Reviewerly/exHarmony huggingface dataset:
Priority evaluation factors for blockchain application services in public sectors

Blockchain is rapidly becoming established as the core technology of the Fourth Industrial Revolution. By combining blockchain to improve processes in existing industries, innovative new services will emerge, but services not effectively applied by blockchain will also develop. This study investigated the factors to be considered when applying the characteristics of blockchain technology to business. We developed a framework of blockchain service utility evaluation indexes using the analytic hierarchy process method. The Delphi method is used to identify highly effective blockchain application service cases by applying the evaluation framework to actual use cases in the public sector. By proposing a framework of utility evaluation factors for blockchain application services, this study provides a systematic foundation for blockchain business review. We address the question of "why blockchain should be applied to this service" by providing a more comprehensive approach than existing research, such as a fragmentary decision tree. Blockchains are expected to become more active along with the full-scale digital transformation of industries, and thus, we must examine the ways to broadly use blockchain as a base technology in a form applicable to the diverse industries and societies constituting the digital economy. Accordingly, this study presents an evaluation solution for promoting efficient policies and developing successful blockchain application services.
Red Alarm for Pre-trained Models: Universal Vulnerability to Neuron-level Backdoor Attacks

The pre-training-then-fine-tuning paradigm has been widely used in deep learning. Due to the huge computation cost for pre-training, practitioners usually download pre-trained models from the Internet and fine-tune them on downstream datasets, while the downloaded models may suffer backdoor attacks. Different from previous attacks aiming at a target task, we show that a backdoored pre-trained model can behave maliciously in various downstream tasks without foreknowing task information. Attackers can restrict the output representations (the values of output neurons) of trigger-embedded samples to arbitrary predefined values through additional training, namely neuron-level backdoor attack (NeuBA). Since fine-tuning has little effect on model parameters, the fine-tuned model will retain the backdoor functionality and predict a specific label for the samples embedded with the same trigger. To provoke multiple labels in a specific task, attackers can introduce several triggers with predefined contrastive values. In the experiments of both natural language processing (NLP) and computer vision (CV), we show that NeuBA can well control the predictions for trigger-embedded instances with different trigger designs. Our findings sound a red alarm for the wide use of pre-trained models. Finally, we apply several defense methods to NeuBA and find that model pruning is a promising technique to resist NeuBA by omitting backdoored neurons.
How social practices inform the future as method: Describing personas in an energy transition while engaging with teleoaffectivities

In the sociology of expectations, the future is seen as performative in the present: how energy futures are imagined has consequences for how the energy transition might be enacted today. This contribution discusses the conceptual and methodological work undertaken to develop personas from the future, on their way to the policy aim of so-called ‘net zero’ carbon emissions by 2050 in Switzerland. Social practice theoretical reflections contribute in three ways: conceptually, in recognizing the future orientation of teleoaffectivities; methodologically, for describing personas engaged in future practices; and towards reflexivity, in how imagining futures might affect social practices today. Moving from policy-relevant to practice-oriented energy scenarios is described as an iterative process that involves different stages of consultation. The process is not without tensions, calling into question how change is understood, and revealing the strengths and limits of trans- and inter-disciplinary approaches. A social practice-based approach provided a method for rich descriptions of futures, moving beyond changes to technologies that are ubiquitous in energy scenarios, to also include changes in meanings and competencies, as well as broader socio-cultural dynamics. The treatment of the social energy futures in cartoon form and through storytelling also allowed for a more compelling and engaging representation of the future, which might affect teleoaffectivities in the present.
International Perspective on Securing Cyberspace Against Terrorist Acts

This research reviews the legal framework for protecting the security of cyberspace from terrorist acts, using the following approaches: the legal approach, the descriptive approach, and the analytical approach. This is achieved by gathering knowledge and data on the protection of cybersecurity and analyzing the different tools and methods that terrorist organizations use to implement their cybercrimes. The goal is to find ways to overcome the various challenges that terrorists pose to regional (Arab-wide) and international cybersecurity systems to find mechanisms and solutions to deal with this phenomenon effectively and to reduce its increasing risks to people and the security, stability, and economies of nations. The most significant conclusion that was reached is the need to continue international efforts to strengthen the fight against cyberterrorism and to establish a binding legal treaty in this regard so as to prevent further harm to the safety of the international community.
Machine Learning-Based Delineation of Geodomain Boundaries: A Proof-of-Concept Study Using Data from the Witwatersrand Goldfields

Abstract Machine-aided geological interpretation provides an opportunity for rapid and data-driven decision-making. In disciplines such as geostatistics, the integration of machine learning has the potential to improve the reliability of mineral resources and ore reserve estimates. In this study, inspired by existing geostatistical approaches that use radial basis functions to delineate domain boundaries, we reformulate the problem into a machine learning task for automated domain boundary delineation to partition the orebody. We use an actual dataset from an operating mine (Driefontein gold mine, Witwatersrand Basin in South Africa) to showcase our new method. Using various machine learning algorithms, domain boundaries were created. We show that based on a combination of in-discipline requirements and heuristic reasoning, some algorithms/models may be more desirable than others, beyond merely cross-validation performance metrics. In particular, the support vector machine algorithm yielded simple (low boundary complexity) but geologically realistic and feasible domain boundaries. In addition to the empirical results, the support vector machine algorithm is also functionally the most resemblant of current approaches that makes use of radial basis functions. The delineated domains were subsequently used to demonstrate the effectiveness of domain delineation by comparing domain-based estimation versus non-domain-based estimation using an identical automated workflow. Analysis of estimation results indicate that domain-based estimation is more likely to result in better metal reconciliation as compared with non-domained based estimation. Through the adoption of the machine learning framework, we realized several benefits including: uncertainty quantification; domain boundary complexity tuning; automation; dynamic updates of models using new data; and simple integration with existing machine learning-based workflows.
Energy efficient resource utilization and load balancing in virtual machines using prediction algorithms

In recent years due to increase in the number of customers and organizations utilize cloud applications for personal and professionalization become greater. As a result of this increase in utilizing the Cloud services, Cloud data centers found to be huge consumption with boundless energy and harmful materials into the environment. The Primary goal of fully virtual machines is to minimize consumption of boundless energy utilization and providing uttermost resources. Balancing the loads on the servers has become a challenging task for the cloud service provider. Load balancing on servers helps to improve the performance of the virtual machines and to minimize energy and processing time in cloud systems. Here we discuss about various load balance r serves as the single and distributes incoming traffic across multiple targets. Reducing the consumption for the data center industries, power supply capacity has more increased and demand will be continued. With an aim to reduce and bring down active server infrastructure burdens. All about, this includes various challenges are taken care of by the environment. Behavior approaches of systems and consumers are not defined clearly. We present a novel resource optimization framework to discover jobs with minimum utilization. This progress ground work encircle boundless energy utilization.
Enhancing Ethereum smart-contracts static analysis by computing a precise Control-Flow Graph of Ethereum bytecode

The immutable nature of Ethereum transactions, and consequently Ethereum smart-contracts, has stimulated the proliferation of many approaches aiming at detecting defects and security issues before the deployment of smart-contracts on the blockchain. Indeed, the actions performed by smart-contracts instantiated on the blockchain, possibly involving substantial financial value, cannot be undone. Unfortunately, smart-contracts source code is not always available, hence approaches based on static analysis have very often to face the problem of inspecting the compiled Ethereum Virtual Machine (EVM) bytecode, retrieved directly from the blockchain. However, due to the intrinsic complexity of EVM bytecode (especially in jumps address resolution), the state-of-the-art static analysis-based solutions have poor accuracy in the automated detection of Ethereum smart-contracts programming defects and vulnerabilities. This paper presents a novel approach based on symbolic execution of the EVM operands stack that allows to resolve jumps address in the EVM bytecode and to construct a precise Control-Flow Graph (CFG) of compiled smart-contracts. Many static analysis techniques are based on a CFG-based representation of the smart-contract to validate, and would therefore benefit from our approach. We have implemented the CFG reconstruction algorithm in a tool called EtherSolve . Then, we have validated the tool on a large dataset of real-world Ethereum smart-contracts, showing that EtherSolve extracts more precise CFGs, w.r.t. state-of-the-art available approaches. Finally, we have extended EtherSolve with two detectors for two of the most prominent Ethereum smart-contracts vulnerabilities (Reentrancy and Tx.origin). Experimental results show that exploiting the proposed CFG reconstruction static analysis, leads to more accurate vulnerabilities detection, w.r.t. state-of-the-art security tools. Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board.
Reducing number of gates in quantum random walk search algorithm via modification of coin operators

This paper examines a way to simplify the circuit of quantum random walk search algorithm, when the traversing coin is constructed by both generalized Householder reflection and an additional phase multiplier. If an appropriate relation between corresponding parameters is realized, our algorithm becomes more robust to deviations in the phases. In this modification marking coin is not needed, and all advantages from above mentioned optimization to the stability, are preserved. It is shown explicitly how to construct such walk coin in order to obtain more robust quantum algorithm.
Steerable pyramid with subpixel compensation for remotely measuring and magnifying dynamic vibration of moving objects

Measuring vibration generated by a moving object is difficult because the accelerometers must be mounted on the moving object. Moreover, the weight and damping ratio of the accelerometers could cause extra mass and damping effect on small moving objects, casting doubt on the accuracy of such measurements. This paper reports a novel method that can remotely measure vibration from the motion of an object captured by a high-speed camera. Aimed at magnifying subtle changes in the presence of large motions, several phase-based video process methods have been proposed previously. Although these methods can capture such subtle changes, they all have deficiencies. For instance, the pure Euler method needs to process every position of movement, making the process very slow if the video file is large. Furthermore, the methods that combine Eulerian and Lagrangian algorithms ignore the subpixel compensation of stabilization. This paper reports a novel dynamic video processing technology by combining phase-based video process technology with visual tracking. The reason for using visual tracking is to extract the target and detect its dynamic position. To compensate for the error induced during the process of video stabilization, the steerable pyramid method was applied to enhance the magnification of subtle vibration with small error. The proposed method was verified by experiments to show that it can effectively improve the signal-to-noise ratio (SNR). The results demonstrated that the vibration and motion generated from the moving object can be detected and measured with satisfactory SNR. With the help of such an innovative method, in the future, both the motion and vibration signals of fast-moving objects, like airplanes, cars, or even small structures, can be measured simultaneously and remotely using high-speed cameras that can also move.
Reversible data hiding based on automatic contrast enhancement using histogram expansion

A lot of Reversible Data Hiding (RDH) methods aim to generate a stego image infinitely approaches the original image while the quality of the original image is leaved out of consideration. Juxtaposed with a plain image, a contrast enhanced version always improves the user experience significantly. Reversible Data Hiding with Contrast Enhancement (RDHCE) enhances the stego image contrast combined with its payloads and enables the cover image to be regained accurately after the payloads have been extracted. This study presents a novel RDHCE method using histogram expansion. First, a new local histogram selecting strategy is proposed to improve the contrast of the whole image. Meanwhile, the global average brightness is used as a reference to determine the shifting direction of the local histogram to prevent the image from being over-enhanced. Moreover, the contrast can be improved adaptively when a reasonable number of data is embedded at the selected embedding points. Experimental results show that, with a given payload, the proposed method achieves better contrast and maintains good visual quality compared with state-of-the-arts.
SOR-TC: Self-attentive octave ResNet with temporal consistency for compressed video action recognition

Modeling and recognizing video activities from videos are key parts of many promising applications such as visual surveillance, human–computer interaction, and video summarization. However, current approaches mainly suffer from two issues: (a) Short-term local and global spatial features are not well represented. The spatial redundancy and dependency have not been well considered on CNN-based action recognition, which may result in a further increase in both memory and computation cost. (b) Long-term temporal consistency is not well captured. The action consistency across multiple clips has been ignored in video-level action recognition approaches. To address these two issues, we propose a Self-Attentive Octave ResNet with Temporal Consistency (SOR-TC) for compressed video action recognition to better capture the short-term and long-term features in video and improve the efficiency and effectiveness of action recognition. In addition, this paper introduces a consistency hypothesis that adjacent clips should predict similar actions. So the consistency loss function is designed to learn the correlation of clips. Finally, extensive experimental results on two benchmark datasets HMDB-51 and UCF-101 verify the effectiveness of our proposed method.
Quantum approximate optimization algorithm applied to the binary perceptron

We apply digitized quantum annealing (QA) and quantum approximate optimization algorithm (QAOA) to a paradigmatic task of supervised learning in artificial neural networks: the optimization of synaptic weights for the binary perceptron. At variance with the usual QAOA applications to MaxCut, or to quantum spin-chains ground-state preparation, here the classical cost function is characterized by highly nonlocal multispin interactions. Yet, we provide evidence for the existence of optimal smooth solutions for the QAOA parameters, which are transferable among typical instances of the same problem, and we prove numerically an enhanced performance of QAOA over traditional QA. We also investigate on the role of the classical cost-function landscape geometry in this problem. By artificially breaking this geometrical structure, we show that the detrimental effect of a gap-closing transition, encountered in QA, is also negatively affecting the performance of our QAOA implementation.
A Quantum-Based Beetle Swarm Optimization Algorithm for Numerical Optimization

The beetle antennae search (BAS) algorithm is an outstanding representative of swarm intelligence algorithms. However, the BAS algorithm still suffers from the deficiency of not being able to handle high-dimensional variables. A quantum-based beetle swarm optimization algorithm (QBSO) is proposed herein to address this deficiency. In order to maintain population diversity and improve the avoidance of falling into local optimal solutions, a novel quantum representation-based position updating strategy is designed. The current best solution is regarded as a linear superposition of two probabilistic states: positive and deceptive. An increase in or reset of the probability of the positive state is performed through a quantum rotation gate to maintain the local and global search ability. Finally, a variable search step strategy is adopted to speed up the ability of the convergence. The QBSO algorithm is verified against several swarm intelligence optimization algorithms, and the results show that the QBSO algorithm still has satisfactory performance at a very small population size.
Dual-UAV Payload Transportation Using Optimized Velocity Profiles via Real-Time Dynamic Programming

In this paper, a real-time dynamic programming (RTDP) approach was developed for the first time to jointly carry a slung load using two unmanned aerial vehicles (UAVs) with a trajectory optimized for time and energy consumption. The novel strategy applies RTDP algorithm, where the journey was discretized into horizons consisting of distance intervals, and for every distance interval, an optimal policy was obtained using a dynamic programming sweep. The RTDP-based strategy is applied for dual-UAV collaborative payload transportation using coordinated motion where UAVs act as actuators on the payload. The RTDP algorithm provides the optimal velocity decisions for the slung load transportation to either minimize the journey time or the energy consumption. The RTDP approach involves minimizing a cost function which is derived after simplifying the combined model of the dual-UAV-payload system. The cost function derivation was also accommodated to dynamically distribute the load/energy between two multi-rotor platforms during a transportation mission. The cost function is used to calculate transition costs for all stages and velocity decisions. A terminal cost is used at the last distance interval during the first phase of the journey when the velocity at the end of the current horizon is not known. In the second phase, the last stage or edge of the horizon includes the destination, hence final velocity is known which is used to calculate the transition cost of the final stage. Once all transition costs are calculated, the minimum cost is traced back from the final stage to the current stage to find the optimal velocity decision. The developed approach was validated in MATLAB simulation, software in the loop Gazebo simulation, and real experiments. The numerical and Gazebo simulations showed the successful optimization of journey time or energy consumption based on the selection of the factor λ. Both simulation and real experiments results show the effectiveness and the applicability of the proposed approach.
Fully Cross-Attention Transformer for Guided Depth Super-Resolution

Modern depth sensors are often characterized by low spatial resolution, which hinders their use in real-world applications. However, the depth map in many scenarios is accompanied by a corresponding high-resolution color image. In light of this, learning-based methods have been extensively used for guided super-resolution of depth maps. A guided super-resolution scheme uses a corresponding high-resolution color image to infer high-resolution depth maps from low-resolution ones. Unfortunately, these methods still have texture copying problems due to improper guidance from color images. Specifically, in most existing methods, guidance from the color image is achieved by a naive concatenation of color and depth features. In this paper, we propose a fully transformer-based network for depth map super-resolution. A cascaded transformer module extracts deep features from a low-resolution depth. It incorporates a novel cross-attention mechanism to seamlessly and continuously guide the color image into the depth upsampling process. Using a window partitioning scheme, linear complexity in image resolution can be achieved, so it can be applied to high-resolution images. The proposed method of guided depth super-resolution outperforms other state-of-the-art methods through extensive experiments.
TeCre: A Novel Temporal Conflict Resolution Method Based on Temporal Knowledge Graph Embedding

Since the facts in the knowledge graph (KG) cannot be updated automatically over time, some facts have temporal conflicts. To discover and eliminate the temporal conflicts in the KG, this paper proposes a novel temporal conflict resolution method based on temporal KG embedding (named TeCre). Firstly, the predicate relation and timestamp information of time series are incorporated into the entity–relation embedding representation by leveraging the temporal KG embedding (KGE) method. Then, taking into account the chronological sequence of the evolution of the entity–relation representation over time, TeCre constrains the temporal relation in the KG according to the principles of time disjoint, time precedence, and time mutually exclusive constraints. Besides that, TeCre further considers the sequence vectorization of predicate relation to discover the temporal conflict facts in the KG. Finally, to eliminate the temporal conflict facts, TeCre deletes the tail entities of the temporal conflict facts, and employs the link prediction method to complete the missing tail entities according to the output of the score function based on the entity–relation embedding. Experimental results on four public datasets show that TeCre is significantly better than the state-of-the-art temporal KG conflict resolution model. The mean reciprocal ranking (MRR) and Hits@10 of TeCre are at least 5.46% and 3.2% higher than the baseline methods, respectively.