Reviewerly/exHarmony huggingface dataset:
Homomorphic Sorting With Better Scalability

Homomorphic sorting is an operation that blindly sorts a given set of encrypted numbers without decrypting them (thus, there is no need for the secret key). In this article, we propose a new, efficient, and scalable method for homomorphic sorting of numbers: polynomial rank sort algorithm. To put the new algorithm in a comparative perspective, we provide an extensive survey of classical sorting algorithms and networks that are not directly suitable for homomorphic computation. We also include, in our discussions, two of our previous algorithms specifically designed for homomorphic sorting operation: direct and greedy sort, and explain how they evolve from classical sorting networks. We theoretically show that the new algorithm is superior in terms of multiplicative depth when compared with all other algorithms. When batched implementation is used, the number of comparisons is reduced from O(N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ) to O(N) provided that the number of slots is larger than or equal to the number of elements in the set. Our software implementation results confirm that the new algorithm is several orders of magnitude faster than many methods in the literature. Also, the polynomial sort algorithm scales better than the fastest algorithm in the literature to the best our knowledge although for small sets the execution times are comparable. The proposed algorithm is amenable to parallel implementation as most time consuming operations in the algorithm can naturally be performed concurrently.
LiDAR-Camera Fusion Based High-Resolution Network for Efficient Road Segmentation

This paper addressed the problem of road segmentation using a novel LiDAR-Camera fusion based high-resolution network. Road segmentation in different road conditions has been challenging due to limitations of single sensor. LiDAR could detect height and distance accurately in all road conditions but its data is too sparse for segmenting road, and camera can capture rich visual features but is susceptible to illumination variations and noises. We tackle this problem by fusing the data of these two sensors to complement each other's disadvantages. To achieve better fusion, the LiDAR data is transformed to image-like data and LiDAR features are also transformed adaptively. For better segmentation, we keep high resolution features throughout the convolutional network to reduce information loss and improve segmentation precision. The LiDAR-camera fusion are incorporated into the high-resolution network at multiple layers and multiple scales to constitute our road segmentation system. Comprehensive experiments on KITTI road dataset have been conducted to verify the effectiveness and efficiency of the proposed method.
Three‐dimensional face modeling technology based on 5G virtual reality binocular stereo vision

Summary 5G virtual reality technology is a technology that has attracted widespread attention in the computer field in recent years. Establishing a harmonious human–computer interaction environment in the multidimensional information space is one of the development goals of information technology. Face information is important information for identifying the identity of others. Three‐dimensional information has important applications in many fields and directions of machine vision. Therefore, the three‐dimensional common face image has a significant meaning. In this paper, binocular stereo vision system is used to collect multiple pairs of facial information images. The image correction method was used to correct the collected image pairs. According to the basic criteria of stereo matching, the advantages and disadvantages of various traditional image matching methods are analyzed. On the basis of completing the image correction, the graph cut matching method is selected as the matching method, and multiple pairs of collected image pairs are matched by this method to obtain a multi‐angle face disparity map. In this paper, after studying the basic dense matching algorithm and its improved algorithm, the SAD, SD, and NC methods of fixed window are used to compare the matching cost and the performance of the adaptive weight method in face matching. This paper studies the virtual roaming technology and the basis of the related Internet of Things (IoT) 3D face graphics theory and develops a building virtual roaming system based on MFC and OpenGL. The system draws on the basic I/0 device of the computer and provides users with a friendly MFC interface and multiple viewing angles and multiple roaming mode options for virtual faces. Experimental research shows that the reconstruction accuracy of the spatial points of the reconstructed face and the actual situation are both about 1%, which shows that the IoT 3D face modeling algorithm designed in this paper has high accuracy.
Designing a gamified reading app with pupils in elementary school

Playing games on mobile devices has become an integral part of younger generations’ lives. Mobile games foster, among other things, deep concentration. This paper reports on design guidelines derived from observations of six elementary school pupils’ engagement over a six-week period during an after-school reading club program. Each meeting consisted of three activities as well as reading text on the Microsoft immersive reader on an iPad, and playing a competitive reading game app, ‘Henry rennt’, which are both designed to support reading. Pupils were engaged in informal conversation with the researchers about both applications, and the authors informally observed the pupils’ engagement with the apps and each other. Patterns of engagement and comments from pupils informed the design of a new reading app. This work reports these general patterns and concludes with new research areas to pursue as a result, including the impact of: social setting on playing, in-game teaching with avatars on engagement, and speed as a measurement of skill mastery. Finally, the applicability of children’s engagement patterns is validated with adult students of German as a second language who used the app.
Wavelength-modulation Fourier interferometry with elimination of DC phase error

Wavelength-modulation Fourier interferometry has been employed to measure the thickness of a glass plate. However, nonlinear phase-modulation error causes a DC error in the evaluated phase distribution. In this study, a 5 N–2 phase-extraction algorithm was derived for the simultaneous measurement of the thickness and surface shape of a glass plate with eliminating the DC phase error. First, the condition for the error elimination on a complex plane was derived and a new algorithm polynomial was proposed to satisfy this condition. Next, using this condition and 5 N–4 algorithm, 5 N–2 algorithm was derived. The newly developed 5 N–2 algorithm was visualized in the frequency domain using the Fourier transform method. The DC error elimination ability of the 5 N–2 algorithm was confirmed through numerical error analysis. Finally, the surface shape and thickness of the glass plate were simultaneously measured using a large-aperture Fizeau interferometer and the 5 N–2 algorithm. The standard deviations of the thickness and surface shape profiling were 3.113 nm and 10.689 nm, respectively, which were smaller than those obtained using other phase-extraction algorithms.
Methods for Recognizing Images of Heterogeneous Objects in Small Training Sample

The article studies the application of computer vision, digital filtering and two-dimensional convolution methods for solving image recognition issues of heterogeneous objects by the example of the in situ plankton recognition in a small training sample. Key focus is on the study of the stability of computer vision template matching methods toward a group of affine transformations. The basic provisions are given alongside with the mathematical substantiation of the application of the methods of two-dimensional convolution with a dynamically generated kernel in order to solve the image recognition problem. The authors carry out a series of targeted synthetic tests for the recognition of plankton images in the conditions of unstable environment and the presence of random geometrical distortions. Moreover, for each class of the training sample, the minimum number (not more than ten) of manually prepared patterns is used. The Euclidean distance and correlation coefficient are used as classifier metrics. It is shown that the developed system successfully solves the issues of detecting objects against an unstable background and determining their class under complex conditions: multiple objects scale, random location and rotation, point and wave noise components on the background image texture, as well as minor brightness changes.
Verified Translation Between Purely Functional and Imperative Domain Specific Languages in HELIX

HELIX is a formally verified language and rewriting engine for generation of high-performance implementation for a variety of numerical algorithms. Based on the existing SPIRAL system, HELIX adds the rigor of formal verification of its correctness using the Coq proof assistant. It formally defines a series of domain-specific languages starting with HCOL, which represents a computation data flow. HELIX works by transforming the original program through a series of intermediate languages, culminating in LLVM IR. In this paper, we will focus on three intermediate languages and the formally verified translation between them. Translation between these three languages is non-trivial, because each subsequent language introduces lower-level abstractions, compared to the previous one. During these steps, we switch from pure-functional language using mixed embedding to a deep-embedded imperative one, also introducing a memory model, lexical scoping, monadic error handling, and transition from abstract algebraic datatype to floating-point numbers. We will demonstrate the design of these languages, the automatic reification between them, and automated proofs of semantic preservation, in Coq.
Sentiment analysis for movie reviews using embedding words with semantic orientation

Sentiment analysis is an opinion mining that analyzes people's emotions and attitudes on a given topic. The field of sentiment analysis is conducted by Natural Language Processing. In this research, a new sentimental method has been proposed, based on semantic analysis of the reviews, incorporated into the word Embedded as a deep learning framework. Also, in this research proposing a semantic classifier based on part of speech technique that used to identify the polarity of reviews as "positive" or "negative". The concept of Deep learning was implemented in the feature extraction step as an embedding word technique, that used to improve the accuracy of classifying. Due to the limitations in recent conventional bag-of-word models and their ability to capture word meaning similarities, that produce highly dimensioned vectors to throw in learn contextual information about words. The dataset of Internet Movie Database movie reviews was concentrated. Multiple tests were carried out, testing and evaluating the effectiveness of the proposed method using dataset. The recorded results showed a better accuracy by the embedding words as a deep learning technique, compared with the other vector representations.
Decision Support for Video-based Detection of Flu Symptoms

The development of decision support systems is a growing domain that can be applied in the area of disease control and diagnostics. Using video-based surveillance data, skeleton features are extracted to perform action recognition, specifically the detection and recognition of coughing and sneezing motions. Providing evidence of flu-like symptoms, a decision support system based on causal networks is capable of providing the operator with vital information for decision-making. A modified residual temporal convolutional network is proposed for action recognition using skeleton features. This paper addresses the capability of using results from a machine-learning model as evidence for a cognitive decision support system. We propose risk and trust measures as a metric to bridge between machine-learning and machine-reasoning. We provide experiments on evaluating the performance of the proposed network and how these performance measures can be combined with risk to generate trust.
Evaluation of Cloud Computing Copyright Protection Based on AHP

To increase the protection level of copyright in cloud computing environment, aiming at the problems that the indicators in the copyright protection evaluation system are difficult to accurately define and cannot be quantified, a comprehensive evaluation model of copyright protection based on the combination of analytic hierarchy process and fuzzy comprehensive evaluation is proposed. First, using the analytic hierarchy process (AHP), the copyright protection evaluation system is constructed and the weight of each evaluation index is determined through the judgment matrix. Then the quantitative evaluation result is fuzzy operated through index weight and evaluated data. Finally, the simulation evaluation results on specific example show that this model is reasonable and effective, and it can provide the evaluation basis and practical reference for copyright protection evaluation in cloud computing environment.
Real-Time Adaptive Color Correction in Dynamic Projection Mapping

Projection mapping augments a real-world object's appearance by projecting digital content on its surface. However, a remaining obstacle to immersive projection mapping is the limitation to white Lambertian surfaces and uniform neutral environment light, if any. Violating one of these assumptions results in a discernible difference between the source material and the appearance of the projected content. For example, some colors may not be visible due to intense environment lighting or pronounced surface colors. We present a system that actively subdues many of those real-world influences, especially environment lighting. Our system supports dynamic (i.e., movable) target objects as well as changing lighting conditions while requiring no prior color calibration of the projector nor any precomputed environment probing. We automatically and continuously estimate these influences during runtime in a real-time feedback-loop and adjust the projected colors accordingly.
Writer Identification based on Writing Individuality and Combination of Features

Individuality in handwriting occurs due to genetic as well as memetic factors. Memetic or cultural factors originate from training-related influences on pen-grip style and writing-strokes. This work presents a writer identification scheme using individuality of handwriting. Performance of such a scheme is greatly influenced by judicious selection off eatures and their combinations. In this work three types of features are selected, namely, line based, word based and character based. Importance of these features in writer identification is explored in the work. A dataset of English handwritten text samples written by subjects of Bengali origin and Bengali-medium schooling background is used for experimentation. Results are given for different combinations of features mentioned above. Emphasis is given on stroke features which include shapes like doughnut, hump, stick and stem-loop. They are found commonly in English lowercase letters. Best results obtained by using classifiers MLP and K-STAR are 93.54 percent and 95.69 percent respectively.
Review of the Application of Blockchain Technology in Traditional Chinese Medicine Field

The concept of blockchain has received a lot of attention and research since it was first proposed in 2008. With the popularization and development of blockchain technology, its huge development opportunities in the field of Traditional Chinese Medicine(TCM) have gradually revealed. This article studies the development path and technical characteristics of blockchain, analyzes the current research status of application of blockchain technology in medical field. It also summarizes the application and future research directions of blockchain technology in four typical TCM fields: TCM big data safe storage, Chinese medicine traceability, TCM electronic medical record privacy protection, TCM cloud health system and wearable devices. It is hoped that this paper can provide useful reference for relevant research.
Integrated Topic Modeling and User Interaction Enhanced WebAPI Recommendation using Regularized Matrix Factorization for Mashup Application Development

Mashup application developers combine relevant web APIs from existing sources. Still, developers often face challenges in finding appropriate web APIs as they have to go through thousands of available ones. Recommending relevant web APIs might help, but very low API invocation from mashup applications creates a sparse dataset for the recommendation models to learn about the mashups and their invocation pattern, ultimately affecting their accuracy. Effectively reducing sparsity and using supplemental information such as mashup and web API specific features that trigger mashups to invoke the same web APIs in their applications and web APIs to be used together by a mashup can help to generate more accurate and useful recommendations. In this work, we developed a novel web API recommendation model for mashup application, which uses two-level topic modeling of mashups and user interaction with mashup and web APIs sequentially to reduce the sparsity of the initial dataset. Then, we applied regularized matrix factorization with the mashup and web API embeddings. These embeddings integrate 'mashup to mashup' and 'web API to web API' relationships with 'mashup to web API' invocation analysis. Compared with existing web API recommendation models, our model achieved 54% more precision, 36.4% more Normalized Discounted Cumulative Gain (NDCG), and 36% more recall value over other baseline models on a dataset collected from programmableWeb <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Accurate Real-Time Traffic Light Detection Using YOLOv4

Traffic light detection has a significant role in every driver assistance system (DAS). Using the information of recognized traffic signals, a DAS provides safe navigating suggestions to drivers. In this paper, we focus on horizontal traffic light formats, which are popular in South Korea, North American and most European countries. Despite their different shapes and sizes, we group important traffic light signals into six categories: red, yellow, green, green-turn, red-turn and no signal. This paper proposes a method to localize all traffic light positions in the current scene; and identify the main ones from them. To detect and classify traffic signals, we use YOLOv4, which is the most recent deep learning framework. Next, based on the relation of detected traffic lights, our system removes irrelevant signals. Finally, with the optimized set of detected traffic lights, we identify the main signal for the whole scene. Experiments were conducted on two large datasets captured in Seoul Korea containing both highways and urban areas. The results achieved 95% accuracy at 30FPS.
Outlier percentage estimation for shape‐ and parameter‐independent outlier detection

Accurate and robust three-dimensional reconstruction of objects allows for applications in many aspects of modern life. Yet, it typically suffers from outliers and noise which often need to be post-processed. Although many algorithms are able to effectively remove the outliers, most require a certain amount of manual tuning of the parameter(s) or to have a parameter(s) set based on the rule of thumb. New machine learning and artificial intelligence-based methods have also been introduced but may require vast parallel computing resources as well as training data. In the present study, a novel combinatory-distance-based method capable of high accuracy outlier detection named as the sorted distance divergence point (SDDP) is introduced. Results show that SDDP is able to achieve an average accuracy of 98% in outlier detection. Moreover, the introduced distance function and outlier percentage allow clear labelling of inliers and outliers cloud points. Therefore, SDDP presents an attractive enhancement to existing methods; namely, the manual parameter(s) tuning may not be necessary. The adaptability and utility of SDDP is further demonstrated by incorporating SDDP with current methods, to produce a high accuracy outlier detector. When tested with 17 objects with 20–50% outliers, attain F1 and F2 scores averaging 0.960 and 0.968, respectively.
Handwritten Digit Recognition by Deep Learning for Automatic Entering of Academic Transcripts

Recently, deep learning has presented a new field of investigation for researchers in machine learning and has made it more artificial intelligence. Many domains of application are concerned by deep learning, such as health, education, robotics, surveillance, sports, etc., and more precisely Convolutional Neural Network (CNN). In fact, the CNN has become a standard to be used broadly in text classification, speech recognition, face detection, object detection, document analysis, and manuscript digit recognition. In this paper, we presented a case study consisting to recognize handwritten digits from academic transcripts. A new six-layered convolutional neural network is developed, trained using the known MNIST dataset, allowing to attain a training accuracy of 98,45% and a validation accuracy of 98,01%. Its efficiency is evaluated based on two experimental scenarios.
Alternative method sentiment analysis using emojis and emoticons

Our research aims to develop an alternative method for analyzing the tonality of the texts. Most of the traditional methods for determining tonality classes are based on text analysis and ignore various emotional indicators that users actively used in social networks. Therefore, it improves the quality of predicting the tonality of the class. The study is focused on three methods of expressing emotions in a text, emojis, emoticons, and punctuation marks that express emotions. We have developed a special lemmatizer for data preprocessing and built several text classifications models to classify the text into two classes, positive and negative, where emotional indicators are used as predictors. We have also used the RuSentiment corpus to create the classifier. The study has demonstrated that the proposed methods improve prediction of tonality classes by 6% compared to the traditional models. We have obtained the best results using a model ensemble based on the emotional indicators model and the Word2vec model. The model has demonstrated convincing results 91% accuracy and 0.937 area under the ROC curve. We have identified several patterns. Emotional indicators have a pronounced connection with tonality classes. Positive emotions are much more than neutral and negative. The model of emotional indicators has 85% accuracy; therefore, emotional indicators are very crucial in the analysis of the text tonality.
The Use of Max-Sat for Optimal Choice of Automated Theory Repairs

The ABC system repairs faulty Datalog theories using a combination of abduction, belief revision and conceptual change via reformation. Abduction and Belief Revision add/delete axioms or delete/add preconditions to rules, respectively. Reformation repairs them by changing the language of the faulty theory. Unfortunately, the ABC system overproduces repair suggestions. Our aim is to prune these suggestions to leave only a Pareto front of the optimal ones. We apply an algorithm for solving Max-Sat problems, which we call the Partial Max-Sat algorithm, to form this Pareto front.