Reviewerly/exHarmony huggingface dataset:
Hunting strategy for multi-robot based on wolf swarm algorithm and artificial potential field

The cooperation and coordination in multi-robot systems is a popular topic in the field of robotics and artificial intelligence, thanks to its important role in solving problems that are better solved by several robots compared to a single robot. Cooperative hunting is one of the important problems that exist in many areas such as military and industry, requiring cooperation between robots in order to accomplish the hunting process effectively. This paper proposed a cooperative hunting strategy for a multi-robot system based on wolf swarm algorithm (WSA) and artificial potential field (APF) in order to hunt by several robots a dynamic target whose behavior is unexpected. The formation of the robots within the multi-robot system contains three types of roles: the leader, the follower, and the antagonist. Each role is characterized by a different cognitive behavior. The robots arrive at the hunting point accurately and rapidly while avoiding static and dynamic obstacles through the artificial potential field algorithm to hunt the moving target. Simulation results are given in this paper to demonstrate the validity and the effectiveness of the proposed strategy.
KFCNet: Knowledge Filtering and Contrastive Learning for Generative Commonsense Reasoning

Pre-trained language models have led to substantial gains over a broad range of natural language processing (NLP) tasks, but have been shown to have limitations for natural language generation tasks with high-quality requirements on the output, such as commonsense generation and ad keyword generation.In this work, we present a novel Knowledge Filtering and Contrastive learning Network (KFCNet) which references external knowledge and achieves better generation performance.Specifically, we propose a BERTbased filter model to remove low-quality candidates, and apply contrastive learning separately to each of the encoder and decoder, within a general encoder-decoder architecture.The encoder contrastive module helps to capture global target semantics during encoding, and the decoder contrastive module enhances the utility of retrieved prototypes while learning general features.Extensive experiments on the CommonGen benchmark show that our model outperforms the previous state of the art by a large margin: +6.6 points (42.5 vs. 35.9)for +3.7 points (33.3 vs. 29.6)for SPICE, and +1.3 points (18.3 vs. 17.0) for CIDEr.We further verify the effectiveness of the proposed contrastive module on ad keyword generation, and show that our model has potential commercial value.
Meta-Analysis of the Use of Augmented Reality Applications in Science Teaching

This study aims to reach the meta-analysis data by analyzing the augmented reality (AR) applications used in Science Education. The search was conducted on specific databases considering the studies carried out between 2000 and 2019 years. While collecting data, inclusion criteria were considered. After searching the related databases, 24 studies were reached. Based on the random-effects model, the study results revealed that the ES value was g = 0.602, which means a medium size in the meta-analysis process according to Thalheimer and Cook's (2002) calculation level. Furthermore, z-test calculations conducted to reveal the statistical significance were found to be z = 4.989, which showed that AR applications had a significant effect on science learning. In this context, it is thought that this technological design, which offers a positive contribution to science education, allows interacting with animations or simulations simultaneously without breaking away from the real world and will serve as a model for future studies.
A Comprehensive Analysis of Handling Imbalanced Dataset

Classification is a major obstacle in Machine Learning generally and also specific when tackling class imbalance problem. A dataset is said to be imbalanced if a class we are interested in falls to the minority class and appears scanty when compared to the majority class, the minority class is also known as the positive class while the majority class is also known as the negative class. Class imbalance has been a major bottleneck for Machine Learning scientist as it often leads to using wrong model for different purposes, this Survey will lead researchers to choose the right model and the best strategies to handle imbalance dataset in the course of tackling machine learning problems. Proper handling of class imbalance dataset could leads to accurate and good result. Handling class imbalance data in a conventional manner, especially when the level of imbalance is high may leads to accuracy paradox (an assumption of realizing 99% accuracy during evaluation process when the class distribution is highly imbalanced), hence imbalance class distribution requires special consideration, and for this purpose we dealt extensively on handling and solving imbalanced class problem in machine learning, such as Data Sampling Approach, Cost sensitive learning approach and Ensemble Approach.
Fast Allan Variance (FAVAR) and Dynamic Fast Allan Variance (D-FAVAR) Algorithms for both Regularly and Irregularly Sampled Data

This work develops algorithms demonstrating fast implementations of Allan variance (AVAR) for regularly and irregularly sampled signals. AVAR is a technique first developed to study the frequency stability of atomic clocks. Typical AVAR algorithms calculate changes in means between differently-sized groupings of data and thus are useful in many data aggregation processes: to select the appropriate window length or timescales for estimating a signal’s moving average, to find the minimum variance of a signal, or to estimate the change in variance of a signal with complex noise contributions as a function of the number of collected data points. Unfortunately, AVAR typically involves very large signal lengths, yet the typical time required to compute AVAR increases quickly with the length of the time-series data. This paper presents a recursive algorithm inspired by the Fast Fourier Transform (FFT), specifically data organization into power-of-two groupings. This enables a fast AVAR implementation, called FAVAR, shown first for regularly sampled data. The results show a computational speed increase of three orders of magnitude versus typical AVAR calculations for data lengths often used with AVAR. Next, the FAVAR algorithm is extended to compute AVAR of irregularly sampled data by modeling these data as weighted but regularly sampled data clusters. Finally, this work analyzes Dynamic Allan variance implementations of FAVAR, called D-FAVAR, wherein AVAR is calculated at every timestep to capture window-varying statistical properties of the data stream. The recursion methods used in FAVAR, when extended to compute D-FAVAR, further increase computational speed by an additional factor of ten compared to computing the FAVAR at every timestep. They result in approximately four orders of magnitude speed improvements versus repeated calculation of AVAR with typical methods. These fast algorithms are demonstrated on signals that illustrate classical Allan variance curves, and the results agree with the classical AVAR formulations within computational accuracy.
Introduction to Phishing

The great majority of work is now carried out by individuals or corporations using the Internet to complete their tasks. People, companies, and governments work together to provide online users with a wide range of online services, such as data transfers, monetary transactions, online shopping, communication services such as chat, and more. Despite the many benefits of the Internet, cyberattacks can lead individuals, organizations, or corporations to lose a substantial amount of information or money. Criminals and attackers steal login credentials, bank information, Social Security numbers, and other personal data from web-based users in cyberattacks. Attackers then use the credentials to purchase items online, withdraw money from financial institutions, apply for credit cards, and request cash from the victim’s contacts through email and social media.
Efectos del programa educativo "Libres de COVID-19" sobre las habilidades sociales preventivas del adolescente

Objective: To determine the effects of the “Free of COVID-19” educational program on the preventive social skills ofteenagers treated at a provincial hospital in Peru. Materials and methods: An explanatory, pre-experimental, longitudinal and prospective research with a pretest andposttest intact group design. The population consisted of 40 male and female teenagers aged between 13 and 19, whowere treated at the Tayabamba provincial hospital. The inclusion criteria were a) teenagers aged between 13 and 19, b)teenagers with complete medical records and c) teenagers treated at the Tayabamba provincial hospital. The variables(before and after the program) were the preventive social skills and their dimensions: assertiveness, communication,self-esteem and decision-making. The “Free of COVID-19” educational program was implemented with a pretest and a posttest. A questionnaire made up of four dimensions and 27 questions was administered for assessing the preventivesocial skills. A non-parametric analysis of longitudinal data was used for a sample of subjects who were evaluated twiceduring the course of the study. Results: Before the implementation of the “Free of COVID-19” educational program, the teenagers’ preventive social skillswere, in general terms, fair (55.00 %) and, after the program, good (80.00 %). Their assertiveness was fair (45.00 %) beforethe program and improved to good (75.00 %) after the program. Their communication was fair (70.00 %) before the programand good (85.00 %) afterwards. Their decision-making was good (50.00 %) before the program and also good (90.00 %) afterthe implementation of the program. Conclusions: The preventive social skills of teenagers treated at the Tayabamba provincial hospital, as well as theirassertiveness, communication, self-esteem and decision-making, significantly improved with the implementation of the“Free of COVID-19” educational program.
Ekstraksi Fitur Tekstur dan Warna pada Kulit Katak Menggunakan GLCM dan Momen Warna

Anura is an order in the Amphibian class consisting of frogs and toads. Anura is very important in the ecosystem, especially its role as part of the food chain. Anura's main role is to maintain the balance of the ecosystem and as a bioindicator agent for changing environmental conditions such as water pollution, habitat destruction, disease and parasites, and climate change. This research applies digital image processing technology which is expected to assist in detecting types of frogs based on color and texture. This research uses 5 types of frogs, namely kongkang gading, kongkang poison, striped trees, small trees and flying trees with 20 images of each type of frog. This research uses the color feature extraction methods such Color Moment and texture extraction GLCM (Gray Level Co-occurance Matrix), then classified using K-Star. The results of the K-Star performance evaluation to classify the 5 types of frogs obtained the Accuracy (Acc) value of 0.93, Precision (Prec) of 0.94, Recall (Rec) of 0.93 and F-measure of 0.93. So that the classification results of frog species on texture and color feature extraction using the GLCM method and the Color Moment with the K-Star classification method have high performance and can work well.
Global Spillovers of the Fed Information Effect

This paper sheds lights on the open economy dimension of the Fed information effect, by evaluating its international spillovers on exchange rates, capital flows, and global economic activity. We provide empirical evidence that in response to unexpected increases in the Federal Funds rate associated with Fed information shocks, the dollar depreciates instead of appreciating. We show that this phenomenon occurs because Fed announcements affect investors’ risk appetite. Expansionary Fed information shocks increase investors’ risk appetite and drive capital towards foreign markets in pursuit of higher yields. Conversely, contractionary Fed information shocks decrease investors’ risk appetite and drive capital towards safe-haven currencies, causing an appreciation of the dollar and safe-haven currencies vis-à-vis foreign currencies. We provide evidence that the Fed information effect is associated with large spillovers onto global safe-haven currencies, risk premia, cross-border credit, and ultimately, on global economic activity. These findings highlight the presence of global spillovers of the Fed information effect.
The impact of global socio-economic changes on the regional role of universities

Economic and regional development and rapid, often unpredictable changes across the globe in technical fields have an effect on everything, including universities, training, research and services as well. Universities must find their place in this continuously changing environment, they need to continuously reinterpret themselves and their own roles, whilst ensuring that education and knowledge transfer take priority. The pace of change is increasingly accelerating with technological novelties and innovations by high-tech industries surpassing themselves at an astounding rate. Therefore, knowledge, as the facilitator and creator of these results, plays an increasingly important role for those who wish to rank among the winners of the competition both locally and globally. Today, the primary creators of knowledge are still educational institutions, and universities are the fundamental platforms of research. However, it is not enough to be «only» a university, they must educate and research and become part of the developed world alongside participants who would like to provide scientific answers to practical problems. In our paper we address the question of breaking points along which the role of universities can be transformed in order to remain open to the needs of both their narrower and wider environment while maintaining their fundamental mission, thus becoming successful players on the global stage. The development of high-tech industries has resulted in a science-driven period when economic development is unimaginable without the scientific results and the interconnection of individual disciplines. This development will presumably lead to the deterioration of certain individual knowledge and the deepening of others. Higher education must also adapt to this with the development of an education system that strengthens digital skills and serves regional expectations. Nowadays entrepreneurial universities have emerged, which are able to operate with an entrepreneurial approach, thereby responding independently to challenges from outside. The ability to co-operate must be a key aspect of university existence, and the deepening of educational, research and business partnerships is a prerequisite for success. In order to spread management approach and create an entrepreneurial ecosystem, successful R&amp;D work and innovation, it is also necessary to involve external, tender resources and grants, besides own resources. However, these resources need to be used well, but the lack of a pre-planned, conscious strategy results in less efficient use. Regional cooperation, common thinking, training and innovation, science workshops and science parks all contribute to the development of institutions and related regions.
A multi-hop cross-blockchain transaction model based on improved hash-locking

Blockchain is a decentralised, trust-free distributed ledger technology that has been applied in various fields such as finance, supply chain, and asset management. However, the network isolation between blockchains has limited their interoperability in asset exchange and business collaboration since it forms blockchain islands. Cross-blockchain is an important technology aiming to realise the interoperability between blockchains, and has become one of the hottest research topics in this area. This paper proposes a multi-hop cross-blockchain transaction model based on an improved hash-locking consulted by the notary and users. It can solve the security problems in the traditional hash-locking, and prevent malicious participants from creating a large number of transactions to block cross-blockchain system. Moreover, a notary multi-signature scheme is designed to solve the problem of lack of trust in the traditional model. A multi-hop cross-blockchain transaction loop is designed based on the loop detection method of directed graphs. The transaction process of key agreement, asset locking, lock releasing and security analysis based on the model is discussed in detail. Experiments of cross-blockchain transactions are carried out in Ethereum private chain, and prove that the proposed model has good applicability.
Chapter 15. Human-Centered Concept Explanations for Neural Networks

Understanding complex machine learning models such as deep neural networks with explanations is crucial in various applications. Many explanations stem from the model perspective, and may not necessarily effectively communicate why the model is making its predictions at the right level of abstraction. For example, providing importance weights to individual pixels in an image can only express which parts of that particular image is important to the model, but humans may prefer an explanation which explains the prediction by concept-based thinking. In this work, we review the emerging area of concept based explanations. We start by introducing concept explanations including the class of Concept Activation Vectors (CAV) which characterize concepts using vectors in appropriate spaces of neural activations, and discuss different properties of useful concepts, and approaches to measure the usefulness of concept vectors. We then discuss approaches to automatically extract concepts, and approaches to address some of their caveats. Finally, we discuss some case studies that showcase the utility of such concept-based explanations in synthetic settings and real world applications.
GLF-Net: A Target Detection Method Based on Global and Local Multiscale Feature Fusion of Remote Sensing Aircraft Images

Due to the influence of weather and shooting height, remote sensing aircraft image has the characteristics of small target samples, fuzzy target samples, and complex background information. Because of these characteristics, most target detection methods applied to original images have the defect of low precision and recall due to insufficient feature extraction. To solve these problems, based on the target features of remote sensing aircraft images, a remote sensing aircraft detection method based on global and local multiscale feature fusion is proposed. The method applies the encoder-decoder architecture, which extracts the local features of the target through the network encoder part, and extracts the global features of the target through the decoder part. The fused features are input into the classifier for evaluation, and a group with high scores is selected as the final detection output. The experimental results show that the proposed method has higher precision, recall, and F1 score. Compared with other detection methods, it has smaller memory usage and fewer parameters.
A New Hierarchical Temporal Memory Algorithm Based on Activation Intensity

As a human-cortex-inspired computing model, hierarchical temporal memory (HTM) has shown great promise in sequence learning and has been applied to various time-series applications. HTM uses the combination of columns and neurons to learn the temporal patterns within the sequence. However, the conventional HTM model compacts the input into two naive column states—active and nonactive, and uses a fixed learning strategy. This simplicity limits the representation capability of HTM and ignores the impacts of active columns on learning the temporal context. To address these issues, we propose a new HTM algorithm based on activation intensity. By introducing the column activation intensity, more useful and fine-grained information from the input is retained for sequence learning. Furthermore, a self-adaptive nonlinear learning strategy is proposed where the synaptic connections are dynamically adjusted according to the activation intensity of columns. Extensive experiments are carried out on two real-world time-series datasets. Compared to the conventional HTM and LSTM model, our method achieved higher accuracy and less time overhead.
The Implementation of Smart Contract via Blockchain Technology in Supply Chain Management: A Case Study from The Automotive Industry in Turkey

Blockchain Technology, underlined as the most revolutionizing innovation after the internet, is still in the growth phase and waits for the practitioners to enlighten its productivity promises. In the current environment, volatile profits require a more digitalized work experience and competitive advantages to get ahead in such a highly competitive automotive industry and innovative applications that lead to more simplified operation management. Accordingly, this paper aims to present a case study via use cases in which Blockchain has been used and smart contract as the sought-out innovation and its application for the digitized spare parts disposal legal process. Blockchain Technology in the automotive sector is discussed by focusing on the supply management process of an automotive company's processes in Turkey. Blockchain technology is expected to develop and simplify spare parts-related transactions in the automotive industry, which deals with more than 500K stock keeping units per company. Paper presents the current, future, and ideal states of spare parts transactions with Blockchain adoption. The implemented application enables the development of an enterprise-level blockchain platform with hyper-ledger fabric as an open-source. The distributed ledger technology provides a smart contract system between actors of the existed supply chain process. The study aims to show the potential of Blockchain Technology in delivering a high degree of competitive advantage especially for automotive service providers with regards to its features related to providing security, transparency, traceability, cost reduction, more efficient data storage in dense supply based industries.
An Automatic Graphic Pattern Generation Algorithm and Its Application to the Multipurpose Camouflage Pattern Design

Graphic patterns are usually drawn manually by graphic designers. Although many methods have been developed to computerize the creation process of graphic patterns, most of them solely provide some facilitating tools for the designers and could not propose an automatic procedure. This article proposes a fully automatic graphic pattern generation (AGPG) model that performs the entire pattern generation process without any human interference. We then customize the proposed model for camouflage pattern design. Most of the existing camouflage pattern creation methods consider only one or a few background images. Since the objects move and their backgrounds may vary significantly, it is essential to generate multipurpose camouflage patterns with appropriate performance in various backgrounds. The previously proposed methods are also heavily dependent on the existing patterns to produce new ones and could not create novel structures in their generated patterns. Our model is equipped with a novel creative drawing engine that can create a wide variety of new graphical structures without using any existing pattern. The drawing module in our model is controlled by several parameters tuned for the desired task employing an evolutionary strategy-based algorithm. The proposed method has no limits for the number of background images and creates the camouflage patterns appropriate for any number of provided images. The experimental results show that the AGPG method can create novel multipurpose camouflage patterns automatically with high concealment capabilities.