Reviewerly/exHarmony huggingface dataset:
Research on Framework and Key Technologies of Text Mining for Operation and Maintenance of Traction Power Supply Equipment

Based on a large number of texts produced by railway traction power supply equipment during service, which is difficult to analyze and apply directly, a text information mining system framework for railway traction power supply equipment operation and maintenance is proposed. This paper covers the process of Data pre-processing, information extraction, analysis and application, and studies the key technologies of Chinese text segmentation, information extraction, status evaluation, etc. Finally, the system architecture and related key technologies are verified by actual traction power supply equipment operation and maintenance text data.
A Content-wise Data Placement Policy for Improving the Performance of MapReduce-based Video Processing Applications in Cloud Computing

This paper introduces a novel data placement policy to distribute large video data across Hadoop cluster nodes. In video processing, it is common that applications process only the Region of Interest, so processing two equal-sized video segments may require different workloads and hence different execution times. Therefore, the MapReduce implementation of such processing inevitably causes a workload imbalance when the default data placement policy of Hadoop is used to distribute video data. The resulting workload imbalance reduces data locality, which in turn results in data migration overhead. The proposed data placement policy improves the performance of such MapReduce-based applications by mitigating the workload imbalance and minimizing the data transfer overhead in homogeneous environments. The experimental results show the achieved performance improvement.
Pedagogical conditions for the formation of the students’ scientific worldview when teaching biology

Abstract The theoretical aspects are revealed regarding the role of biology as one of the leading sciences of nature, in the formation of the students’ scientific worldview. It is emphasized that the content of the school subject “Biology”, methods, forms and means of study thereof, should be aimed at the formation of the students’ worldview. There are highlighted the main pedagogical conditions of the stepwise formation of the students’ worldview when teaching biology, taking into account the specific kinds of activity of the students and the teacher. As a structural unit of the pedagogical process there are designated the person-developing learning situations based on the relationship between the educational activity and life experience as well as the independent creative activity of the student, for the implementation of which the traditional content and techniques of teaching biology are reconstructed-the biological concepts of the worldview nature, creative tasks, problem situations, worldview-oriented issues with regard to the age-specific features of the students are emphasized. The obtained results of the pedagogical diagnostics, aimed at identifying the level of the formedness of the students’ scientific worldview, confirmed the effectiveness of the chosen pedagogical conditions.
Active jumping motion platform hardware in immersive virtual reality environment

We introduce an active jumping motion platform hardware device that enables a natural jump experience in an immersive virtual reality environment. The motion platform consists of four units: (1) base plate unit, (2) motor and sprung wire unit, (3) hanging seat unit, and (4) column unit. The base plate unit is a flat plate with pressure sensors that supports the user's jumping motion. The motor and the sprung wire unit support the user's body while using the multi-spring recoil to enable jumping. The hanging seat unit allows a person to sit halfway, sitting only slightly on the hips. Finally, the column unit is placed on the base plate unit and supports the motor and wire unit. This hardware provides a safe jumping experience while wearing a head-mounted display. Using the proposed jumping motion platform, the user can enable a jump operation in conjunction with a virtual character. Additionally, it could be used to measure exercise calories or to analyze knee disease.
The System to Measure Around Shape for Health Management

At present, a physical examination is often implemented manually by using measuring instruments, such as tape measures, or the like. Therefore, when the number of participants to be measured increases, it takes a long time to measure. In order to resolve the problem, we are developing a device that can measure around the hip and the waist automatically. Although our system increases the speed of measuring by using a light section method, the problem still is remained in terms of accuracy.
How Office Layouts Influence Software Development?

Background: Organizations are constantly looking for performance improvements, and office layout has been widely studied because of its hypothetical influences on the social dynamics of software engineering projects. Aim: In this article, we investigate the perceived outcomes of different workspace characteristics, from the perspective of software engineering professionals. Methods: To achieve that, we conducted a survey with software engineering practitioners, and collected data on the perceptions about their current workspaces and performance from 47 participants. We used the results of a previous systematic review to design the survey questionnaire, and focused on the four human aspects known to be influenced by the office layout. Results: Different workspace settings exhibited similar perceptions in most of the investigated factors. However, we reveal 14 items that responsible for significant differences in the performance outcomes, such as communication quality, collaboration, team learning, privacy and others. In general, open spaces were the most effective office layout to enable all these factors. Conclusions: As a conclusion, our study demonstrates that there is not a generally accepted best model for software development workspace design, as all types of setting have positive and negative aspects. Organizations that are considering investing any budget in such things as radical workspace redesign should ponder the change very carefully. Also, there is still much room for investigation in this topic.
High Frame-Rate Virtual View Synthesis Based on Low Frame-Rate Input

In the paper we investigated the methods of obtaining high-resolution, high frame-rate virtual views based on low frame-rate cameras for applications in high-performance multiview systems. We demonstrated how to set up synchronization for multiview acquisition systems to record required data and then how to process the data to create virtual views at a higher frame rate, while preserving high resolution of the views. We analyzed various ways to combine time frame interpolation with an alternative side-view synthesis technique which allows us to create a required high frame-rate video of a virtual viewpoint. The results prove that the proposed methods are capable of delivering the expected high-quality, high-resolution and high frame-rate virtual views.
Ginput: a tool for fast hi-fi prototyping of gestural interactions in virtual reality

Gestural interfaces in virtual reality (VR) expand the design space for user interaction, allowing spatial metaphors with the environment and more natural and immersive experiences. Typically, machine learning approaches recognize gestures with models that rely on a large number of samples for the training phase, which is an obstacle for rapidly prototyping gestural interactions. In this paper, we propose a solution designed for hi-fi prototyping of gestures within a virtual reality environment through a high-level Domain-Specific Language (DSL), as a subset of the natural language. The proposed DSL allows non-programmer users to intuitively describe a broad domain of poses and connect them for compound gestures. Our DSL was designed to be general enough for multiple input classes, such as body tracking, hand tracking, head movement, motion controllers, and buttons. We tested our solution for wands with VR designers and developers. Results showed that the tool gives non-programmers the ability to prototype gestures with ease and refine its recognition within a few minutes.
Analysis of Elements of Mathematics Learning on Cloud Technology for Pre-Service Teachers

This study aims to analyze the elements of mathematics learning on cloud technology for pre-service teachers. The data were analyzed by content analysis. The results indicate that the framework of elements of mathematics learning on cloud technology for pre-service teachers has three key elements: 1) cloud service model, 2) cloud technology for mathematics learning and 3) cloud mathematics learning activities. Cloud service model consists of IaaS, PaaS and SaaS. Cloud technology for mathematics learning includes 1) mobile mathematics application, 2) online mathematics learning application, and3)online mathematical software and programming. Cloud mathematics learning activities include Collaboration, Communication, Discussion, Numerical calculation and verify calculated solutions, Explore geometric relationships, Explore graph and function, Mathematical programming, Mathematical proofs, Assignment and Quiz.
Reducing Blocking Artifacts in CNN-Based Image Steganography by Additional Loss Functions

Our work improves the encoded image quality from HiDDeN framework, an end-to-end image steganography based on deep convolution neural network. In the encoding phase of HiDDeN framework, to embed a message in a cover image, it is required to split the cover image into smaller image blocks and embed the message bits in each block in parallel. These embedded blocks are then combined to form an encoded image that has the same size as the cover image. This image reconstruction process causes artifacts that appear on the boundaries of the blocks. This can be explained by the fact that when message bits are embedded in the image blocks, the pixel-level information of each image block is unequally alternated. In order to reduce block artifacts, in this work we propose a blocking loss as an additional objective function in HiDDeN framework. This loss measures the difference between encoded images and modified versions of the cover images. The proposed method is evaluated on COCO 2014 and BOSS datasets and the experimental results show the effectiveness in reducing the block artifacts that appeared in the encoded images of HiDDeN framework. This has an important impact on increasing the invisibility or transparency of the steganography system. In addition, the experimental result on secrecy of the proposed method also indicates the same performance as the HiDDeN pipeline.
POS Scaling Attention Model for Joint Slot Filling and Intent Classification

Intention recognition is a subtask of the natural language understanding in human-machine dialogue system. To understand what the user says, we need to determine the intention expressed by the user. Intent classification is to classify the sentence into a predefined intention category. Considering that slot and intent have the strong correlation, joint slot filling and intent classification with attention mechanism are actively explored in recent years. Most joint methods focus on the interactive mode and hierarchy of slot filling and intent classification. However, part-of-speech (POS) is ignored. As we all know, nouns and verbs occupy important positions in sentence, verbs represent user behavior, and nouns correspond to the object of operation. In this paper, we propose a part-of-speech scaling attention module which makes our model focus on the verbs and nouns for joint slot filling and intent classification. The experiments show that our proposed model significantly improves sentence-level semantic frame accuracy, reaching 85.8 percent and 76.8 percent on the benchmark ATIS and Snips datasets respectively. In addition, compared with the baseline methods, our model has better convergence speed.
Performance Analysis of Chatbots and Brief Study About Technical Aspects

Machines using Artificial intelligence is a very challenging task. It contains the formulation of machines by which it can simulate intelligence. In this paper, we will discuss about implementation and design of the chatbot using machine learning which is a part of artificial intelligence. Nowadays, chatbot using machine learning is a hot topic and widely popular, and we need an application for computer communication to catching the speed of natural language. There is statement that is “can machine think” which gives a idea that there can be intelligent application that respond like human, and this application we can name as chatbot [17]. Smart assistants such as cortana, siri, and text-based search engines are recently on boom in today’s generation. For analyzing, text techniques of Python like NLTK can be applied in natural language processing and for getting the convenient human like responses by designing an engine for finding the intelligent responses.
Mining Domain-specific Component-Action Links for Technical Support Documents

IT service support data such as user queries, resolutions done by agents, technical documents are very complex. It is very important to accurately identify the key components and the actions performed on them to better assist applications, such as knowledge extraction and ingestion, search, query, and support chat-bots. Extracting components and correctly linking them to the required actions would also benefit the problem remediation process by drastically reducing the time to resolve and helping the engineers to focus on the steps or section of the document that are relevant and needed to solve the issue. In this paper, we evaluate the performance of four approaches to establish links between mentioned components and actions on technical support documents.
Research of Association Rules Based on Improved Ant Colony Optimization

This paper proposed an association rules mining algorithm based on improved ant colony optimization, using association rules to explore the regularity of traditional Chinese medicines. We first build a frequent itemset association graph and turn the transaction database into a bitmap matrix. Then the improved ant colony optimization is used to find the frequent itemsets, based on which, strong association rules are produced. To increase the diversity of frequent itemsets, the roulette selection method is used in the improved ant colony optimization. Experimental results show the feasibility and effectiveness of our proposed algorithm.
Reasoning and Simulation of Mental Attitudes of a Customer

In this chapter, we employ logic programming to simulate the mental world. A Theory of Mind engine is introduced that takes an initial mental state and produces the consecutive mental states as plausible to a real-world scenario as possible. We simulate a multiagent decision-making environment taking into account intentions, knowledge and beliefs of itself and others. The simulation results are evaluated with respect to precision, completeness and complexity. Metaprogramming techniques of introspection is outlined for putting a CRM component in “customers’ shoes,” better predicting how she would think and act. We conclude that the Theory of Mind engine is adequate to support a broad range of CRM tasks requiring simulation of human mental attitudes.
Local binary pattern encoding schemes <scp>for computed</scp> tomography image segmentation: An experimental and comparative study

Abstract Local binary patterns (LBPs) are used for effective texture representation in various applications. This study explores the clustering consistency and stability of image segmentation when distance‐based clustering methods are used with an LBP. Because data are described by features and attributes, distances among data are also dominated by the definition of features. Moreover, four popular LBP encoding schemes for segmenting computed tomography (CT) images by using fuzzy C‐means are discussed and compared. The experimental results demonstrate several notable phenomena: When the pixels are encoded in different LBP encoding schemes, the distance between these pixels varies considerably. The experimental results indicate that each LBP encoding scheme emphasizes a specific image texture that dominates how pixels are described in the feature space and thus affects the clustering results. Based on the evaluation of CT image segmentation by using the clustering inconsistency index, linear and corner‐like LBPs are particularly suitable for clustering longitudinal sections and cross sections of CT images, respectively.
Difficulty in estimating visual information from randomly sampled images

In this paper, we evaluate dimensionality reduction methods in terms of difficulty in estimating visual information on original images from dimensionally reduced ones. Recently, dimensionality reduction has been receiving attention as the process of not only reducing the number of random variables, but also protecting visual information for privacy-preserving machine learning. For such a reason, difficulty in estimating visual information is discussed. In particular, the random sampling method that was proposed for privacy-preserving machine learning, is compared with typical dimensionality reduction methods. In an image classification experiment, the random sampling method is demonstrated not only to have high difficulty, but also to be comparable to other dimensionality reduction methods, while maintaining the property of spatial information invariant.
HGE2MED: Heterogeneous Graph Embedding for Multi-domain Event Detection

Multi-domain event detection (MED) task aims to discover real-world events from multi-domain and multi-view data sources. Most of the existing methods process multiple data modalities alone or concatenate single modality for the MED task, ignoring diverse intrinsic structures and underlying relations between each modality. In this paper, we address the heterogeneity of modalities' structure and data views' relations, and present a novel heterogeneous graph embedding method HGE2MED to learn the heterogeneous underlying relations and robust representation of each node. In particular, we construct heterogeneous graph for multi-domain and multi-view data, and obtain heterogeneous walking sequences by heterogeneous random walk. Using our heterogeneous graph embedding method to sample the triplets from above sequences, we are able to obtain robust embedding of each node by optimizing the network. Finally, taking the embedding of news as input, the event labels are predicted by a simple linear classifier. Comprehensive experiments are conducted to demonstrate the effectiveness of our proposed approach over state-of-the-art event detection algorithms on a real-world dataset.
A Joint Scheme for PAPR Reduction of OFDM Signals based on 3-D Constellation Rotation Encryption

Orthogonal frequency division multiplexing (OFDM) is the high rate data transmission technology widely used in wireless communication systems. High peak to average power ratio (PAPR) could directly affect the application of OFDM in the uplink of mobile communication systems. Based on the physical layer, this paper proposes a joint scheme for PAPR reduction of OFDM signals based on 3-d constellation rotation encryption. Simulation and analysis show that this scheme not only realizes physical layer security, but also reduces PAPR and greatly improves system performance.
Face Recognition Based on Deep Features

Abstract A face recognition method based on deep feature decision fusion is proposed. First, a convolutional neural network (CNN) is designed for deep feature learning on face images. Then, all the feature are mapped to construct a single depth feature vector. In the classification stage, the sparse representation-based classification is used to characterize the constructed depth feature vector, and the face category of the object to be recognized is determined according to the overall reconstruction error. Experiments are carried out on ORL and Yale-B datasets and compared with several existing face recognition methods. The results verify the effectiveness and robustness of the proposed method.
HLS Based Implementation of Modified DE-RIW Algorithm on FPGA and P-SoC

Being lossless, Reversible Image Watermarking (RIW) with Difference Expansion (DE) algorithm is crucial for content authentication, especially for high security medical and military images. In DE-RIW, the secret data bit can be embedded into the Least Significant Bit (LSB) of the difference between two neighboring pixels. Very few researchers have worked on High Level Synthesis (HLS) based implementation of its modified algorithm on Field Programmable Gate Array (FPGA) and Programmable System-On-Chip (P-SoC). Here, to get accelerated performance a modified DE-RIW algorithm is proposed along with its VLSI based hardware implementation both on Xilinx FPGA and P-SoC. In the proposed approach, scheduling is done considering resource constraint criteria. Implementation results through simulation up to burning the design on board needs fewer resources (adder, subtractor, multiplier, divider, register, multiplexer, and comparator) as compared with the similar existing architectures in the literature based on RIW.
Hints for Memory Optimization of Asylo-based SGX Enclave Applications

Intel SGX is a technology for providing the hardware-based trusted execution environment, that is available on the 6th generation Intel CPUs and above. Despite the strong security model, SGX has a fundamental limitation in its memory capacity. An SGX-enabled processor has 128 MB of the trusted memory region, called enclave page cache. Due to the limited EPC size, loading and running memory-intensive secure applications is limited. We can use SGX paging, adjusting heap sizes to accommodate secure applications in SGX enclaves. However, the performance impact of memory pressure has not been thoroughly investigated. In this paper, we analyze the relationship between the memory footprint of the secure application with the performance. According to our analysis, we show that the memory footprint of the enclave heap could negatively affect the performance of the latency-sensitive application. Besides, we show that we can effectively mitigate performance degradation by adjusting the maximum heap size.
Lineup Mining and Balance Analysis of Auto Battler

Auto battler is currently one of the most popular types of video games on the market. In such type of games, players set up their own lineup of characters which combat automatically with other competitors. There are numerous kinds of chess pieces, lineups and bonuses in the game, which make the balance problem of game design extremely critical and challenging. In this paper, we use Chess Rush to investigate the methodology of evaluating the strength standards of lineups in auto battler games, and build a system for implementing such measurement. A neural network model is firstly trained for quick strength evaluation of the lineup. The genetic algorithm is then adopted and modified for the purpose of lineup mining of auto battler. Finally, strong lineups can be obtained under certain constraints. In this way, game developers can complete a balanced analysis before the game goes online. Moreover, the proposed approach can be generalized to balance analysis tasks of other games.
Burrows-Wheeler Transformation for Medical Image Compression

Medical imaging is a very useful component in diagnosing diseases. For future use, and further study and analysis, hospitals must keep all patients' medical images in databases. In this work, a new lossless image compression technique is proposed for efficient storage and transmission of medical images. The newly proposed technique is based on encoding prediction errors with a suitable entropy coder upon transforming them with the Burrows-Wheeler Transformation (BWT). We show that the newly proposed technique yields better compression than the mainstream lossless compression algorithms JPEG-2000 and JPEG-LS.