Reviewerly/exHarmony huggingface dataset:
Research on Real Estate Transaction Platform Based on Blockchain Technology

Abstract This paper analyzes the current status of real estate transactions in China and puts forward some shortcomings. In response to these shortcomings, a real estate transaction platform based on blockchain technology is proposed. This platform uses the Hyperledger Fabric platform to effectively connect purchasers, sellers, financial institutions and government departments. Firstly, the whole transaction process of the platform is proposed. Then, the design framework of the system is proposed and the implementable scheme is given. At last, this work summarizes some points of the program, and points out the shortcomings and difficulties in the future work.
Modeling of Smart Contracts in Blockchain Solution for Renewable Energy Grid

The paper presents the manner of smart contracts’ modeling in blockchain solution. The authors illustrate the modeling approach using the example of a renewable energy system. The paper proposes extension of Unified Modeling Language (UML) in form of UML Profile for Smart Contracts, appropriate for smart contract design. Moreover, the standard smart contract design and implementation method, in Corda environment, has been made more flexible. The authors present static aspect of newly designed Smart Contract Design Pattern.
Unsupervised Person Re-Identification via Multi-Label Classification

The challenge of unsupervised person re-identification (ReID) lies in learning discriminative features without true labels. Most of previous works predict single-class pseudo labels through clustering. To improve the quality of generated pseudo labels, this paper formulates unsupervised person ReID as a multi-label classification task to progressively seek true labels. Our method starts by assigning each person image with a single-class label, then evolves to multi-label classification by leveraging the updated ReID model for label prediction. We first investigate the effect of precision and recall rates of pseudo labels to the ReID accuracy. This study motivates the Clustering-guided Multi-class Label Prediction (CMLP), which adopts clustering and cycle consistency to ensure high recall rate and reasonably good precision rate in pseudo labels. To boost the unsupervised learning efficiency, we further propose the Memory-based Multi-label Classification Loss (MMCL). MMCL works with memory-based non-parametric classifier and integrates local loss and global loss to seek high optimization efficiency. CMLP and MMCL work iteratively and substantially boost the ReID performance. Experiments on several large-scale person ReID datasets demonstrate the superiority of our method in unsupervised person ReID. For instance, with fully unsupervised setting we achieve rank-1 accuracy of 90.1% on Market-1501, already outperforming many transfer learning and supervised learning methods.
Hypergraph based clustering for document similarity using FP growth algorithm

Modelling multiple documents for different applications is a major field of research due to the tremendous growth in Web data. To find the document similarity, we require clustering to determine the grouping of unlabelled data. Graph models have the capability or knowledge of capturing the structural information in texts. It organizes high dimensional data in such a way that the user can effortlessly access the desired information. In this paper, we use a hypergraph with the help of an association rule mining to model a collection of text documents and find similarity between them using a hypergraph partitioning algorithm. Here we use FP-Growth algorithm to find the association relationship which is a recursive elimination scheme. We then uses a spectral clustering algorithm which uses eigenvalues and vectors which is found out from the matrices to find similar documents. Experiment shows that this algorithm gave better clusters compared to others which commonly take higher eigenvectors.
A Human-Robot Collaboration Framework Based on Human Motion Prediction and Task Model in Virtual Environment

Human-robot collaboration is one of the hot topics in the field of robotics, which has attracted much recent interests. This paper proposes a human-robot collaboration framework that combines a human motion prediction model with a finite state machine-based task model. A virtual human-robot collaboration environment is created, including a robot model, an interactive object model, and a human body model that can be controlled by a skeleton point tracking algorithm. The experimental results show that the precision and recall based on our model are much higher than those based on the traditional Gaussian mixture model. The task model based on finite state machine can effectively model human-robot collaboration tasks.
The Practical Implication of Software Quality Assurance of Change Control Management: Why Overall IT Project Activities Matters?

Changes can be made frequently throughout a project. Software change control (SCC) as a subset of software quality assurance (SQA) is responsible for examining, approving changes, assuring quality of changes made and quality of the newer version of system with changes implemented. While a system is often broken down into a number of releases, these SCC tasks can be fulfilled by integrating approved changes to planned releases. As such, SCC tasks can be achieved simultaneously with project releases. However, a number of relevant discussions are focusing on the process of handling change request as a cycle itself and neglected the integration of changes to project. The overall view of IT project activities and sequence is not readily presented has too lead to the blurred vision of how changes can be integrated. This paper then addresses two main areas. One on establishing the association between project life cycle (PLC) and system development lifecycle (SDLC) to present an overall picture of IT project activities since changes can impact any area of a project and eventually integrate in it; and the other is on the integration between IT project and change requests that can achieve SCC concurrently considering the project throughput as a whole.
Image Enhancement in Varying Light Conditions Based on Wavelet Transform

The following topics are dealt with: learning (artificial intelligence); neural nets; pattern classification; feature extraction; optimisation; computer vision; convolutional neural nets; health care; gradient methods; medical computing.
Security of quantum key distribution with detection-efficiency mismatch in the multiphoton case

Detection-efficiency mismatch is a common problem in practical quantum key distribution (QKD) systems. Current security proofs of QKD with detection-efficiency mismatch rely either on the assumption of the single-photon light source on the sender side or on the assumption of the single-photon input of the receiver side. These assumptions impose restrictions on the class of possible eavesdropping strategies. Here we present a rigorous security proof without these assumptions and, thus, solve this important problem and prove the security of QKD with detection-efficiency mismatch against general attacks (in the asymptotic regime). In particular, we adapt the decoy state method to the case of detection-efficiency mismatch.
A Logistic Regression Approach for Generating Movies Reputation Based on Mining User Reviews

The paper aims to present an approach for generating a single reputation value towards a target movie based on mining movie reviews and their attached ratings with the use of Logistic Regression classifier and Latent Semantic Indexing (LSI) method. The contribution of the paper is fourfold. First, we apply Logistic Regression classifier to determine the sentiment orientation of movie reviews (positive or negative). Second, we use LSI method and cosine similarity to compute the semantic similarity between reviews. Third, we compute a custom reputation value separately for positive opinions group and negative opinions group. Finally, we use the weighted arithmetic mean to generate a single reputation value towards the target movie.
Three-dimensional steering for an articulated mobile robot with prismatic joints with consideration of hardware limitations

The paper presents a three-dimensional steering method for an articulated mobile robot that contains links, rotational joints, prismatic joints, and active wheels. The robot can change the angles of its links using the rotational joints and vary the lengths of its links using the prismatic joints. The target motion of this robot is represented by a continuous curve and the motions of the joints and the wheels are calculated by fitting the entire robot to this target curve. The forward velocity of the robot's head is adjusted to satisfy the hardware limitations of the robot; e.g. joint angle, joint velocity, and wheel velocity limitations. In addition, a terrain-following method is presented that considers the changes in the lengths of the links. An experimental articulated mobile robot was developed and experiments were carried out to demonstrate the effectiveness of the proposed method.
A Face Recognition Method Based on Broad Learning of Feature Block

Recently, deep learning methods are widely used in face recognition, but the model training time is long, and the problem of global optimal solution cannot be guaranteed. In order to solve the problem of high time cost in face recognition training, this paper applies Broad Learning System (BLS) to face recognition. At the same time, BLS is sensitive to the input features, and the occlusion and illumination problems in the recognition process are considered. The idea of feature block is introduced to BLS. Final weighted facial features and face recognition. Experiments were carried out on ORL and Yale-B datasets, and compared with BLS, PCA and improved deep learning algorithm. The results show that our method is not affected by the number of features on the face dataset with strong illumination and occlusion, maintaining a high recognition accuracy.
An Ontology Based on the Timeline of Log2timeline and Psort Using Abstraction Approach in Digital Forensics

Digital forensics practitioners encounter numerous new terminologies during time-intensive digital investigation processes because of the explosive growth of the web, an immense amount of data, and rapid changes in technology. In such a scenario, the time needed to find and interpret the cause of the potential digital incident can be affected by the complexity involved in understanding the meaning of newly encountered terminologies. Although various approaches have been designed to assist digital practitioners in understanding the newly encountered terminologies during the investigation of the accident, none of them is capable of supporting investigators to interpret new terminologies. Our work focuses on reconstructing and analyzing the timeline of events and artifacts backed by the abstraction concept to help practitioners in reasoning about the perceived meaning of different digital forensics terminologies that are encountered during the investigation. This paper introduces an ontological approach based on the abstraction concept to reconstruct the timeline provided by command-based digital forensic tools, i.e., Log2timeline and Psort in the L2TCSV format, and assist in resolving the meaning of new encountered concepts. The performed experiments show that the novel methodology is capable of enhancing the timeline and assisting practitioners in determining the significance of encountered terminologies or concepts.
A Performance Comparison of Supervised and Unsupervised Image Segmentation Methods

Image processing plays a vital role in many recent computer applications in the association with machine learning technology. The supervised training on dataset of features can only be successful if the segmentation process is accurate in the computer vision phase. The term segmentation is the process of extracting or identification of distinguishable regions in an image. This is performed based on the properties of image pixel intensity values and their proximities. This paper mainly focuses on an investigation of various latest image segmentation techniques performed in the field of computer vision and image processing. Segmentation plays a vital role in computer vision since any fault in segmentation will led to inaccurate extraction of features which results in wrong prediction of the decision support systems.
Too big to fail but big enough to pay for their mistakes

In this paper, we illustrate the usefulness of the family of methods collectively known as Collostructional Analysis for phraseological research. Investigating two patterns, [too ADJ to V] and [ADJ enough to V], we show how a technique originally developed for the investigation of words and constructions can be fruitfully applied to issues pertinent to phraseology, such as the co-existence of compositional and idiomatic semantics and the analysis of semantically complementary patterns more generally. To this end, we use the three conventional methods (Simple, Distinctive and Co-varying Collexeme Analyses) and propose a novel extension (Distinctive Co-varying Collexeme Analysis) particularly suitable for the investigation of complementary patterns. We show that collostructional analysis is suitable for confirming hypotheses derived from qualitative analyses, as well as uncovering subtle differences that are otherwise inaccessible for non-empirical research.
Identifying cross-lingual plagiarism using rich semantic features and deep neural networks: A study on Arabic-English plagiarism cases

The rapid growth in the digital era initiates the need to inculcate and preserve the academic originality of translated texts. Cross-lingual semantic similarity is concerned with identifying the degree of similarity of textual pairs written in two different languages and determining whether they are plagiarized. Unlike existing approaches, which exploit lexical and syntax features for mono-lingual similarity, this work proposed rich semantic features extracted from cross-language textual pairs, including topic similarity, semantic role labeling, spatial role labeling, named entities recognition, bag-of-stop words, bag-of-meanings for all terms, n-most frequent terms, n-least frequent terms, and different sets of their combinations. Knowledge-based semantic networks such as BabelNet and WordNet were used for computing semantic relatedness across different languages. This paper attempts to investigate two tasks, namely, cross-lingual semantic text similarity (CL-STS) and plagiarism detection and judgement (PD) using deep neural networks, which, to the best of our knowledge, have not been implemented before for STS and PD in cross-lingual setting, and using such combination of features. For this purpose, we proposed different neural network architectures to solve the PD task as either binary classification (plagiarism/independently written), or even deeper classification (literally translated/paraphrased/summarized/independently written). Deep neural networks were also used as regressors to predict semantic connotations for CL-STS tasks. Experimental results were performed on a large number of handmade data taken from multiple sources consisting of 71,910 Arabic-English pairs. Overall, experimental results showed that using deep neural networks with rich semantic features achieves encouraging results in comparison to the baselines. The proposed classifiers and regressors tend to show comparable performances when using different architectures of neural networks, but both the binary and multi-class classifiers outperform the regressors. Finally, the evaluation and analysis of using different sets of features reflected the supremacy of deeper semantic features on the classification results.
Preventive Measures to Secure Issues in Cloud Computing

This paper sheds light over the security issues and challenges in cloud computing and puts emphasis on cloud computing service types and the different types of delivery along with their security challenges and threats. While the benefits of cloud computing are clear, there are several issues that keep enterprises from adopting it. Problems including account control, data control, multi-tenancy, data segregation and more need to be addressed and have made several propositions to reduce some of these issues and facilitate management of security in cloud computing. These propositions include biometric validations using facial recognition and fingerprint scan.
Secure DNA and Morse code based Profile access control models for Cloud Computing Environment

Cloud computing is an emerging field in the Information Technology sector which allows users to access resources from cloud servers irrespective of its geographical location. It utilizes the concept of virtualization, service-oriented architecture and parallel computing. It is an on-demand service which is cost-effective, resilient, efficient, and explandable. Since data is generating in big numbers from various fields of life, we require a more innovative way to manage and secure that data. With the growing social platforms, managing Big Data calls for the need for cloud computing. Nowadays access control and security are recent major research problem in cloud computing. Access control is a paradigm by which a specific consumer access resources from cloud server. This paper proposes a novel access control methodology to cope up with the above issues. The model is based on an integrated DNA morse code pattern that provides data security against collision, man-in-the-middle, and internal attacks. It also compares the proposed model with existing approaches.
Edge Camera based Dynamic Lighting Control System for Smart Streetlights

In this paper, we propose a novel dynamic lighting control system for smart streetlights using an edge camera which detects objects for itself using a light weight deep neural network at local. The proposed system consists of an edge camera, a light control unit, and a control server. The proposed system dynamically adjusts the brightness of a dimming module based on event messages from the edge camera. The proposed system maintains low brightness when there are no pedestrians and switches to high brightness when pedestrians appear. And when a pedestrian leaves the service radius, it returns to low brightness. We designed the protocol between the edge camera and the light control unit, and developed the pedestrian detector using a lightweight deep neural network, which provides real-time processing and the high detection accuracy.
Analysis of New Distributed Differential Evolution Algorithm with Best Determination Method and Species Evolution

This is a new semi-distributed differential evolution algorithm with best determination method and species evolution (DESBS). This algorithm is based on fact- evolution of species around niche. In DESBS, the best determination method determines the best individuals of population. These best individuals act as niches and the species are evolved around these best individuals. Over the period of time, each species evolved separately, using standard differential evolution algorithm. The evolving efficiency of each species evaluated separately and inefficient species are merge to nearby species. The scale-up study is performed to find out best parameter setting and the results are compared with other state-of-art algorithms like CoDE, EPSDE and standard differential evolution algorithm. The results show that the DESBS perform better than SDE in multimodal functions and CoDE, EPSDE in rotating optimization function
Content-based Image Retrieval Speedup

Content-based image retrieval (CBIR) is a task of retrieving images from their contents. Since retrieval process is a time-consuming task in large image databases, acceleration methods can be very useful. This paper presents a novel method to speed up CBIR systems. In the proposed method, first Zernike moments are extracted from query image and an interval is calculated for that query. Images in database which are out of the interval are ignored in retrieval process. Therefore, a database reduction occurs before retrieval which leads to speed up. It is shown that in reduced database, relevant images to query image are preserved and irrelevant images are throwed away. Therefore, the proposed method speed up retrieval process and preserve CBIR accuracy, simultaneously.
Dimensions that characterize and mechanisms that cause the misuse of information systems for corrupt practices in the Nigerian public sector

Abstract This article explores the dimensions and structural mechanisms that can foster the misuse of information systems (IS) for corrupt practices. Using the abductive‐retroductive strategy native to critical realism, we present a realist conceptualization of why the misuse of IS for corrupt practices occurs in the Nigerian public sector. Routine activity, reintegrative shaming, model of emergent IT use, and normalization theories were adopted as theoretical lenses. Danermark et al's six‐stage explanatory framework embedded with a single case study was adopted as the methodology. Focus group and semistructured interviews were used as primary sources of data, while archival documents and press media reports were used as secondary data sources. From the data analysis, malleability structures of the IS artifacts, dysfunctional structures of the Nigerian state, embeddedness of corrupt routines into IS artifacts, institutionalization, socialization, rationalization, and negative reintegrative shaming were identified as core causal structural mechanisms generating the misuse of IS for corrupt practices in the study context. Such corrupt practices were clustered in motivated offenders, suitable targets, and absence of a capable guardian as dimensions that characterized the misuse of IS by our findings. Our findings contribute to theory, practice, and the methodology of critical realism.
How much vocabulary is needed to use a concordance?

Abstract Vocabulary load is a predictor of comprehension and a common concern in relation to learner use of concordances; however, vocabulary load figures for whole texts have limited relevance to learner use of concordances. This paper explores the average vocabulary load of the citations (or lines) in a concordance, reflecting how learners use concordances as reading or reference resources. Non-parametric tests are used to compare the vocabulary loads of citations from three authentic written corpora and a corpus of graded readers. The results indicate that citations from authentic corpora have an average vocabulary load of 4,000–5,000 word families, there are reliable differences in vocabulary load between citations from different corpora, and the magnitude of difference between citations from authentic corpora can be equivalent to the magnitude of difference between authentic corpora and graded reader corpora. The paper concludes with a discussion of the results in relation to language learner use of concordances.
A Scheme of Answer Selection In Community Question Answering Using Machine Learning Techniques

Question Answering System is a very useful system in most of the deep learning related problems and it can be modeled as a question answering problem. Answer selection in Community Question Answering (CQA) is a challenging and important task to develop the automatic Question Answering System in a Natural Language Processing (NLP). The system requires semantic gap between questions answer pairs is the main problem in the QA system. It also needs a serious modeling of contextual factor. An attentive deep neural network architecture is proposed in this paper. The architecture is having three layers which are namely; Convolution Neural Network, Long Short Term Memory and Conditional Random Field. The SemEval-2015 CQA dataset is used to develop the experiment.