Search

Numerous state-of-the-art recommendation frameworks employ deep neural networks in Collaborative Filtering (CF). In this paper, we propose a cross feature fusion neural network (CFFNN) for the enhancement of CF. Existing studies overlook either user preferences for various item features or the relationship between item features and user features. To solve this problem, we construct a cross feature fusion network to enable the fusion of user features and item features as well as a self-attention network to determine users' preferences for items. Specifically, we design a feature extraction layer with multiple MLP (Multilayer Perceptrons) modules to extract both user features and item features. Then, we introduce a cross feature fusion mechanism for an accurate determination of the relationship between different user-item interactions. The features of users and items are crossly embedded and then fed into a prediction network. The attention mechanism enables the model to focus on more effective features. The effectiveness of CFFNN model is demonstrated through extensive experiments on four real-world datasets. The experimental results indicate that CFFNN significantly outperforms the existing state-of-the-art models, with a relative improvement of 3.0\% to 12.1\% on hit ratio (HR) and normalized discounted cumulative gain (NDCG) compared with the baselines.

In social networks, one of the most significant challenges is how to estimate the tweet popularity. Prior studies focus on leveraging different aspects of just a single tweet, while ignoring the impact of historical tweets. In this paper, we propose to leverage such historical information and rethink the problem of tweet popularity estimation. From historical information, there are two important factors that can be extracted: (1) user reputation feature, which can represent coarse-grained level of tweet popularity; (2) tweet related features, which can represent fine-grained level of tweet popularity. To incorporate these two factors from historical information, we design a novel deep neural architecture, a Bi-layered LSTM for tweet popUlarity Estimation, called DeepBlue. Specifically, we first propose a user-reputation aware mechanism to combine coarse-grained and fine-grained level estimation into a united LSTM model. We also design a content attention mechanism to consider different impacts of historical tweets in terms of content similarity. We then propose a time aware mechanism to address the time interval irregularity issue. Finally, we apply the Poisson regression model to obtain the overall loss for tweet popularity estimation. Extensive experiments demonstrate the superiority of our proposed approach to other state-of-the-arts in terms of MAE and SRC.

Card fraud is an important issue and incurs a considerable cost for both cardholders and issuing banks. Contemporary methods apply machine learning-based approaches to detect fraudulent behavior from transaction records. But manually generating features needs domain knowledge and may lay behind the modus operandi of fraud, which means we need to automatically focus on the most relevant fraudulent behavior patterns in the online detection system. Therefore, in this work, we propose a spatial-temporal attention-based graph network (STAGN) for credit card fraud detection. In particular, we learn the temporal and location-based transaction graph features by a graph neural network first. Afterwards, we employ the spatial-temporal attention on top of learned tensor representations, which are then fed into a 3D convolution network. The attentional weights are jointly learned in an end-to-end manner with 3D convolution and detection networks. After that, we conduct extensive experiments on the real-word card transaction dataset. The result shows that STAGN performs better than other state-of-the-art baselines in both AUC and precision-recall curves. Moreover, we conduct empirical studies with domain experts on the proposed method for fraud detection and knowledge discovery; the result demonstrates its superiority in detecting suspicious transactions, mining spatial and temporal fraud hotspots, and uncover fraud patterns. The effectiveness of the proposed method in other user behavior-based tasks is also demonstrated. Finally, in order to tackle the challenges of big data, we integrate our proposed STAGN into the fraud detection system as the predictive model and present the implementation detail of each module in the system.

Code review is a powerful technique to ensure high quality software and spread knowledge of best coding practices between engineers. Unfortunately, code reviewers may have biases about authors of the code they are reviewing, which can lead to inequitable experiences and outcomes. In principle, anonymous author code review can reduce the impact of such biases by withholding an author's identity from a reviewer. In this paper, to understand the engineering effects of using author anonymous code review in a practical setting, we applied the technique to 5217 code reviews performed by 300 software engineers at Google. Our results suggest that during anonymous author code review, reviewers can frequently guess authors identities; that focus is reduced on reviewer-author power dynamics; and that the practice poses a barrier to offline, high-bandwidth conversations. Based on our findings, we recommend that those who choose to implement anonymous author code review should reveal the time zone of the author by default, have a break-the-glass option for revealing author identity, and reveal author identity directly after the review.

Soft gripping provides the potential for high performance in challenging tasks through morphological computing; however, design explorations are limited by a combination of a difficulty in generating useful models and use of laborious fabrication techniques. We focus on a class of grippers based on granular jamming that are particularly difficult to model and introduce a "one shot" technique that exploits multimaterial three-dimensional (3D) printing to create entire grippers, including membrane and grains, in a single print run. This technique fully supports the de facto physical generate-and-test methodology used for this class of grippers, as entire design iterations can be fitted onto a single print bed and fabricated from Computer-Aided Design (CAD) files in a matter of hours. Initial results demonstrate the approach by rapidly prototyping in materio solutions for two challenging problems in unconventional design spaces; a twisting gripper that uses programmed deformations to reliably pick a coin, and a multifunctional legged robot paw that offers the ability for compliant locomotion over rough terrains, as well as being able to pick objects in cluttered natural environments. The technique also allows us to easily characterize the design space of multimaterial printed jamming grippers and provide some useful design rules. The simplicity of our technique encourages and facilitates creativity and innovation. As such, we see our approach as an enabling tool to make informed principled forays into unconventional design spaces and support the creation of a new breed of novel soft actuators.

Profiling side-channel attacks in which an adversary creates a “profile” of a sensitive device and uses such profile to model a target device with similar implementation has received the lion’s share of attention in the recent years. In particular, template attacks are known to be the most powerful profiling side-channel attacks from an information theoretic point of view. When launching such attack, the adversary first builds a model based on the leakage of the profiling (training) device in his disposal which is then exploited in the second phase of the attack (i.e., matching) to extract the key from the target device. Discrepancies between the device used for modeling and the target device affect the attack success. The effect of process variation and temperature misalignment between the profiling and target devices in the template attack’s success has been studied extensively in literature, while the impact of device aging on the template attack’s success is yet to be investigated thoroughly. This paper moves one step forward and studies the impact of device aging, mainly Bias temperature Instability (BTI) and Hot Carrier Injection (HCI), in the devices that have been protected against power analysis attacks via dual rail logics. In particular, we focus on the Wave Dynamic Differential Logic (WDDL) circuits, and via extensive transistor-level simulations we will show how device aging misalignments between the profiling and target devices can hinder template attacks for both unprotected and WDDL protected counterparts. We mounted several attacks on the PRESENT cipher, with and without WDDL protection, at different temperatures and aging times. Our results shows that the attack is more difficult if there is an aging-duration mismatch between the training and target devices, and the attack-efficiency decrease is especially significant for mismatches of few weeks.

Recent years have witnessed a successful rise of the time synchronized gossiping comment, or so-called danmu combined with online videos. This new business mode has enriched communication among users by sending users' feelings through danmus and sharing these danmus on time synchronized videos. Can danmu communication be helpful for better user behavior modeling or video analyzing? To this question, in this paper, preliminary attempts are made on analysis of users and videos by introducing a Danmu dataset which is collected from a real-world danmu-enabled video sharing platform. The dataset contains 1.7 TB of videos and danmus in total across 8 video categories. With a focus on the 7.9 million danmus records and 4.8 million video frames, we first perform the basic statistic analysis and high-level semantic analysis. After that, we show some of the previous work on this area, including user behavior modeling, fine-grained video understanding and labeling, video plot generation and image-enhanced semantic understanding. For each application, we also propose its possible future directions. We hope this new dataset will inspire new ideas in areas among language, multimedia and user understanding.

Text summarization automatically produces a summary containing important sentences and includes all relevant important information from the original document. One of the main approaches, when viewed from the summary results, are extractive and abstractive. An extractive summary is heading towards maturity and now research has shifted towards abstractive summation and real-time summarization. Although there have been so many achievements in the acquisition of datasets, methods, and techniques published, there are not many papers that can provide a broad picture of the current state of research in this field. This paper provides a broad and systematic review of research in the field of text summarization published from 2008 to 2019. There are 85 journal and conference publications which are the results of the extraction of selected studies for identification and analysis to describe research topics/trends, datasets, preprocessing, features, techniques, methods, evaluations, and problems in this field of research. The results of the analysis provide an in-depth explanation of the topics/trends that are the focus of their research in the field of text summarization; provide references to public datasets, preprocessing and features that have been used; describes the techniques and methods that are often used by researchers as a comparison and means for developing methods. At the end of this paper, several recommendations for opportunities and challenges related to text summarization research are mentioned.

Knowledge Graphs (KGs) are the most widely used representation of structured information about a particular domain consisting of billions of facts in the form of entities (nodes) and relations (edges) between them. Besides, the KGs also encapsulate the semantic type information of the entities. The last two decades have witnessed a constant growth of KGs in various domains such as government, scholarly data, biomedical domains, etc. KGs have been used in Machine Learning based applications such as entity linking, question answering, recommender systems, etc. Open KGs are mostly heuristically created, automatically generated from heterogeneous resources such as text, images, etc., or are human-curated. However, these KGs are often incomplete, i.e., there are missing links between the entities and missing links between the entities and their corresponding entity types. This thesis focuses on addressing these two challenges of link prediction for Knowledge Graph Completion (KGC): \textbf{(i)} General Link Prediction in KGs that include head and tail prediction, triple classification, and \textbf{(ii)} Entity Type Prediction. Most of the graph mining algorithms are proven to be of high complexity, deterring their usage in KG-based applications. In recent years, KG embeddings have been trained to represent the entities and relations in the KG in a low-dimensional vector space preserving the graph structure. In most published works such as the translational models, convolutional models, semantic matching, etc., the triple information is used to generate the latent representation of the entities and relations. In this dissertation, it is argued that contextual information about the entities obtained from the random walks, and textual entity descriptions, are the keys to improving the latent representation of the entities for KGC. The experimental results show that the knowledge obtained from the context of the entities supports the hypothesis. Several methods have been proposed for KGC and their effectiveness is shown empirically in this thesis. Firstly, a novel multi-hop attentive KG embedding model MADLINK is proposed for Link Prediction. It considers the contextual information of the entities by using random walks as well as textual entity descriptions of the entities. Secondly, a novel architecture exploiting the information contained in a pre-trained contextual Neural Language Model (NLM) is proposed for Triple Classification. Thirdly, the limitations of the current state-of-the-art (SoTA) entity type prediction models have been analysed and a novel entity typing model CAT2Type is proposed that exploits the Wikipedia Categories which is one of the most under-treated features of the KGs. This model can also be used to predict missing types of unseen entities i.e., the newly added entities in the KG. Finally, another novel architecture GRAND is proposed to predict the missing entity types in KGs using multi-label, multi-class, and hierarchical classification by leveraging different strategic graph walks in the KGs. The extensive experiments and ablation studies show that all the proposed models outperform the current SoTA models and set new baselines for KGC. The proposed models establish that the NLMs and the contextual information of the entities in the KGs together with the different neural network architectures benefit KGC. The promising results and observations open up interesting scopes for future research involving exploiting the proposed models in domain-specific KGs such as scholarly data, biomedical data, etc. Furthermore, the link prediction model can be exploited as a base model for the entity alignment task as it considers the neighbourhood information of the entities.

Badges are commonly used in online platforms as incentives for promoting contributions. It is widely accepted that badges "steer" people's behavior toward increasing their rate of contributions before obtaining the badge. This paper provides a new probabilistic model of user behavior in the presence of badges. By applying the model to data from thousands of users on the Q&A site Stack Overflow, we find that steering is not as widely applicable as was previously understood. Rather, the majority of users remain apathetic toward badges, while still providing a substantial number of contributions to the site. An interesting statistical phenomenon, termed "Phantom Steering," accounts for the interaction data of these users and this may have contributed to some previous conclusions about steering. Our results suggest that a small population, approximately 20%, of users respond to the badge incentives. Moreover, we conduct a qualitative survey of the users on Stack Overflow which provides further evidence that the insights from the model reflect the true behavior of the community. We argue that while badges might contribute toward a suite of effective rewards in an online system, research into other aspects of reward systems such as Stack Overflow reputation points should become a focus of the community., To appear in IEEE ICDM2020

Attention mechanisms are now widely used in image captioning models. However, most attention models only focus on visual features. When generating syntax related words, little visual information is needed. In this case, these attention models could mislead the word generation. In this paper, we propose Task-Adaptive Attention module for image captioning, which can alleviate this misleading problem and learn implicit non-visual clues which can be helpful for the generation of non-visual words. We further introduce a diversity regularization to enhance the expression ability of the Task-Adaptive Attention module. Extensive experiments on the MSCOCO captioning dataset demonstrate that by plugging our Task-Adaptive Attention module into a vanilla Transformer-based image captioning model, performance improvement can be achieved.

Multimodal human sentiment comprehension refers to recognizing human affection from multiple modalities. There exist two key issues for this problem. Firstly, it is difficult to explore time-dependent interactions between modalities and focus on the important time steps. Secondly, processing the long fused sequence of utterances is susceptible to the forgetting problem due to the long-term temporal dependency. In this paper, we introduce a hierarchical learning architecture to classify utterance-level sentiment. To address the first issue, we perform time-step level fusion to generate fused features for each time step, which explicitly models time-restricted interactions by incorporating information across modalities at the same time step. Furthermore, based on the assumption that acoustic features directly reflect emotional intensity, we pioneer emotion intensity attention to focus on the time steps where emotion changes or intense affections take place. To handle the second issue, we propose Residual Memory Network (RMN) to process the fused sequence. RMN utilizes some techniques such as directly passing the previous state into the next time step, which helps to retain the information from many time steps ago. We show that our method achieves state-of-the-art performance on multiple datasets. Results also suggest that RMN yields competitive performance on sequence modeling tasks.

Fraudulent financial statements (FFS) are the results of manipulating financial elements by overvaluing incomes, assets, sales, and profits while underrating expenses, debts, or losses. To identify such fraudulent statements, traditional methods, including manual auditing and inspections, are costly, imprecise, and time-consuming. Intelligent methods can significantly help auditors in analyzing a large number of financial statements. In this study, we systematically review and synthesize the existing literature on intelligent fraud detection in corporate financial statements. In particular, the focus of this review is on exploring machine learning and data mining methods, as well as the various datasets that are studied for detecting financial fraud. We adopted the Kitchenham methodology as a well-defined protocol to extract, synthesize, and report the results. Accordingly, 47 articles were selected, synthesized, and analyzed. We present the key issues, gaps, and limitations in the area of fraud detection in financial statements and suggest areas for future research. Since supervised algorithms were employed more than unsupervised approaches like clustering, the future research should focus on unsupervised, semi-supervised, as well as bio-inspired and evolutionary heuristic methods for anomaly (fraud) detection. In terms of datasets, it is envisaged that future research making use of textual and audio data. While imposing new challenges, this unstructured data deserves further study as it can show interesting results for intelligent fraud detection.

Traditionally, much of the focus of the mechanism/auction design community has been on revenue optimal mechanisms for settings where bidders’ private valuations over outcomes can be reasonably thought of as independent of each other. This has been the case even though there is good reason to believe that valuations are often correlated and there are theoretical results suggesting that mechanisms designed with this correlation in mind can generate much higher revenue. In “Mechanism Design for Correlated Valuations: Efficient Methods for Revenue Maximization,” we look at the setting where there is correlation, but the exact distribution is unknown and must be estimated from samples. We show that in this setting, the previous extremely strong theoretical results around the usefulness of correlation are now very sensitive to the degree of correlation in the underlying distribution and the number of samples that the mechanism designer has access to. However, we also show that if correlation is sufficient, we can construct mechanisms, using a computationally efficient procedure, that significantly outperform traditional mechanism design paradigms.

This article surveys the literature on human-robot object handovers. A handover is a collaborative joint action where an agent, the giver, gives an object to another agent, the receiver. The physical exchange starts when the receiver first contacts the object held by the giver and ends when the giver fully releases the object to the receiver. However, important cognitive and physical processes begin before the physical exchange, including initiating implicit agreement with respect to the location and timing of the exchange. From this perspective, we structure our review into the two main phases delimited by the aforementioned events: 1) a pre-handover phase, and 2) the physical exchange. We focus our analysis on the two actors (giver and receiver) and report the state of the art of robotic givers (robot-to-human handovers) and the robotic receivers (human-to-robot handovers). We report a comprehensive list of qualitative and quantitative metrics commonly used to assess the interaction. While focusing our review on the cognitive level (e.g., prediction, perception, motion planning, learning) and the physical level (e.g., motion, grasping, grip release) of the handover, we briefly discuss also the concepts of safety, social context, and ergonomics. We compare the behaviours displayed during human-to-human handovers to the state of the art of robotic assistants, and identify the major areas of improvement for robotic assistants to reach performance comparable to human interactions. Finally, we propose a minimal set of metrics that should be used in order to enable a fair comparison among the approaches., Comment: Review paper, 19 pages

This paper presents a hybrid method to assess the rail health with the focus on a specific type of rail surface defect called head check. The proposed method uses physics-based and data-driven models in order to model defect initiation and defect evolution on a rail for a given rail traffic tonnage. Ultrasonic (US) and Eddy Current (EC) defect detection measurements are used to provide Infrastructure Managers (IMs) with insight in the current rail condition. The defect initiation results obtained from the first part of the hybrid method which consists of the physics-based model is successfully validated with the EC measurements. Furthermore, the US and EC measurements are utilized to derive a data-driven model for defect evolution. Finally, a set of robust and predictive Key Performance Indicators (KPIs) are proposed to quantify the future condition of the rail based on different characteristics of rail health resulting from the defect initiation and defect evolution analysis.

Robots, as the creation of human, became an irreplaceable component in human society. With the advancement of technologies, robots become more and more intelligent and have been widely used in many fields, such as disease diagnosis, customer services, healthcare for the older people, and so on. As robots made our lives much more convenient than ever before, they also brought many potential risks and challenges in technology, security, and ethic. To better understand the development of robots, we proposed a concept of a robot’s living space and analyzed the role of robots in our society. In this paper, we focus on setting a theoretical framework of the robot’s living space to further understand the human-robot relationship. The research in this paper contains three central aspects. First, we interpret the concept of the robot’s living space and functions of each space. Second, we analyze and summarize the relative technologies which support robots living well in each space. Finally, we provide advice and improvement measures based on a discussion of potential problems caused by the developments of robots. With the trend of robots humanization and human-robot society integration, we should seriously consider how to make a collaboration with intelligent robots to achieve hybrid intelligence. To build a harmonious human-robot integrated society, studying the robot’s living space and its relationship with humans is the pre-requisite and roadmap.

Location statistics collective release provides essential information to understand crucial phenomena, including points of interest and movement patterns. Sharing location statistics without compromising users’ privacy is critical. The new standard method for calculating private statistical information is differential privacy. Several research works focus on the centralized environment where individuals share their real data with a trusted curator. To calculate private statistics, the trusted curator then adds carefully measured noise. However, this centralized approach is susceptible to privacy breaches in which, by targeting the trustworthy curator, an adversary accesses the true data. By enabling every individual to perturb their records before reaching the curator, local differential privacy overcomes this form of attack. However, the fundamental challenge is that existing local differential privacy algorithms degrade the privacy guarantee when data collection spans over time. Therefore, a methodology to allocate the privacy budget over multiple timestamps is needed to achieve a collective location statistics release. In this paper, using a sliding window approach, we tackle the issue of releasing location statistics with local differential privacy over multiple timestamps. We develop a privacy budget allocation methodology to release collective location statistics with formal local differential privacy proof. Then, we present an approximation strategy to share the closest private statistics to the current timestamp. This strategy optimizes the released statistics’ utility. We demonstrate our solution enables collective location statistics release with a robust privacy guarantee on two datasets (real-time counts of nearby users and historical counts of bike owners close to each bike station).

Next point-of-interest (POI) recommendation is recently proposed to predict user's next destination and has attracted considerable research interest. Most of the previous studies failed to incorporate the spatiotemporal contextual information, which plays a critical role in analyzing user check-in behavior, into recommending the next POI. In recent years, embedding learning and recurrent neural network (RNN) based approaches show promising performance for modeling sequential patterns of check-in behavior in next POI recommendation. However, not all of the historical check-in records contribute equally to the next-step check-in behavior. To provide better next POI recommendation performance, we first proposed a spatiotemporal long and short-term memory (ST-LSTM) network. By feeding the spatiotemporal contextual information into the LSTM network in each step, ST-LSTM can model the spatial and temporal information better. Also, we developed an attention-based spatiotemporal LSTM (ATST-LSTM) network for next POI recommendation. By using the attention mechanism, ATST-LSTM can focus on the relevant historical check-in records in a check-in sequence selectively using the spatiotemporal contextual information. Besides, we conducted a comprehensive performance evaluation using large-scale real-world datasets collected from two popular location-based social networks. Experimental results indicated that the proposed ATST-LSTM network outperformed two state-of-the-art next POI recommendation approaches regarding three evaluation metrics.

Coded caching is beneficial due to its ability to leverage the multicast property of a wireless channel. Under specific network settings, the coded caching scheme has been shown to reveal the user file requests. In prior work, a privacy attack by a single attacker on coded caching was presented. In this letter, we focus on a relatively secure setting whereby only the intended users access the files. Nevertheless, we show that, even in this setting, privacy is compromised if the users are colluding. The time complexity of the attack on a network with $N$ files, $K$ users, and $l \leq K$ colluding users is shown to be in the order of $\sqrt {\ln (N(K-l))}N^{(K-l)/2}$ steps.

The comparably small scope of municipal politics creates a unique setting within which elected officials act as representatives. Here, we explore how mayors and councillors in Canadian cities do so...

Internet of Things (IoT) embodies a vision of merging heterogeneous objects to establish seamless interaction among physical and virtual entities. IoT has given the Internet a shift from connecting networks to interconnecting the physical world. The IoT devices are capable of sensing, processing, communicating and storing the data acquired from the physical world. Most of the applications of IoT are pervasive in nature, the pervasive IoT environment poses many challenges due to constrained resources in these miniature and unattended IoT devices. This paper presents a survey of physical and virtual resource management in IoT systems. The main focus of the paper is on resource management in pervasive IoT environment with limited resources. This paper also presents a use case of IoT based Body Area Network and proposes a model for resource management in personal and community healthcare.

Augmented Reality (AR) technology has increasingly been applied to facilitate manufacturing tasks such as training, maintenance, and safety management, in which manual assembly frequently occurs. Previously, studies confirmed the values of AR-based assembly systems, but few explored support of spatially restricted assembly where components are visually occluded from operator. In this regard, this research aims to develop new AR functions that assist manual assembly in such situations. The focus is on validating the effectiveness of various assistive information in AR, including assembly interface, operator’s hand movement, and operator held components. Subjective and objective measures are used to evaluate assembly experiments of different degrees of difficulty. Analyses of the experimental data reveal whether or not and how effectively each information offers guidance in occluded condition. In particular, a time reduction in more difficult assembly is realized by showing operator hand movement in AR. The hand model initially offers an operator a visual clue for quickly and roughly locating the assembly interface in a large unseen area, prior to precisely localizing in a smaller region guided by tactile sensing. However, the effectiveness of incorporating the held component is not evident, as positional deviation between real and virtual objects may reduce human’s hand-eye coordination. These findings not only provide preliminary design guidelines of AR assembly functions for occluded components but also demonstrates a novel yet practical application of AR technology in smart manufacturing.

5G networks provide novel features that have generated huge expectations. Therefore, implementation and validation become crucial. In order to achieve this, the following concepts are important: first, to have a technical insight on the 5G technology and to understand better its advantages; second, to understand the specific needs and requirements of the use cases; and third, to know from an implementation point of view how to match the 5G technical features with the needs of the use cases. In this context, the main focus of this article is to describe the implementation approach followed to deliver 5G services to specific use cases with concrete high throughput and low latency requirements, and to present the current lessons learned, challenges, and open issues identified. This article uses as reference the integration work done by the authors of the Horizon 2020 projects, where the target is to design and implement an advanced and accessible 5G facility (5G-VINNI), and validate vertical use cases by the use of 5G technology (5G-HEART), providing the technology, use cases, and integration knowledge needed to understand better how to bring from theory to practice the 5G technology into the verticals world.

We present results of a grounded analysis of individual interviews in which students play Vector Unknown — a digital game designed to introduce visualizing vectors, scaling vectors, vector addition, and vector equations, attending to the geometric and algebraic representations of vectors. The game was designed to be used at the beginning of a linear algebra course, and participants in the study were students who had not previously taken such a course. We categorize the strategies the students employed while playing the game and analyze how these strategies evolved throughout the students’ gameplaying. They range from less anticipatory button-pushing to more sophisticated strategies based on approximating solutions and choosing vectors based on their direction. We found that student focus alternated between numerical and geometric aspects of the game interface, which provided additional insight into their strategies. These results have provided revisions to the game and also informed our team’s plans for incorporating it into classroom instruction.

Surface tension is an important property of liquids. It has diverse uses such as testing water contamination, measuring alcohol concentration in drinks, and identifying the presence of protein in urine to detect the onset of kidney failure. Today, measurements of surface tension are done in a lab environment using costly instruments, making it hard to leverage this property in ubiquitous applications. In contrast, we show how to measure surface tension using only a smartphone. We introduce a new algorithm that uses the small waves on the liquid surface as a series of lenses that focus light and generate a characteristic pattern. We then use the phone camera to capture this pattern and measure the surface tension. Our approach is simple, accurate, and available to anyone with a smartphone. Empirical evaluations show that our mobile app can detect water contamination and measure alcohol concentration. Furthermore, it can track protein concentration in the urine, providing an initial at-home test for proteinuria, a dangerous complication that can lead to kidney failure.

Digital contact tracing has emerged as a viable tool supplementing manual contact tracing. To date, more than 100 contact tracing applications have been published to slow down the spread of highly contagious Covid-19. Despite subtle variabilities among these applications, all of them achieve contact tracing by manipulating the following three components: a) use a personal device to identify the user while designing a secure protocol to anonymize the user's identity; b) leverage networking technologies to analyze and store the data; c) exploit rich sensing features on the user device to detect the interaction among users and thus estimate the exposure risk. This paper reviews the current digital contact tracing based on these three components. We focus on two personal devices that are intimate to the user: smartphones and wearables. We discuss the centralized and decentralized networking approaches that use to facilitate the data flow. Lastly, we investigate the sensing feature available on smartphones and wearables to detect the proximity between any two users and present experiments comparing the proximity sensing performance between these two personal devices., Accepted at the IEEE Communications Magazine

Attention mechanisms have achieved success in video-based person re-identification (re-ID). However, current global attentions tend to focus on the most salient parts, e.g., clothes, and ignore other subtle but valuable cues, e.g., hair, bag, and shoes. They still do not make full use of valuable information from diverse parts of human bodies. To tackle this issue, we propose a Diverse Part Attentive Network (DPAN) to exploit discriminative and diverse body cues. The framework consists of two modules: spatial diverse part attention and temporal diverse part attention. The spatial module utilizes channel grouping to exploit diverse parts of human bodies including salient and subtle parts. The temporal module aims to learn diverse weights for fusing learned features. Besides, this framework is lightweight, which introduces marginal parameters and computational complexities. Extensive experiments were conducted on three popular benchmarks, i.e. iLIDS-VID, PRID2011 and MARS. Our method achieves competitive performance on these datasets compared with state-of-the-art methods.

Live video hosted by streamer is being sought after by more and more Internet users. A few streamers show inappropriate action in normal live video content for profit and popularity, who bring great harm to the network environment. In order to effectively regulate the streamer behavior in live video, a streamer action recognition method in live video with spatial-temporal attention and deep dictionary learning is proposed in this paper. First, deep features with spatial context are extracted by a spatial attention network to focus on action region of streamer after sampling video frames from live video. Then, deep features of video are fused by assigning weights with a temporal attention network to learn the frame attention from an action. Finally, deep dictionary learning is used to sparsely represent the deep features to further recognize streamer actions. Four experiments are conducted on a real-world dataset, and the competitive results demonstrate that our method can improve the accuracy and speed of streamer action recognition in live video.

The rocketing number of photos shared on social media leads to the increasing demand for photo editing. We here focus on a specific scenario - group dinner photo and tackle two user-demanding problems - to add a person or replace the tabletop. The target objects are determined according to the saliency detection results. We developed a novel application to solve these problems. With our system, non-professional users can accomplish semantic editing within a few seconds, including inserting human and tidying up tabletops. Our system contributes to the state-of-the-art by (1) efficiently selecting the saliency area by its semantic meaning, (2) accurately compositing the salient content with the target image, based on the contextual knowledge. The context refers to the key factors, including occlusion and artifacts during the composition. The feedback from users shows that the authenticity of inserting human is satisfying. A comparative study shows that our system can more efficiently produce pictures with comparable quality as those edited by professional editing software in the tidying up tabletops work.

Recommendation with implicit feedback such as “bought” in e-commerce sites and “like” in online-to-offline services is an important problem because of the abundance of users’ online behaviors. Previous works for modeling users’ implicit feedback mainly focus on designing some predefined similarity, learned similarity, or hybrid similarity between two users (or two items). However, these similarities either can not capture both the local and global relations among users (or items) simultaneously, or they are not easy to be tuned due to the tradeoff parameter in the linear hybridization. Moreover, information from one single group, either a user group or an item group, may not be sufficient for modeling users’ preferences well. To this end, in this paper, we first propose a heterogeneous similarity to capture rich correlations among users and items via a concise integration, and then design a novel recommendation algorithm, i.e., pointwise factored heterogeneous similarity model (Poi-FHSM), in which we fully exploit the proposed similarity in a flexible pointwise preference learning paradigm. Finally, we conduct extensive empirical studies on five real-world datasets, and find that our Poi-FHSM performs better than those based on the predefined, learned and hybrid similarity, as well as the state-of-the-art deep learning-based methods, showcasing the effectiveness of our heterogeneous similarity and recommendation algorithm.

The transparent elements of the building envelope have a crucial role not only in term of heat and mass transfers control, but also for natural light penetration, sound insulation, thermal and visual comfort of the occupants and their health. Among passive technologies, the light shelves could be architectural solutions for improving daylight penetration and for controlling thermal loads. The available research papers usually focus on one aspect. For this reason, the aim of the present study is to analyse the application of the light shelves with multidisciplinary approach and thus, taking into account: daylight, electricity for lighting, cooling and heating needs and thermo-hygrometric comfort. The case study is a real dormitory building placed in Athens and subject to a deep energy renovation toward the nearly zero energy building target. EnergyPlus, by means of DesignBuilder interface, has been used as dynamic simulation tool. Among ten different configurations, the optimal one turns out to be the internal horizontal light shelf placed at 50 cm from the top of the window with a depth of 90 cm or 60 cm. It has been found that in some cases the reduction of electricity for lighting cannot balance the variation in heating and cooling needs.

The number of Web services on the Internet has been growing rapidly. This has made it increasingly difficult for users to find the right services from a large number of functionally equivalent candidate services. Inspecting every Web service for their quality value is impractical because it is very resource consuming. Therefore, the problem of quality prediction for Web services has attracted a lot of attention in the past several years, with a focus on the application of the Matrix Factorization (MF) technique. Recently, researchers have started to employ user similarity to improve MF-based prediction methods for Web services. However, none of the existing methods has properly and systematically addressed two of the major issues: 1) retrieving appropriate neighborhood information, i.e., similar users and services; 2) utilizing full neighborhood information, i.e., both users’ and services’ neighborhood information. In this paper, we propose CNMF, a c overing-based quality prediction method for Web services via n eighborhood-aware m atrix f actorization. The novelty of CNMF is twofold. First, it employs a covering-based clustering method to find similar users and services, which does not require the number of clusters and cluster centroids to be prespecified. Second, it utilizes neighborhood information on both users and services to improve the prediction accuracy. The results of experiments conducted on a real-world dataset containing 1,974,675 Web service invocation records demonstrate that CNMF significantly outperforms eight existing quality prediction methods, including two state-of-the-art methods that also utilize neighborhood information with MF.

Purpose Additive manufacturing has rapidly developed in terms of technology and its application in various types of industries. With this rapid development, there has been significant research in the area of materials. This has led to the invention of Smart Materials (SMs). The 4D printing is basically 3D printing of these SMs. This paper aims to focus on novel materials and their useful application in various industries using the technology of 4D printing. Design/methodology/approach Research studies in 4D printing have increased since the time when this idea was first introduced in the year 2013. The present research study will deeply focus on the introduction to 4D printing, types of SMs and its application based on the various types of stimulus. The application of each type of SM has been explained along with its functioning with respect to the stimulus. Findings SMs have multiple functional applications pertaining to appropriate industries. The 4D printed parts have a distinctive capability to change its shape and self-assembly to carry out a specific function according to the requirement. Afterward, the fabricated part can recover to its 3D printed “memorized” shape once it is triggered by the stimulus. Originality/value The present study highlights the various capabilities of SMs, which is used as a raw material in 4D printing. Graphical abstract

Many IoT applications and services such as smart parking and smart traffic control contain a network of different analytical components, which are composed in the form of a workflow to make better decisions. These workflows are also known as stream workflows. The focus of existing research works is on the streaming operator graph, which differs from stream workflow application as it involves heterogeneity, multiple data sources and multiple outputs. Considering the complexity and dynamism of stream workflow, meeting real-time data analysis requirements at deployment time is not the whole story as the velocity of data changes over time. This change is the most dynamic form of stream workflow that occurs frequently during the execution of this application. In this article, we propose a new dynamic scheduling technique that manages cloud resources over time to handle data velocity changes in stream workflow while maintaining user-defined real-time data analysis requirements and minimising execution cost. The efficiency of the proposed technique is evaluated, and experimental results showed that this technique outperformed its competitors and is close to the lower bound. Refereed/Peer-reviewed

Biomaterials offer a promising approach to repair bone defects. Whereas traditional studies predominantly focused on optimizing the osteogenic capacity of biomaterials, less focus has been on the immune response elicited by them. However, the immune and skeletal systems extensively interact, a concept which is referred to as 'osteoimmunology'. This realization has fuelled the development of biomaterials with favourable osteoimmunomodulatory (OIM) properties, aiming to modulate the immune response and to support bone regeneration, thereby affecting the success of an implant. Given the plethora of in vitro assays used to evaluate the OIM properties of biomaterials, it may be challenging to select the right methods to produce conclusive results. In this review, we aim to provide a comprehensive and practical guide for researchers interested in studying the OIM properties of biomaterials in vitro. After a concise overview of the concept of osteoimmunology, emphasis is put on the methodologies that are regularly used to evaluate the OIM properties of biomaterials. First, a description of the most commonly used cell types and cell culture media is provided. Second, typical experimental set-ups and their relevant characteristics are discussed. Third, a detailed overview of the generally used methodologies and readouts, including cell type-specific markers and time points of analysis, is given. Finally, we highlight the promise of advanced approaches, namely microarrays, bioreactors and microfluidic-based systems, and the potential that these may offer to the osteoimmunology field. STATEMENT OF SIGNIFICANCE: Osteoimmunology focuses on the connection and communication between the skeletal and immune systems. This interaction has been recognized to play an important role in the clinical success of biomaterials, which has resulted in an increasing amount of research on the osteoimmunomodulatory (OIM) properties of biomaterials. However, the amount of literature makes it challenging to extract the information needed to design experiments from beginning to end, and to compare obtained results to existing work. This article intends to serve as a guide for those aiming to learn more about the commonly used experimental approaches in the field. We cover early-stage choices, such as cell types and experimental set-ups, but also discuss specific assays, including cell markers and time points of analysis.

We study time-varying semidefinite programs (TV-SDPs), which are semidefinite programs whose data (and solutions) are functions of time. Our focus is on the setting where the data varies polynomially with time. We show that under a strict feasibility assumption, restricting the solutions to also be polynomial functions of time does not change the optimal value of the TV-SDP. Moreover, by using a Positivstellensatz on univariate polynomial matrices, we show that the best polynomial solution of a given degree to a TV-SDP can be found by solving a semidefinite program of tractable size. We also provide a sequence of dual problems which can be cast as SDPs and that give upper bounds on the optimal value of a TV-SDP (in maximization form). We prove that under a boundedness assumption, this sequence of upper bounds converges to the optimal value of the TV-SDP. Under the same assumption, we also show that the optimal value of the TV-SDP is attained. We demonstrate the efficacy of our algorithms on a maximum-flow problem with time-varying edge capacities, a wireless coverage problem with time-varying coverage requirements, and on bi-objective semidefinite optimization where the goal is to approximate the Pareto curve in one shot., Comment: Minor revision

Snowplowing is an indispensable part of winter road maintenance because it contributes to improving drivers’ mobility and safety. Most prior studies, however, limit focus to operational aspects without considering interaction with road users. This paper aims to minimize travel time in the traffic system while still maintaining the efficiency of plowing activities. The k-truck plowing with the precedence model was formulated to link road users with snowplow activities. The tabu search algorithm then followed to optimize the order of precedence of snowplow routes. Furthermore, the discrete-event simulation was used to quantify the effectiveness of the proposed method based on different scenarios and fleet sizes, and to illustrate the potential benefit of integrating both the operators’ and users’ perspectives. The methodological framework developed herein can be used to design a routing strategy that improves the performance of the snowplow trucks by reducing both plowing completion and road users’ total travel time.

In the 21st century, with the exponential rise in global population and a greater demand for environmental resources, the focus of engineering is on integrated solutions within a multifarious context. To successfully navigate today’s multidisciplinary and multicultural environment, engineers require soft skills in addition to their technical or hard skills: these include leadership, interpersonal, communication and problem-solving skills. They support engineering professionals in meeting the challenges of working in multidisciplinary teams which make decisions that will help shape define the future. This study explores the perspectives of engineering professionals working in different engineering disciplines to understand the role soft skills play in their professions. It finds that technical competence is not considered enough; to succeed in the complex world of engineering, employers look for graduates who can enhance their technical expertise with complementary soft skills. Engineers have to work alongside various stakeholders to develop technologies for the benefit of humankind. Most of the content in engineering education institutions focuses on technical aspects, and it is imperative to highlight and implement soft skills training.

In this article, we focus on fake news detection task and aim to automatically identify the fake news from vast amount of social media posts. To date, many approaches have been proposed to detect fake news, which includes traditional learning methods and deep learning-based models. However, there are three existing challenges: (i) How to represent social media posts effectively, since the post content is various and highly complicated; (ii) how to propose a data-driven method to increase the flexibility of the model to deal with the samples in different contexts and news backgrounds; and (iii) how to fully utilize the additional auxiliary information (the background knowledge and multi-modal information) of posts for better representation learning. To tackle the above challenges, we propose a novel Knowledge-aware Multi-modal Adaptive Graph Convolutional Networks (KMAGCN) to capture the semantic representations by jointly modeling the textual information, knowledge concepts, and visual information into a unified framework for fake news detection. We model posts as graphs and use a knowledge-aware multi-modal adaptive graph learning principal for the effective feature learning. Compared with existing methods, the proposed KMAGCN addresses challenges from three aspects: (1) It models posts as graphs to capture the non-consecutive and long-range semantic relations; (2) it proposes a novel adaptive graph convolutional network to handle the variability of graph data; and (3) it leverages textual information, knowledge concepts and visual information jointly for model learning. We have conducted extensive experiments on three public real-world datasets and superior results demonstrate the effectiveness of KMAGCN compared with other state-of-the-art algorithms.

More than 20 years ago, Elizabeth O. Smith published her points of reference that documented the research trajectory of technical communication from 1988 to 1997. Her results indicated a focus on rhetorical analyses, a decrease in collaborative research, and a disproportionate representation of male authors. This study builds on these points with a quantitative content analysis of 1,271 articles that were published in five leading technical communication journals and Intercom, the trade magazine for the Society for Technical Communication, from 1996 to 2017. The results show that both the research journals and Intercom have pivoted to process-driven rather than product-driven content. The results also suggest that the primary topics of communication strategy and collaboration might be the most likely places to foster future industry–academic ties and that the greatest division between the two populations is the primary topic of rhetoric. This study offers an updated baseline for future investigations by offering an evaluation of disparate content foci between the publication types.

Product identification using barcodes has gained good astounding, especially at the point of sale, where different products are being scanned individually before making the payment receipt. These products when at the warehouse meets issues like individual scanning demands significant time and labor to be involved in the human error. Barcodes have been in a process of replacement by Radio Frequency Identification (RFID). RFID is a technology that enables data transfer wirelessly and provides several advantages over barcodes which mandatory requires line of sight. Much of the work has been done on RFID tag antenna design, but efficient reader antenna design has been a major need of the hour for big warehouses, that came across bulk data to manage on daily basis. RFID reader antennas are unable to focus their RF energy on a desired zone. This paper proposes a novel multi-linear polarized RFID reader antenna design that utilizes the RF energy efficiently to meet the bulk reading application that will help in managing the tagged stock for industrial application.

Scholarly debates on civil–military relations often focus on how the military impacts society. Adding to the vast literature of civil–military relations, this article examines how socio-cultural practices and societal developments in the host society affect the military. Based on long-term ethnographic engagement with the Ghana Armed Forces, the piece presents empirical observations of how culturally informed practices, such as begging via proxies ( djuan toa), infiltrate the Ghanaian military barracks and affect the institutions’ functioning. The article illustrates how two additional elements, skewed recruitment practices and the politicisation of the rank and file, are used as tools by political factions, such as Ghana’s two most prominent parties (the New Patriotic Party and National Democratic Congress), seeking to gain control over the Ghanaian military. The article analyses how these approaches contribute to undermining the armed forces’ discipline and military professionalism and consequently affect the military institution as a whole.

The paper investigates the multi-actor processes of knowledge co-production in the implementation of research-based on-farm demonstration with a focus on collaboration arrangements and areas of con...

Although it has become easier for individuals to track their personal health data (e.g., heart rate, step count, and nutrient intake data), there is still a wide chasm between the collection of data and the generation of meaningful summaries to help users better understand what their data means to them. With an increased comprehension of their data, users will be able to act upon the newfound information and work toward striving closer to their health goals. We aim to bridge the gap between data collection and summary generation by mining the data for interesting behavioral findings that may provide hints about a user’s tendencies. Our focus is on improving the explainability of temporal personal health data via a set of informative summary templates, or “protoforms.” These protoforms span both evaluation-based summaries that help users evaluate their health goals and pattern-based summaries that explain their implicit behaviors. In addition to individual-level summaries, the protoforms we use are also designed for population-level summaries. We apply our approach to generate summaries (both univariate and multivariate) from real user health data and show that the summaries our system generates are both interesting and useful.

Within the current transportation infrastructure, we have seen a steady increase in the use of sensor technologies. These sensors, individually produce large amounts of data that then need to be fused and understood. Data commingling and data integration are difficult tasks when it comes to processing such data centrally, which can require costly hardware and software techniques. Over the past few years, high-utility pattern mining (HUPM) has gained popularity due to its growing capability in identifying useful information and knowledge from stored database data, as compared to the traditional frequent pattern mining. Existing works of HUPM mostly focus on mining the set of HUPs from one data source, which cannot be implemented in real-world scenarios. In this paper, we present a pre-large weighted high-utility pattern (PWHUP) fusion framework for integrating HUPs from different sensed data sources. The proposed PWHUP algorithm considers the size of the data source for discovering more relevant HUPs for integration, which is more applicable to real-life applications and scenarios in transportation and also within other data fusion scenarios. Moreover, the pre-large concept is applied to maintain the suggested pattern for later integration, which greatly improves the effectiveness of the proposed algorithm. Our in-depth experiments show that the designed approach has good performance for knowledge integration and outperforms existing non-integration solutions in precision, recall, and runtime.

Improving the lubricating performance of tribo-pairs using engineering textured surfaces has been the main focus of tribology research in recent years. The use of a suitably designed micro-texture on the bearing surface may have a beneficial effect on the performance of fluid film bearings. In the present paper, a mathematical model of a hybrid spherical thrust bearing is developed considering the effect of shear thinning and piezoviscous behaviour of a lubricant. The modified Reynolds equation for a hybrid spherical thrust bearing configuration together with a restrictor flow equation for a capillary restrictor is solved using a finite-element method. In this work, the effect of various micro-textures shapes (spherical, circular, conical and square) and non-Newtonian lubricant behaviour having shear thinning and piezoviscous effects are analysed. The numerically simulated result shows a strong dependence on the combined effect of shear thinning and piezoviscous lubricant behaviour and a chosen geometric shape of a texture. The frictional power loss is seen to reduce nearly by 24.05%, and the stiffness gets enhanced by 11.08%.

Due to the daily use of web applications in several critical domains such as banking and online shopping, cybersecurity has become a challenge. Recently, deep learning techniques have achieved promising results and attracted cybersecurity researchers. In this paper, we explore and evaluate deep learning techniques used for the security of web applications. We analyze through experiments the different factors influencing the performance of the Convolutional Neural Network (CNN) technique for web attacks detection. The experiments done in this paper focus on CNN and have three goals. First, we evaluate the performance of different CNN models using two different methods of data input presentation and data input splitting. Second, we study the impact of the different CNN hyper-parameters on the attack detection rate. Third, we select the best deep learning toolbox that will be used in our future proposed detection technique. Through the experiments conducted in this paper, we reveal that an adequate tuning of hyper-parameters and the way of pre-processing data input have a significant impact on the attack detection rate.

Polycentric leadership is a growing issue in the global missions movement. The focus of this article is to help readers understand polycentrism broadly and examine what it means for those seeking to lead fruitfully in God’s mission. Examples will be provided to illustrate what polycentric leadership can look like. Biblical and theological convictions that shape leaders who work out of this paradigm will be examined. The reality of tension points experienced by people who desire to lead from this paradigm will be explored, as well as practical strategies and insights for those who have to navigate these tensions in their missional journeys. Through bringing together these different threads, the goal is for this article is to provide both academic insights and practical assistance for anyone who cares deeply about God’s work around the world and desires to lead fruitfully in a polycentric mission context.

Young people's digital lives are bigger than they have ever been. This means that realizing young people's rights now requires a concerted focus on the digital world as well. Terms and Conditions (from Cookies Policies to Terms of Service) are an important part of young people's digital worlds because they set the “rules of engagement” between digital products and young service users. However important, these Terms and Conditions rarely recognize young people's rights, let alone uphold them. This article outlines some of the ways Terms and Conditions fail young people, and why this is problematic from moral, legal, and commercial perspectives. This suggests there is a critical need for Terms and Conditions that uphold rights, and that a Standard around Terms and Conditions may be an effective way of addressing this problem.