Reviewerly/exHarmony huggingface dataset:
Key Expansion in Cryptographic Systems

The manner in which a secret key is used in symmetric cryptographic systems is a permanent challenge for cryptographers due to its importance in overall security required for information protection. In order to mitigate the success rate of an attacker, a key expansion stage is included in cryptographic algorithms. This article presents an innovative solution that maximizes the key space, without reveal any information to an attacker who might monitor the communication channel. The prerequisites of this approach are the secret keys, a pseudorandom numbers generator and a random number generator, all of these being aggregate by a challenge-response mutual authentication protocol and a permutation function. For an efficient implementation, the already existing conductive mesh and the associated electronic circuits, dedicated to protect the cryptographic module against physical intrusion attempts of an attacker, are used to generate random numbers, without being necessary other electronic circuits. The numbers established as a result of the mutual authentication protocol will be used further to permute the secret key so, after each logical (re)synchronization, different keys will be used in cipher algorithm. Due to this key space extension process, the confusion and diffusion properties belonging to cypher algorithm are improved.
Analysis of Teaching Reform of Agricultural Water Conservancy Engineering Specialty Based on Engineering Education Accreditation

Based on the requirements of engineering education accreditation, the requirements for the teaching of agricultural water conservancy engineering are getting higher and higher. In order to implement the new curriculum standards and the concept of comprehensive quality education, it is necessary to innovate the teaching model of the Agricultural Water Conservancy Engineering major, combine the main problems in teaching, enrich teaching content, innovate teaching methods, and continuously improve teaching quality and level. This article focuses on the issues related to the teaching reform of the Agricultural Water Conservancy Engineering major based on engineering education accreditation.
Presentation in English language teaching in the context of higher education digitalization

Abstract The paper is devoted to one of the main multifunctional elements of any foreign language training course–multimedia presentation. The authors describe basic requirements to a presentation, its aims and advantages, consider different types of presentation tasks. Special attention is paid to the analysis of using presentations in their practical experience and feedback from students. The study allowed to examine students’ attitude to presentations considering 5 aspects such as the choice of presentation topics, students’ willingness to do this task, their self-assessment, the level of PC skills, literacy and speech competences. The questionnaire was used to analyze effectiveness of this task in different periods of time (2008-2010, 2018-2020), to prove the influence of computerization of higher education on students’ scientific and academic work, to identify the main difficulties for students in creating presentations and offer possible ways of overcoming them. The intensive use of information technologies contributes to the formation of an intellectually developed creative personality, well-oriented in the information space, ready for self-development and application of this knowledge in their future professional activities.
Real-Time Path Planning with Application for Multi-UAVs to Track Moving Target

We propose a coordinated tracking strategy based on the Lyapunov Guidance Vector Field (LGVF) to solve the problem of moving target tracking in real-time path planning for multiple unmanned aerial vehicles (UAVs). First, a tracking strategy is proposed for high/low-altitude UAVs cooperative target tracking, the high altitude UAV monitors target, the low-altitude UAV detects and collects the status information of moving target. Second, to track a moving target and maintain wireless communication in a 3D environment, the LGVF method is improved by introducing flight height into the Lyapunov function, the limit cycle in the horizontal plane and the optimal height in the vertical plane are constantly being adjusted by attraction and repulsion function. Third, as the quality of the route is mainly influenced by the attraction parameters, repulsive parameters and the rate of convergence in the vertical direction, the real-time path can be planned by the rolling optimization according to the predicted motion. The experimental results validate the effectiveness of the tracking strategy.
Statistical Method of Bubble Distribution in Ship Wake Based on Digital Image

Abstract A large number of bubbles are formed in the wake of a ship when it is sailing. Statistical analysis of the wake bubbles can infer the important information of the ship such as draft, ship width and speed, which is of great significance in the military field. The experimental device is used to simulate the actual wake bubbles in this paper, and the detection method combining the sobel operator and the regionprop function is used to make effective statistics on the wake distribution. Experiments show that this paper can perform distribution statistics of wake bubbles in different situations and obtain better statistical results.
Multilingual acoustic model training based on phoneme set conversion

For multilingual automatic speech recognition (ASR), acoustic models are often trained on multiple language datasets. The ASR of a target language can be improved by using auxiliary language data. Generally, universal phoneme sets are used in order to eliminate the differences between phoneme sets. However, this approach is inefficient for languages whose phoneme sets are similar. We propose a more efficient training method based on phoneme set conversion, in which phonemes from the auxiliary language are converted into those of the target language. In addition, we examined the construction of context trees.
Transmission of climate parameters using low budget wireless communication modules in Ventanilla, Callao, Peru

Climate change has affected our ecosystem in many aspects such as social and economic, one of the reasons being sudden changes in temperature in the different seasons of the year. Populous places like these located in the Ventanilla district (Callao), are not alien to conditions, living in unacceptable situations due to climatic factors, combined with extreme poverty and little access to communication networks; That is why it has been proposed to implement a meteorological station by incorporating wireless communication modules to establish the transmission and reception of climatic parameters. In this way, the information obtained can be sent to a center that allows its dissemination through social networks. The parameters that have been established are temperature, relative humidity, and atmospheric pressure; With this, it is sought, through local entities, to generate a culture of prevention and awareness in favor of the mentioned human settlement and, a posteriori, to be able to implement a meteorological network capable of obtaining data in real-time for the entire Constitutional Province of Callao.
Comprehensive Evaluation Method of Multi-meter Integrated Energy Metering Operating State Based on Hierarchical Structure

In view of the current lack of comprehensive evaluation of energy measurement system evaluation and the need for comprehensive measurement of electricity, water, gas and heat energy, a multi-table integrated energy measurement status evaluation method based on hierarchical structure is proposed. Based on the analysis of the comprehensive measurement indicators of electricity, water, gas and heat energy and the evaluation needs, a hierarchical evaluation index of the operating status of the multi-table integrated energy measurement system has been established. The comprehensive evaluation of the operation status of multimeter integration of energy measurement is realized through a hierarchical structure analysis method. Experimental analysis shows that this method can scientifically quantify the multimeter integrated energy measurement.
The Upper Confidence Bound Algorithm: Minimax Optimality

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Cyber Crime Threats, Strategies to Overcome, and Future Trends in the Banking Industry

Electronic banking, with its inherent advantages for the banking industry as well as the customer, is an area with tremendous growth potential. This field has also seen a corresponding increase in network security breaches, data thefts/loss, identity thefts and other white-collar crimes resulting in huge losses to the banking industry as well as the banking clientele. Losses by the banking industry worldwide due to white- collar crimes are in billions of dollars and far outstrip conventional methods of bank robbery. The unprecedented speed at which net banking has evolved, the ubiquitous and global nature of open networks and the increasing reliance on information technology have all added up to provide an environment of enhanced security challenges. Amendments in the IT act, banking regulations and the coming explosion in WAP are issues that need to be taken into account by the industry.
Learning a Video-Text Joint Embedding using Korean Tagged Movie Clips

For intelligent multimedia services, video contents understanding is a major challenge. In the existing video retrieval approaches, manual descriptive sentence data is necessary for retrieving desired videos against user's search intent. To overcome these limitations, modeling visual concepts included in video and sentence is necessary to learn a mapping of video and text into a common vector space, where relevant videos and texts are close to each other. In this study, we construct a new dataset containing 250 Korean movies with manual text description in Korean. Also, video-text joint embedding model and its quantitative and qualitative search results are introduced. With our proposed model, video manual tagging is no longer necessary for video retrieval services.
Static and Dynamic Video Summarization

Video represented by a large number of frames synchronized with audio making video saving requires more storage, it's delivery slower, and computation cost expensive. Video summarization provides entire video information in minimum amount of time. This paper proposes static and dynamic video summarizationmethods. The proposed static video summarization method includes several steps which are extracting frames from video, keyframes selection, feature extraction and description, and matching feature descriptor with bag of visual words, and finally save frames when features matched. The proposed dynamic video summarizationmethod includes in general extracting audio from video, calculating audio features using the average of samples in windows and find the highest average which reflects portion of video with loudest sound. The experimental results for the proposed static video summarization show that there is no redundancy between selected representative keyframes and the subjective evaluation results ensure the importance of the selected keyframes. While the experimental results for the proposed static video summarization show that all the segments of goals have been extracted to provide video summary. Static and dynamic video summarization methods done to football or soccer video type.
Research on Factors Influencing College Students’ Initiative in Physical Learning

In college physical education teaching, students’ physical education learning initiative has a great impact on teaching efficiency and quality. At present, many college physical education teachers often focus on the reform of their teaching content and methods, while ignoring the study of students’ learning initiative, resulting in the dilemma of low teaching efficiency and low quality. This paper finds that students’ subjective consciousness, learning objectives, learning methods, campus sports culture and teachers’ comprehensive quality have great influence on college students’ physical education learning initiative.
Robust Visual Tracking Via An Imbalance-Elimination Mechanism

The competitive performances in visual tracking are achieved mostly by tracking-by-detection based approaches, whose accuracy highly relies on a binary classifier that distinguishes targets from distractors in a set of candidates. However, severe class imbalance, with few positives (e.g., targets) relative to negatives (e.g., backgrounds), leads to degrade accuracy of classification or increase bias of tracking. In this paper, we propose an imbalance-elimination mechanism, which adopts a multi-class paradigm and utilizes a novel candidate generation strategy. Specifically, our multi-class model assigns samples into one positive class and four proposed negative classes, naturally alleviating class imbalance. We define negative classes by introducing proportions of targets in samples, which values explicitly reveal relative scales between targets and backgrounds. Further-more, during candidate generation, we exploit such scale-aware negative patterns to help adjust searching areas of candidates to incorporate larger target proportions, thus more accurate target candidates are obtained and more positive samples are included to ease class imbalance simultaneously. Extensive experiments on standard benchmarks show that our tracker achieves favorable performance against the state-of-the-art approaches, and offers robust discrimination of positive targets and negative patterns.
Self-Attention Based Neural Network for Few Shot Classification

This paper shows results of experiments carried on few shot classification using attention based neural network plus modifications to its formulation.
Existence of Periodic Solutions for an Output Hidden Feedback Elman Neural Network

We first recall the sufficient conditions for the existence of a periodic output of a modified Elman neural network with a periodic input found by using Mawhin's continuation theorem of coincidence degree theory.Using this result, we obtain sufficient conditions for the existence of a periodic output for an output hidden feedback Elman neural network with a periodic input.Examples illustrating these sufficient conditions are given.
A Tool for Reducing Implementation Defects for Requirements based on Large Decision Tables Towards a Semantic Gap Reduction

The growth and complexity of Information Systems demands tools and methodologies aiming to reduce the time for development as well as to improve the quality of such systems. The use of business rules to model the behavior of information systems is an adopted technique in software engineering. Business rules are described using multiple techniques such as Decision Tables. A Decision Table is a tabular representation combining conditions and responses. Empirical industrial experience showed that the existence of defects associated to the bad understanding of large decision tables representing business rules. This paper describes the first set of results in a project that aims to reduce the defects generated during the implementation of business rules based on the hypothesis that the automatic generation of code for decision tables can reduce the semantic gap between what is described and what is implemented. The results include the description of a fully functional prototype that allows the definition of large decision tables as well as the code generation. Additionally, the paper also describes the experiments performed so far to test our hypothesis for defect reduction together with some new paths for research.
Homomorphic Encryption Based Secure Sensor Data Processing

Novel sensor processing algorithms face many hurdles to their adoption. Sensor processing environments have become increasingly difficult with an ever increasing array of threats. These threats have, in turn, raised the bar on deploying new capabilities. Many novel sensor processing algorithms exploit or induce randomness to boost algorithm performance. Co-designing this randomness with cryptographic features could be a powerful combination providing both improved algorithm performance and increased resiliency. The emerging field of signal processing in the encrypted domain has begun to explore such approaches. The development of this new class of algorithms will require new classes of tools. In particular, the foundational linear algebraic mathematics will need to be enhanced with cryptographic concepts to allow researchers to explore this new domain. This work highlights a relatively low overhead method that uses homomorphic encryption to enhance the resiliency of a part of a larger sensor processing pipeline.
A Spatial Attention-Enhanced Multi-Timescale Graph Convolutional Network for Skeleton-Based Action Recognition

How to effectively extract discriminative spatial and temporal features is important for skeleton-based action recognition. However, current researches on skeleton-based action recognition mainly focus on the natural connections of the skeleton and original temporal sequences of the skeleton frames, which ignore the inter-related relation of non-adjacent joints and the variant velocities of action instances. To overcome these limitations and therefore enhance the spatial and temporal features extraction for action recognition, we propose a novel Spatial Attention-Enhanced Multi-Timescale Graph Convolutional Network (SA-MTGCN) for skeleton-based action recognition. Specifically, as the relation of non-adjacent but inter-related joints is beneficial for action recognition, we propose an Attention-Enhanced Spatial Graph Convolutional Network (A-SGCN) to use both natural connection and inter-related relation of joints. Furthermore, a Multi-Timescale (MT) structure is proposed to enhance temporal feature extraction by gathering different network layers to model different velocities of action instances. Experimental results on the two widely used NTU and Kinetics datasets demonstrate the effectiveness of our approach.
Auto-Encoder based Structured Dictinoary Learning

Dictionary learning and deep learning are two popular representation learning paradigms, which can be combined to boost the classification task. However, existing combination methods often learn multiple dictionaries embedded in a cascade of layers, and a specialized classifier accordingly. This may inattentively lead to overfitting and high computational cost. In this paper, we present a novel deep auto-encoding architecture to learn only a dictionary for classification. To empower the dictionary with discrimination, we construct the dictionary with class-specific sub-dictionaries, and introduce supervision by imposing category constraints. The proposed framework is inspired by a sparse optimization method, namely Iterative Shrinkage Thresholding Algorithm, which characterizes the learning process by the forward-propagation based optimization w.r.t the dictionary only, reducing the number of parameters to learn and the computational cost dramatically. Extensive experiments demonstrate the effectiveness of our method in image classification.
A Feasibility Study for MPI over HDFS

With the increasing prominence of integrating highperformance computing (HPC) with big-data (BIGDATA) processing, running MPI over the Hadoop Distributed File System (HDFS) offers a promising approach for delivering better scalability and fault tolerance to traditional HPC applications. However, it comes with challenges that discourage such an approach: (1) two-sided MPI communication to support intermediate data processing, (2) a focus on enabling N-1 writes that is subject to the default HDFS block-placement policy, and (3) a pipelined writing mode in HDFS that cannot fully utilize the underlying HPC hardware. So, while directly integrating MPI with HDFS may deliver better scalability and fault tolerance to MPI applications, it will fall short of delivering competitive performance. Consequently, we present a performance study to evaluate the feasibility of integrating MPI applications to run over HDFS. Specifically, we show that by aggregating and reordering intermediate data and coordinating computation and 110 when running MPI over HDFS, we can deliver up to 1.92x and 1.78x speedup over MPI I/O and HDFS pipelined-write implementations, respectively. Consequently, we present a performance study to evaluate the feasibility of integrating MPI applications to run over HDFS. Specifically, we show that by aggregating and reordering intermediate data and coordinating computation and 110 when running MPI over HDFS, we can deliver up to 1.92x and 1.78x speedup over MPI I/O and HDFS pipelined-write implementations, respectively.
Decidability in robot manipulation planning

Abstract Consider the problem of planning collision-free motion of n objects movable through contact with a robot that can autonomously translate in the plane and that can move a maximum of $$m \le n$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>m</mml:mi> <mml:mo>≤</mml:mo> <mml:mi>n</mml:mi> </mml:mrow> </mml:math> objects simultaneously. This represents the abstract formulation of a general class of manipulation planning problems that are proven to be decidable in this paper. The tools used for proving decidability of this simplified manipulation planning problem are, in fact, general enough to handle the decidability problem for the wider class of systems characterized by a stratified configuration space. These include, e.g., problems of legged and multi-contact locomotion, bi-manual manipulation. In addition, the approach described does not restrict the dynamics of the manipulation system modeled.
Reflections on the role of nudges in human-computer interaction for behavior change

Considering the increase in the use of digital technologies, there is also an increase in the number of decisions made through these ways. In this context, designers must understand the effects of their interfaces on users' decisions, thus acting as choice architects. Digital nudges are presented as tools to enable people to change their behavior in a predictable way without prohibiting options. Although they have similarities with persuasive technologies, such aspects of the choice architecture have new characteristics. This work intends to generate reflections on the adoption of digital nudges in the different aspects of Human-Computer Interaction.
Can you really trust the sensor's PRNU? How image content might impact the finger vein sensor identification performance

We study the impact of highly correlated image content on the estimated photo response non-uniformity (PRNU) of a sensor unit and its impact on the sensor identification performance. Based on eight publicly available finger vein datasets, we show formally and experimentally that the nature of finger vein imagery can cause the estimated PRNU to be biased by image content and lead to a fairly bad PRNU estimate. Such bias can cause a false increase in sensor identification performance depending on the dataset composition. Our results indicate that independent of the biometric modality, examining the quality of the estimated PRNU is essential before the sensor identification performance can be claimed to be good.
An Entailment Analysis Based Entity Mapping To Improve Automatic Question Generation

Automatic generation of assessment questions to enhance learning requires natural language understanding. To enhance the process, the entities extracted from the subject domain, may be mapped to form knowledge graphs. But entity mapping faces the challenge of analysing entailment. The proposed work analyses the semantic relevance in the process and aims at generating heuristics to map entities.