Reviewerly/exHarmony huggingface dataset:
Blockchain-based collaborative edge computing: efficiency, incentive and trust

Abstract The rise of 5G technology has driven the development of edge computing. Computation offloading is the key and challenging point in edge computing, which investigates offloading resource-intensive computing tasks from the user side to the cloud or edge side for processing. More consideration needs to be given to load balancing, user variability, and the heterogeneity of edge facilities in relevant research. In addition, most of the research around edge collaboration also revolves around cloud-side collaboration, which pays relatively little attention to the collaboration process between edge nodes, and the incentive and trust issues of the collaboration process need to be addressed. In this paper, we consider the impact of the user demand variability and the edge facility heterogeneity, then propose a method based on Vickrey-Clarke-Groves (VCG) auction theory to accommodate the edge demand response (EDR) process where the number of users and service facilities do not match. The method makes users’ bidding rules satisfy the Nash equilibrium and weakly dominant strategy, which can improve the load balancing of edge nodes, has positive significance in improving the edge resource utilization and reducing the system energy consumption. In particular, combined with blockchain, we further optimize the incentive and trust mechanism of edge collaboration and consider three scenarios: no collaboration, internal collaboration, and incentive collaboration. We also consider the impact of the user task’s transmission distance on the quality of experience (QoE). In addition, we illustrate the possible forking attack of blockchain in collaborative edge computing and propose a solution. We test the performance of the proposed algorithm on a real-world dataset, and the experimental results verify the algorithm’s effectiveness and the edge collaboration’s necessity.
Analysis and optimization of surgical electromagnetic tracking systems by using magnetic field gradients

Background: Electromagnetic tracking systems (EMTSs) are widely used in surgical navigation, allowing to improve the outcome of diagnosis and surgical interventions, by providing the surgeon with real-time position of surgical instruments during medical procedures. Objective: the main goal is to improve the limited range of current commercial systems, which strongly affects the freedom of movement of the medical team. Studies are currently being conducted to optimize the magnetic field generator (FG) configuration (both geometrical arrangements and electrical properties) since it affects tracking accuracy. Methods: In this paper, we discuss experimental data from an EMTS based on a developed 5-coils FG prototype, and we show the correlation between position tracking accuracy and the gradients of the magnetic field. Therefore, we optimize the configuration of the FG by employing two different metrics based on i) the maximization of the amplitude of the magnetic field as reported in literature, and ii) the maximization of its gradients. Results: The two optimized configurations are compared in terms of position tracking accuracy, showing that choosing the magnetic field gradients as objective function for optimization leads to higher position tracking accuracy than maximizing the magnetic field amplitude.
Developments of Computer Vision and Image Processing: Methodologies and Applications

The rapid advancement of technology has enabled a vast and ever-growing number of computer applications in real scenarios of our daily life [...]
Synergy of Nominees of the Author’s Artistic Subconcept (Based on the Subconcept Zersto..rung/Destruction in E. M. Remarque’s Novels)

The article considers the author’s artistic subconcept Zerstörung/Destruction, which is included in the nominative field of the dominant concept Krieg/War. Among all the subconcepts that form the nominative field of the Krieg/War dominant concept, this subconcept is the most common. For the first time, the article interprets the nominees describing the destruction implicitly. The joint use of explicit and implicit nominees of the Zerstörung/Destruction subconcept is revealed, which creates a synergy effect when describing the consequences of war.
Are our clone detectors good enough? An empirical study of code effects by obfuscation

Abstract Clone detection has received much attention in many fields such as malicious code detection, vulnerability hunting, and code copyright infringement detection. However, cyber criminals may obfuscate code to impede violation detection. To date, few studies have investigated the robustness of clone detectors, especially in-fashion deep learning-based ones, against obfuscation. Meanwhile, most of these studies only measure the difference between one code snippet and its obfuscation version. However, in reality, the attackers may modify the original code before obfuscating it. Then what we should evaluate is the detection of obfuscated code from cloned code, not the original code. For this, we conduct a comprehensive study evaluating 3 popular deep-learning based clone detectors and 6 commonly used traditional ones. Regarding the data, we collect 6512 clone pairs of five types from the dataset BigCloneBench and obfuscate one program of each pair via 64 strategies of 6 state-of-art commercial obfuscators. We also collect 1424 non-clone pairs to evaluate the false positives. In sum, a benchmark of 524,148 code pairs (either clone or not) are generated, which are passed to clone detectors for evaluation. To automate the evaluation, we develop one uniform evaluation framework, integrating the clone detectors and obfuscators. The results bring us interesting findings on how obfuscation affects the performance of clone detection and what is the difference between traditional and deep learning-based clone detectors. In addition, we conduct manual code reviews to uncover the root cause of the phenomenon and give suggestions to users from different perspectives.
PENGEMBANGAN MEDIA SPACE BOOK BERBASIS QR CODE MATERI TATA SURYA DENGAN PENGUATAN KARAKTER GEMAR MEMBACA PADA KELAS VI SEKOLAH DASAR

Learning media are tools used to deliver and clarify the material at once stimulate and motivate students to learn in order to achieve the learning process. The aim of research and development are to produce a valid, pratical, and interesting space book based on QR Code with the character of likes to read for learning solar system 6th grade elementary school 03 Candimulyo Madiun as media. The method was used research and development method of Dick &amp; Carey. The results of the material expert is 90,6%, media expert 96,4%, and user 100%. Students respon of small scale test 100% and the character likes to read after used space book 92,2%. And then, students respon of large scale test 92,3% and the character likes to read 86,9%. Based on the results analysis of space book based on QR Code very suitable for learning solar system.
What you can read is what you can't execute

Due to the address space layout randomization (ASLR), code reuse attacks (CRAs) require memory probes to get available gadgets. Code reading is the basic way to obtain code information. In theory, setting the code to be unreadable can prevent code reading. However, the pages are loaded dynamically, and the existing methods cannot set all code as unreadable at one time. They can only control code permissions page-by-page via time-consuming page tracking. Moreover, since some special users need to read code, turning off the read permission will affect their execution. To solve these problems, this paper proposes a method AntiRead. It rebuilds the buddy system for memory allocation. The new buddy system places code pages in a specific memory pool to manage their read permissions. In the presence of AntiRead, what is obtained by adversaries through code reading is either randomized code or non-executable code. Experiments and analysis show that AntiRead can prevent the code that has been read from being used as gadgets without affecting the normal code reading. In addition, the CPU overhead introduced by AntiRead is 1.8%.
Evaluation of breast tumor morphologies from African American and Caucasian patients

The primary aim of this research was to investigate potential differences of breast tumor morphologies across African American and Caucasian racial groups by utilizing machine learning (ML) and artificial intelligence (AI) methods. While breast cancer disparities can partially be attributed to social determinants of health, tumor biology also contributes to survival outcomes. The rate of breast tumor growth is largely dependent on the extracellular matrix (ECM). Current research suggests the cellular components of the ECM may vary among racial and ethnic populations, and this may contribute to the incidence of cancer in African Americans. We utilized a supervised AI method to evaluate morphological differences between African American and Caucasian breast cancer tumors. Images used for analysis were downloaded from the Cancer Genome Atlas (TCGA) biorepository stored in the NIH Genomic Data Commons (GDC) data portal. We designed an ML classifier using the AlexNet model provided in PyTorch’s torchvision package. The model was pre-trained and adapted via transfer learning resulting in a classification accuracy of 92.1% when using our breast cancer tumor image database split into 80% of training set and 20% of testing set. We interpreted the results of the AlexNet and ResNet50 models using LIME and saliency mapping as the explainers. Based on the images from our bi-racial testing set, this study confirmed significant variations of tumor and ECM regions in the different racial groups evaluated. Based on these findings, further analysis and characterization may provide new insight into disparities associated with the incidence of breast cancer.
Leasing in IaaS Cloud Using Queuing Model

In the present scenario, most of the IaaS clouds use simple resource leasing policies for eg., Advance Reservation (AR), immediate (IL), and best-effort (BE). Resources must be provided for immediate leasing either immediately or not at all. The scheduler executes these leases in the event that resources are available, otherwise, not. Resources are assigned to the lease under the BE policy as soon as they become available. The ability to allocate resources depends on the service provider. When the requested resources are available, the scheduler processes the request. There are no time restrictions with this kind of lease. If resources are not available the request is placed in a FIFO queue. Another sort of lease that necessitates resources within a constrained time frame is the AR lease. Due to limited resources, a cloud provider cannot accommodate all requests at once. There will be no issues in the Best-effort leases. But in critical applications in the cloud, there are many applications that need immediate attention from the cloud provider and the delay in processing such leases have a great impact on such applications. There needs attention for immediate leases. An analytical Markov model for the proposed scheme is developed and analysed.
An Intelligence Security Architecture for Mitigating DDOS Attack in CloudIoT Environment

In the field of information technology, IoT and Cloud play a crucial role. IoT aims to connect disparate objects so that smart services and applications can be accessed from anywhere. Even though IoT and Cloud have been developed independently, combining these two technologies results in a renaissance in the construction of smart environments and future networks. CloudIoT is the name of the latest evolution. The major concern with CloudIoT is security. Researchers from around the world work to incorporate innovative CloudIoT services to satisfy users needs. But so far, no well-known structure has been established. DDoS is one of the destructive attacks that can potentially impair CloudIoT data transfer. Therefore, a comprehensive resolution to this problem is required. To safeguard against DDoS attacks against a network of clients and a network of providers, a special security technique is suggested.
Disguised Repression: Targeting Opponents with Non-Political Crimes to Undermine Dissent

Why do authoritarian regimes charge political opponents with non-political crimes when they can levy charges directly related to opponents’ political activism? We argue that doing so disguises political repression and undermines the moral authority of opponents, minimizing backlash and mobilization. To test this argument, we conduct a case study of the arrests of vocal government critics in China in 2013. Analyzing millions of Weibo posts made before and after the crackdown shows that individuals with larger online followings are more likely to be charged with non-political crimes, and those charged with non-political crimes are less likely to receive public sympathy and support. We then conduct an experiment, which shows that disguised repression decreases perceptions of dissidents’ morality, decreases people’s willingness to en- gage in dissent, and increases support for repression. These results challenge the assumption of public opposition to repression, showing instead why the public may support repression.
Proposed Framework for Implementation of Biometrics in Banking KYC

Cybersecurity is a collection of technologies, policies, and operations that are used to protect networks, devices, software, and data against intrusions, damage, malware, viruses, hacking, and security breaches. The protection of the customer’s assets is the primary objective of data security in banks. As more people become cashless, additional acts or transactions go online. Transactions are carried out using electronic payment methods like debit and credit cards, which require cyber complete security. In this paper, we are proposing a framework for KYC based on biometric retina recognition which is more secure than the existing framework. During the onboarding process, our proposed framework employs the unique digital identity of each customer and lessens the chance of digital identity theft. Retinal biometric recognition may be implemented in ATMs in the future to guard against fraud risks such as cash trapping, card skimming, bogus assistance, eavesdropping, shoulder surfing, fake PIN pad overlays, hold-ups.
Sublinear information bottleneck based two-stage deep learning approach to genealogy layout recognition

As an important part of human cultural heritage, the recognition of genealogy layout is of great significance for genealogy research and preservation. This paper proposes a novel method for genealogy layout recognition using our introduced sublinear information bottleneck (SIB) and two-stage deep learning approach. We first proposed an SIB for extracting relevant features from the input image, and then uses the deep learning classifier SIB-ResNet and object detector SIB-YOLOv5 to identify and localize different components of the genealogy layout. The proposed method is evaluated on a dataset of genealogy images and achieves promising results, outperforming existing state-of-the-art methods. This work demonstrates the potential of using information bottleneck and deep learning object detection for genealogy layout recognition, which can have applications in genealogy research and preservation.
THE UTILIZATION OF ‘ONLINE DICTATION’ CONTENT TO INCREASE STUDENTS LISTENING ABILITY IN STIKES MADANI YOGYAKARTA

Listening is one of the skills considered hard for the students in learning English. Rapid listening practice can be used to overcome this. This is experimental research aimed to find out the effect of online listening dictation content using pre and post-tests design. 15 students were taken randomly. Paired T-Test shows an increase in the value of the pre and post-test results of all study participants with a p-value of 0.003 and
Design and Development of a Solar- Powered UAV Using IoT and Machine Learning

The proposed solar-powered UAV utilizes photovoltaic panels to convert solar energy into electrical power to supply the onboard electronic systems, including the propulsion system and sensors. To enhance the UAV's performance, loT technology is employed to enable the communication and coordination of the UAV with other connected devices, such as ground stations and sensors. This allows for real-time data collection and analysis, as well as improved situational awareness for the UAV. To optimize the performance of the UAV, SVM is used as a machine learning algorithm for object detection and classification. SVM has been widely used in UAVs to detect and classify objects in aerial images, and has been proven to be effective in a variety of applications. The proposed design utilizes SVM to detect and classify objects of interest, such as crops, buildings, and infrastructure, and to assist in the navigation and control of the UAV. The design and development of the proposed solar-powered UAV involves several key components, including the solar panels, propulsion system, control system, and communication system.
La promoción literaria como actividad extensionista frente a la Covid-19

The university extension process encompasses various modalities as a substantive process of higher education, within which students with literary concerns are served, included in the Movement of Amateur Artists that exists in Cuban universities.It is a task that includes teaching and research and has an impact on the comprehensive training of university students.This article shows the results in the promotion of creative writing of the members of the Pablo Neruda Literary Creation Workshop, but also the changes that occur in the various actors involved in said task.Among the actions, the writing and debate of significant works stand out, collective readings, books presentation, elaboration and visualization of promotional videos stand out, while the knowledge, skills or experiences acquired are applied, which enriches the spiritual world of those who live in the community university and contributes to the advancement of cultural life, despite the Covid-19 pandemic.
PARENT INVOLVEMENT IN ACCOMPANYING DEAF STUDENTS IN LEARNING ENGLISH ONLINE WHILE COVID-19 PANDEMIC

The purpose of this study was to obtain information about parent involvement in accompanying deaf students in learning English online while the Covid-19 pandemic. The involvement of parents is very important for children's education especially in students with the deaf. Parental involvement in deaf students learning English online as a form of parental support that aims at the development of child potential where the parent take part in their role in deaf students learning English online through related relationships that support each other and the parental involvement indicators in learning English online are; prepare facilities in learning English activities from home, assisting the deaf student in learning English activities from home, guiding and motivating deaf student, benefits in learning English online, barriers in learning English online.
Beyond Pairwise Reasoning in Multi-Agent Path Finding

In Multi-Agent Path Finding (MAPF), we are asked to plan collision-free paths for teams of moving agents. Among the leading methods for optimal MAPF is Conflict-Based Search (CBS), an algorithmic family which has received intense attention in recent years and for which large advancements in efficiency and effectiveness have been reported. Yet all of the recent CBS gains come from reasoning over pairs of agents only. In this paper, we show how to further improve CBS by reasoning about more than two agents at the same time. Our new cluster reasoning techniques allow us to generate stronger bounds for CBS and to identify more bypasses (alternative cost-equivalent paths), which reduce the number of nodes in the CBS conflict tree.
An Efficient Hybrid Genetic Algorithm for the Quadratic Traveling Salesman Problem

The traveling salesman problem (TSP) is the most well-known problem in combinatorial optimization which has been studied for many decades. This paper focuses on dealing with one of the most difficult TSP variants named the quadratic traveling salesman problem (QTSP) that has numerous planning applications in robotics and bioinformatics. The goal of QTSP is similar to TSP which finds a cycle visiting all nodes exactly once with minimum total costs. However, the costs in QTSP are associated with three vertices traversed in succession (instead of two like in TSP). This leads to a quadratic objective function that is much harder to solve. To efficiently solve the problem, we propose a hybrid genetic algorithm including a local search procedure for intensification and a new mutation operator for diversification. The local search is composed of a restricted double-bridge move (a variant of 4-Opt); and we show the neighborhood can be evaluated in O(n^2), the same complexity as for the classical TSP. The mutation phase is inspired by a ruin-and-recreate scheme. Experimental results conducted on benchmark instances show that our method significantly outperforms state-of-the-art algorithms in terms of solution quality. Out of 800 considered instances, it finds 437 new best-known solutions.
A Fast Rescheduling Algorithm for Real-Time Multi-Robot Coordination [Extended Abstract]

One area of research in Multi-Agent Path Finding (MAPF) is to determine how re-planning can be efficiently achieved in the case of the delay of an agent. One option is to determine a new wait ordering to find the most optimal new solution that can be produced by re-ordering the wait order. We propose to use an Edge-Switchable Temporal Plan Graph and an augmented A* algorithm, called Switchable-Edge Search, to approach finding a new optimal wait order. While this is a work in progress still, we have discovered several optimizations for this algorithm, and the results show promising increases in efficiency for the algorithm. We have analyzed our present efficiency in a variety of conditions by measuring re-planning speed in different maps, with varying numbers of agents and randomized scenarios for agents' start and goal locations. We hope that, as we proceed with optimization, we can show that such an approach can be more efficient in practice and be used instead of re-running MAPF to perform cheap re-planning in such situations.
Binary Branching Multi-Objective Conflict-Based Search for Multi-Agent Path Finding

This paper considers a multi-agent multi-objective path-finding problem that requires not only finding collision-free paths for multiple agents from their respective start locations to their respective goal locations but also optimizing multiple objectives simultaneously. In general, there is no single solution that optimizes all the objectives simultaneously, and the problem is thus to find the so-called Pareto-optimal frontier. To solve this problem, an algorithm called Multi-Objective Conflict-Based Search (MO-CBS) was recently developed and is guaranteed to find the exact Pareto-optimal frontier. However, MO-CBS does not scale well with the number of agents due to the large branching factor of the search, which leads to a lot of duplicated effort in agent-agent collision resolution. This paper therefore develops a new algorithm called Binary Branching MO-CBS (BB-MO-CBS) that reduces the branching factor as well as the duplicated collision resolution during the search, which expedites the search as a result. Our experimental results show that BB-MO-CBS reduces the number of conflicts by up to two orders of magnitude and often doubles or triples the success rates of MO-CBS on various maps given a runtime limit.