Reviewerly/exHarmony huggingface dataset:
An Efficient, Hybrid, Double-Hash StringMatching Algorithm

In this paper we show that combining some of the good features of the existing popular algorithms can be even more efficient. This new algorithm is hybrid as it employs features from Boyer-Moore-Horspool, Rabin-Karp and Raita algorithms. We compare the right most character as well as use two independent hash functions and no character by character checking - hence leaving a very small probability for a false positive result if there is any. The proposed algorithm particularly does well when the pattern is very long as it will skip checking character by character comparison.
Development of a Novel Accurate Analysis System Regarding Information Processing within the Gazing Point

Over the last 10 years, tablets have spread to the point where we can now read electronic books (e-books) like paper books. There is a long history of studies of eye movement during reading. Remarkable results have been reported for reading experiments in which displayed letters are changed in conjunction with eye movement during reading. However, these studies were conducted in the 1970s, and it is difficult to judge the detailed descriptions of the experimental techniques and whether the display time was correctly controlled when changing letters. Here, we propose an experimental system to control the display information exactly, as well as the display time, and inspect the results of past reading research, with the aim of being at the forefront of reading research in the e-book era.
Heterogeneous Implementation of a Voronoi Cell-Based SVP Solver

This paper presents a new, heterogeneous CPU+GPU attacks against lattice-based (post-quantum) cryptosystems based on the Shortest Vector Problem (SVP), a central problem in lattice-based cryptanalysis. To the best of our knowledge, this is the first SVP-attack against lattice-based cryptosystems using CPUs and GPUs simultaneously. We show that Voronoi-cell based CPU+GPU attacks, algorithmically improved in previous work, are suitable for the proposed massively parallel platforms. Results show that 1) heterogeneous platforms are useful in this scenario, as they increment the overall memory available in the system (as GPU’s memory can be used effectively), a typical bottleneck for Voronoi-cell algorithms, and we have also been able to increase the performance of the algorithm on such a platform, by successfully using the GPU as a co-processor, 2) this attack can be successfully accelerated using conventional GPUs and 3) we can take advantage of multiple GPUs to attack lattice-based cryptosystems. Experimental results show a speedup up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$7.6 \times $ </tex-math></inline-formula> for 2 GPUs hosted by an Intel Xeon E5-2695 v2 CPU (12 cores <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times 2$ </tex-math></inline-formula> sockets) using only 1 core and gains in the order of 20% for 2 GPUs hosted by the same machine using all 22 CPU threads (2 are reserved for orchestrating the GPUs), compared to single-CPU execution using the entire 24 threads available.
Multi-agent Logics for Reasoning About Higher-Order Upper and Lower Probabilities

We present a propositional and a first-order logic for reasoning about higher-order upper and lower probabilities. We provide sound and complete axiomatizations for the logics and we prove decidability in the propositional case. Furthermore, we show that the introduced logics generalize some existing probability logics.
TextWiller: Collection of functions for text mining, specially devoted to the Italian language

TextWiller is
Human Behavior Modeling During Dialogue by Using Generative Adversarial Networks

In this research, human behavior during dialogue is modeled, with the goal of generating human-like motion for humanoid robots. Most of the previous studies on the human motion modeling aimed to model the single motion of human [8] and produce complicated behaviors by combining the models of the single motions [11]. In these studies, each model was associated with a label such as waving a hand, bowing and so on. However, since the human-like behavior in a daily life is diverse and ambiguous, defining a clear label for each motion of such behavior is difficult. To treat with this problem, in this paper, we collected the human motion data during a dialogue and propose a modeling method using Generative Adversarial Networks (GAN) [15]. The result of the human-robot experiment based on the subjective evaluation of the participants suggested that the human-like behavior of the robot could be generated by the proposed method.
A Multi-Objective Video Crowdsourcing Method in Mobile Environment

With the rapid development of mobile video services, HD and UHD videos are attractive for mobile users due to the realistic visual enjoyment and the accurate representation. However, the limited transmission bit rate in 4G communication network affects the experience of the users for watching videos. Crowdsourcing is considered as a reasonable and effective solution to alleviate the resource limitation. Through employing the crowdsourcing participants to download and transmit video segments, mobile users can get enhanced video services. However, it is still a significant challenge that how to avoid excessive payment and energy consumption when the crowdsourcing participants download the video segments for the mobile users. To address this challenge, a multi-objective video crowdsourcing method in mobile environment is proposed in this paper. Technically, the crowdsourcing participants apply device-to-device (D2D) communication technique rather than the cellular network or bluetooth transmission to transmit video segments to the mobile users. Here, we divide our problem into two situations, the single participant case and the multi-participants case. In the single participant case, we apply the improved dynamic programming algorithm to find strategies with more enhanced video service time that the crowdsourcing participants provide for the mobile users. Then Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Multiple Criteria Decision Making (MCDM) techniques are applied to find a balanced strategy to maximize the enhanced video service time and minimize the payment and the energy consumption. In the multi-participants case, through DBSCAN clustering, the problem with multi-participants is divided into several problems with single participant. Finally, extensive experimental evaluations are conducted to demonstrate the effectiveness and efficiency of our proposed method.
Finding Near-Duplicate Videos in Large-Scale Collections

This chapter discusses the problem of Near-Duplicate Video Retrieval (NDVR). The main objective of a typical NDVR approach is: given a query video, retrieve all near-duplicate videos in a video repository and rank them based on their similarity to the query. Several approaches have been introduced in the literature, which can be roughly classified in three categories based on the level of video matching, i.e., (i) video-level, (ii) frame-level, and (iii) filter-and-refine matching. Two methods based on video-level matching are presented in this chapter. The first is an unsupervised scheme that relies on a modified Bag-of-Words (BoW) video representation. The second is a s upervised method based on Deep Metric Learning (DML). For the development of both methods, features are extracted from the intermediate layers of Convolutional Neural NetworksConvolutional Neural Networks and leveraged as frame descriptors, since they offer a compact and informative image representation, and lead to increased system efficiency. Extensive evaluation has been conducted on publicly available benchmark datasets, and the presented methods are compared with state-of-the-art approaches, achieving the best results in all evaluation setups.
Production of a favorable calibration target for the determination of distortions of lenses in image processing

Lenses have a number of faults. The distortion is an error which has a negative effect especially in metrological applications. In extreme cases, distortion can also be visible to the naked eye. The distortion of lenses is a systematic error that can be corrected numerically. For this purpose, a target with a smaller local deviation is necessary. These targets are produced and measured with great effort. Especially for the area of large object fields the effort increases enormously. For this reason, no correction has been made for larger measuring ranges. In order to be able to apply a correction nevertheless, a compromise solution is required that produces a target with little effort, with which a correction becomes possible that is better than not applying any correction at all. Here, calibration targets were printed and examined with commercially available printers.
Use of Deep Learning for Firearms Detection in Images

Demand for weapons has grown along with crime rates, a contemporary problem haunting countries. This has motivated scientists to devise solutions that can aid in public safety in general. This paper proposes the detection of ﬁrearms in images through convolutional neural networks, using the YOLO (You Only Look Once) object detector. To improve learning, YOLO was used to generate annotations in an unmarked database, integrating a new database. This proposal was evaluated in a database containing 608 images, in which 304 images had weapons. Experiments carried out indicated an accuracy of 89.15% and a sensitivity of 100.00%, surpassing results presented in the current literature. These results show that the proposed methodology can be applied for the detection of ﬁrearms in images.
Trajectory Classification Using Feature Selection by Genetic Algorithm

Trajectory classificationSaini, Rajkumar helps in understanding the behaviorKumar, Pradeep of objects being monitoredRoy, Partha Pratim. The raw trajectories may not yield satisfactoryPal, Umapada classification results. Therefore, features are extracted from raw trajectories to improve classification results. All the extracted features may not be useful for classification. Hence, an automatic selection scheme is essential to find optimal features from the pool of handcrafted features. This paper uses a genetic framework to choose the optimal set of features for trajectory classification purpose. Seven features costing 18 dimensions have been extracted from raw trajectories. Next, Genetic Algorithm (GA) has been used to find the optimal set of features from them. The binary encoding scheme has been used in GA. The 7-bit long chromosomes have been coded in this work. Bits of chromosomes represent trajectory features to be used in classification. Finally, trajectories have been classified using optimal features. Trajectory classification has been done using Random Forest (RF) based classifier and compared with Support Vector Machine (SVM). The results are evaluated using three trajectory datasets, namely I5, LabOmni2, and T15. The classification rates of 99.87%, 93.32%, and 90.58% have been recorded for datasets I5, LabOmni2, and T15, respectively.
Enhanced Evolutionary Computing Assisted Robust SLA-Centric Load Balancing System for Mega Cloud Data Centers

Abstract Considering significance of a robust and Quality of Service (QoS) centric cloud computing, virtualization assisted load-balancing has been found a potential solution. However, assuring optimal Virtual-Machine (VM) migration with minimum violation of Service-Level-Agreement (SLA) and QoS degradation has been the challenge for academia-industries. VM allocation or scheduling being an NP-hard problem has been solved by numerous heuristic approaches such as classical Genetic Algorithm (GA), Ant Colony Optimization (ACO), etc. However they have been found confined due to local minima and convergence issues, especially for Mega Data Centres (MDCs). To alleviate such issues, in this paper an enhanced Evolutionary Computing algorithm named Adaptive Re-sampling GA (ARGA) algorithm has been developed that in conjunction with a stochastic prediction based dynamic load–measurement and Maximum Correlation (MC) assisted VM selection perform optimal load balancing over IaaS MDC infrastructures. The proposed ARGA VM allocation model with dual-level dynamic threshold assisted load estimation and MC based VM selection has exhibited lower SLA violation, performance degradation, downtime and minimum VM migration as compared to classical ACO based load balancing.
The DECODE Database Collection of Historical Ciphers and Keys.

We present an on-line database DECODE consisting of encrypted historical manuscripts, aiming at the systematic collection of ciphers and keys to create infrastructural support for historical resear ...
Density Peak Clustering Algorithm Based on Differential Privacy Preserving

Clustering by fast search and find of density peaks (CFSFDP) is an efficient algorithm for density-based clustering. However, such algorithm inevitably results in privacy leakage. In this paper, we propose DP-CFSFDP to address this problem with differential privacy, which adds random noise in order to distort the data but preserve its statistical properties. Besides, due to the poor performance of CFSFDP on evenly distributed data, we further optimize the clustering process with reachable-centers and propose DP-rcCFSFDP. The experimental results show that, under the same privacy budget, DP-rcCFSFDP can improve the clustering effectiveness while preserving data privacy compared with DP-CFSFDP.
Path Planning Optimization with Flexible Remote Sensing Application

The purpose of the path planning optimization is to find the most favorable route between starting and arrival points based on defined criteria and target functions. The change in the characteristics of each route becomes complicated when there is an increase in the number of intermediate points. This study predominately analyses the monitoring of a limited area. The author demonstrates how the path of the autonomous systems will change in different conditions and further introduces the possibility of using mobile remote sensing systems. The test is performed firstly in 2D flat area, then 3D spaces, and then—taking a forest fire as an example—the ideal conditions changed to reality. The study reveals findings on efficiency, based both on professional and economic considerations. The utilization of remote sensing technologies was found to optimize the observation of the given area generating new problems, such as what is the size of the monitored area at a given moment and how can we increase it for the higher effectiveness. An increase in the size of the monitored area results into an efficient and functional autonomous system albeit generating a shorter and modified path. Mobile autonomous systems therefore can be replaced by stable systems; simultaneously under real conditions, they can be more efficient than stable ones.
USING OF DIGITAL TECHNOLOGIES FOR FORMATIVE ASSESSMENT

The article deals with the problem of using modern digital technologies for formative assessment. The main objectives of the assessment, such as "assessment for learning", "assessment as learning" and "assessment of learning", are analyzed, and it is stated that the first two objectives are realized precisely by means of formative assessment. The evaluation strategies that the teacher can use to collect data on student achievements is analyzed. The objectives of formative assessment before the beginning, during the process of work and at the end of the lesson are determined; as well as methods of formative assessment. The structure of the formative assessment process consisting of six steps is analyzed. In today's conditions, for the implementation of formative assessment, it is expedient to involve digital technologies. Therefore, the article proposes a classification of digital resources that can be used for formative assessment at different stages of the occupation and for different purposes, examples are given and a description of possible use of resources of each specified category. In particular, such digital resources as quizzes, surveys, online tests, virtual boards, multiservices for creating interactive tasks with the possibility of self-checking their correctness, word-of-mouth word services, mindmaps, services for creating infographics and visualization, keeping diaries are considered. Different ways of using virtual boards for formative assessment are offered, and examples are given of their application before the beginning of the class, in the process of work and at the end of the lesson for reflection. The research leading to these results received, within the framework of the Modernization of Pedagogical Higher Education by Innovative Teaching Instruments. MoPED – KA2 CBHE – 586098-EPP-1- 2017-1-UA-EPPKA2-CBHE-JP.
A Novel Unsupervised Learning Approach for Assessing Web Services Refactoring

During the last years, the development of Service-Oriented applications has become a trend. Given the characteristics and challenges posed by current systems, it has become essential to adopt this solution since it provides a great performance in distributed and heterogeneous environments. At the same time, the necessity of flexibility and great capacity of adaptation introduce a process of constant modifications and growth. Thus, developers easily make mistakes such as code duplication or unnecessary code, generating a negative impact on quality attributes such as performance and maintainability. Refactoring is considered a technique that greatly improves the quality of software and provides a solution to this issue. In this context, our work proposes an approach for comparing manual service groupings and automatic groupings that allows analyzing, evaluating and validating clustering techniques applied to improve service cohesion and fragmentation. We used V-Measure with homogeneity and completeness as the evaluation metrics. Additionally, we have performed improvements in existing clustering techniques of a previous work, VizSOC, that reach 20% of gain regarding the aforementioned metrics. Moreover, we added an implementation of the COBWEB clustering algorithm yielding fruitful results.
SQL Injection Attacks Detection and Prevention Based on Neuro-Fuzzy Technique

A Structured Query Language (SQL) injection attack (SQLIA) is one of most famous code injection techniques that threaten web applications, as it could compromise the confidentiality, integrity and availability of the database system of an online application. Whereas other known attacks follow specific patterns, SQLIAs are often unpredictable and demonstrate no specific pattern, which has been greatly problematic to both researchers and developers. Therefore, the detection and prevention of SQLIAs has been a hot topic. This paper proposes a system to provide better results for SQLIA prevention than previous methodologies, taking in consideration the accuracy of the system and its learning capability and flexibility to deal with the issue of uncertainty. The proposed system for SQLIA detection and prevention has been realized on an Adaptive Neuro-Fuzzy Inference System (ANFIS). In addition, the developed system has been enhanced through the use of Fuzzy C-Means (FCM) to deal with the uncertainty problem associated with SQL features. Moreover, Scaled Conjugate Gradient algorithm (SCG) has been utilized to increase the speed of the proposed system drastically. The proposed system has been evaluated using a well-known dataset, and the results show a significant enhancement in the detection and prevention of SQLIAs.
Optimal Truth-Tracking Rules for the Aggregation of Incomplete Judgments

Suppose you need to determine the correct answer to a complex question that depends on two logically independent premises. You can ask several agents to each evaluate either just one of those premises (which they can do with relatively high accuracy) or both premises (in which case their need to multitask will lower their individual accuracy). We first determine the optimal rule to aggregate the individual judgments reported by the agents and then analyse their strategic incentives, depending on whether they are motivated by (i) the group tracking the truth, by (ii) maximising their own reputation, or by (iii) maximising the agreement of the group's findings with their own private judgments. We also study the problem of deciding how many agents to ask for two judgments and how many to ask for just a single judgment.
Research on Key Technologies and Applications of Energy Internet Blockchain

In view of the challenges facing the current development of energy Internet, the basic theory, architecture implementation and application mode of energy blockchain technology are proposed from the communication layer, data layer and contract layer. In this paper, we analyze the value distribution mode of blockchain in energy internet, integrate business form, information system, Internet of Things and blockchain, and build secure and autonomous peer-to-peer energy Internet environment based on blockchain. The key technologies are constructed from the network layer, system layer and business layer. The problems of network layer equipment and data access authentication, secure and trusted sharing are solved. The system layer blockchain-based power payment, power settlement, power audit and other business support capabilities are improved. The system enhances the security and credibility of business, and drives the future global energy Internet development.
NOXOLOGICAL APPROACH TO ENVIRONMENTAL PROTECTION AND HUMAN LIFE ACTIVITY SAFETY: RISKS OF MODERN DANGERS

The purpose of the article: The purpose of the article is to theoretically substantiate the noxological approach to the definition of mechanisms to counter the risks of modern hazards.&#x0D; Materials and methods: The key ideas of the noxological approach in the framework of this study are environmental preservation and human life-activity safety. The research is based on the methodological principles and leading ideas of environmental protection and human life-activity safety, presented in the context of social ecology, the concept of sustainable development of environmental safety, in the environmental paradigm, in the co-evolutionary paradigm of environmental ethics.&#x0D; Results of the research: The authors attempt to create a theoretical model of noxological approach to countering the risks of modern hazards in environmental preservation and human life-activity safety. The systematization of the basic concepts of the noxological approach as an independent scientific direction is carried out; the regularities and sources of the hazards’ emergency are established; the taxonomy of hazards is determined; the prototype of the hazard passport is justified.&#x0D; Applications: This research can be used for universities, teachers, and students.&#x0D; Novelty/Originality: In this research, the model of noxological approach to environmental protection and human life activity safety: risks of modern dangers is presented in a comprehensive and complete manner.