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Hardware platforms for socially interactive robotics can be limited by cost or lack of functionality. This paper presents the overall system -- design, hardware, and software -- for Quori, a novel, affordable, socially interactive humanoid robot platform for facilitating non-contact human-robot interaction (HRI) research. The design of the system is motivated by feedback sampled from the HRI research community. The overall design maintains a balance of affordability and functionality. Initial Quori testing and a six-month deployment are presented. Ten Quori platforms have been awarded to a diverse group of researchers from across the United States to facilitate HRI research to build a community database from a common platform., 20 pages. 21 figures. This was accepted to and will be published to the IEEE Transactions on Robotics Journal

Burst is a common pattern in data streams which is characterized by a sudden increase in terms of arrival rate followed by a sudden decrease. Burst detection has attracted extensive attention from the research community. In this paper, we propose a novel sketch, namely BurstSketch, to detect bursts accurately in real time. BurstSketch first uses the technique Running Track to select potential burst items efficiently, and then monitors the potential burst items and capture the key features of burst pattern by a technique called Snapshotting. Experimental results show that our sketch achieves a 1.75 times higher recall rate than the strawman solution.

Since 2005 the Italian Research Conference on Digital Libraries is a yearly date for researchers on Digital Libraries and related topics, organized by the Italian Research Community. Over the years, IRCDL has become an essential national forum focused on digital libraries and associated technical, practical, and social issues. IRCDL encompasses the many meanings of the term digital libraries, including new forms of information institutions; operational information systems with all manner of digital content; new means of selecting, collecting, organizing, and distributing digital content; and theoretical models of information media, including document genres and electronic publishing.

COVID-19 has accelerated broad trends already in place toward remote research data collection and monitoring. This move implicates novel ethical and regulatory challenges which have not yet received due attention. Existing work is preliminary and does not seek to identify or grapple with the issues in a rigorous and sophisticated way. Here, we provide a framework for identifying and addressing challenges that we believe can help the research community realize the benefits of remote technologies while preserving ethical ideals and public trust. We organize issues into several distinct categories and provide points to consider in a table that can help facilitate ethical design and review of research studies using remote health instruments.

Arsenic contamination above allowable limit in groundwater is universally been identified as a critical threat with a high alarm in the African countries, Western USA and a lot of different developing countries. The increasing numbers of arsenicosis patients with their extremely grave and fatal fates, imposes severe stringency to the treatment strategies. Though numerous types of technologies were adopted regarding the abatement of arsenic from water but as far the generated sludge from arsenic removing groundwater treatment plant is concerned, scanty efforts were put forward towards wise stabilization/capturing/encapsulation of arsenic sludge to provide zero chances of leaching. Thus, arsenic with the ability of leaching back to soil and water while mixing with surface and under-ground water, still stands as a serious and open challenge to the scientific communities for their safe disposal. With an extensive review towards available technologies for safe arsenic sludge disposal, this paper reviews the potential of stabilization of arsenic recalcitrant sludge employing concrete, polymer compounds, fly ash, adding chemicals and by formation of bricks and cement blocks. Such approaches stand out as flexible and economic strategies while permanently capturing stabilized arsenic in solid matrices while sustaining the strength of the building material. Thus, the requirements of coarse aggregate for the formation of concrete blocks could also be reduced. Such a review is expected to initiate influential policies among the research community to combat against the widespread risk of reluctant disposal practices of lethal arsenic sludge generated from arsenic abating water purification methodologies.

There is an upsurge in the drive for better understanding of the processes, which control the combustion in modern engines. Large-scale intake generated flow structures, such as swirl and tumble, are known to have a significant effect on the combustion behaviour and have been widely studied by the automotive research community. Traditionally, the standard tools available to assess the in-cylinder flow behaviour inside an engine are either steady-state flow rigs or research engines with optical access. Steady-state flow rigs are used to evaluate the bulk motion using some integral parameters. Whilst the optical measurements are used to investigate detailed in-cylinder flow conditions. To assess the recent developments, principles and applications of stereoscopic particle image velocimetry to engine flow measurements are presented and discussed. © 2021 THE AUTHORS

Broad impact in the research community may be anticipated when a material's properties are capable of being manipulated artificially. Such a possibility has been explored here in the FAPbI3 perovskite structure of perovskite solar cells, which involves undesirable phase transition at working temperature, despite many attempts to resolve the issue. Essential steps have been taken here toward solving this problem by adopting an opposite strategy to incorporate the water molecules into the perovskite structure under the current materials framework by new structural physics maneuvering. The secondary bonding of the perovskite structure has been relocated, which altered the microstructure to remove the internal strain that caused the phase transition, resulting in not only a 10-fold enhancement in the moisture/structure stability but also a bandgap comparable to that of the favored α-FAPbI3. All this opens an unprecedented avenue in perovskite research, which will hopefully be of intrinsic interest to the broad materials research community as well.

Robotic manipulation is fundamental to many realworld applications; however, it is an unsolved problem that remains a very active research area. New algorithms for robot perception and control are frequently proposed by the research community. These methods must be thoroughly evaluated in realistic conditions before they can be adopted by industry. This process can be extremely time consuming, mainly due to the complexity of integrating different hardware and software components.

Individuals throughout the world are being recruited into studies to examine the social impacts of coronavirus disease 2019 (COVID-19). While previous literature has illustrated how research participation can impact distress and wellbeing, to the authors’ best knowledge no study has examined this in the COVID-19 context. Using an innovative approach, this study analyses the impacts of participation in a COVID-19 survey in Australia on subjective wellbeing through a survey experiment. At a population level, we find no evidence that participation impacts subjective wellbeing. However, this may not hold for those with mental health concerns and those living in financial insecurity. These findings provide the research community with a deeper understanding of the potential wellbeing impacts from COVID-19-related research participation.

Considerable work assesses research productivity in political science. Yet, the latter work has not explicated how research careers can differ across time and individuals and neither has it considered such careers in teaching institutions. This article presents an empirically validated typology for research career paths for political scientists who began their career in a teaching institution. The typology demonstrates that many scholars follow paths different from the most conventional expectations, and research “success” by measures of publications and citations is associated with only some of those paths. Thus, existing research on aggregate publications and citations likely addresses only a subset of the career paths described here. Understanding research activities in our profession requires accounting for various career paths, their motivations, and their place in our research community.

Food has a direct impact on an individual's life and is a significant area for the research community. Computational techniques in food-related computing are performed to address various food-related issues in the field of agronomy, medicine, biology etc. In this paper, we present a comprehensive review of research exclusively on segmentation techniques used for food computing. This paper illustrates the viable segmentation techniques used for food image segmentation. It also provides a comprehensive review of the same. A relevant survey on 66 research papers has been done to provide different food image segmentation techniques. A comparative study among these techniques is also done based on different parameters like type of algorithm, segmentation technique, dataset, and accuracy. Moreover, this paper focuses on research challenges in food recognition. Also a framework has been proposed in this paper to overcome the problem of watershed and OTSU algorithm.

Lung cancer is the most dangerous of all forms of cancer and it has the highest occurrence rate, world over. Early detection of lung cancer is a difficult task. Medical images generated by computer tomography (CT) are being used extensively for lung cancer analysis and research. However, it is essential to have a well-organized image database in order to design a reliable computer-aided diagnosis (CAD) tool. Identifying the most appropriate dataset for the research is another big challenge. The objective of this paper is to present a review of literature related to lung CT datasets. The Cancer Imaging Archive (TCIA) consortium collates different types of cancer datasets and permits public access through an integrated search engine. This survey summarizes the research work done using lung CT datasets maintained by TCIA. The motivation to present this survey was to help the research community in selecting the right lung dataset and to provide a comprehensive summary of the research developments in the field.

The continuously growing universal demand for energy and declining production is fascinating the research community to invent novel technologies. The oil and gas industry is not only focusing on th...

Nowadays, the impact of refrigeration on the environment is considered as an important problem in the research community. This phenomenon necessitates the need to improve the performance of refrige...

Facial feature extraction (FFE) is considered as a challenging area for computer vision and artificial intelligence research community. There are numerous application domains of face recognition such as demographic classification and facial disease classification. In last few years, numerous feature extraction approaches are proposed. This review focuses on studies that exclusively use facial asymmetry as one of the main subject-specific facial characteristics for FFE. This paper provides a review about the related research conducted in the past two decades. First, we summarize the conventional FFE approaches and their main algorithms. Application of deep networks to facial asymmetry based FFE approaches is then presented. Multi-network deep models suitable for asymmetry-based FFE for different applications are also focused in this review. We presented the details about the publicly available face datasets, evaluation metrics, and comparison of the state-of-the-art results is also presented. The directions of asymmetry-based FFE for future research is also presented to provide an awareness about the existing and future trends. This review is presented to provide directions and ideas for future research in the field of facial asymmetry.

Multi-Source Information Fusion (MSIF) is a comprehensive and interdisciplinary subject, and is referred to as, multi-sensor information fusion which was originated in the 1970s. Nowadays, the types and updates of data are becoming more multifarious and frequent, which bring new challenges for information fusion to deal with the multi-source data. Consequently, the construction of MSIF models suitable for different scenarios and the application of different fusion technologies are the core problems that need to be solved urgently. Rough set theory (RST) provides a computing paradigm for uncertain data modeling and reasoning, especially for classification issues with noisy, inaccurate or incomplete data. Furthermore, due to the rapid development of MSIF in recent years, the methodologies of learning under RST are becoming increasingly mature and systematic, unveiling a framework which has not been mentioned in the literature. In order to better clarify the approaches and application of MSIF in RST research community, this paper reviews the existing models and technologies from the perspectives of MSIF model (i.e., homogeneous and heterogeneous MSIF model), multi-view rough sets information fusion model (i.e., multi-granulation, multi-scale and multi-view decisions information fusion models), parallel computing information fusion model, incremental learning fusion technology and cluster ensembles fusion technology. Finally, RST based MSIF related research directions and challenges are also covered and discussed. By providing state-of-the-art understanding in specialized literature, this survey will directly help researchers understand the research developments of MSIF under RST.

Selective laser melting (SLM) has received huge attention recently by the research community due to feasibility of realization of tailored intricate metallic structures having applications in biomedical, aviation and energy industries. SLM of Ti6Al4 V is found to be one of the most demandable and potential area. Present work is aimed for critical review of noteworthy contributions in terms of SLM process parameters, defects and various post processing techniques considered by various researchers. Proper selection of process parameters can arrest the tendency of defect generation and appropriate post treatment results in improvement in mechanical behavior in comparison with as-built SLMed Ti6Al4 V components. Efforts have been made to identify correlations between SLM parameters, specially scan speed, laser power, hatch spacing and layer thickness, with prominent defects. Different post processing approaches considered by important research studies have been extensively analyzed and their impact on defects and mechanical behavior especially fatigue performance and biocompatibility of SLM-fabricated Ti6Al4 V alloy is revealed. Many remedies have been identified in the form of optimization of process parameters and post processing techniques. The outcomes of work have clearly facilitated in developing a better scientific understanding of SLMed Ti6Al4V alloy and have also opened many dimensions for future investigations.

Today Perovskite solar cell (PSC) has achieved efficiency close to 26%, surpassing the efficiencies of well- known Dye-Sensitized Solar cells (DSSC), CdTe-based solar cells, etc. Ease of preparing perovskite solutions and convenient deposition technique has given them added advantage over other contemporary competitors. This has also made them an economically feasible option. Since the inception of PSCs in 2009, a lot of improvements have been done in various aspects like composition, synthesis technique, fabrication method, and interface study. However, today it is crucial to think about the commercialization of PSCs. In this direction, the stability of PSCs has been a long-standing question. This review focuses on the key aspects of perovskite stability. Challenges posed by environmental factors like moisture, oxygen, temperature, and light are still unanswered. The lead toxicity of PSCs demands a potential substitute of lead with no compromise in their efficiency. The lead-free approach of PSCs and their commercialization has been discussed separately. A suitable emphasis has been given to the encapsulation of solar cells, as we find it a necessary part of the future commercialization process. This review has been addressed with an ideology to provide overall knowledge on the stability issues and challenges concerned with the PSCs, to attract young research community towards this emerging field.

Software engineering (SE) researchers and research networks from emerging communities are often not visible in already established Software Engineering venues for a multitude of reasons. This limits the opportunities and mutual bene ts that can arise from collaborations between global and emerging Software Engineer- ing networks. This article focuses on a rst attempt to provide a map of the African software engineering research community with focus on the networks of two big East African Universities. We hope that this very initial mapping e ort will help to raise aware- ness in the international community about the variety of software engineering research in Africa. We formulate some suggestions for making our academic Software Engineering community more inclusive.

The optimal scheduling of water pumping systems has gained a lot of attention in the research community. Nevertheless, the advancements obtained in water distribution networks were not transferred ...

Artificial intelligence (AI) is defined as the ability of machines to perform tasks that are usually associated with intelligent beings. Argument and debate are fundamental capabilities of human intelligence, essential for a wide range of human activities, and common to all human societies. The development of computational argumentation technologies is therefore an important emerging discipline in AI research1. Here we present Project Debater, an autonomous debating system that can engage in a competitive debate with humans. We provide a complete description of the system’s architecture, a thorough and systematic evaluation of its operation across a wide range of debate topics, and a detailed account of the system’s performance in its public debut against three expert human debaters. We also highlight the fundamental differences between debating with humans as opposed to challenging humans in game competitions, the latter being the focus of classical ‘grand challenges’ pursued by the AI research community over the past few decades. We suggest that such challenges lie in the ‘comfort zone’ of AI, whereas debating with humans lies in a different territory, in which humans still prevail, and for which novel paradigms are required to make substantial progress. An artificial intelligence system that can engage in a competitive debate with humans is presented.

This article aims at collecting and discussing the results reached by the research community regarding the study of the ground effect on small rotorcraft unmanned aerial vehicles, especially from the modeling and control point of view. Rotorcraft performance is affected by the presence of the ground or any other boundary that alters the flow into the rotors. Specifically, the ground effect can induce perturbations in the flight stability, when operating near the ground. For a rotorcraft, an accident is likely to happen when the vehicle leaves or enters the ground effect region, which may cause crashes and property damages. Today, the use of unmanned aerial vehicles has grown widespread, which raises safety concerns when they are flying at very low altitudes and near the ground. Consequently, studying the influence of the ground over rotorcrafts is of paramount importance for general safety. Also, these investigations can be used to design systems of guidance, navigation, and control. In this review, we break down the most relevant works to date. We discuss aspects related to modeling, control, and application of the ground effect for small-scale multirotors, as well as other aerodynamic proximity effects, such as the ceiling and wall effects. We conclude by mentioning potential avenues of research when studying the ground effect from the point of view of the robotics and artificial intelligence fields.

Considerable research assesses research success in political science. Yet this work has not considered widespread findings that scholars can follow various career research paths that complicate how we envision scholarly success. Further, we have no systematic information on these career paths in any scientific discipline. I present an empirically validated research career-path typology for political scientists who began their career in a research institution. The typology demonstrates that many scholars follow paths different from the most conventional expectations, and research success by measures of publications and citations is associated with only some of these paths. Thus, existing research on aggregate publications and citations likely addresses only a subset of the career paths revealed in this article. Understanding research activities in our profession requires consideration of the various career paths, their motivation, and their place in our research community.

Graphyne, a new two-dimensional carbon allotrope containing both sp and sp2 hybridized states, is a current topic of focus in nanomaterial research community. Taking gamma (γ)-graphyne as an exampl...

The influential stage of Internet of Things (IoT) has reformed all fields of life in general but specifically with the emergence of artificial intelligence (AI) has drawn the attention of researchers into a new paradigm of life standard. This revolution has been accepted around the globe for making life easier with the use of intelligent devices such as smart sensors, actuators, and many other devices. AI-enabled devices are more intelligent and capable of doing a specific task which saves a lot of resources and time. Different approaches are available in the existing literature to tackle diverse issues of real life based on AI and IoT systems. The role of decision-making has its own importance in the AI-enabled and IoT systems. In-depth knowledge of the existing literature is dire need of the research community to summarize the literature in effective way by which practitioners and researchers can benefit from the prevailing proofs and suggest new solutions for solving a particular problem of AI-enabled sensing and decision-making for the IoT system. To facilitate research community, the proposed study presents a systematic literature review of the existing literature, organizes the evidences in a systematic way, and then analyzes it for future research. The study reported the literature of the last 5 years based on the research questions, inclusion and exclusion criteria, and quality assessment of the selected study. Finally, derivations are drawn from the included paper for future research. National Natural Science Foundation of China (51804248).

In recent times, text detection in the wild has significantly raised its ability due to tremendous success of deep learning models. Applications of computer vision have emerged and got reshaped in a new way in this booming era of deep learning. In the last decade, research community has witnessed drastic changes in the area of text detection from natural scene images in terms of approach, coverage and performance due to huge advancement of deep neural network based models. In this paper, we present (1) a comprehensive review of deep learning approaches towards scene text detection, (2) suitable deep frameworks for this task followed by critical analysis, (3) a categorical study of publicly available scene image datasets and applicable standard evaluation protocols with their pros and cons, and (4) comparative results and analysis of reported methods. Moreover, based on this review and analysis, we precisely mention possible future scopes and thrust areas of deep learning approaches towards text detection from natural scene images on which upcoming researchers may focus.

Li metal has been widely recognized as a promising anode candidate for high-energy-density batteries. However, the inherent limitations of Li metal, that is, the low Coulombic efficiency and dendrite issues, make it still far from practical applications. In short, the low Coulombic efficiency shortens the cycle life of Li metal batteries, while the dendrite issue raises safety concerns. Thanks to the great efforts of the research community, prolific fundamental understanding as well as approaches for mitigating Li metal anode safety have been extensively explored. In this Review, Li electrochemical deposition behaviors have been systematically summarized, and recent progress in electrode design and electrolyte system optimization is reviewed. Finally, we discuss the future directions, opportunities, and challenges of Li metal anodes.

Identification and detection of pathogens are the strategies towards the elucidation of infectious disease. There are growth and significance in the field of Nanomedicine. Present circumstance here is a need in research surrounding the topic of nanoparticle (NP) properties, mainly concerned with mechanisms of action with the bacterial cell. Metal NPs are now showing strong antibacterial activity. The bacterial cells have more resistance towards the antibiotics, which has met the research community to innovate novel antibiotic agents. The ability to detect micro-organisms has become an essential requirement in food-processing technology for clinical and environmental monitoring. In keeping with traditional methods of detection, those are time taking and develop the result solitary has to wait for long hours. Nowadays, there are many rapid and sensitive methods developed by the effort of researchers and inventors. Nanoparticles act as the alternative to pathogen detection. The size, shape and morphology of nanoparticles play a role for the pathogen nanoparticle communication. Here, in this review article, we discuss the nanoparticle and bacterial cell interaction. The different methods based on culture-based, optical sensing-based and nanoparticle-based methods all discussed in this chapter.

Route planning satisfied multiple requests is an emerging branch in the route planning field and has attracted significant attention from the research community in recent years. The prevailing studies focus only on seeking a route by minimizing a single kind of Travel Cost, such as trip time or distance, among others. In reality, most users would like to choose an appropriate route, neither fastest nor shortest route. Usually, a user may have multiple requirements, and an appropriate route would satisfy all requirements requested by the user. In fact, planning an appropriate route could be formulated as a problem of Multi-weight Multi-destination Route Planning with Deadlines Constraints (MWMDRP-DC). In this article, we propose a framework, namely, MWMD-Router, which addresses the MWMDRP-DC problem comprehensively. To consider the travel costs with time-variation, we propose not only four novel dynamic graph miner to extract travel costs that reveal users’ requirements but also two new algorithms, namely, Basic MWMD Route Planning and Advanced MWMD Route Planning , to plan a route that satisfies deadline requirements and optimizes another criterion like travel cost with time-variation efficiently. To the best of our knowledge, this is the first work on route planning that considers handling multiple deadlines for multi-destination planning as well as optimizing multiple travel costs with time-variation simultaneously. Experimental results demonstrate that our proposed algorithms deliver excellent performance with respect to efficiency and effectiveness.

Micro-nanoscale core–shell particles are distinguishable from other particle types because of their unique composition. Core-shell particles combine the features of both the core and shell materials, while exhibiting smart properties resulting from their materials. In the past few years, the research community has paid increasing attention to the generation and application of core–shell structures. The present review focuses on the core–shell microparticles, which have found practical applications in various fields. The novel properties of the core–shell microparticles make them extremely suitable for pharmaceutical and biomedical applications, including cell encapsulation, cell study, targeted drug delivery, controlled drug release, food industry, catalysis, and environmental monitoring. This paper also systematically reviews the different classes of core–shell microparticles based on their respective materials. Moreover, an overview of conventional and more recent microfluidic methods for the generation of core–shell microparticles is presented. The unique advantages of the microfluidic approaches are highlighted.

In the late 1970s, the embryonic UK research community in molecular simulation – physicists and physical chemists – organised itself around CCP5, one of a set of Collaborative Computational Projects in different fields. CCP5 acted to develop and use the software required by an evolving and expanding scientific agenda, to exploit quickly and efficiently the revolution in computing hardware and to educate and nurture the careers of future generations of researchers in the field. This collaboration formally began in 1980, and is still fully active now, 40 years later. Today, molecular simulation techniques, many of them pioneered by CCP5, are now used very widely, including in several other CCPs in the UK’s current family of Collaborative Computational Projects. This article tells the story of molecular simulation in the UK, with CCP5 itself at centre stage, using the written records in the CCP archives. The authors were, or are, all personally involved in this story.

The weakness of the security measures implemented on Internet of Things (IoT) devices, added to the sensitivity of the data that they handle, has created an attractive environment for cybercriminals to carry out attacks. This has caused a substantial increase in the number of cyberincidents, requiring the opening of digital investigations in order to shed light on what has occurred. However, the characteristics of this new environment, such as its variety of contexts, make it impossible to use the methodology followed until now in conventional analysis. Therefore, a new common procedure is needed to ensure that IoT examinations are carried out in a complete and efficient manner. In this article, after reviewing the methodological requirements of IoT forensics, and studying the suggestions made by the research community, a methodology to perform investigations in a certain context of the IoT environment is proposed. In addition, its practicality is evaluated in three different security incident scenarios, proving its effectiveness and appropriateness to be used in future cases.

The biosolids management is becoming an increased concern for the wastewater sector in recent times due to production of large volume of biosolids, their higher processing costs and the presence of emerging contaminants. The pyrolysis of biosolids is gaining significant interest in the industry sector as well as research community over the last decade due to its ability to reduce biosolids volume, produce high-value biochar product and minimise the risk associated with contaminants. This paper aims to critically review the literature on biosolids management techniques and their current challenges, biosolids characteristics and its suitability for pyrolysis, pyrolysis product characterisation from different reactor designs and biochar application as a soil amendment, adsorbent and catalyst. The efforts have also been made to critically summarise studies on the process modelling activities and techno-economic assessments including some key pilot-scale demonstrations of recent time. The review concludes that biosolids to biochar can be an effective alternative to biosolids management; however, its commercial viability is limited in the current scenario. In the end, efforts have been made to highlight current challenges including research gaps and future perspectives in improving its commercial viability.

We report quantum phenomena in spin-orbit-coupled single crystals that are synthesized using an innovative technology that “field-alters” crystal structures via application of magnetic field during crystal growth. This study addresses a major challenge facing the research community today: A great deal of theoretical work predicting exotic states for strongly spin-orbit-coupled, correlated materials has thus far met very limited experimental confirmation. These conspicuous discrepancies are due in part to the extreme sensitivity of these materials to structural distortions. The results presented here demonstrate that the field-altered materials not only are much less distorted but also exhibit phenomena absent in their non-altered counterparts. The field-altered materials include an array of 4d and 5d transition metal oxides, and three representative materials presented here are Ba4Ir3O10, Ca2RuO4, and Sr2IrO4. This study provides an approach for discovery of quantum states and materials otherwise unavailable.

This paper reports on the different engineering, social and financial challenges behind the building and deploying electric energy monitoring and eco-feedback technology in real-world scenarios, which despite being relevant to the research community are seldom reported in the literature. The objectives of this paper are two-fold: First, discuss the technical and social constraints of real-world deployments. This includes, for example, hardware and software requirements, and issues related to security and intrusiveness of the monitoring solutions. Second, identify and understand the costs associated with developing and deploying such systems. These include hardware costs and consumed energy. To this end, we rely on over five years of experience developing and improving a non-intrusive energy monitoring research platform to enable the deployment of long and short-term studies of eco-feedback technology. During this time, two versions of that platform were deployed in 50 homes for periods that lasted between 6 and 18 consecutive months. By iteratively developing and deploying our sensing and eco-feedback infrastructures, we managed to build upon previous findings and lessons learned to understand how to create, deploy, and maintain such systems. Concurrently, we gained insights regarding what are some of the most relevant costs associated with running such experiments.

For the past decade, literatures in sampling theory show that estimation of mean is one of the challenging aspects which has received wide attention from research community with the aim of obtainin...

Today, several applications and technologies require a suitable jitter characterization. For this reason, measurement methods and instruments aimed at quantifying and analyzing jitter continue to be proposed and developed by the research community and manufacturers.

Orodispersible Film (ODF) is a promising and progressive dosage form that offers exceptional drug delivery benefits to patients. Indeed, they are the most transformational alternatives to traditional/conventional dosage forms such as tablets and capsules. ODFs are portable and highly comfortable for self-administration by patients with swallowing problems. The key to gain end-user acceptance is to have an ODF with outstanding quality. Poor quality may lead to choking or spitting, accordingly leading to a lack of compliance. It is vital to employ suitable experimental methodologies that facilitate characterization or determination of the quality of ODF. Nonetheless, there are no standard techniques prescribed in official compendia of any country. But, there is a consensus in the thin-film research community about the characterization techniques that one relies on deciding the quality of an ODF. We review various experimental techniques and highlight its importance in determining the performance and quality of an ODF. We provide a relatively novel and inventive disintegration test apparatus, which works using ‘Light Dependent Resistor (LDR) and Light Emitting Diode (LED) sensors’ for clear and accurate determination of start and end disintegration time of an ODF.

Growing awareness about sustainable development and the environmental problems involved in using nonbiodegradable materials has motivated the research community to develop environment-friendly materials. Developments have been achieved in the field of natural fibers and biopolymers, still there remain unanswered questions regarding the high-quality cost-effective manufacturing of natural fiber reinforced composites. The natural fiber-based polymeric composites are being used extensively in engineering applications, especially in the nonstructural parts and components. Near-net processing techniques such as compression molding, extrusion, and injection molding are well-developed for natural fiber reinforced composites. However, secondary processes such as joining, machining, and surface modification are still unexplored and need to be investigated in detail. The present research endeavor is an attempt to experimentally investigate the adhesive joining behavior of jute/sisal reinforced epoxy composites. The laminates based on three different material configurations in woven mat form, namely, pure jute, pure sisal, and hybrid jute/sisal reinforced epoxy have been fabricated by hand layup process. Different lap joint configurations with through holes in adherends overlapping area have been investigated. It has been established experimentally that the holes in the adherends provide a hinge-effect in the overlapping area and help in defining the failure load of the composite joint. The different arrangement of holes has been investigated and the best design of hole arrangement has been proposed for adhesive joining of jute/sisal fiber reinforced epoxy laminates. It was found that the holes (filled with an epoxy adhesive) in the overlap area result in 6–18% improvement in the failure load for different materials as compared to the joints with only adhesive bonding. Moreover, the field-emission scanning electron microscopy micrographs have been used to understand the failure mechanism of the adhesively bonded natural fiber reinforced composite laminates.

Due to the complex and dynamic environment of social media, user generated contents (UGCs) may inadvertently leak users’ personal aspects, such as the personal attributes, relationships and even the health condition, and thus place users at high privacy risks. Limited research efforts, thus far, have been dedicated to the privacy detection from users’ unstructured data (i.e., UGCs). Moreover, existing efforts mainly focus on applying conventional machine learning techniques directly to traditional hand-crafted privacy-oriented features, ignoring the powerful representing capability of the advanced neural networks. In light of this, in this article, we present a fine-grained privacy detection network (GrHA) equipped with graph-regularized hierarchical attentive representation learning. In particular, the proposed GrHA explores the semantic correlations among personal aspects with graph convolutional networks to enhance the regularization for the UGC representation learning, and, hence, fulfil effective fine-grained privacy detection. Extensive experiments on a real-world dataset demonstrate the superiority of the proposed model over state-of-the-art competitors in terms of eight standard metrics. As a byproduct, we have released the codes and involved parameters to facilitate the research community.

In last years Artificial Intelligence presented a tremendous progress by offering a variety of novel methods, tools and their spectacular applications. Besides showing scientific breakthroughs it attracted interest both of the general public and industry. It also opened heated debates on the impact of Artificial Intelligence on changing the economy and society. Having in mind this international landscape, in this short paper we discuss the Polish AI research community, some of its main achievements, opportunities and limitations. We put this discussion in the context of the current developments in the international AI community. Moreover, we refer to activities of Polish scientific associations and their initiative of founding Polish Alliance for the Development of Artificial Intelligence (PP-RAI). Finally two last editions of PP-RAI joint conferences are summarized.

Certain research strands can yield “forbidden knowledge”. This term refers to knowledge that is considered too sensitive, dangerous or taboo to be produced or shared. Discourses about such publication restrictions are already entrenched in scientific fields like IT security, synthetic biology or nuclear physics research. This paper makes the case for transferring this discourse to machine learning research. Some machine learning applications can very easily be misused and unfold harmful consequences, for instance, with regard to generative video or text synthesis, personality analysis, behavior manipulation, software vulnerability detection and the like. Up till now, the machine learning research community embraces the idea of open access. However, this is opposed to precautionary efforts to prevent the malicious use of machine learning applications. Information about or from such applications may, if improperly disclosed, cause harm to people, organizations or whole societies. Hence, the goal of this work is to outline deliberations on how to deal with questions concerning the dissemination of such information. It proposes a tentative ethical framework for the machine learning community on how to deal with forbidden knowledge and dual-use applications.

Visual content has a protagonist role in this age of data revolution. These days, computer vision research community is fascinated towards application of convolution neural networks and transfer le...

Reidentification-risk analysis and anonymity have received a great deal of attention in the last two decades. While the research community has been developing several privacy notions and the algorithms to achieve them, these tools have faced difficulties in being transferred to the wider audience of practitioners, for they require a considerable amount of data privacy technical knowledge. We demonstrate X 2 R 2 (Explainable Explorative Reidentification Risk), a data anonymization tool for the laymen. X 2 R 2 guides the user through a transparent explorative process, during which the existing reidentification risks are explained and quantified, possible data transformation options are recommended, and the consequences of these operations, in terms of privacy risk and data utility, are clearly shown.

Purpose This paper aims to report an interview study investigating knowledge protection practices in a collaborative research and innovation project centred around the semi-conductor industry. The authors explore which and how knowledge protection practices are applied and zoom in on a particular one to investigate the perspective of three stakeholders which collaborate: the SUPPLIER of a specialised machine, the APPLIER of this machine and a SCHOLAR who collaborates with both, in an effort to develop a grey-box model of the machine and its operation. Design/methodology/approach A total of 33 interviews have been conducted in two rounds: 30 interviews explore knowledge protection practices applied across a large project. Qualitative content analysis is applied to determine practices not well covered by the research community. A total of three follow-up interviews inspect one specific collaboration case of three partners. Quotes from all interviews are used to illustrate the participants’ viewpoints and motivation. Findings SCHOLAR and APPLIER communicate using a data-centric knowledge protection practice, in that concrete parameter values are sensitive and hidden by communicating data within a wider parameter range. This practice balances the benefit that all three stakeholders have from communicating about specifics of machine design and operations. The grey-box model combines engineering knowledge of both SUPPLIER and APPLIER. Practical implications The line of thought described in this study is applicable to comparable collaboration constellations of a SUPPLIER of a machine, an APPLIER of a machine and a SCHOLAR who analyses and draws insights out of data. Originality/value The paper fills a research gap by reporting on applied knowledge protection practices and characterising a data-centric knowledge protection practice around a grey-box model.

Metal-organic frameworks (MOFs) have captivated the research community due to their modular crystal structure that can be tailored to suit diverse applications. However, identifying ideal MOFs for ...

Over the last couple of decades, the synchrotron radiation research community has witnessed tremendous advancement in the field of X-ray imaging and microscopy. Continuing enhancement of the light ...

The SUM'20 workshop was held at the 13th ACM International WSDM Conference on Web Search and Data Mining (WSDM 2020) in Houston, Texas. The purpose of the workshop was to stimulate the research community to explore open challenges in building systems that can capture the user's state, context and goals, as well as effectively use these for leveraging intelligent user-centric systems in a wide range of applications. The workshop incorporated different plenary sessions and contributed talks. The workshop website and proceedings are available at https://www.k4all.org/event/wsdmsum20.

Scheduling with multiple agents and learning effects has attracted growing attention of the scheduling research community. However, scheduling research together with learning, multiple agents and r...

Given the central role of webcams in monitoring physical surroundings, it behooves the research community to understand the characteristics of webcams' distribution and their privacy/security implications. In this paper, we conduct the first systematic study on live webcams from both aggregation sites and individual webcams (webpages/IP hosts). We propose a series of efficient, automated techniques for detecting and fingerprinting live webcams. In particular, we leverage distributed algorithms to detect aggregation sites and generate webcam fingerprints by utilizing the Graphical User Interface (GUI) of the built-in web server of a device. Overall, we observe 0.85 million webpages from aggregation sites hosting live webcams and 2.2 million live webcams in the public IPv4 space. Our study reveals that aggregation sites have a typical long-tail distribution in hosting live streams (5.8% of sites contain 90.44% of live streaming contents), and 85.4% of aggregation websites scrape webcams from others. Further, we observe that (1) 277,239 webcams from aggregation sites and IP hosts (11.7%) directly expose live streams to the public, (2) aggregation sites expose 187,897 geolocation names and more detailed 23,083 longitude/latitude pairs of webcams, (3) the default usernames and passwords of 38,942 webcams are visible on aggregation sites in plaintext, and (4) 1,237 webcams are detected as having been compromised to conduct malicious behaviors.