Reviewerly/exHarmony huggingface dataset:
TERA: Screen-to-Camera Image Code With Transparency, Efficiency, Robustness and Adaptability

With the rapid development of digital devices, the issue of how to transmit information among different devices with multimedia carriers has drawn much attention from the research community. This paper focuses on the important user scenario of “screen-to-camera information transmission”. Along this direction, image coding-based techniques have been shown to be the most popular and effective methods in the past decades. However, after careful study, we find that none of the existing methods can satisfy the four important properties simultaneously, i.e., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">high transparency</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">high embedding efficiency</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">strong transmission robustness</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">high adaptability to device types</i> . This is mainly because these properties are contradictory with each other. In this paper, we thus propose a screen-to-camera image code dubbed “TERA” ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</b> ransparency, <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</b> fficiency, <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</b> obustness and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</b> daptability), which makes it possible to circumvent the contradiction among the above four properties for the first time. Generally, TERA adopts the color decomposition principle to ensure the visual quality and the superposition-based scheme to ensure embedding efficiency. BCH-coding-based information arrangement and a powerful attention-guided information decoding network are further designed to guarantee the robustness and adaptability. Through extensive experiments, the superiority and broad applications of our method are demonstrated.
Double Low-Rank Matrix Decomposition for Hyperspectral Image Denoising and Destriping

Hyperspectral images (HSIs) have a wealth of applications in many areas, due to their fine spectral discrimination ability. However, in the practical imaging process, HSIs are often degraded by a mixture of various types of noise, for example, Gaussian noise, impulse noise, dead pixels, dead lines, and stripe noise. Low-rank matrix decomposition theory has been widely used in HSI denoising, and has achieved competitive results by modeling the impulse noise, dead pixels, dead lines, and stripe noise as sparse components. However, the existing low-rank-based methods for HSI denoising cannot completely remove stripe noise when the stripe noise is no longer sparse. In this article, we extend the HSI observation model and propose a double low-rank (DLR) matrix decomposition method for HSI denoising and destriping. By simultaneously exploring the low-rank characteristic of the lexicographically ordered noise-free HSI and the low-rank structure of the stripe noise on each band of the HSI, the two low-rank constraints are formulated into one unified framework, to achieve separation of the noise-free HSI, stripe noise, and other mixed noise. The proposed DLR model is then solved by the augmented Lagrange multiplier (ALM) algorithm efficiently. Both simulation and real HSI data experiments were carried out to verify the superiority of the proposed DLR method.
Training Strategies and Data Augmentations in CNN-based DeepFake Video Detection

The fast and continuous growth in number and quality of deepfake videos calls for the development of reliable de-tection systems capable of automatically warning users on social media and on the Internet about the potential untruthfulness of such contents. While algorithms, software, and smartphone apps are getting better every day in generating manipulated videos and swapping faces, the accuracy of automated systems for face forgery detection in videos is still quite limited and generally biased toward the dataset used to design and train a specific detection system. In this paper we analyze how different training strategies and data augmentation techniques affect CNN-based deepfake detectors when training and testing on the same dataset or across different datasets.
An Ontological Framework for Information Extraction From Diverse Scientific Sources

Automatic information extraction from online published scientific documents is useful in various applications such as tagging, web indexing and search engine optimization. As a result, automatic information extraction has become among the hottest areas of research in text mining. Although various information extraction techniques have been proposed in the literature, their efficiency demands domain specific documents with static and well-defined format. Furthermore, their accuracy is challenged with a slight modification in the format. To overcome these issues, a novel ontological framework for information extraction (OFIE) using fuzzy rule-base (FRB) and word sense disambiguation (WSD) is proposed. The proposed approach is validated with a significantly wider document domains sourced from well-known publishing services such as IEEE, ACM, Elsevier, and Springer. We have also compared the proposed information extraction approach against state-of-the-art techniques. The results of the experiment show that the proposed approach is less sensitive to changes in the document format and has a significantly better average accuracy of 89.14% and F-score as 89%.
Identifying and Analyzing Cryptocurrency Manipulations in Social Media

Interest surrounding cryptocurrencies, digital or virtual currencies that are used as a medium for financial transactions, has grown tremendously in the recent years. The anonymity surrounding these currencies makes investors particularly susceptible to fraudity-such as “pump and dump” scams-where the goal is to artificially inflate the perceived worth of a currency, luring victims into investing before the fraudsters can sell their holdings. Because of the speed and relative anonymity offered by social platforms such as Twitter and Telegram, social media has become a preferred platform for scammers who wish to spread false hype about the cryptocurrency they are trying to pump. In this work, we propose and evaluate a computational approach that can automatically identify pump and dump scams as they unfold by combining information across social media platforms. We also develop a multi-modal approach for predicting whether a particular pump attempt will succeed or not. Finally, we analyze the prevalence of bots in cryptocurrency related tweets, and observe a significant increase in bot activity during the pump attempts.
A Resource-Efficient Pipelined Architecture for Real-Time Semi-Global Stereo Matching

It is still a grand challenge to implement a high-accuracy and high-performance stereo matching algorithm on a resource-limited hardware platform in stereo vision systems. This paper proposes a resource-efficient pipelined hardware architecture with four-cycle time-sharing for the semi-global matching (SGM) algorithm with weighted path cost aggregation. To save hardware resources, we also combined image down-sampling and disparity skipping in the SGM algorithm. The presented architecture is synthesized and implemented on a Zynq-7 FPGA board, which results in a throughput of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${1280 \times 960/62.5}$ </tex-math></inline-formula> fps with 75 disparity levels at the maximum frequency of 216 MHz. To improve the accuracy of the disparity map at close range, we also adapt the presented architecture with two-cycle time-sharing, and the disparity range is increased to 128, which attains the processing of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${1280 \times 960/116}$ </tex-math></inline-formula> fps at 200 MHz on VCU-118 FPGA board; the throughput reaches 18245 MDE/s. The result shows that the whole architecture only takes 50465 LUTs, 48046 Registers, 125.5 BRAMs with 128 disparity levels, which is much more efficient than the latest reference work.
Summarization of legal documents: Where are we now and the way forward

Due to huge amount of legal information availability on the internet, as well as other sources, it is important for the research community to do more extensive research on the area of legal text processing, which can help us make sense out of the vast amount of available data. This information growth has compelled the requirement to develop systems that can help legal professionals as well as ordinary citizens get relevant legal information with very little effort. In this survey paper, different text summarization techniques are surveyed, with a specific focus on legal document summarization, as this is one of the most important areas in the legal field, which can help with the quick understanding of legal documents. This paper starts with the general introduction to text summarization, following which various legal text summarization techniques are discussed. Various available tools are also described in this paper which is used for summarization of legal text. Two case studies are also presented in this work, where the automatic summarization of heterogeneous legal documents from two countries is considered. With the presented detailed review of the state of the art approaches, comparative analysis from the case studies and also discussions on several important research questions, this work is expected to provide a good starting point for researchers to perform a more in-depth exploration of the area of legal document summarization, more specifically with respect to the key future research directions identified in this work.
HANSEL: Adaptive horizontal scaling of microservices using Bi-LSTM

With the rapid development of 5G network, business scenarios such as intelligent service and new retail are becoming more and more popular. The demand for more flexible and scalable real-time data processing, in particular, the AI-related data processing has also increased in edge computing. Therefore, how to meet such business development has become a major challenge. Focusing on this requirement, microservice architecture, proposed and developed by some big cloud computing companies’ platform, such as Google Kubernetes platform, has gradually become a mainstream technology solution in edge computing. However, many microservices used in edge computing cannot achieve an even time distribution, which is random or sudden. Kubernetes built-in Horizontal POD Autoscaling (HPA) is unable to well handle the change of microservice load, which inevitably leads to the waste of system resources and affects the SLA of microservice. To solve this issue, this paper proposes a HANSEL system based on Kubernetes platform, which can optimize the horizontal elastic scaling policy of Kubernetes by accurately predicting the load of microservices based on the Bi-LSTM load prediction algorithm with attention mechanism. Furthermore, active elastic scaling is realized through reinforcement learning method, and we design a hybrid elastic scaling mechanism through combining reactive and active methods, so as to construct an elastic scaling system for automatic scheduling of working nodes. Our experimental results show that HANSEL system can improve the system resource utilization by about 20% when meeting the microservice SLA of edge computing.
Reachability-Based Trajectory Safeguard (RTS): A Safe and Fast Reinforcement Learning Safety Layer for Continuous Control

Reinforcement Learning (RL) algorithms have achieved remarkable performance in decision making and control tasks by reasoning about long-term, cumulative reward using trial and error. However, during RL training, applying this trial-and-error approach to real-world robots operating in safety critical environment may lead to collisions. To address this challenge, this letter proposes a Reachability-based Trajectory Safeguard (RTS), which leverages reachability analysis to ensure safety during training and operation. Given a known (but uncertain) model of a robot, RTS precomputes a Forward Reachable Set of the robot tracking a continuum of parameterized trajectories. At runtime, the RL agent selects from this continuum in a receding-horizon way to control the robot; the FRS is used to identify if the agent's choice is safe or not, and to adjust unsafe choices. The efficacy of this method is illustrated in static environments on three nonlinear robot models, including a 12-D quadrotor drone, in simulation and in comparison with state-of-the-art safe motion planning methods.
Bump hunting in latent space

Unsupervised anomaly detection could be crucial in future analyses searching for rare phenomena in large datasets, as for example collected at the LHC. To this end, we introduce a physics inspired variational autoencoder (VAE) architecture which performs competitively and robustly on the LHC Olympics Machine Learning Challenge datasets. We demonstrate how embedding some physical observables directly into the VAE latent space, while at the same time keeping the classifier manifestly agnostic to them, can help to identify and characterise features in measured spectra as caused by the presence of anomalies in a dataset.
Smooth Operator

Can we influence how a robot is perceived by designing the sound of its movement? Drawing from practices in film sound, we overlaid a video depicting a robot's movement routine with three types of artificial movement sound. In a between-subject study design, participants saw either one of the three designs or a quiet control condition and rated the robot's movement quality, safety, capability, and attractiveness. We found that, compared to our control, the sound designs both increased and decreased perceived movement quality. Coupling the same robotic movement with different sounds lead to the motions being rated as more or less precise, elegant, jerky, or uncontrolled, among others. We further found that the sound conditions decreased perceived safety, and did not affect perceived capability and attractiveness. More unrealistic sound conditions led to larger differences in ratings, while the subtle addition of harmonic material was not rated differently to the control condition in any of the measures. Based on these findings, we discuss the challenges and opportunities regarding the use of artificial movement sound as an implicit channel of communication that may eventually be able to selectively target specific characteristics, helping designers in creating more refined and nuanced human-robot interactions.
4.6 A 144Kb Annealing System Composed of 9×16Kb Annealing Processor Chips with Scalable Chip-to-Chip Connections for Large-Scale Combinatorial Optimization Problems

Substantial progress has been made on a new computer architecture, known as an annealing processor (AP) [1-4]. The AP can effectively solve NP-hard combinatorial optimization problems by providing a fast method for finding the grand state of an Ising model. In particular, various types of APs based on a CMOS process (CMOS-AP) significantly improve the scalability and power efficiency of the annealing system by utilizing fast parallel spin updates on the basis of simulated annealing (SA) [2-4]. Further development of CMOS-APs requires overcoming two challenges: improving the accuracy of the annealing processing by expanding the bitwidth of coefficients and attaining a multi-chip annealing system consisting of AP chips with 8-way connectivity. In this work, we developed a scalable CMOS-AP with two key technologies: (i) A flip-flop (FF)-based spin circuit allowing expandable bitwidth by reproducing the Metropolis algorithm, which is SA with a fixed temperature, and (ii) an inter-chip interface (I/F) with a data-compression method utilizing annealing characteristics to obtain multi-chip operation without degrading annealing speed and accuracy. The CMOS-AP demonstrated multichip operation of the 9×16k spin system with an annealing speed 233× faster and a calculation energy 972× lower than running SG3 on a CPU.
KArSL

Sign language is the major means of communication for the deaf community. It uses body language and gestures such as hand shapes, lib patterns, and facial expressions to convey a message. Sign language is geography-specific, as it differs from one country to another. Arabic Sign language is used in all Arab countries. The availability of a comprehensive benchmarking database for ArSL is one of the challenges of the automatic recognition of Arabic Sign language. This article introduces KArSL database for ArSL, consisting of 502 signs that cover 11 chapters of ArSL dictionary. Signs in KArSL database are performed by three professional signers, and each sign is repeated 50 times by each signer. The database is recorded using state-of-art multi-modal Microsoft Kinect V2. We also propose three approaches for sign language recognition using this database. The proposed systems are Hidden Markov Models, deep learning images’ classification model applied on an image composed of shots of the video of the sign, and attention-based deep learning captioning system. Recognition accuracies of these systems indicate their suitability for such a large number of Arabic signs. The techniques are also tested on a publicly available database. KArSL database will be made freely available for interested researchers.
The Effect of Organizational Information Security Climate on Information Security Policy Compliance: The Mediating Effect of Social Bonding towards Healthcare Nurses

The advancement of information communication technology in healthcare institutions has increased information security breaches. Scholars and industry practitioners have reported that most security breaches are due to negligence towards organizational information security policy compliance (ISPC) by healthcare employees such as nurses. There is, however, a lack of understanding of the factors that ensure ISPC among nurses, especially in developing countries such as Malaysia. This paper develops and examines a research framework that draws upon the factors of organizational climate of information security (OCIS) and social bond theory to enhance ISPC among nurses. A questionnaire was adopted in which responses were obtained from 241 nurses employed in 30 hospitals in Malaysia. The findings from the study demonstrated that the ISPC among nurses is enhanced through OCIS factors. The influence on ISPC was even more significant when examined by the mediating effect of the social bond. It implies that influential OCIS factors reinforce social bonds among nurses and eventually increase the ISPC. For information security practitioners, the study findings emphasize the prevalence of socio-active information security culture in healthcare organizations to enhance ISP compliance among nurses.
SAEA: A security-aware and energy-aware task scheduling strategy by Parallel Squirrel Search Algorithm in cloud environment

The rapid growth of networking technologies resulted in the execution of an extensive data-centric task, which needs the critical quality of service by cloud data centers. The task scheduling problem is difficult to attain an optimal solution, so we use the Squirrel Search Algorithm to approximate the optimal solution. Traditional scheduling algorithms attempt to reduce execution time without taking into account the energetic cost and security issues. In this scheme, a fuzzy-based task scheduling (SAEA) algorithm is developed which closely combines energy cost, makespan, degree of imbalance, and security levels for multi-objective optimization scheduling problems. In addition, SAEA tries to find a high-quality knowledge base that accurately describes the fuzzy system by parallel squirrels search algorithm (PSSA). The automatic design of a fuzzy rule-based system is currently attracting the interest due to the inherently dynamic nature and the typical complex search spaces of cloud. Extensive experiments prove that SAEA algorithm obtains superior performances in energy cost around 45% compared with MGA and has a better result in terms of total execution time, makespan, degree of imbalance, and security value than other similar scheduling algorithms under high load condition.
Convergence of the Random Batch Method for Interacting Particles with Disparate Species and Weights

We consider in this work the convergence of the random batch method proposed in our previous work [Jin et al., J. Comput. Phys., 400(2020), 108877] for interacting particles to the case of disparate species and weights. We show that the strong error is of $O(\sqrt{\tau})$ while the weak error is of $O(\tau)$ where $\tau$ is the time step between two random divisions of batches. Both types of convergence are uniform in $N$, the number of particles. The proof of strong convergence follows closely the proof in [Jin et al., J. Comput. Phys., 400(2020), 108877] for indistinguishable particles, but there are still some differences: Since there is no exchangeability now, we have to use a certain weighted average of the errors; some refined auxiliary lemmas have to be proved compared with our previous work. To show that the weak convergence of empirical measure is uniform in $N$, certain sharp estimates for the derivatives of the backward equations have been used. The weak convergence analysis is also illustrating for the convergence of the Random Batch Method for $N$-body Liouville equations.
A Framework for Edge-Assisted Healthcare Data Analytics using Federated Learning

With the emergence of wearable technology, IoT, and Edge computing, the nature of healthcare is rapidly shifting towards digital health aided by these ICT technologies. At the same time, consumer devices, such as smart, wearable fitness watches are gaining market share as a way to monitor physical activity and wellness. Despite these advances, and their ability to capture longitudinal behavioural patterns, these devices have yet to be fully leveraged within the healthcare system. If the user-generated data from such devices could be collected without com-promising an individual’s privacy, these insights could comprise part of a more holistic and preventative healthcare solution. In this article, we propose an Edge-assisted data analytics frame-work that uses Federated Learning to re-train local machine learning models using user-generated data. This framework could leverage pre-trained models to extract user-customized insights while preserving privacy and Cloud resources. We also identify some potential application scenarios and discuss research challenges to be explored within the proposed framework.