Reviewerly/exHarmony huggingface dataset:
Comparison of Different Machine Learning Algorithms in Classification

Classification, as a basic problem in the field of data analysis and machine learning, plays a more and more important role in human life. People are faced with huge amounts of information every day. How to classify information and how to extract useful information has gradually become a hot topic for scholars. There are many kinds of classifiers, such as neural network, support vector machine, decision tree, Bayesian classification algorithm and so on. This paper compares the classification results and accuracy of decision tree, support vector machine and naive Bayesian method by selecting data sets, and briefly describes its operation principle. The results show that the three machine learning classifiers perform well in dealing with the binary classification problem, but compared with the other two models, the decision tree model has higher accuracy.
O(1) Communication for Distributed SGD through Two-Level Gradient Averaging

Large neural network models present a hefty communication challenge to distributed Stochastic Gradient Descent (SGD), with a per-iteration communication complexity of $\mathcal{O}(n)$ per worker for a model of n parameters. Many sparsification and quantization techniques have been proposed to compress the gradients, some reducing the per-iteration communication complexity to $\mathcal{O}(k)$, where $k\ll n$. In this paper, we introduce a strategy called two-level gradient averaging (A2SGD) to consolidate all gradients down to merely two local averages per worker before the computation of two global averages for an updated model. A2SGD also retains local errors to maintain the variance for fast convergence. Our analysis shows that A2SGD converges similar to the default distributed SGD algorithm. Our evaluation validates the conclusion and demonstrates that A2SGD significantly reduces the communication traffic per worker, and improves the overall training time of LSTM-PTB by $3.2\times$ and $23.2\times$, compared to Top-K and QSGD, respectively. We evaluate the effectiveness of our approach using two kinds of optimizers, SGD and Adam. Also, our evaluation with various communication options demonstrates the strength of our approach both in terms of communication reduction and convergence. To the best of our knowledge, A2SGD is the first to achieve $\mathcal{O}$ (1) communication complexity per worker without incurring a significant accuracy degradation of DNN models while communicating only two scalars representing gradients per worker for distributed SGD.
A Method of Source Code Authorship Attribution Based on Graph Neural Network

Source code authorship attribution aids in resolving software infringement and plagiarism issues, it is also helpful with the identification of the author of malware in the field of cybersecurity. However, traditional de-anonymization methods mainly extract semantic and lexical features, ignoring code structural features such as control flow and data flow, and the feature vectors generated by them are sparse vectors which are prone to overfitting when dealing with large-scale programmer’s de-anonymization. In this paper, we proposed a novel code de-anonymization model, which is based on the AST, by extracting both AST and structural features, the model builds the feature graph representation of Python file and then uses graph neural network to realize code de-anonymization. Experimental results show the high accuracy, we achieve an accuracy of 98.06% with 117 programmers, and 95.60% with 1000 programmers on Google Code Jam Python datasets.
MSDF: A General Open-Domain Multi-skill Dialog Framework

Dialog systems have achieved significant progress and have been widely used in various scenarios. The previous researches mainly focused on designing dialog generation models in a single scenario, while comprehensive abilities are required to handle tasks under various scenarios in the real world. In this paper, we propose a general Multi-Skill Dialog Framework, namely MSDF, which can be applied in different dialog tasks (e.g. knowledge grounded dialog and persona based dialog). Specifically, we propose a transferable response generator pre-trained on diverse large-scale dialog corpora as the backbone of MSDF, consisting of BERT-based encoders and a GPT-based decoder. To select the response consistent with dialog history, we propose a consistency selector trained through negative sampling. Moreover, the flexible copy mechanism of external knowledge is also employed to enhance the utilization of multiform knowledge in various scenarios. We conduct experiments on knowledge grounded dialog, recommendation dialog, and persona based dialog tasks. The experimental results indicate that our MSDF outperforms the baseline models with a large margin. In the Multi-skill Dialog of 2021 Language and Intelligence Challenge, our general MSDF won the 3rd prize, which proves our MSDF is effective and competitive.
A CRT-LZW-Based Compression of Multiple and Large Size Images for Clustered Embedding in Image Cover

The main target of steganographic schemes is to enforce privacy and security of wireless mode of communication between parties. The image steganography attempts to embed secret information inside an image cover in order to evade suspicion of attackers. This approach ensures that the cover image is relatively altered in a manner that cannot be easily noticeable by the HVS. Many existing image steganographic schemes are short of the desired level of imperceptibility of cover media, leading to several security lapses. This paper attempts to remove redundancy in the secret message (or images) in order to allow multiple or clustered embedding inside an image cover. To achieve this, Lempel–Ziv-Welch and Chinese Remainder Theorem (LZW-CRT) compression algorithms were applied on the larger secret and cover images. Then, the entropy embedding procedure (or pixels superimposition) is used to conceal the secret message (or clustered images) inside the cover image. Thereafter, the large stego-item is compressed to improve the memory consumption, imperceptibility, bandwidth and quantity of secret messages required during exchanges between parties. The proposed approach outperforms existing approaches in terms of the amount of payload and quality of reconstructed media with a mean (70%) and PNSR (48%).
Reconciling Instructors’ and Students’ Course Overlap Perspectives via Linked Data Visualization

Research on the use of modeling and mapping tools in curriculum management is thriving, often focusing on the perspectives of the faculty alone. However, scholarly works that also incorporate the students’ curriculum concerns are rare. A recurring theme in students’ curriculum concerns is the perceived overlap among courses, usually expressed at the level of common concepts across courses. This common “concept” emphasis imposes a challenge for modelers, who often focus on the course-level comparisons because they usually lack tools with subcourse (concept)-level granularity. This article investigates how to model and represent curriculum information to help in reconciling the gap between instructors’ and students’ views of cross-course overlap. The proposed approach involves the design and development of a digital environment to model a curriculum via linked data through an ontology representing concept-level granularity, offer instructors aid in populating course content, and facilitate the visualization and manipulation of data. The visualization tools are designed to offer functions for perceived common concept overlap identification and rectification. This digital environment was deployed and evaluated in the context of a curriculum review process, in which 25 course instructors employed the visualization tools to address a perceived course overlap problem. The preliminary results demonstrate, first, the usefulness of the approach in reconciling the views of instructors and students regarding perceived course overlap. Second, the results highlight that the approach contributes to transforming course overlap from a fuzzy notion to a more concrete and actionable construct defined as either <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">repetition</i> or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">reinforcement</i> .
Improving Pseudo Relevance Feedback with Term Relationship using Firefly Algorithm

When searching for information with an information retrieval (IR) system, sometimes the results of the search documents provided by the system do not match the information needs of the user. Pseudo Relevance Feedback (PRF) based Query expansion (QE) tries to overcome these problems by adding words that are expected to improve retrieval results from top N ranked documents retrieved. The use of firefly algorithm (FA) as one of the optimization methods has been proven by the previous study to improve the performance of the IR system. However, in that study the weighting of words was done using the rocchio function of the Pseudo Relevant Document (PRD), so it is feared that the performance of IR system will be reduced if the number of relevant documents in PRD is little or none at all. Therefore, scoring by term relationship between query and PRD is used in this study combined with rocchio algorithm. The results of the study showed that usage of term relationship word co-occurrence or word similarity can improve the performance of the IRS that was previously built. In addition, word co-occurrence with jaccard have the best performance compared to the previous study and other combinations. FA itself was able to choose the optimal terms, even though the number of top N ranked documents increased. Furthermore, the combination of term relationship and rocchio algorithm can increase the convergence rate than the ones without rocchio algorithm.
Awareness level on the relevance of forensics in criminal investigation in Nigeria

The main aim of forensic science is to gather intelligence to enable the judge to credible and logical decisions in the court by means of scientific approach through evaluation of evidence for the administration of justice, and country around the world now considers forensic methodology as the gold standard for criminal investigation. Therefore, the present study examined the level of awareness on the relevance of forensics in criminal investigation in Nigeria. The design used in this study is the survey research design and the sample size of this study was a total of one hundred personnel of law enforcement and the judiciary. The study adopted descriptive statistics which involves the use of frequency and percentage. The result of the present study revealed that the participants were distributed socio-demographically as follows; there was an observable higher number of male participants (68%) relative to the female participants (32%), As per age distribution, a larger population of the participants were found to be &gt; 40 years of age with 55%, and it was observed that age between 35-39 years ranked the least with 15%. On educational level, the result of the present study revealed that majority of the participants possesses a bachelor’s degree as the highest level of educational qualification with 75% from a pool of 100% of participants. The present study further examined responses on the relevance of forensics in criminal investigation, and the result revealed an inadequate level of awareness on the relevance of forensics in criminal investigation. Therefore, the study recommends that the Nigerian Police Force and the Judiciary should collaborate with Universities running programs on forensics for trainings.
Efficacious Data Transfer Accomplished by Trustworthy Nodes in Cognitive Radio

Cognitive Radio (CR) utilizes the free spectrum through a process called spectrum sensing efficiently. This free spectrum is shared by many CR’s in the wireless environment. Many CR can shape a network and utilize the resources. A cognitive node can dynamically be part of the cluster or organization. The members of the network are managed and monitored by master controller called Network Manager (NM). To reduce the burden of NM sub-controller called Group Head (GH) are assigned. Suppose if a CR wish to become member of the network, then it is the responsibility of the NM and GH to check the reliability of the CR and then decide whether to accept it or not as the member of the network. With the above back ground this work focusses on two things. First one is to check the trustworthiness of the CR and to decide whether the cognitive radio can end up as a member of the group so that it can get access to all the features available in that group. This is done by asking many questions to the CR by the NM and GH. To overcome the malicious attack in the wireless medium the communication between CR and NM and also the communication between CR and GH are encrypted. Here Rijndael AES encryption algorithm is used to encrypt the questions. The key to the algorithm is generated using sutras wheel. Once the CR becomes the member of the CR network it is assigned with a free channel if available. Utilizing this channel, the CR can transmit the data to its intended receiver. To protect the data from intruders the information is encrypted. Here DNA algorithm used to encrypt the information to be transmitted and the same is decrypted at its destination. The dynamic spectrum access and security information transfer via the free channel integrated together makes the wireless communication the most successful one.
MODEL OF THE ADOLESCENTS’ COMMUNICATIVE CULTURE DEVELOPMENT IN ADDITIONAL EDUCATION SYSTEM IN THE INFORMATIZATION AND DIGITALIZATION ERA

Рассматривается проблема развития коммуникативной культуры подростков в системе дополнительного образования в эпоху информатизации и цифровизации. Для решения данной проблемы предлагается модель развития коммуникативной культуры подростков, учитывающая особенности представителей цифрового поколения и специфику организации образовательного процесса в учреждении дополнительного образования в условиях его информатизации и цифровизации. Исследуется структурно-функциональная модель развития коммуникативной культуры в системе дополнительного образования, представленная целевым, концептуальным, диагностическо-аналитическим, содержательно-технологическим и результативно-оценочным блоками. The article is devoted to the problem of the development of the communicative culture of adolescents in the system of additional education in the era of informatization and digitalization. To solve this problem, the author proposes a model for the development of the communicative culture of adolescents, taking into account the peculiarities of the representatives of the digital generation and the specifics of the organization of the educational process in the institution of additional education in the conditions of its informatization and digitalization. The article considers the structural and functional model of the development of communicative culture in the system of additional education, represented by the target, conceptual, diagnostic-analytical, content-technological and performance-evaluation blocks. All components of the model are interconnected and serve to achieve the result – the transition to a higher level of development of the communicative culture of adolescents. And this, in turn, is one of the priority tasks of the education system in the direction of developing communication skills and socializing the personality of a teenager. The implementation of the model in institutions of additional education will increase the effectiveness of the communicative development of adolescents, form a culture of interpersonal communication, organize the interaction of the family, school and institutions of additional education. It is worth noting that when implementing this model in the long term, it is possible to increase the academic performance of adolescents in the humanities, the formation of a culture of communication among adolescents in social networks, and the reduction of destructive conflicts in the chi ld-adult community.
OABC: An Otsu-Artificial Bee Colony Multilevel Image Threshold Optimization for Liver Tumor Segmentation

Grey scale or color image conversion involves thresholding technique which can be either at single or multiple threshold levels. Resultant images have reduced pixel level classes. As level of thresholding required increases, the process becomes complex involving exhaustive search and thereby increasing computational resources and time complexity. Meta-heuristic optimization algorithms show promise to reduce time-complexity, especially the Artificial Bee Colony (ABC) algorithm mimicking honeybee foraging activities. It provides optimal results when sampling a large solution space. Since threshold-based image segmentation is a multidimensional discreet optimization problem, for fitness evaluation of threshold level candidates, Otsu’s thresholding method has been widely employed. In this paper, proposed is an Otsu-ABC multilevel image thresholding model for CT images using MATLAB for Liver tumor segmentation. The developed model is evaluated for increased dimensionality and computation time. It is also benchmarked against other commonly used algorithms. Results demonstrate that the proposed model is immune to dimensionality increase and outperforms all other models with regards to quality of images segmented and computation time proving that the model is ideal for optimizing multilevel image thresholding problems involving large thresholds for complex computer vision problems in medical diagnostics, biometrics, surveillance, satellite imagery and gaming.
Colorization of 3D Objects Based on Geometric Generation Model

Colorization of gray image is a challenging research topic which is often discussed. It can be used to restore photos and assist artistic work. There are many approaches to colorize. From the beginning of the use of artificial colorization, and then the method of automatic colorization appeared. Most automatic colorization methods are based on deep learning. Most previous studies have focused on 2D image, the colorization of 3D objects is rarely discussed. In this paper, we introduce the colorization of 3D objects based on the geometric generation model. Unlike 2D images, 3D objects contain different information. Therefore, there are different problems in colorization. Colorization of 2D image can be regarded as the mapping of grayscale texture and color information. This mapping can be simulated by neural network, such as Variational Autoencoder and Generative Adversarial Networks. For 3D objects, we can use similar method for automatic colorization. In this paper, we introduce a generation model for colorization of 3D objects.
Comparative study of students’ writing achievement using Roundtable and STAD techniques

This research was aimed to compare the two language teaching techniques of writing, that is Roundtable and STAD techniques, and  to investigate the student’s appraisal after being taught through Roundtable technique, and to know the student’s appraisal after being taught through STAD technique. This research was conducted at the tenth grade of SMAN 1 Pringsewu in academic year 2019/2020 using two groups pretest posttest design. The data were collected using tests and questionnaire then analyzed using independent samples t-test and through descriptive analysis in SPSS ver. 20. The results show that: first, there is no significant difference of students’ writing achievement on descriptive text. Second, Roundtable technique helped students to learn writing of descriptive text through the activities, the material, and the teacher’s explanation during the learning process since this technique was well implemented regarding to its procedures. Third, STAD technique is effective to help students to write descriptive text through the teacher’s explanation, the activities, and the material in learning process. It provided evidence that STAD technique is well applied in the class regarding to its procedures. Keywords: writing, descriptive text, roundtable, STAD, student appraisal
A Manifold Semantic Canonical Correlation Framework for Effective Feature Fusion

In this paper, we present a manifold semantic canonical correlation (MSCC) framework with application to feature fusion. In the proposed framework, a manifold method is first employed to preserve the local structural information of multi-view feature spaces. Afterwards, a semantic canonical correlation algorithm is integrated with the manifold method to accomplish the task of feature fusion. Since the semantic canonical correlation algorithm is capable of measuring the global correlation across multiple variables, both the local structural information and the global correlation are incorporated into the proposed framework, resulting in a new feature representation of high quality. To demonstrate the effectiveness and the generality of the proposed solution, we conduct experiments on audio emotion recognition and object recognition by utilizing classic and deep neural network (DNN) based features, respectively. Experimental results show the superiority of the proposed solution on feature fusion.
SINGA-Easy: An Easy-to-Use Framework for MultiModal Analysis

Deep learning has achieved great success in a wide spectrum of multimedia applications such as image classification, natural language processing and multimodal data analysis. Recent years have seen the development of many deep learning frameworks that provide a high-level programming interface for users to design models, conduct training and deploy inference. However, it remains challenging to build an efficient end-to-end multimedia application with most existing frameworks. Specifically, in terms of usability, it is demanding for non-experts to implement deep learning models, obtain the right settings for the entire machine learning pipeline, manage models and datasets, and exploit external data sources all together. Further, in terms of adaptability, elastic computation solutions are much needed as the actual serving workload fluctuates constantly, and scaling the hardware resources to handle the fluctuating workload is typically infeasible. To address these challenges, we introduce SINGA-Easy, a new deep learning framework that provides distributed hyper-parameter tuning at the training stage, dynamic computational cost control at the inference stage, and intuitive user interactions with multimedia contents facilitated by model explanation. Our experiments on the training and deployment of multi-modality data analysis applications show that the framework is both usable and adaptable to dynamic inference loads. We implement SINGA-Easy on top of Apache SINGA and demonstrate our system with the entire machine learning life cycle.
A Context-Free Symbiosis of Runtime Verification and Automata Learning

We present an approach for efficient life-long learning of systems modeled in terms of our recently developed formalism of systems of procedural automata (SPAs). SPAs describe context-free/procedural systems in which entering and exiting procedures is observable. Key to the efficiency of our life-long learning approach is an SPA-based monitor that verifies individual procedures without having to consider the complete trace of the global system. This in itself allows for an efficient monitor implementation that is able to detect runtime verification errors and—if such a behavioral mismatch is observed—an efficient extraction of on-the-fly reduced counterexamples, thus further boosting performance in the life-long learning context. Our experimental evaluation based on (synthetic) scenarios of various profiles indicates the tractability of counterexamples of lengths of billions of symbols, a number that we believe suffices for most practical applications.