Reviewerly/exHarmony huggingface dataset:
Learning the distribution of latent variables in paired comparison models with round-robin scheduling

Paired comparison data considered in this paper originate from the comparison of a large number N of individuals in couples.The dataset is a collection of results of contests between two individuals when each of them has faced n opponents, where n N. Individuals are represented by independent and identically distributed random parameters characterizing their abilities.The paper studies the maximum likelihood estimator of the parameters distribution.The analysis relies on the construction of a graphical model encoding conditional dependencies of the observations which are the outcomes of the first n contests each individual is involved in.This graphical model allows to prove geometric loss of memory properties and deduce the asymptotic behavior of the likelihood function.This paper sets the focus on graphical models obtained from round-robin scheduling of these contests.Following a classical construction in learning theory, the asymptotic likelihood is used to measure performance of the maximum likelihood estimator.Risk bounds for this estimator are finally obtained by sub-Gaussian deviation results for Markov chains applied to the graphical model.
Unveiling Cybercrime in a Developing Country

This article unveils the pervasiveness of cybercriminal activities in Ghana from the point of view of key stakeholders in the fight against the crime. From a critical realist's perspective, the authors employed a qualitative approach to understand cybercrime from the point of view of different key players—four cybercriminals, eight lawyers, eight bankers, six café operators and personnel from the cybercrime unit of the Ghana Police—for the study. Findings from the article pointed out that cybercrime has gained ground among Ghanaian youth between the ages of 12 and 35, mostly males with female decoys. The findings of the article also suggested lack of confidence in the Ghana Police service to crackdown on cybercriminal activities in Ghana. The research finally suggested that there seem to be the nonexistence of cybercrime policies and laws for lawyers to arraign or defend suspected criminals in Ghanaian courts of law.
A Study of Contrast Enhancement Methods in Various Color Spaces on Biometric Recognition System

Image enhancement especially contrast enhancement has a significant role in the biometric recognition system. Contrast image enhancement exposes an important feature in an image that is concealed around the dark area before the transformation. This brings important improvement in the system of biometric recognition. In this paper, we studied the effect of contrast enhancement methods such as histogram equalization, image adjustment, mathematical morphology in top-bottom hat algorithm and CLAHE in HSV, YCbCr and RGB color spaces. Using non-exhaustive cross-validation and nearest neighbor distance calculation, 400 images of androgenic hair were observed. The best results were obtained from saturation in HSV and Cr component from YCbCr using normal histogram equalization method. Overall, the contrast enhancement methods, in certain combination, improved precision recognition until 20% better. The best result in precision was derived from saturation component in HSV color spaces, around 89% precision recognition and Cr component in YCbCr color spaces, around 88% precision recognition. Meanwhile, the best contrast enhancement method was obtained from histogram equalization, around 89% precision recognition.
Reordering Search Results to Support Learning

Although many learning activities involve search engines, their ranking criteria are focused on providing factual rather than procedural information. In the context of Searching as Learning, providing factual information may not be the best approach. In this paper, we discuss the relevance criteria according to traditional learning theories to support search engine results reordering based on content suitability to learning purposes. We proceeded on the investigation by selecting some self-proclaimed search literacy experts to answer thoroughly questions about their views on the reordered results. We take into account that literacy expert’s judgment may reveal issues regarded to technical side on learning supported by search tools. Experienced users claimed a preference for reliable sources and direct answers to what they are looking for, as they have exploratory skills to overcome information incompleteness.
Research on News Value Feature Item Based on User Attention

With the update of network technologies and the large increase in the number of news releases, it is more important to choose news topics that are more in line with the audience entry point and higher news value. Based on the random forest algorithm, this experiment utlizes the news value knowledge system to construct the news value feature items, and formulates the algorithm to quantitatively calculate the feature importance of the new value feature items to provide a more accurate and usable method for the research of news value prediction, which can help journalism works better and more effectively.
Visualization of Spatial-Temporal Distribution of Damage Reports

This paper presents our visualization tool developed to address Mini Challenge 1 of the 2019 VAST Challenge [1]. The insights obtained using this tool are described separately in our main competition entry, which will be archived in the Visual Analytics Benchmark Repository [2].
Research on Mission Planning for MAV/UAV Cooperative Combat

MAV/UAV cooperative combat mission planning: based on the integration task of reconnaissance, decision, attack and damage assessment, the heterogeneity of clusters, the limitation of airborne resources and the constraints of multitask time order are all considered. By utilizing graph theory and combinatorial optimization theory, an optimization model for MAV/UAV cooperative mission planning is established. An improved genetic algorithm is proposed to solve the problem. By designing genetic operators such as population initialization, crossover and mutation, the heterogeneity and resource limitation are successfully solved. Then an optimal solution is obtained in the process of population evolution. Finally, an example is given to prove the feasibility and effectiveness of the proposed model and method.
Sentiment Translation Model for Expressing Positive Sentimental Statements

Natural Language Processing enables computers to understand human natural languages and assists humans to perform many tasks, such as information retrieval, question answering, automatic summarization, text categorization, machine translation, etc. To our knowledge, there is still no NLP employed on the application of translating negative sentimental statements into positive sentimental statements with similar semantics although it is an important application in communications between humans. In this paper, we propose the concepts and implementations of translating negative sentimental statements into positive sentimental statements. First, we build negative-positive sentimental statement data sets for a sentiment translation model. Then, the sentiment translation model is trained using deep learning techniques. In the experiments, we use perplexity, BLEU, and subjective human assessments to evaluate the model, and the experimental results are satisfactory. Finally, we think the methods employed in translating sentimental statements are feasible if the trained data sets can be built as expected, and more advanced models are developed.
Solutions to Achieve Virtual Machine Placement in Cloud Computing Environment

Cloud providers have enormous expectations in terms of maintaining Quality-of-Service (QoS) to optimize energy efficiency levels, since cloud computing has become very popular. Several mechanisms are discussed in research papers to achieve it. One such mechanism is Virtual Machine Placement (VMP). In this paper, we discuss variants of bin packing algorithms which enhances VMP placements. Further, it summarizes them with different parameters. Additionally, it also points out the challenges involved in bin packing based algorithms.
Learning Fuzzy SPARQL User Preferences

In this paper, we propose an adaptive fuzzy user profiling method for SPARQL: an RDF query language [1]. This work extends the study [2] where we proposed a manner by which we enrich SPARQL with fuzzy user preferences expression. According to our approach, users issue generic fuzzy quantified queries that are further refined based on his/her past interactions with the system. Unlike [2], we avoid prompting the user for manual expression of his/her preferences. Online preference learning approaches are by definition adaptive to changes over time of the user preferences which make them more attractive than their static counter-part. In order to achieve online learning, we resort to stochastic search and propose to integrate two different types of user feedback, namely rank-based and score-based. The efficiency of this approach was validated by some experimental results.
An adaptive stenographic technique, ascertaining upper bounds of embedding

In the world of advanced technologies, we mostly work on the networks by sending and receiving credential information, which can cause huge loss if the data or information leaks outside. Thus the data transmission should be performed with some security rules by which the third party person cannot steal the data while transmission. There are many techniques used to secure the data. But, all those technique didn't include rate-distortion bound of the image. Thus all these techniques became complex. In this paper, we implement a new methodology known as “An adaptive stenographic technique, ascertaining upper bounds of embedding” where we initially embed the information with the cover image, after that, first find out the good distortion rate of the cover image and stegano image. Later, feature extraction is performed using stegnalysis of both cover image and stegano image. After finishing the feature extraction for both cover and stegano image, it will be given to the classifier to evaluate the rate of detection. We use factors such as SRM (Spatial-Rich-Model), ensemble classifier and maxSRM for evaluation of results for steganalyzer. After performing the evaluation, our results are compared with the previously existing techniques result to show that our implemented technique is much better than the other techniques.
Research and Application of Small Object Recognition Based on TX2 in the Field Scene

In the field environment target detection, there are problems such as low grab rate due to fast moving objects and poor quality of acquired image data. Considering these issues, this paper designs a field small target detection system that is mounted on the TX2 embedded development board. To solve the problem of poor image data quality, a super-resolution method DRCN (deep-recursive convolutional network) is introduced to enhance the data clarity. Besides, the structure of DRCN is set to be deeper. To handle the problem of low capture rate of fast moving targets and the problem of memory shortage in TX2, a new method based on increasing inter-frame difference threshold judgment is employed. The new method namely the three-frame difference method based on difference threshold judgment can filter the "blank" differential image. Differential mapping preservation only when fast moving targets occur.It can improve the capture rate of fast moving targets and save storage space at the same time. Experiments show that the designed system can adapt to TX2 and effectively improve the effect of field environment detection.
Kineticist: Kinetic Sculpture Design Using Multilevel Skeletons

This article presents a rapid parametric design system of the rotary kinetic sculpture. The multilevel skeletons enable users to model propeller-like units rapidly, arrange and deform them collaboratively, and generate transmission mechanisms automatically without any specialized machine knowledge. Experimental results show that our system can help users get diverse rotary kinetic sculptures effectively.
Hybrid Genetic Algorithm &amp; Learning Vector Quantization for Classification of Social Assistance Recipients

The social assistance program called “Rastra” is a government program that aims to ease the burden on poor families by providing food. However, the distribution of the food to prospective beneficiaries is still not accurate. So, a classification method is needed that can help to estimate the right target. In this study, the classification of social assistance recipients by using the Learning Vector Quantization (LVQ) method. LVQ weight vector is very important in the classification process because it affects the classification results. This study applies the Genetic Algorithm to optimize the LVQ weight vector to improve accuracy. The results obtained from this study indicate an LVQ accuracy of 84.16% and GA-LVQ gives a higher accuracy of 87.08%. Produces the best parameters: population size (popSize) 100, crossover rate (cr) 0.5, mutation rate (mr) 0.5, max generation 80, learning rate (a) 0.1 and reduce learning rate (dec a) 0, 1. The use of the LVQ method that is optimized using GA has been shown to provide better results, with higher accuracy values compared to the LVQ method without being optimized.
Method for Calculating Number of Backup Applications in Server Cluster of Regulation Cloud

Regulation cloud platform is clearly proposed in the 13th Five-Year Plan of State Grid Corporation of China's informatization. In order to meet the growing demand for business and technology development. At present, applications of regulation cloud need to run in server clusters. To ensure the stable operation of the platform, each application needs to be backed up by several other machines. This paper proposes a method which can calculate the number of backup server for an application, make the use of hardware source more rational and the platform more stable.
Towards a time editor for orchestrating connected objects in the Web of Things

Web of Things is a new paradigm, it constitutes the heart of a great research activity. However, most of this work does not take into account its temporal aspect, whereas it is a critical dimension directly related to customer satisfaction, optimization and is considered as a very effective strategy for cost reduction. For this matter, we propose a tool to edit and verify the time constraints added to an abstract BPEL specification. Furthermore, the editor allows the user to edit abstract BPEL specification that orchestrates Web services offered by objects connected to the Web of Things. Through the latter, the input specification is enriched with constraints and time attributes. Then, a temporal verification and validation process is applied to detect any temporal errors or conflicts.
Context-Aware weighted for visual tracking

To solve the problem of target drift during the tracking process, this paper proposes a general framework based on the context-aware tracking framework. In the tracking process, the status of the correlation detection response map is observed in real-time through the APCE. When there is a single sharp peak on the correlation detection response map indicating that the tracking result is stable, the position of the correlation response maximum is taken as the target position, and the filter will perform zero-regression learning on the context of the target region. Conversely, when there are multiple peaks on the correlation detection response map indicating that the tracking result is unreliable, the position of the target is redetected in the multi-peak region, and the filter will perform weighted regression learning on these multi-peak regions. We apply the modified framework to the basic trackers DCF and SAMF for experimentation and compare the performance of it with the basic tracker and the corresponding tracker using the context-aware framework. The experimental results show that the tracker using our modified framework can accurately locate the target and avoid the target drift problem. Meanwhile, the precision rate and the success rate of the tracking are improved.
Toward Automatic Identification of Dataset Names in Scholarly Articles

As the number of scholarly articles we can access is increasing, it becomes possible to read them freely. However, it is difficult to understand scholarly articles since they are basically written for experts. Our big goal is, developing methods to extract essential elements of articles, to facilitate open innovation. To this end, this paper is devoted to considering automatic identification of dataset names in articles. Because a dictionary of datasets is necessary for evaluation, existing methods have focused on some specific discipline. To achieve applicability to any disciplines, we adopt a machine learning approach with a huge amount of scholarly papers. Because we treat papers in multi-disciplines, it is challenging how to evaluate experimental results. To solve it, we quantitatively evaluate experimental results with precision@N, which does not require to know all the dataset names in the papers we use, and qualitatively check if candidate tokens are dataset names or not using a GUI tool we have developed. While about 1/3 tokens of the top 20 output by our method were dataset names, the other ones are names of methods, models, or organizations. So it is important future work to remove such noise results, using additive compositionality of word vectors.
Diversity-based JADE Algorithm

In adaptive DE, the parameter p is used to balance the algorithm exploration and exploitation ability. Selection of suitable p in the evolution process is an ongoing challenge for adaptive DE. Population diversity is an effective measure showing the distribution of individuals in the search space. In this paper, use of diversity as a search strategy selection criterion for calculating p in adaptive DE is proposed. The proposed algorithm normalizes the calculated diversity to [0, 1] and calculates p according to the normalized diversity using an exponential function. Higher diversity yields higher p, providing the proposal with higher exploration ability, and vice versa. The accuracy of the proposed algorithm was experimentally compared to those of five other advanced DE variants using benchmark functions from IEEE Congress on Evolutionary Computation 2017. The results show that the proposed algorithm achieved the best performance among all variants for most benchmark functions.
A New Probe into the Security Problems and Prevention Strategies of Computer Network Communication

Analysis of the current status of computer network communication security at this stage can be seen that it is affected by many factors, which leads to more problems in this work, which is not conducive to people's normal life, production and other activities. It also leads frequent information leakage problems, making the privacy is not guaranteed, threatening people to use the computer network to carry out the security and effectiveness of activities. Therefore, in this case, the technical department concerned with ensuring the security of computer network communication needs to be deeply researched and analyzed for the emerging security problems. The prevention strategy significantly improves the security of the computer network and effectively protects the user's information security. Based on this paper, the current computer network communication security issues are summarized. Several problems are proposed to help solve the problem. It is hoped that it can provide benefits for the subsequent improvement on computer network communication security in China.
Unsupervised Cross-Lingual Mapping for Phrase Embedding Spaces

Cross-lingual embedding has shown an effective way to learn cross-lingual representation in a joint embedding space. Recent work showed that cross-lingual phrase embedding is important to induce phrase table for unsupervised phrase-based machine translation. However, most of the cross-lingual representation from the literature are either limited to word level embedding or uses bilingual supervision for shared phrase embedding space. Therefore, in this paper, we explore the ways to map phrase embeddings of two languages into a common embedding space without supervision. Our model uses a three-step process: first we identify phrase in a sentence by using their mutual information, and combine component words of the phrase in the preprocessing stage; then we independently learn phrase embedding for each language based on their distributional properties, finally a fully unsupervised linear transformation method based on self-learning is used to map the phrase embeddings into a shared space. We extracted bilingual phrase translation as a gold standard to evaluate the result of the system. Besides its simplicity, the proposed method has shown a promising result for phrase embedding mapping.
A Distributed Scheme for Accelerating Semantic Video Segmentation on An Embedded Cluster

We present a methodology for enhancing the throughput of semantic video segmentation tasks on an embedded cluster containing multiple embedded processing elements (ePEs). The methodology embraces a scalable master-slave hierarchy and features a global and local key management scheme for allocating video frames to different ePEs. The master ePE divides each video frame into frame regions, and dynamically distributes different regions to different slave ePEs. Each slave ePE executes either a segmentation path or a flow path: the former is highly accurate but slower, while the latter is faster but less accurate. A lightweight decision network is employed to determine the execution path for each slave ePE. We propose a global and local key management scheme to facilitate the execution of the embedded cluster, such that the average processing latency of each frame is significantly reduced. We evaluate the performance of our methodology on a real embedded cluster in terms of accuracy and frame rate, and validate its effectiveness and efficiency for various ePE configurations. We further provide a detailed latency analysis for different configurations of ePEs.
An Improved Method of ADHDP for Optimal Control in Wiped Film Molecular Distillation

The wiped film molecular distiller is a complex system which has features of multivariate, nonlinearity, strong coupling and large time delay. It's production adjustment is mainly based on artificial experience. In order to improve the purification rate of molecular distillation and increase productivity, the paper proposes to adopt extreme learning machine to improve action-dependent heuristic dynamic programming algorithm. Through constructing three kinds of network, the paper gives the specific theoretical realization process of ADHDP based on ELM network. For the purpose of verifying the effectiveness of the algorithm, the algorithm is applied to the optimal control of wiped film molecular distillation. By comparing with BP-ADHDP and BP-HDP, the simulation results indicate that the controller based on ELMADHDP can realize better optimizing control and improve the stability of the molecular distillation process.