Reviewerly/exHarmony huggingface dataset:
On Development of Information Communications in Human Society

Information is very important.Information is also very complicated, making that people have no common understanding and conclusion for the nature of it up today.There are too many papers and some books to describe information; however it is rather difficult to find the description and analysing for the whole history of information from the advent of human beings to the present day.Two parts of information in prehistoric period and the time interred divinization are described.Every part is separated according to several succeeded stages for description.It is near impossible to describe in detail such entire historical facts of information in human society in a paper, so the description and discussion is focused on their comprehensiveness and integrity.By knowing and analysing all these solid historical facts of information, some relative issues e.g."did information age really exist in the development of material civilization in human society" can be recognized easily.
ISHIGAKI Retrieval through Combinatorial Optimization

Two strong earthquakes that hit Kumamoto in April 2016 damaged the infrastructure and many buildings in the area, significantly impacting the people and traditional cultural assets of Kumamoto. In this project, using information technology, we aim to reconstruct the stone walls of Kumamoto castle, which is an important nationally designated special historical site in Japan. A stone wall is called "Ishigaki'' in Japanese. During the earthquake, many of the stones of the Kumamoto castle wall fell to the ground. Therefore, to reconstruct the stone wall, we must identify the original locations of the collapsed stones, similar to a jigsaw puzzle. In this paper, we formulate the problem by defining the cost function of the similarity between before and after collapse of the stones and propose the application of a combinatorial optimization algorithm. We tested the proposed solver by identifying 269 actual collapsed stones of the castle wall, and the results demonstrated that the proposed method can identify 253 stones correctly (94% accuracy).
Exorcising Spectres with Secure Compilers

Attackers can access sensitive information of programs by exploiting the side-effects of speculatively-executed instructions using Spectre attacks. To mitigate theses attacks, popular compilers deployed a wide range of countermeasures. The security of these countermeasures, however, has not been ascertained: while some of them are believed to be secure, others are known to be insecure and result in vulnerable programs. To reason about the security guarantees of these compiler-inserted countermeasures, this paper presents a framework comprising several secure compilation criteria characterizing when compilers produce code resistant against Spectre attacks. With this framework, we perform a comprehensive security analysis of compiler-level countermeasures against Spectre attacks implemented in major compilers. This work provides sound foundations to formally reason about the security of compiler-level countermeasures against Spectre attacks as well as the first proofs of security and insecurity of said countermeasures.
A Multi-Server Data Security with Public Auditing in Cloud Computing

In cloud computing, user share data while sharing data providing security and protecting the data from a unauthorized users is still a demanding problem, so we implement a multilevel data security popular a cloud computing in order to preserve the information using multi-security levels, and provide sufficient multilevel access and validate authorized identity. The recommended scheme is dynamic in multilevel data security, adaptable to certified resource share in, and protected against different malignant strike. Experimentation result checks the feasibleness. The processed data have multiple security level and access permission.
College Student Competition Recommendation and Development Planning Prediction Platform Based on BP Neural Network Algorithm

Abstract With the development of today’s society, the number of college students in China has steadily increased, and the number of college students is increasing. More and more college students choose to participate in competitions to improve their abilities. Therefore, our team has integrated the relevant competition resources of major universities and established a competition system with high efficiency, real-time interaction and high communication for the purpose of college students. The ordinary users are college students. In addition to registration, briefing, mutual help discussion forum and video broadcast of the event, the platform also provides users with professional classification based recommendation, college student development planning prediction, live broadcast, teammate recommendation, Special services such as rapid team formation, fast pipeline, experience sharing and authoritative guidance improve the user experience while simplifying the registration process. Form a unique platform for college students.
Research on voice search behavior in the last 10 years

ABSTRACT Voice search enables users to issue spoken information requests and retrieve desired information on web/mobile search interfaces. We conducted a literature review to examine the extent to which existing studies have addressed voice search behavior, with a focus on the employed methodologies and the focused topics in these studies. Fourteen relevant studies conducted since 2008 were identified from four publication databases. These studies were mainly focused on three aspects of search behavior: voice query characteristics, reformulation strategies, and users' perception/satisfaction. Our results provide a snapshot of how users perform and perceive voice search in order to inform future research and design of voice search systems.
Online Learning For Indoor Asset Detection

Building floor plans with locations of safety, security, and energy assets such as IoT sensors, fire alarms, etc. are vital for climate control, emergency response, safety, and maintenance of building infrastructure. Existing approaches to building survey are tedious, error prone, and involve an operator with a clipboard and pen, enumerating and localizing assets in each room. We propose an interactive method for a human operator to use an app on a smartphone, which can detect, classify, and localize assets of interest, to expedite such a task. We must overcome the fact that appearances of a single type of asset, e.g. power outlet, vary greatly from building to building or even from room to room. We propose an online ”one-shot learning” approach using a Neural Turing Machine (NTM) architecture with augmented memory capacity, which allows us to rapidly incorporate new data into our model, improving prediction accuracy after only a few examples, without compromising its ability to remember previously learned data. This approach reduces the training time needed to update the model between building survey sessions by up to a factor of 10. Experiments show that our proposed method outperforms the prediction accuracy attained by using more traditional, batch processing deep learning methods where new data is combined with all old data to train the model. The advantage is especially pronounced for assets in never-before-seen buildings.
Professional and institutional support for RDM: A case of the National Institutes of Health (NIH)

ABSTRACT This study explored biomedical researchers' Research Data Management (RDM) practices and investigated how institute and professional services support their practices through the RDM processes with a case of NIH. It was found that institutional information technology (IT) and library services are focused on certain stages of RDM processes. Also, it was found that having a close collaboration among service units within an institute is important to maximize their resources for services, reduce duplicated services, and publicize their services to a wider range of users.
The Development of Furniture Assembly Instruction Based on Augmented-Reality

This research utilized Augmented Reality (AR) technology along with interactive multimedia applications to improve visual guidance and assist nonexpert users in assembling furniture. Rather than paperbased or video instructions, AR enhanced learning and manipulative-interactive approach. This research used the Waterfall model for development. It was divided into a couple of stages. It consisted of specifying the problem and requirements, designing the system, implementing and integrating it, and testing the system. Identifying problems and requirements was conducted through an online questionnaire and related works study. The graphics tool used was 3D Studio Max. Unity3d was used to develop the system with the software library from Qualcomm’s Vuforia for the AR implementation. The results show the mobile application called ARsembly. It serves as the early stage of concept model as it only assists the simple assembly. Through system testing performed by both researchers and end-users, ARsembly is proven to be useful and helpful in learning and performing furniture assembly.
Continuous Sign Language Recognition Based on Pseudo-supervised Learning

Continuous sign language recognition task is challenging for the reason that the ordered words have no exact temporal locations in the video. Aiming at this problem, we propose a method based on pseudo-supervised learning. First, we use a 3D residual convolutional network (3D-ResNet) pre-trained on the UCF101 dataset to extract visual features. Second, we employ a sequence model with connectionist temporal classification (CTC) loss for learning the mapping between the visual features and sentence-level labels, which can be used to generate clip-level pseudo-labels. Since the CTC objective function has limited effects on visual features extracted from early 3D-ResNet, we fine-tune the 3D-ResNet by feeding the clip-level pseudo-labels and video clips to obtain better feature representation. The feature extractor and the sequence model are optimized alternately with CTC loss. The effectiveness of the proposed method is verified on the large datasets RWTH-PHOENIX-Weather-2014.
Automatic Detection of Suspicious Bangla Text Using Logistic Regression

Suspicious Bangla text detection is a text classification problem of determining Bangla texts into suspicious and non suspicious categories. In this paper, we have proposed a machine learning based system that can classify Bangla texts into suspicious and non-suspicious. For this purpose, a corpus is developed and logistic regression algorithm is used for classification task. In order to measure the effectiveness of the proposed system a comparison of accuracy among other algorithms such as Naive Bayes, SVM, KNN, and decision tree also performed. The experimental result with 1500 training documents and 500 testing documents shows that the logistic regression provides the highest accuracy (92%) than other algorithms.
Watch, Reason and Code

Humans have a surprising capacity to induce general rules that describe the specific actions portrayed in a video sequence. The rules learned through this kind of process allow us to achieve similar goals to those shown in the video but in more general circumstances. Enabling an agent to achieve the same capacity represents a significant challenge. In this paper, we propose a Watch-Reason-Code(WRC) model to synthesise programs that describe the process carried out in a set of video sequences. The 'watch' stage is simply a video encoder that encodes videos to multiple feature vectors. The 'reason' stage takes as input the features from multiple diverse videos and generates a compact feature representation via a novel deviation-pooling method. The 'code' stage is a multi-sound decoder that the first step leverages to generate a draft program layout with possible useful statements and perceptions. Further steps then take these outputs and generate a fully structured, compile-able and executable program. We evaluate the effectiveness of our model in two video-to-program synthesis environments, Karel andVizDoom, showing that we can achieve the state-of-the-art under a variety of settings.
Expert Survey—Triggers for State Reactance

A qualitative survey among 21 usability experts was conducted to integrate the situations from the scientific literature into observations from practice. The expert survey resulted in five clusters of situations that showed a different distribution of situations. While five of the twelve scientific papers from the literature search investigated state reactanceState reactance in connection with persuasive attempts, none of the experts from the expert survey reported such a situation. Instead, multiple experts mentioned reduced freedom of choice, high effort or costs or scarceness as situations that triggered reactance, all of which were only covered by one scientific paper. Also, five experts reported to have experienced state reactanceState reactance as a consequence of system errors, a state reactanceState reactance trigger that has not been investigated in scientific literature, at all. The following research question asks for the consequences for the interaction if technical systems triggered state reactanceState reactance in their users. The largest amount of information regarding this question was obtained from the scientific literature. Numerous consequences, including boomerang effects and avoidance behavior have been reported in the reviewed papers. In contrast to this, the reported consequences for interaction with technical systems from the survey were scarce but drastic. Two experts reported that they completely refrained from using the systems that caused state reactanceState reactance, anymore.
Diametrical Risk Minimization: Theory and Computations

The theoretical and empirical performance of Empirical Risk Minimization (ERM) often suffers when loss functions are poorly behaved with large Lipschitz moduli and spurious sharp minimizers. We propose and analyze a counterpart to ERM called Diametrical Risk Minimization (DRM), which accounts for worst-case empirical risks within neighborhoods in parameter space. DRM has generalization bounds that are independent of Lipschitz moduli for convex as well as nonconvex problems and it can be implemented using a practical algorithm based on stochastic gradient descent. Numerical results illustrate the ability of DRM to find quality solutions with low generalization error in sharp empirical risk landscapes from benchmark neural network classification problems with corrupted labels.
Comparing Two Models of Document Similarity Search over a Text Stream of Articles from Online News Sites

In this paper, we compare two models of document similarity search over a text stream of articles which are collected daily from online News sites. The first model uses the word to vector (Word2Vec), neural-network-based document embedding is known as the document to vector (Doc2Vec) and k-NN technique to perform similarity search in a tree structure called M-Tree. The second model applies Gensim model to do the same job of document similarity search. We use the metric which measures the accuracy of the documents similar to document d when considering if they are in the same category with the document d or not. We also do the experiment and evaluation, analyze experimental results, discuss and propose solutions for improvement. Our main contributions are to compare the two solutions in performing document similarity queries.
Towards Fine-Grained Recognition: Joint Learning for Object Detection and Fine-Grained Classification

Fine-grained classification is a challenging problem due to subtle differences between intra-class categories. In practice, fine-grained classification is often used in conjunction with object detection algorithms to locate and identify object categories. Despite recent achievements in both fine-grained classification and object detection, few works have demonstrated datasets or solutions to simultaneously handle both tasks. We make two contributions to this problem. Firstly, we construct a fine-grained classification and detection benchmark. Secondly, we show an end-to-end convolutional neural networks (CNNs) architecture to detect and classify fine-grained objects. Experimental results verify that our networks perform favorably against alternatives.
Clinical Pathways Formal Modelling Using Bigraphical Reactive Systems

Clinical pathways are multidisciplinary structured care plans that aim at increasing the quality of healthcare delivery. Despite the wide-spread adoption of clinical pathways by governments and healthcare authorities, works that associate formal semantics to clinical pathways do not model clearly the roles of health professionals during the care process. This has motivated us to propose a formal modelling approach for clinical pathways, based on Bigraphical Reactive Systems. We concentrate on showing graphically relationships among the healthcare stakeholders. To meet the control flow requirement of clinical pathways, we apply the Maude strategies language over a Maude implementation of Bigraphical reactive Systems key concepts.
Parallel Clique-Like Subgraph Counting and Listing

Cliques and clique-like subgraphs (e.g., quasi-cliques) are important dense structures whose counting or listing are essential in applications like complex network analysis and community detection. These problems are usually solved by divide and conquer, where a task over a big graph can be recursively divided into subtasks over smaller subgraphs whose search spaces are disjoint. This divisible algorithmic paradigm brings enormous potential for parallelism, since different subtasks can run concurrently to drastically reduce the overall running time. In this paper, we explore this potential by proposing a unified framework for counting and listing clique-like subgraphs. We study how to divide and distribute the counting and listing tasks, and meanwhile, to balance the assigned workloads of each thread dynamically. Four applications are studied under our parallel framework, i.e., triangle counting, clique counting, maximal clique listing and quasi-clique listing. Extensive experiments are conducted which demonstrate that our solution achieves an ideal speedup on various real graph datasets.
CAP-based Examination of Popular NoSQL Database Technologies in Streaming Data Processing

Nowadays, when the Internet, software and hardware technologies develop rapidly, we are in a digital era where a wide range of resources generate data in different formats and 7/24. It is essential for organizations to quickly process data in near-real time and store it in a scalable form, especially without loss of data. In addition, the importance of streaming data processing due to the desire to get information quickly instantly increases. In this study, the popular NoSQL databases, which are prominent in streaming data processing, have been examined based on CAP's theorem. This work will help software developers in the selection and use of NoSQL databases for streaming data processing.
Building Formal Semantic Domain Model: An Event-B Based Approach

Ontologies are structured data models used to describe a set of concepts related to a specific domain, they describe also the semantic properties of these concepts. Formal development process aims to develop a system with respect to properties or constraints. The IMPEX project is interested in involving domain constraints as soon as possible into formal development process, it proposes to integrate the ontologies descriptions in an Event-B development process. This approach assumes to develop a transformation step of the ontologies constructs from their initial description in an ontological language (OWL, OntoML ...) to Event-B. A first version of this transformation approach for OWL ontologies based on a generic and extensible architecture has been developed, it is supported by the OntoEventB tool. An evolution of this approach to include OntoML ontologies and new features is presented in this paper.
Diverse Video Captioning Through Latent Variable Expansion with Conditional GAN.

Automatically describing video content with text description is challenging but important task, which has been attracting a lot of attention in computer vision community. Previous works mainly strive for the accuracy of the generated sentences, while ignoring the sentences diversity, which is inconsistent with human behavior. In this paper, we aim to caption each video with multiple descriptions and propose a novel framework. Concretely, for a given video, the intermediate latent variables of conventional encode-decode process are utilized as input to the conditional generative adversarial network (CGAN) with the purpose of generating diverse sentences. We adopt different Convolutional Neural Networks (CNNs) as our generator that produces descriptions conditioned on latent variables and discriminator that assesses the quality of generated sentences. Simultaneously, a novel DCE metric is designed to assess the diverse captions. We evaluate our method on the benchmark datasets, where it demonstrates its ability to generate diverse descriptions and achieves superior results against other state-of-the-art methods.
The Creativity Process behind GRIS

GRIS is the journey of a girl dealing with the most painful experience in her life. Explaining this story with no words or dialogue has been a great challenge for Nomada Studio. Adrian Cuevas will explain the process of creation for GRIS, showing the different tools, references and ideas that were used during the production of the game. Art, Music, Animation, Game Design, ... everything is extremely important to have a consistent narrative and engage the player with a story that will not be told. On top of that GRIS targets all kind of audience, even people that are not used to play video games. This will brought more challenges to the design and construction of the game. Adrian will talk about some of the decisions that were made to make the game more appealing to non-gamers.
Operation of Solar Power Plant with Solar Tracker in Orenburg Region During the Winter

The article provides the experience of operating a solar power station with dual-axis solar tracking system in the winter of 2019 in Orenburg (Russia). A comparative analysis of the generation of “green” energy by stationary solar panels and a solar module with a tracking system is presented. Data recorded by the station's information-measuring complex are used as a material for analyzing the operation of the photovoltaic plant. This complex consists of a developed system for remote monitoring and diagnostics of solar power station parameters and an automated weather station. The authors consider the structure and capabilities of this complex in detail, the features of the station in the abovementioned period and make recommendations for improving the energy efficiency of photovoltaic plants in the Orenburg region.
Performance analysis of the LapRSSLG algorithm in learning theory

It is known that one aim of semi-supervised learning is to improve the prediction performance using a few labeled data with a large set of unlabeled data. Recently, a Laplacian regularized semi-supervised learning gradient (LapRSSLG) algorithm associated with data adjacency graph edge weights is proposed in the literature. The algorithm receives success in applications, but there is no theory on the performance analysis. In this paper, an explicit learning rate estimate for the algorithm is provided, which shows that the convergence is indeed controlled by the unlabeled data.