Reviewerly/exHarmony huggingface dataset:
A Study of Big Data and Classification of NoSQL Databases

Today, Big Data is the main topic of discussion everywhere due to its huge popularity as its getting generated in a huge volume in every second. It is getting huge consideration and gratitude because of its wide research area and application scenarios. Large scale, bulky, quick changes, huge growth in data is generally stated as Big Data. Data that is obtained from a wide variety of sources are usually in a format of structured, unstructured or semi-structured data. Many times big data is collected from multiple application sources, so there is a presence of structural heterogeneity. This problem of structural heterogeneity is one of the major challenges for researchers around the world and can be overcome using big data Integration. As big data refers to the data in large volumes, available in different formats and generated at extraordinary speed so to capture, process and analyze this kind of data becomes difficult using traditional data processing tools. These difficulties can be overcome using big data management tools and techniques. Big Data Integration and Management are very crucial, revolutionizing the industries and has many applications in all sectors of human life. This paper discusses brief information about big data, its history, integration issues, and available management methods and tools.
Learning Analytics: A Literature Review and its Challenges

Learning analytics is used to measure, collect and analyse educational data. It extracts valuable information from educational data of students for the purpose of understanding and optimizing educational practices. It helps in developing predictive models using past educational data of students to improve future practices. It benefits stakeholders to improve learning processes by providing timely feedback regarding their progress. Although sharing and using of data include privacy risks for data subjects, privacy is the right of individuals to secure his/her personal information. Data privacy is a concern of sharing the data with a third party. The k-anonymization is the most popular existing privacy technique which helps to resolve privacy issues, but it also has some drawbacks. It leads to loss of information that is dependent on the value of k parameter. This paper discusses in detail about learning analytic, need for privacy in learning analytics, existing privacy preservation techniques and various existing privacy issues in learning analytics. An exhaustive literature survey has been done, and comparison between various existing privacy techniques has been presented.
Base Argumentation as an Abstraction of Deductive Argumentation

Base argumentation is a logic-based instantiation of abstract argumentation. Each base argument is a subset of the given knowledge base. In this paper, we show that base argumentation satisfies some rationality postulates, and that base argumentation is equivalent to deductive argumentation under complete semantics. Due to its simplicity, base argumentation can be seen as an abstraction of deductive argumentation.
Security System Aided by Voice Fingerprint

Abstract The goal of this paper is to create a security system to identify a specific person who wants to access private information or enter a building using their voice. To perform this system, we identified a database containing the audio files of the users who will be able to authenticate with this system. Several steps were sequentially performed in order to extract the characteristics of the Mel Frequency Cepstral Coefficients from the audio files. Based on the k-Nearest Neighbor algorithm with an Euclidean distance and 4 neighbors, a training model was created. Through experimental results we prove in two ways, using confusion matrix and scatter plot, that the overall voice fingerprint recognition is 100%, for this particular configuration.
Understanding Media Control in the Digital Age

Media control comprises multifaceted and amorphous phenomena, combining a variety of forms, tools, and practices. Today media control takes place in a sphere where national politics meet global technology, resulting in practices that bear features of both the (global) platforms and the affordances of national politics. At the intersection of these fields, we try to understand current practices of media control and the ways in which it may be resisted. This thematic issue is an endeavour to bring together conceptual, methodological, and empirical contributions to revise the scholarly discussion on media control. First, authors of this thematic issue re-assemble the notion of media control itself, as not being holistic and discrete (control vs freedom) but by considering it from a more critical perspective as having various modes and regimes. Second, this thematic issue brings a “micro” perspective into understanding and theorising media control. In comparison to structural and institutional perspectives on control, this perspective focuses on the agency of various actors (objects and subjects of media pressure) and their practices, motivations, and the resources with which they exert or resist control. Featuring cases from a broad range of countries with political systems ranging from democracy to electoral authoritarian regime, this issue also draws attention to the question of how media control relates to regime type.
Research on Federated learning algorithms under communication constraints

Federated learning is a kind of distributed machine learning, due to the limited communication resources of Internet of things devices, it is very inefficient to train various neural network models through federated learning (FL) mode. In this paper, we propose two new efficient federated learning algorithms: static quantization federated average algorithm (SQFedAvg) and dynamic quantization federated average algorithm (DQFedAvg). They are composed of two parts, which are optimized locally on the client side, and then the updated parameters are quantified and sent to the server, thus reducing computational storage and communication costs and speeding up the training process. Finally, two models: convolutional neural network (CNN) model, a two-layer perceptron (2NN) model and two standard detection data sets (CIFAR-10, MINIST) are used to verify the effectiveness and efficiency of the proposed federated learning algorithm.
Research on Traffic Sign Detection Algorithm Based on Improved YOLOv3

As the traffic sign target detection has the defects of small size, low resolution and unobvious features, an improved YOLOv3 network model is proposed. Firstly, using a K-means algorithm TT100K traffic sign data set clustering analysis and redefine the initial candidate frame of the network. Secondly, this paper improves the FPN structure in the original network, retain the large-scale prediction of 52×52 in the original network, and then use the feature map output from the second down-sampling in the YOLOv3 network to establish a larger-scale prediction of 108×108. For the purpose of solving the size of image and distortion problems, the pyramid pooling operation with fixed block sizes of 5, 9, 13 is used before the detection layer, and then the output features are merged with the original feature map, so as to achieve the same size for inputs of different sizes output. We use the improved YOLOv3 network model with its original model and other small target detection algorithms to conduct comparative experiments on the TT100K data set. The results show that the improved YOLOv3 network model can detect traffic signs more effectively, with better detection accuracy and real-time performance.
Neural-Based Adversarial Encryption of Images in ECB Mode with 16-Bit Blocks

Digital images possess rich features that are highly correlated among neighboring pixels and highly redundant bits. Thus, the study of encryption algorithms over images is a subject of study to determine encryption quality. In this chapter, we study the applicability of neural networks in learning to use secret keys to protect information (e.g., images) from other neural networks. We implement a well-known adversarial neural network architecture that is designed to learn to establish its own encryption method to protect itself from an attacker that is also a neural network. Our preliminary results suggest that this type of neural architecture is capable of securing communications in the midst of a neural-based attacker using only 16-bit blocks.
Cage’s Imitation Game: <i>Cheap Imitation</i> and <i>Song Books</i> through the sketches

The theme of John Cage’s Song Books (1970), according to Cage, is contained in the statement “We connect Satie with Thoreau” (".fn_cite($cage_1970).", 1). Previous studies of Cage’s Song Books have not asked what I feel to be obvious questions: how, precisely, does Cage connect Satie with Thoreau? To what end? And how does Cage connect to Satie and Thoreau (and to the other sources from which he borrows)? I make use of Cage’s sketch materials to seek answers. I examine three of the Solos for Voice from Song Books that make use of the cheap-imitation procedure that Cage had devised for his work of that name in 1969. Because Song Books is a work for vocalists while Cheap Imitation is a work for solo piano, Cage needed to apply analogous processes of textual “imitation” and mixture to the words of Thoreau to accompany the cheap imitations of the music of Satie. This article explores the persistence of compositional choice in Song Books as revealed by the sketches, in so doing exploring themes of duality in Cage’s pursuit of “poetry as I need it” in the music of Erik Satie, the words of Henry David Thoreau, and in the imitation game that he devises to connect them with one another.
Penggunaan Model Pembelajaran Jigsaw untuk Meningkatkan Hasil Belajar Matematika

The purpose of this study was to improve student learning outcomes in learning mathematics through the Jigsaw learning model. This research method is classroom action research using a cycle model. Each cycle consists of 4 stages, namely: planning, implementation, observation, and reflection. This research was conducted in 2 cycles which were divided into 4 meetings. At the end of each cycle,  evaluation will be carried out to determine the level of achievement and completeness of students in learning. In the pre-cycle activities, the number of students whose score is 70 (KKM) is only 30% then in the first cycle it increases to 70% and in the second cycle the student learning completeness increased to 90%. Based on these results, it can be concluded that the use of the Jigsaw Learning Model can improve mathematics learning outcomes in fifth grade students of SDN 2 Randusari.
Multiscale Reference-Aided Attentive Feature Aggregation for Person Re-Identification

In person re-identification (Re-ID), increasing the diversity of pedestrian features can improve recognition accuracy. In standard convolutional neural networks (CNNs), the receptive fields of neurons in each layer are designed to have the same size. Therefore, in complex pedestrian re-identification tasks, the standard CNNs extract local features but are unable to obtain satisfactory results for global features extracted from the images. Local feature learning methods are helpful for obtaining more abundant features, which focus on the most significant local features and ignore the correlations between features of various parts of the human body. To solve the above problems, a new multiscale reference-aided attentive feature aggregation (MS-RAFA) mechanism is proposed, consisting of three main modules. First, to extract the most significant local features and strengthen the correlations between the features of various parts of the human body, an autoselect module (ASM) is designed, an attentional mechanism that can stack the structural information and spatial relations to form new features. Then, to realize multiscale feature fusion of the multiple output branches of the backbone network and increase feature diversity, we propose a multilayer feature fusion module (MFFM), which enables the model to mine the features hidden by salient features and to learn features better. Finally, to supervise the MFFM and make the network obtain better recognition features, we propose a multiple supervision mechanism. Finally, experimental results demonstrate that our proposed method outperforms the state-of-the-art methods on three large-scale datasets.
Extracting Small Flying Airplane With Spatially Accurate and Temporally Consistent Foreground Modeling

Extracting the flying airplane from the satellite video has received a great deal of attention in remote sensing applications. Nevertheless, the challenge arises in two aspects: the limited spatial resolution of space-borne imaging devices and the small size of the airplane with low contrast to the background. This article presents a novel spatially accurate and temporally consistent model for foreground airplane extraction. Specifically, each frame is decomposed as a linear combination of the airplane and background with a kind of mixture ratios. This enables a more accurate spatial representation of the small airplane of low resolution. Then, the airplanes from different frames obey the common mixture ratios based on the subpixel motion estimation. This enables the temporally consistent extraction through frames. The effectiveness of our method is demonstrated by the qualitative and quantitative evaluation of both synthetic and real data.
The incidence relation research of Shanghai distribution network construction cost based on big data analytics

This paper uses the incidence relation algorithm based on big data, studies the incidence relation between the various influencing factors of the construction cost of distribution network in Shanghai, and obtains an effective incidence relation model, which provides effective guidance for distribution network engineering projects and is of great significance to the future green development of distribution network.
Evidence Collection and Qualitative Analysis of Electronic Data in the Background of Artificial Intelligence

With the rise and rapid development of mobile communication, intelligent terminal and artificial intelligence technology, in recent years, China has made some progress in the credibility of digital forensics. This paper proposes a forensics method based on artificial intelligence, which makes use of the sequence and dependence characteristics of the previous stage of artificial intelligence to establish an ex post forensics model. When problems occur in the forensics stage, it can feed back to the previous stage and find the problem stage. This paper mainly introduces the forensics and cross examination analysis of electronic data under the background of artificial intelligence. In this paper, we use electronic data forensics and cross examination analysis under the background of artificial intelligence, use a * algorithm for indoor path planning and design, and reasonably analyze the feasibility of Dijkstra algorithm and adjacency table model. This paper analyzes the proof system of the validity of artificial intelligence forensics, uses the evolved expert system to judge the validity of forensics methods and data, and formally proves the integrity and consistency of data in the process of forensics from a theoretical point of view. The experimental results show that the forensic and cross examination analysis of electronic data under the background of artificial intelligence increases the efficiency of the algorithm by 16%. The limitations of the design and research of indoor path planning algorithm for complex environment, and the methods and ways of providing good indoor path planning for the application of ant colony algorithm are analyzed, discussed and summarized, so as to enrich the research results of the academic community.
Creative derivatives the poetics of derivation and its formative assumptions

The creative derivatives phrase has in itself two terms: creativity (lat. creatus - having been created) and derivation (lat. derivatio - derivation, departure). Creativity presupposes the realisation of the new, the non-existent. Derivation, on the other hand, means transition, formation or arrangement. A derivative is what is derived or comes from something else (like gasoline which is a petroleum derivative). Creative derivations would therefore be processes in which a new is derived from the existing; procedures of rearranging the existing, conversion (transitioning) from one system to another. There are two basic requirements that are necessary for the realisation of these and such actions: an adequate poetic means and a common denominator of two or more phenomena, i.e. two or more systems that are brought into contact. We define the poetic means here in Jakobson's terms as the axis of combination (syntagm) and the axis of selection (paradigm). The paper systematises the poetic possibilities of artistic modeling, which is based on the template of already existing works of art. Different versions of the approach to modern and postmodern practice of taking over the already existing form and content aspects of a work of art are briefly explained and described. When choosing examples, the author adheres to the principle of representativeness instead of compendial comprehensiveness. The outcome of the paper should be twofold. On the one hand, the aim is to get to know and understand the poetics of taking over, which is one of the preconditions for aesthetic pleasure and cognitive insight when encountering works of art of that provenance. On the other hand, the work should be useful to students in their own creative work. The poetic means exhibited in it should facilitate a creative approach to the inexhaustible source of tradition.
A Study on the User Privacy-Preserving Personalized Recommendation Based on Random Perturbation for online Services in the Background of Big Data

In the background of big data, how to realize personalized online service recommendation while protecting user privacy is the current research hotspot. This paper first analyzes the collaborative filtering algorithm and the user privacy-preserving problems of the online service personalized recommendation on the big data background, and then introduces the user personal characteristic model in the collaborative filtering recommendation to improve the accuracy of the recommendation and the user privacy is preserved at the same time. Considering that the current privacy-preserving method does not reflect user participation and balance between privacy preserving and recommendation accuracy, this paper improves the privacy-preserving method through user preference policy, and uses distributed cloud storage for data storage and processing to further prevent malicious attacks by attackers. Finally, the effectiveness of the improved random perturbation-based method is demonstrated through data comparative analysis. Experiments demonstrate that the method proposed in this paper effectively achieves privacy protection while ensuring a high recommendation accuracy of personalized services.
The notion of “progress” from the philosophy of language perspective

This paper discusses the notion of progress and the idea proposed by Alexander Nikiforov that the term “progress” belongs to the same class of words as predicates of personal taste. This claim is based on the observation that our assessment of progress depends on our subjective point of view. The paper argues that there is a substantive difference between terms of personal taste and the term “progress”. Specifically, it is shown that “progress” does not necessarily make reference to a personal point of view. The subjectivity of our assessment of “progress” in certain areas seems to be rooted in the choice of the scale and our ideas of the ideal state of an object that is the endpoint of that scale. The paper also discusses A. Nikiforov’s idea that the evolution of the Universe can provide us with the objective scale for evaluation of progress in all areas. I argue against this idea by showing that it does not help us overcome the subjectivity of progress assessment in many cases.
Assistant human facial recognition based on convolution feature-map

Face recognition is a very important research direction in the field of image processing. In daily life, there are various problems that require face recognition everywhere, but how to accurately identify faces from images is a very important and urgent problem. To this end, we propose an assistant face recognition method that can effectively improve the recognition accuracy. The face recognition method based on deep learning will convolve the input image into five different convolution kernels to obtain the convolution feature-map of each layer. At the end, the corresponding description vector of the input image is generated by using the two layers fully connected layer and the pooling layer. Therefore, in the process of face recognition, it is only necessary to calculate the similarity between the description vectors of the two frames of images. Because of its recognition accuracy and room for improvement, we use the features of the fifth-level convolution feature-map, combined with the image description vector finally generated by the deep learning method, can significantly improve the recognition accuracy in the face recognition process. Through a large number of experiments, the method can be used to assist the image recognition problem based on the deep learning method, which can effectively improve the recognition accuracy.
The Effect of Grammar Translation Method in Description Text on Writing Ability of Class XI Vocational High School Students

The topic of this research is The Effect of Grammar Translation Method in Descriptive Text for Students’ Writing Skill. The objective of this research were to find how the Grammar Translation Method can improve students’ writing skill through descriptive text for eleventh grade students at SMK Yapalis Krian in the academic year of 2019/2020 and whether the grammar translation method can be used as a method in improving the students’ achievement of writing skill. The researchers was applied pre-experimental research in this study. The steps of pre-experimental research were given test and treatment. In gathering the data, the researcher used a test in the form of writing content about descriptive text. The result of this research showed that using grammar translation method was good. It was proven by the improvement mean difference of pre-test and post-test after treatment. The means of the pre-test was 60.85, and the post-test was 74.63. Therefore, it can be suggessted that grammar translation method can be used as the alternative method in teaching which especially to improve writing skill.
Multi-Sensor Spatial Association Using Joint Range-Doppler Features

We investigate the problem of localizing multiple targets using a single set of measurements from a network of radar sensors. Such "single snapshot imaging" provides timely situational awareness, but can utilize neither platform motion, as in synthetic aperture radar, nor track targets across time, as in Kalman filtering and its variants. Associating measurements with targets becomes a fundamental bottleneck in this setting. In this paper, we present a computationally efficient method to extract 2D position and velocity of multiple targets using a linear array of FMCW radar sensors by identifying and exploiting inherent geometric features to drastically reduce the complexity of spatial association. The proposed framework is robust to detection anomalies, and achieves order of magnitude lower complexity compared to conventional methods. While our approach is compatible with conventional FFT-based range-Doppler processing, we show that more sophisticated techniques for range-Doppler estimation lead to reduced data association complexity as well as higher accuracy estimates of target positions and velocities.