Reviewerly/exHarmony huggingface dataset:
ARGUS: Interactive visual analysis of disruptions in smartphone-detected Bio-Behavioral Rhythms

Human Bio-Behavioral Rhythms (HBRs) such as sleep-wake cycles (Circadian Rhythms), and the degree of regularity of sleep and physical activity have important health ramifications. Ubiquitous devices such as smartphones can sense HBRs by continuously analyzing data gathered passively by built-in sensors to discover important clues about the degree of regularity and disruptions in behavioral patterns. As human behavior is complex and smartphone data is voluminous with many channels (sensor types), it can be challenging to make meaningful observations, detect unhealthy HBR deviations and most importantly pin-point the causes of disruptions. Prior work has largely utilized computational methods such as machine and deep learning approaches, which while accurate, are often not explainable and present few actionable insights on HBR patterns or causes. To assist analysts in the discovery and understanding of HBR patterns, disruptions and causes, we propose ARGUS, an interactive visual analytics framework. As a foundation of ARGUS, we design an intuitive Rhythm Deviation Score (RDS) that analyzes users’ smartphone sensor data, extracts underlying twenty-four-hour rhythms and quantifies their degree of irregularity.​ This score is then visualized using a glyph that makes it easy to recognize disruptions in the regularity of HBRs. ARGUS also facilitates deeper HBR insights and understanding of causes by linking multiple visualization panes that are overlaid with objective sensor information such as geo-locations and phone state (screen locked, charging), and user-provided or smartphone-inferred ground truth information. This array of visualization overlays in ARGUS enables​ analysts to gain a more comprehensive picture of HBRs, behavioral patterns and deviations from regularity. The design of ARGUS was guided by a goal and task analysis study involving an expert versed in HBR and smartphone sensing. To demonstrate its utility and generalizability, two different datasets were explored using ARGUS and our use cases and designs were strongly validated in evaluation sessions with expert and non-expert users.
Multiple Kernel Clustering With Compressed Subspace Alignment

Multiple kernel clustering (MKC) has recently achieved remarkable progress in fusing multisource information to boost the clustering performance. However, the O(n2) memory consumption and O(n3) computational complexity prohibit these methods from being applied into median- or large-scale applications, where n denotes the number of samples. To address these issues, we carefully redesign the formulation of subspace segmentation-based MKC, which reduces the memory and computational complexity to O(n) and O(n2) , respectively. The proposed algorithm adopts a novel sampling strategy to enhance the performance and accelerate the speed of MKC. Specifically, we first mathematically model the sampling process and then learn it simultaneously during the procedure of information fusion. By this way, the generated anchor point set can better serve data reconstruction across different views, leading to improved discriminative capability of the reconstruction matrix and boosted clustering performance. Although the integrated sampling process makes the proposed algorithm less efficient than the linear complexity algorithms, the elaborate formulation makes our algorithm straightforward for parallelization. Through the acceleration of GPU and multicore techniques, our algorithm achieves superior performance against the compared state-of-the-art methods on six datasets with comparable time cost to the linear complexity algorithms.
Vulnerability disclosure and cybersecurity awareness campaigns on twitter during <scp>COVID</scp>‐19

Abstract The COVID‐19 pandemic introduced the new norm that changed the way we work and live. During these unprecedented times, most of the organizations expected their employees to work from home. Remote working created new opportunities for hackers since more users were making use of digital platforms for online shopping, accessing Virtual Private Network (VPN), videoconferencing platforms, and software alike. Consequently, cybercrime increased due to the increase in the attack surface, and software vulnerabilities were exploited for launching cyberattacks. There is existing research that explores vulnerability disclosure on Twitter. However, there is a lack of study on opportunistic targeted attacks where specific vulnerabilities are exploited in a way that benefit adversaries the most in times such as COVID‐19. The primary aim of this work is to study the effectiveness of vulnerability disclosure pattern on Twitter in COVID‐19, and discuss how Twitter can be leveraged as Open‐Source Intelligence (OSINT) during a pandemic where the global users can follow a coordinated approach to share security‐related information and conduct awareness campaigns. The study identifies Twitter as an apt source for conducting cybersecurity awareness campaigns as 99.83% of the security vulnerabilities are found to be accurate. The information can help global cybersecurity agencies to proactively identify vulnerabilities, coordinate activities, and plan for mitigation strategies since releasing patches from the vendor might take time.
DAM-SE: A Blockchain-Based Optimized Solution for the Counterattacks in the Internet of Federated Learning Systems

The rapid development in network technology has resulted in the proliferation of Internet of Things (IoT). This trend has led to a widespread utilization of decentralized data and distributed computing power. While machine learning can benefit from the massive amount of IoT data, privacy concerns and communication costs have caused data silos. Although the adoption of blockchain and federated learning technologies addresses the security issues related to collusion attacks and privacy leakage in data sharing, the “free-rider attacks” and “model poisoning attacks” in the federated learning process require auditing of the training models one by one. However, that increases the communication cost of the entire training process. Hence, to address the problem of increased communication cost due to node security verification in the blockchain-based federated learning process, we propose a communication cost optimization method based on security evaluation. By studying the verification mechanism for useless or malicious nodes, we also introduce a double-layer aggregation model into the federated learning process by combining the competing voting verification methods and aggregation algorithms. The experimental comparisons verify that the proposed model effectively reduces the communication cost of the node security verification in the blockchain-based federated learning process.
Extreme random forest method for machine fault classification

Recently, random forest (RF) as a highly flexible machine learning algorithm has been applied to medicine, biology, machine learning, computer vision and other fields, and has shown good application performance. Nevertheless, the operation efficiency and identification accuracy of RF algorithm are actually affected by the number of the decision trees. A novel RF model, referred to as the extreme random forest (ERF), was proposed to improve the ability of feature extraction and reduce the computation burden. In the ERF method, the dimensionality of the high-dimensional data is randomly reduced through the random mapping matrix, and the classification performance after dimensionality reduction is improved. In this way, the sample dimension of the input random forest is greatly reduced, which improves the operation efficiency of the random forest. Both theoretical analysis and experiment tests have verified the superiority of the proposed method. In the experimental part, the present ERF method was compared with other peer method in terms of diagnostic performance and computational efficiency. The comparison results showed that the ERF method has more advantages both in diagnostic accuracy and computational efficiency. In addition to mechanical fault diagnosis, the proposed ERF can also be used in other machine learning fields.
Grabbing the Long Tail: A data normalization method for diverse and informative dialogue generation

Recent neural models have shown significant progress in dialogue generation. Among those models, most of them are based on language models, yielding the generation word by word according to the previous context. Due to the inherent mechanism in language models, as well as the most frequently used cross-entropy function (making the distribution of generations approximate that of training data continuously), trained generation models inevitably tend to generate frequent words in training datasets, leading to low diversity and poor informativeness issues. By investigating a few mainstream dialogue generation models, we find that the probable cause is the intrinsic Long Tail Phenomenon in linguistics. To address these issues of low diversity and poor informativeness, we explore and analyze a large corpus from Wikipedia, and then propose an efficient frequency-based data normalization method, i.e., Log Normalization. Furthermore, we explore another two methods, Mutual Normalization and Log-Mutual Normalization, to eliminate the mutual information effect. In order to validate the effectiveness of the proposed methods, we conduct extensive experiments on three datasets with different subjects, including social media, film subtitles, and online customer service. Compared with the vanilla transformers, generation models augmented with our proposed methods achieve significant improvements in generated responses, in terms of both diversity and informativeness. Specifically, the unigram and bigram diversity in the responses are improved by 8.5%–14.1% and 19.7%–25.8% on the three datasets, respectively. The informativeness (defined as amounts of nouns and verbs) is increased by 13.1%–31.0% and 30.4%–59.0%, respectively. Moreover, our methods can be adapted to new generation models efficiently and effectively, with their model-agnostic characteristics.
Image Noise Removal Method Based on Improved Nonlocal Mean Algorithm

Aiming at the problem of unclear images acquired in interactive systems, an improved image processing algorithm for nonlocal mean denoising is proposed. This algorithm combines the adaptive median filter algorithm with the traditional nonlocal mean algorithm, first adjusts the image window adaptively, selects the corresponding pixel weight, and then denoises the image, which can have a good filtering effect on the mixed noise. The experimental results show that, compared with the traditional nonlocal mean algorithm, the algorithm proposed in this paper has better results in the visual quality and peak signal-to-noise ratio (PSNR) of complex noise images.
Blockchain Technology for Preventing Counterfeit in Health Insurance

The huge volume of data and requests for health insurance led to an increased fraud (counterfeit) in the sector either from stakeholders or users. Therefore, the weakness of traditional systems and lack of transparency in obtaining health insurance cards led to a crisis of confidence on part of both parties, regarding both privacy of the patient's data or the fraudulent insurance cards. In addition to that, governments are spending a lot of time and money to get rid of this dilemma. Hence, a system that can solve the counterfeiting problem is highly needed. In this paper, we have proposed and implemented a Blockchain (BC) system to prevent counterfeiting in health insurance sector. We carried out several experiments in order to demonstrate the usability and efficiency of the designed system. The results show system strength and effectiveness in term of speed, security and privacy.
Collecting High-Dimensional and Correlation-Constrained Data with Local Differential Privacy

Local differential privacy (LDP) is a promising privacy model for distributed data collection. It has been widely deployed in real-world systems (e.g. Chrome, iOS, macOS). In LDP-based mechanisms, an aggregator collects private values perturbed by each user and then analyses these values to estimate their statistics, such as frequency and mean. Most existing works focus on simple scalar value types, such as boolean and categorical values. However, with the emergence of smart sensors and internet of things, high-dimensional data are gaining increasing popularity. In many cases, correlations exist between various attributes of such data, e.g. temperature and luminance. To ensure LDP for high-dimensional data, existing solutions either partition the privacy budget &#x03F5; among these correlated attributes or adopt sampling, both of which dilute the density of useful information and thus result in poor data utility.In this paper, we propose a relaxed LDP model, namely, univariate dominance local differential privacy (UDLDP), for high-dimensional data. We quantify the correlations between attributes and present a correlation-bounded perturbation (CBP) mechanism that optimizes the partitioning of privacy budget on each correlated attribute. Furthermore, we extend CBP to support sampling, which is a common bandwidth reduction technique in sensor networks and Internet of Things. We derive the best allocation strategy of sampling probabilities among attributes in terms of data utility, which leads to the correlation-bounded perturbation mechanism with sampling (CBPS). The performance of both mechanisms is evaluated and compared with state-of-the-art LDP mechanisms on real-world and synthetic datasets.
Automated driver management for Selenium WebDriver

Selenium WebDriver is a framework used to control web browsers automatically. It provides a cross-browser Application Programming Interface (API) for different languages (e.g., Java, Python, or JavaScript) that allows automatic navigation, user impersonation, and verification of web applications. Internally, Selenium WebDriver makes use of the native automation support of each browser. Hence, a platform-dependent binary file (the so-called driver) must be placed between the Selenium WebDriver script and the browser to support this native communication. The management (i.e., download, setup, and maintenance) of these drivers is cumbersome for practitioners. This paper provides a complete methodology to automate this management process. Particularly, we present WebDriverManager, the reference tool implementing this methodology. WebDriverManager provides different execution methods: as a Java dependency, as a Command-Line Interface (CLI) tool, as a server, as a Docker container, and as a Java agent. To provide empirical validation of the proposed approach, we surveyed the WebDriverManager users. The aim of this study is twofold. First, we assessed the extent to which WebDriverManager is adopted and used. Second, we evaluated the WebDriverManager API following Clarke’s usability dimensions. A total of 148 participants worldwide completed this survey in 2020. The results show a remarkable assessment of the automation capabilities and API usability of WebDriverManager by Java users, but a scarce adoption for other languages.
Interferometric measurement of high-order aspheric surface parameter errors based on a virtual-real combination iterative algorithm

Aspheric surface parameters, including vertex radius of curvature, conic constant, and high-order aspheric coefficients, decide the optical properties of aspheric surfaces. The measurement of aspheric surface parameter errors (SPEs) is a substantial issue for the fabrication of aspheric surfaces. Interferometry is a mature high-accuracy method in aspheric surface figure error measurement, but challenges still exist in the measurement of SPEs for high-order aspheric surfaces or convex aspheric surfaces. We propose an interferometric measurement method for high-order aspheric SPEs based on a virtual-real combination iterative algorithm (VRCIA). We also propose a recommended measurement system including a partial compensation interferometer to obtain the partial compensated wavefront and a laser differential confocal system to obtain the best compensation distance for calculating SPEs through the VRCIA. A high-order convex aspheric surface is measured to demonstrate the feasibility of the method. The relative accuracy of vertex radius of curvature error, conic constant error and fourth-order aspheric coefficient error can reach 0.025%, 0.095% and 3.02%, respectively.
<i>BinDiff<sub>NN</sub> </i>: Learning Distributed Representation of Assembly for Robust Binary Diffing Against Semantic Differences

Binary diffing is a process to discover the differences and similarities in functionality between two binary programs. Previous research on binary diffing approaches it as a <i>function matching</i> problem to formulate an initial 1:1 mapping between functions, and later a sequence matching ratio is computed to classify two functions being an <i>exact</i> match, a <i>partial</i> match or <i>no-match</i> . The accuracy of existing techniques is best only when detecting exact matches and they are not efficient in detecting partially changed functions; especially those with minor patches. These drawbacks are due to two major challenges (i) In the 1:1 mapping phase, using a strict policy to match function features (ii) In the classification phase, considering an assembly snippet as a normal text, and using sequence matching for similarity comparison. Instruction has a unique structure i.e. mnemonics and registers have a specific position in instruction and also have a semantic relationship, which makes assembly code different from general text. Sequence matching performs best for general text but it fails to detect structural and semantic changes at an instruction level thus, its use for classification produces many false results. In this research, we have addressed the aforementioned underlying challenges by proposing a two-fold solution. For the 1:1 mapping phase, we have proposed computationally inexpensive features, which are compared with distance-based selection criteria to map similar functions and filter unmatched functions. For the classification phase, we have proposed a Siamese binary-classification neural network where each branch is an attention-based distributed learning embedding neural network — that learn the semantic similarity among assembly instructions, learn to highlight the changes at an instruction level and a final stage fully connected layer learn to accurately classify two 1:1 mapped function either an <i>exact</i> or a partial match. We have used x86 kernel binaries for training and achieved <inline-formula><tex-math notation="LaTeX">$\sim 99\%$</tex-math></inline-formula> classification accuracy; which is higher than existing binary <i>diffing</i> techniques and tools.
Partially Observable Monte Carlo Planning with state variable constraints for mobile robot navigation

Autonomous mobile robots employed in industrial applications often operate in complex and uncertain environments. In this paper we propose an approach based on an extension of Partially Observable Monte Carlo Planning (POMCP) for robot velocity regulation in industrial-like environments characterized by uncertain motion difficulties. The velocity selected by POMCP is used by a standard engine controller which deals with path planning. This two-layer approach allows POMCP to exploit prior knowledge on the relationships between task similarities to improve performance in terms of time spent to traverse a path with obstacles. We also propose three measures to support human-understanding of the strategy used by POMCP to improve the performance. The overall architecture is tested on a Turtlebot3 in two environments, a rectangular path and a realistic production line in a research lab. Tests performed on a C++ simulator confirm the capability of the proposed approach to profitably use prior knowledge, achieving a performance improvement from 0.7% to 3.1% depending on the complexity of the path. Experiments on a Unity simulator show that the proposed two-layer approach outperforms also single-layer approaches based only on the engine controller (i.e., without the POMCP layer). In this case the performance improvement is up to 37% comparing to a state-of-the-art deep reinforcement learning engine controller, and up to 51% comparing to the standard ROS engine controller. Finally, experiments in a real-world testing arena confirm the possibility to run the approach on real robots.
TDS-Net: Towards Fast Dynamic Random Hand Gesture Authentication via Temporal Difference Symbiotic Neural Network

Hand gesture is a new emerging biometric trait containing both physiological and behavioral characteristics. With the popularity of various cameras, and the rich identity features and contactless authentication mode embedded in gestures themselves, vision-based hand gesture authentication has great potential value. However, current hand gesture authentication methods heavily rely on defined gestures and require identical enrollment and verification gestures, which limits the user-friendliness and efficiency of authentication. It is arguably true that authentication in a simpler and faster way, without the need to remember gestures, will be more approachable. Thus, a fast dynamic random hand gesture authentication method is introduced, in which users can perform a random improvised gesture in both the enrollment and verification stage. To better utilize the physiological and behavioral characteristics of hand gestures, an efficient network named Temporal Difference Symbiotic Neural Network (TDS-Net) equipped with our designed behavioral energy-based feature fusion module (BE-Fusion module) is proposed. Extensive experiments on the SCUT-DHGA dataset demonstrate that TDS-Net outperforms the recent state-of-the-art methods.
Attribute-aware explainable complementary clothing recommendation

Modelling mix-and-match relationships among fashion items has become increasingly demanding yet challenging for modern E-commerce recommender systems. When performing clothes matching, most existing approaches leverage the latent visual features extracted from fashion item images for compatibility modelling, which lacks explainability of generated matching results and can hardly convince users of the recommendations. Though recent methods start to incorporate pre-defined attribute information (e.g., colour, style, length, etc.) for learning item representations and improving the model interpretability, their utilisation of attribute information is still mainly reserved for enhancing the learned item representations and generating explanations via post-processing. As a result, this creates a severe bottleneck when we are trying to advance the recommendation accuracy and generating fine-grained explanations since the explicit attributes have only loose connections to the actual recommendation process. This work aims to tackle the explainability challenge in fashion recommendation tasks by proposing a novel Attribute-aware Fashion Recommender (AFRec). Specifically, AFRec recommender assesses the outfit compatibility by explicitly leveraging the extracted attribute-level representations from each item’s visual feature. The attributes serve as the bridge between two fashion items, where we quantify the affinity of a pair of items through the learned compatibility between their attributes. Extensive experiments have demonstrated that, by making full use of the explicit attributes in the recommendation process, AFRec is able to achieve state-of-the-art recommendation accuracy and generate intuitive explanations at the same time.