Reviewerly/exHarmony huggingface dataset:
Understanding formal scholarly communication in information science by exploring generational evolution of academic writing patterns

ABSTRACT Professional academic writing skills can improve the validity of scholarly communication. Aiming to reveal the generational differences of academic writing patterns, by applying quantitative linguistic analysis to explore 53 information scientists in China and 2,328 papers of them, we found that (1) the writing pattern evolves smoothly, (2) sentences that scientists compose in a paper have become longer and longer, and syntax has become more and more complex as time goes by, and (3) scientists tend to be more and more cautious on choosing expression manners and norm words. This research will continue the matters of exact relationships between the age differences and writing patterns, and will be extended to other languages.
Study and Implementation of Sun Tracker Design

The world is using up all the resources to meet the daily demands of energy, renewable energy solution has achieved a great step today to save the natural resources and solar technique is rapidly gaining its popularity as an important source of renewable energy. In this paper, we have constructed and studied the efficiency of a two axis solar tracker, which can track the sun throughout the day to obtain the maximum efficiency.
Analysis of The Community Satisfaction Index on Medan Rumah Kita Public Service Application

Abstract This study is about the Public Satisfaction index of the management of public service applications managed by the Office of Communication and Information of the City of Medan using a Play Store-based system on smart cell phones or android. This application system provides information, facilities and service categories for the people of Medan City in submitting complaints to problems of public facilities. The management of this application is called Medan Rumah Kita or MRK. The main objective is to fulfil the NPS (New Public Service) as part of the implementation managed by the Medan City government with the main goal being community satisfaction in the city of Medan. The MRK (Medan Rumah Kita) application, which began in 2016, is expected to increase the contribution of the role of the community in improving the system of government work that is more effective and efficient in accordance with the principles of good governance and e-government. This study has an important objective, namely to provide a measurable analysis of the ability of state civil apparatus in managing the Medan Rumah Kita application for the public interest so that it becomes one of the stages and achievements that become a reference for improving access to public services in Medan City. The population in this study were people who had downloaded and used Medan Rumah Kita (MRK) application. This study is using a questionnaire as a data collection tool, this study also uses research respondents which is gathered from the Play Store application that consist of 1500 people who have downloaded MRK application, with the total sample is 306 people. The result shows that 75.95% of the respondents are in the “GOOD LESS” category (at intervals of 2.60 - 3.064 or 65.00 - 76.60).
A Robust and Efficient Pedestrian Detection Approach for Autonomous Driving

Due to the particularity of the autonomous driving scene, the pedestrian detection for autonomous vehicles needs to meet the requirements of good robustness and real-time performance. For this scenario, we propose a robust and efficient one-stage approach, which resorts to a single convolutional neural network to derive bounding box coordinates and pedestrian probabilities directly from the original image, and achieves end-to-end detection. The residual unit used by the deep residual network can improve the process of deep network model optimization and reduce the time for model convergence. Therefore, resNet is taken as the basic network of the one-stage method. Training and testing results on the Caltech pedestrian dataset demonstrate that the proposed approach can achieve higher accuracy than the traditional pedestrian detection methods, while maintaining the real-time performance.
Auto-Generating Examination Paper Based on Genetic Algorithms

With the acceleration of education informatization, the social demand for online examination papers is increasing. However, there are some problems in the generation of online examination papers. Firstly, it is impossible to randomly generate examination papers quickly. Besides, it is impossible to dynamically adjust examination papers according to test results. Thirdly, it is impossible to generate examination papers based on individual characteristics of students. In order to solve these problems, this paper proposes a new auto-generation examination paper model based on genetic algorithm. The model dynamically adjusts the difficulty factor of individual test questions by analyzing the online learning data and historical user test result data, and then guarantees the difficulty of generating examination papers in line with the changes in the current educational environment. The simulation results show that the algorithm improves the efficiency and accuracy of the generation examination paper, and effectively controls the difficulty coefficient of the examination paper.
Power Characterization of Memory Intensive Applications: Analysis and Implications

DRAM is a significant source of server power consumption especially when the server runs memory intensive applications. Current power aware scheduling assumes that DRAM is as energy proportional as other components. However, the non-energy proportionality of DRAM significantly affects the power and energy consumption of the whole server system when running memory intensive applications. Thus good knowledge of server power characterization under memory intensive workloads can help better workload placement with power reduction. In this paper, we investigate the power characteristics of memory intensive applications on real rack servers of different generations. Through comprehensive analysis we find that (1) Server power consumption changes with workload intensity and concurrent execution threads. However, fully utilized memory systems are not the most energy efficient. (2) Powered memory modules of installed memory capacity, i.e. the memory capacity per processor core has significant impact on the application’s performance and server power consumption even if the memory system is not fully utilized. (3) Memory utilization is not always a good indicator for server power consumption when it is running memory intensive applications. Our experiments show that hardware configuration, workload types, as well as concurrently running threads have significant impact on a server’s energy efficiency when running memory intensive applications. Our findings presented in this paper provide useful insights and guidance to system designers, as well as data center operators for energy efficiency aware job scheduling and power reductions.
Virtual Window Manipulation Method for Head-mounted Display Using Smart Device

In this paper, we propose a virtual window manipulation method used for information search while utilizing a head-mounted display (HMD). Existing HMD operation methods have several issues like causing user fatigue and processing input tasks inefficiently. Such problems are difficult to solve simultaneously. Therefore, we propose using head tracking cursors and smart devices. The suggested method aims to operate a head tracking cursor by swiping input on the smart device. In this paper, we compared the operability of this new method and the classic hand tracking one based on the results of user experiments. As a result, it was confirmed that operability of the proposed method is deemed to be high.
Blurring Scene Recognition in Short Video

In order to solve the problems of blur and jitter in short video scenes, this paper proposes a multi-scale fusion network based on VGGNet. First, a fuzzy image data set is obtained by performing different blur processing on the clear image data set. Then, the fuzzy feature is extracted from the blurred image by using the fused depth network, and compared with the clear image extraction feature for back propagation training. Finally, the classification of fuzzy scene recognition is performed. The top3 of the Charades short video dataset has achieved 78.9% of the results, which proves that the proposed method has a good performance in blurring scene recognition.
Digital Transformation

This chapter is a research on the effects of the concept of information literacy on the economic, social, economic, and digital epochs. When examining the information, the authors also examined the facts. In defining information, it was discussed within the framework of a national statement. While focusing on how information changes technology, the effects of knowledge on society are also examined. The authors socialized the information by showing the correlations between the types of information and society and units. By socializing, they have used the concept of renewing the process, re-using the process, to provide systematic coordination. The authors saw that the problem of knowledge and the solution phases emerged. In fact, information literacy has a characteristic that affects and supports other types of literacy. In other words, it is possible for an individual to develop other literacy skills with the help of information literacy skills.
GenSeq+: A Scalable High-Performance Accelerator for Genome Sequencing

Genome sequencing is one of the most challenging problems in computational biology and bioinformatics. As a traditional algorithm, the string match meets a challenge with the development of the massive volume of data because of gene sequencing. Surveys show that there will be a huge amount of short read segments during the process of gene sequencing and the need for a highly efficient is urgent. As a classic fast and exact single pattern matching algorithm, Knuth-Morris-Pratt (KMP) algorithm has been demonstrated in network security and computational biology. However, with the increasing amount of data in the modern society, it becomes increasingly important and essential to provide a High-performance implementation of KMP algorithm. In this article, we implement a scalable KMP accelerator based on FPGA, named GeneKMP. The accelerator is composed of different computing units to achieve a pipelined organization for higher throughput with satisfying scalability. A novel programming model is provided to alleviate the burden of the high-level programmers. We provide a greedy-based partitioning algorithm for the software/hardware design paradigms. Experimental results on the state-of-the-art Xilinx FPGA hardware prototype show that our accelerator can achieve up to a promising speedup with insignificant hardware cost and power consumption.
Trial Introduction of Group Work in Law Education for College Students

The demand for education of college students on radiation protection is strongly emphasized recently in Japan. We here aimed at developing an effective way to teach the radiation protection-related laws to students who have so little practical experience in radiation protection that the learning motivation tends to be very low, and a high training effectiveness is hardly expected. For this purpose, we tested a combination of an ordinary classroom lecture and a group work in which the students positively and cooperatively tackled the specific task that was given by the lecturer. The tasks included such as making up an application form to be submitted to Nuclear Regulatory Agency to respond to an altered condition of utilization of a radiation source in a radiation-controlled area. The students had to investigate and solve what is necessary to comply with the law by themselves. As a result, it was suggested that the group work had an effect on improvement of learning motivation of the students.
GRAMMATICAL ASPECTS OF CODE-SWITCHING: MODELS AND APPROACHES

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A Dual-Link Residual Convolution Neural Network for Image Super-Resolution

In recent years, the application of deep convolution neural network has achieved great success in image super-resolution (SR) fields. However, most of these deep convolution neural network methods are based on increasing network depth, width or input image size to achieve more accurate fitting, which will lead to a surplus of parameters and slow the convergence speed. Compared to target recognition tasks, the neural network for image super resolution often remove pooling layers to prevent losing some learned information. In this paper, we propose a dual-link residual network with pooling and deconvolution layers(RPDN). By adding pooling layers and corresponding deconvolution layers, the model can extract more non-adjacent pixel structure information, reduce the computation, and increase the sparsity of the model. Inspired by DenseNet, we propose a dual-link structure to achieve feature fusion of shallow and deep layers. One chain is to connect the convolution layers, and another chain is to connect the pooling layers. RPDN also uses Intergroup Connection(IC) to help gradient back propagation. Experiments on benchmark datasets shows our network can effectively reduce artifacts.
Secure Cloud Data Storage: From Single to Multi-Cloud Environment

Cloud based storage services are most popular among organizations due to its high computing power at low cost with saving in IT infrastructure.Major threat found by organizations before storing data in public cloud storage is security of the information stored.Even after strong provision of various security mechanism at various cloud service level, still full security is not achieved yet.Malicious system administrator can access data of any organization .Our proposed solution deals with the cloud storage security issues by distributing trust and security of user's data among multiple clouds by distributing fragmented data among them.Since no entity will get complete set of data at any instance of time hence system is secure and reliable.
Fast and Accurate Super-Resolution of FY-2 Infrared Cloud Images via Multi-Scale Fusion Network

This paper proposes an effective method to improve the spatial resolution of FengYun-2 (FY-2) infrared cloud images via deep convolutional neural networks. The proposed model consists of four parts: shallow feature representation block, stacked multi-scale fusion blocks, global feature fusion block, and feature reconstruction block. The multi-scale fusion block combines dilated convolution, local feature fusion and local residual learning to extract multi-scale local features from the original low-resolution image directly. Then these local features are all merged by the global feature fusion block to reconstruct the residual representations in high-resolution space. For training and testing, we have specially built a dataset of infrared cloud images. We evaluated the proposed method both on simulated and real data. Experimental results demonstrate that the proposed approach achieves improved reconstruction accuracy than the state-of-the-art methods. Besides, the concise structure of the proposed model enables it to be applicable in practice.
Building Trust in Wearables for Health Behavior

Advances in Internet of Things (IoT) have given users the ability to monitor heart rate, calories burned, steps walked, time spent exercising, and the electrical activity of the heartbeat. Although major players in the wearable industry have marketed their wearables using the health and activity tracking features, a noteworthy health behavior change has not been observed at individual or societal level. A prominent barrier to adoption of healthcare features in these devices is lack of user trust. This research conceptualizes the formation of user’s initial trust in wearables. Here, wearable systems are proposed as three-dimensional framework constituting the device, the organization (manufacturer or app-maker), and the Internet. Understanding the formation of initial trust on wearable systems’ healthcare features can lead to improvement in user’s health-related behaviors, which in turn has the potential to cause a societal change in primary healthcare delivery.
Integration of TeSLA in Assessment Activities

During Large Scale Pilots of the TeSLA system, teachers in six European universities constructed case studies of the use of the TeSLA instruments in assessment activities in authentic courses. A thematic analysis of these case studies identified seven common categories of TeSLA instrument use, which varied according to the design of the assessment activity and the teacher’s purpose in seeking to authenticate student identity and/or check the authorship of assignments. These seven categories are described in detail in this chapter. These descriptions are intended to provide a starting point for teachers new to the use of student authentication and authorship checking instruments to identify appropriate ways to integrate the instruments in assessment activities in their courses. The chapter goes on to identify some of the issues arising from the context of the collected data and implications of the study for the future implementation of TeSLA and similar systems incorporating instruments for student authentication and authorship checking.
A Structured Approach for Designing Adaptive Interactive Systems by Unifying Situation-Analytics with Model- and Pattern-Based User Interface Development

This paper discusses the SitAdapt 2.0 development approach for constructing adaptive interactive systems. Resulting applications provide situation-awareness and respond to changing contexts, environments, user emotions, and biometric signals. An observer component is watching the user during interaction with the system. The adaptation process is triggered by situation changes or the recognition of new situations. The necessary software modifications are established in real-time by activating situation rules and exploiting the pattern- and model-based resources of the PaMGIS development framework for model based user interface development. It is shown in detail, how the adaptation process exploits the different PaMGIS models before and during runtime. The SitAdapt approach may also serve for finding user personas, optimizing user experience, and allowing for individualized digital marketing activities. The approach is currently being evaluated for applications from the e-business domain.
Cray Compute Node Linux

Unix, and now Linux, haveLarry Kaplan Jim Harrellbecome prevalent in high-performance computing (HPC) systems, including systems at the very high end. These operating systems were not originally intended for scalable HPC usage, butCNL (Compute Node Linux) Cray|( have been adapted to fulfill that role, while preserving their ability to support a wide range of applications and use cases. While Cray Research and some related commercial efforts had started with proprietary operating systems, Unix or Linux was eventually chosen to provide this wider applicability. This chapter describes the evolution of the use of Unix and Linux at Cray, along with similar activities at two other companies that followed similar paths, BBN Advanced Computers Inc. and Tera Computer Company (later merged with Cray to form Cray Inc.).
Random spanning trees for expanders, sparsifiers, and virtual network security

This work describes probabilistic methods for utilizing random spanning trees generated via a random walk process. We generalize a method by Goyal et al. for weighted graphs and show that it is possible to approximate the expansion of every cut in a weighted graph with the union of random spanning trees generated by a random walk on a weighted graph. Particularly, we show that our union of random spanning trees is a spectral sparsifier of the graph, and we show that for 1n<ϵ≤1, O(logn/ϵ2) random spanning trees are required in order to spectrally approximate a bounded degree expander graph. In another part of our research work, we show that our random spanning trees based construction provides security features for virtual networks, in context of Software-Defined Networking. Namely, we demonstrate that our construction allows on-demand efficient monitoring or anonymity services.
Unsupervised Learning of Depth and Visual Odometry using Photometric Calibration

Abstract Depth and visual odometry estimation are two essential parts in SLAM systems. Compared with traditional algorithms, supervised learning methods have shown promising results in single view depth estimation and visual odometry estimation. However, they require large amounts of labeled data. Recently, some unsupervised approaches to estimate depth and odometry via minimizing photometric error draw great attention. In this paper, we present a novel approach to learn depth and odometry via unsupervised learning. Our method ameliorates the original photometric loss to enhance the robustness to illumination change in real scenarios. In addition, we propose a new structure of Pose-net and Explainability-net to achieve rotation-sensitive odometry results and more accurate explainability masks. The experimental results have demonstrated that our approach achieves better performance than existing unsupervised methods in both depth and odometry results.
Multi-Features Fusion Based on Boolean Map for Video Saliency Detection

Video saliency detection aims to extract salient objects in video. In order to resolve the problems of incomplete extraction salient area and the mixing between background and salient objects at boundary, this paper proposes a video saliency detection method via multi-features fusion based on Boolean Map. The main innovation in this method is that global topological relationship of video frames are utilized to compute saliency values. Firstly, boolean maps are generated according to the Boolean Map theory via combine motion features and color features in videos, and then attention maps are calculated by masking unsurroundedness area, finally, video saliency maps are obtained by fusing all attention maps. Experiments on SegtrackV2 and Fukuchi benchmark datesets show that the proposed method successfully obtains complete and clear boundary salient area, and it outperforms the other general models.
Chinese Historical Term Translation Pairs Extraction Using Modern Chinese as a Pivot Language

Term translation of Chinese historical classics is very difficult and time-consuming work, and using term alignment methods to extract term translation pairs is of great help for historical term translation. However, the limited bilingual corpora resources of historical classics and special morphology of the ancient Chinese result in poor performance of term alignment. To this end, this paper proposes a historical term alignment method using modern Chinese as a pivot language. The method first identifies English terms by rules, then aligns them from English to modern Chinese and then from modern Chinese to ancient Chinese. The use of English-modern Chinese corpus and modern-ancient Chinese corpus instead of English-ancient Chinese corpus solves the shortage problem of the parallel corpus. Moreover, using modern Chinese as a pivot language effectively reduces the alignment errors caused by the abbreviations and the interchangeable characters of ancient Chinese. In the term alignment experiment on Shiji, our method outperformed the direct alignment method significantly, which proves the validity of our method.