Reviewerly/exHarmony huggingface dataset:
A New Augmented Method for Processing Video Datasets Based on Deep Neural Network

Large datasets are required for deep learning to achieve good performance. However, there is a lack of sufficient training datasets in many research fields, which may become a shortcoming of computer vision applications. This article provided a new data augmentation method for making training small datasets, which could be divided into two steps: 1. Unbalanced sampling based on information density. 2. Splicing images to form a dataset. Different information density dataset combinations had been used for testing the model generalization. The enhanced loss function which consisted of label smoothing loss and cross-entropy loss had been used to minimize the model preference during training models. Finally, with the same amount of data, the Mean Absolute Error (MAE) of the model with our sampling method could get 55% increase compared with the traditional sampling method. The best MAE could reach 0.98 if the splicing method had been adopted. The results showed that this augmented method was suitable for scenarios with small sample size, especially video datasets. To get the best performance, the splicing method was a nice choice to optimal model generalization performance.
Face Privacy Protection Based on Attribute Manipulation

Recent studies have shown the possibility of inferring soft biometric attributes from individual facial images, such as age, gender, and race. This has aroused people's concern about privacy. To solve this problem, we designed a generation confrontation network to achieve the purpose of privacy protection by confusing or preserving the soft biometrics of face images and preserving the identity matching function of the original face. Our method can complete the conversion between multiple facial attributes by using only one generation model. Users can flexibly select the retention or modification of facial attributes without affecting the accuracy of identity matching. A large number of experiments show the effectiveness of our method.
Automatic Question Answering System for Semantic Similarity Calculation

The automatic question answering system based on semantic similarity calculation includes three modules: word segmentation module, question understanding module and FAQ database module. Jieba, an open-source tool, is used in the word segmentation module. The problem understanding module can be further divided into problem classification, keyword extraction, and keyword expansion. The hierarchical classification method based on self-learning rules is used for problem classification. The common question database module distinguishes sentence similarity calculation and question matching. Sentence similarity calculation is based on the How Net semantic dictionary. The core algorithm is the rule algorithm design based on the corpus. The system relies heavily on each module, so it is difficult to establish a more perfect test scheme. Therefore, we only test the sentence similarity calculation which ultimately determines the accuracy of the problem matching, and finally realize the function of each module, and test and evaluate each module. The test results can be summarized that the sentence segmentation is relatively short, the part of speech contains less, and the similarity judgment is relatively concentrated, which is caused by the absence of specified parts of speech in both sentences. According to the part of speech coverage specified by the system, the more comprehensive the coverage, the more accurate the similarity calculation.
A Comparative Study of Neural Computing Approaches for Semantic Segmentation of Breast Tumors on Ultrasound Images

This paper compares two approaches for semantic segmentation of breast tumors on ultrasound. The first approach, called conventional, follows the typical pattern classification process to extract hand-crafted features, followed by pixel classification with a Multilayer Perceptron (MLP) network. The second approach, called convolutional, uses a Convolutional Neural Network (CNN) to learn features automatically. For evaluating both approaches, a breast ultrasound dataset with 1200 images is considered. Experimental results reveal that the CNNs called VGG16 and ResNet50 outperformed the conventional approach in various segmentation quality indices. Thus, extracting hand-crafted discriminant features is challenging since it depends on the problem domain and the designer’s skills. On the other hand, through transfer learning, it is possible to adjust a pre-trained CNN for addressing the problem of tumor segmentation satisfactorily. This performance is because CNN learns general features in its first layers, and more subtle features are activated as depth increases.
Semiautomatic Identification of Pulmonary Embolism in Electronic Health Records Through Sentence Labeling

In this study, we tested the quality of the information extraction algorithm proposed by our group to detect pulmonary embolism (PE) in medical cases through sentence labeling. Having shown a comparable result (F1 = 0.921) to the best machine learning method (random forest, F1 = 0.937), our approach proved not to miss the information of interest. Scoping the number of texts under review down to distinct sentences and introducing labeling rules contributes to the efficiency and quality of information extraction by experts and makes the challenging tasks of labeling large textual datasets solvable.
Data-Hungry Issue in Personalized Product Search

Product search has been receiving significant attention with the development of e-commerce . Existing works recognize the importance of personalization and focus on personalized product search. While these works have confirmed that personalization can improve the performance of product search, they all ignore the few-shot learning problems caused by personalization. Under the few-shot setting, personalized methods may suffer from the data-hungry issue. In this paper, we explore the data-hungry issue in personalized product search. We find that data-hungry issue exists under the few-shot setting caused by personalization, and degrades the performance under the few-shot setting when the input query consists of diverse intents. Furthermore, we illustrate that with such a data-hungry issue, the returned search results tend to be close to the products the user purchases most often, or the products the most users purchase in the market given the same query. The result in the further experiment confirms our conclusions.
High-quality Voxel Reconstruction from Stereoscopic Images

Volumetric reconstruction from one or multiple RGB images has shown significant advances in recent years, but the approaches used so far do not take advantage of stereoscopic features such as distance blur, perspective disparity, textures, etc. that are useful to shape the object volumes. Our study is to evaluate a convolutional neural network architecture for reconstruction of 128³ voxel models from 960 pairs of stereoscopic images. The preliminary results show an 80% of coincidence with the original models in 2 categories using the Intersection over Union metric. These results indicate that good reconstructions can be made from a small dataset. This will reduce the time and memory usage for this task.
Enhancing Behavioral Dependency for Effective Computing in Software

Software plays an important role in effective computing and communication of any services. It become crucial to identify some critical parts of the software that can lead to enhanced computing and increases efficiency of the software. Dependency plays a significant role in finding relationship amongst classes and predicting change prone classes. This paper aims to enhance Behavioral Dependency by defining 6 types of dependencies amongst classes. These are (i) direct behavioral dependency (ii) indirect behavioral dependency (iii) internal behavioral dependency (iv) external behavioral dependency (v) indirect internal behavioral dependency and (vi) Indirect External Behavioral Dependency. Evaluating these dependencies, gives accurate results for the prediction of change prone classes. Further, paper compares proposed approach with existing methods.
Verification and Validation Plan in a Software Product: A Practical Study

In Software Engineering practice, it is a challenge to develop quality software. In this way, several proposals have emerged, such as verification and validation process (V&V), which is composed of procedures, activities and tasks to verify and validate if the software is correct and if it satisfies user needs. For this reason, we based this study on Design Science Research (DSR) approach to answer the question: Does the implementation of a VVP verification and validation plan using the IEEE 1012-2016/Cor 1-2017 standard work? The method used to answer this question consisted of creating and applying a VVP to the ad-hoc SAREL software and then documenting the findings. The results show that the application of the VVP reached a compliance of 96.50%, according to the evaluation criteria of each V&V task granting a satisfactory degree of quality.
DIGITAL DIVIDE PELAKSANAAN UJIAN NASIONAL BERBASIS KOMPUTER

Indonesia has a fairly serious problem in the distribution of technology, the Computer-Based National Examination (UTBK) policy implemented in Pesawaran Regency is a concern because of some differences with other regions that also implement UTBK. This study aims to analyze the Digital Gap in Pesawaran Regency, especially the implementation of UTBK at SMPN 25 Pesawaran. The results of this study indicate that there are several factors causing the digital divide to occur at SMPN 25 Pesawaran, namely First, technological factors related to roads to telecommunication access, and student factors as users who are not yet literate with ICT. Second, the socio-economic conditions of the people at SMPN 25 Pesawaran are still in the lower middle position so that ICT has not become a basic need.
Multimodal Fusion Representation Learning Based on Differential Privacy

Multimodal data for a certain target can often play a complementary role in information integration, but the diversification of the modal brings difficulties to the training of the model. Further, previous differential privacy works are only performed on a single modality. To tackle the problem, we choose deep representation learning to map different modalities data into the same subspace. This method of fusing multiple modalities uses low-rank decomposition based on Canonical Polyadic (CP) decomposition to implicitly obtain a high-dimensional tensor rich in mutual fusion information between multiple modalities, but explicitly obtain a low-dimensional representation. The perturbation that satisfies differential privacy is then carried out in the dimensional subspace. Experimental results show that it satisfies the data utility requirement while remaining suited privacy guarantee.
Retrospective Review of Activation Functions in Artificial Neural Networks

Deep learning is a subfield of machine learning and artificial intelligence technique. It employs neural network tasks like image processing, computer vision, voice recognition, machine translation, medical information processing, self-driving vehicles, predictive forecasting, robotics and control, cybersecurity, natural language processing, bioinformatics, and countless others. The performance of a neural network is determined by a variety of factors, and activation functions are an essential element in the design of a neural network. The hidden layer’s activation feature defines the extent to which the network model learns the training data set. The type of prognostications made by the model is regulated by the activation functions present at the output layer. This paper presents a comprehensive review of research studies on different activation functions aimed toward deep learning applications.
Pattern Recognition of GIS Typical Defects Based on Convolutional Neural Network

With the continuous development of power system, people’s requirements for power quality are constantly improving. Safe, stable and reliable power equipment is the premise of power system stability. As an important equipment in power system, GIS has been widely concerned by scholars domestic and abroad. Insulation faults account for the highest proportion in GIS faults, and most of them are presented in the form of partial discharge. Partial discharge is not only the symptom and manifestation of GIS insulation deterioration, but also the cause of further insulation deterioration. At the initial stage of faults, they have little impact on equipment, with the aggravation of partial discharge, they eventually seriously threaten the safe operation of power grid. In this paper, a GIS single-phase single-cavity model is established by using the finite-difference time-domain method, and then data sets are obtained for classification of four typical GIS defects. Secondly, convolutional neural network is used to classify GIS insulation faults, which lays a foundation for the research of GIS equipment online monitoring system.
Medical Image Denoising Method Based on Total Variational Model and Adaptive Wavelet Threshold

Wavelet threshold denoising can easily cause edge blur, while total variational denoising is insufficient to achieve image smooth area denoising. Therefore, this paper proposes a new denoising method combining the total variational model and the adaptive wavelet threshold. Firstly, the original noisy image is processed by the total variational model, the improved Prewitt operator is used to extract the edge of the processed image, and the edge denoising image is obtained. Then, the original noisy image is divided into edge and non-edge images by the improved Prewitt operator, and the non-edge image is de-noised by the adaptive wavelet threshold method to obtain the non-edge denoising image. Finally, after denoising, the edge image and non-edge image are superimposed to achieve high-definition restoration of medical images. Experimental results show that the proposed method can effectively denoise medical images and preserve the original edges and details in the images.
Software Methodologies and War Strategies

Innovation is permanent in all fields of human activity. Strategy and tactics adopted by one discipline may be used to communicate through disciplines or may be transposed and adapted in other fields. The contemporary activity of software development brought to emerging different methodologies that exercise their influence to other fields and may be applied in other disciplines. On the other hand, as people showed the highest inventiveness in attacking and conquering others, the basic military notions are used across disciplines. Adapting the attacking and war strategies to different fields resulted naturally. The article will examine the trans-disciplinary communication through the Agile incremental methodology and similarities between its application in software development and adopting it in different fields. We'll analyze how to transpose to other fields Agile concepts as adding business value and getting to the business goals, conducting the work the incremental and non-incremental way, and how the basic concepts from Agile are used beyond their original creation, through other disciplines. The article also analyzes war strategies and the possibility to apply them to other disciplines, with extended references to one of the most renowned military treaties, Sun Tzu's The Art of War. We'll investigate what things as initial estimations, attack by stratagem, tactical dispositions, energy, weak points, and strong points, maneuvering, variation in tactics, the army on the march, terrain, arriving on unknown ground, concrete situations on the ground, the use of spies or the attack by fire may mean to communicate across disciplines.
The differences in the levels of interleukin 1 beta (IL-1ß) and tumor necrosis factor alpha (TNF-Α) based on the storage of thrombocyte concentrates

Thrombocyte concentrate (TC) transfusion plays an important role in preventing bleeding in patients with severe thrombocytopenia. Febrile non hemolytic transfusion reaction (FNHTR) may occur after TC administration. IL-1ß and TNF-α cytokines released by thrombocytes and leucocytes during TC storage play important roles in the occurrence of FNHTR after TC transfusion. The purpose of this study was to analyze changes in levels of IL-1ß and TNF-α on the duration of TC storage. This was an observational analytical research with time series design carried out at the Clinical Pathology Laboratory and Blood Bank of the Dr. Soetomo Hospital Surabaya in September - October 2019. IL-1ß and TNF -α levels in 20 bags of Thrombocyte Concentrate blood components derived from Platelet Rich Plasma during storage for day 1, day 3 and day 5 were measured using ELISA Sandwich method. The statistical analysis was performed using the Subject Same Variant Test or Friedman Test. The results showed no significant differences in the levels of IL-1ß and TNF-α based on the storage duration of TCs on day 1, 3 and 5, p = 0.262 and p = 0.534 respectively. There was a significant difference of IL-1ß levels between day 1 and day 3 (p=0.032).
Neural Style Transfer

Neural style transfer takes in a content image and a style image to blend them together so that the output looks like the content image, but is painted in the style of the reference image. This can be done by using the features present in a previously trained network and well defined loss functions. A style loss is defined to represent how close the image is in terms of style to the reference. The content loss is defined to ensure important features of the original image is preserved.
Identifying Complex Patterns in Online Information Retrieval Processes

We define a computable benchmark framework that replicates the behavior of users as they proceed through the alternatives ranked by a search engine and highlights the problems faced by artificial intelligence techniques to categorize the retrieval patterns generated by users endowed with different information assimilation capacities. The research is developed through three specific stages.First, we define a decision theoretical model that relates the threshold values determining the retrieval behavior of decision makers (DMs) to the distance in terms of semantic similarity between the descriptions observed in the snippets and the ideal ones considered by the user. The model is designed to highlight the complexity of the search process defined by DMs, who must consider combinations of the variables defining the alternatives, both observed and expected, together with the number of satisficing alternatives aimed to be observed. Second, we design a set of heuristic algorithms that mimic the online information retrieval behavior of DMs as reflected in their click through rates (CTRs). We illustrate how requiring DMs to observe two satisficing alternatives provides a sufficient approximation to their CTRs. Adding a third alternative delivers an almost identical set of CTRs to those displayed by DMs ([1], [2]). The mimicking quality of the heuristic algorithms prevails as alternatives are added up to include the ten ranked within the first page of search results. Third, the set of heuristic algorithms provides two different strings of data, the pages clicked by the DMs and, more importantly, a numerical representation of each of the observations and evaluations that determine the retrieval behavior of DMs. We illustrate how, even when providing several artificial intelligence techniques with both strings of data, the models face considerable problems categorizing DMs correctly as their information assimilation capacities are enhanced.References[1] Chitika, The value of Google result positioning. Chitika Insights June 7, 2013. Chitika, Westborough (2013) Available at perma.cc/7AGCHTDH [2] B. Dean, We analyzed 5 million Google search results. Here’s what we learned about organic click through rate (2019) Available at https://backlinko.com/google-ctr-stats
Improving Conversation Task with Visual Scene Dataset

To build good conversation agents, an accurate conversation context is assumed to be required. We argue that a conversation scene that includes speakers can provide more information on the context because using images as conversation contexts has proven effective. We constructed a visual conversation scene dataset (VCSD) that provided scenic images corresponding to conversations. This dataset provides a combination of (1) conversation scene image (third-person view), (2) the corresponding first utterance and its response, and (3) the corresponding speaker, respondent, and topic object. In our experiments on the response-selection task, we first examined BERT (text only) as a baseline. Although BERT managed to perform well in general conversations, where a response continued from the previous utterance, it failed to deal with cases where visual information was necessary to understand the context. Our error analysis found that conversations requiring visual contexts can be categorized into three types: visual question-answering, image-referring response, and scene understanding. To optimize the usage of conversation scene images and their focused parts, that is, speaker, respondent, and topic object, we proposed a model that received texts and multiple image features as inputs. Our model can capture this information and achieve 91% accuracy.
Clustering method of hybrid English teaching resources based on Information Retrieval

In order to improve the availability of mixed English teaching resources, it is necessary to cluster the teaching resources, but the traditional clustering method is difficult to retrieve all the teaching resources information, resulting in the low accuracy of mixed English teaching resources. Therefore, a mixed English teaching resources clustering method based on information retrieval is proposed. Firstly, the structural characteristics of mixed English teaching resources are analyzed, and the piecewise linear fusion retrieval method is used to construct the multi-dimensional feature distribution constraints of mixed English teaching resources. Secondly, according to the beam domain calculation results of mixed English teaching resources retrieval, the distribution structure model of mixed English teaching resources is constructed to complete the retrieval of English teaching resources. Finally, in the collection of mixed English teaching resources, the distance between the sample and the centroid in each cluster is the distance within the cluster, and the clustering objective function completes the clustering of mixed English teaching resources. The experimental results show that the retrieval accuracy and clustering accuracy of this method are significantly improved compared with the traditional teaching resource clustering method.
Analysis and Comparison Between Reversible DS GATE and Irreversible NXOR Logic Circuit

In today’s scenario, the system is configured with electronic simulated modules. To run this kind of system, a lot of functional digital electronic elements are required. Especially this specific configuration of holding some data in them for some period so that the system could perform some intelligent works. This field is termed as digital signal processing, used in various phases like smart doors, or mobile, and there are countless many folds in its merit list. Here, in the study, the function is to manage the circuit to limit elements while maintaining the efficiency on output calibrations. The main concern of this study is quantum computing in addition to make it more efficient, and optimize the leftover residues. The system here is confined to the functional ability and the concern reversibility in the logic gates. Not all of them but only few are studied under reversibility condition. The proposed system has been constructed to make the analysis clearer.