Reviewerly/exHarmony huggingface dataset:
Proposal and evaluation of adjusting resource amount for automatically offloaded applications

Heterogeneous hardware other than a small-core central processing unit (CPU) such as a graphics processing unit (GPU), field-programmable gate array (FPGA), or many-core CPU is increasingly being used. However, to use heterogeneous hardware, programmers must have sufficient technical skills to utilize OpenMP, CUDA, and OpenCL. On the basis of this, I have proposed environment-adaptive software that enables automatic conversion, configuration, and high-performance operation of once-written code, in accordance with the hardware to be placed. Although it has been considered to convert the code in accordance with the offload devices, no method has been developed to set the amount of resources appropriately after automatic conversion. In this paper, as a new element of environment-adapted software, I examine a method of optimizing the amount of resources of the CPU and offload device in order to operate the application with high-cost performance. Through the offloading of multiple existing applications, I demonstrate that the proposed method can meet the user’s request and set an appropriate amount of resources.
Privacy-preserving <i>k</i>-NN interpolation over two encrypted databases

Cloud computing enables users to outsource their databases and the computing functionalities to a cloud service provider to avoid the cost of maintaining a private storage and computational requirements. It also provides universal access to data, applications, and services without location dependency. While cloud computing provides many benefits, it possesses a number of security and privacy concerns. Outsourcing data to a cloud service provider in encrypted form may help to overcome these concerns. However, dealing with the encrypted data makes it difficult for the cloud service providers to perform some operations over the data that will especially be required in query processing tasks. Among the techniques employed in query processing task, the k-nearest neighbor method draws attention due to its simplicity and efficiency, particularly on massive data sets. A number of k-nearest neighbor algorithms for query processing task on a single encrypted database have been proposed. However, the performance of k-nearest neighbor algorithms on a single database may create accuracy and reliability problems. It is a fact that collaboration among different cloud service providers yields more accurate and more reliable results in query processing. By considering this fact, we focus on the k-nearest neighbor (k-NN) problem over two encrypted databases. We introduce a secure two-party k-NN interpolation protocol that enables a query owner to extract the interpolation of the k-nearest neighbors of a query point from two different databases outsourced to two different cloud service providers. We also show that our protocol protects the confidentiality of the data and the query point, and hides data access patterns. Furthermore, we conducted a number of experiment to demonstrate the efficiency of our protocol. The results show that the running time of our protocol is linearly dependent on both the number of nearest neighbours and data size.
Analysis of Badminton Movement Cognition Algorithm Based on Track Linear Capture

At present, more and more sports science and technology are being explored and applied in competitive sports. The birth and popularization of video tracking and capturing technology have provided more fair and just perspectives for many sports events. Track linear capture can replay the player’s behavior in real time, the flight path of the badminton can be analyzed in 3D stereoscopic analysis, and the ball’s motion trajectory can be calculated more accurately. In this paper, an objective trajectory tracking and prediction model is constructed based on the motion cognition algorithm, and the motion characteristics of the objective are extracted from the limited historical trajectory of the objective to achieve more accurate trajectory tracking. Then, the trajectory tracking model is applied to the objective tracking framework to obtain ideal objective tracking results. At the same time, in order to make use of the interaction between scene information and objective, this paper improves the trajectory tracking model. The trajectory prediction model based on neural network is constructed, which learns the pedestrian motion characteristics from the pedestrian trajectory data of the target tracking scene offline and uses its “memory” online to generate the implicit depth motion characteristics of the target according to the limited historical information of the target. It also predicts the most likely location of the future target and calculates the motion similarity between the targets. Finally, a simulation experiment platform is built to prove the effectiveness of the trajectory tracking model and objective tracking algorithm proposed in this paper. Through the research results of this paper, it can play a role in verifying the referee’s judgment on the penalty of some key balls, which is more conducive to maintaining the fairness of the game, and more helpful for athletes to optimize their exercise results according to scientific basis, and has the function of improving their performance.
Deep Feature Learning for Intrinsic Signature Based Camera Discrimination

In this paper we consider the problem of "end-to-end" digital camera identification by considering sequence of images obtained from the cameras.The problem of digital camera identification is harder than the problem of identifying its analog counterpart since the process of analog to digital conversion smooths out the intrinsic noise in the analog signal.However it is known that identifying a digital camera is possible by analyzing the camera's intrinsic sensor artifacts that are introduced into the images/videos during the process of photo/video capture.It is known that such methods are computationally intensive requiring expensive pre-processing steps.In this paper we propose an end-to-end deep feature learning framework for identifying cameras using images obtained from them.We conduct experiments using three custom datasets: the first containing two cameras in an indoor environment where each camera may observe different scenes having no overlapping features, the second containing images from four cameras in an outdoor setting but where each camera observes scenes having overlapping features and the third containing images from two cameras observing the same checkerboard pattern in an indoor setting.Our results show that it is possible to capture the intrinsic hardware signature of the cameras using deep feature representations in an end-to-end framework.These deep feature maps can in turn be used to disambiguate the cameras from each another.Our system is end-to-end, requires no complicated pre-processing steps and the trained model is computationally efficient during testing, paving a way to have near instantaneous decisions for the problem of digital camera identification in production environments.Finally we present comparisons against the current state-of-the-art in digital camera identification which clearly establishes the superiority of the end-to-end solution.
Examining the Impact of COVID-19 on Cryptocurrency Enforcement in the United States

This chapter examines how COVID-19 has impacted cryptocurrency enforcement at the state level. This author employs a qualitative single case study method and investigates the cryptocurrency enforcement actions of the United States Securities and Exchange Commission (SEC) in 2020. The data were collected from SEC cryptocurrency press releases and public statements. The US Securities and Exchange Commission (SEC) has brought 28 enforcement actions against companies and individuals in the crypto industry in 2020 regarding the three types of cryptocurrency enforcement actions and trading suspensions (trading suspension, litigation, and administrative proceeding). Among them, litigation is the most common type of cryptocurrency enforcement action taken by the SEC. This author concludes that the law enforcement agencies in the United States faced several challenges before and during the pandemic. Finally, the author suggests some measures that law enforcement agencies can take to address the above challenges.
New precise model of studentized principal components

If the sample Mahalanobis distance (SMD) is atypically large, it is essential to statistically estimate or test the contribution of each studentized principal component (SPC) decomposed from the SMD to consider its cause. However, there are no appropriate probability models for the SPCs of small samples. This study proposes a precise probability model for the SPCs of small samples without estimating the population eigenvalues or eigenvectors. The proposed model for an SPC comprises an elementary formula of sample size and the SPC’s index multiplied by one random variable following the t-distribution, which is simpler and requires no further computing compared with previous models. Numerical experiments demonstrated that the proposed model performs well under the weak condition that population eigenvalues are closely distinct with various dimensions and sample size. For practical implementation, the proposed model was applied for correcting the SMD to the population Mahalanobis distance, demonstrating better performance than other models. Additionally, the proposed model enables precise statistical testing of SPCs for discriminant analysis, cluster analysis, and projection pursuit, and the model improves the expectation–maximization algorithm for Gaussian mixture models.
A Collaborative Grouping Aggregation Query Scheme on Heterogeneous Computing Systems

Aggregation is one of the most common operations in data analytics systems, but complex aggregation query like grouping aggregation on massive data sets is a relatively expensive operation. Thus, improving the efficiency of aggregation query has become a hot topic for database researchers. The emergence of massively parallel coprocessors provides a way to solve this issue. Prior works focus mainly on aggregation optimization using one kind of coprocessors, but little is known about the performance impact on the heterogeneous computing system with diverse coprocessors, such as GPGPUs, MICs or FPGAs. To investigate the potential benefits of the use of heterogeneous computing systems in improving the performance of aggregation operation, in this paper, we design a grouping aggregation scheme with GPUs and MICs to work collaboratively. The scheme includes task partitioning method, data transfer mode, and aggregation algorithm optimization. To verify the effectiveness of our collective aggregation scheme, we performed a series of experiments on a heterogeneous platform with one Intel Xeon Phi coprocessor and one NVIDIA GPU coprocessor, and the results have shown that our aggregation processing strategy for the heterogeneous systems is effective and can achieve noticeable performance improvement comparing the CPU-only parallel computing system, even taking the communication overhead via PCI Express bus into account.
Image Recognition With Haar Wavelet and Pseudoinverse Learning Algorithm Based Autoencoders

Abstract In this work, a recognition model with low computation and high training efficiency is proposed for images recognition. The proposed model consists of two modules: 1) haar wavelet transformation extracts features and compresses images; 2) a representation learning module using autoencoder. Because of its powerful function, Haar wavelet transformation has wide use in the feature extraction of images. The mother wavelet and scale wavelet can extract features from different scales. The representation learning module is trained with a non-gradient descent algorithm based on autoencoder structure. Two benchmark image datasets, MNIST and Fashion-MNIST, have been used to validate the proposed model. Experimental results show that our proposed model achieves its expected effect. Compared with filters, CNN, and pseudoinverse learning autoencoders, our model takes less training time, at the same time it acquires comparative recognition accuracy.
Qualitative Abnormalities of Peripheral Blood Smear Images Using Deep Learning Techniques

In recent years, Peripheral blood smear is a generic analysis to assess the person's health status.Manual testing of Peripheral blood smear images are difficult, time-consuming and is subject to human intervention and visual error.This method encouraged for researchers to present algorithms and techniques to perform the peripheral blood smear analysis with the help of computer-assisted and decision-making techniques.Existing CAD based methods are lacks in attaining the accurate detection of abnormalities present in the images.In order to mitigate this issue Deep Convolution Neural Network (DCNN) based automatic classification technique is introduced with the classification of eight groups of peripheral blood cells such as basophil, eosinophil, lymphocyte, monocyte, neutrophil, erythroblast, platelet, myocyte, promyocyte and metamyocyte.The proposed DCNN model employs transfer learning approach and additionally it carries three stages such as pre-processing, feature extraction and classification.Initially the pre-processing steps are incorporated to eliminate noisy contents present in the image by using Histogram Equalization (HE).It is enclosed to improve an image contrast.In order to distinguish the dissimilar class and segmentation approach is carried out with the help of Fuzzy C-Means (FCM) model whereas its centroid point optimality method with Slap Swarm based optimization strategy.Moreover some specific set of Gray Level Co-occurrence Matrix (GLCM) features of the segmented images are extracted to augment the performance of proposed detection algorithm.Finally the extracted features are recorded by DCNN and the proposed classifier has the capability to extract their own features.Based on this the diverse set of classes are classified and distinguished from qualitative abnormalities found in the image.
THE APPLICATION OF THE MINDMAPPING TO SOLVING PROBLEMS OF MATHEMATICAL ANALYSIS

The accelerated process of innovation is one of the decisive factors, without which it is impossible to maintain leadership in today's world. The development of technology is actively influencing education, changing the pace of learning. At present, acquiring a knowledge system in an educational institution is no longer a key priority. It is much more valuable to develop students' analytical and modern digital skills, the ability to effectively apply the acquired knowledge in practice. The article is devoted to the problem of using mental maps in the educational process as one of the effective innovative pedagogical technologies for visualizing the process of thinking and an alternative technique for recording information. The features of the use of mental maps as a means of visualizing the level of understanding and application of theoretical material in the process of solving problems in the course “Mathematical Analysis” by future teachers of mathematics, physics and computer science in institutions of higher pedagogical education are described. It is concluded that it is advisable to make mental maps when performing practical tasks on the topics “Graphing a function”, “Finding boundaries”, “Investigation of a function and plotting its graph”, “Finding the largest and smallest values of a function of one and several variables”, “Geometric Applications of the Integral”, etc. It has been established that this technique should not be used in algorithms designed for several steps. It is proved that in combination with digital services designed to create maps, this technology allows you to optimize the process of solving problems, visualize the process of thinking, positively influence the formation of digital competence in future teachers in the context of digitalization of education and is an effective means of increasing the efficiency of the educational process since it helps to restore skills focusing attention and contributes to the structuring of theoretical material.&#x0D; Keywords: education; teacher training; thinking process; mental map; visualization; mathematical analysis; limit; graph; innovation.
Reinforcement Learning Model With Dynamic State Space Tested on Target Search Tasks for Monkeys: Extension to Learning Task Events

Learning is a crucial basis for biological systems to adapt to environments. Environments include various states or episodes, and episode-dependent learning is essential in adaptation to such complex situations. Here, we developed a model for learning a two-target search task used in primate physiological experiments. In the task, the agent is required to gaze one of the four presented light spots. Two neighboring spots are served as the correct target alternately, and the correct target pair is switched after a certain number of consecutive successes. In order for the agent to obtain rewards with a high probability, it is necessary to make decisions based on the actions and results of the previous two trials. Our previous work achieved this by using a dynamic state space. However, to learn a task that includes events such as fixation to the initial central spot, the model framework should be extended. For this purpose, here we propose a "history-in-episode architecture." Specifically, we divide states into episodes and histories, and actions are selected based on the histories within each episode. When we compared the proposed model including the dynamic state space with the conventional SARSA method in the two-target search task, the former performed close to the theoretical optimum, while the latter never achieved target-pair switch because it had to re-learn each correct target each time. The reinforcement learning model including the proposed history-in-episode architecture and dynamic state scape enables episode-dependent learning and provides a basis for highly adaptable learning systems to complex environments.
Approach to Develop an Intelligent User Support System of the Educational Platform

The article describes an approach to the development and the results of testing of an intelligent user support system (USS), applicable, as well, to the educational platform of online courses to solve problems of information support for students.
Exploring Laughter Sound Visualizations for Self Reflection

We present an exploratory design study on visualizing laughter sounds for personal reflection. We experimented with a variety of graphic design elements to visualize temporal, spatial, and social aspects of laughter sounds. In order to experience their own laughter being visualized, our participants collected audio recordings of everyday conversations with their loved ones. We extracted laughter from the participants’ audio files using a machine learning algorithm, then visualized selected laughter in five different types of visual representations, and shared the result with each participant. Through the journey of collecting, seeing, listening to, and interacting with their personal laughter visualizations, participants explored what laughter means for them in different contexts. The study reveals that interactive laughter visualizations have the potential to evoke memories, support emotional expressions, and promote relationships.
Web archiving and digital libraries (WADL) 2022

The 2022 edition of the Workshop on Web Archiving and Digital Libraries (WADL) will explore the integration of web archiving and digital libraries. The workshop aims at addressing aspects covering the entire life cycle of digital resources, including creation/authoring, uploading/publishing, crawling, indexing, exploration, and archiving. It will also explore areas such as archiving processes and tools for "non-traditional" resources such as social media, scholarly and government datasets, 3D objects, and digital online art.
SMIFD-1000: Social media image forgery detection database

Image forgery/manipulation is one of the most alarming topics and becomes a major concern about different social media platforms regarding one’s privacy and safety. Therefore, the detection of the manipulated images is of immense interest to the researchers in the recent years. Despite the availability of numerous image forgery detection (IFD) datasets, very few particularly address the actual challenge by collecting the manipulated images from real-world scenario, e.g., collection of images from social media. Consequently, the contextual knowledge behind using the manipulated images remains unachieved. In order to address these issues, we propose an indigenous social media image forgery detection database, naming SMIFD-1000. This dataset provides rich annotations from several aspects: (a) image level: image regions that helps to classify pixel-level information; (b) forgery type: provide rich information about manipulation and (c) target and motif of manipulations: provide contextual rich knowledge about manipulation, which is significantly important from the perspective of social science. Finally, we would examine and benchmark the effectiveness of several publicly available algorithms on this dataset to demonstrate its usefulness. Results show that the dataset is highly challenging and will serve as an important benchmark for the existing and future IFD algorithms. Keywords: Image Manipulation, Digital Forensics, Image Dataset.
Transmission of Biometric Feature Using Facial Features Securely For Long Distance Biometric Recognition System

Assuring biometric data security and integrity is crucial. The widespread use of biometric authentication technologies creates a demand for data security and privacy that is both effective and dependable. To solve these difficulties, watermarking approach is preferred for biometric data protection. Multi biometric systems have enhanced the accuracy and reliability of biometric systems. Though a biometric system can be hacked in a variety of ways, unauthorised access to biometric template information poses a severe security and privacy risk. Here description of biometric watermarking strategies for safe user verification on a remote, biometric system that uses both facial and multi iris information, and statistically assess their effects on verification accuracy is presented. The Haar wavelet, LBP, and LTP iris characteristics are combined using average fusion and concatenation fusion. A single secure sketch is created after messing with the images. This biometric feature is hidden within the cover image, facial image. The results of the experiments reveal that the preset's security, performance, and accuracy are all satisfactory.
Junior High School Pupils' Perceptions and Self-Efficacy of Using Mobile Devices in the Learning Procedure

The study of this chapter investigated junior high school pupils' perceptions and self-efficacy of using mobile devices in the learning procedure. A 33-item questionnaire was administered to 91 pupils aged 12-15 years old in different Greek schools. Most of the pupils had showed favorable perceptions about the use of mobile devices for educational purposes. Nevertheless, the majority of pupils had a relatively medium degree of self-efficacy of using mobile devices in learning activities. Perceived usefulness was indicated as the major factor in predicting the adoption and use of mobile devices for educational purposes. The higher the level of perceived usefulness pupils have about mobile devices, the higher the possibility to use mobile devices as a learning tool. Pupils were more interested in using mobile devices for learning mathematics, history, English, and ancient Greek language. The findings of this study have implications for secondary education instructors, policy makers, and researchers.