Reviewerly/exHarmony huggingface dataset:
Context-Aware Automatic Sign Language Video Transcription in Psychiatric Interviews

Sign language (SL) translation constitutes an extremely challenging task when undertaken in a general unconstrained setup, especially in the absence of vast training datasets that enable the use of end-to-end solutions employing deep architectures. In such cases, the ability to incorporate prior information can yield a significant improvement in the translation results by greatly restricting the search space of the potential solutions. In this work, we treat the translation problem in the limited confines of psychiatric interviews involving doctor-patient diagnostic sessions for deaf and hard of hearing patients with mental health problems.To overcome the lack of extensive training data and be able to improve the obtained translation performance, we follow a domain-specific approach combining data-driven feature extraction with the incorporation of prior information drawn from the available domain knowledge. This knowledge enables us to model the context of the interviews by using an appropriately defined hierarchical ontology for the contained dialogue, allowing for the classification of the current state of the interview, based on the doctor's question. Utilizing this information, video transcription is treated as a sentence retrieval problem. The goal is predicting the patient's sentence that has been signed in the SL video based on the available pool of possible responses, given the context of the current exchange. Our experimental evaluation using simulated scenarios of psychiatric interviews demonstrate the significant gains of incorporating context awareness in the system's decisions.
Matlab-based Graphical User Interface for IoT Sensor Measurements Subject to Outlier

Graphical user interface is addressed for outlier detection in sensor measurement. One of the essential steps toward sensor measurement analysis and decision making is anomaly detection. Specifically, measurement noise may encounter abnormal readings, which affect the model algorithm and hence misleading decisions; hence, data must be explored, cleaned, and corrected before being considered for statistical analysis. In this paper, we propose an interactive, easy to navigate, and friendly user interface to extract and explore the sensor's abnormal measurement records. Extraction, visualization, and detection are the three main tasks of such an interface, particularly providing the user with a variety of predefined detection techniques as the first step toward data analysis. The interface is operated via Matlab, and the effectiveness of such a tool is demonstrated by the mean of real-time measurements extraction where different outlier detection is considered.
Emerging Cybersecurity Capability Gaps in the Industrial Internet of Things: Overview and Research Agenda

Internet of Things (IoT)-enabled devices are becoming integrated into a significant and increasing proportion of critical infrastructures, changing the cybersecurity-risk landscape. Risk is being introduced to industry sectors such as transport, energy, and manufacturing, with new attack surfaces exposed and potential for increased harm. Furthermore, risk and harm arising in the Industrial IoT (IIoT) could propagate across interconnected organisations and sectors, resulting in systemic risk. Aspects of this changing risk landscape are not addressed by current cybersecurity approaches, leaving cybersecurity-capability gaps. In this article, we show how current and emerging cybersecurity needs in the IIoT align with a key industry cybersecurity standard, the NIST Cyber Security Framework. The key capability gaps emerging in the IIoT are identified based on our findings from a series of workshops with over 100 expert participants. We present a comprehensive research agenda to enable researchers to prioritise research focus to address these gaps; this research agenda covers the full lifecycle of IIoT development (design, implementation, use and decommission). Furthermore, we conclude that there is a significant gap in understanding of the nature of systemic risk, which should be a key priority if we are to develop effective solutions for cybersecurity and safety in IIoT environments.
Lightweight CNN Frameworks and their Optimization using Evolutionary Algorithms

This paper presents novel frameworks for efficient and lightweight convolutional neural networks that are suitable for use in embedded system applications. Population-based metaheuristic approaches including the genetic algorithm, cuckoo search, the multifactorial evolutionary algorithm and a proposed hybrid approach are used to optimize their performance on image classification tasks. The methods utilize small population sizes without requiring weight-sharing or a surrogate function, and both binary and integer encoding methods are applied in the optimization. The results from these various strategies are evaluated using metrics of computation time and classification accuracy. The multifactorial-based approach is found to give the highest classification accuracy and it requires only a moderate evaluation time. Also, comparisons with prior approaches demonstrate that these methods show a favorable tradeoff between accuracy and computational cost.
Treeago: Tree-structure aggregation and optimization for graph neural network

Graph Convolutional Networks (GCNs) have achieved much success in various graph learning tasks. However, as the number of layers increases, the smoothing of GCNs will over-mix the neighbors’ information, leading output towards space with low expressivity. It is known as the over-smoothing issue. Although several works have refined deep GCNs by optimizing network structure, receptive field, and topology, the over-smoothing issue cannot be completely avoided. In this paper, we propose a recurrent neural network framework for learning graph representation while avoiding over-smoothing effectively, which is the tree-structure aggregation and optimization framework named Treeago. Treeago firstly transforms the irregularly distributed graph into sequential trees. Then, Treeago adopts Tree-LSTM with attention to aggregate important neighbors’ feature information to the graph representation. Tree-LSTM with attention can prevent the mixing of noise neighbors’ information to avoid the over-smoothing issue. Finally, Treeago uses an edge pruning optimization framework based on reinforcement learning to enhance the model’s performance further. Experimental results on multiple real-world datasets show that Treeago effectively avoids over-smoothing and yields state-of-the-art results.
Benchmarking and Configuring Security Levels in Intermittent Computing

Intermittent computing derives its name from the intermittent character of the power source used to drive the computing, typically an energy harvester of ambient energy sources. Intermittent computing is characterized by frequent transitions between the powered and the non-powered state. To enable the processor to quickly recover from unexpected power loss, regular checkpoints store the run-time state of the program, including variables, control information, and machine state. In sensitive applications such as logged measurements, checkpoints must be secured against tamper and replay. We investigate the overhead of creating, securing, and restoring checkpoints with respect to the application. We propose a configurable checkpoint security setting that leverages application properties to reduce overhead of checkpoint security and implement the same using a secure checkpointing protocol. We discuss a prototype implementation for a FRAM-based micro-controller, and we characterize the cost of adding and configuring security to traditional checkpointing using a suite of embedded benchmark applications.
Adaptive open domain recognition by coarse-to-fine prototype-based network

Open domain recognition has attracted great attention in recent two years, which aims to assign a specific identification for each target sample in the presence of large domain discrepancy both in label space and data distributions. Most existing approaches rely on abundant prior information about the relationship of the label sets between the source and the target domain, which is a great limitation for their applications in practical wild. In this paper, a new Adaptive Open Domain Recognition (AODR) task is introduced, which can generalize to various openness and requires no prior information on the label set. To achieve this adaptive transfer task, a two-stage Progressive Adaptation Network is designed, whose learning process consists of multiple episodes. Each episode is performed to simulate an AODR task. Through training and refining multiple episodes, the basic model has progressively accumulated wealthy experience on predicting unseen categories in the presence of large domain discrepancy, which will well generalize to various openness. More specifically, Fusion Information Guided Feature Prototype Generation module is proposed to synthesize visual feature prototype conditioned on category semantic prototype in training stage. Further, Class-Aware Feature Prototype Alignment module is designed in refining stage to align the global feature prototype for each class between two domains. Experimental results verify that the proposed model not only has superiority on classifying the image instances of known and unknown classes, but also well adapts to various openness.
Grammatical Evolution of Complex Digital Circuits in SystemVerilog

The evolution of complex circuits remains a challenge for the Evolvable Hardware field in spite much effort. There are two major issues: the amount of testing required and the low evolvability of representation structures to handle complex circuitry, at least partially due to the destructive effects of genetic operators. A 64-bit × 64-bit add-shift multiplier circuit modelled at register-transfer level in SystemVerilog would require approximately 33,200 gates when synthesized using Yosys Open SYnthesis Suite tool. This enormous gate count makes evolving such a circuit at the gate-level difficult. We use Grammatical Evolution (GE) and SystemVerilog, a hardware description language (HDL), to evolve fully functional parameterized Adder, Multiplier, Selective Parity and Up-Down Counter circuits at a more abstract level other than gate level-register transfer level. Parameterized modules have the additional benefit of not requiring a re-run of evolutionary experiments if multiple instances with different input sizes are required. For example, a 64-bit × 64-bit and 128-bit × 128-bit multipliers etc., can be instantiated from a fully evolved functional and parameterized N-bit × N-bit multiplier. The Adder (6.4 × ), Multiplier (10.7 × ) and Selective Parity (6.7 × ) circuits are substantially larger than the current state of the art for evolutionary approaches. We are able to scale so dramatically because of the use of a HDL, which permits us to operate at a register-transfer level. Furthermore, we adopt a well known technique for reducing testing from digital circuit design known as corner case testing. Skilled circuit designers rely on this to avoid time-consuming exhaustive testing. We demonstrate a simple way to identify and use corner cases for evolutionary testing and show that it enables the generation of massively complex circuits. All circuits were successfully evolved without resorting to the use of any standard decomposition methods, due to our ability to use programming constructs and operators available in SystemVerilog.
Performance Evaluation Of Hsrp, Glbp And Vrrp With Interior Gateway Routing Protocol And Exterior Gateway Routing Protocol

The year of 2019 and 2020, saw the world shifting from physical classes, meetings etc., to online mode due to the commence of worldwide pandemic called COVID-19. Due to this, new network infrastructures were required and among the networks, efficient protocols were required in order to ensure 100&#x0025; uptime and availability. Among the network routing protocols used, it is prudent to find which routing protocol works best with which redundancy protocol, as they have different switching algorithms. Therefore, in this paper, interior gateway routing protocol (OSPF) and exterior gateway protocol (BGP) are being evaluated on their performance with a redundancy protocol (HSRP, GLBP and VRRP). The criterion of measuring the performance includes jitter, downtime, throughput and number of packets lost. The result will help to analyze which first hop redundancy protocol works best with which routing protocol. The result of this study indicates that OSPF has 4.52&#x0025; better QoS values with HSRP and BGP has 5.12&#x0025; better QoS values with GLBP.
Convolution-Based Design for Real-Time Pose Recognition and Character Animation Generation

Human pose recognition and its generation are an important animation design key point. To this end, this paper designs new neural network structures for 2D and 3D pose extraction tasks and corresponding GPU-oriented acceleration schemes. The scheme first takes an image as input, extracts the human pose from it, converts it into an abstract pose data structure, and then uses the converted dataset as a basis to generate the desired character animation based on the input at runtime. The scheme in this paper has been tested on pose recognition datasets and different levels of hardware showing that 2D pose recognition can reach speeds above 60 fps on common computer hardware, 3D pose recognition can be estimated to reach speeds above 24 fps with an average error of only 110 mm, and real-time animation generation can reach speeds above 30 frames per second.
Dynamic Live VM Migration Mechanism in OpenStack-Based Cloud

Live Migration of Virtual Machine (VM) is an action of transferring a working VM from one host to another host on a different physical machine without disturbing the activities of the VM. Live migration enables flexibility in resource optimization, fault tolerance, and load balancing in data center networks. However, migration of VM&#x0027;s from failed or over-utilized hosts to available hosts is an important research issue. OpenStack uses worst-fit as the default migration technique, which does not consider the CPU utilization of hosts. Furthermore, the running instances may consume nearly the entire CPU, which impacts the performance of a particular host. This paper considers how CPU utilization aspects can be added to OpenStack. We calculate the CPU utilization of each host and dynamically migrate the instance from the over-utilized host to the under-utilized host. We observe variation in migration time and downtime considering the CPU utilization aspects for live migration of instances, which significantly impacts the host performance.
Image Registration Based on Redundant Keypoint Elimination SARSIFT Algorithm and MROGH Descriptor

In this article, a new approach is suggested in remote-sensing images registration. In the suggested approach, first, the features extraction process is done based on proposed redundant keypoint elimination method synthetic aperture radar-SIFT (RKEM-SARSIFT). Second, creating descriptors is based on the Multi-Support Region Order-Based Gradient Histogram (MROGH) algorithm. Finally, matching process is done based on nearest neighbor distance ratio (NNDR) and transformation model is done based affine transform. The simulation results on several remote sensing image datasets affirm the suggested approach advantage in comparison with some other basic registration methods in terms of precision matching, SITMMR and SITMMC.
BIoT (Blockchain-based IoT) Framework for Disaster Management

Disasters have a global influence on human life, the environment, wealth, and infrastructure. Information and communication technologies (ICT) play a significant part in dealing with these calamities by allowing and combining diverse technical efforts to build disaster management systems. The Internet of Things (IoT) and Blockchain have sought tremendous attention throughout the globe and across industries. The significant success with the adoption of IoT and Blockchain in many fields has piqued the interest of many stakeholders in disaster management. This study describes an overview of how IoT and blockchain can enhance the performance of disaster management practices.
Intelligent Risk Management using Artificial Intelligence

Effective information security risk management is essential for survival of any business that is dependent on IT. In this paper we present an efficient and effective solution to find best parameters for managing cyber risks using artificial intelligence. Genetic algorithm is use as it can provide our required optimization and intelligence. Results show that GA is professional in finding the best parameters and minimizing the risk.
Blockchains to curb Fake News in an Online World

Fake news has become a significant problem in today&#x0027;s online world. Misinformation is easily propagated through online social media platforms. It is essential to curb the spread of fake news. Blockchain has provided a framework for tamperproof and traceable data, and can therefore be used for developing a news platform. Applying blockchain technology to news tracing has formed the groundwork for supressing the spread of fake news. However, the scale of online platforms and the characteristics of blockchain add several new challenges that hinder the real world deployment of such solutions. This study reviews various blockchain based methods that combat the spread of fake news on online platforms and discusses the challenges faced in realizing such solutions.
Blockchain-Supported Health Registry: The Claim for a Personal Health Trajectory Traceability and How It Can Be Achieved

The digitalization of health processes is a reality. Each time, there are more services and institutions generating and interacting with the health data of a patient. This put in manifest some deficiencies of actual health systems, such as the need for data no longer revolve around the institutions that generate them and start to revolve around the users or patients to whom they belong. Otherwise, patients will end up losing focus and control of their data, which is distributed among different information systems. To address this, many researchers around the world have proposed software solutions that integrate a patient’s data into a global view, even though it is still stored in a distributed way in the systems that generate it. However, the mere integration of data does not allow a patient to have real knowledge of the entire life cycle of her different records. To this end, data integration solutions must go a step further and convert the structure that maintains the overall view of a patient’s health, her Personal Health Trajectory, into a registry ensuring the traceability of the patient’s health. As a result, patients will have a real understanding of everything that surrounds their health data and true patient-centered healthcare systems will be one step closer.
Robust and Deployable Gesture Recognition for Smartwatches

Gesture recognition on smartwatches is challenging not only due to resource constraints but also due to the dynamically changing conditions of users. It is currently an open problem how to engineer gesture recognisers that are robust and yet deployable on smartwatches. Recent research has found that common everyday events, such as a user removing and wearing their smartwatch again, can deteriorate recognition accuracy significantly. In this paper, we suggest that prior understanding of causes behind everyday variability and false positives should be exploited in the development of recognisers. To this end, first, we present a data collection method that aims at diversifying gesture data in a representative way, in which users are taken through experimental conditions that resemble known causes of variability (e.g., walking while gesturing) and are asked to produce deliberately varied, but realistic gestures. Secondly, we review known approaches in machine learning for recogniser design on constrained hardware. We propose convolution-based network variations for classifying raw sensor data, achieving greater than 98% accuracy reliably under both individual and situational variations where previous approaches have reported significant performance deterioration. This performance is achieved with a model that is two orders of magnitude less complex than previous state-of-the-art models. Our work suggests that deployable and robust recognition is feasible but requires systematic efforts in data collection and network design to address known causes of gesture variability.
Gambling Domain Name Recognition via Certificate and Textual Analysis

Abstract On-line gambling is the key illegal behaviour of public security department in most countries due to the potential threat to cyberspace security and social stability. Hence, the research on gambling domain names (GDN) classification is quite important and in great demand for academia and industry. Till now, there is very little research work on this topic. Most of the GDN training datasets in previous work were chosen from GDN blacklists provided by publicly available data sources, and the authors did not verify the authenticity and accuracy of these datasets, and the classification results are not particularly satisfactory. In this paper, certificated and textual analysis-based classification method CT-GDNC is proposed to get GDN training data set with an accuracy of 0.9776 and significantly improve the classification results of GDN. The exhaustive comparative experiments on 10K GDN obtained via Bert fine-tuning model and 10K benign data collected from Alex Top 1 million list show that the proposed method achieves new baseline result for GDN classification with classification accuracy 0.9936, precision 0.9936, F1 0.9936 and recall 0.9939.
Assessment of rare earth and actinides (U and Th) elements in soil samples from Kapar industrial area, Selangor

Abstract The study of rare earth and actinide elements have received attention nowadays due to rapid development in the extraction of the rare earth industry in Malaysia. Soil samples (18 areas) collected from the Kapar industrial area were analysed by using the neutron activation analysis (NAA) technique for the determination of rare earth elements and actinides (U, Th) concentration. The concentration of rare earth element such as La, Ce, Nd, Sm, Eu, Tb, Dy, Yb and Lu were ranged from 14.7-61.4, 30.6-132.8, 11.7-44.0, 2.12-9.60, 0.37-1.43, 0.03-1.57, 2.20-7.45, 0.28-0.81, and 1.99-4.15 mg/kg, respectively. The concentrations of uranium and thorium were a range of &lt; 0.1-6.74 and 12.6-30.1 mg/kg, respectively. The results of geo-accumulation indicate the all-rare earth elements can be categorised as uncontaminated in all areas with an I geo value below 0. The concentration of U can be categorised as uncontaminated to moderately contaminated (I geo class 1) with I geo values of 0 to 1 in the area of D, F, H, I, L, and N, whilst other areas can be categorised as uncontaminated with I geo value &lt; 0 (I geo class 0). The thorium concentration in B, L, and N areas can be categorised as moderately contaminated with I geo value 1 to 2 (I geo class 2), whilst most of the area can be categorised as uncontaminated to moderately contaminated (I geo values 0 to 1). The sources of Th contamination most likely originated from the anthropogenic and geogenic processes.
Cape: compiler-aided program transformation for HTM-based cache side-channel defense

Cache side-channel attacks pose real threats to computer system security. Prior work called Cloak leverages commodity hardware transactional memory (HTM) to protect sensitive data and code from cache side-channel attacks. However, Cloak requires tedious and error-prone manual modifications to vulnerable software by programmers. This paper presents Cape, a compiler analysis and transformation that soundly and automatically protects programs from cache side-channel attacks using Cloak’s defense. An evaluation shows that Cape provides protection that is as strong as Cloak’s, while performing competitively with Cloak.