Reviewerly/exHarmony huggingface dataset:
A Sorting and Searching Scheme for Relational Database Using Searchable Encryption

This paper adopts a sorting and searching (S&S) scheme using searchable encryption (SE) that searches encrypted data over the relational database. The key concept of SE is that the user sends a ciphertext with a key, and the corresponding recipient obtains this message with another key. The distributed asymmetric SE scans the document and constructs a word list by scanning and parsing, and thus finds the frequency of words in the document. Also, the correlation score and optimally matched documents lead to attaining the desired outcome. Thereby, the exploited search overcomes the deficiency of asymmetric SE. For this, it combines the qualities of both asymmetric SE and symmetric SE. Hence, the scheme can be applied in the domain of relational database settings to promote its users for protecting their data. Finally, the experimental results, comparisons, analyses, etc. confirm the usefulness of the scheme.
Indie Games Popularity Prediction by Considering Multimodal Features

We present a popularity prediction system for independent computer games (indie games), by jointly considering visual, text, and metadata information. An indie game dataset is first collected and labeled. According to the number of sales, we label an indie game as popular or not. Different types of information is extracted by specific feature extractors, and then is fused to construct a neural network-based classifier. We demonstrate that jointly considering multimodal information yields promising performance. In addition, we show that, with helps of state-of-the-art feature embeddings, the proposed method outperforms the only existing SVM-based method.
Research on Application of Computer Big Data Technology in Physical Electricity Experiment

This article uses 3D Max, Vitriols and other software tools to design and develop junior high school physics simulation experiments based on the above-mentioned curriculum design. The development process, key technologies and module architecture are introduced in more detail. Finally, the article tests, evaluates and improves the simulation experiment developed.
Design and implementation of patient monitoring robot in hospitals

This project portrays the strategies for breaking down, planning, improving and controlling the medical services the board framework. Self-sufficient Intelligent Robots can perform wanted errands in a climate without the ceaseless direction of human being. A line adherent robot is robotized gadget customized to follow a particular way. A line following bots conveys the medication to the patient at whatever point they need it dependent on the predefined way that can be either noticeable on a dark line on a white surface or the other way around It utilizes three degrees of input for arrangement, pivot balance and for keeping away from obstructions. Since the way of the wards stays same in medical clinics, so a fixed way is characterized and is stacked. In this venture the robot utilized is equipped for gathering the boundaries like temperature, pulse and of the patients. It utilizes three degrees of criticism for methodment, pivot counterbalance and for keeping away from snags. Since the way of the wards stays same in the clinics, so a fixed way is characterized and is stacked. In this venture the robot utilized is equipped for gathering the boundaries l ike temperature, pulse and life of the patients.
Cross Domain Person ReID Based on Symmetric Coding and Pedestrian Similarity

Person re-identification (ReID) aims to find images of the same pedestrian in cross camera scenarios. Recent years have witnessed extensive studies on person ReID by exploiting machine learning techniques under single domain settings. In this paper, we propose a novel cross domain person ReID method based on symmetric coding and pedestrian similarity. Specifically, we first introduce a symmetric coding technique for pedestrian attributes, and compute the attribute similarity matrix. Then, we extract pedestrian features by utilizing convolutional neural networks, and compute the feature similarity matrix. By fusing the pedestrian attribute similarity and pedestrian feature similarity, we obtain the fusion similarity accordingly. The attribute-tensor graph can be represented by the edges composed of the pedestrian similarity, which is produced by pedestrian attribute similarity and pedestrian feature similarity. Finally, the person ReID results can be obtained by similarity ranking. We conduct comprehensive experiments to evaluate the performance of our proposed algorithm for cross domain person ReID tasks, in which the encouraging results validate the efficacy of our proposed cross domain person ReID method.
Recommendation System: Posts Filtering in a Women Empowerment Platform

The women empowerment platform develops a medium, which would help the women in the society get more job opportunities and an equal stand in the society as compared to men, to get inspired from success of other women and find the right way to work on their weak sections as well in getting the job. The portal also helps to explore the different crime zones in the society by encouraging the women to post about any sort of harm they felt at their workplace or any other place, so that police departments can work on them thus building a safer place for women, where with the pillars of support and safety, women can work with the peace of mind. In this system, the women are allowed to participate in the discussions and help the topic in an open environment. The system also suggests the post the other users based on the content-based recommendation system. The main advantage of using the content-based system is that it does not map the user to user, instead it will map the content to the user. The mapping of the content helps the user to focus on the exactly the post that is required as they may be chance of getting the post liked by the other may not exactly to the other user choice.
An intelligent software testing method for online advertising strategy

In recent years, with the continuous development of Internet technology, Internet products have penetrated into People&#x0027;s Daily life and become an indispensable part of people&#x0027;s life. With the continuous development and innovation of modern artificial intelligence, big data and Internet information technology, the profit model of the Internet is also constantly updated, entering the vision of most researchers. Among them, the income brought by Internet advertising [1] is particularly prominent. So how to quickly test a kind of online advertising strategy has become the main problem facing at present. As for Internet online advertising strategies, the main research direction of major Internet companies is to design different strategies for different groups of people and businesses, and each strategy needs to go through a series of testing procedures to ensure that there will be no serious problems before a certain strategy goes online. However, Whether a real online advertising strategy can meet the established target groups and target businesses, as well as how to really bring revenue to the company can only observe the corresponding results after the online advertising strategy.
Attention-Based Convolution Bidirectional Recurrent Neural Network for Sentiment Analysis

Customer conveys their opinion in natural language about an entity. Applying sentiment analysis to those reviews is a very complex task. The significance terms that are influencing the polarity of a review are not examined. The terms that are having contextual meaning are not recognized which are present across multiple sentences in a review. To address the above two issues, we have proposed an Attention-based Convolution Bi-directional Recurrent Neural Network (ACBRNN). In this model, two convolution layer captures phrase-level feature, while Self-Attention in the middle assigns high weight to the significant terms and Bi-directional GRU performs a conceptual scanning of review through forward and backward direction. We have conducted four different experiments viz., Unidirectional, Bidirectional, Hybrid and Proposed model on IMDB dataset to show the significance of the proposed model. The proposed model has obtained an F1 score of 87.94% on IMDB dataset which is 5.41% higher than CNN. Thus the proposed architecture performs well while comparing with all other baseline models.
Silicon Photonics Beyond Optical Interconnects

In this paper we present the use of a 300mm Si-Photonic platform for applications beyond the data communication. Beam steering and beam shaping for free-space-optics and hybrid III - V/ Si optical switch for computing applications are discussed.
Single-task Temporal Face Synthesis Model Based on Migration Confrontation

Quality of face images generated by existing methods is not high and there is a lack of research on Asian face datasets. For synthesizing face images in a specific age domain, single-task temporal face synthesis model based on migration confrontation is proposed here. Transfer learning is used to redesign the network structure of the generated confrontation network and network structure of the discriminant network. And we also improve the loss function, so that model can better obtain feature information in a small amount of data set in a single-task scenario. Through experimental analysis, the model proposed in this paper performs better under objective evaluation indicators than existing models, and the model is more scalable and diverse.
Dhdip: An Interpretable Model for Hypertension and Hyperlipidemia Prediction Based on Emr Data☆

Background and ObjectiveTraditional hypertension and hyperlipidemia prediction models suffer from uneven modeling data sources, short sample sizes, and a lack of uniform standards for the index system, resulting in the model failing to fulfill clinical applications. To address this issue, this work will offer DHDIP, an interpretable hypertension and hyperlipidemia prediction model based on EMR data.MethodsFirst, we will select massive high-dimensional, unstructured EMR data as a unified modeling data source, and propose a pre-processing algorithm for EMR data to solve the problem that EMR data cannot be directly processed by machine learning algorithms. Second, a variety of mainstream models such as XGBoost, CatBoost, and RandomForest are selected for modeling, and the best adaptation algorithms are identified by performance comparison. Finally, the SHAP framework was introduced into the DHDIP model, thus identifying the main factors contributing to hypertension and hyperlipidemia, effectively enhancing the interpretability of the model.ResultsThe DHDIP model's MSE value is 0.0285, and its LOSS value is 0.0054, both of which are better than previous studies.ConclusionThe model balances performance and interpretability. Multi-objective learning allows for a more thorough analysis and prediction of the condition, which not only lowers the cost of disease prediction but also aids physicians in clinical diagnosis.
Examining Continuance Intention of Using Ugunduzi App in Farmer-Led Research of Agro-Ecological Practices Among Tanzanian Smallholder Farmers

Transformation of food production system to meet increasing global population demand without harming environment is a critical agenda debated worldwide. Agro-ecological practices inspired most agricultural stakeholders that they can improve food production sustainably. Farmer-led research of agro-ecological practices viewed could support farmers to identify best agro-ecological practices which can increase productivity with low costs. To support farmers in their research activities, a smartphone based app named ugunduzi was developed. This paper examines continuance intention of using the ugunduzi app. Qualitative and quantitative primary data were collected from 60 smallholder farmers who participated in farmer-led research of agro-ecological practices and use of the ugunduzi app from Masasi, Bagamoyo and Mvomero districts in Tanzania. Partial least square structural equation modeling using smartpls 3 was used to analyze the data. Technological acceptance model (TAM) and Expectance Confirmation Theory (ECT) were applied to test theoretical hypotheses of the study. The results indicated that, the app satisfied smallholder farmers and showed possibility of continuous usage by them (51.9%). This was due to its usefulness on supporting farmers to identify best-fit agro-ecological practices and to have better future season plan through records kept in the app. The app also showed greater potential on increasing agro-ecological knowledge through sharing information to the ugunduzi web-site via the ugunduzi app. However, some difficulties on using the app among smallholder farmers were observed especially at the early stage of using it due to some processes involved. Promotion of the ugunduzi app to be used by many farmers is strongly recommended to key stakeholders. Further studies to recognize best practices identified by farmers from the field are also recommended.
COMPUTATIONAL MODELING AND PHYSICS-INFORMED MACHINE LEARNING OF METAL ADDITIVE MANUFACTURING: STATE-OF-THE-ART AND FUTURE PERSPECTIVE

Metal additive manufacturing (AM) processes, such as powder bed fusion and directed energy deposition, involves very complex thermal dynamics phenomena including repetitive rapid heating, fast solidification, and melt-back, which are not encountered in conventional manufacturing processes, such as casting, forging, and heat treatment. Understanding and predicting the thermal behaviors in metal AM remain one central challenge for printing high-quality metal parts. This paper presents a systematic analysis of the state of the art in physics-based computational modeling, data-driven machine learning (ML) modeling, and physics-informed ML modeling for metal AM processes. The physical laws, recent developments, and limitations in modeling metal AM processes are analyzed in depth for the physics-based computational methods including computational fluid dynamics, finite volume method, finite element method, lattice Boltzmann method, and discrete particle method. The complementary pure data-driven ML and physics-informed machine learning methods and applications in metal AM are also reviewed. The key findings, common challenges, research needs, and an outlook regarding new and emerging modeling methods are outlined, which may serve as a roadmap for the computational AM community for future synergy that enables smart metal AM.
The Economics of Information and Human Factors in Cybersecurity

To design and apply effective countermeasures to hacking and related threats, it helps to have some way to structure one’s educated guesses and inferences about hackers’ current activities and future moves. This is especially the case when the decision-maker must consider not only the risk of attack but the unquantifiable risk of an attack (i.e., uncertainty). Computer science is not a decision-making framework and, while obviously necessary, is not sufficient to guide the design and implementation of effective countermeasures. We show how to use Schelling’s focal points together with Kahneman & Tversky’s reference points and related concepts from behavioural economics to complement technical innovations and improve threat detection and deterrence.
Memorability Based No-Reference Quality Assessment for Multiply-Distorted Images

In the paper, we proposed an effective no-reference image quality assessment (NR-IQA) model based on image memorability for multiply-distortion images, which is motivated by the image memorability property that the distortion happened in high memorable areas is more obvious to human perception. In our proposed method, we first extract memorable feature through attention-driven memorability predictor. Besides, structure feature is captured based on local binary pattern in gradient domain since human visual system (HVS) is susceptible to structure degradation. Considering that memorable and structure features are less sensitive to luminance change, luminance feature is extracted by the entropy of first derivative of image which improves the performance of our method significantly. Finally, we use support vector regression (SVR) to learn the mapping from feature space to quality space. A large number of experiments on two benchmark multiply-distorted datasets verify the advantage and efficiency of our proposed NR-IQA model compared with the state-of-the-art methods.
Virtual beam generation and guidance command calculation in FLS advanced approach process

FLS approach is an effective means to improve approach perception and reduce operational risk, and it is one of the key functions of flight management system of advanced commercial aircraft. This paper studies the generating virtual beam, calculating beam deviation and guidance command in FLS advanced approach guidance technology, and proposes a method of generating virtual beam based on navigation database information and a method of calculating beam deviation based on spatial geometric relationship. The guidance law was constructed based on angle deviation and lateral distance between the actual path and FLS beam, and the simulation test examle was designed to verity the deviation and guidance command calculation algorithm. The simulation results show that the FLS guidance algorithm proposed in this paper can achieve high-accuracy approach guidance.
An orthogonal calibration method for the multi-core fiber shape sensor

The multi-fiber shape sensor has important application value due to its high precision and anti- interference. The installation error of core fiber affects the measurement accuracy of the sensor but there is no universal calibration method in present sensor application. In this paper, an orthogonal calibration method is proposed to get the position parameters of each optical fiber. Based the calibration parameters, the shape measured can be reconstructed. Through digital simulation and sensor experiments, the measurement error of the multi-fiber shape sensor can be reduced to 1% of the traditional method.
Turn-of-the-candle effect in Bitcoin returns

This study discovers a statistically and economically significant intraday anomaly on Bitcoin markets. Positive returns of 0.58 bps per minute are disproportionately concentrated at the turn of 15-minute candles (in minutes 0, 15, 30, and 45 of each trading hour). Average returns in other trading minutes are negative. The effect is consistent across Bitcoin exchanges, in quantile regression, and TGARCH-M estimations with heavy tails. A high-frequency strategy that exploits this “turn-of-the-candle” effect can be net-outperforming with initial investment as low as $5,000. The anomaly is detected in the data starting from mid-to-late 2020, and its discovery contributes significantly to the understanding of cryptocurrency adaptive market efficiency.
Application of Improved ISM in the Analysis of Undergraduate Textbooks

In order to improve the quality and efficiency of the overall design and analysis of undergraduate professional courses, this paper analyzes and discusses Questionnaire Design through the improved ISM. As a result, 13 knowledge elements related to the content were successfully divided into 6 layers to form 5 effective teaching sequences. It fully verified the effectiveness of this method and provided data support for the analysis of the undergraduate textbook “Educational Technology Research Methods”. Finally, some suggestions are put forward to related teachers to help them innovate and develop in curriculum teaching.
Multilingual Novel Summarizer for Visually Challenged Peoples

In our busy lives, most of us hardly have time to read books. This habit of reading is slowly diminishing because of people's busy lives. The situation is significantly difficult for persons who are visually challenged or have lost their vision. As a result, the authors provide a method based on the sense of sound that is better and more accurate than the sense of touch for visually impaired people. This chapter discusses an effective method for condensing books into important keywords in order to avoid having to read the entire text each time. This work employs a variety of APIs and modules, including Gensim, Text Ranking Algorithm, and other functions for translating summary text to speech, allowing the system to assist even the blind.
Prediction for Categorical Data

Steve Fienberg was well-known for his pioneering work on categorical data analysis. Based on his work, there was an explosion of papers on this topic. Most of this work focuses on questions related to inference. A problem that has received less attention is prediction. In this paper we review two methods for prediction of categorical data. However, our goal is not point prediction, but set-valued prediction as we explain in Sect. 1.2.
A Pixel-Level Segmentation-Synthesis Framework for Dynamic Texture Video Compression

Dynamic textures are time-varying motion patterns that exhibit certain temporal stationarity. The motion of such patterns is usually anisotropic, which is a great challenge for video coding. In this paper, we firstly analyze the predictive and temporal characteristics of dynamic textures and, based on the analysis, a pixel-level segmentation-synthesis framework for dynamic texture compression is proposed to improve predictive-coding efficiency. This framework consists of three sub-modules: dynamic texture segmentation, dynamic texture synthesis and fusion process. A deep-learning-based segmentation method is introduced to obtain a pixel-level mask. Subsequently, optical flow fields are used to generate flowlines and help analyze the motion periodicity of a given sequence. The flowlines and period values are utilized as inputs for flow-based dynamic texture synthesis. The segmented dynamic texture area is then reconstructed using the synthesized results at the decoder side at pixel-level, such that the overhead for transmitting these contents could be saved. The segmented mask is transmitted through an All-Zero-Run-Length coding algorithm and an effective fusion module is further proposed to reduce edge artifacts that occur at the boundary of dynamic texture areas. Coding blocks have been classified into three types and a weighting reconstruction scheme is performed. The proposed framework has been integrated into VTM-10.0. Experimental results on a collected dynamic texture test set demonstrate that 15.1% and 16.1% MOS BD-rate savings can be achieved on average for LDB and LDP configurations, respectively.
Text hashing by semantic information based on BERT model

Text hashing transforms a text into a binary hash code, and similar texts correspond to similar hash codes. By using text hashing, we can accelerate the similarity retrieval of text in big data environment and reduce the storage costs. In view of most existing text hashing methods having not integrated contextual semantic information into hash codes, a text hashing method based on BERT (Bidirectional Encoder Representation from Transformers) model is proposed in this paper. Firstly, feature sentences are selected from the text, then the BERT pre-training language model is used to get the feature vector of each feature sentence, next, the weighted feature vectors are summed and binarized to obtain the text hash code. Experimental results show that both the precision and the recall rate of the proposed method outperform other text hashing methods