Reviewerly/exHarmony huggingface dataset:
Simple and effective deep hand shape and pose regression from a single depth image

Simultaneously estimating the 3D shape and pose of a hand in real time is a new and challenging computer graphics problem, which is important for animation and interactions with 3D objects in virtual environments with personalized hand shapes. CNN-based direct hand pose estimation methods are the state-of-the-art approaches, but they can only regress a 3D hand pose from a single depth image. In this study, we developed a simple and effective real-time CNN-based direct regression approach for simultaneously estimating the 3D hand shape and pose, as well as structure constraints for both egocentric and third person viewpoints by learning from the synthetic depth. In addition, we produced the first million-scale egocentric synthetic dataset called SynHandEgo, which contains egocentric depth images with accurate shape and pose annotations, as well as color segmentation of the hand parts. Our network is trained based on combined real and synthetic datasets with full supervision of the hand pose and structure constraints, and semi-supervision of the hand mesh. Our approach performed better than the state-of-the-art methods based on the SynHand5M synthetic dataset in terms of both the 3D shape and pose recovery. By learning simultaneously using real and synthetic data, we demonstrated the feasibility of hand mesh recovery from two real hand pose datasets, i.e., BigHand2.2M and NYU. Moreover, our method obtained more accurate estimates of the 3D hand poses based on the NYU dataset compared with the existing methods that output more than joint positions. The SynHandEgo dataset has been made publicly available to promote further research in the emerging domain of hand shape and pose recovery from egocentric viewpoints (https://bit.ly/2WMWM5u).
Hidden constraints to digital financial inclusion: the oral-literate divide

The vision that digital finance can achieve universal financial inclusion is premised on the rarely questioned assumption that the world is rapidly moving towards universal literacy and numeracy. In fact, text and arithmetic notation shape the relationship between formal finance and about a billion of the world’s poorest adults. This “oral” population, stranded outside the reach of formal employment, have neither the capabilities nor the incentives to engage in digital finance as it is currently being offered. Empirical observation of the actual capabilities and incentives of oral adults can offer transformative solutions for mobile wallet providers, NGO projects, designers and governments.
Trusted Remote Patient Monitoring Using Blockchain-Based Smart Contracts

With an increase in the development of the Internet of Things (IoT), people have started using medical sensors for health monitoring purpose. The huge amount of health data generated by these sensors must be recorded and conveyed in a secure manner in order to take appropriate measures in critical conditions of patients. Additionally, privacy of the personal information of users must be preserved and the health records must be stored in a secure manner. Possession details of IoT devices must be stored electronically for eradication of counterfeited actions. The emerging blockchain is a distributed and transparent technology that provides a trusted and unalterable log of transactions. We have made a healthcare system using blockchain-based smart contracts which support enrollments of patients and doctors in a health center thereby increasing user participation in remote patient monitoring. Our system monitors the patients at distant places and generates alerts in case of emergency. We have used smart contracts for authorization of its devices and provided a legalized and secure way of using medical sensors. Using the blockchain technology, forgery and privacy hack in healthcare settings is reduced, thereby increasing the trust of people in remote monitoring. We have provided a graphical comparison of costs that verifies the successful deployment of contracts.
Embracing and controlling risk dependency in cyber-insurance policy underwriting

Abstract This article highlights how cyber risk dependencies can be taken into consideration when underwriting cyber-insurance policies. This is done within the context of a base rate insurance policy framework, which is widely used in practice. Specifically, we show that there is an opportunity for an underwriter to better control the risk dependency and the risk spill-over, ultimately resulting in lower overall cyber risks across its portfolio. To do so, we consider a Service Provider (SP) and its customers as the interdependent insurer’s customers: a data breach suffered by the SP can cause business interruption to its customers. In underwriting both the SP and its customers, we show that the insurer can increase its profit by incentivizing the SP (through a discount on its premium) to invest more in security, thereby decreasing the chance of business interruption to the customers, and increasing social welfare. For comparison, we also consider a scenario where the insurer underwrites only the SP’s customers (but not the SP), and receives compensation from the SP’s insurance carrier when losses are attributed to the SP. We show how the insurer’s best strategy is to underwrite both the SP and its customers. We use an actual cyber-insurance policy and claims data to calibrate and substantiate our analytical findings.
Metric learning-based kernel transformer with triplets and label constraints for feature fusion

Feature fusion is an important skill to improve the performance in computer vision, the difficult problem of feature fusion is how to learn the complementary properties of different features. We recognize that feature fusion can benefit from kernel metric learning. Thus, a metric learning-based kernel transformer method for feature fusion is proposed in this paper. First, we propose a kernel transformer to convert data from data space to kernel space, which makes feature fusion and metric learning can be performed in the transformed kernel space. Second, in order to realize supervised learning, both triplets and label constraints are embedded into our model. Third, in order to solve the unknown kernel matrices, LogDet divergence is also introduced into our model. Finally, a complete optimization objective function is formed. Based on an alternating direction method of multipliers (ADMM) solver and the Karush-Kuhn-Tucker (KKT) theorem, the proposed optimization problem is solved with the rigorous theoretical analysis. Experimental results on image retrieval demonstrate the effectiveness of the proposed methods.
Smart Virtual Product Development (SVPD) to Enhance Product Manufacturing in Industry 4.0

This paper presents a system capable of enhancing product development process for industrial manufactured products. The system is known as Smart Virtual Product Development (SVPD), and it helps in decision making by using explicit knowledge of formal decision events. It stores and reuses the past decisional events or set of experiences related to different activities involved in industrial product development process i.e. product design, manufacturing, and product inspection. This system can be potentially used in large enterprises manufacturing a range of similar products in mass production, or a group of small and medium enterprises (SMEs). This research explains that how product manufacturing can be enhanced by using SVPD in Industry 4.0, where cyber-physical systems have to play the key role. The analysis of basic concepts and implementation method proves that this is an expert system facilitating product manufacturing which can play a vital role towards Industry 4.0.
Dynamical creation and detection of entangled many-body states in a chiral atom chain

Abstract Open quantum systems with chiral interactions can be realized by coupling atoms to guided radiation modes in photonic waveguides or optical fibers. In their steady state these systems can feature intricate many-body phases such as entangled dark states, but their detection and characterization remains a challenge. Here we show how such collective phenomena can be uncovered through monitoring the record of photons emitted into the guided modes. This permits the identification of dark entangled states but furthermore offers novel capabilities for probing complex dynamical behavior, such as the coexistence of a dark entangled and a mixed phase. Our results are of direct relevance for current optical experiments, as they provide a framework for probing, characterizing and classifying classical and quantum dynamical features of chiral light–matter systems.
Perspectives on development: why does studying information and communication technology for development (ICT4D) matter?

The concept of development has been used by scholars and practitioners to demonstrate changes for the better in the lives of individuals, communities, nations and regions. Historically, the practice of development has been associated with interventions and categorizations that have had the opposite effect in countries and regions deemed to be developing. Current notions of development predicated on respect for individual rights, human freedoms, environmental sustainability, health and well-being and education have been more successful in achieving improvements in the lives of people. Yet, historic notions of development persist and continue to drive failing projects. This editorial explores the historical notions of development that persist to this day and offers a view of contemporary perspectives on development. As a growing set of development interventions and papers in this issue involve Information and Communication Technologies (ICTs), the question of why Information and Communication Technology for Development matters? is addressed in the light of development perspectives.
Trust-Based Game-Theoretical Decision Making for Food-Energy-Water Management

Decision making has been an essential aspect of the life of both individuals or organizations. We all have to face situations where we need to decide whether a daily one such as having coffee or tea in the breakfast or selecting a graduate school for Ph.D. The importance of a decision increases as the duration of the impact of its results and the number of people that are affected increases. Food-Energy-Water (FEW) is one of the fields where the impacts can stay for a long time and affect many people and areas. In this paper, we proposed a game theory-based approach for decision making among FEW actors sharing a finite amount of continuous resource where actors have different weights on their trust and the amount of share that they receive in their payoff functions. Then, we run simulations on scenarios utilizing a more realistic discrete solution set for actors. Results have shown that when actors place more weight on trust in their payoff function, they tend to propose fairer solutions that are closer to the consensus point. Also, they move towards that point faster compared to actors with low trust weight.
From one-class to two-class classification by incorporating expert knowledge: Novelty detection in human behaviour

One-class classification is the standard procedure for novelty detection. Novelty detection aims to identify observations that deviate from a determined normal behaviour. Only instances of one class are known, whereas so called novelties are unlabelled. Traditional novelty detection applies methods from the field of outlier detection. These standard one-class classification approaches have limited performance in many real business cases. The traditional techniques are mainly developed for industrial problems such as machine condition monitoring. When applying these to human behaviour, the performance drops significantly. This paper proposes a method that improves existing approaches by creating semi-synthetic novelties in order to have labelled data for the two classes. Expert knowledge is incorporated in the initial phase of this data generation process. The method was deployed on a real-life test case where the goal was to detect fraudulent subscriptions to a telecom family plan. This research demonstrates that the two-class expert model outperforms a one-class model on the semi-synthetic dataset. In a next step the model was validated on a real dataset. A fraud detection team of the company manually checked the top predicted novelties. The results show that incorporating expert knowledge to transform a one-class problem into a two-class problem is a valuable method.
LIVE: the Human Role in Learning in Immersive Virtual Environments

This work studies the role of a human instructor within an immersive VR lesson. Our system allows the instructor to perform “contact teaching” by demonstrating concepts through interaction with the environment, and the student to experiment with interaction prompts. We conducted a between-subjects user study with two groups of students: one experienced the VR lesson while immersed together with an instructor; the other experienced the same contents demonstrated through animation sequences simulating the actions that the instructor would take. Results show that the Two-User version received significantly higher scores than the Single-User version in terms of overall preference, clarity, and helpfulness of the explanations. When immersed together with an instructor, users were more inclined to engage and progress further with the interaction prompts, than when the instructor was absent. Based on the analysis of videos and interviews, we identified design recommendations for future immersive VR educational experiences.
Research into the integrated navigation of a deep-sea towed vehicle with USBL/DVL and pressure gauge

Underwater Towed Vehicle navigation and localization in deep sea environments are particularly challenging without high-precision inertial sensors. In this paper, the multi-sensors navigation fusion method of a towed underwater vehicle based on the ultrashort baseline (USBL) system, Doppler velocity log (DVL) and a pressure gauge in the deep sea is investigated. Combined with GPS real-time tide detection technology, the positioning principle of the USBL based on the depth constraint is given, and a straight-line model be proposed to estimate the position of the vehicle used the average heading when underwater towed vehicle is working normally in deep sea without DVL. The interacting multiple model (IMM) algorithm based on smoothing algorithm is proposed to improve the positioning accuracy in the deep sea, and the nonlinearity of the model, precision and stability of filtering are considered simultaneously using the adaptive robust square root cubature Kalman filter. The results of the simulated and practical experiments at 4800 m verify that the new algorithm can take full use of high-precision observation information and greatly improve the positioning accuracy and robustness of a deep sea underwater towed system.
A Big Data visualization layer meta-model proposition

The evolution and democratization of technologies have created a veritable explosion of information, this, of course, gives rise to an urgent need to analyze and deal with the huge masses of data. In fact, the problems raised by the accumulation of data (storage, processing time, heterogeneity, capture rate/generation, etc.) become stronger because the data are massive, complex and varied. It is clear that the representation of information has the capacity to synthesize and condense data, and constitutes an efficient approach for analysis. Nevertheless, it remains ineffective and does not solve these problems. Besides that, conventional visualization techniques are rarely adapted to manage and process this mass of information. To face the complex big data challenges, various types of technologies have been developed. This paper talks about the visualization layer. This layer is located just above the Data Sources, Ingestion, Hadoop storage and Hadoop Platform Management layers for which we have already proposed a meta-modeling. It has a very important role at the level of Big Data Structure. In a continuous effort, we shall present in this paper a universal meta-modeling for the visualization layer and its relationship with the other layers of a Big Data system.
MvsGCN

Multi-video summarization, which tries to generate a single summary for a collection of video, is an important task in dealing with ever-growing video data. In this paper, we are the first to propose a graph convolutional network for multi-video summarization. The novel network measures the importance and relevance of each video shot in its own video as well as in the whole video collection. The important node sampling method is proposed to emphasize the effective features which are more possible to be selected as the final video summary. Two strategies are proposed to integrate into the network to solve the inherent class imbalance problem in the task of video summarization. The loss regularization for diversity is used to encourage a diverse summary to be generated. Extensive experiments are carried out, and in comparison with traditional and recent graph models and the state-of-the-art video summarization methods, our proposed model is effective in generating a representative summary for multiple videos with good diversity. It also achieves state-of-the-art performance on two standard video summarization datasets.
Social Media Popularity Prediction Based on Visual-Textual Features with XGBoost

Popularity prediction for social media is an efficient way for scientists to explore advanced predictive trend and make better strategic decisions for future. In this paper, we propose a framework that uses visual-textual features combined with XGBoost for popularity prediction. More specifically, the framework contains three procedures, including visual-textual features extraction, features fusion and XGBoost regression. In order to extract the visual-textual data, on the one hand, we first adopt one-hot encoder to encode the metadata of the posts, and then apply a word2vec model to produce word embeddings. On the other hand, we adopt a shape descriptor called Hu moment to extract the visual features from the images. What's more, we exploit user's information, e.g. users' followings and users' followers, for providing extra social features to the regression. After that, we fuse the multi-modal features and input them to the XGBoost directly for popularity prediction. Extensive experiments conducted on the SMPD2019 dataset manifest the effectiveness of our system. Furthermore, our approach achieves the 3nd place on the leader board of the Grand Challenge in ACM Multimedia 2019.
Collaborative Human–UAV Search and Rescue for Missing Tourists in Nature Reserves

The authors present a highly complex search-planning problem in which unmanned aerial vehicles and humans collaborate to minimize the expected time at which human rescuers reach a target who is missing. They propose an evolutionary algorithm that uses biogeography-inspired operators to find an optimal or near-optimal solution within an acceptable time.
WaistonBelt X:A Belt-Type Wearable Device with Sensing and Intervention Toward Health Behavior Change

Changing behavior related to improper lifestyle habits has attracted attention as a solution to prevent lifestyle diseases, such as diabetes, heart disease, arteriosclerosis, and stroke. To drive health behavior changes, wearable devices are needed, and they must not only provide accurate sensing and visualization functions but also effective intervention functions. In this paper, we propose a health support system, WaistonBelt X, that consists of a belt-type wearable device with sensing and intervention functions and a smartphone application. WaistonBelt X can automatically measure a waistline with a magnetometer that detects the movements of a blade installed in the buckle, and monitor the basic activities of daily living with inertial sensors. Furthermore, WaistonBelt X intervenes with the user to correct lifestyle habits by using a built-in vibrator. Through evaluation experiments, we confirmed that our proposed device achieves measurement of the circumference on the belt position (mean absolute error of 0.93 cm) and basic activity recognition (F1 score of 0.95) with high accuracy. In addition, we confirmed that the intervention via belt vibration effectively improves the sitting posture of the user.
Gerenuk

Big Data systems are typically implemented in object-oriented languages such as Java and Scala due to the quick development cycle they provide. These systems are executed on top of a managed runtime such as the Java Virtual Machine (JVM), which requires each data item to be represented as an object before it can be processed. This representation is the direct cause of many kinds of severe inefficiencies.
Supervised Discrete Hashing With Mutual Linear Regression

Supervised linear hashing can compress high-dimensional data into compact binary codes owing to its efficiency. Generally, the relation between label and hash codes is widely used in the existing hashing methods because of its effectiveness of improving the accuracy. The existing hashing methods always use two different projections to represent the mutual regression between hash codes and class labels. In contrast to the existing methods, we propose a novel learning-based hashing method termed supervised discrete hashing with mutual linear regression (SDHMLR) in this study, where only one stable projection is used to describe the linear correlation between hash codes and corresponding labels. To the best of our knowledge, this strategy has not been used for hashing previously. In addition, we further use a boosting strategy to improve the final performance of the proposed method without adding extra constraints and with little extra expenditure in terms of time and space. Extensive experiments conducted on three image benchmarks demonstrate the superior performance of the proposed method.
Improved Attribute-Based Encryption Using Chaos Synchronization and Its Application to MQTT Security

In recent years, Internet of Things (IoT) has developed rapidly and been widely used in industry, agriculture, e-health, smart cities, and families. As the total amount of data transmission will increase dramatically, security will become a very important issue in data communication in the IoT. There are many communication protocols for Device to Device (D2D) or Machine to Machine (M2M) in IoT. One of them is Message Queuing Telemetry Transport (MQTT), which is quite prevalent and easy to use. MQTT is designed for resource-constrained devices, so its security is not as strong as other communication protocols. To enhance MQTT security, it needs an additional function to overcome its weakness. However, considering the limited computational abilities of resource-constrained devices, they cannot use too powerful or complicated cryptographic algorithms. Therefore, this paper proposes novel improved attribute-based encryption (ABE) integrated with chaos synchronization to enhance the MQTT security. Finally, a small size of IoT is implemented to simulate resource-constrained devices equipped with a human–machine interface and monitoring software to show and verify the performance of MQTT communication with this improved ABE algorithm.