Reviewerly/exHarmony huggingface dataset:
Architecture of a Hydroacoustic Navigation System with a Cotton Basis for Conversion of the Underwater Apparatus to the Docking Module

The object of the study are the principles and methods of hydroacoustic reduction of an autonomous underwater vehicle to a carrier. When solving the problem of constructing a hydroacoustic navigation system (HNS), the main systemic issues are the choice of an acceptable method for locating an object in the reduction zone and estimating the options of the proposed navigation system from the point of view of technical implementation. The feasibility of constructing the equipment of a highfrequency hydroacoustic reduction system in the form of a combined information and navigation antenna system combined in base, in which hydroacoustic navigation systems with a short base can be used, is justified; as navigation it is suggested to use data signals exchanged between the docking module and the autonomous underwater vehicle based on the results of measuring the mutual navigation options. The developed sample of equipment is part of a multifunctional network of hydroacoustic communication and should ensure that in a near zone at distances not exceeding 300 m, an autonomous underwater vehicle is brought into contact with the carrier. For this purpose, mutual determination of the distance and angular position of the docking module of the carrier and the autonomous underwater vehicle relative to each other is carried out. Determination of the distance and angular position of the docking module of the carrier and the autonomous underwater vehicle relative to each other is accompanied by the transfer of data between them along the hydroacoustic communication channel. The proposed sample of high-frequency hydroacoustic reduction system (HHRS) equipment of short-range action is intended for operation as part of a complex of technical means providing underwater docking of an autonomous underwater vehicle with a carrier. The obtained quantitative options of short-based HNS are the initial indicators of the first approximation when selecting the necessary parameters of the navigation signal and developing the structure of the AGSS designed to solve the problem of automatically bringing the autonomous underwater vehicle (AU V ) to the docking module (DM) of the given carrier.
Scale the Authoring of High Quality Lexicon Knowledge

In this document, we describe T-Lex, a tool designed to solve high quality Lexicon knowledge's collection issue for natural language generation (NLG) purpose. To allow the computer to speak naturally and fluently, it's critical to collect large amount of high quality Lexicon knowledge. T- Lex has 3 main contributions: 1, scales tooling support for new locale's Lexicon model; 2, provides filter/annotate/arbitration workflow to fasten data collection and ensure data quality; 3, offers proper way to validate and fix data issues quickly. The result shows that the system is capable to collect high quality Lexicon knowledge with better scalability.
Performance Bounds of Decentralized Search in Expert Networks for Query Answering

Expert networks are formed by a group of expert-professionals with different specialties to collaboratively resolve specific queries posted to the network. In such networks, when a query reaches an expert who does not have sufficient expertise, this query needs to be routed to other experts for further processing until it is completely solved; therefore, query answering efficiency is sensitive to the underlying query routing mechanism being used. Among all possible query routing mechanisms, decentralized search, operating purely on each expert’s local information without any knowledge of network global structure, represents the most basic and scalable routing mechanism, which is applicable to any network scenarios even in dynamic networks. However, there is still a lack of fundamental understanding of the efficiency of decentralized search in expert networks. In this regard, we investigate decentralized search by quantifying its performance under a variety of network settings. Our key findings reveal the existence of network conditions, under which decentralized search can achieve significantly short query routing paths (i.e., between O (log n ) and O (log 2 n ) hops, n : total number of experts in the network). Based on such theoretical foundation, we further study how the unique properties of decentralized search in expert networks are related to the anecdotal small-world phenomenon. In addition, we demonstrate that decentralized search is robust against estimation errors introduced by misinterpreting the required expertise levels. The developed performance bounds, confirmed by real datasets, are able to assist in predicting network performance and designing complex expert networks.
Spanning the Boundaries of Data Visualization Work: An Exploration of Functional Affordances and Disciplinary Values

Creating data visualizations requires diverse skills including computer programming, statistics, and graphic design. Visualization practitioners, often formally trained in one but not all of these areas, increasingly face the challenge of reconciling, integrating and prioritizing competing disciplinary values, norms and priorities. To inform multidisciplinary visualization pedagogy, we analyze the negotiation of values in the rhetoric and affordances of two common tools for creating visual representations of data: R and Adobe Illustrator. Features of, and discourse around, these standard visualization tools illustrate both a convergence of values and priorities (clear, attractive, and communicative data-driven graphics) side-by-side with a retention of rhetorical divisions between disciplinary communities (statistical analysis in contrast to creative expression). We discuss implications for data-driven work and data science curricula within the current environment where data visualization practice is converging while values in rhetoric remain divided.
A Comparative Study of Biological Scientists’ Data Sharing Between Genome Sequence Data and Lab Experiment Data

This research aims to explore how the institutional pressure, resource, and individual motivation factors all affect biological scientists' data sharing behaviors in different data types. This research utilized a combined theoretical framework including institutional theory and theory of planned behavior to examine institutional pressure, resource, and individual motivation factors influencing biological scientists' data sharing intentions between different data types including genome sequence data and lab experiment data. A total of 342 survey responses from biological sciences were employed for a series of statistical analyses including Cronbach's alpha, factor analysis, hierarchical regression, and t-test. This research shows that biological scientists' data sharing intentions are led by institutional pressure, resource, and individual motivation factors, and the levels of those factors are significantly different between genome sequence data and lab experiment data. This research shows that biological scientists' data sharing differs depending on the data they share, and different policies and support needs to be applied to encourage biological scientists' data sharing of different data types.
Self-attentional Convolution for Neural Networks

Convolutional neural networks (CNNs) have proven to be effective models for tackling a variety of visual tasks. For each convolutional layer, a set of filters are learned to express local spatial connectivity patterns along inputs, but with the complexity of the structure growing, the network training becoming harder and harder, one of the reasons is over-fitting. Many methods are proposed recently in order to reduce this factor by network regularization or normalization, but few of them put the thought of structural control into consideration. In this paper, we propose a new method called Self-attentional Convolution for learning and regularizing the structure of convolutional layers by using the thoughts of attention models, which is more efficient than the original convolutional layers. For calculable and reasonable, we divide the attention weight factors into two parts, channels and shape of the kernels, as the structural constraints for each convolution layer from different views, and multiply them as the global attentional factors for the weights of the convolutional kernel. At last, several experiments are designed, and the final improvement is about 1% on 20, 32, 44, 56 and 110 layers of ResNets on CIFAR-10, and 0.8% of AlexNet on ImageNet dataset, which can prove the effectiveness of our method.
Case based image retrieval and clinical analysis of tumor and cyst

Case based reasoning (CBR) with image retrieval can be used to implement a clinical decision support system for supporting diagnosis of space occupying lesions . We present a case based image retrieval (CBIR) system to retrieve images with lesion similar to the input test image. Here we consider only glioblasoma and lung cancer lesions. The lung cancer lesions can be either nodules or cysts. A feature database has been created and the processing of a query is conducted in real time. By using bag of visual words (BOVW), histogram of features is compared with the codebook to retrieve similar images. The experiments performed at various levels retrieved relevant and similar images of lesion images with a mean average precision of 0.85. The system presented is expected aid and improve the effectiveness of diagnosis performed by radiologist.
Using Physics-Based M&amp;S for Training and Testing Machine Learning Algorithms

Machine learning algorithms have been used to successfully solve many complex and diverse problems especially in the domain of unmanned vehicle systems. However, machine learning algorithms require training data that contain extensive variations in specimens. These variations include variations of the sensor settings, terrain conditions, environmental conditions, and variants of the objects of interest themselves. Capturing training specimens that span these variants is time consuming, expensive, and in some cases impossible. Training data in a narrow range of variations lead to decreases in performance such as overfitting. Therefore, collecting training data is often the limiting factor in developing robust machine learning algorithms for applications such as object detection and classification. Another time-consuming task is labeling training specimens with metadata needed in some training approaches. In this paper, we demonstrate using a physics-based modeling and simulation (M&S) capability to generate simulated training data spanning variations in sensor settings, terrain conditions, and environmental conditions that include a versatile automated labeling process. The product of prior efforts, the Virtual Autonomous Navigation Environment (VANE), is a high-fidelity physics-based M&S tool for simulating sensors commonly used on unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs).
Causation, Process Metaphor, and Reductionism

The limitations of deductive logic are addressed with the practical syllogism. Linking verbs within premises in deduction are state-of-being verbs that establish membership ultimately in the same group, tautologies. Socrates is a member of all humans and humans are members of the set of all mortal things, thus Socrates is mortal. In science, relationships between entities are not necessarily in terms of membership. The practical syllogism extends deduction by changing state-of-being linking verbs to causal verbs critical to the generation of hypotheses that can be submitted to the scientific method. While extending the utility of logic, it also exposes such logic to fallacies. Understanding biology in terms of causation leads to reductionism in order to reduce the number of explanatory causes, further risking error and unproductive irreproducibility. The problem is compounded by the often necessity of a chain of causal events, which, if violating the Principle of Causational Synonymy, risks irreproducibility.
An improved TLD algorithm with selective detection

This paper proposes an improved TLD algorithm with selective detection. In the tracking module, KCF is used as the short-term tracker, and the idea of backward tracking is introduced to judge the accuracy of the result, when the result is inaccurate, the proposed algorithm starts up the detection module; in the detection module, H component of the color image is used as the input, and the mean filter serves as the first layer of the cascaded classifier, the color histogram similarity measure method is used to judge the accuracy of the detection results; finally a fusion strategy is formulated according to the output results of the tracking module and the detection module. The experiments carried on the OTB-2013 data platform show that the tracking accuracy and success rate of the proposed algorithm are respectively 0.801 and 0.591, which are 20.4% and 16.5% higher than the TLD algorithm, besides the proposed algorithm have good adaptability in scenarios such as illumination change, occlusion, and scale change, which shows superior tracking robustness.
Comparison of different machine learning models on feature extraction for human activity recognition from RGB-depth datasets

Recognizing human body activities from the video sequences are directly depends on the features extraction for motion analysis, which is each activity can be presented by certain motion features. Therefore, by using corresponding features, we can probably classify different activities. This idea inspires us to form the activity recognition as a classification problem and verify its feasibility. In this work, two important goals are presented. The first one is extracting the motion and texture features from RGBD sequences by proposing a feature extracting method to extract feature vector values based on the Gray-Level Co-occurrence matrices (GLCM) of the dense optical flow pattern and the well-known Haralick features from these matrices by measuring meaningful properties such as energy, contrast, homogeneity, entropy, sum average, and correlation to capture local spatial and temporal characteristics of the motion through the neighboring optical flow fields (orientation and magnitude). Secondly, we present a performance comparison of five different classifiers such as Artificial Neural Networks, Naive Bayes classifier, Random Forest, K-Nearest Neighbors, and Support Vector Machine. Various numerical experiments results are carried on four well-known public datasets (Gaming Datasets, Cornell Activity Datasets, MSR Daily Activity 3D and Online RGBD Datasets) to verify the effectiveness of these classification algorithms. From experiments, the classifiers show different performance according to the features that computed and the set of classes from different activities. And the results demonstrate that all the five algorithms achieve satisfactory activity recognition performance.
Kill Your Darlings (a Holonovel)

This paper presents a shady holonovel, which is situated in the vile, short-term future. The main protagonists are two teenagers, Angela and Veronica, who are immersed in an adverse Mixed Reality online game. The girls are, at first, seduced by the comfy Sherlock Holmes-type of suspenseful narration that involves engaging mystery-solving challenges. Thereafter, the adolescent girls are fascinated by the virtual social status that the game network offers, as well as the persuasive end-reward, which will allegedly answer the ultimate existential question, “Is there life after death?”. Behind the scenes, the game is controlled by a group of anonymous, extreme transhumanists who have virtual appearances in the game as holographic avatars. To avoid lawsuits, the game is moderated by Artificial Intelligence—a curator with a virtual presence of Professor Moriarty—that aggregates the gameplay and command streams of the savage community of teen-hunters.
Research on key escrow technology Based on software

With the rapid development of e-commerce, security issues have attracted more and more attention. In this paper, PKI-based electronic commerce security key escrow technology and protocol are studied, the role of PKI in e-commerce security and the application characteristics of PKI are explained, and the design scheme of a digital authentication system based on PKI is described.
Study on Destruction Transformation of Error System Structure

Firstly this paper analyzes the basic structures of error system, which can be broken down to six basic types including series, parallel, expansion & contraction, inclusion, feedback and others. Then gives the physical meaning of destruction, Thsw (things destruction) → things that do not exist → nothing to be discussed, or there’s no need to discuss the things or the things are killed, extinct, destroyed, disappeared, fired, sold, thrown, transferred, moved and so on. And next we describe the logic rules of destruction transformation by corresponding logic connectives, quantitatively represents the relationship between system structure and error under destruction transformation by using error function and structure function. Finally the transformation system is given.
Electromagnetic Activity vs. Logical Activity: Near Field Scans for Reverse Engineering

Electromagnetic Near Field Scanning has formerly been proposed to guide side channel and fault injection attacks. However very few studies support its use for reverse-engineering. This absence could be explained by difficulties linked to the diffusion of currents in the power supply network, which are the root of EM radiations. This diffusion has for consequence that a local electrical activity in an IC can be observed quite far from its origin point, thus limiting the interest of EM near field scans for reverse engineering. This paper proposes a solution to this problem by describing a method to extract the source areas of an IC where electrical activity is occurring from EM near field scans. Experimental results are given for an ARM based microcontroller designed in a 90 nm process.
A Responsive Templating Approach for Generating Collaborative Spaces

The business world is a competitive world and the success of entrepreneurs and businesses today is linked to their ability to be inventive which, in turn, means that innovation has become increasingly important for businesses. One response to this has been the introduction of special environments, tailored to support innovation, namely Innovation-Labs (i-Labs). Research has shown that creativity thrives in spaces that are playful and customizable, rather than in the somewhat sterile environments of most workplaces. In this paper, we describe a Responsive Templating Model (RTM) for creating an online immersive collaborative business innovation space that can be customized and dynamically adapted to the needs of individuals and specific business innovation sessions. Our approach has been inspired by earlier pioneering work in innovation Labs, virtual reality, HCI, end-user programming and the World Wide Web. This paper presents a model that allows space and task reconfigurations to be undertaken by a facilitator using template interfaces that are both dynamic and extensible, so as to support human–machine and machine–machine interactions thereby enabling the creation of personalised business innovation spaces.
Knowledge Based System for Composing Sentences to Summarize Documents

This chapter provides the details on how to build a knowledge-based system that is capable of composing new sentences to summarize multiple documents. The system is also capable of identifying the main topics of the given documents and is able to derive new concepts based on the given text data. In order to process the documents conceptually to create abstractive summaries, the system makes use of the Cyc development platform that consists of the world’s largest knowledge base and one of the most powerful inference engines. The resultant knowledge based system first uses natural language processing techniques to extracts syntactic structure of the documents and then maps the words of the sentences into related concepts in the knowledge base. It then uses the inference engine to generalize and fuse concepts to form more abstract concepts. Since a word can be mapped into multiple concepts, the system also includes new techniques to handle word-sense disambiguation by using concept weights. After the generalization, the system is able to identify the main topics and the key concepts of the documents. The system then composes new sentences based on the key concepts by linking subject concepts with their related predicate concepts. The syntactic structure of the newly created sentences extends beyond simple subject-predicate-object triplets by incorporating adjective and adverb modifiers. The final stage is then to map the linked concepts back to words to form the abstractive sentences. The system has been implemented and tested. The implementation encodes a process that consists of seven stages: syntactic analysis, words mapping, concept propagation, concept weights and relations accumulation, topic derivation, subject identification, and new sentence generation. The implementation has been tested on various documents and webpages. The test results showed that the system is capable of creating new sentences that include abstracted concepts not explicitly mentioned in the original documents and that contain information synthesized from different parts of the documents to compose a summary.
Detection of the Information Hidden in Image by Convolutional Neural Networks

This article shows the possibility of detection of the hidden information in images. This is the approach to steganalysis than the basic data about the image and the information about the hiding method of the information are unknown. The architecture of the convolutional neural network makes it possible to detect small changes in the image with high probability.
A Method for Extracting Influential People for the Improvement of Contents

It is very important to find influential users who have useful information as well as extract helpful knowledge that affect the efficiency of user’s intellectual work; however, it is difficult to evaluate how such helpful comments and conversations from influential users can affect works of other users. In this study, we propose a method for extracting user comments that influenced other users to improve their contents such as a presentation slide by analyzing correlation between user’s comments and improved parts of contents. Our method allows us to make actual evaluation to influential users for some specific users. In the experiment, we evaluate the feasibility of the proposed method using the actual communication history that is collected from a communication service, Slack.
Salt and Pepper Noise Suppression for Medical Image by Using Non-local Homogenous Information

In this paper, we propose a method to suppress salt and pepper noise for medical images based on the homogenous information obtained by non-symmetrical and anti-packing model (NAM). The NAM could divide the image into several homogenous blocks and it is sensitive to the additive extra energy. Thus the noise could be detected effectively due to the usage of bit-plane during the division. Then corrupted points are estimated by using a distance based weighted mean filter according to the homogenous information in its non-local region, which could keep local structure. Experimental results show that our method can obtain denoising results with high quality.