Reviewerly/exHarmony huggingface dataset:
MagThief: Stealing Private App Usage Data on Mobile Devices via Built-in Magnetometer

Various characteristics of mobile applications (apps) and associated in-app services have been used reveal potentially-sensitive user information; however, privacy concerns have prompted third-party apps to rigorously restrict access to data related to mobile app usage. This paper outlines a novel approach to the extraction of detailed app usage information based on analysis of the electromagnetic (EM) signals emitted from mobile devices when executing app-related tasks. Note that this type of EM leakage becomes high-complex when multiple apps are used simultaneously and is subject to interference from geomagnetic signals generated by device movement. This paper proposes a deep learning-based multi-label classification system to identify apps and in-app services based on magnetometer readings. The proposed MAGTHIEF system uses accelerometer and gyroscope data to cancel out the offset in geomagnetic signals followed by an elaborate deep region convolution neural network (DRCNN) to differentiate among multiple apps and the corresponding inapp services. Experiments on 50 apps demonstrated the efficacy of MAGTHIEF in identifying multiple apps and in-app services, achieving high average macro F1 scores of 0.87 and 0.95, respectively. MAGTHIEF also achieved time duration accuracy of 89.5% in recognizing app trajectory in the real-world scene.
Blockchain governance: innovating sustainable energy financing in Global South

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Towards evolutionary knowledge representation under the big data circumstance

Purpose The purpose of this paper is to propose a graph-based representation approach for evolutionary knowledge under the big data circumstance, aiming to gradually build conceptual models from data. Design/methodology/approach A semantic data model named meaning graph (MGraph) is introduced to represent knowledge concepts to organize the knowledge instances in a graph-based knowledge base. MGraph uses directed acyclic graph–like types as concept schemas to specify the structural features of knowledge with intention variety. It also proposes several specialization mechanisms to enable knowledge evolution. Based on MGraph, a paradigm is introduced to model the evolutionary concept schemas, and a scenario on video semantics modeling is introduced in detail. Findings MGraph is fit for the evolution features of representing knowledge from big data and lays the foundation for building a knowledge base under the big data circumstance. Originality/value The representation approach based on MGraph can effectively and coherently address the major issues of evolutionary knowledge from big data. The new approach is promising in building a big knowledge base.
Classification utility aware data stream anonymization

Data streams are continuous, infinite and ordered sequences of data. In comparison to static dataset anonymization, data stream anonymization confront with a number of constraints and difficulties due to the dynamic nature of data flow. The literature already addressed the k-anonymization of data streams which contain quasi-identifier attributes. However, today most data streams contain sensitive and classification target attributes as well. This work's main motivation is to develop a k-anonymization method for data streams which additionally protects the sensitivity and enables effective classification models. The k-anonymization, as a result, is formulated as a weighted multi-objective optimization problem. There are three objectives with respective weights as user parameters. A clustering based k-anonymization algorithm is developed as the solution. An extensive experimental evaluation on three real datasets shows the effectiveness of our proposal in various configurations. Moreover, the experimental results also confirm that our proposal attains better classification accuracies in comparison to popular data stream anonymization techniques.
Data Stream Management for CPS-based Healthcare: A Contemporary Review

Data stream management (DSM) for cyber-physical systems (CPSs) provides good quality care services in the medical domain. This is a very prominent field of research that includes sensing, processing, and networking of various medical devices. The DSM for CPS is a combination of computation using Cyber world (computers or WBANs), including wearable sensors and smart meters as well as communication between the processes through the networks. Data analytics and mobile computing include the usage of wireless sensors which plays a very significant role while handling uncertainties of data stream in healthcare domain. This paper presents a comprehensive review of DSM techniques, including the problem of concept drift in the healthcare domain using CPS, and the challenges associated with the domain. The complete taxonomy characterizes and classifies all the components and methods required for data management in healthcare. This paper provides a glimpse of futuristic techniques used for DSM in view of concept drift while handling real-time data management in healthcare and also identifies the fields for future research. The prime objective of this review is to provide a solution to aggregate health data streams using DSM, generated from different sources, clean and normalize them, and improve them for analysis, diagnosis, pattern identification for data analytics and complex event processing. It is supposed that the techniques discussed in this paper are relevant and useful for further research in the area of DSM for CPS in healthcare.
Key-sifting algorithms for continuous-variable quantum key distribution

We propose key-sifting algorithms for a continuous-variable quantum key distribution (CV-QKD) protocol using a machine-learning framework based on the isolation forest and a traditional digital signal-processing scheme based on the Wiener filter. In previous works, the key-sifting procedure performs by discarding bits on different measurement basis, but a few useless portions are still retained, so that the transmission distance of CV-QKD systems is limited by excess noise. Here, we develop sifting schemes based on anomaly detection, which provide better feasibility in noisy environments. We compare the detection results for both approaches and estimate the excess noise to confirm the utility of our proposal in real-life applications. Results obtained over a 100-km fiber link indicate that the key-sifting algorithms can ensure low excess noise. Moreover, some of the techniques we develop are applicable to other QKD protocols.
Deep learning-based semantic classification of EMF-related scientific literature

Semantic classification of scientific literature using machine learning approaches is challenging due to the difficulties in labeling data and the length of the texts [2, 7]. Most of the work has been done for keyword-based categorization tasks, which take care of occurrence of important terms, whereas semantic classification requires understanding of terms and the meaning of sentences in a context. In this study, we have evaluated neural network models on a semantic classification task using 1091 labeled EMF-related scientific papers listed in the Powerwatch study. The EMF-related papers are labeled into three categories: positive, null finding, and neither. We have conducted neural architecture and hyperparameter search to find the most suitable model for the task. In experiments, we compared the performance of several neural network models in terms of classification accuracy. In addition, we have tested two different types of attention mechanisms. First, a Fully Convolutional Neural Network (FCN) has been used to identify important sentences in the text for the semantic classification. Second, the Transformer, a self-attention-based model, has been tested on the dataset. The experimental result showed that the BiLSTM performed best on both unbalanced and balanced data and the FCN was able to identify important parts in input texts.
Upgrading BRICKS—The Context-Aware Semantic Rule-Based System for Intelligent Building Energy and Security Management

Building management systems (BMSs) are being implemented broadly by industries in recent decades. However, BMSs focus on specific domains, and when installed on the same building, they lack interoperability to work on a centralized user interface. On the other hand, BMSs interoperability allows the implementation of complex rules based on multi-domain contexts. The Building’s Reasoning for Intelligent Control Knowledge-based System (BRICKS) is a context-aware semantic rule-based system for the intelligent management of buildings’ energy and security. It uses ontologies and semantic web technologies to interact with different domains, taking advantage of cross-domain knowledge to apply context-based rules. This work upgrades the previously presented version of BRICKS by including services for energy consumption and generation forecast, demand response, a configuration user interface (UI), and a dynamic building monitoring and management UI. The case study demonstrates BRICKS deployed at different aggregation levels in the authors’ laboratory building, managing a demand response event and interacting autonomously with other BRICKS instances. The results validate the correct functioning of the proposed tool, which contributes to the flexibility, efficiency, and security of building energy systems.
Incentive Mechanisms for Acceptance and Adoption of Automated Systems

The advancement of information technology has increased the prevalence of autonomous systems within day-today activities. Autonomous systems save time for users, performing set tasks with increased speed and efficiency while simultaneously providing financial benefits. However, one of the biggest issues faced by designers and decision makers is the acceptance and adoption of such technologies. As such, research has pulled from an established body of literature regarding incentive mechanisms, in order to motivate users to accept and adopt automated and autonomous systems. The object of this paper is to provide a brief literature review on incentive mechanisms, and subsequently provide design ideas for their inclusion. We will compare financial, social/reputation, and gamification-based incentive mechanisms and their relative efficacy on the changing of user behavior. Finally, we provide some avenues for future research that we believe to be particularly important which could prove fruitful.
Monitoring sound levels in the intensive care unit: A mixed-methods system development project to optimize design features for a new electronic interface in the healthcare environment

Intensive care units (ICU) are busy round the clock and it is difficult to maintain low sound levels that support patient rest. To help ICU staff manage activities we developed a visual display that monitors and reports sound levels in real-time. This facilitates immediate feedback, encouraging proactive behavior change to limit disturbances. Following the principles of user-centered design practices we created our ‘user persona’ to understand the needs and goals of potential users of the system. We then conducted iterative user testing with current members of the ICU team, primarily using the ‘think aloud’ method to refine the design and functionality of our novel system. Ethnography evaluated team use of the display. The final design was simple, clear, and efficient, and both functional and aesthetically pleasing for the key user demographic. We identified challenges in the implementation and adoption process that were separate from the ‘usability’ of the system itself. Embedding the design process within the core user demographic ensured the final product delivered relevant information for key users, and that this information was intuitive to interpret. Initiating sustainable change is not straightforward. It requires recognition of cultural practices within teams, departments, professions, organizations, and strategies to maximize engagement.
Learning Human Activity From Visual Data Using Deep Learning

Advances in wearable technologies have the ability to revolutionize and improve people's lives. The gains go beyond the personal sphere, encompassing business and, by extension, the global economy. The technologies are incorporated in electronic devices that collect data from consumers' bodies and their immediate environment. Human activities recognition, which involves the use of various body sensors and modalities either separately or simultaneously, is one of the most important areas of wearable technology development. In real-life scenarios, the number of sensors deployed is dictated by practical and financial considerations. In the research for this article, we reviewed our earlier efforts and have accordingly reduced the number of required sensors, limiting ourselves to first-person vision data for activities recognition. Nonetheless, our results beat state of the art by more than 4% of F1 score.
Democratizing Design and Fabrication Using Speech

Recent advances in artificial intelligence (AI) and voice interfaces have made possible the digital reincarnation of the draftsperson and craftsperson, capable of designing and producing custom work "to spec" for a reasonable cost. We present findings from an experiment using a Wizard-of-Oz-backed recreation of a voice assistant-based designer; participants describe a holiday ornament of their own imagination to the voice assistant, which elicits details and offers design choices before ultimately "creating" the ornament for the participant. For 8 out of 16 participants, a video channel allows the participant to see the designed object as it is iteratively designed and refined. We offer observations about the nature of the interactions between participants and the voice assistant, as well as quantitative measures of participant-reported cognitive load and predicted and actual satisfaction and accuracy of reproduction. Participants report greater satisfaction with their ornaments and experience reduced cognitive load if they can see them being designed; their expectations are higher if they cannot. 15 of 16 participants would use a voice assistant again for a design task.
A GPU -accelerated Deep Stereo- LiDAR Fusion for Real-time High-precision Dense Depth Sensing

Active LiDAR and stereo vision are the most commonly used depth sensing techniques in autonomous vehicles. Each of them alone has weaknesses in terms of density and reliability and thus cannot perform well on all practical scenarios. Recent works use deep neural networks (DNNs) to exploit their complementary properties, achieving a superior depth-sensing. However, these state-of-the-art solutions are not satisfactory on real-time responsiveness due to the high computational complexities of DNNs. In this paper, we present FastFusion, a fast deep stereo-LiDAR fusion framework for real-time high-precision depth estimation. FastFusion provides an efficient two-stage fusion strategy that leverages binary neural network to integrate stereo-LiDAR information as input and use cross-based LiDAR trust aggregation to further fuse the sparse LiDAR measurements in the back-end of stereo matching. More importantly, we present a GPU-based acceleration framework for providing a low latency implementation of FastFusion, gaining both accuracy improvement and real-time responsiveness. In the experiments, we demonstrate the effectiveness and practicability of FastFusion, which obtains a significant speedup over state-of-the-art baselines while achieving comparable accuracy on depth sensing.
Game-Theoretic Strategic Coordination and Navigation of Multiple Wheeled Robots

Multiple robots negotiating in a dynamic workspace may lead to collisions. To avoid such issues, multi-robot navigation and coordination becomes necessary but is computationally very challenging, particularly when there are many robots. This article addresses the problem of multi-robot navigation where individual robots require coordination. Although a few such attempts for modeling multi-robot coordination and navigation have been studied, this work proposes a game-theoretic coordination strategy, also referred to as strategic coordination. We make use of a genetic algorithm tuned fuzzy logic–based motion planner. The proposed strategic coordination strategy has been pitted against a basic potential field-based motion planner, also referred to as the heuristic method, for performance comparison. Results are compared through computer simulation with 8 to 17 robots at different rounds. From the obtained results, it was observed that the proposed coordination scheme’s efficacy is strong for a larger number of robots. In addition, the proposed strategic coordination scheme with the genetic-fuzzy-based motion planner was found to outperform other combinations as far as the quality of solutions and time to reach the goal positions. The computational complexity of different methods has also been compared and presented.
End-to-End Key-Player-Based Group Activity Recognition Network Applied to Basketball Offensive Tactic Identification in Limited Data Scenarios

In this paper, we propose an end-to-end key-player-based group activity recognition network specially applied to the identification of basketball offensive tactics in limited data scenarios. Our previous studies show that basketball tactics can be better recognized via key player detection with multiple instance learning (MIL) using the support vector machine (SVM). However, the SVM in that work is required to extract features depending on basketball- and tactic-specific knowledge for good performance. Thus, in this study, we develop an end-to-end trainable neural network without prior knowledge and integrate MIL into it. As long as a tactic label is given, MIL can train the network to identify tactic's key players. For testing, our network can recognize the key players in a video clip and provide a tag of the tactic related to them. Like other neural network models, our network requires a large annotated dataset. At the same time, we could collect only a few labeled data, which is common in dealing with group activity recognition. To overcome such a limitation, we propose a novel data augmentation framework, the tactical-based conditional generative adversarial network (GAN), for generating new labeled trajectories. The experimental results show that our method significantly improves 9.13 % in tactic recognition and 4.965 % in key player detection.
Efficient Human Pose Estimation by Maximizing Fusion and High-Level Spatial Attention

In this paper, we propose an efficient human pose estimation network-SFM (slender fusion model) by fusing multi-level features and adding lightweight attention blocks-HSA (High-Level Spatial Attention). Many existing methods on efficient network have already taken feature fusion into consideration, which largely boosts the performance. However, its performance is far inferior to large network such as ResNet and HRNet due to its limited fusion operation in the network. Specifically, we expand the number of fusion operation by building bridges between two pyramid frameworks without adding layers. Meanwhile, to capture long-range dependency, we propose a lightweight attention block-HSA, which computes second-order attention map. In summary, SFM maximizes the number of feature fusion in a limited number of layers. HSA learns high precise spatial information by computing the attention of spatial attention map. With the help of SFM and HSA, our network is able to generate multi-level feature and extract precise global spatial information with little computing resource. Thus, our method achieve comparable or even better accuracy with less parameters and computational cost. Our SFM achieve 89.0 in PCKh@0.5, 42.0 in PCKh@0.1 on MPII validation set and 71.7 in AP, 90.7 in AP@0.5 on COCO validation with only 1.7G FLOPs and 1.5M parameters. The source code will be public soon.
Implementation of a Chaos-Based Symmetric Text Encryption Using Arduino Microcontrollers

Abstract Over the last few years, security becomes more important in any communication system. Several techniques have been implemented to transfer information in a secure way. One of the most important encryption techniques used in security is encryption based on a chaotic system. In this paper, a security chaotic algorithm has been proposed, studied and implemented for encrypting a text message. This work based on using two levels of one-dimensional chaotic maps (logistic map) with different initial conditions. In the experimental process, the original text on the receiver side was decrypted correctly 100% (without any error). The key space measured by this system was (16×1064) which is greater than the minimum key space value necessary to resist any brute-force attack. Also another analysis has been studied in this paper proved that the time plays a very important limitation in the encryption. The time effect on the attacker during its attempts to obtain the original text was very large and it could not get the text in the real or nearly the real-time.
Latent label mining for group activity recognition in basketball videos

Motion information has been widely exploited for group activity recognition in sports video. However, in order to model and extract the various motion information between the adjacent frames, existing algorithms only use the coarse video-level labels as supervision cues. This may lead to the ambiguity of extracted features and the omission of changing rules of motion patterns that are also important sports video recognition. In this paper, a latent label mining strategy for group activity recognition in basketball videos is proposed. The authors' novel strategy allows them to obtain the latent labels set for marking different frames in an unsupervised way, and build the frame-level and video-level representations with two separate levels of supervision signal. Firstly, the latent labels of motion patterns are digged using the unsupervised hierarchical clustering technique. The generated latent labels are then taken as the frame-level supervision signal to train a deep CNN for the frame-level features extraction. Lastly, the frame-level features are fed into an LSTM network to build the spatio-temporal representation for group activity recognition. Experimental results on the public NCAA dataset demonstrate that the proposed algorithm achieves state-of-the-art performance.
Decision Support System on Determination of Contraception Tools as an Effort to Suppress the Number of Growth Ratios in Indonesia

This study aims to recommend the selection of contraception as an effort to reduce the number of growth ratios in Indonesia with the Multi-Objective Optimization method on the basis of Ratio Analysis (MOORA). The data comes from the National Population and Family Planning Agency and the Central Statistics Agency using 5 assessment criteria i.e. security (C1), time period (C2), age (C3), cost (C4), size (C5) and 4 alternatives family planning used i.e. birth control pills (A1), KB injections (A2), implants (A3) and spiral (A4). The sample of this study were female aged 15–49 years and married who are used to family planning program. The results by using the MOORA method showed that from the 4 alternatives used, it was obtained the recommendation of implant contraceptive selection (A3) as the highest rank and birth control pills (A1) as the second rank 2. This study will help the family planner in choosing appropriate and safe family planning program and it further can assist the government in suppressing the number of growth ratios in Indonesia.