Reviewerly/exHarmony huggingface dataset:
Blockchain-based online education content ranking

Nowadays, internet technology plays a vital role in all the fields of our daily lives ranging from the world economy, professional careers, higher education, and almost all the spheres that are deeply impacted. In the current situation, due to COVID19, the dependence on the Internet for almost everything, including learning, getting daily needs, etc., is heavily dependent on the Internet. Online learning is made possible by the Internet, and today most students, educators, researchers are leveraging online learning platforms to enhance their knowledge at their own pace. Generally, the quality of the E-learning courses is evaluated with the help of the courses’ review and rating mechanisms. In the present context, review systems are centralized, storing highly valuable information at one location and are liable to manipulation, hacking, and tampering. In this paper, the Blockchain-based Online Education Content Ranking system is proposed for an online review and ranking system that offers a decentralized trustworthy system, ensuring the integrity of the rating and the independence and integrity of content reviews by Subject Matter Experts (SME).
Fine‐grained semantic ethnic costume high‐resolution image colorization with conditional GAN

Grayscale image colorization, especially for ethnic costume images, is highly challenging due to its rich and complex color features. The existing image colorization methods usually take the costume image as a whole in practical applications that lead to the ignorance of the semantic information of different parts of the costume. It is known that each part's color distribution of the ethnic costume is different. So, the color mapping of other parts is also diverse, which is determined by distinctive ethnic characteristics. This study introduces fine-grained level semantic information and proposes a high-resolution image colorization model for ethnic costumes targeting enhancement, inspired by semantic-level colorization. The semantic information of different regions of ethnic costumes has a significant impact on the performance of the coloring task. Using Pix2PixHD as the backbone network, we create a new network architecture that maintains color distribution correspondence and spatial consistency of costume images using fine-grained semantic information. In our network, we take the splice result of fine-grained semantic for ethnic costume and grayscale image as the conditions and then feed them into the generative adversarial networks. We also discuss and analyze the influences of the grayscale channel and fine-grained semantics on discriminator. Extensive experiments demonstrate that our method performs well compared with other state-of-the-art automatic colorization methods.
Coal elemental (compositional) data analysis with hierarchical clustering algorithms

The modes of occurrence for elements in coal are extremely important for deciphering geological process of coal formation and for anticipating the technological behavior and environmental and health impacts derived from coal utilization. Hierarchical clustering algorithm has been widely adopted to investigate the modes of occurrence of elements in coal. The traditional statistics (e.g., Pearson correlation, Euclidean distance) for the elemental data of coal may lead to misinterpretation because the elemental data of coal are of compositional nature and follow the rules of Aitchison geometry. This work applied log-ratio transformations in order to overcome this problem. Different hierarchical clustering algorithms with various data transformations can infer modes of occurrence for coal elements, but which algorithm is optimum deserves to be investigated. In this paper, we discuss four commonly used hierarchical clustering algorithms utilizing pivot coordinates and weighted symmetric pivot coordinates (WSPC), two types of log-ratio transformations, to infer modes of occurrence of elements in coal, based on published coal elemental data. Results showed that the Pearson correlation produces more meaningful results than the Euclidean distance in clustering rare earth elements and Y. WSPC produces more interpretable results than those from pivot coordinates transformed data for these coal elemental data. Compared with the single, complete, and centroid, the average-linkage algorithm is indeed the optimum.
FOFS: Firefly Optimization for Feature Selection to Predict Fault-Prone Software Modules

Early prediction of fault-prone modules in software development is desirable to ensure the high-quality end product. It highlights the modules which are susceptible to faults and incur high development cost. Curse of dimensionality hinders the performance of classifiers and threatens the accuracy of classifiers to predict fault-prone software modules. This study proposes firefly optimization to select most informative features from high-dimensional defect dataset. It works on the swarm intelligence of fireflies and searches the high dimensional feature space using multi-objective optimization criteria. It looks for a minimal feature subset giving maximum accuracy of classifier. The experimental study utilizes the publicly available NASA corpus. The study compares the proposed model with traditional models statistically. It is inferred from the experiments that the proposed model performs better than the competing techniques.
Low-precision Floating-point Arithmetic for High-performance FPGA-based CNN Acceleration

Low-precision data representation is important to reduce storage size and memory access for convolutional neural networks (CNNs). Yet, existing methods have two major limitations: (1) requiring re-training to maintain accuracy for deep CNNs and (2) needing 16-bit floating-point or 8-bit fixed-point for a good accuracy. In this article, we propose a low-precision (8-bit) floating-point (LPFP) quantization method for FPGA-based acceleration to overcome the above limitations. Without any re-training, LPFP finds an optimal 8-bit data representation with negligible top-1/top-5 accuracy loss (within 0.5%/0.3% in our experiments, respectively, and significantly better than existing methods for deep CNNs). Furthermore, we implement one 8-bit LPFP multiplication by one 4-bit multiply-adder and one 3-bit adder, and therefore implement four 8-bit LPFP multiplications using one DSP48E1 of Xilinx Kintex-7 family or DSP48E2 of Xilinx Ultrascale/Ultrascale+ family, whereas one DSP can implement only two 8-bit fixed-point multiplications. Experiments on six typical CNNs for inference show that on average, we improve throughput by over existing FPGA accelerators. Particularly for VGG16 and YOLO, compared to six recent FPGA accelerators, we improve average throughput by 3.5 and 27.5 and average throughput per DSP by 4.1 and 5 , respectively.
Accurate hyperspectral imaging of mineralised outcrops: An example from lithium-bearing pegmatites at Uis, Namibia

Efficient, socially acceptable and rapid methods of exploration are required to discover new deposits and enable the green energy transition. Sustainable exploration requires a combination of innovative thinking and new technologies. Hyperspectral imaging (HSI) is a rapidly developing technology and allows for fast and systematic mineral mapping, facilitating exploration of the Earth's surface at various scales on a variety of platforms. Newly available sensors allow data capture over a wide spectral range, and provide information about the abundance and spatial location of ore and pathfinder minerals in drill-core, hand samples and outcrops with mm to cm precision. Conversely, the complex geometries of the imaged surfaces affect the spectral quality and signal-to-noise ratio (SnR) of HSI data at these very narrow spatial samplings. Additionally, the complex mineral assemblages found in hydrothermally altered ore deposits can make interpretation of spectral results a challenge. In this contribution, we propose an innovative approach that integrates multiple sensors and scales of data acquisition to help disentangle complex mineralogy associated with lithium and tin mineralisation in the Uis pegmatite complex, Namibia. We train this method using hand samples and finally produce a three-dimensional (3D) point cloud for mapping lithium mineralisation in the open pit. We were able to identify and map lithium-bearing cookeite and montebrasite at outcrop scale. The accuracy of the approach was validated by drill-core data, XRD analysis and LIBS measurements. This approach facilitates efficient mapping of complex terrains, as well as important monitoring and optimisation of ore extraction. Our method can easily be adapted to other minerals relevant to the mining industry.
Inductive logic programming at 30

Abstract Inductive logic programming (ILP) is a form of logic-based machine learning. The goal is to induce a hypothesis (a logic program) that generalises given training examples and background knowledge. As ILP turns 30, we review the last decade of research. We focus on (i) new meta-level search methods, (ii) techniques for learning recursive programs, (iii) new approaches for predicate invention, and (iv) the use of different technologies. We conclude by discussing current limitations of ILP and directions for future research.
Articulating the Role of Artificial Intelligence in Collective Intelligence: A Transactive Systems Framework

Human society faces increasingly complex problems that require coordinated collective action. Artificial intelligence (AI) holds the potential to bring together the knowledge and associated action needed to find solutions at scale. In order to unleash the potential of human and AI systems, we need to understand the core functions of collective intelligence. To this end, we describe a socio-cognitive architecture that conceptualizes how boundedly rational individuals coordinate their cognitive resources and diverse goals to accomplish joint action. Our transactive systems framework articulates the inter-member processes underlying the emergence of collective memory, attention, and reasoning, which are fundamental to intelligence in any system. Much like the cognitive architectures that have guided the development of artificial intelligence, our transactive systems framework holds the potential to be formalized in computational terms to deepen our understanding of collective intelligence and pinpoint roles that AI can play in enhancing it.
Movement Estimation Using Mediapipe BlazePose

The paper describes a system to track the body movement of a person from a video source while augmenting the labelled skeleton joints onto the body of the person. This work has endless applications in the real world especially in the physical-demanding working environment as well as in the sports industry by implementing deep learning, the techniques can recognize the joints on a person’s body. An algorithm namely Mediapipe Blazepose has been applied using PoseNet dataset to detect and estimate curated movements specifically designed for body injury during heavy workload. The propose method has been compared to IMU based motion capture and the difference accuracy is within 10% since IMU capture real data of the sensors while the deep learning method using 2D image analysis. The expected outcome from this project is a working system that is able to correctly identify and label the skeleton joints on a person’s body as well as perform various calculation such as movement velocity and the angle of joints which could be crucial for determining whether certain body movements could result in injuries either in the short- or long-term period.
Real-Time Short-Range Human Posture Estimation Using mmWave Radars and Neural Networks

Millimetre-wave (mmWave) radar is increasing in popularity for human activity recognition, due to its advantages of high resolution, non-intrusive nature and suitability for various environments. In this paper, we present a novel human posture estimation system using mmWave radars. The system detects people with arbitrary postures in indoor environments at close distances (within two metres), and estimates the posture by localising the key joints. We use two mmWave radars to capture the scene and a neural network model to estimate the posture. The neural network model consists of a part detector that estimates the subject’s joint positions, and a spatial model that learns the correlation between the joints. A temporal correlation step is introduced to further refine the estimate when in real-time operation. The system can provide an accurate posture estimate of the person in real-time at 20 fps, with a mean localisation error of 12.2 cm and an average precision of 71.3%.
An identity-based proxy re-encryption for data deduplication in cloud

Cloud computing offers a huge resource pool by centralizing various resources with the rapid growth of digital data. Data storage is the most acceptable service in cloud computing. As one of the key technologies of cloud storage service, data deduplication technology allows cloud servers to save storage space by deleting redundant data. In order to protect data privacy, data holders generally encrypt their data first, and then upload the encrypted data to the cloud server, which clearly brings a new challenge for cloud data deduplication, as traditional deduplication technology cannot deduplicate encrypted data. Existing solutions have various security issues. They cannot flexibly support data access control, and also require data users to remain online. In this paper, we propose an identity-based proxy re-encryption data deduplication scheme using identity-based proxy re-encryption (IB-PRE) and identity-based proof of ownership (IB-PoW). The proposed scheme integrates cloud data deduplication with access control. By detailed security analysis and performance evaluation, we show that the proposed scheme is security, efficient and effective.
Non-Separable Linear Canonical Wavelet Transform

This study aims to achieve an efficient time-frequency representation of higher-dimensional signals by introducing the notion of a non-separable linear canonical wavelet transform in L2(Rn). The preliminary analysis encompasses the derivation of fundamental properties of the novel integral transform including the orthogonality relation, inversion formula, and the range theorem. To extend the scope of the study, we formulate several uncertainty inequalities, including the Heisenberg’s, logarithmic, and Nazorav’s inequalities for the proposed transform in the linear canonical domain. The obtained results are reinforced with illustrative examples.
A Novel Middleware for Efficiently Implementing Complex Cloud-Native SLOs

Service Level Objectives (SLOs) guide the elasticity of cloud applications, e.g., by deciding when and how much the resources provisioned to an application should be changed. Evaluating SLOs requires metrics, which can be directly measured on the application or system, or, more elaborately, be composed from multiple low-level metrics. The implementation of such metrics and SLOs, the triggering of elasticity strategies, and allowing configurability by the user deploying an application, requires a flexible middleware. In this paper, we present a middleware that provides an orchestrator-independent SLO controller for periodically evaluating SLOs and triggering elasticity strategies, while decoupling SLOs from the elasticity strategies to increase flexibility, and provider-independent services for obtaining low-level metrics and composing them into higher-level metrics. We evaluate our middleware by implementing a motivating use case, featuring a cost efficiency SLO for an application deployed on Kubernetes.
Extreme Speech, Democratic Deliberation, and Social Media

Abstract Social media are now central sites of democratic discourse among citizens. But are some contributions to social media too extreme to be permitted? This entry considers the permissibility of suppressing extreme speech on social media, such as terrorist propaganda and racist hate speech. It begins by considering the argument that such restrictions on speech would wrong democratic citizens, violating their freedom of expression. It proceeds to investigate the moral responsibilities of social media companies to suppress extreme speech, and whether these ought to be enforced through the law. Finally, it explores an alternative mechanism for combatting extreme speech on social media—counter-speech—and evaluates its prospects.
All-Optical Scalable Spatial Coherent Ising Machine

Networks of optical oscillators simulating coupled Ising spins have been recently proposed as a heuristic platform to solve hard optimization problems. These networks, called coherent Ising machines (CIMs), exploit the fact that the collective nonlinear dynamics of coupled oscillators can drive the system close to the global minimum of the classical Ising Hamiltonian, encoded in the coupling matrix of the network. To date, realizations of large-scale CIMs have been demonstrated using hybrid optical-electronic setups, where optical oscillators simulating different spins are subject to electronic feedback mechanisms emulating their mutual interaction. While the optical evolution ensures an ultrafast computation, the electronic coupling represents a bottleneck that causes the computational time to severely depend on the system size. Here, we propose an all-optical scalable CIM with fully-programmable coupling. Our setup consists of an optical parametric amplifier with a spatial light modulator (SLM) within the parametric cavity. The spin variables are encoded in the binary phases of the optical wavefront of the signal beam at different spatial points, defined by the pixels of the SLM. We first discuss how different coupling topologies can be achieved by different configurations of the SLM, and then benchmark our setup with a numerical simulation that mimics the dynamics of the proposed machine. In our proposal, both the spin dynamics and the coupling are fully performed in parallel, paving the way towards the realization of size-independent ultrafast optical hardware for large-scale computation purposes.
SoK: Attacks on Industrial Control Logic and Formal Verification-Based Defenses

Programmable Logic Controllers (PLCs) play a critical role in the industrial control systems. Vulnerabilities in PLC programs might lead to attacks causing devastating consequences to the critical infrastructure, as shown in Stuxnet and similar attacks. In recent years, we have seen an exponential increase in vulnerabilities reported for PLC control logic. Looking back on past research, we found extensive studies explored control logic modification attacks, as well as formal verification-based security solutions. We performed systematization on these studies, and found attacks that can compromise a full chain of control and evade detection. However, the majority of the formal verification research investigated ad-hoc techniques targeting PLC programs. We discovered challenges in every aspect of formal verification, rising from (1) the ever-expanding attack surface from evolved system design, (2) the real-time constraint during the program execution, and (3) the barrier in security evaluation given proprietary and vendor-specific dependencies on different techniques. Based on the knowledge systematization, we provide a set of recommendations for future research directions, and we highlight the need of defending security issues besides safety issues.
A Fully Integrated Cryo-CMOS SoC for State Manipulation, Readout, and High-Speed Gate Pulsing of Spin Qubits

This article presents a fully integrated Cryo-CMOS system on chip (SoC) for quantum computing. The proposed SoC integrates a radio frequency (RF) pulse modulator for qubit state manipulation, a multi-tone signal generator and a coherent receiver for qubit state readout, and 22 DACs for high-speed voltage pulsing of qubit gates. By adopting frequency division multiplexing and direct digital synthesis (DDS), the RF pulse modulator can control up to 16 qubits over a single RF line, and the readout receiver can detect the state of up to six qubits simultaneously. The proposed SoC also integrates a microcontroller for low latency on-chip signal processing and increased flexibility in implementing quantum instruction sets. A detailed analysis of qubit-state readout fidelity and the impact of finite DAC resolution on two-qubit gate fidelity is also included in this article, together with an electrical specification summary. The SoC is implemented in Intel’s 22 nm FFL fin field-effect transistor (FinFET) process, and it is characterized both at room and 4 K temperatures. The performance of each specific block is measured, with the readout characterized in a loop-back configuration. Generation of the control signals required for a full Rabi oscillation experiment is also demonstrated. This article also describes the cryogenic thermalization techniques used to integrate the SoC in the dilution refrigerator and shows temperature measurements during operation.
Impact of chaotic dynamics on the performance of metaheuristic optimization algorithms: An experimental analysis

Random mechanisms including mutations are an internal part of evolutionary algorithms, which are based on the fundamental ideas of Darwin’s theory of evolution as well as Mendel’s theory of genetic heritage. In this paper, we debate whether pseudo-random processes are needed for evolutionary algorithms or whether deterministic chaos, which is not a random process, can be suitably used instead. Specifically, we compare the performance of 10 evolutionary algorithms driven by chaotic dynamics and pseudo-random number generators using chaotic processes as a comparative study. In this study, the logistic equation is employed for generating periodical sequences of different lengths, which are used in evolutionary algorithms instead of randomness. We suggest that, instead of pseudo-random number generators, a specific class of deterministic processes (based on deterministic chaos) can be used to improve the performance of evolutionary algorithms. Finally, based on our findings, we propose new research questions.
Deep learning based detection and localization of road accidents from traffic surveillance videos

Real-world traffic surveillance videos need continuous supervision to monitor and take appropriate actions in case of fatal accidents. However, continuously monitoring them with human supervision is error prone and tedious. Therefore, a deep learning approach for automatic detection and localization of road accidents has been proposed by formulating the problem as anomaly detection. The method follows one-class classification approach and applies spatio-temporal autoencoder and sequence-to-sequence long short-term memory autoencoder for modeling spatial and temporal representations in the video. The model is executed on a real-world video traffic surveillance datasets and significant results have been achieved both qualitatively and quantitatively.
Abnormality Identification in Video Surveillance System using DCT

In the present world, video surveillance methods play a vital role in observing the activities that take place across secured and unsecured environment. The main aim with which a surveillance system is deployed is to spot abnormalities in specific areas like airport, military, forests and other remote areas, etc. A new block-based strategy is represented in this paper. This strategy is used to identify unusual circumstances by examining the pixel-wise frame movement instead of the standard object-based approaches. The density and also the speed of the movement is extorted by utilizing optical flow. The proposed strategy recognizes the unusual movement and differences by using discrete cosine transform coefficient. Our goal is to attain a trouble-free block-based Discrete Cosine Transform (DCT) strategy that promotes real-time abnormality detection. The proposed approach has been evaluated against an airport dataset and the outcome of unusual happenings occurred in is evaluated and reported.