Reviewerly/exHarmony huggingface dataset:
Bet and Attack: Incentive Compatible Collaborative Attacks Using Smart Contracts

Smart contract-enabled blockchains allow building decentralized applications in which mutually-distrusted parties can work together. Recently, oracle services emerged to provide these applications with real-world data feeds. Unfortunately, these capabilities have been used for malicious purposes under what is called criminal smart contracts. A few works explored this dark side and showed a variety of such attacks. However, none of them considered collaborative attacks against targets that reside outside the blockchain ecosystem. In this paper, we bridge this gap and introduce a smart contract-based framework that allows a sponsor to orchestrate a collaborative attack among (pseudo)anonymous attackers and reward them for that. While all previous works required a technique to quantify an attacker's individual contribution, which could be infeasible with respect to real-world targets, our framework avoids that. This is done by developing a novel scheme for trustless collaboration through betting. That is, attackers bet on an event (i.e., the attack takes place) and then work on making that event happen (i.e., perform the attack). By taking DDoS as a usecase, we formulate attackers' interaction as a game, and formally prove that these attackers will collaborate in proportion to the amount of their bets in the game's unique equilibrium. We also model our framework and its reward function as an incentive mechanism and prove that it is a strategy proof and budget-balanced one. Finally, we conduct numerical simulations to demonstrate the equilibrium behavior of our framework.
Hypergraph Neural Networks with Attention Mechanism for Session-based Recommendation

Abstract The session-based recommendation task is designed to predict the behavior of the current session at the next moment based on multiple anonymous sessions. Due to the lack of user information in the session, the traditional recommendation model cannot be used directly to model the interest of specific users. In this paper, a session recommendation model based on hypergraph neural networks and attention mechanism (HGNNA) is proposed. Firstly, the features of items are learned by constructing hypergraph neural networks, then the conversation information is aggregated by self-attention mechanism, and finally the information among similar sessions is aggregated by graph attention networks. The hypergraph neural networks can capture the correlation between items, the self-attention mechanism can show the interest of the current session, and the graph attention networks can find the interest pattern between similar sessions, so that the representation vector of the session includes the information of the items in the session, other items outside the session and other sessions. In the experiments on two datasets, Yoochoose1/4 and Diginetica, the recommendation effect of HGNNA is higher than that of other relevant methods, especially in the P@20, which is improved by 0.69 and 1.40 respectively.
Regular Path Query Evaluation Sharing a Reduced Transitive Closure Based on Graph Reduction

Regular path queries (RPQs) find pairs of vertices of paths satisfying given regular expressions on an edge-labeled, directed multigraph. When evaluating an RPQ, the evaluation of a Kleene closure is very expensive. Furthermore, when multiple RPQs include a Kleene closure as a common sub-query, repeated evaluations of the common sub-query cause serious performance degradation. In this paper, we present a novel concept of RPQ-based graph reduction, which significantly simplifies the original graph through edge-level and vertex-level reductions. Interestingly, RPQ-based graph reduction can replace the evaluation of the Kleene closure on the large original graph to that of the transitive closure to the small reduced graph. We then propose a reduced transitive closure (RTC) as a lightweight structure for efficiently sharing the result of a Kleene closure. We also present an RPQ evaluation algorithm, RTCSharing, which treats each clause in the disjunctive normal form of the given RPQ as a batch unit. If the batch units include a Kleene closure as a common sub-query, we share the lightweight RTC instead of the heavyweight result of the Kleene closure. RPQ-based graph reduction further enables us to formally represent the result of an RPQ including a Kleene closure as a relational algebra expression including the RTC. Through the formal expression, we optimize the evaluation of the batch unit by eliminating useless and redundant operations of the previous method. Experiments show that RTCSharing improves the performance significantly by up to 73.86 times compared with existing methods in terms of query response time.
Smart Hybrid Scanner: Detection of Versatile International entity Codes using IoT

Nowadays, scanners are widely using in the marketing field. The scanners available at supermarket check-out counters support the manufacturer and retailer to assess product sales, promotional strategies and compare the performance with competing products in the market. The scanner at the bill counter used to read the cost of a product based on its code for generating customer bills. A variety of scanning mechanisms were introduced for promoting market analysis as per technological advances. Still enhancing the capacity of the scanner is a big research challenge because some scanners are not supported to read few countries product codes. The advanced technology consists of GPS and IoT which enables the scanner to read any kind of code such as bar code or QR Code of any country product codes. The proposed system makes scanners are compatible with any code to read on the entities available in the market. The advantages would read any kind of code accurately that reveals the details of the product or an entity, process the request in less time, and will enhance the features of scanning. The report is generated and alert to the admin whenever the location change is detected in the sense the country is changed. The components involved in that intended device are described and are integrated for achieving the motto of the proposed ideology. The running of this device is USB and charging type.
An investigation into the effects of label noise on Dynamic Selection algorithms

In the literature on classification problems, it is widely discussed how the presence of label noise can bring about severe degradation in performance. Several works have applied Prototype Selection techniques, Ensemble Methods, or both, in an attempt to alleviate this issue. Nevertheless, these methods are not always able to sufficiently counteract the effects of noise. In this work, we investigate the effects of noise on a particular class of Ensemble Methods, that of Dynamic Selection algorithms, and we are especially interested in the behavior of the Fire-DES++ algorithm, a state of the art algorithm which applies the Edited Nearest Neighbors (ENN) algorithm to deal with the effects of noise and imbalance. We propose a method which employs multiple Dynamic Selection sets, based on the Bagging-IH algorithm, which we dub Multiple-Set Dynamic Selection (MSDS), in an attempt to supplant the ENN algorithm on the filtering step. We find that almost all methods based on Dynamic Selection are severely affected by the presence of label noise, with the exception of the K-Nearest Oracles-Union algorithm. We also find that our proposed method can alleviate the issues caused by noise in some scenarios. We have made the code for our method available at https://github.com/fnw/baggingds.
Multi-Server Private Linear Transformation with Joint Privacy

This paper focuses on the Private Linear Transformation (PLT) problem in the multi-server scenario. In this problem, there are N servers, each of which stores an identical copy of a database consisting of K independent messages, and there is a user who wishes to compute L independent linear combinations of a subset of D messages in the database while leaking no information to the servers about the identity of the entire set of these D messages required for the computation. We focus on the setting in which the coefficient matrix of the desired L linear combinations generates a Maximum Distance Separable (MDS) code. We characterize the capacity of the PLT problem, defined as the supremum of all achievable download rates, for all parameters N, K, D ≥ 1 and L = 1, i.e., when the user wishes to compute one linear combination of D messages. We show that the capacity in this case is Φ(1/N, K − D + 1), where Φ(A, B) ≜ (1 + A + A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> + ⋯ + A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B−1</sup> ) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup> . Moreover, we establish an upper bound and lower bounds on the capacity of the PLT problem for all parameters N, K, D, L ≥ 1. We show that for the regime where K − D is a non-negative integer multiple of L, the proposed capacity upper bound is equal to Φ(1/N, (K −D +L)/L). Also, leveraging some known capacity results, we show the tightness of this bound in the following regimes: (i) the case when there is a single server (i.e., N = 1), (ii) the case when L = 1, and (iii) the case when L = D.
Validation of a survey to contrast online education during the COVID-19 pandemic and face-to-face pre-pandemic education

In order to develop quality pedagogical processes to alleviate the negative effects of the Cov19 pandemic, as the loss of interest in school subjects -caused by accessibility difficulties and lack of resources to purchase equipment and internet services, and to take advantage of the positive effects of online educational modality, as flexibility and housework, it is necessary to have valid and reliable instruments with which the information can be collected. This document presents the validation of a survey to contrast pre-pandemic online and face-to-face education. Beginning with the evaluation of the judgment of experts and teachers for preparation and proposal, which according to the methodology, was administered for a self-evaluation of university students in three areas of knowledge, and consisting of four different dimensions: attitudes during the Covid19 pandemic, applications of ICTs in education, problems when studying online, and exploration of face-to-face education The results show that it is a valid and reliable instrument to measure the four aspects described.
A Survey on Sentiment Analysis Techniques for Twitter

People regularly use social media to express their opinions about a wide variety of topics, goods, and services which make it rich in text mining and sentiment analysis. Sentiment analysis is a form of text analysis determining polarity (positive, negative, or neutral) in text, document, paragraph, or clause. This chapter offers an overview of the subject by examining the proposed algorithms for sentiment analysis on Twitter and briefly explaining them. In addition, the authors also address fields related to monitoring sentiments over time, regional view of views, neutral tweet analysis, sarcasm detection, and various other tasks in this area that have drawn the researchers ' attention to this subject nearby. Within this chapter, all the services used are briefly summarized. The key contribution of this survey is the taxonomy based on the methods suggested and the debate on the theme's recent research developments and related fields.
Determining Elements in Mobile Learning Implemention Among Instructors in Vocational Colleges: A Fuzzy Delphi Method

This study aims to identify the elements of implementing mobile learning based on Competency-based Education. The confirmation of elements was based on the opinions and consensus of experts. The consensus survey was constructed based on the emergent themes that experts raised during their interview sessions. Sixteen experts in Competency-based Education were included in the survey. The data was analyzed using the Fuzzy Delphi method (FDM). The results indicate that eight elements met the FDM requirements. Its threshold value is ≤ 0.2, the percentage expert consensus is ≥ 75%, and the average score of the Fuzzy number is over 0.5. These eight elements are students, teachers, technology, learning environment, content, assessment, learning strategy, and learning activity. The outcomes of this research will be useful for stakeholders within the educational sector to address student competency, including the Ministry of Education Malaysia, the Technical and Vocational Education Division, teachers, students, and developers and designers of mobile learning applications.
An Improved DCP-Based Image Defogging Algorithm Combined with Adaptive Fusion Strategy

Dark channel prior (DCP) has been widely used in single image defogging because of its simple implementation and satisfactory performance. This paper addresses the shortcomings of the DCP-based defogging algorithm and proposes an optimized method by using an adaptive fusion mechanism. This proposed method makes full use of the smoothing and “squeezing” characteristics of the Logistic Function to obtain more reasonable dark channels avoiding further refining the transmission map. In addition, a maximum filtering on dark channels is taken to improve the accuracy of dark channels around the object boundaries and the overall brightness of the defogged clear images. Meanwhile, the location information and brightness information of fog image are weighed to obtain more accurate atmosphere light. Quantitative and qualitative comparisons show that the proposed method outperforms state-of-the-art image defogging algorithms.
On the Necessity of Aligning Gradients for Wireless Federated Learning

In this paper, we consider the problem of wireless federated learning, where the users wish to jointly train a machine learning model with the help of a parameter server. During the training, the local gradients from the users are aggregated over a wireless channel. Typically, coefficients of the local gradients are aligned by power control techniques to ensure that the estimated aggregated gradient is an unbiased estimator of the true gradient. However, schemes that align gradients require coordination, can be challenging to implement in practice, and often lead to degraded performance due to heterogeneity of users’ channels. In this paper, we show that alignment of gradients for wireless FL is not always necessary for convergence. Specifically, we consider non-convex loss functions, and derive conditions under which misaligned wireless gradient aggregation still converges to a stationary point. We also present experimental results to show that transmitting at full power can outperform aligned gradient aggregation depending on the heterogeneity of users’ channels.
A mapping method between EXPRESS and OWL based on text similarity analysis

To translate product data described by STEP to Ontology models can enhance data integration and knowledge sharing among different platforms throughout the product lifecycle. This translation process requires mapping between two languages, i.e. EXPRESS for STEP and OWL for ontology. However, limited research has explored the automatic way of determining the mapping relationships. This paper introduces a model to measure the semantic textual similarity score between the official reference manuals that explains EXPRESS and OWL. Based on the model, a method is proposed to automatically find the mapping relationships between the two languages. The experiment shows that the proposed model achieves better performance comparing with other pre-trained models on the STS-B dataset. Furthermore, this semantic analysis based method is not specific to any EXPRESS schema so that this paper gets more general purpose conversion rules of mapping EXPRESS to OWL.
Reviewing Dynamic Feature Location Techniques: Basic Elements and Challenges

Dynamic Feature Location Techniques (DFLTs) seek to pertain the software functionalities artifacts which are known as software features, to the relevant source code based on execution trace. These techniques enhance developers to comprehend a software system to perform various activities such as software maintenance, code refactoring, and others. For instance, comprehend a software application at runtime support the developers to map source code to the relevant software feature based on the feature software execution trace analysis. This work introduces a systematic literature review for several works in DFLTs top. An illustration is presented to show their common structure approach and idealize the used solution on an industrial software application. Moreover, we review their characterization, advantages, disadvantages and administer a possible framework that would be utilized by researchers and developers to boost their decision to opt for the correct DFLTs to solve a problem. A basic comparison between DFLTs is provided by classifying the located techniques into corpus-based analysis, execution trace analysis, and dependencies analysis. The three points of comparison results and the taxonomy diagram lead to examine possible directions for standardizing DFLTs architecture to improve the accuracy and increase the ease of mapping software functionalities to the related software artifact based on the execution trace. The review paper also discusses unsolved issues and defines future directions in the field of Dynamic Feature Location (DFL).
ReDroId: Remote Drone Identification Based on Visual RSA SecurID Tokens

In an “open skies” era in which drones are flying among us, a new question arises: how can we distinguish between a friend drone (e.g., carrying a pizza delivery) and a foe drone (e.g., carrying a bomb) in areas that allow drone flights if both drones are identical? In this paper, we propose an authentication scheme that can help an authenticatee distinguish between a foe and friend drone. The authentication is implemented by optically modulating an RSA token using flashing lights which are mounted on the drone. Using a video camera, a trained machine learning model recognizes the flashing lights from the drone and converts them to a binary code which is used to authenticate the drone's identity.
Online Sensorless Solar Power Forecasting for Microgrid Control and Automation

Meteorological conditions such as air density, temperature, solar radiation etc. strongly affect the power generation from solar, and thus, the prediction and estimation process should consider weather conditions as critical inputs. The nature of weather forecast is highly unpredictable, so many applications use meteorological data from in-place on-site sensors to add to the forecast and some use complex networks with complicated mapping. The in-situ sensor approach and dense mapping methods, however, present several drawbacks. First, the use of sensors give rise to extra operational, installation and maintenance cost. Second, it requires significant amount of time to capture and accumulate data for various occasions and scenarios, and in addition, sensor itself can be the cause of error measurements. The complex methods are computational inefficient and may present suboptimal convergence. This paper presents a sensorless solar output power forecasting based on historical weather (publicly available from met office) and PV data. The algorithm uses simple to implement neural networks with few neurons and hidden layers for its training and allows for day a head forecast. The proposed methodology presents a guideline on how to select the relevant data from weather and how it affects the accuracy and training time of neural network.
Data Center Air Handling Unit Fan Speed Optimization Using Machine Learning Techniques

An Increase in Data Center power requirements has placed significant pressure on traditional Data Center cooling management Systems. The temperature in the Data Center is controlled using Air handling units (AHUs) and plays a critical role in a Data Center to maintain the required temperature to ensure the best possible performance. As the targeted Data Center is quite Old and using backdated technologies and does not have sensor-based technologies implemented. One of the issues faced by the target Data Center was that AHU fan speed was set to the static setting which impacts the Supplied Temperature in Data Center and results in excessive hot & cold temperature inside a Data Center. The proposed model resolves the problem faced by the targeted Data Center to operate the AHU fan speed to maintain the required DC Temperature on the predicted range by using machine learning techniques. This model not only solves the problem of maintaining the necessary temperature in the Data Center, but it can also regulate the fan speed within the expected range, contributing to the Data Center's energy efficiency. Supervised machine learning with linear regression and logistical regression approaches are utilized to investigate which methodology produces the best prediction results for adjusting the AHU unit fan speed for better control of the supplied Data Center temperature. In the targeted Data Center, it has no scope to expand more rack space or host IT load. It is desired that the predicted or recommended range for controlling AHU fan speed be determined so that the needed temperature can be sustained with the suggested setting without requiring extensive manual task. Henceforth as the data generated by the Data Center is historical, supervised regression machine learning models using Linear and Logistic Regression techniques are used. Both regression models are compared to see which regression methodology predicts the best variable fan speed range for maintaining the data center's required temperature.
Deep Learning For Knowledge Graph Completion With XLNET

Knowledge Graph is a graph knowledge base composed of fact entities and relations. Recently, the adoption of Knowledge Graph in Natural Language Processing tasks has proved the efficiency and convenience of KG. Therefore, the plausibility of Knowledge Graph become an import subject, which is also named as KG Completion or Link Prediction. The plausibility of Knowledge Graph reflects in the validness of triples which is structured representation of the entities and relations of Knowledge Graph. Some research work has devoted to KG Completion tasks. The typical methods include semantic matching models like TransE or TransH and Pre-trained models like KG-BERT. In this article, we propose a novel method based on the pre-trained model XLNET and the classification model to verify whether the triples of Knowledge Graph are valid or not. This method takes description of entities or relations as the input sentence text for fine-tuning. Meanwhile contextualized representations with rich semantic information can be obtained by XLNET, avoiding limitations and shortcomings of other typical neural network models. Then these representations are fed into a classifier for classification. Experimental results show that there is an improvement in KG Completion Tasks that the proposed method has achieved.