Reviewerly/exHarmony huggingface dataset:
A Dynamic Path Planning Algorithm Based on the Improved DWA Algorithm

Because of its superior obstacle avoidance capability, the Dynamic Window Approach (DWA) algorithm has been widely used in local dynamic path planning nowadays. However, in areas with dense obstacles, the DWA algorithm prefers to go around the outside of the dense obstacle area, which increases the total distance. In addition, when encountering a "C" shaped obstacle, the objective cost function will fail and the path will not be found. Therefore, this paper proposes a method to improve the DWA algorithm. Based on the existing constraints, we also propose to score the distance between the current point and the target. In our experiments, we use the traditional DWA algorithm as a reference method and compare the two algorithms in maps with different characteristics. The experimental results demonstrate that the improved DWA algorithm achieves better results in obstacle avoidance.
Review Study on Blockchain Frameworks for Security Issues in IoT Devices

A coalition of IoT devices, such as the smart houses, smart health care system, the smart device and many more, can share resources through communication lines. The Internet is integrated with the physical world over existing network infrastructure, allowing intelligent devices to share data more efficiently. As IOT devices have specific characteristics such as having low processing power, limited memory and energy, securing access to them requires special requirements. Because of their complex management of access, and their incredulity due to centralized control, conventional access control systems do not offer the security of data that IoT systems need. Blockchain technology is expected to enhance the IoT technology by ensuring trust and reducing overall overhead. We can address IoT's architectural shortcomings by including transparency, data encryption, auditable data, operational resilience, and immutability. In this paper, the most relevant findings are presented in the area of understanding blockchain applications to solve authorization and access control issues in IoT environments. Additionally, future suggestions and ideas are discussed in an effort to improve authentication and access control solutions over time.
Personalized persuasion: Quantifying susceptibility to information exploitation in spear-phishing attacks

Many cyberattacks begin with a malicious email message, known as spear phishing, targeted at unsuspecting victims. Although security technologies have improved significantly in recent years, spear phishing continues to be successful due to the bespoke nature of such attacks. Crafting such emails requires attackers to conduct careful research about their victims and collect personal information about them and their acquaintances. Despite the widespread nature of spear-phishing attacks, little is understood about the human factors behind them. This is particularly the case when considering the role of attack personalization on end-user vulnerability. To study spear-phishing attacks in the laboratory, we developed a simulation environment called SpearSim that simulates the tasks involved in the generation and reception of spear-phishing messages. Using SpearSim, we conducted a laboratory experiment with human subjects to study the effect of information availability and information exploitation end-user vulnerability. The results of the experiment show that end-users in the high information-availability condition were 2.97 times more vulnerable to spear-phishing attacks than those in the low information-availability condition. We found that access to more personal information about targets can result in attacks involving contextually meaningful impersonation and narratives. We discuss the implications of this research for the design of anti-phishing training solutions.
Evaluations of Deep Learning Methods for Pathology Image Classification

While the state-of-the-art deep neural networks have been shown their effectiveness to achieve the general image classification tasks, the performance evaluations of these networks on the pathology image classification have not been well discussed. In this paper, we aim to evaluate the state-of-the-art deep learning methods including the convolutional neural network, deep ensemble network and transformers on several public pathology datasets. By comparing these methods, we empirically find that the transformers and deep ensemble network serve as good backbone networks for pathology image classification.
On Improving Adaptive Problem Decomposition Using Differential Evolution for Large-Scale Optimization Problems

Modern computational mathematics and informatics for Digital Environments deal with the high dimensionality when designing and optimizing models for various real-world phenomena. Large-scale global black-box optimization (LSGO) is still a hard problem for search metaheuristics, including bio-inspired algorithms. Such optimization problems are usually extremely multi-modal, and require significant computing resources for discovering and converging to the global optimum. The majority of state-of-the-art LSGO algorithms are based on problem decomposition with the cooperative co-evolution (CC) approach, which divides the search space into a set of lower dimensional subspaces (or subcomponents), which are expected to be easier to explore independently by an optimization algorithm. The question of the choice of the decomposition method remains open, and an adaptive decomposition looks more promising. As we can see from the most recent LSGO competitions, winner-approaches are focused on modifying advanced DE algorithms through integrating them with local search techniques. In this study, an approach that combines multiple ideas from state-of-the-art algorithms and implements Coordination of Self-adaptive Cooperative Co-evolution algorithms with Local Search (COSACC-LS1) is proposed. The self-adaptation method tunes both the structure of the complete approach and the parameters of each algorithm in the cooperation. The performance of COSACC-LS1 has been investigated using the CEC LSGO 2013 benchmark and the experimental results has been compared with leading LSGO approaches. The main contribution of the study is a new self-adaptive approach that is preferable for solving hard real-world problems because it is not overfitted with the LSGO benchmark due to self-adaptation during the search process instead of a manual benchmark-specific fine-tuning.
Deep Neural Networks for Object Detection

Object detection is one of the most basic and challenging tasks in image and video domains. The research on computer vision tasks is getting more and more attention, such as some tasks: object classification, object monitoring, etc. This paper provides a literature review that summarizes the detailed algorithms and application scenarios for object detection. Analysing and summarizing the latest research results in the current Object detection field, and summarize the relevant data sets and evaluation indicators, and based on this, summarize the current research problems in the Object detection field, and discuss the future research challenges in the Object detection field. possible future research directions.
Imaging Stars with Quantum Error Correction

The development of high-resolution, large-baseline optical interferometers would revolutionize astronomical imaging. However, classical techniques are hindered by physical limitations including loss, noise, and the fact that the received light is generally quantum in nature. We show how to overcome these issues using quantum communication techniques. We present a general framework for using quantum error correction codes for protecting and imaging starlight received at distant telescope sites. In our scheme, the quantum state of light is coherently captured into a nonradiative atomic state via stimulated Raman adiabatic passage, which is then imprinted into a quantum error correction code. The code protects the signal during subsequent potentially noisy operations necessary to extract the image parameters. We show that even a small quantum error correction code can offer significant protection against noise. For large codes, we find noise thresholds below which the information can be preserved. Our scheme represents an application for near-term quantum devices that can increase imaging resolution beyond what is feasible using classical techniques.
Evaluating the decentralisation of filecoin

Web3 is progressively decentralising Internet services with the introduction of the blockchain, from social networks to online trading. Filecoin is a Web3 storage marketplace implemented on top of the Interplanetary File System that aims at decentralising storage by rewarding users who pledge storage to the system. Clients form deals with storage miners, who store deals on the Filecoin blockchain for transparency and auditability. Storage miners can also obtain extra rewards when they mine a block. In this paper, we show that although Filecoin aims at complete decentralisation, the rewarding system acts as the opposite. We download a dataset composed of 47.5M messages and expose that most of the blocks are created by the same storage miners, who are also the ones that pledge more storage. An analysis of their identity shows that they belong to cloud companies, that have a lot of extra storage resources. The effect observed is due to the fact that storage miners are able to commit all extra storage to the system, even when there are no deals in them. Additionally, this effect is further intensified by recent updates to Filecoin, which let storage miners update committed storage to deal-containing storage effortlessly.
Rao-combined artificial bee colony algorithm for minimum dose path planning in complex radioactive environments

Nuclear power plant workers inevitably work in a radiation environment. Minimum dose path planning becomes an important issue in ensuring that they receive as little radiation as possible. However, a work environment with complex obstacles or narrow passages makes the problem of finding the path with the minimum dose more difficult. In this paper, we propose a meta-heuristic method to solve this problem. An algorithm called Rao-combined artificial bee colony (RABC) is proposed with a novel obstacle detour technique. The RABC algorithm combines the directional solution update method of the Rao algorithm with the traditional ABC algorithm to further improve performance. To evaluate the effectiveness of the proposed method, three hypothetical environments are simulated. In the first hypothetical environment where there are no obstacles and many radiation sources are scattered, the meta-heuristic algorithms, PSO, ABC and RABC are compared, and in the second hypothetical environment with narrow passages and obstacles, the grid-based A* algorithm and the sampling-based RRT* algorithm are used for comparison with RABC. The third hypothetical environment is a complex environment where many radiation sources and obstacles are distributed and there are narrow passages. In this case, the proposed algorithm is compared with the Dijkstra algorithm and the GB-RRT* algorithm, which is known as one of the best in obstacle avoidance. Experimental results show that the proposed method is superior in exploration and exploitation, convergence and robustness compared to other meta-heuristic algorithms as well as grid-based or sampling-based algorithms.
Quantum entanglement in low-dimensional metal complexes: an outlook

The development of quantum technologies at room temperature is an actual challenge to overcome. The quantum properties of advanced materials must be optimized to preserve their quantumness in such high temperatures. In this scenario, low-dimensional metal complexes (LDMC) present themselves as robust platforms for quantum devices due to the thermal stability of their quantum features. This work reviews the literature on studying quantum entanglement in LDMC, exploring its observation through macroscopic properties.
A comprehensive forensic preservation methodology for crypto wallets

Studies have shown that the existing methodology of digital forensics preservation, which is to acquire and hash the evidence, is insufficient for cryptocurrencies as it does not secure the value. To address this issue, investigators secure the cryptocurrency by transferring it to a crypto wallet controlled by the Law Enforcement Agencies(LEAs). This process will unavoidably modify some data. Despite the criticality of this issue, inadequate studies have been made in this area. In addition, current guidelines on securing the cryptocurrency lack a comprehensive description from the perspective of digital evidence preservation principles. Crucial data to be documented throughout the preservation process were also not properly listed. Therefore, this study aims to address the gap in preserving cryptocurrencies from crypto wallets. Three objectives were then laid out; (1) to develop a methodology that is mapped comprehensively with digital evidence preservation principle, (2) to describe and provide justification on the inevitably modified data, and (3) to list crucial data to be documented during preservation process. The methods to achieve the objectives were critical examinations on various types of crypto wallets and by using simulation. The result shows that the study is able to provide a comprehensive crypto wallets preservation methodology to forensic investigators. It is hoped that the outcome from this study will promote better understanding, ensure consistency of implementation, and to aid investigators in explaining and justifying their actions during search and seizure in court.
Zero Trust Validation: from Practice to Theory : An empirical research project to improve Zero Trust implementations

How can high-level directives concerning risk, cybersecurity and compliance be operationalized in the central nervous system of any organization above a certain complexity? How can the effectiveness of technological solutions for security be proven and measured, and how can this technology be aligned with the governance and financial goals at the board level? These are the essential questions for any CEO, CIO or CISO that is concerned with the wellbeing of the firm. The concept of Zero Trust (ZT) approaches information and cybersecurity from the perspective of the asset to be protected, and from the value that asset represents. Zero Trust has been around for quite some time. Most professionals associate Zero Trust with a particular architectural approach to cybersecurity, involving concepts such as segments, resources that are accessed in a secure manner and the maxim “always verify never trust”. This paper describes the current state of the art in Zero Trust usage. We investigate the limitations of current approaches and how these are addressed in the form of Critical Success Factors in the Zero Trust Framework developed by ON2IT ‘Zero Trust Innovators’ (1). Furthermore, this paper describes the design and engineering of a Zero Trust artefact that addresses the problems at hand (2), according to Design Science Research (DSR). The last part of this paper outlines the setup of an empirical validation trough practitioner oriented research, in order to gain a broader acceptance and implementation of Zero Trust strategies (3). The final result is a proposed framework and associated technology which, via Zero Trust principles, addresses multiple layers of the organization to grasp and align cybersecurity risks and understand the readiness and fitness of the organization and its measures to counter cybersecurity risks.
Stable and actionable explanations of black-box models through factual and counterfactual rules

Abstract Recent years have witnessed the rise of accurate but obscure classification models that hide the logic of their internal decision processes. Explaining the decision taken by a black-box classifier on a specific input instance is therefore of striking interest. We propose a local rule-based model-agnostic explanation method providing stable and actionable explanations. An explanation consists of a factual logic rule, stating the reasons for the black-box decision, and a set of actionable counterfactual logic rules, proactively suggesting the changes in the instance that lead to a different outcome. Explanations are computed from a decision tree that mimics the behavior of the black-box locally to the instance to explain. The decision tree is obtained through a bagging-like approach that favors stability and fidelity: first, an ensemble of decision trees is learned from neighborhoods of the instance under investigation; then, the ensemble is merged into a single decision tree. Neighbor instances are synthetically generated through a genetic algorithm whose fitness function is driven by the black-box behavior. Experiments show that the proposed method advances the state-of-the-art towards a comprehensive approach that successfully covers stability and actionability of factual and counterfactual explanations.
Developer discussion topics on the adoption and barriers of low code software development platforms

Low-code software development (LCSD) is an emerging approach to democratize application development for software practitioners from diverse backgrounds. LCSD platforms promote rapid application development with a drag-and-drop interface and minimal programming by hand. As it is a relatively new paradigm, it is vital to study developers' difficulties when adopting LCSD platforms. Software engineers frequently use the online developer forum Stack Overflow (SO) to seek assistance with technical issues. We observe a growing body of LCSD-related posts in SO. This paper presents an empirical study of around 33K SO posts (questions + accepted answers) containing discussions of 38 popular LCSD platforms. We use Topic Modeling to determine the topics discussed in those posts. Additionally, we examine how these topics are spread across the various phases of the agile software development life cycle (SDLC) and which part of LCSD is the most popular and challenging. Our study offers several interesting findings. First, we find 40 LCSD topics that we group into five categories: Application Customization, Database and File Management, Platform Adoption, Platform Maintenance, and Third-party API Integration. Second, while the Application Customization (30%) and Data Storage (25%) topic categories are the most common, inquiries relating to several other categories (e.g., the Platform Adoption topic category) have gained considerable attention in recent years. Third, all topic categories are evolving rapidly, especially during the Covid-19 pandemic. Fourth, the How-type questions are prevalent in all topics, but the What-type and Why-type (i.e., detail information for clarification) questions are more prevalent in the Platform Adoption and Platform Maintenance category. Fifth, LCSD practitioners find topics related to Platform Query the most popular, while topics related to Message Queue and Library Dependency Management as the most difficult to get accepted answers to. Sixth, the Why-type and What-type questions and Agile Maintenance and Deployment phase are the most challenging among practitioners. The findings of this study have implications for all three LCSD stakeholders: LCSD platform vendors, LCSD developers/practitioners, Researchers, and Educators. Researchers and LCSD platform vendors can collaborate to improve different aspects of LCSD, such as better tutorial-based documentation, testing, and DevOps support.
Content and complexity of stakeholders’ mental models of socio-ecological systems

Stakeholders' interactions with environmental resources are influenced by their mental models of the socio-ecological system of the environmental resource. Individual differences in such mental models are particularly important to identify, as diverse mental models may be associated with different behaviour or policy preferences and affect collaborative conservation efforts. In the present work, we explore stakeholders' mental models of a socio-ecological system and assess content and complexity differences across fishing experience levels, migration status, and regions. We mapped Tanzanian fishers' (N = 185) mental models of the drivers of the Nile perch stock fluctuation at Lake Victoria. The findings show that (1) fishers’ mental models were complex and diverse, (2) mental models focused on the causal influence of destructive fishing activities, (3) mental model complexity, but not content, varied across regions, and (4) fishing experience and migration status were not consistently related to mental model complexity or content. These results have important implications for environmental resource management at Lake Victoria.
BdSLW-11: Dataset of Bangladeshi sign language words for recognizing 11 daily useful BdSL words

The dataset of Bangladeshi sign language words (BdSLW) is rare. Though there are lots of datasets of BdSL sign alphabets, numbers, or characters, there are not enough datasets of sign words. This is the first dataset about sign words of BdSL according to the author(s) knowledge. So, this dataset is developed by collecting data from people. This is an image dataset. This dataset is a collection of 1105 images of sign words. A total of 11 sign word categories are selected which are important and daily use in our life. As this is an image dataset, so the images of sign words are taken by camera from the sign users of Bangladesh. Authors have gone to the individuals of sign users and captured images from them with their permission. Then the images are analyzed and segmented into the images which have quality such as no background, clear, bright, etc. This dataset is used for recognizing BdSL sign words.
Statistical Data Privacy: A Song of Privacy and Utility

To quantify trade-offs between increasing demand for open data sharing and concerns about sensitive information disclosure, statistical data privacy (SDP) methodology analyzes data release mechanisms that sanitize outputs based on confidential data. Two dominant frameworks exist: statistical disclosure control (SDC) and the more recent differential privacy (DP). Despite framing differences, both SDC and DP share the same statistical problems at their core. For inference problems, either we may design optimal release mechanisms and associated estimators that satisfy bounds on disclosure risk measures, or we may adjust existing sanitized output to create new statistically valid and optimal estimators. Regardless of design or adjustment, in evaluating risk and utility, valid statistical inferences from mechanism outputs require uncertainty quantification that accounts for the effect of the sanitization mechanism that introduces bias and/or variance. In this review, we discuss the statistical foundations common to both SDC and DP, highlight major developments in SDP, and present exciting open research problems in private inference.