Reviewerly/exHarmony huggingface dataset:
MAFAWD: An Adaptive Weight Distribution Clustering Algorithm Based on Multi-layer Attribute Fusion

With the rapid development of social networks, the structure of the data presents more and more complex, and the graph data appears now. This paper proposes an adaptive clustering algorithm based on Multi-layer Attribute Fusion for the Adaptive Weight Distribution (MAFAWD) algorithm to solve the graph data clustering problem. It combines the graph node attribute and the structure relation through the pre-set merger. In this paper, we use some rules to unify the graph node attribute and structure into the same network for clustering, and the influence of node attribute and structure relation on clustering results is considered synthetically. In the multi-layer attribute fusion model, we divide attribute layer and structure layer. Considering the different clustering influence of the graph node attribute and structure, we set different weight layer coefficients. This paper uses affine propagation clustering algorithm and node voting mechanism to change the different weight layer coefficients. It makes the data reflects the original distribution and has a better clustering result. Finally, we verify the algorithm on the real DBLP data set. The experimental result on real data set shows that the MAFAWD algorithm has a better clustering result through compared with the traditional graph clustering algorithms.
Towards mining bilingual lexicons and parallel phrases from large-scale monolingual corpora

Bilingual lexicons and parallel phrases have a great effect on certain tasks of natural language processing (NLP). Recent researches have proved that the high-quality bilingual lexicons can hence the performance of the machine translation. When it comes to some special tasks of NLP, the incorporation of bilingual lexicons can bring about obvious effectiveness. The bilingual lexicons and parallel phrases can be easily extracted from parallel corpora, but in contrast to the monolingual corpora, the number of parallel corpora is still scarce. Actually, the monolingual corpora also have the potential to mine a large amount of parallel word and phrase pairs. In this paper, we propose two strategies to extract parallel words and phrases from monolingual corpora. On one hand, we present the indirect mining strategy, Anchored Mining (AM), which injects the anchoring point into each mining procedure to improve the accuracy. On the other hand, inspired by the process of humans learning a foreign language, we further propose another novel, direct algorithm named Bootstrapping Mining (BM), which mimics the human learning process and aims to learn parallel phrases automatically in a self-iterative way. Additionally, we propose a novel metric, phrase probability-sub item average probability (PP-SAP), which is applied to quantitatively evaluate the rationality of each extracted parallel phrase pair in the monolingual corpora. We conduct the experiments on large-scale English-Chinese, English-Russia, and English-France monolingual corpora, and the results show that our methods can mine high-quality bilingual lexicons and parallel phrases. We also evaluate our algorithms on low-resource monolingual corpora and get good results as well.
INVESTIGATION OF ENGLISH GRAMMAR APPLICATION ABILITY OF COLLEGE STUDENTS USING DYNAMIC NONLINEAR SYSTEM THEORY

The purpose of this paper is to improve the English teaching levels of colleges and universities. First, the primary attributes of dynamic nonlinear system theory in second language acquisition are analyzed. Second, the influencing factors of English grammar application ability in theoretical dimension and measurement methods are proposed. Six first-year students majoring in English in a university were randomly selected as research objects, who were followed up for a semester, during which three identical oral English tasks were finished (in September, October, and November of 2020). The fluency, accuracy, and complexity of students’ utterances are statistically analyzed using SPSS 26.0. Results demonstrate that the fluency of students’ utterances has increased from 110 syllables per minute on average in September to 129 syllables per minute on average in November. The proportion of error-free clauses has increased from 0.5 to 0.55, and the number of clauses in the T unit has increased from 1.42 to 1.51. Students [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] have a gradually increasing accuracy of utterance, while student [Formula: see text] shows a gradually decreasing utterance accuracy. Students [Formula: see text] and [Formula: see text] have a decreasing utterance accuracy, while students [Formula: see text] and [Formula: see text] present an increasing utterance accuracy. Besides, the utterance accuracy of students [Formula: see text] and [Formula: see text] first decreases and then increases. The performances in utterance complexity of students [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] have been increasing gradually, while the performance of student [Formula: see text] has been decreasing gradually. The normal distribution of each student’s test scores is different, as well. Hence, any minor changes will affect the overall oral English levels of students.
Learning by Environment Clusters for Face Presentation Attack Detection

Face recognition has been used widely for personal authentication. However, there is a problem that it is vulnerable to a presentation attack in which a counterfeit such as a photo is presented to a camera to impersonate another person. Although various presentation attack detection methods have been proposed, these methods have not been able to sufficiently cope with the diversity of the heterogeneous environments including presentation attack instruments (PAIs) and lighting conditions. In this paper, we propose Learning by Environment Clusters (LEC) which divides training data into some clusters of similar photographic environments and trains bona-fide and attack classification models for each cluster. Experimental results using Replay-Attack, OULU-NPU, and CelebA-Spoof show the EER of the conventional method which trains one classification model from all data was 20.0%, but LEC can achieve 13.8% EER when using binarized statistical image features (BSIFs) and support vector machine used as the classification method.
Appearance design of intelligent garbage robot through computer visual recognition technology

In this paper, STM32 single-chip microcomputer is used as the motion control center of intelligent garbage robot. The track of ground runway is tracked by visual recognition technology. The position of garbage is determined by ultrasonic technology and visual recognition technology. The robot is recharged continuously by solar energy technology. The intelligent garbage robot is developed and established. The robot has the functions of road information detection, tracking, looking for plastic bottles and picking up garbage. Experiments show that the intelligent garbage robot based on visual recognition technology can meet the functional requirements well.
RGB-Based No-Reference Depth Map Quality Assessment

Depth maps play an important role in computer vision and robotics tasks. However, it is difficult to obtain ground truth (GT) of depth maps by either depth sensors or calculation methods. Therefore, there is a great need for depth quality assessment without GT in relevant tasks. This paper proposes a new method to assess the quality of depth map with the guidance of its associated RGB image, which divides the depth map into two parts: structure area and smooth area. On the one hand, it uses the same-view RGB image as a reference to detect the error pixels in depth map structure area, and calculates the objective index as the quality score of the structure area. On the other hand, by using the feature that the pixel values in the smooth area of the depth map are all the same, a sliding window is set to detect the error pixels in the smooth area affected by noise, and the metric is calculated as the quality score of the smooth area. Finally, the assessment scores of the depth map structure area and the smooth area are weighted and integrated as the quality assessment result of the entire depth map. The proposed method is tested on the Middlebury dataset. It can be clearly seen that compared with the assessment results of the other two existing no-reference depth map quality assessment methods, the assessment results of the method proposed in this paper have the strongest consistency with the objective quality assessment results with ground truth (PBMP), and it shows that this method can objectively assess the quality of depth maps.
DeepTracking from Aerial Platforms

Object tracking is a pivotal part of video processing. Defense based UAVs generally fly at an altitude of 20,000-30,000ft. Conventionally, it's quite easy to track objects from a stationary camera's feed. Videos from UAVs are hard to process beca <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> use of abrupt motion of the UAV, jitters, noise and cluttering produced in the video. Tracking and surveillance from such altitudes is quite challenging. This paper illustrates the combination of mod- ified YOLOv4 algorithm with Darknet framework to perform object detection and the DeepSORT tracker to perform object tracking in such challenging environments. The detection model achieves an 11% improvement in Mean Average Precision(mAP) when compared with its predecessors on custom images. When paired with DeepSORT and inferred on a high-end GPU it renders output at 28 FPS, making it suitable for real-time tracking. It is also immune to occlusion and camera movement. The proposed model addresses problems in the existing models that require manual locking of target and high computational complexity. In addition to this, it aims to illustrate automatic tracking of moving objects in real-time.
Preliminary research on the design of an ambient intelligence application utilizing the exploitation of landmarks for the promotion of ancient greek technology exhibits

A preliminary research is presented about development of a platform where storytelling can be created and managed through portable technology devices taking place outside the exhibition premises of Kotsanas Museum in Greece or abroad aiming to attract new visitors. An ambient intelligence approach is utilized where tourists of Athens during their walks in the historical center of Athens can discover with the use of a mobile application 3d visualized parts of ancient Greek technology inventions and after collecting them to watch 3D simulations of their usage in augmented reality. The user’s preferences will be acquired through multiple ways (gamification, points of interests, rule base system combining interests of users and ancient inventions) and the right story for the users will be suggested to the user. Soft computing techniques such as fuzzy cognitive maps will be used for setting initially the reasoning rules.
A Compilation Method for Dynamic Typing in ML

This paper develops a systematic method for extending a polymorphic type system of ML with dynamic typing, and implements the extension in SML#. The core of the extension consists of an adaptation of the type-directed compilation method for non-parametric polymorphism to type tag abstraction and type tag generation. To support existentially bound type variables in dynamic value elimination and user-level manipulation of dynamic values, the conventional type-directed compilation framework is extended with existential types and a mechanism to reify dynamic values to user-level datatypes. The resulting language achieves orthogonal integration of dynamic typing in ML: it supports all the standard features, including polymorphic type inference, user-defined datatypes and pattern matching, in programming with dynamic typing. The implementation readily provides various practical features, including polymorphic first-class pretty-printer, polymorphic deserialization, type-safe interface to database systems, and type-safe meta-programming.
A Proposed Model for Smart Public Distribution System of Ration Commodities to Indian Citizens using an IoT based Design

The paper deals with the methods adopted by India to eradicate the malpractices that take place while Ration Distribution. The responsibility of providing food and non-food items to the poor at subsidized rates is of the Government of India under the Ministry of Consumer Affairs, Food and Public Distribution. Food grains, such as wheat, rice, sugar and fuels like kerosene are the goods provided through a network of fair price shops also known as ration shops established in several states across the India. The main motive of such scheme is to ensure that the citizens of India get ample food material whatever their financial condition. But still the goal is not achieved due to problems like ration hijacking, delivery of false ration, corruption, etc. and these troubles have been resolved by the developed solution.
Application of Mathematics in Computer Science

No one escape the learning of mathematics in one way or other, ranging from our kitchen to our journey from earth to Moon or Mars. Mathematics persists everywhere around us. It can be perceived in our garden or park from symmetry of leaves, flowers, fruits etc. and by so many examples of Geometry and symmetry can be seen in nature. God used mathematics in creation of the universe in one form or the other. Likewise, Mathematics is the queen of all sciences. Scientists and researchers can not perfectly accomplish their work without including mathematics. Mathematics is the foundation of Computer Science. If one is eager to learn any arena of Computer Science, first he/she has to imbibe a love of Mathematics that will be supportive for progressive learning of the said subject. Mathematics is friendly for analytical skills needed in Computer Science. Concepts of binary number system, Boolean algebra, Calculus, Discrete mathematics, linear algebra, number theory, and graph theory are the most applicable to the subject of computer science with the emergence of new concepts like machine learning, artificial intelligence, virtual reality and augmented reality.
The research of case retrieval system based on deep learning

A large number of criminal investigation images are collected for modern case detection, and these images not only contain many valuable clues, but also can provide powerful evidence. At present, most of the current criminal investigation image retrieval methods for public security investigation applications use text-based or traditional shallow feature-based image retrieval methods, and the accuracy and efficiency of retrieval can hardly meet the needs of modern criminal investigation cases. In this paper, we summarize and analyze the current research results and technologies in the field of image retrieval, and adopt the image retrieval method based on depth features to improve the accuracy and efficiency of the criminal investigation present investigation image retrieval. In the depth feature-based image retrieval, VGGNet and Res Net are fine-tuned using the criminal investigation image dataset, and then the image depth features are extracted for retrieval experiments. The experimental results show that the retrieval model has the following two shortcomings: the model cannot adapt to the target scale change; the retrieval accuracy is lower than the average in the category with fewer samples. Two optimizations are proposed to address these problems: introducing pyramid pooling to improve the robustness of the model to target scale changes; retraining the network after enhancing the data samples to make the retrieval accuracy of the model more balanced for different categories of samples. In addition, query expansion is introduced to enhance the image feature representation during retrieval. After using the above optimization methods, the retrieval accuracy is improved by 5.7%.
Bayesian hybrid automata: Reconciling formal methods with metrology

Abstract Hybrid system dynamics arises when discrete actions meet continuous behaviour due to physical processes and continuous control. A natural domain of such systems are emerging smart technologies which add elements of intelligence, co-operation, and adaptivity to physical entities. Various flavours of hybrid automata have been suggested as a means to formally analyse dynamics of such systems. In this article, we present our current work on a revised formal model that is able to represent state tracking and estimation in hybrid systems and thereby enhancing precision of verification verdicts.
Architectures Based on Blockchain Technology for Information Systems in Public Institutions

Public institutions are challenged to innovate their management processes to better serve citizens. Blockchain is a disruptive technology that provides security and integrity to information systems. The objective of the article is to show the results found in models of administrative systems in public institutions that use architectures based on blockchain technology. A brief bibliographic review was carried out to analyze the articles published in prestigious journals during the last three years. As a result, 102 potential articles were found and 15 articles were selected. From the results, it is concluded that the use of blockchain through the use of smart contracts offers security, privacy, transparency and data integrity in the public organization.
Cross-Layer Learning

Deep learning-based investigation mechanisms are available for conventional forensics, but not for IoT forensics. Dividing the system into different layers according to their functionalities, collecting data from each layer, finding the correlating factor, and using it for pattern detection is the fundamental concept behind the proposed intelligent system. The authors utilize this notion for embedding intelligence in forensics and speed up the investigation process by providing hints to the examiner. They propose a novel cross-layer learning architecture (CCLA) for IoT forensics. To the best of their knowledge, this is the first attempt to incorporate deep learning into the forensics of the IoT ecosystem.
Representations for Content Creation, Manipulation and Animation

What I cannot create, I do not understand" said the famous writing on Dr. Feynman's blackboard. The ability to create or to change objects requires us to understand their structure and factors of variation. For example, to draw a face an artist is required to know its composition and have a good command of drawing skills (the latter is particularly challenging for the presenter). Animation additionally requires the knowledge of rigid and non-rigid motion patterns of the object. This talk shows that generation, manipulation and animation skills of deep generative models substantially benefit from such understanding. Moreover we see, the better the models can explain the data they see during training, the higher quality content they are able to generate. Understanding and generation form a loop in which improved understanding improves generation, improving understanding even more. To show this, I detail our works in three areas: video synthesis and prediction, image animation by motion retargeting. I will further introduce a new direction in video generation which allows the user to play videos as they're generated. In each of these works, the internal representation was designed to facilitate better understanding of the task, resulting in improved generation abilities. Without a single labeled example, our models are able to understand factors of variation, object parts, their shapes, their motion patterns and perform creative manipulations previously only available to trained professionals equipped with specialized software and hardware.
Cognitive Study of Data Mining Techniques in Educational Data Mining for Higher Education

Data mining is a subset of Data Science. The future is bright for data science as the amount of data only increases. Educational data mining (EDM) involves mining similar valuable information from data sets that stores academic work information. It focuses on using data mining techniques to discover valuable knowledge from the educational datasets. EDM predicts the various performance metrics of students is one of the most important activities. In this paper, we present a cognitive study of data mining techniques in educational data mining for data analytics. Also, we illustrate how data mining is used to acquire useful vital information through the educational datasets in the higher educational institutions. We have tried to explore the field of education data mining for further research in higher educational institutions. The ultimate goal of this paper is to apply effective data mining techniques in higher education.
A False Alarm Suppression Method Via Selective Anchor Generator for Ship Detection in Sar Images

Mainstream CNN-based SAR ship detectors are prone to produce false targets in the land area, which has a great relationship with the anchor generation mechanism in the network structure. The anchor generator will indiscriminately generate a set of anchors for each point on the predicted feature map and map it back to the original image. These anchors will become the initial candidate boxes after screening. Considering that ship targets in SAR images are usually small and sparsely distributed, this dense anchor generation method can easily generate candidate boxes in land areas. To solve this problem, this paper proposes a method to guide the generation of anchors through image area information, called selective anchor generator (SAG). Unlike previous anchor generator, SAG will do sea-land segmentation for image first and then establish a mapping relationship with the feature map. Points on the feature map that only contain land feature information are marked as negative points with no anchors being generated. In this way, the number of candidate prediction boxes in the land area is greatly reduced, thereby achieving the effect of false alarm suppression. Experiments on test data show that this method can effectively reduce the number of false targets generated in land areas.
Isotopic composition and geochemical features of ores of Dzhusa pyrite-polymetallic deposit (South Urals)

The sulfur isotopic composition was studied for the main morphological types and generation of sulfides composing the ore of the Dzhusinsky deposit, and the content of trace elements was analyzed using the highly sensitive method of mass spectrometry with inductively coupled plasma and laser sampling (LA-ICP-MS). Both types of studies were performed for ore deposits for the first time. According to the geochemistry of sulfur isotopes, its deep magmatic source was reconstructed, which made a predominant contribution to the ore-forming fluid. Using the modules of the STATISTICA, correlations were calculated and the patterns of distribution of micro impurities in sulfide minerals were revealed.