Reviewerly/exHarmony huggingface dataset:
Deep Structural Feature Learning

Vehicle re-identification (re-ID) has received more attention in recent years as a significant work, making huge contribution to the intelligent video surveillance. The complex intra-class and inter-class variation of vehicle images bring huge challenges for vehicle re-ID, especially for the similar vehicle re-ID. In this paper we focus on an interesting and challenging problem, vehicle re-ID of the same/similar model. Previous works mainly focus on extracting global features using deep models, ignoring the individual loa-cal regions in vehicle front window, such as decorations and stickers attached to the windshield, that can be more discriminative for vehicle re-ID. Instead of directly embedding these regions to learn their features, we propose a Regional Structure-Aware model (RSA) to learn structure-aware cues with the position distribution of individual local regions in vehicle front window area, constructing a FW structural map space. In this map sapce, deep models are able to learn more robust and discriminative spatial structure-aware features to improve the performance for vehicle re-ID of the same/similar model. We evaluate our method on a large-scale vehicle re-ID dataset Vehicle-1M. The experimental results show that our method can achieve promising performance and outperforms several recent state-of-the-art approaches.
InnerBehavior

Internet surfing is one of the most frequent activities in our daily lives. Investigation of web user behaviors may help to disclose the Internet usage habits, the life-styles and even the personality of users. Previous studies on web user Behaviors mainly based on data collected from questionnaires or log files of a particular website and the extracted features are manually designed for a specific application. In this paper, we propose a two-stage feature extraction framework called "InnerBehavior", which aims to reconstruct a general representation for web user behaviors from millions of access records. The proposed framework is employed to the detection of Internet addiction users. Experiment results demonstrate the effectiveness of the framework.
A Lexical Conversion as Preprocessing Method for Text Intention Classification

The structure and style of scientific paper can be analyzing by classifying the sentences in scientific paper according to intention. Existing researches on text classification tasks, such as emotion classification, mostly improve the model structure and ignore text preprocessing. In order to get better classification performance in intention classification task, this paper proposes a preprocessing method for lexical conversion of original text statements. First, a set of sentence labels for intention classification of scientific paper is proposed and annotated. Then the words in the sentence are divided into intent-mark words and non-intent-mark words, and the rules are designed to convert the non-intent-mark words. After vectorization, the processed text is input into LSTM network for training and acquisition of classification model. It is shown in several comparative experiments that the classification model obtained by using this method is higher than that obtained by the untreated text model.
단위학교 교사공동체의 실행공동체 특성 및 참여의 기제

This study interpreted the teachers’ community in a school from the viewpoint of community of practice. We investigated the characteristics of the teachers' community and the participation factors for forming such communities so that teachers can improve their professionalism and research in their schools. A school in Seoul, which is actively interacting with its colleagues and open to the school's educational activities, was selected for the case study, and a survey was conducted in-depth interviews with five teachers. The research results indicate that the characteristics of the community of practice (COP) were shared with the school's educational goals and core vision and were confirmed with the public-informal discussions about class and educational activities that were common. In addition, we have formed a reciprocal relationship based on the trust of colleagues, and it is confirmed that teacher research study was actively explored (research) and there were applied educational activities with teachers possessing a sense of ownership of the school. Finally, program operations such as cloud systems, open meetings, and a self-training system were analyzed. We specifically analyzed the participation patterns of teachers in consideration of the participation level of COP with respect to these characteristics. This study analyzed the human, environmental, and performance aspects of the factors of forming a community of practice and discussed ways to support the unit school as a community of practice.
Application of the Big Data Technology in the Enterprise Management

With the rapid development of the information technology, especially in the new situation of the deep implementation of the “Internet +” strategy, the big data technology has been applied to all aspects and all walks of life. This will have a certain impact on the management mode of enterprises. Enterprises should have a clear understanding of this, and in a specific process of its implementation, we should combine the big data technology with the enterprise management closely. We should not only give full play to the positive role of the big data technology, but also make great efforts to innovate the enterprise management mode, so that the big data can play a greater role in promoting the enterprise management reform and development.
A Low Delay and Bit-saving IntMDCT for Lossless Codec

Modified Discrete Cosine Transform (MDCT) is widely used in current audio codec for its excellent performance in frequency selectivity during critical sampling. To avoid lossy codec which produced by floating MDCT coefficients from integer input samples, Integer Modified Discrete Cosine Transform (IntMDCT) is a derivation of MDCT and utilized in MPEG-4 SLS to generate integer outputs which could lead lossless codec. However, IntMDCT also causes one-frame-length samples decoder delay and big quantization noise. In this paper, power-complementary windows are introduced into IntMDCT to achieve lower samples delay. More dense inputs for reducing quantization noise method can contribute to saving bit rate.
Self-Coherent Detection in Stokes Space

We review self-coherent (SCOH) modulation in Stokes space with varying degrees of freedom. We show that such an electrical spectrum-efficient linear modulation with simple and low-cost direct detection can be potentially suitable for short-reach communications.
Dependent choice as a termination principle

We introduce a new formulation of the axiom of dependent choice, which can be viewed as an abstract termination principle that in particular generalises recursive path orderings, the latter being fundamental tools used to establish termination of rewrite systems. We consider several variants of our termination principle, and relate them to general termination theorems in the literature.
Recommending Trips in the Archipelago of Refactorings

The essence of refactoring is to improve source code quality, in a principled, behavior preserving, one step at the time, process. To this end, the developer has to figure out the refactoring steps, while working on a specific source code fragment. To facilitate this task, the documentation that explains each primitive refactoring typically provides guidelines and tips on how to combine it with further refactorings. However, the developer has to cope with many refactorings and lots of guidelines. To deal with this problem, we propose a graph-based model that formally specifies refactoring guidelines and tips in terms of nodes that correspond to refactorings and edges that denote part-of, instead-of and succession relations. We refer to this model as the Map of the Archipelago of Refactorings and we use it as the premise of the Refactoring Trip Advisor, a refactoring recommendation tool that facilitates the combination of refactorings. A first assessment of the tool in a practical scenario that involves 16 developers and a limited set of refactorings for composing and moving methods brought out positive results that motivate further studies of a larger scale and scope.
Data protection method for charging pile based on blockchain technology

Aiming at the lack of information maintenance technology after the charging piles left the factory, the double encryption technology in the data transmission process causes a lot of computing power and storage space waste. A data protection method for charging piles based on blockchain technology is proposed. In order to ensure the security and integrity of the data, the blockchain technology is used to protect all the processes after the charging pile left the factory. The scalar multiplication of elliptic curve encryption algorithm is optimized and improved. Represent the random number as a binary number, reducing the amount of computation of the operation. In addition, in order to improve the efficiency of elliptic curve encryption algorithm, the Hamming weight is used to reduce the inverse operation.
Neuro-Fuzzy Inference Approach for Detecting Stages of Lung Cancer

A hybridized diagnosis system called Neuro-Fuzzy Inference System (NFIS) uses neural network for the classification of lung nodule into benign or malignant and then a fuzzy logic for detecting various stages of lung cancer is proposed in this paper. Using fuzzy logic based algorithms such as Enhanced Fuzzy C-Means (EFCM) and Enhanced Fuzzy Possibilistic C-Means (EFPCM), the required features from the lung CT scan image are segmented and extracted using GLCM and GLDM matrix. Then the features are selected using DRASS algorithm. These features are fed as input for Radial Basis Function Neural Network (RBFNN) classifier with k-means learning algorithm for detecting the lung cancer. Once the lung cancerous nodules are detected, the result of RBFNN is combined with fuzzy inference system that determines appropriate stage of the lung cancer. Experiments have been conducted on ILD lung image datasets with 104 cases. Results reveal that our proposed NFIS effectively classify lung nodule into benign or malignant along with the appropriate stage with considerable improvement in respect of Recall/Sensitivity 94.44%, Specificity 92%, Precision 96.22%, Accuracy 93.67%, and False Positive Rate 0.08.
A Stochastic Variance Reduced Extragradient Method for Sparse Machine Learning Problems

This paper mainly considers the optimization problem of minimizing the average of a number of smooth convex functions and a general convex function capable of proximal mapping. In particular, we consider non-smooth functions, and assume that the smooth convex function is strongly convex, which often occur in data mining and deep learning. For non-smooth large-scale high-dimensional machine learning problems, this paper introduces both the variance reduction technique in SVRG and extragradient descent, and proposes an efficient proximal stochastic variance reduced extragradient algorithm, called EProx-SVRG. From our theoretical results, we know that in the expected form, EProx-SVRG converges to the optimum at a linear convergence rate for strongly convex problems, and the convergence guarantee for non-strongly convex problems is also provided. Moreover, EProx-SVRG achieves the same oracle complexity as Prox-SVRG. Finally, our experimental results show that our algorithm can obtain an improved performance compared with Prox-SVRG and Katyusha.
Secure Email Login Based on Lightweight Asymmetric Identities

In this paper, the authors analyze existing email login schemes, point out their flaws and shortcomings of, and give a secure login scheme based on a lightweight asymmetric identity. For the first time, the new scheme adopts a lightweight asymmetric identity of a user account as a user login credentials. The lightweight asymmetric identity implicates a user account (namely user name), private key, IP source address, and random number, and is verified by an email server, which can prevent an attacker from intercepting the user information and directly logging in the user mailbox. Analysis manifests the scheme can resist replay attack, birthday attack, dictionary attack, phishing website attack, and their combinatorial attacks.
Obscured 3D Point-of-Gaze Estimation by Multipoint Cloud Data

Point cloud sensor is currently used to sense point cloud data information which is 3D position on the surface of target object. Point cloud data information can be input to improve for 3D point of gaze estimation (3D POG). Presently, there are limitation on creating point cloud data information on target object since point cloud data cannot be found if any obstacle in front of the sensor, there are shadow projection. This paper proposes method of multipoint cloud data to create point cloud data on the surface of target object and obscured point cloud data in the shadow projection. Eye tracker sensor provides 3D eyes position data and 2D POG on screen data which each origin represents center of eye tracker and center of screen respectively. These mentioned data are integrated by model fitting to draw a straight line, originating from the center point between left pupil and right pupil, which passes through the2D POG on virtual screen and ends when the line meets the closest point on the target object. In performance evaluation of proposed method, firstly the obscured point cloud data are successfully defined. Secondly, experiment by 4 participants by watching 9 units of testing objects at 2 seconds in free move provide the result of 3D POG estimation at average distance errors by 1.09 cm.
Development of Gaussian filter for Preserving Edge by Edge Template

Making image filters through only the Gaussian function even if the image is blended to reduce interference or disappear, but the result will lack one important feature of the image that is sharpness. This article, therefore, presents a way to spread the image that is based on the Gaussian filter but still possesses sharpness. The method is to check at any location that wants to filter images, and the system will compare with the template of the created image. To decide the mask adjustment from the Gaussian Function in order to be suitable for the data to be filtered When examining and deciding which image edge is from the system, the mask will be applied through the adjustment to match the appropriate edge angle data. And for any position that does not match the image edge, will spread the image as normal, which the results of the proposed method will result in Gaussian image filtering function but still maintaining the edge of the image.
Development of creative thinking in children

In the article, it is stated that the development of children's creative thinking is a requirement of time, and responsibility is given to educators and psychologists employed at educational institutions, and new approaches are needed to develop creative thinking in young people.
Fine-tuning SMPL

In this paper, we consider the task of statistical modeling of 3D human shape and pose. Current advances in computer graphics, 3D scanning and reconstruction technologies and emergence of new application areas for human body models raise the need not only in highly detailed human body model that can qualitatively represent details of human appearance, but also in a framework for fine-tuning this model based on newly collected data. We address both of these issues in our work by presenting a framework for fully automatic creation of highly detailed human body model based on SMPL model. The key features of our framework are mesh subdivision technique that increases the granularity of the model, modified Non-Rigid Deformation algorithm for smooth and precise registration of 3D scans and weighted registration process that allows controlling registration in low confidence areas of the 3D scan (holes, artifacts and voluminous haircuts). We propose and evaluate two body models with different detail level and show that even low detailed model outperform existing body models in terms of registration accuracy and cumulative relative variance.
An Amalgamation of Three Modulus Method and LSB4 Image Encryption Technique for Hiding text in an Image

In this paper, we have proposed a stenographic algorithm that advantageously disguises an instant message inside an image. The hidden message is concealed in the cover image using an amalgamation of a hybrid Least Significant Bit method and Three Modulus characters by character encryption. The entire encryption methodology is secured by a password such that only the designated receiver can decrypt the hidden message from the cover image.
Trasformazione di diatesi nella traduzione di testi legali in ucraino e russo: evidenze dall'analisi contrastiva

The article deals with the translation of European Union legal texts into Russian and Ukrainian.The translated texts are compared with the source text to trace the translation patterns of English passive constructions.In Ukrainian, there is a tendency to use a diathesis transformation, while in Russian a parallel passive construction is used.
What Business Leaders Should Know About Cloud Computing

Cloud computing represents a popular and frequent notion in media and scientific research. However, in post-transition countries, there remains a low offer of cloud computing solutions and users have not still accepted this concept besides its broad spectrum of benefits. This chapter examines the possible reasons why, despite high media attention, there are not many examples of using cloud-computing solutions by companies in post-transition countries, using Croatia as a case study. For that purpose, the survey among Croatian enterprises has been conducted regarding cloud-computing usage in business. Collected data is analyzed using knowledge discovery from databases aiming to define firms' readiness when using cloud-computing solutions. Results indicate that Croatian enterprises recognize the value of cloud computing as a software-as-a-service, but significant barriers are present in terms of slow recognition of its business value. Steps for overcoming this obstacle are proposed in the chapter.
Sensual and conceptual component of the conceptual binary opposition HUMAN BEING – TECHNOLOGY in the works by Ray Bradbury

The article is focused on the study of the sensual and conceptual component of the conceptual binary opposition human being – technology in Ray Bradbury’s works. The relevance of the research is based on the constant interest of the scientists in the study of binary opposition. The duality of world perception results in writers’ (including Ray Bradbury ) using binary oppositions as a means of conveying their own attitude to the spiritual values on mankind and the very sense of the world. The research identifies the theoretical prerequisites for the duality of human perception; reveals the methodology of frame analysis of concepts as members of binary oppositions; investigates the characteristics of concepts human being and technology as oppositions in Ray Bradbury’s works. The study shows that binary oppositions in the fictional text are preconditioned by the very nature of fiction. Binary oppositions in the fictional text have sensual and conceptual content, thus, the analysis of binary oppositions in the works of a writer gives the opportunity to identify the peculiarities of the writer’s worldview and to understand it in a more profound way. Binary oppositions are realized in the form of opposition of concepts as basic units of the cognitive code of humans with a relatively ordered internal structure. The study of concepts is carried out through the construction of frames as a means of generalized visual concept scheme. It is based and modeled on the relevant sources, collected in a single system of research and illustrative resources, and their graphical representation. This gives the possibility to identify the components of each concept and to analyze the parameters which the author considers to oppose the concepts. It is revealed that the binary opposition human being – technology in Ray Bradbury’s works is represented as an opposition of the key slots of these concept. Thus, it can be considered as a direct opposition of such slots as: animate – inanimate on the basis of functioning; feeling – insensibility on the basis of emotional capability of the world perception; interest – staticity on the basis of the cognitive abilities, and creativity – predation on the basis of the principle aim of a human being and technology.
Fault-injection Attack and Improvement of a CRT-RSA Exponentiation Algorithm

RSA cryptosystem is a widely-used public-key cryptographic algorithm in TLS/SSL and IPSec protocols. Fault-injection attack has a powerful threat on the CRT-based implementation of RSA cryptosystem. In 2016, Y. Choi et al. proposed a new right-to-left square-always exponentiation algorithm and a test-based CRT-RSA exponentiation algorithm to defeat the fault-injection attack. In this paper, we propose a fault-injection attack on Y. Choi et al.'s test-based CRT-RSA exponentiation algorithm. By inducing a permanent fault in the computation process of CRT-RSA cryptosystem, the attacker can obtain a faulty RSA signature and then recover the RSA private key. Furthermore, we give an improved CRT-RSA exponentiation algorithm to fix the security flaw. The security analysis shows that the improved algorithm can resist the fault-injection attack.
Collecting representative social media samples from a search engine by adaptive query generation

Studies in computational social science often require collecting data about users via a search engine interface: a list of keywords is provided as a query to the interface and documents matching this query are returned. The validity of a study will hence critically depend on the representativeness of the data returned by the search engine. In this paper, we develop a multi-objective approach to build queries yielding documents that are both relevant to the study and representative of the larger population of documents. We then specify measures to evaluate the relevance and the representativeness of documents retrieved by a query system. Using these measures, we experiment on three real-world datasets and show that our method outperforms baselines commonly used to solve this data collection problem.
A Target Tracking Algorithm Based on Correlation Filter and Least Squares Estimation

Target tracking plays a very important role in the civil field and has always been a hot topic for scholars. But there are also problems such as the difficulty of tracking goals. The core part of the algorithm framework proposed in this paper includes target recognition and target tracking models. The target apparent model is solved by using the correlation filter-based tracking algorithm, and the reliability of the block itself is calculated by using each block response graph, and calculate the relationship between multiple blocks in the same frame, and define the contribution of each block to the overall tracking result. In order to solve the problem of difficult target tracking, the core part of the algorithm framework proposed in this paper includes target recognition and target tracking model. In this paper, a tracking algorithm based on correlation filtering is used to solve the apparent model of the target, and the reliability of each block is calculated by the response graph of each block. The experimental results show that the parallel algorithm framework designed in this paper can not only achieve stable and accurate tracking under the interference of masking, deformation and scale change, but also quotes the idea of least squares method. The models and characteristics of the previous least squares method are analyzed and summarized. The target initial value least squares estimation method is improved.