Reviewerly/exHarmony huggingface dataset:
Automated Black-box Testing of Mass Assignment Vulnerabilities in RESTful APIs

Mass assignment is one of the most prominent vulnerabilities in RESTful APIs. This vulnerability originates from a misconfiguration in common web frameworks, such that naming convention and automatic binding can be exploited by an attacker to craft malicious requests writing confidential resources and (massively) overriding data, that should be read-only and/or confidential. In this paper, we adopt a black-box testing perspective to automatically detect mass assignment vulnerabilities in RESTful APIs. Execution scenarios are generated purely based on the OpenAPI specification, that lists the available operations and their message format. Clustering is used to group similar operations and reveal read-only fields, the latter are candidate for mass assignment. Then, interaction sequences are automatically generated by instantiating abstract testing templates, trying to exploit the potential vulnerabilities. Finally, test cases are run, and their execution is assessed by a specific oracle, in order to reveal whether the vulnerability could be successfully exploited. The proposed novel approach has been implemented and evaluated on a set of case studies written in different programming languages. The evaluation highlights that the approach is quite effective in detecting seeded vulnerabilities, with a remarkably high accuracy.
Sample efficient graph classification using binary Gaussian boson sampling

We present a variation of a quantum algorithm for the machine learning task of classification with graph-structured data. The algorithm implements a feature extraction strategy that is based on Gaussian boson sampling (GBS) a near term model of quantum computing. However, unlike the currently proposed algorithms for this problem, our GBS setup only requires binary (light/no light) detectors, as opposed to photon number resolving detectors. These detectors are technologically simpler and can operate at room temperature, making our algorithm less complex and less costly to implement on the physical hardware. We also investigate the connection between graph theory and the matrix function called the Torontonian which characterizes the probabilities of binary GBS detection events.
The Role of Learning in Complex Problem Solving using MicroDYN

It is still an open question which cognitive and non-cognitive personality traits are useful for describing and explaining behavior and performance in complex problems. During complex problem solving (CPS), problem solvers have to interact with the task in a way in which learning ability might be beneficial for successful task completion. By investigating the relationship between learning ability and CPS, while accounting for interactions between complex system characteristics and person characteristics, this paper aims to understand the role of learning processes in CPS more closely. In a sample of N = 241 participants, we performed a preregistered analysis to investigate the relationship between knowledge acquisition performance in a CPS test (MicroDYN) and learning test performance (ADAFI) with a multilevel modelling approach across 10 CPS systems with various characteristics. In line with our expectations, we replicated previous findings on a relationship between learning test and MicroDYN performance and found this relationship to be more pronounced in systems with (vs. without) autonomous changes. Further system and person characteristics also showed effects as expected, with better performance in systems with lower complexity, with more experience with the task, and with more strategic exploration behaviour. Our results provide further evidence for the notion that learning is an important component for the successful completion of CPS tasks.
Intelligent Bat Algorithm for Finding Eps Parameter of DbScan Clustering Algorithm

Clustering is an unsupervised learning method that classified data according to similarity probabilities. DBScan as a high-quality algorithm has been introduced for clustering spatial data due to its ability to remove noise (outlier) and constructing arbitrarily shapes. However, it has a problem in determining a suitable value of Eps parameter. This paper proposes a new clustering method, termed as DBScanBAT, that it optimizes DBScan algorithm by BAT algorithm. The proposed method automatically sets the DBScan parameters (Eps) and finds the optimal value for it. The results of the proposed DBScanBAT automatically generates near original number of clusters better than DBScanPSO and original DBScan. Furthermore, the proposed method has the ability to generate high quality clusters with minimum entropy [ 0.2752, 0.4291] in TR11 and TR12 datasets.
In search for the simplest example that proves Huffman coding overperforms Shannon-Fano coding

Abstract Shannon-Fano coding (SFC) and Huffman coding (HC) are classic and well-known algorithms, but still in use today. The search for the simplest example that proves HC overperforms SFC is still of interest. The problem is not as trivial as it looks like at first view because of several decisions that must be considered. We perform a full-search of the stream data space for a maximum stream length of 100. Depending on additional requests we impose, the simplest solution we found is {1,1,1,1,3} when we accept to select a specific cutting, {2,3,3,3,7} when we accept only deterministic (unique) cuttings and {4,5,6,7,14} when we also ask for different frequencies for symbols as well.
How Metaverse will influence Industrial Innovation (Extended Abstract SID2022)

Abstract This paper will explore how Metaverse will facilitate the acceleration of industrial innovation and help the industry digitalize their global high-volume manufacturing environment. The authors conducted a live workshop (with around 30 participants) as part of the international conference Sibiu Innovation Days 2022. The workshop’s proceedings and output is described here below.
Investigation on Research Knowledge Map and Frontier Development of College Spoken English in China based on CiteSpace Analyses of Literature

With the help of CiteSpace, this study analyzed the knowledge base, research hotspots, research frontiers and development trends of EFL (English as a Foreign Language) speaking research in China from the aspects of cited literature and cited author, key words, bursts and co-author. In terms of knowledge base, the studies of cited authors such as Wen Qiufang represent classic work, namely CSP teaching. Frontier development has changed from the theoretical basis to the empirical research, that is, from oral teaching to ability training, and then to technical assistance, ability evaluation and so on. The newer frontier is the measurement and evaluation of Spoken English, especially for College spoken English (CSP). However, there are still some problems in the research including insufficient attention from scholars, short duration of research, and weak cooperative relationship. The improvement in these aspects will greatly promote the development of relevant research in the direction of depth and breadth.
Analysis of the Application of Cooperative Learning Method in English Teaching

Cooperative learning is a form of teaching activity that takes group as the basic form and uses classroom interaction to promote students' learning so as to achieve common teaching objectives. This paper will look at the advantages and disadvantages of the cooperative learning method. In general, cooperative learning is a method worth studying, which has a lot of inspiration for current foreign language teaching. For example, cooperative learning can provide a variety of activities and increase the frequency and diversity of language learning. Cooperative learning can help improve the ability of cooperation and communication among students. Cooperative learning can reduce students' learning anxiety and make them devote themselves to study better. Cooperative learning can give students more opportunities to communicate in class and promote student center.
Management and Information Collection System for Small and Medium-sized Books in Remote Areas

A book is the crystallization of human wisdom and the ladder of human progress. Opening a good book is a profound dialogue with the wise. However, some children have never even seen extracurricular books. The only thing they can read is textbooks. As we all know, reading is the basis of learning, and childhood is the key period to develop reading ability. Reading is crucial to the growth of young people, especially for children in need. Reading is not only an important way to broaden their horizons, but also an important way to change their destiny. Next, this article will discuss in detail the modernization of education in remote areas, equal rights in education and the nature of public welfare under the condition of popularizing remote areas.
Study on the Lexical and Syntactic Features and Translation Strategies of Ice Hockey English

Translation is essential to the popularization of and international exchange activities about ice and snow sports. This paper, through analyzing the features of vocabulary and syntax in the text of ice hockey, finds that specialized words terms and abbreviation are widely used in the texts and passive sentences as well as long sentences are frequently adopted in the texts. In addition, this paper takes IIHF OFFICIAL RULE BOOK as an example to discuss the strategies and methods applied in translation practice, hoping to provide some enlightenments for the translation practice in the field of ice hockey.
Review of the Application Scenarios of Blockchain Technology

Blockchain is a distributed decentralized data structure that forms data blocks in a chronological manner similar to a chain list, and is not tampered with and fafiable. Therefore, blockchain has the characteristics of decentralization, tamper, transparency, security and so on. With the use of digital cryptocurrencies such as Bitcoin and Ethereic coin and the landing of a large number of decentralized applications, the blockchain technology derived from the blockchain is being actively explored in many industries, and this paper will study the development process, technical characteristics, and application scenarios of the blockchain.
The The Use of Ecoprocedure Text to Develop the English Reading Materials

This research is the development of reading material for environmental-based learning to read English. This research designed the types of eco-procedure text to develop the English reading. The purpose of this research is the use of eco-procedure text to develop English reading materials. This research uses the method of educational research and development. The step of this research modifies into three development steps which consist of 1) research and collecting data, (2) planning, and (3) developing a preliminary form of the product. The results of the research are The eco procedure text prototypes that have been developed are how to make sepat banang, how to make serune pade, how to make soko Sumbawa, and how to make minyak Sumbawa. Then, the elements analyzed in each of the eco procedure prototypes text are the text structure and the linguistic elements of each text. The conclusion of this research is eco procedure text very useful for learning to read students because the eco procedure text is contextual.
Analysis of Bullet and Ball Collision using Theoretical Approach

Collisions between two objects can be analyzed using the concept of the law of momentum conservation. In the case of a collision between two objects, namely a bullet and a ball, it is necessary to analyze using a theoretical approach. This research aims to analyze the velocity and height after a collision. Newton's law of conservation of momentum and Newton's second law in friction conditions are used as the basis of Calculation analysis using analytical and numerical methods. There are air friction coefficients with variations of 0.1, 0.2, 0.3, 0.4, and 0.5 N.s/m. This type of bullet used a 9 mm calibre with a velocity of 380 m/s. The mass of the bullet and ball is 0.008 kg and 0.3 kg, respectively. The results showed that increasing the air fraction coefficient resulted in lower velocity and height achieved by bullet and ball. The increasing discretization value makes the results of numerical calculations consistent with analytical calculations.
Integrated Document Segmentation and Region Identification- Textual, Equation and Graphical

Abstract With the advancement in the world of digitization, storing information in the form of scanned copies, images, etc. becomes a new normal. This new normal leads to the need for a system that can extract accurate information from the scanned documents or images with respect to every component they may have, such as textual, graphical, etc. The first step in extracting document information is to segment the document layout: divide the document into textual and non-textual regions of interest. There have been various studies over document layout segmentation, and this study observed that the majority of the existing studies face one common challenge, i.e., accurate segmentation of graphical components with sparsely clustered pixels such as flowcharts, block diagrams, etc. The study addresses it with a two-tier feedback-based framework. The first tier segments and classifies the textual and mathematical equation components, while the second tier segments and classifies the graphical regions using the feedback information from the first tier. The information provided by the first tier is the regional information of the equation and textual components to get a different copy of the original input document image in such a way that most of the foreground pix-els are part of graphical regions. The proposed framework outperforms various existing studies (when evaluated against multiple datasets).
The reference-frame independence of quantum probabilities

Abstract We investigate the transformation rule of a single particle wave-function under a change of reference frame. A postulate is raised, and some fundamental aspects regarding the reference-frame independence of quantum probabilities are explored.
Principled Composition of Function Variants for Dynamic Software Diversity and Program Protection

Artificial diversification of a software program can be a versatile tool in a wide range of software engineering and security scenarios. For example, randomizing implementation aspects can increase the costs for attackers as it prevents them from benefiting of precise knowledge of their target. A promising angle for diversification can be having two runs of a program on the same input yield inherently diverse instruction traces. Inspired by on-stack replacement designs for managed runtimes, in this paper we study how to transform a C program to realize continuous transfers of control and program state among function variants as they run. We discuss the technical challenges toward such goal and propose effective compiler techniques for it that enable the re-use of existing techniques for static diversification with no modifications. We implement our approach in LLVM and evaluate it on both synthetic and real-world subjects.
Chaotic Evolution for Multimodal Optimization Based on Multi-rule Generation Strategy

We introduce a multi-rule generation strategy into the conventional chaotic evolution (CE) to solve multimodal optimization, that is, a single run can find as many different optimal solutions as possible. To maintain the diversity of the population and avoid the over-density of individuals in the same local area, our proposal provides a variety of different rules to search the parameter space. Specifically, the proposed strategy will choose an appropriate rule for each individual to generate the offspring individual according to the distribution of individuals in its local area. To evaluate the effectiveness of our proposed strategy, we run the conventional CE and (the conventional CE + our proposed strategy) on 8 benchmark functions that have different characteristics and multiple levels of optimization difficulty. Besides, we also apply the Wilcoxon signed-rank test to detect significant differences between the two comparison algorithms at the stop condition. The experimental results confirmed that our proposed strategy can significantly find more different optimal solutions.
Artificial Neural Network Models to Predict the Cost and Time of Wastewater Projects

Infrastructure, especially wastewater projects, plays an important role in the life of residential communities. Due to the increasing population growth, there is also a significant increase in residential and commercial facilities. This research aims to develop two models for predicting the cost and time of wastewater projects according to independent variables affecting them. These variables have been determined through a questionnaire distributed to 20 projects under construction in Al-Kut City/ Wasit Governorate/Iraq. The researcher used artificial neural network technology to develop the models. The results showed that the coefficient of correlation R between actual and predicted values were 99.4% and 99 %, MAPE was (26.24%), and (5.5%), and AA was (74%), and (94.5%), for cost and time model, respectively. The researcher concluded that the ANN model has a strong correlation and high accuracy, indicating that these models are characterized by high efficiency and good performance in predicting cost and time.
Cloud ubiquitous learning approach based on multi-agents system

Information technology (IT) plays an important role in the education field, with ubiquitous computing technology the ubiquitous learning (U-learning) is now a practical possibility. Cloud computing can offer many benefits to the learning field, as it provides all resources such as software (SaaS), platforms (PaaS), and infrastructure (IaaS) as a service. The multi-agent systems approach is an excellent way to implement flexible systems whose structure evolves dynamically. Based on the discussion of the benefits of cloud computing and the multi-agent systems approach to support adaptive and personalized learning content, the paper presents a hybrid approach based on cloud computing that uses the multi-tenant architecture where tenants share the system software. Multi-agent system is used in this approach to support a ubiquitous learning system. A multi-tenant SaaS application addresses the unique requirements of each tenant by enabling customization at the tenant level with the adoption of the concept of virtualization (virtual machines). This paper explores the challenges of achieving performance isolation in the context of multi-tenant SaaS applications. As an output of this study, U-learning on the cloud-based multi-agent system architecture has been proposed. To achieve lower cost of resources and server allocation and provide isolation among learners in the ubiquitous learning environment.
Study and structural investigation of methyl thioformate from carbon monoxide oxidation by copper nano-catalyst

Abstract Volatile sulfur compounds (VSCs) are one of the most important in food products. Chemical reactions play an important role in the production of these compounds, which has often not been scientifically evaluated. In this study, are investigated the mechanisms of methyl thio-formate production from carbon monoxide (CO) against copper oxide nano-catalyst (CuO-NC). Changing the concentration of reactants in the reaction leads to the production of different sulfur products. Three different routes for the production of methyl thioformate with different concentrations of carbon monoxide and methanethiol (CH 3 SH) were investigated by DFT method. The results obtained from the thermodynamic and structural point of view show the possibility of forming a reaction with equal ratio of materials (A path). By examining the charge transfer of the compounds around the copper oxide nano catalyst, it shows that in the A path, the copper atom is more positive than other reactions (0.25 │e│) ), and more charge transfer takes place in this reaction. One of the reasons for the stability of the structure of volatile sulfur compounds is the configuration of the structure of the compound. For methyl thioformate, there are two structural forms, cis and trans, which the analysis of the NBO method shows that the bond length and electron accumulation show the stability of the trans form.
Transformation of Communication Rituals in Remote Learning

Communication rituals are transforming together with the development of media and technology. This article aims to show how the new forms of these rituals have developed over the years and whether they achieved communication goals and satisfied users’ expectations when implementing substitutional education.
Cooperative Position-based Formation-pursuit of Moving Targets by Multi-UAVs with Collision Avoidance

The paper focuses on the issue of capturing a moving target for multiple unmanned aerial vehicles (UAVs). The problem involves a group of UAVs to create a formation-pursuit in the encirclement of a moving target. Dynamic task allocation algorithm is used in 3D dynamic environments to efficiently allocate the target to several existing UAVs. Target information is disseminated to neighbor UAVs by the temporary leader of UAVs. For the formation-pursuit using a position-based strategy, destination points to create formation are made at the sphere coordinates around a moving target. Then the destination points are tracked using a fuzzy state feedback controller. Optimized artificial potential field (APF) algorithm is used to avoid collisions with targets, other UAVs, and static obstacles. Each UAV can choose the optimal trajectory to avoid obstacles and reset the formation after passing them. The simulation results show that multi-UAVs successfully surrounded and formed formation-pursuit of a moving target without colliding with the closest Euclidean distance between UAVs of 1.32957 m. UAVs with a target is 1.94359 m, and UAVs with static obstacles within a range of 1.60632 m.Keywords—formation-pursuit, multi-UAVs, obstacle avoidance, task allocation, tracking control.
Self-Instruction Training Untuk Meningkatkan Derajat Academic Buoyancy Mahasiswa

This study aims to determine the effect of self-instruction training interventions in increasing the level of student academic buoyancy. Students need to have a high level of academic buoyancy in order to be able to overcome daily academic challenges and overcome the decline in academic self-regulation (Martin &amp; Marsh, 2009). Students who have a low or moderate degree of academic buoyancy will have obstacles in overcoming daily academic challenges and academic setbacks. This condition causes low academic achievement that students have. In order to overcome this problem, this research examines whether self-instruction training interventions with CBT can help students increase their degree of academic buoyancy. This self-instruction technique is a behavior modification technique that has two uses, namely improving maladaptive mindsets, becoming positive mindsets, which are then used to direct individuals by giving positive instructions to themselves to make behavior changes to become more adaptive (Meichenbaum, in Martin &amp; Pear 2015). This study uses a single subject design with AB research pattern. The participant of this study was a male student at the Faculty of Medicine, with a moderate degree of academic buoyancy. The results of the study show that the self-instruction training technique can increase the degree of academic buoyancy.