Reviewerly/exHarmony huggingface dataset:
Research on Data Mining of Intelligent Education Evaluation from the Perspective of the Knowledge Graph

Abstract The continuous development of Internet information technology given birth to data mining technology. Data mining is applied to analyze the internal relationships of data and provides a more optimized advancement way. The paper focuses on how to improve the data mining and analysis capabilities of intelligent education to associate students of the same characteristics. A cluster analysis method was utilized to construct a students’ knowledge graph model, which used a text classification algorithm to classify the evaluation questions and attribute them into various knowledge points. On the basis of the analysis on the data generated from online education, the clustering algorithm was adopted to classify the learning characteristics of the students. A knowledge graph developed by associating the students with similar characteristics was visualized be means of information technology in order to realize visualized and intelligent teaching.
Query Reformulation Based on Word Embeddings: A Comparative Study

Formulating effective queries for retrieving domain-specific content from the Web and social media is very important for practitioners in several fields, including law enforcement analysts involved in terrorism-related investigations. Query reformulation aims at transforming the original query in such a way, so as to increase the search effectiveness by addressing the vocabulary mismatch problem. This work presents a study comparing the performance of global versus local word embedding models when applied for query expansion. Two query expansion methods are employed (i.e. CombSUM and Centroid) for defining the most similar terms to each query term, based on GloVe pre-trained global embeddings and local models trained on four large-scale benchmark and one terrorism-related datasets. We assessed the performance of the global and local models on the benchmark datasets based on commonly used evaluation metrics and performed a qualitative evaluation of the respective models on the terrorism-related dataset. Our findings indicate that the local models yield promising results on all datasets.
A pipelining strategy for accelerating convolution neural networks on ARM CPUs

Abstract Convolution is a primary operation in convolution neural networks. The speed of inference is mainly decided by the speed of the convolutional layer. Improving the performance of embedded processors makes it possible to process the inference on embedded devices. In this article, a pipelining strategy of single instruction and multiple data (SIMD) instructions is proposed to finely optimize the process of the 3 × 3 convolution on ARM‐based CPUs. We implement the SIMD group to improve the efficiency of the SIMD pipeline. A tiling method is exploited to increase data reuse during the process. An evaluation model is proposed to guide the design of the tiling method and register allocation. The speed of our implementation is 5.18 times of the GNU compiler collection compiled unoptimized version on RK3288. The effect of our optimizing method is measured by a performance profiling tool, the performance information suggests that the pipelining strategy has a significant effect for both normal and depthwise separable convolution. By implementing multithread processing, the speedup achieves 18.3 compared with the single thread unoptimized version.
An Improved K-means Algorithm Based on Multiple Clustering and Density

The initial clustering center set of the k-means algorithm is randomly selected, which leads to unstable clustering results. To address this shortcoming, many improved k-means algorithms based on density have propersed, but the time complexity of these algorithms is too high. In order to improve clustering stability and reduce the clustering time, this paper proposes an improved algorithm based on multiple clustering and density. This algorithm firstly calls the k-means algorithm for many time, and adaptively selects excellent sample set according to the distance between samples and the corresponding cluster center. Then the initial cluster center set is selected according to the principle of the furthest distance and high density. The experiment on the UCI data sets shows that the algorithm in this paper not only improves the performance but also ensures the stability of clustering result compared with the k-means algorithm and the kmeans++ algorithm. Compare to improved density-based k-means algorithms, the proposed algorithm can greatly save the clustering time.
In-Hindsight Quantization Range Estimation for Quantized Training

Quantization techniques applied to the inference of deep neural networks have enabled fast and efficient execution on resource-constraint devices. The success of quantization during inference has motivated the academic community to explore fully quantized training, i.e. quantizing back-propagation as well. However, effective gradient quantization is still an open problem. Gradients are unbounded and their distribution changes significantly during training, which leads to the need for dynamic quantization. As we show, dynamic quantization can lead to significant memory overhead and additional data traffic slowing down training. We propose a simple alternative to dynamic quantization, in-hindsight range estimation, that uses the quantization ranges estimated on previous iterations to quantize the present. Our approach enables fast static quantization of gradients and activations while requiring only minimal hardware support from the neural network accelerator to keep track of output statistics in an online fashion. It is in-tended as a drop-in replacement for estimating quantization ranges and can be used in conjunction with other advances in quantized training. We compare our method to existing methods for range estimation from the quantized training literature and demonstrate its effectiveness with a range of architectures, including MobileNetV2, on image classification benchmarks (Tiny ImageNet & ImageNet).
Content-Based Textual File Type Detection at Scale

Programming language detection is a common need in the analysis of large source code bases. It is supported by a number of existing tools that rely on several features, and most notably file extensions, to determine file types. We consider the problem of accurately detecting the type of files commonly found in software code bases, based solely on textual file content. Doing so is helpful to classify source code that lack file extensions (e.g., code snippets posted on the Web or executable scripts), to avoid misclassifying source code that has been recorded with wrong or uncommon file extensions, and also shed some light on the intrinsic recognizability of source code files. We propose a simple model that (a) use a language-agnostic word tokenizer for textual files, (b) group tokens in 1-/2-grams, (c) build feature vectors based on N-gram frequencies, and (d) use a simple fully connected neural network as classifier. As training set we use textual files extracted from GitHub repositories with at least 1000 stars, using existing file extensions as ground truth. Despite its simplicity the proposed model reaches ≈ 85% in our experiments for a relatively high number of recognized classes (more than 130 file types).
Detection and Classification of Incoming Ambulance Vehicle using Artificial Intelligence Technology

Traffic congestion increases due to a number of vehicles on the road. Drivers cannot see the arrival of emergency ambulance. Then, personal vehicles block emergency ambulance. As a result, the chance of patient's dead increases due to such problems. Currently, personal vehicles equipped with Car DVR. This article introduces the implementation of AI for ambulance classification from features of ambulance (Text Ambulance, ambulance lights, Red Cross symbol, Star of life symbol) using Faster-RCNN by TensorFlow. Initially, various image pre-processing techniques are performed. Intersection over Union (IoU) are used for accuracy representation. The results of this research found that the accuracy of classification, star of life symbol give accuracy 98%, red cross symbol 99%, text Ambulance 95.6%, and Ambulance light 97.9%. Our results confirm the proposed system can classify and detection an incoming of Ambulance vehicles.
Word-building means in a political leader’s speech portrait (implicit pragmalinguistics)

Abstraсt. The article is concerned with the implicit pragmalinguistics which studies a person’s unconscious choice of verbal means to maximize the impact on the listener. The principles of hidden or implicit pragmalinguistics are put forward. They reveal the dependence of the actualization of hidden grammatical meanings on the speech situation of communication. The present article focuses on the characteristics of blogging as a form of comprehensive political communication. The paper aims at examining the specific features of political online written diaries in open social networks. It is established that the implicit speech influence on mass addressee is carried out by means of speech signals. These verbal signals actualize the plans of emotive and conative-aimed implied speech strategies of the hidden influence of the text sender on its recipient, so the author’s speech portrait is drawn up. Grammatical and textual forms of grammatical and textual categories are considered as speech signals. Along with them implicitly influencing derived lexical units formed by various word-building means are presented as verbal signals in the paper. It is formed the concept of speech behavior. According to the key features of a New Zealand politician’s speech behavior his personality is revealed. It has been developed in the form of the political leader’s speech habits based in the conditions of his national culture.
A Intrusion Detection Algorithm Based on Improved Slime Mould Algorithm and Weighted Extreme Learning Machine

Aiming at the problem of intrusion detection, the extreme learning machine is not effective in processing imbalanced data. Using Levy flight to improve the slime mold optimization algorithm, an improved slime mold algorithm is proposed to optimize the weighted extreme learning machine intrusion detection algorithm. The improved algorithm can jump out of the local optimum with a higher probability, and the weighted extreme learning machine has the advantages of short training time and good generalization performance. It increases the weight of minority attacks and makes the detection rate of rare attacks in network attacks higher than that of traditional machines. The learning method has been greatly improved. Global optimization is performed on the input weights and biases of the hidden layer in the weighted extreme learning machine to avoid the algorithm from falling into the local optimal solution and realize the classification of the intrusion detection data set. Experiments show that the detection rate and classification accuracy of this algorithm for rare attacks are improved, and the false alarm rate is reduced.
Generalized Intersection over Union Based Online Weighted Multiple Instance Learning Algorithm for Object Tracking

The traditional weighted multiple instance learning based online object tracking methods often use the Euclidean distance between the centers of the bounding boxes of the target and the instance to weight the instances in the positive bag, which can not effectively measure the contribution degree of the instances of the positive and negative bags and easily causes the object drifting problem. This paper proposes a generalized intersection over union based online weighted multiple instance learning algorithm (named GIoU-WMIL) for object tracking. This algorithm introduces a novel generalized intersection over union (GIoU) to calculate the overlap degree between the bounding boxes of the target and each instance in the bags, in order to effectively measure the contribution of the different instances. Furthermore, a new objective function is designed by employing the GIoU-based weights of all the instances in the positive and negative bags. Experiments show that the proposed algorithm has the good robustness and accuracy on several challenging video sequences.
Employment Environment for Overseas Students Based on Big Data

In recent years, the number of college graduates in China has increased tenfold in the past decade . It is more and more difficult for college graduates to find a job, while the number of overseas students is also increasing, which makes it more and more difficult for students to find a job in China. This paper mainly researches and analyzes the employment environment of overseas students based on big data. Based on big data on campus, this paper analyzes and calculates students' behaviors on campus, and finally uses machine learning algorithms to build a prediction model for college students' employment. On the one hand, this research can provide decision-making suggestions for relevant departments; on the other hand to provide employment guidance for college students to provide early warning and guidance services.
Features of managerial communication in network management

The issue of implementing BIM technologies in design organizations is becoming increasingly acute. The use of information technology raises complex questions about the organization of production. Management communication should be effective and effective. The purpose of this study is to identify the priority tasks in establishing effective communication when implementing BIM technologies in a project organization. Persistent attention to the most important issues of research methodology is a sure indicator of the growth of self-awareness in the science of management and management. The criteria of accuracy in philology and production management are reinterpreted. The problems of combining the objective and the subjective in the management text are discussed. There is an obvious need for initiative in the search for new, more subtle methods of penetration into the world of complex and increasingly complex phenomena of the labor management system. The increased attention of managers to the active participation of the employee in the improvement and implementation of the company’s document flow, to the improvement of the ergonomics of production management, to the complex issues of the psychology of perception of the management text is indicative.
Computerized Accounting System of E-commerce Platform

In 1979, China first referred to the application of computer in accounting as “accounting by electronic data processing”. After learning from foreign references, the concept of “electronic data processing accounting” appeared. Relationship between E-business and accounting computerization becomes more and more prominent. Based on the in-depth analysis of e-commerce and accounting computerization, this paper systematically discusses how the computerized accounting information system with management department and financial department as the center can effectively adapt to e-commerce, so as to guide the development of enterprises.
AI augmentation in the field of digital image processing

All of us are very well aware that identifying the information visually is more effective and efficient for human intelligence. In this aspect, one of the primary modes of interacting within and around for understanding the situation through images can act as crucial sources of information for the activities related to human intelligence. This paper's primary focus is to accomplish a better image processing impact using AI as part of image processing. Initially, this paper deals with introduction, elaboration, and mathematical representation on the essential theory of the ant colony algorithm. Furthermore, part of this paper deals with improvising the global search by introducing the crowding function of fish into the algorithm. Later, the improvised algorithm is utilised to segment the images to improve the impact of the segmentation process. Using this algorithm, the results demonstrate its feasibility, significant improvement in performance and the optimisation while segmenting the images.
CFP RRT for an automatic robot in the narrow space and the trapped goal environment

RRT algorithm is a popular obstacle avoidance path planning algorithm of the sampling-based algorithm to tackle the path planning issues of the robot's high degree of freedom in the high dimensional working environment. Moreover, it has been developed in many versions to improve its performance depending on the working space, but it still has been some weak performance in the trapping goal environment. To gain the efficiency of the previously improved RRT algorithm, this paper is studied a CFP-RRT algorithm by combining three different techniques of the previous algorithm. First, the changing sampling area (CSA) technique is used to reduce the number of random nodes, which are lengthier than the distance between extension nodes and a goal node. Second, the following obstacle (FO) technique is utilized to find quickly the motion planning in the trapped goal environment by the tree is grown following obstacle to encounter the goal. Third, the heuristic probabilistic biased goal (PBG) technique is used heuristic probabilistic to select the extension node equation, to avoid searching nodes for invalid areas of the target gravity equation, and changing the biased goal factor in the target gravity equation is applied to prevent the local minimum problem. Finally, the result of the proposed algorithm is evaluated and verified via the performance comparison of the previous RRT algorithm, i.e., the average number of nodes in the tree, the average number of nodes in the path, the average length of the waypoints, and the average time computation.
The Unknown Unknowns Are Not Totally Unknown

The question of whether “handling unanticipated changes is the ultimate challenge for self-adaptation” is impossible to evaluate without looking closely at what “unanticipated” means. In this position paper I try to bring a little clarity to this issue by arguing that the common distinction between “known unknowns” and “unknown unknowns” is too crude: for most systems there are changes that are not directly handled by “first-order” adaptation, but can, with appropriate engineering, be addressed naturally through “second-order” adaptation. I explain what I mean by this and consider ways in which such systems might be engineered.
Optimization of the Randomness Extraction Based on Toeplitz Matrix for High-Speed QRNG Post-Processing on GPU

As one of the applications of randomness extraction, post-processing is important for the high-speed and real-time QRNG (Quantum Random Number Generator) system. This work proposes a multi-thread and streamed-processing algorithm to optimize post-processing based on Toeplitz matrix on GPU (Graphics Processing Unit) and achieves a high speed over 6.8 Gbps. Furthermore, random tests are done to evaluate the quality of the random bits generated. The results show that both the execution speed and the randomness can satisfy the requirements of the high-speed and real-time QRNG system.
Blockchain Leveraged Incentive Providing Waste Management System

Garbage management has been an important issue worldwide. A good number of research has been done in order to efficiently collect and manage garbage in an urban area. But, most smart management systems are not suitable for implementing in the developing countries due to poor economy and people’s mentality. In Bangladesh, the government recently has taken some steps to appropriately manage garbage produced in the city. All kinds of governments initiative regarding garbage management have been proved to be failed because people are not aware of diseases that spread due to the mismanagement of wastes. General people are not educated and encouraged to put garbage in a designated place or make dustbin empty on a regular time basis. In this paper, we designed a conceptual cost-efficient and incentive providing Blockchain-based smart garbage management system for the developing countries, particularly Bangladesh. This proposal includes a smart dustbin that lid is automatically opened when a person approaches it and the system can provide people with incentives depending on the amount of garbage mined by an individual.
MDDA: A Multi-scene Recognition Model with Multi-dimensional Domain Adaptation

Optical Character Recognition is widely used in scenarios from license plate recognition, chip detection to text translation. However, in some special scenarios, there are very few data for training or the target datasets are not marked. This paper mainly aims to establish a multi-scene text recognition model that can achieve accurate results even when the target test dataset is not marked. The model reduces the distance between domains at the feature level by using an adversarial loss and flipping the gradient. For the adversarial loss, each letter has a different domain discriminator to align the features at the character level. We also align the source domain features and target domain features in the background. Experimental results show that our model achieves good performance when the training dataset and test dataset have domain shift, and suggest possible scenarios for potential applications.
Context-Aware Question-Answer for Interactive Media Experiences

Media content has become a primary source of information, entertainment, and even education. The ability to provide video content querying as well as interactive experiences is a new challenge. To this end, question answering (QA) systems such as Alexa and Google Assistant have become quite established in consumer markets but are limited to general information and lack context awareness. In this paper, we propose Context-QA, a light-weight context-aware QA framework, to provide QA experiences on multimedia content. The context awareness is achieved through our innovative Staged QA Controller algorithm that keeps the search for answers in the context most relevant to the question. Our evaluation results show that Context-QA improves the quality of the answers by up to 49% and uses up to 56% less time compared to the conventional QA model. Subjective tests show Context-QA improved results over conventional QA models, with 90% reporting enjoying this new media form.
A Robust Color Image Watermarking Improvement Scheme Based on Image Normalization

Many researchers concerned with watermarking for copyright protection purposes and several watermarking methods have been developed in order to overcome that problems caused by attacks especially geometric attacks which have more challenging issue.. There is a main issue related to the image copyrights protections watermarking and its robustness against geometric attacks. One of the solutions is watermarking based normalization technique which provides invariant properties against geometric attacks. There is a need for a deep investigation of the robust watermarking based normalization technique to guarantee achieving the robustness against geometric attacks while conserving the image quality. This paper is mainly focuses on the topic of digital image watermarking. It presents investigation and improvement of one existing algorithm which was introduced to be a robust color image watermarking scheme based on image normalization technique in order to make the system resistant to signal processing and geometric attacks. The improvement makes this algorithm robust to geometric attacks such as rotation, scaling and translation. Related theories used in watermark embedding and extraction procedures are illustrated in this work. Results and evaluation of watermarking system are described will be included in this paper.