Search

This article investigates cohesion in the spoken and written registers of constrained language varieties to highlight the similarities and differences in the cohesion patterns of mediated (i.e., interpreted and translated) and non-native texts with respect to original texts produced by native speakers. In particular, it examines how different types of cohesive devices are distributed across spoken and written native, non-native, and mediated speeches originally delivered impromptu and read out at the plenary sessions of the European Parliament. The dataset comes from the European Translation and Interpreting Corpus (EPTIC) (Ferraresi and Bernardini 2019). The context provides a rare opportunity to examine the spoken and written registers of professional communication, both mono- and multilingual, in a relatively homogenous setting. First, in the exploratory analysis, I investigate the distribution of different types of cohesive devices across the investigated varieties drawing on mosaic plots and correspondence analysis. Thereafter, I make use of regression modelling of the overall frequency of cohesive devices across the examined varieties to evaluate the effect of constrainedness, mode of delivery, and individual variation. The results indicate that non-native and mediated texts do diverge from native production in the use of cohesive devices, but in different ways.

International audience; The impromptu closure of schools and universities and the strict lockdown decided by the French State in March 2020 forced us to find -urgently and without preparation- alternative solutions to conventional face-to-face teaching. Due to their experimental nature, the transposition of labs on pilot plants was particularly challenging. Despite this inherent difficulty, we succeeded in implementing a remote educational device within a few weeks, designed as a viable alternative to conventional food engineering labs for Master level students. Four engineering labs (corresponding to four unit operations widely found in the food industry) were transposed: appertization of cans; concentration in a falling film evaporator; frontal filtration in a plate filter; and spray drying. In the remote labs, hands-on experiments were replaced with various types of virtual tours of the equipment, a detailed description and illustration of its operation, and analysis of real data (previously collected). Evaluation was done via individual quizzes (counting for a small part of the mark, because their aim was above all formative), individual and collective written reports and an individual online oral exam. The teaching team comprised six lecturers, one pedagogical engineer and the person in charge of the technology platform, who also had long experience in distance learning. This experiment took place in the second half of May 2020, with approximately thirty first-year Master students. The effectiveness of the implemented system was evaluated through direct observation and discussions, marks obtained by the students and an anonymous survey. This teaching experiment presents certain flaws induced by the very peculiar conditions of its conception (short time frame, no direct access to the pilot plants). While an “all-digital” approach is not our ultimate aim for these lab sessions, where handling and testing are inherent to the learning process of the student, the educational resources deployed here can be seen as a great incentive to further develop hybrid teaching approaches in food engineering. Moreover, the educational resources and practices implemented in these exceptional circumstances are an opportunity to develop new teaching methods in the future. The present paper reports on this teaching experiment during lockdown due to the COVID-19 pandemic and tries to draw exciting lessons for the future and pitfalls to avoid.

This paper presents a new strategy for choosing an ideal way between the hubs for information transmission in portable impromptu organization for industrial automation on improving communication between sensors for defining materials. Portable impromptu organization is a brief, framework less organization that conveys in multi-jump way. Directing is a significant issue in versatile impromptu organizations. Regularly, directing convention may chooses the way between two hubs dependent on numerous measurements like separation between the hubs, bounces check, delay, trust esteems, leftover vitality, gotten signal quality and so forth. In this proposed strategy, two courses are found between the source and objective and afterward by considering the separation metric ideal course is chosen for information transmission between the hubs. Reenactment results show that the proposed Multiple Metric based Twofold Route Selection AODV Routing protocol (MMBTRS_AODV) beats the ordinary AODV directing convention. The proposed steering convention brings about high bundle conveyance proportion with less normal start to finish deferral and low directing overhead.

The article experimentally justifies the effectiveness of the biological feedback simulator «Blazepod» to improve the speed of disjunctive and differentiated responses, which condition the effectiveness of unannounced and deliberately impromptu combat in sporting fights. It shows the desirability of preferentially using special exercises, simulating combat situations with selection and switching reactions.

High-accuracy trajectory tracking is critical to many robotic applications, including search and rescue, advanced manufacturing, and industrial inspection, to name a few. Yet the unmodeled dynamics and parametric uncertainties of operating in such complex environments make it difficult to design controllers that are capable of accurately tracking arbitrary, feasible trajectories from the first attempt (i.e., impromptu trajectory tracking). This article proposes a platform-independent, learning-based “add-on” module to enhance the tracking performance of black-box control systems in impromptu tracking tasks. Our approach is to pre-cascade a deep neural network (DNN) to a stabilized baseline control system, in order to establish an identity mapping from the desired output to the actual output. Previous research involving quadrotors showed that, for 30 arbitrary hand-drawn trajectories, the DNN-enhancement control architecture reduces tracking errors by 43% on average, as compared with the baseline controller. In this article, we provide a platform-independent formulation and practical design guidelines for the DNN-enhancement approach. In particular, we: (1) characterize the underlying function of the DNN module; (2) identify necessary conditions for the approach to be effective; (3) provide theoretical insights into the stability of the overall DNN-enhancement control architecture; (4) derive a condition that supports data-efficient training of the DNN module; and (5) compare the novel theory-driven DNN design with the prior trial-and-error design using detailed quadrotor experiments. We show that, as compared with the prior trial-and-error design, the novel theory-driven design allows us to reduce the input dimension of the DNN by two thirds while achieving similar tracking performance.

Recent years have witnessed a surge of professional user-generated content (PUGC) based video services, coinciding with the accelerated proliferation of video acquisition devices such as mobile phones, wearable cameras, and unmanned aerial vehicles. Different from traditional UGC videos by impromptu shooting, PUGC videos produced by professional users tend to be carefully designed and edited, receiving high popularity with a relatively satisfactory playing count. In this paper, we systematically conduct the comprehensive study on the perceptual quality of PUGC videos and introduce a database consisting of 10,000 PUGC videos with subjective ratings. In particular, during the subjective testing, we collect the human opinions based upon not only the MOS, but also the attributes that could potentially influence the visual quality including face, noise, blur, brightness, and color. We make the attempt to analyze the large-scale PUGC database with a series of video quality assessment (VQA) algorithms and a dedicated baseline model based on pretrained deep neural network is further presented. The cross-dataset experiments reveal a large domain gap between the PUGC and the traditional user-generated videos, which are critical in learning based VQA. These results shed light on developing next-generation PUGC quality assessment algorithms with desired properties including promising generalization capability, high accuracy, and effectiveness in perceptual optimization. The dataset and the codes are released at https://github.com/wlkdb/pugcq_create.

A ubiquitous problem faced by students and teachers of an educational institute is to arrange for meetings in an effortless manner and at a time which suits both. Teachers usually have a fixed time table for their regular classes, but they often get busy with impromptu meetings, extra classes, official work, etc. Since these situations are frequent and unpredictable, students willing to meet the teachers outside the regular class time often find it difficult to do so. To reduce the hassle of waiting for teachers and searching for them around the campus, this paper proposes a teacher-student appointment system named ‘MeetUp’. This application allows a teacher member to upload their timetable from which free slots of the teacher will be extracted and shown against the respective teacher on the student portal, which will help students request for an appointment time that is viable for both the members. This system not only benefits the students but also helps the teachers manage their time as students would only show up with prior appointments and the teachers have the authority to accept an appointment request at their convenience. Meetup is built for native android users using Dart language based Flutter framework and python based Django Framework.

Skyline processing, an established preference evaluation technique, aims at discovering the best, most preferred objects, i.e. those that are not dominated by other objects, in satisfying the user’s preferences. In today’s society, due to the advancement of technology, ad-hoc meetings or impromptu gathering are becoming more and more common. Deciding on a suitable meeting point (object)for a group of people (users) to meet is not a straightforward task especially when these users are located at different places with distinct preferences. A place which is close by to the users might not provide the facilities/services that meet all the users’ preferences; while a place having the facilities/services that meet most of the users’ preferences might be too distant from these users. Although the skyline operator can be utilised to filter the dominated objects among the objects that fall in the region of interest of these users, computing the skylines for various groups of users in similar region would mean rescanning the objects of the region and repeating the process of pair wise comparisons among the objects which are undoubtedly unwise. On this account, this study presents a region-based skyline computation framework which attempts to resolve the above issues by fragmenting the search region of a group of users and utilising the past computed skyline results of the fragments. The skylines, which are the objects recommended to be visited by a group of users, are derived by analysing both the locations of the users, i.e. spatial attributes, as well as the spatial and non-spatial attributes of the objects. Several experiments have been conducted and the results show that our proposed framework outperforms the previous works with respect to CPU time.

The purpose of this research was to find out the types of grammatical errors frequently made by students in delivering the impromptu speech. The aim of learning a language is to be able to communicate in a right way. There is no a silent language knowledge, a language must be produced in words to achieve its goals in learning second or foreign language comprehending. Trough speaking, we can see how is a speaker skill in mastering a lang uage. A good speaker is who able to produce language which arranged as its rule. In speaking, appropriate sentence structure and context are very helping in order to gain listener‟s comprehending. In collecting the data, the researcher used documentation of video of impromptu speech. A method to analyze the error is called error analysis. The indicators are: (a) Morphology error (b) Syntax error. From these indicators, the researcher find out students‟ grammatical error in delivering the impromptu speech. In this research, the researcher found 127 errors in morphology. Those were 34 (14,5%) errors in noun, 77 (33%) errors in verb, 4 (2%) errors in adjective, 2 (1%) errors in adverb, 10 (4%) errors in preposition. In syntax was found 107 errors. Those were 26 (11%) errors in phrase, 33 (14%) errors in clause, 21 (9%) errors in sentence, and 27(11,5%) errors in intersentence. From the result, the researcher concluded that the most frequent grammatical error found in verb.

As the range of handheld, mobile and desktop devices expands and worldwide demand for collaborative application tools increases, there is a growing need for higher speed impromptu cross-device application sharing to keep up with workplace requirements for on-site or remote collaborations. To address this, we have developed CamCutter, a cross-device interaction technique enabling a user to quickly select and share an application running on another screen using the camera of a handheld device. This technique can accurately identify the targeted application on a display using our adapted computer vision algorithm, system architecture and software implementation, allowing impromptu real-time and synchronized application sharing between devices. For desktop and meeting room set-ups, we performed a technical evaluation, measuring accuracy and speed of migration. For a single-user reading task and a collaborative composition task, we carried out a user study comparing our technique with commercial screen sharing applications. The results of this study showed both higher performance and preference for our system. Finally, we discuss CamCutter’s limitations and present insights for future vision-based cross-device application sharing.

Cyber–physical interactive performance is a new style of performance art that facilitates impromptu interactions among dancers and virtual characters. This paper presents the design and implementation of a full-stack service platform, called WISE (Wearable Item Service runtimE), that supports such kind of performance. The proposed service platform deals with core issues such as connectivity, manageability and simplicity, behind the scene. Two successful real-world applications have verified the feasibility of WISE: a live public performance called Future Circus and a virtual reality (VR) art exhibition called Step In and Out of the Dreams. The evaluation results show that WISE is not only easy to use but also portable and extensible to fit various cyber–physical interactive performances.

This research provides a new model for team formation problems for Impromptu task activities in geo-social networks, called Geo-team formation. The team formation problem (TFP) is the process of dedicating the users from Social Networks to activities as teams in a collaborative functioning environment for an effective outcome based on their skills. It was proven to be an NP-hard problem. Impromptu activities deployment requires users with required skills who are socially close to each other and spatially close to the activity location. Most existing researches tackle the geo-team formation problem as a single social constraint or skills constraints query while optimizing the spatial closeness. To cover this gap, we present a model that efficiently narrows down the search space and then applies the required constraint. Efficient processing of the geo-team formation model is challenging considering (1) required skills of users, (2) weight of user skills, (3) the maximum contribution of users skills, (4) social cohesiveness between users, and (5) spatial closeness, which needs to be carefully examined and made timely invitations. The weight of user's skills helps the model dedicate users with high expertise for each team's required activities. In this approach, the time and the search cost of the team formation process are efficiently restricted. Our preliminary results on real-world datasets determine that the proposed algorithms can efficiently apply the geo-team formation model under various parameter settings. the results confirm the effectiveness of the proposed method.

Data-driven scrollytelling has become a prevalent way of visual communication because of its comprehensive delivery of perspectives derived from the data. However, creating an expressive scrollytelling story requires both data and design literacy and is time-consuming. As a result, scrollytelling has been mainly used only by professional journalists to disseminate opinions. In this paper, we present an automatic method to generate expressive scrollytelling visualization, which can present easy-to-understand data facts through a carefully arranged sequence of views. The method first enumerates data facts of a given dataset, and scores and organizes them. The facts are further assembled, sequenced into a story, with reader input taken into consideration. Finally, visual graphs, transitions, and text descriptions are generated to synthesize the scrollytelling visualization. In this way, non-professionals can easily explore and share interesting perspectives from selected data attributes and fact types. We demonstrate the effectiveness and usability of our method through both use cases and an in-lab user study.

Vehicular ad hoc networks (VANETs)are most of the numerous ITS advances that permit V2V multi-jump impromptu organizations. Because of the profoundly unique nature of the vehicles, the grouping viably mitigates numerous discussion interferences. These days, the vast majority of the exploration work experienced that grouping can improve the bunch’s genuineness by utilizing similar versatility designs for vehicles. A review in spected the bunching calculations of VANET to meet the prerequisites of its novel highlights. This past exploration is principally founded on the single bounce gathering, which gives the best collaboration between the bunch head (CH) and the group part (CM) with a multi-jump bunch. The bunch size is little in one jump group, which deals with the planning of CHs and high upkeep costs. Along these lines, a fewmulti-bounce grouping calculations have been participated as of late. These calculations can build group inclusion, lessen CHs, and increment bunch opposition. In any case, there was trouble with multi-bounce gathering for VANET, for example, mass awkwardness and security esteem. This paper proposes a dynamic mobility-based multi-hop clustering algorithm (DMMHC) that relies primarily on dynamic navigation in a multi-hop clustering scenario.The proposed framework utilizes vehicle travel heading, interface lifetime assessment, and dependent circumstance to address group opposition and diminish support charges between CMs. The reproduction tests are executed utilizing MATLAB to clarify the suggested calculation. The outcomes uncover that the proposed calculation can improve the group execution regarding CM span and normal CH term. Likewise, the quantity of re-broadcast hi messages is decreased.

Internet of Things, has opened many new possibilities in the world of technology. The interconnection among sensors, wearables, mobile devices, etc. is used to collect data. The collected data is processed and analyzed on fog and cloud through various algorithms to obtain an efficient solution to diverse problems. IoT has a wide range of spectrum for its implementation, and several industries have already started deploying a smart and efficient ecosystem. The Education sector is among these industries. There are many implementations of IoT for Smart Learning such as the use of smart boards in the classrooms, mobile applications allowing impromptu access to learning resources anywhere. These advancements are only the tip of the iceberg. The network of smart things can be utilized to make the learning process even smarter and more efficient. Even though the classes are equipped with smart devices, it is hard for educators to attend each student individually and find out the areas where they are facing problems, as every individual has a different learning pattern. Keeping track of the students and to assist them individually requires a lot of time and effort. The level of difficulty increases further when face-to-face learning is switched to online learning. The research focuses on enhancing the online learning and teaching experience through the implementation of IoT using available devices, sensors, and other technologies such as machine learning and artificial intelligence.

To date, there are many speech data sets with emotional classes, but with impromptu or intentional actors. The native speakers are given a stimulus in each emotion expression. Because natural conversation from secretly recorded daily communication still raises ethical issues, then using voice data that takes samples from movies and podcasts is the most appropriate step to take the best insights from speech. Professional actors are trained to induce the most real emotions close to natural, through the Stanislavski acting method. The speech dataset that meets this qualification is the Human voice Natural Language from On-demand media (HENLO). Within HENLO, there are basic per-emotion audio clips of films and podcasts originating from Media On-Demand, a motion video entertainment media platform with the freedom to play and download at any time. In this paper, we describe the use of sound clips from HENLO, then conduct learning using Support Vector Machine (SVM) and Long Short-Term Memory (LSTM). In these two methods, we found the best strategy by training LSTMs first, then then feeding the model to SVM, with a data split strategy at 80:20 scale. The results of the five training phases show that the last accuracy results increased by more than 17% compared to the first training. These results mean both complement and methods are important for improving classification accuracy., Hingga saat ini, terdapat banyak dataset wicara dengan kelas emosi, namun dengan aktor dadakan atau disengaja. Penutur asli diberi stimulus serta petunjuk emosi apa yang harus ditampakkan selanjutnya. Karena percakapan natural dari komunikasi sehari-hari masih menimbulkan isu etik ketika direkam secara diam-diam, maka, menggunakan data suara yang mengambil sampel dari film dan podcast adalah langkah paling tepat untuk mengambil insight terbaik dari wicara. Aktor profesional terlatih untuk menginduksikan emosi paling nyata mendekati natural, melalui metode akting Stanislavski. Dataset suara yang memenuhi kualifikasi ini adalah Human voice Natural Language from On-demand media (HENLO). Di dalam HENLO, terdapat klip audio per emosi dasar dari film dan podcast yang berasal dari Media On-Demand, yaitu platform media hiburan motion video dengan kebebasan play dan download kapanpun. Dalam makalah ini, kami menjabarkan penggunaan klip suara dari HENLO, lalu melakukan learning menggunakan Support Vector Machine (SVM) dan Long Short-Term Memory (LSTM). Pada dua metode tersebut, kami menemukan strategi terbaik dengan melatih LSTM terlebih dahulu, lalu selanjutnya memberi feed model ke SVM, dengan strategi split data di skala 80:20. Hasil dari lima kali fase training menunjukkan hasil akurasi terakhir meningkat lebih dari 17% dibandingkan dengan training pertama. Hasil ini berarti kedua metode komplemen dan penting untuk peningkatan akurasi klasifikasi.

We transform traditional experience writing into in-situ voice-based multimedia authoring. Documenting experiences digitally in blogs and journals is a common activity that allows people to socially connect with others by sharing their experiences (e.g. travelogue). However, documenting such experiences can be time-consuming and cognitively demanding as it is typically done OUT-OF-CONTEXT (after the actual experience). We propose in-situ voice-based multimedia authoring (IVA), an alternative workflow to allow IN-CONTEXT experience documentation. Unlike the traditional approach, IVA encourages in-context content creations using voice-based multimedia input and stores them in multi-modal “snippets”. The snippets can be rearranged to form multimedia articles and can be published with light copy-editing. To improve the output quality from impromptu speech, Q&A scaffolding was introduced to guide the content creation. We implement the IVA workflow in an android application, LiveSnippets - and qualitatively evaluate it under three scenarios (travel writing, recipe creation, product review). Results demonstrated that IVA can effectively lower the barrier of writing with acceptable trade-offs in multitasking.

Geo-social group search aims to find a group of people proximate to a location while socially related. One of the driven applications for geo-social group search is organizing an impromptu activity. This is because the social cohesiveness of a found geo-social group ensures a good communication atmosphere for the activity and the spatial closeness of the geo-social group reduces the preparation time for the activity. Most existing works treat geo-social group search as a problem that finds a group satisfying a single social constraint while optimizing the spatial proximity. However, since different impromptu activities have diverse demands on attendees, e.g. an activity could require (or prefer) the attendees to have skills (or favorites) related to the activity, the existing works cannot find this kind of geo-social groups effectively. In this paper, we propose a novel geo-social group model, equipped with elegant keyword constraints, to fill this gap. We propose a novel search framework which first significantly narrows down the search space with theoretical guarantees and then efficiently finds the optimum result. To evaluate the effectiveness, we conduct experiments on real datasets, demonstrating the superiority of our proposed model. We conduct extensive experiments on large semi-synthetic datasets for justifying the efficiency of the proposed search algorithms.

Security in daily transactions, not only in the financial sector, but also in various upcoming Industrial smart application areas needs a generic solution, out of the cat and mouse race, where frequently vulnerabilities are pointed out and a quick patch is fabricated to resolve the issue temporarily. An impromptu solution to this protracted issue will not be enough to solve on the long run, thus dedicated researchers have been putting efforts to their best for finding the real problem by asking the right questions. Through this paper authors have attempted to trace the current issues in handling decentralised ledger system using blockchain technology and discussed state of the art management in auditing hardware obfuscation techniques.

As the Internet of Things (IoT) proliferates, the potential for its opportunistic interaction with traditional mobile apps becomes apparent. We argue that to fully take advantage of this potential, mobile apps must become things themselves, and interact in a smart space like their hardware counterparts. We present an extension to our Atlas thing architecture on smartphones, allowing mobile apps to behave as things and provide powerful services and functionalities. To this end, we also consider the role of the mobile app developer, and introduce actionable keywords (AKWs)---a dynamically programmable description---to enable potential thing to thing interactions. The AKWs empower the mobile app to dynamically react to services provided by other things, without being known a priori by the original app developer. In this paper, we present the mobile-apps-as-things (MAAT) concept along with its AKW concept and programming construct. For MAAT to be adopted by developers, changes to the existing development environments (IDE) should remain minimal to stay acceptable and practically usable, thus we also propose an IDE plugin to simplify the addition of this dynamic behavior. We present details of MAAT, along with the implementation of the IDE plugin, and give a detailed benchmarking evaluation to assess the responsiveness of our implementation to impromptu interactions and dynamic app behavioral changes. We also investigate another study, targeting Android developers, which evaluates the acceptability and usability of the MAAT IDE plugin.

Symbolic music generation has long been an attractive topic in machine intelligence, which aims to automatically learn musical distribution from musical corpora and then to generate samples from the estimated distribution. As one of the most popular kinds, pop music is usually polyphonic and multi-track which makes difficulties in music generation. Furthermore, different from other tasks in machine intelligence such as image processing, the piano roll representation of music is discrete and binary, thus leading to a non-differentiable problem. In other words, most state-of-the-art models such as neural networks cannot be directly applied to achieve piano-roll-based symbolic music generation. To address these two issues, we propose a coupled latent variable model with binary regularizer. On the one hand, the proposed model employs a coupled mechanism to learn a latent variable that simultaneously captures the internal distribution of each track and the joint distribution of multiple tracks. On the other hand, we propose to reformulate the discrete and binary properties into a convex constraint in an elegant way, thus obtaining a differentiable optimization problem and smoothly cooperating with neural networks. To show the effectiveness of our method, we carry out the experiment in impromptu accompaniment generation which is a popular application of music generation, and both the quantitative evaluation and human evaluation demonstrate promising performance of our model compared with some state-of-the-art models.1

With the development of event-based social network, the ways and tools of people’s online and offline social activities have changed dramatically. IEBSN emphasizes the impromptu of social events, which will lead to inaccurate and inefficient event recommendation. To address this problem, we propose a novel dynamic scene recognition method for IEBSN, which combines supervised learning and unsupervised learning. Aiming at the similarity and inconsistency between classes in scene recognition, we propose a convolution feature encoder, which can extract more scene visual information. In order to meet the unexpected requirement of application level, the images whose convolution module is lower than a certain threshold are clustered with k-means. Experiments show that this method can automatically identify the scene in IEBSN application efficiently, and alleviate the problems in scene recognition.

Real-time remote interaction has become easier and richer powered by recent advances in mobile computing and communication. A number of research have been explored on enriching family interaction by augmenting an interaction channel with asynchronous communication [6] or additional sensory stimuli [5]. However, it is still far from achieving a sense of living together for family members involuntarily living apart, especially in context-aware impromptu interaction. For families living together, it is trivial to naturally perceive behavioral and situational contexts of the other and initiate a relevant interaction intuitively. For example, a wife starts a casual chat with asking her husband what he is going to cook when she sees him going to the kitchen or hears a simmering sound.

In this paper, we propose an online learning approach that enables the inverse dynamics model learned for a source robot to be transferred to a target robot (e.g., from one quadrotor to another quadrotor with different mass or aerodynamic properties). The goal is to leverage knowledge from the source robot such that the target robot achieves high-accuracy trajectory tracking on arbitrary trajectories from the first attempt with minimal data recollection and training. Most existing approaches for multi-robot knowledge transfer are based on post-analysis of datasets collected from both robots. In this work, we study the feasibility of impromptu transfer of models across robots by learning an error prediction module online. In particular, we analytically derive the form of the mapping to be learned by the online module for exact tracking, propose an approach for characterizing similarity between robots, and use these results to analyze the stability of the overall system. The proposed approach is illustrated in simulation and verified experimentally on two different quadrotors performing impromptu trajectory tracking tasks, where the quadrotors are required to accurately track arbitrary hand-drawn trajectories from the first attempt., European Control Conference (ECC) 2019

The increasing complexity of data and analysis methods has created an environment where scientists, who may not have formal training, are finding themselves playing the impromptu role of software engineer. While several resources are available for introducing scientists to the basics of programming, researchers have been left with little guidance on approaches needed to advance to the next level for the development of robust, large-scale data analysis tools that are amenable to integration into workflow management systems, tools, and frameworks. The integration into such workflow systems necessitates additional requirements on computational tools, such as adherence to standard conventions for robustness, data input, output, logging, and flow control. Here we provide a set of 10 guidelines to steer the creation of command-line computational tools that are usable, reliable, extensible, and in line with standards of modern coding practices.

Health is the utmost importance to any human being. To take care of oneself in this busty world we need some assistance. The world is revolving with smart technologies so we need a smart health care assistance that tracks one's activities, moods and suggest precautious actions when required. The primary objective of this product is to be time efficient along with being as an easily accesses able personal health assistant. To build a cost efficient personal assistance with good quality, monitoring of the messages from user is necessary. At present they are managed in an impromptu way. The paper will put forward the use of a Personal assistance chatbot to handle the messages from users and collectively provide the appropriate answers to it. A model has been proposed to enumerate precautions for the health related issues.

Post-disaster need assessment deals with the accurate assessment of the need (i.e. demand and utility) for emergency resource at the shelters. While demand signifies the amount of resource required, utility represents the exigency of that requirement. Due to lack of, or imprecise need assessments immediately after a disaster, relief requirements are generally set up based on coarse estimates by logisticians regarding what people would normally need. The effectiveness of this estimation depends on the competencies and experience of the logistician in control, often leading to impromptu allocation of typically scarce emergency resources. Thus, forecasting the exact demand and enumerating the correct utility of emergency resources are inevitable.

Air quality is a source of increasing concern in several cities, due to the adverse health effects of significant pollution levels. For this reason, the need to assess the concentration of pollutants at high temporal and spatial resolution is perceived as very urgent. As such, there is a growing interest in building pervasive networks integrating different sensing technologies to achieve this capability. In this view, low cost smart sensors data could be fused together with fixed but more accurate multisensor devices and certified analyzers data to build hi-res maps (

Present Days multi-day, sight and sound applications winding up progressively well known for the clients of specially appointed systems. To help these applications the investigation of QoS issues in MANETs is fundamental. Hidden hub and exposed hubs are the fundamental drivers of execution corruption of these specially appointed systems. There is no thorough investigation to completely address these issues. Here in this paper novel double bustling tone based MAC convention is proposed to address every one of these issues. Utilizing reproduction we close with the relative Analysis of throughput and access postpone proportion of Ad Hoc systems for mixed media applications. We additionally consider various information rate backing of the correspondence divert in remote impromptu systems which rely upon the bit mistake rate of the correspondence channel.

As an important part of vocal guidance, piano performance and singing guidance are important ways to cultivate students' interest in piano and improve their improvisational level. With the development of education, especially the rapid popularization and promotion of big data and other information technologies, traditional teacher-led performance and singing teaching models have gradually met the learning needs of students and affected their learning effects. Therefore, it is indispensable to reform and innovate the guiding philosophy and methods of performance and songs. Therefore, how to introduce big data technology into piano performance and singing guidance is worthy of in-depth discussion. Online education has obvious advantages in auditory training. Whether it is impromptu feedback through online classrooms, or completion of homework through audio, it can have the effect of "grinding ears". On the one hand, the clear audio allows students to think aurally in a good auditory environment; on the other hand, through online interactive answering, teachers can accurately understand the development of the auditory perception of the entire class, so as to adjust the training content immediately. The subject studied the problems existing in the teaching of traditional piano playing and singing, and proposed different solutions for the computer artificial intelligence system and virtual image processing technology, so that image classification and video teaching have a better recognition effect, and promote vocal music teaching forward development of.

Consumers often schedule their activities in an attempt to use their time more efficiently. Although the benefits of scheduling are well established, its potential downsides are not well understood. The authors examine whether scheduling uniquely undermines the benefits of leisure activities. In 13 studies using unambiguous leisure activities that consumers commonly schedule (e.g., movies, a coffee break), they find that scheduling a leisure activity (vs. experiencing it impromptu) makes it feel less free-flowing and more work-like. Furthermore, scheduling diminishes utility from leisure activities, in terms of both excitement in anticipation of the activities and experienced enjoyment. Importantly, the authors find that maintaining the free-flowing nature of the activity by “roughly scheduling” (without prespecified times) eliminates this effect, thus indicating that the effect is driven by a detriment from scheduling rather than by a boost from spontaneity. The reported findings highlight an important opportunity for marketers to improve consumers’ experiences and utility by leveraging scheduling behavior while also providing important implications for consumer well-being from leisure consumption.

The rise of mobile food vending in US cities combines urban space and mobility with continuous online communication. Unlike traditional urban spaces that are predictable and known, contemporary vendors use information technology to generate impromptu social settings in unconventional and often underutilized spaces. This unique condition requires new methods that interpret online communication as a critical component in the production of new forms of public life. We suggest qualitative approaches combined with data-driven analyses are necessary when planning for emergent behavior. In Charlotte, NC, we investigate the daily operations, tweet content, and spatial and temporal sequencing of six vendors over an extended period of time. The study illustrates the interrelationship between data, urban space, and time and finds that a significant proportion of tweet content is used to announce vending locations in a time-based pattern and that the spatial construction of events is often independent of traditional urban form.

Due to the complexity of an open multi-agent system, agents&rsquo, interactions are instantiated spontaneously, resulting in beneficent collaborations with one another for mutual actions that are beyond one&rsquo, s current capabilities. Repeated patterns of interactions shape a feature of their organizational structure when those agents self-organize themselves for a long-term objective. This paper, therefore, aims to provide an understanding of social capital in organizations that are open membership multi-agent systems with an emphasis in our formulation on the dynamic network of social interactions that, in part, elucidate evolving structures and impromptu topologies of networks. We model an open source project as an organizational network and provide definitions and formulations to correlate the proposed mechanism of social capital with the achievement of an organizational charter, for example, optimized productivity. To empirically evaluate our model, we conducted a case study of an open source software project to demonstrate how social capital can be created and measured within this type of organization. The results indicate that the values of social capital are positively proportional towards optimizing agents&rsquo, productivity into successful completion of the project.

The designer of the virtual environment have been trying for decades to provide the player with more enjoyable, comfortable and also informative user experiences, and yet still fail to ensure that the player follow the preset instructions and even implicit suggestions faithfully and naturally, due to the designer's invisibility during runtime, and the player's individual diversity and individual impromptu in manipulations. We believe that the camera is the mainly messenger for the designer and the player to communicate, and intend to build a bridge between them. By binding the designer's aesthetic ideas to the parameters of the camera's movement, we enable the player to roam in the virtual scene with the guidance from the designer. We also propose a navigation guiding language (NGL) to assist the binding and the guiding process. A user study is made to evaluate the performance of our method. Experiments and questionnaires have shown that our method can offer a more attentive and pleasing experience to the player with implicit guidance.

Opportunistic social matching is the concept of introducing individuals for various social interests when user-contexts are conducive to impromptu face-to-face meetings. Early prototypes have received positive feedback, yet they are susceptible to inaccurately reported match preferences and false-negative match recognition. This paper presents an alternative design for opportunistic social matching called the encounter opportunity browsing interface. The interface lets users indiscriminately browse and react to opportunities for social encounters nearby, which enables the gradual learning of users' match preferences. A preliminary qualitative assessment of the interface indicates that users seek trusted signals regarding the enjoyability of social encounter opportunities displayed.

In this work-in-progress we introduce our prototype, a portable speaker augmented to be used as an electronic music sketchpad which allows musicians to play, record and compose multi-track music on the fly. We developed the MusicFabrik cover for the portable speaker, a playable textile cover which, although in functional sketch stage now, shows promise of becoming a useful tool for impromptu loop creation and collaborative music playing. We present our initial user encounters of the sketch. See video: https://youtu.be/pe2QaYv1Imo.

The Software Defined Networking (SDN) approach has recently demonstrated its effectiveness in simplifying the dynamic management of networking capabilities of infrastructure environments such as datacenters, e.g., by greatly enhancing the flexibility of dispatching features provided by industrial-grade switches. Inspired by the previous and more traditional scenario, we propose SDN adoption in infrastructure-less distributed multi-hop spontaneous networks based on the impromptu collaboration of fixed/mobile devices, with the goal of significantly improving the Quality of Service perceived by final users. To this purpose, the paper outlines our primary guidelines and reference architecture to support quality-aware packet dispatching. In particular, we present how collaborative nodes can exploit the SDN approach to appropriately manage the quality of different traffic flows, by avoiding undesired interference and by taking into consideration network capabilities/conditions and application-level requirements.

This electronic Air quality is nowadays a primary concern in several cities. More generally pollutant and toxic gases continuously, incidentally or purposedly released are now considered to be extremely relevant for their severe adverse health effects to exposed citizens. In this view, low cost smart chemical multisensors are arising as a reliable source for indicative data on Air Quality. Low cost UAVs are versatile platforms capable to host playloads integrating multi sensor technologies for short term, mobile monitoring tasks. The recently proposed tethered UAV platforms can be deemed as interesting solutions to build and rapidly deploy impromptu networks of air quality analyzers for short-term and focused environmental monitoring actions. In this work, we propose the integration of the ENEA MONICA multisensor platform as a measurement payload for the TopView SAV-ES UAV. The prototype has been flown on board of the SAV-ES platform for a test flight targeting the measurement of a plume generated with a brushwood controlled fire. The functional proof of concept flight have confirmed the possibility to use MONICA as a measurement payload for the targeted platform. © 2018 IEEE.

It is under the purview of many librarians to teach library and information literacy concepts to students, researchers, and professionals alike. Those of us who provide this instruction often find ourselves facing similar issues, such as: How can we increase the engagement of our pupils, and how can we determine whether learning has actually taken place? Socrative is a free online assessment tool that allows instructors to administer in-class quizzes, view responses in real time, and generate reports to monitor and visualize student learning [1]. It, therefore, provides a means for carrying out pre-class testing so that instructors understand what students know prior to the session, as well as conduct formative and summative assessment. Formative assessment, which is employed continuously to monitor and improve teaching and learning, contrasts with summative assessment, which is used to evaluate student retention of subject matter [2]. The Socrative platform provides an additional tool for library instruction, beneficial to both teachers and students. Teachers and students will utilize and view Socrative differently. As such, there are differences in how each group will log in. If you are an instructor, you must sign up for a teacher account, which can be linked to Google Drive if you provide a Gmail address. Upon creating an account, you will be assigned a unique room code that students will use to identify your virtual classroom and gain access to your quizzes. You have the option to change this code after creating your account. Socrative allows you to create customized quizzes that can include any combination of multiple choice, true or false, and short-answer questions. Additional customization options include the ability to attach images to questions as well as add explanations of correct answers so that your students can have immediate feedback. Each quiz is given a unique SOC number, which you can share with colleagues. Using this SOC code, you can import preexisting quizzes into your virtual classrooms, allowing smooth collaboration with colleagues. The process of creating and reordering questions is very intuitive, and you should have no problems; however, should any questions arise, Socrative's website contains very helpful documentation. Socrative has four different assessment options: (1) Start a Quiz, (2) Quick Question, (3) Space Race, and (4) Exit Ticket, with various pros and cons: ▪ Start a Quiz allows you to administer a quiz that you have already created or imported, with the pace of questions controlled either by the student or the instructor. There are two forms of student-paced quizzes in Socrative: The student-paced immediate feedback option gives learners instant feedback about which questions they answered correctly and which they got wrong as they work through the quiz, whereas the student-paced student navigation option allows learners to progress in a nonlinear fashion by skipping or returning to questions and modifying their answers. Teachers will see student answers populate a live results table as they occur. Conversely, teacher-paced quizzes allow you to control the delivery of questions and view live question-by-question results that can be made viewable or hidden. I have found the ability to reveal results after everyone has answered provides an excellent opportunity to engage students in class discussions. ▪ Quick Question provides you the opportunity to pose a true/false, multiple choice, or open-ended question to the class instantly and without prior preparation. This option is great for impromptu polls; for instance, you can ask students to identify gaps in their understanding of information literacy concepts or get a sense of their research topics of interest. The disadvantage of this option lies in its spontaneous nature: you cannot provide a text explanation for multiple choice questions. The question and answer fields will appear blank, so students must remember which answer corresponds to which letter. This option is therefore best suited for asking open-ended questions or easily remembered true/false or multiple choice questions. ▪ Space Race allows groups of students to work through your quizzes in a race to answer the most questions correctly (Figure 1). Correct answers move an avatar forward and incorrect answers keep it in place. Teams are distinguished by color and can either be assigned randomly or selected by students. Unfortunately, the avatar graphics seem to be geared more toward a juvenile audience, including a rocket, bear, bicycle, bee, unicorn, or spaceship. However, I would argue that this activity is still valuable for librarian-educators in the professional world and in higher education, as it provides a unique way to encourage and assess group learning. Figure 1 Live student results viewed from a teacher-paced quiz in Socrative ▪ Exit Ticket comprises three fixed questions: one asks how well students understood the material, another requires students to explain what they learned in class, and a final open-ended question gives you the freedom to ask a query of your choosing. It is meant to be administered at the end of a session as a means of formative assessment [3]. Exit Ticket provides you the opportunity to collect invaluable feedback from students. This information allows you to identify problem areas in student learning and address them via improvements to teaching methods going forward. You may choose to employ one or more of these assessment methods, depending on your needs and the purpose of the session. What the methods all have in common is the ability for you to visualize student responses as they happen in real time. Unique to Starting a Quiz and Quick Question assessments is the ability to anonymize student names, which can be useful in situations where sensitive questions are posed and student privacy is a concern. When quizzes are completed, you have the option to generate reports to email, save to Google Drive (if you provide a Gmail address at sign-up), or download as Excel or portable document format (PDF) files. Three different report types are available: (1) whole class Excel, (2) individual students' PDF files, and (3) question-specific PDF. Whole-class summary tables include a color-coded, question-by-question breakdown of student responses; the percentage of students that correctly answered each question; and individuals' and mean class scores. Individual student reports provide a graded quiz for each person in the class, which can easily be returned to your students. These reports may not be as valuable for noncredit or one-shot library sessions but can provide insight into the strengths and weaknesses of individual students. You may find this information beneficial when providing personalized or one-on-one research assistance. Lastly, question-specific reports include data for each question in a quiz, including the number of correct answers per question, those with the most and fewest correct answers, and students' full answers to open-ended questions. Reports generated by Socrative are easy to read and interpret, and provide assessment records so that you can chronicle teaching and learning over time. Not all of Socrative's features will be useful for librarians in a medical or higher education setting, however. For instance, you have the option to categorize the questions you create using predefined tags, but these are limited to Common Core curricula subject matter. Furthermore, the juvenile avatar options available in the Space Race make it evident that this assessment platform was designed with K–12 students in mind. There is also a small learning curve for students who have not used Socrative before. Specifically, it does take a few minutes for students to log on to Socrative using the room code. Additionally, I have found that students often forget to select Submit Answer after making their choices on true/false and multiple choice questions, which can delay the class. The first issue can be remedied by simply changing the room code to a word that is easy to spell and remember; the second issue may require continuously reminding students to select Submit for their answers to go through. Though these matters may not seem a significant concern, you should nonetheless keep them in mind if you teach one-shot sessions, where time is especially valuable. All things considered, Socrative is a user-friendly tool that allows librarian-educators to understand what students already know, assess their learning as it happens, and collect feedback about the learning process itself. It allows increased student engagement in the classroom via group activities such as Space Race and discussions arising out of students' in-class answers. It provides an opportunity for everyone to contribute and participate in library sessions. Moreover, it offers instructors many ways to visualize and organize assessment—both in real-time and through post-assessment reports. In addition to the online interface, Socrative has made free apps for both teacher and student available for iOS and Android mobile devices, as well as for Google Chrome and Windows 8.1. The ability to use this tool across multiple devices such as laptops, phones, or tablets is very convenient both for librarians and students on the go. Overall, Socrative is very useful, and I would highly recommend its use for librarians involved in instruction who are looking for a means of assessing student learning as well as increasing classroom engagement.

One important validity question with regard to writing assessment is the degree to which performance on a timed writing test can predict performance on future academic writing. Recent developments in corpus linguistics have allowed scholars to describe in detail the linguistic features of a variety of academic texts, including genres of disciplinary writing and writing on essay tests, which can aid in answering this question. The purpose of this paper is to compare the linguistic features of test essays written by native and non-native speakers with a comparison corpus of successful student writing across a range of disciplines using Biber's (1988) multidimensional analysis framework. Essays written on two different test prompts were analyzed along dimensions of successful student writing revealed by an analysis of the Michigan Corpus of Upper-level Student Writing (MICUSP) conducted by Hardy and Romer (2013) . Results demonstrated that test essays differed in significant ways from disciplinary writing, particularly in the natural and health sciences. Furthermore, language background (native vs. non-native), prompt, and language proficiency (i.e., essay scores) were systematically related to scores on all four dimensions. Implications for pedagogy and language assessment are discussed.

Secure correspondence amongst vehicle and Framework/Street side unit(V to I/R) over Vehicular impromptu system utilizing Wi-Fi IEEE 802.11p standard and recognizing exact assailant vehicle is a noteworthy test over VANET in present day age. In this paper, executing three encryption systems i.e. DES, AES and RSA calculation and correlation their execution with proposed technique in view of the examination of its incitement time at the season of encryption and unscrambling procedure, throughput and furthermore its cradle measure tentatively. Executing of limit with numerous RSU has been reproduced utilizing MATLAB programming. In this work, a half breed procedure for validation of a vehicle and street side unit (RSU) is proposed. Such condition can be utilized while outlining better communicating plans, security includes in VANETs. The hugeness that MATLAB give generally accessible, ceaselessly refreshed and has more extensive reach. By relegating trust levels to each hub the malevolent hubs or getting out of hand hubs are expelled from the system.

Being a popular computing device, smartphone (or tablet, wearable device) system's screen unlocking mechanisms have recently come under numerous successful attacks. In an aim to deter the attacks, we explored users' spontaneous touch behavioral traits in order to identify the users which we opine could be an added layer of security. We experimented with four conventional unlocking schemes: 4 Digit Pin, 3×3 “Connect the Dots”, Glow Pad, Slide Unlock and proposed one new unlocking mechanism, “Pressure Circle”. Six high-level features have been extracted from user touch activities and we designed our own verifier to identify users. For each unlocking scheme, experimental results generated from 992 impostor attacks performed on 32 user samples, show that users can be identified with an accuracy of up to 77.3% solely by considering their touch behavior. Overall, we achieve an average accuracy of 68.8%. We argue that, this inherent behavioral biometric trait when combined with the conventional unlocking schemes, would be less susceptible to attacks.

In this paper we discuss the potential of ordinary objects acting as human computer interfaces with an Inertial Measurement Unit, the Twiz, to capture a body's orientation and acceleration. The motivation behind this research is to develop a toolkit that enables end users to quickly prototype custom interfaces for artistic expressions through movement. Through an iterative design process we have enhanced existing technical implementations such as wireless data transfer, battery lifespan, two-way communication and data analysis including machine-learning techniques. We conducted object-making sessions and developed software prototypes for audio and visual feedback. We explored a range of experiments related to visual arts, dance, and music by attaching the Twiz to different types of objects to allow users to carry out impromptu interactions. As a result of this process we have gained a better understand of an object's expressive potential whilst capturing and analyzing its movement.

In this paper we present a preliminary design and initial assessment of a computational musical tabletop exhibit for children and teenagers at the Museum of Design Atlanta (MODA). We explore how participatory workshops can promote hands-on learning of computational concepts through making music. We also use a hands-on approach to assess informal learning based on maker interviews. Maker interviews serve to subjectively capture impromptu reflections of the visitors' achievements from casual interactions with the exhibit. Findings from our workshops and preliminary assessment indicate that experiencing and taking ownership of tangible programming on a musical tabletop is related to: ownership of failure, ownership through collaboration, ownership of the design, and ownership of code. Overall, this work suggests how to better support ownership of computational concepts in tangible programming, which can inform how to design self-learning experiences at the museum, and future trajectories between the museum and the school or home.

This paper introduces deep neural networks (DNNs) as add-on blocks to baseline feedback control systems to enhance tracking performance of arbitrary desired trajectories. The DNNs are trained to adapt the reference signals to the feedback control loop. The goal is to achieve a unity map between the desired and the actual outputs. In previous work, the efficacy of this approach was demonstrated on quadrotors; on 30 unseen test trajectories, the proposed DNN approach achieved an average impromptu tracking error reduction of 43% as compared to the baseline feedback controller. Motivated by these results, this work aims to provide platform-independent design guidelines for the proposed DNN-enhanced control architecture. In particular, we provide specific guidelines for the DNN feature selection, derive conditions for when the proposed approach is effective, and show in which cases the training efficiency can be further increased., Comment: 56th IEEE Conference on Decision and Control

This paper presents a learning-based approach for impromptu trajectory tracking for non-minimum phase systems, i.e., systems with unstable inverse dynamics. Inversion-based feedforward approaches are commonly used for improving tracking performance; however, these approaches are not directly applicable to non-minimum phase systems due to their inherent instability. In order to resolve the instability issue, existing methods have assumed that the system model is known and used pre-actuation or inverse approximation techniques. In this work, we propose an approach for learning a stable, approximate inverse of a non-minimum phase baseline system directly from its input-output data. Through theoretical discussions, simulations, and experiments on two different platforms, we show the stability of our proposed approach and its effectiveness for high-accuracy, impromptu tracking. Our approach also shows that including more information in the training, as is commonly assumed to be useful, does not lead to better performance but may trigger instability and impact the effectiveness of the overall approach., Comment: Accepted for publication in the IEEE Robotics and Automation Letters (RA-L), July 2018

Fervent and agile communication on social networking sites and in virtual organizations provides opportunities for potential issues to trigger individuals into individual action as well as the attraction and mobilization of like-minded individuals into an organization that is both physically and virtually emergent. Examples are the rapid pace of Arab Spring and the diffusion rate of the occupy movement. Previous organizational models lack the representational power to model spontaneous exigencies of a network organization that accounts for rapid rates of dissemination in impromptu networks. This model, therefore, is conceived in a life cycle for a prototypical, emergent networked organization and description of operations therein from formation to dissolution. After describing the life cycle, this article offers insights for a model of a successful emergent organization and an implemented example of a spacecraft organization of satellites.

The remote specially appointed systems comprise of a gathering of remote hubs that impart over a typical remote medium. The hubs impart without a framework, for example, base station, wired passage, and so forth. The foundation of the systems must be in an appropriated and decentralized way. In this manner, the many-sided quality of the systems is in the hubs self. The hubs must have the capacity to take care of system’s concern, for example, directing and security. Regardless of the specialized difficulties, the interests of the impromptu systems increment quickly as of late, in light of the fact that they bolster portability and are exceptionally appropriate for some troublesome circumstances, such protect mission, military, vehicular interchanges, and so forth. In this review article, I present the idea of remote impromptu systems and uncommonly portable specially appointed system (MANET), their engineering, purposes, applications, preferences, burdens, and correlation with framework systems.