Reviewerly/exHarmony huggingface dataset:
Automatic Meta-evaluation of Low-Resource Machine Translation Evaluation Metrics

Meta-evaluation is a method to assess machine translation (MT) evaluation metrics according to certain theories and standards. This paper addresses an automatic meta-evaluation method of machine translation evaluation based on ORANGE- Limited ORANGE, which is applied in low-resource machine translation evaluation. It is adopted when the resources are limited. And take the three n-gram-based metrics - BLEUS, ROUGE-L and ROUGE-S for experiment, which is called horizontal comparison. Also, vertical comparison is used to compare the different forms of the same evaluation metric. Compared with the traditional human method, this method can evaluate metrics automatically without extra human involvement except for a set of references. It only needs the average rank of the references, and will not be influenced by the subjective factors. And it costs less and expends less time than the traditional one. It is good for the machine translation system parameter optimization and shortens the system development period. In this paper, we use this automatic meta-evaluation method to evaluate BLEUS, ROUGE-L, ROUGE-S and their different forms based on Cilin on the Russian-Chinese dataset. The result shows the same as that of the traditional human meta-evaluation. In this way, the consistency and effectiveness of Limited ORANGE are verified.
Data-Driven Learning at the English Drafting Stage

Integrating corpus consultation to help students' re-use of vocabulary items for essay-drafting has not been sufficiently examined in instructional contexts. This chapter investigated the linguistic features of target words for which students consulted concordance programs to include in essays by documenting the DDL of three groups of EFL students in a Taiwanese college writing program. Participant essays, video files of consultation processes, responses to questionnaires, interviews, and written records of corpus consultation were examined. Researchers found 88.14% of all consulted words were incorporated into essays, with learners looking up 3.18 words. The lexical profile of consulted words indicates that learners mainly used the most common 1,000 to 2,000 words with more verbs more frequently consulted than nouns. Students queried words for confirmation or incorporated the discovered vocabulary into essays. As emergent pattern hunters, the participants made only limited use of dictionaries and bilingual resources. Implications of the findings are discussed.
Ranking Like Human: Global-View Matching via Reinforcement Learning for Answer Selection

Answer Selection (AS) is of great importance for open-domain Question Answering (QA). Previous approaches typically model each pair of the question and the candidate answers independently. However, when selecting correct answers from the candidate set, the question is usually too brief to provide enough matching information for the right decision. In this paper, we propose a reinforcement learning framework that utilizes the rich overlapping information among answer candidates to help judge the correctness of each candidate. In particular, we design a policy network, whose state aggregates both the question-candidate matching information and the candidate-candidate matching information through a global-view encoder. Experiments on the benchmark of WikiQA and SelQA demonstrate that our RL framework substantially improves the ranking performance.
Human-Robot Interaction Design Based on Specific Person Finding and Localization of a Mobile Robot

In this paper we propose a novel human-robot interaction system to find a specific person in public for providing service. The system combines indoor localization, face recognition and robot navigation. The indoor localization uses deep neural network (DNN) and particle filtering to estimate the user position. A face recognition module provides the user identification to the robot. The robot first uses localization data to navigate to the vicinity of the user and then uses the face recognition to move to the front of the user to provide service. To verify the effectiveness of the design, we implemented the system to a mobile robot and integrated the application through a smart phone. The integrated experiments demonstrated that a user can call the robot to come to his/her front by using the proposed design. One also can order the robot via a smart phone to find a specific person and interact with him/her.
Robot arm operating interface using a touch panel for gripping an object on the floor by designating the end effector

Some of the electric wheelchair users have limb dysfunction due to muscle weakness and paralysis. Recently, welfare robot arms are being developed for self-reliance supporting for them. In the conventional robot arm operating interface, it is necessary for users to confirm the position of the target objects before operation. However, when an object on the floor gets into the blind spot of the operator such as under an electric wheelchair or desk, it can't be found and will be difficult to pick up. In this study, we developed a touch panel interface that a user specifies the end point of the robot arm by drawing a line to the target object as a simple operation method for grasping the object on the floor. In the experiment to compare the operation by the proposed method with the operation by a game pad. And the result shows that the proposed method reduces operation time in the grasping operation of the object on the floor.
OCPAN: multi‐person pose estimation with more attention on residual information

Redundant information generated in the process of feature fusion and residual information always remain challenging in multi-person pose estimation. In this Letter, the authors present an optimised cascaded pyramid attention network composed of two novel modules to reduce the redundant information and highlight the residual information for more accurate results. The first module, called channel optimisation module (COM), optimises the channel at different levels. The proposed COM consisting of convolution layers and two pooling layers performs on the feature maps Conv-25 of different resolution to reduce the redundant information after convolution operation. The second module, called residual attention bottleneck module (RABM), highlights the residual information in the bottleneck. Through adopting the attention mechanism in the bottleneck, RABM performs a significant role in reducing the redundant information. In contrast to the previous work, they attach more attention to residual information, helping to tackle the challenging problem for the ‘hard’ keypoints. By merging COM and RABM into Cascaded Pyramid Network, the authors' method achieves competitive results of 74.6 AP on the COCO keypoint benchmark, outperforming the state-of-the-art results.
Fuzzy Logic Alghorithms for Target Classification in Radar Observations

The article is devoted to solving the task of classifying targets in radar observations. A technique for recognizing target classes based on fuzzy logic algorithms is proposed. The results of recognition of four classes of targets (UAVs, birds, helicopters, airplanes) in detection and tracking radar are presented.
Efficient Use of Data Centres - Factors Affecting Service Availability in Large Organisations based on IT and Data Centre Selection

IT services are an increasingly critical part of conducting business and everyday life, the IT systems and data centres that host them are also improving. The relationship between how IT systems are designed and where they are placed between an array of different data centre options makes it a challenge to plan for appropriate levels of uptime for the services being provided. The current IT landscape provides organisations to target required availability of services in an effort to reduce costs and waste while meeting there required service levels. However, reference architectures for designing IT services often fail to take the data centre into account, while the design standards of data centres typically only take the power and cooling specifications into consideration and not the configuration of the IT infrastructure or the nature of the services they host. This paper shows that by spreading a highly available IT system between data centres of various tier ratings, as opposed to placing the same highly available IT system within a standalone data centre, superior levels of service availability can be achieved while potentially utilising lower cost of data centres.
Gray Scale Medical Image Compression using LM Algorithm

Medical images of MRI, CT and X-ray are the digital form of biological features of human organs. The bulk data of medical images generated by medical imaging processes requires different image compression techniques to reduce the storage space with considerable image quality. This paper presents the compression technique of Feed Forward Back Propagation Neural Network (FFBPNN) for compression and decompression grayscale medical images i.e. MRI of brain image. The FFBPNN with back propagation training algorithms is used to increase the performance by giving better image quality for different compression ratios. This compressed output is encoded with Huffman encoding techniques for much better compression of medical images. From the results, it can be concluded that FFBPNN technique using Levenberg Marquardt (LM) algorithm provides low Mean Squared Error (MSE), high Peak Signal to Noise Ratio (PSNR), good Structural Similarity Index Measurement (SSIM) compared to the existing algorithms.
Data Driven Processing Via Two-Dimensional Spearman Correlation Analysis (2D-SCA)

This paper introduces an algorithm two-dimensional Spearman correlation analysis; the present algorithm is the extension of classical Spearman correlation analysis through algebraic solution for multivariate two-dimensional monotonic (linear or non-linear) multi-media datasets. In a way, two different images with nonlinearity challenges like different dimensions are processed with correspondence techniques such as reshaping images into 1D or vectors. Further, it can reduce dimension reduction along with quadratic algorithm complexity. Due to segmentation of matrices and tied rank ability of spearman correlation analysis. The implementation of proposed algorithm performs on four remarkable dataset. The results demonstration is helpful for researchers to choose finger image impression dataset with algorithm performance and sensors related techniques.
Analyzing Internet Use Of Students in Learning English Through Their Learning Approach at IAIN Bukittinggi

Abstract The research was due to several problems found in the field; Most of students used internet in learning English and doing tasks. However, some of them were not able to explain their task results. They did not comprehend the material of the lesson eventhough they used internet. For some others, internet was worthwhile for them. Based on that, the researchers assumed that the students had varieties way in using internet. This research belongs to quantitative research by survey. The population was the all of English students which study actively in the classroom at IAIN Bukittinggi in academic year 2019/2020. There are three different grades of semester; semester 1, 3 and 5. To choose sample, the researcher used purposive sampling. The purpose of the researcher is to choose sample which use internetmostly. The finding reveals that 70.6% of the students use deep approach while using internet that consists of 25.4% in seeking meaning, 15.9% inrelating ideas, 19.8 % in using evidence and 9.5 % interested in ideas. There are 29.4 % of the students use surface approach, that consist of 3.9% in lacking of purpose, 7.9 % in unrelated memory, 12% in syllabus boundness, and 5.6% in fear failure. In conclusion, the students prefer using deep approach to surface approach.
User Experience of Online Shopping Clothing Display Based on VR Technology

With the development of economy, society and technology are constantly improving, people’s quality of life is also constantly improving, so the requirements for clothing stores are also increasingly high, not only more physical stores are required to operate, but also virtual stores should meet their requirements, so that people can enjoy the shopping experience without leaving home. From this point of view, this paper puts forward VR technology, and specifically analyzes the impact of VR technology on online shopping clothing display and on consumers. This paper first gives a brief overview of the concept of VR technology and the main application areas of VR technology. VR technology, namely virtual reality technology, is a relatively mature technology in recent years. Its application is very extensive, but it is mainly used in commercial and military fields. Secondly, it puts forward the user experience planning scheme of VR technology applied to online shopping clothing display, and points out the vision, hearing and touch The specific application of the factors and the influence of these three sensory factors on consumers’ shopping. Finally, through the investigation and research of a clothing store for four years, it can be concluded that the sales amount of the virtual store in this store is very good, which has been steadily increasing, with the sales rate of 3.2%, 4.7%, 5.9% and 6.5% respectively. However, the sales amount of physical stores has a small increase, with the sales rate of 2.3%, 2.6%, 3.2% and 3.7% respectively.
Bayesian identification of state-space models via adaptive thermostats

Bayesian modeling has been recognized as a powerful approach to system identification, not least due to its intrinsic uncertainty quantification. However, despite many recent developments, Bayesian identification of nonlinear state space models still poses major computational challenges. We propose a new method to tackle this problem. The technique is based on simulating a so-called thermostat, a stochastic differential equation constructed to have the posterior parameter distribution as its limiting distribution. Simulating the thermostat requires access to unbiased estimates of the gradient of the log-posterior. To handle this, we make use of a recent method for debiasing particle-filter-based smoothing estimates. Numerical results show a clear benefit of this approach compared to a direct application of (biased) particle-filter-based gradient estimates within the thermostat.
EASTD: A New Energy-Aware Scheduling with Target Deadline Constraint for Real Workflows in DVFS Cloud Environment

One of the main concerns that attract researchers is minimizing the energy consumption in cloud environment. This results in decreasing both the operating cost and gas emission in cloud environment. As consequence, this paper presents a new Energy-Aware Scheduling with Target Deadline Constraint algorithm (EASTD) that minimizes the energy consumption for tasks executing in cloud environment. The EASTD algorithm uses the DVFS technique, which is considered one of the promising techniques that decreases the energy consumption by lowering the operating frequency and voltage of processors. Furthermore, the proposed approach divides the target deadline across tasks to achieve both the precedence constraint and reduction in processors' operating frequencies. Moreover, EASTD exploits the spare time slots to decrease the operating frequency of processors. Three real workflows generated using scientific toolkit are used to evaluate the proposed algorithm. Moreover, three different experimental scenarios are conducted to evaluate the performance of EASTD. The simulation results showed that the proposed algorithm competes their competitors.
Post-quantum Cryptography: An Introduction

We present a brief introduction to post-quantum cryptography. This note introduces the concept of post-quantum cryptography, discusses its importance and provides a short overview of the mathematical techniques that are currently used to develop this field.
Modified Algorithm for Phylogenetic Tree Construction using Ant Colony Optimization

Constructing Phylogenetic tree from DNA of species is an important problem in Bioinformatics. These trees are used to study the evolutionary relationship between species and to study which species are more closely related or distantly related. Ant colony optimization (ACO) paradigm is one of the best solutions through meta heuristics solutions which can be used to construct phylogenetic trees. Some researches present solutions using ACO, better than mostly used methods like Neighbor-Joining, UPGMA, etc. In this research we propose a modified ACO algorithm which can perform better than conventional ACO approaches in more divergent species by abstracting some features in developed ACO solutions. We construct distance matrix calculating Kolmogorov Complexity as distance between species from complete mitochondrial genome without applying Multiple Sequence Alignment (MSA). Then we obtain optimal sequence of combinations of species applying modified ACO algorithm. Using a new scenario, we construct phylogenetic tree from that sequence of combinations and test with two types of datasets. First, we tuned the model using twenty species of mammalians dataset. As second type ten divergent datasets are obtained from different divergent levels of real benchmark trees which are used to evaluate our method. Our results show that our proposed method performs better than conventional methods for divergent species. As well as it gives results in less processing time than the conventional ACO algorithm.
Learning a Lightweight Representation: First Step Towards Automatic Detection of Multidimensional Relationships between Ideas

Moving ideation from a closed paradigm (companies) to an open one (crowd) yields several benefits: (1) the crowd allows the generation of a large number of ideas and (2) its heterogeneity increases the potential in obtaining creative ideas. In practice, however, the crowd often fails at generating innovative solutions, leading to duplicate or ideas that use each other's description. Thus, it is practically and economically unfeasible to sift through this large number of ideas to select valuable ones. One promising solution to overcome this issue is identifying relationships between idea text descriptions, such as duplicate, generalize, disjoint, alternative solution. Existing approaches rely either on human judgment, which is expensive and requires domain experts or automatic approaches which compute similarity i.e. one dimension and do not consider other relations. To find complex relationships between idea texts, a first logical step is to fully structure ideas into logic-based representation. However, logic-based representations are very expensive to obtain since ideas' texts do not adhere to any specific structure. Our goal is then to come up with a representation that can be learned by the machine, while still being expressive to allow establishing relationships between ideas. This research in progress introduces an approach based on a sequence-to-sequence learning approach, which allows the machine to learn a lightweight structural representation that is used next to establishing multidimensional relationships between ideas (i.e. different kind of relations between ideas). Based on our investigation, we found out that ideas contain the following patterns: what the idea is about (e.g. window with heat-sensitive material), how it works (e.g. it lights up) and when it works (e.g. in case of fire). Those extracted patterns are then compared with the corresponding patterns of other ideas to establish relations. Our preliminary investigation shows promising results to learn such lightweight structural representation and leverages it in identifying complex relationships between ideas.
The extent to which a correlation between global irradiation and temperature developed for a single site can be applied to nearby sites: A case study for Israel

Solar irradiation is the critical parameter required for the design of a solar conversion system but is not always available for the site under consideration. Empirical equations based upon data available at a second nearby site have, at times, been applied to fill this void. The aim of this study is to evaluate the accuracy of an empirical correlation to estimate solar irradiation, based upon a single commonly measured parameter, average daily dry bulb temperature, and the calculated extraterrestrial irradiation, developed for a single site and applied to nearby sites. The per cent mean bias error (%MBE) for the five sites studied is in the range of −6.59 – 1.68%, whereas the per cent root mean square error (%RMSE) is in the range of 13.58–16.95%. The prediction accuracy of the empirical correlation as given by the mean absolute percentage error (MAPE) is in the range of 14.53–21.09% and the correlation coefficient is in the range of 0.917–0.925. The prediction accuracy of this relatively simple, single parameter correlation developed for a single site and then applied to five nearby sites compares favorably with that for more complicated multi-parameter generalized correlations.
In the Era of Computers, Internet and Multimedia, are we still Teaching Composers to become Chapel-Masters?

The ongoing paradigm shift from traditional methods of teaching music composition towards new approaches based on computer technology for sound production is the main focus of the article. The paper reviews the evolution of teaching methods throughout the Classical, Romantic and Modern periods, demonstrating how certain standard practices from former ages, based on the professional profile of the Chapel-Master, become theoretical golden principles for subsequent periods. After the French Revolution the traditional method of individual practical exchange between master and disciple was upgraded at the Conservatory. Classes for groups of students required a new approach based on theory abstraction. Although none of these teaching methods have been abandoned up today, a new paradigm appears when the tools of computer technology became widespread. An unpredictable equilibrium between the traditional methods and the new techniques of computer-assisted or computer-generated music composition tends to emerge.
Information Security Risk Communication: A User-Centric Approach

Users have difficulties in understanding and reacting to security-related threats. Moreover, users only try to protect themselves from risks salient to them. In contrast to the traditional one-message/one-size-fits-all approach when communicating risks, this paper aims to propose an individualized and persuasive approach to information security risk communication that goes beyond alerting the user of his insecure behavior to providing a level of security education. By focusing on the user and that different users react differently to the same stimuli, the authors proposed a targeted user-centric approach that communicates risks in a timely and continuous manner using a proposed gradual response mechanism. This user-centric approach is anticipated to help the user in making security-related decisions by educating him about his risk taking behavior in an individualized way. A scenario is assumed to demonstrate how a response decision is made within the proposed approach. This was useful in demonstrating how risk is not the same for all users and how the proposed approach is effective in adapting to differences between users offering a novel approach to communicating information security risks.