Reviewerly/exHarmony huggingface dataset:
Knowledge Graph Construction and Intelligent Question Answering on Science and Technology Intermediary Service

With the rapid development of the Internet, while it facilitates users to obtain information, it also increases information overload. Although a lot of data have been divided into categories, it is still a big challenge to retrieve effective information from thousands of categories and their subcategories. For professional business, we need more efficient information organization and interactive interface to reduce the complexity of information retrieval. This paper designs an intelligent question-and-answer system for science and technology intermediary services based on knowledge graphs, which uses knowledge graphs to store domain data, combines intelligent question-and-answer technology and website design technology to realize efficient information query. On the basis of building the knowledge graph database of professional domain, the problem of information efficient search is effectively solved by combining natural language processing with relational graph.
InteractDiff: A 3D Sandbox Game for Online Teaching and Campus Life Experience

The coronavirus pandemic forced teachers to use online meeting software to teach courses remotely. However, without on-site supervision from teachers, students can get distracted and lead to learning inefficiencies. Distraction also leads to low interaction, and teachers may lose enthusiasm in teaching. In addition, social interaction is necessary for campus life. To address these problems, we present InteractDiff, a 3D sandbox game to improve the online teaching experience and interactivity. We designed several school scenarios and mechanisms where users act as an avatar inside to imitate real-life interactions in university. Our preliminary study shows potential feasibility and positive feedback from users.
Prior knowledge guided few-shot classification with class disentanglement

Few-shot classification, aiming at learning a precise classifier for novel classes with a few annotated support samples, is a challenging task. Current few-shot classification algorithms firstly extract the prototypes from the support images and further leverage the prototypes to classify the query images with diverse elaborately designed matching methods. However, current algorithms all ignore the essentiality of prior knowledge, which would seriously hamper the generalized prototype learning and weaken the classification performance. Meanwhile, the query features obtained from the extractor are indeed the confounders, which would result in the disruption between different class and dramatically limit the classification performance. To tackle these challenges, we propose a novel prior knowledge guided few-shot classification network with class disentanglement. Specifically, the prior knowledge guided module is constructed to boost the original prototypes with more generalized knowledge in a selective combination manner. Meanwhile, class disentanglement module is designed to disentangle the confounder (extracted query features) with the disruption between different classes eliminating. By combing the prior knowledge and disentangling the confounder, our designed network efficiently addresses the few-shot classification. Comprehensive experiments on the miniImageNet and tieredImageNet powerfully demonstrate the superiority of our proposed network.
Combating Unbalanced Datasets with Generative Adversarial Network

Data is a highly important factor in Deep Learning. The presence of a large, labelled, noise-free and balanced dataset is often essential for a model to perform optimally at a particular task. However, finding such data in the real world is rare, and creating these datasets is a tedious task. The aim of this project is to overcome one of the aspects of difficulties with datasets: unbalanced data across different classes. In more detail, Generative Adversarial Networks (GANs) have been used on an existing unbalanced dataset to create synthetic data of the lesser represented classes. A survey of existing literature shows that GANs have not been used for such a purpose before. Using Deep Convolutional GANs after pertaining the Discriminator on the dataset gave promising results which surpassed the baseline method and almost matched the original dataset.
Document Encryption Method with Mechanism of Enigma Machine

Document encryption and decryption ensure document security by protecting the security in data storage and transition, which prevents possible serious information leakage. Recent encryption methods tend to rely on the internet or the powerful calculation ability of computers highly. However, information is shared online and shared offline, requiring encrypting methods that are comparatively less complicated to implement, in some cases, i.e., paper or flash drives. To solve the problem, this paper presents document encryption and decryption method using the mechanism of the Enigma machine. First, we read the document and process it to leave characters only. Then, we encrypt the document into ciphertext using a given key. Finally, we decrypt the ciphertext using the key and obtain the plaintext, the original document. To verify the effectiveness of the proposed method, we encrypt documents and decrypt the ciphertext. The experimental results show that the proposed method outperforms related benchmarks.
Determination of the optimal inclination angle for solar collectors using the Box - Behnken design in the Peruvian highlands

The solar resource is important for sustainable energy development, which can be applied to different thermal systems to heat water and photovoltaic systems to generate electricity and use it at any time. The objective of the research was to determine the optimal inclination angle for vacuum tube solar collectors using the Box - Behnken design in the Peruvian highlands. This work used the response surface model (quadratic regression) design of Box - Behnken, to determine the optimal angle of inclination of the vacuum tubes of the solar collector, which can capture the maximum solar radiation. The results show that the optimal tilt angle of the solar collector was 11.62°, close to the latitude of the Peruvian highlands, oriented north on sunny days. Therefore, the response surface model optimized the studied factors, to make the most of the solar resource in thermal systems.
Binomial Distribution based K-means for Graph Partitioning Approach in Partially Reconfigurable Computing system

Graph partitioning algorithms have been utilized to execute complex applications, where there is no enough space to run the whole application once, like in limited reconfigurable computing resources. If we have found an “optimal” clustering of a data set, it can be proved that optimal partitioning can be achieved. K-means based algorithms are widely used to partition subjects where there is no information about the number of clusters. A vital issue in the mentioned method is how to define a good centroid, which has the principal role in “good” clustering. In this paper, we introduced a new way to determine purposive centroids, based on Binomial Distribution to reduce the risk of randomly seeds selection, Elbow Diagram to achieve the optimum number of clusters, and finally, Bin Packing to classify nodes in defined clusters with considering Utilization Factor (UF) due to the limited area of Run Space. The proposed algorithm, called Binomial Distribution based K-means (BDK), is compared with common graph partitioning algorithms like Simulated Annealing Algorithm (SA), Density K-means (DK), and a link elimination partitioning with different scenarios such as simple and complex applications. The concluding results show that the proposed algorithm decreases the error of partitioning by 24% compared to the other clustering techniques. On the other hand, the Quality Factor (QF) is increased 41% in this way. Execution Time (EX.T) to achieve the required number of clusters is reduced significantly.
Research on Multi-objective Task Assignment Scheme Based on Group Isomorphism UUV

The traditional ant colony algorithm is improved for the TSP extension problem when multi-objective task assignment is performed by a group homogeneous UUV. Based on the traditional ant colony algorithm and the TSP problem, the TSP problem model is extended to improve the initial information, the taboo table, the location state probability calculation formula, and the pheromone concentration update method to create an improved ant colony algorithm, and simulations are performed with three different evaluation functions: path, time, and path-time, respectively. The experimental results show that the optimal solution can be reached simultaneously by using path evaluation function and time evaluation function, and the solution is gradually optimized by using path-time evaluation function, and finally reaches the optimal solution. The improved ant colony algorithm not only solves the multi-individual and multi-objective task assignment, but also can significantly improve the work efficiency and effectively reduce the cost.
Re-exploration of ε-Greedy in Deep Reinforcement Learning

This paper presents re-exploration as a method for improving the existing method for balancing the exploration/exploitation problem integral to reinforcement learning. The proposed method uses a ε-greedy method called "decreasing epsilon," which reiterate the method after a certain period of episodes in the middle of the learning. The experiment was conducted using Turtlebot3 simulation under the Robot Operating System (ROS) environment. The evaluation involved comparing the existing method, which is pure exploitation (totally greedy), conventional ε-greedy method and proposed method, which is decreasing-epsilon with the re-exploration method. The preliminary results indicate that applying re-exploration method is easier to implement and yet able to improve the reward obtained with in shorter time (episode) compared to the conventional method.
Analysis of Feature Extraction Methods Used for Handwritten Marathi Compound Character Recognition

Feature extraction is essential in the classification of handwritten character recognition. We have studied histogram of gradient (HOG) feature, local binary pattern (LBP), principal component analysis (PCA), and PCA can be used in feature extraction of Marathi handwritten compound character recognition. In feature extraction process, the data need to be reduced into a high dimensional space to a profound dimensional space. In feature selection, a subset of the primary set of features, or variables, get a modest subset which can be used to illustrate the problem.
GicoFace: A Deep Face Recognition Model Based on Global-Information Loss Function

As CNNs have a strong capacity to learn discriminative facial features, CNNs have greatly promoted the development of face recognition, where the loss function plays a key role in this process. Nonetheless, most of the existing loss functions do not simultaneously apply weight normalization, apply feature normalization and follow the two goals of enhancing the discriminative capacity (optimizing intra-class/inter-class variance). In addition, they are updated by only considering the feedback information of each mini-batch, but ignore the information from the entire training set. This paper presents a new loss function called Gico loss. The deep model trained with Gico loss in this paper is then called GicoFace. Gico loss satisfies the four aforementioned key points, and is calculated with the global information extracted from the entire training set. The experiments are carried out on five benchmark datasets including LFW, SLLFW, YTF, MegaFace and FaceScrub. Experimental results confirm the efficacy of the proposed method and show the state-of-the-art performance of the method.
Frame Rate Latency Reduction for Real-time Vehicle Tracking using Network Cameras

Traffic monitoring and vehicle counting systems that use surveillance cameras employ several computer vision techniques, one of which is object tracking, which approximates the trajectory of the vehicle throughout the scene. However, a major challenge in processing videos from network camera feeds is the irregular and low frame rates, affecting the performance of object tracking. In this paper, we present a concurrent implementation framework intended to increase the input network video frame rate.
Case-based reasoning (CBR) in digital well planning and construction

Unforeseen problems happen in drilling because these events are not fully predictable, which could be due to lack of sufficient data or improper modeling to simulate real conditions in the drilling process. Traditional mathematical models have not totally eliminated oil well problems because of the many simplifications, uncertainties, and incomplete information involved. This chapter proposes a new approach for overcoming these challenges. The main objective is to merge two scientific fields, case-based reasoning (CBR) and petroleum engineering, to implement a new methodology. This chapter focuses on the theoretical concepts related to the CBR method and the rule-based reasoning method, and the integration of the methods into digital well planning and construction. The success of the CBR system depends on the quality of the case data. The data quality is related to case base competence which is measured by the range of problems that can be satisfactorily solved. Modeling case base competence is a much-discussed issue in the discipline of CBR.
Application of Data Mining and Fuzzy Location Estimation in Supply Chain Management System

Application of the data mining and fuzzy location estimation in supply chain management system is studied in this research. When locating the communication system fault accurately, in order to be able to quickly locate the system obstacles, the technicians should clarify the true demands of the locating work, and improve work efficiency in this way. With the reference to this technique novelty, this paper designed the novel fuzzy clustering model combined with the neural networks to construct the intelligent supply chain management system. The real data is tested on the different scenarios. The experiment results compared with the latest approaches have proven that the designed framework can effectively analyze the information.
An Approach to Reduce the Number of Conditional Independence Tests in the PC Algorithm

The PC algorithm is one of the most prominent constraint-based methods for learning causal structures from observational data. The algorithm relies on conditional independence (CI) tests to infer the structure and its time consumption heavily depends on the number of performed CI tests. We present a modification, called ED-PC, such that – in the oracle model – both ED-PC and the original PC algorithm infer the same structure. However, by using a new idea allowing the detection of a v-structure without explicit knowledge of a separating set, our method reduces the number of needed CI tests significantly. This is made possible by detecting nonadjacencies considerably earlier.
A Decomposed Projective Method based on FBT-VMD and t-ELM for Facial Emotion Recognition

The performance of affective computing, in particular, of facial emotion recognition, is often limited by the low interpretability of the network structure, parameter redundancy, the high computational complexity with large consumption caused by CNN strategy in extracting features. The decomposed projective method, which integrates dimensional conversion, signal analysis, and dimensionality reduction for facial emotion image processing, is considered in this presentation. The main contributions of this work are the following: 1) a dimensional conversion for a facial image, which transforming the 2-D image matrix into a 1-D signal using Fan Beam Transform (FBT); 2) a hierarchical analysis method for the signal with mode decomposition (VMD) for feature selection; and 3) a method combining the t-SNE algorithm and Extreme Learning Machine (ELM) is introduced to reduce the dimension of feature vectors and predict classification. Experimental results present a satisfactory performance with 80.17% accuracy.
Image Depth Analysis: From Deep Learning to Parallel Cluster Computing

This research study begins with deep learning and progresses to cluster computing to complete the image depth analysis pipeline. The deep neural model is taken into account in designing the proposed model. The convolutional layer is composed of several convolutional units in morphology, and the feature value of the related image is obtained through the convolution and operation. The parallel structure is utilized to optimize this layer. Further, the original data is taken as input, and complete the construction of the proposed model through a series of operations such as convolution, pooling, and nonlinear activation function mapping. The depth image analysis is selected as the verification target. Through the simulation, the analysis accuracy has been much higher than the traditional methods.
Improved VIBE Shadow Elimination Method with Adaptive Threshold in the Environment of Tarmac Monitoring

The detection and counting of airport passengers and baggage is an important task of airport management. When passengers arrive at the aircraft docking area from the terminal building through a shuttle bus and boarding on foot, the counting of people and baggage based on airport surveillance video is often affected by complicated climate, Dynamic background, cast shadows and other factors, the paper improves the VIBE (Visual Background Extractor) foreground detection algorithm, adds a threshold adaptive mechanism, can accurately extract the foreground image under the dynamic background of the airport, and uses the pixel distribution histogram matching method to detect ghosts Finally, based on the Gaussian mixture model, the moving target is removed from the shadow, and the moving target is accurately counted on this basis. The experimental results show that the method proposed in the paper is better than the traditional shadow removal algorithm based on traditional VIBE in removing the shadow cast by passengers.
Human-Oriented Software Engineering Experiments: The Large Gap in Experiment Reports

Context: The lack of information in experiment reports impairs external replications and decreases experiment quality and reliability, causing a lack of knowledge dissemination and making it impossible to confirm the results, despite the existence of well-known guidelines for planning, conducting, and reporting experiments. Objective: Provide an in-depth study on how information from human-oriented controlled experiments in software engineering is reported after the emergence of supporting guidelines. Method: A systematic mapping study was conducted in the main empirical software engineering and software engineering venues, considering the period following the supporting guidelines publication. Results: We analyzed 412 articles from three conferences and three journals reporting experiments where we did not find crucial information about the experiments in most of them. Examples of such information are participant reward, target population, hypothesis, and conclusion and construct validity. Conclusion: There is a gap between the information the guidelines suggest reporting and what is reported. From 27 elements that should be on the reports, according to the guidelines, 65% of the analyzed articles failed to report at least 13 (almost half). Such finding opposes the natural intuition that with the appearance and maturation of guidelines, studies’ reports would increasingly comply with them over the years. As a consequence, a flawed report may raise doubts about the quality and validity of the study.
Reparameterized Sampling for Generative Adversarial Networks

Recently, sampling methods have been successfully applied to enhance the sample quality of Generative Adversarial Networks (GANs). However, in practice, they typically have poor sample efficiency because of the independent proposal sampling from the generator. In this work, we propose REP-GAN, a novel sampling method that allows general dependent proposals by REParameterizing the Markov chains into the latent space of the generator. Theoretically, we show that our reparameterized proposal admits a closed-form Metropolis-Hastings acceptance ratio. Empirically, extensive experiments on synthetic and real datasets demonstrate that our REP-GAN largely improves the sample efficiency and obtains better sample quality simultaneously.
Usability Evaluation by Nurses of a Knowledge-Based System of Care Plans for People Hospitalized by COVID-19

The aim of this study is to analyze the usability by nurses of the Knowledge-Based System “Diagnostics care for COVID-19.” A convenience sample of 16 nurses was selected, among hospital workers and external experts. The group was divided into three subgroups intentionally to obtain different usability perspectives. Usability was evaluated by the System Usability Scale questionnaire. The participants completed the questionnaire on general usability, data inputs, and information output, after completing a minimum of 12 care plans. The first subgroup used real cases and the “think aloud” technique, the second simulated cases from the same hospital, and the third subgroup performed the external simulation. The highest scores were obtained in data inputs (94.38–97.50); and the lowest, in general usability (90.00–95.00). The subgroup of external experts scored the highest (93.13–95.63), and the first subgroup, which carried out real cases, gave the lowest score (90.00–94.38). The “think aloud” technique found an improvement in including more diagnoses and being able to carry out several plans for one person at the same time. The usability obtained was “excellent” in all subgroups and questionnaires, although the application showed limitations related to its characteristics imposed in the requirements specification.
Research on Building Chinese Blog Semantic Lexicon Based on Semantic Comprehension

Abstract Nowadays, investigators of phrases and text sentimental orientation identification mainly divided into two ways: semantic comprehension and machine learning. Machine learning needs a lot of training corpus and also cannot handle general field words effectively, meanwhile, semantic comprehension cannot get high scores at precision and recall. Therefore, in this paper, we propose a new fusion method based on our semantic comprehension for judging phrases’ polarity. In this paper, firstly, we modify the knowledge of Hownet, on the basis of its four primitives, the fifth primitive—sentimental primitive was proposed by our research, and annotated it into Hownet manually; secondly, we propose a new phrase sentimental similarity calculation method to compute word’s sentimental value; at last, we integrate transductive learning into this method to identify phrases’ sentimental orientation. The performance of experiment show that, compared with SVM or traditional semantic comprehension, it can get better results.
Object Recognition Algorithm for Complex Scenes Based on Improved YOLO v3

YOLO v3 is widely used in industry because of its high detection accuracy and speed, but there is a problem that it can only output accurate position coordinates and cannot predict the localization uncertainty of bbox. To solve this problem, an improved YOLO v3 algorithm is proposed. By increasing the output of position parameters and predicting localization uncertainty of bbox with Gaussian modeling to remove the boxes with high bbox uncertainty in the detection process. A new Localization loss function is designed on the basis of increasing the output of bbox coordinates. Batch Normalization layer and Convolution layer are combined to reduce the use of video memory space and improve network performance. The experimental results show that the mAP50 of the improved YOLO v3 algorithm in the helmet wearing test set is improved by 7.99%.