Reviewerly/exHarmony huggingface dataset:
Deep Water: Predicting Water Meter Failures Through a Human-Machine Intelligence Collaboration

Take an AI learning algorithm and a human trainer with an experience in machine intelligence. Take piles of data, in the form of labeled examples. If you think that the task of training a machine that makes accurate decisions is easy as pie, you could not be further from reality. That is what has been missing from this story: getting off on the right foot. For a good start, crucial is the value of data which come large in quantity but low in quality. Beyond how we design our AI, fundamental is making our data valid for learning. We report here our experience in the creation of highly accurate training examples, based on the idea of filtering out all the impurities from a dataset containing 15 million water readings, provided by an Italian water supply company. This was accomplished allowing a human-machine collaboration, down to the implementation of an AI model capable to predict water meter failure.
Augmented Reality and 3-D Visualization Effects to Enhance Battlefield Situational Awareness

This study presents the augmented reality technology and 3D information visualization for Army’s military education to realize the interactive tactical course teaching and consider the user center research and human-computer interaction architecture as a solution to improve the common operational picture. The purpose is to improve the Army’s command and general staff officers in tactical operations to learn battlefield situational awareness to enhance interest in learning and effective decision support. We constructed a prototype augmented reality program, the traditional tactical image and symbols of war games into 3-D virtual images, Which includes military equipment, urban architecture and geography scenes and other models, teaching applications to import tactical operations, not only to break through the limitations of a traditional 2-D image, enhanced visual effects and digital technology information, revealing a more complete battlefield scene. At the same time, through virtual operation, students are more interactive. In order to verify and collect the utility and data of learning, quasi-experimental research has been used. Experiments have shown that using 3-D information Visualization for better understanding of military tasks and space environments can enhance learning interest and perception of situation awareness.
Modelling RNA‐Seq data with a zero‐inflated mixture Poisson linear model

Abstract RNA sequencing (RNA‐Seq) has been frequently used in genomic studies and has generated a vast amount of data. The RNA‐Seq data are composed of two parts: (a) a sequence of nucleotides of the genome; and (b) a corresponding sequence of counts, standing for the number of short reads whose mapped positions start at each position of the genome. One common feature of these count data is that they are typically nonuniform; recent studies have revealed that the nonuniformity is partially owing to a systematic bias resulted from the sequencing preference. Existing works in the literature model the nonuniformity with a single component Poisson linear model that incorporates the effects of the sequencing preference. However, we observe consistently that the short reads mapped to a gene may have a mixture structure and can be zero‐inflated. A single component model may not suffice to model the complexity of such data. In this paper, we propose a zero‐inflated mixture Poisson linear model for the RNA‐Seq count data and derive a fast expectation–maximisation‐based algorithm for estimating the unknown parameters. Numerical studies are conducted to illustrate the effectiveness of our method.
Learning Ising Models with Independent Failures.

We give the first efficient algorithm for learning the structure of an Ising model that tolerates independent failures; that is, each entry of the observed sample is missing with some unknown probability p. Our algorithm matches the essentially optimal runtime and sample complexity bounds of recent work for learning Ising models due to Klivans and Meka (2017). We devise a novel unbiased estimator for the gradient of the Interaction Screening Objective (ISO) due to Vuffray et al. (2016) and apply a stochastic multiplicative gradient descent algorithm to minimize this objective. Solutions to this minimization recover the neighborhood information of the underlying Ising model on a node by node basis.
Improving Interpretable Prediction Models for Antimicrobial Resistance

One of the major problems of healthcare institutions is the treatment of infections caused by bacteria that are resistant to antimicrobials. The early prediction of such infections can improve the patient's evolution as well as minimise the spread of antimicrobial resistance. The creation of effective prediction models is particularly limited due to the high dimensionality of data, the imbalanced datasets and the concept drift problem. In this paper, we face these challenges from a machine learning perspective, considering the interpretability of the resulting models as essential. In particular, we present a study of multiple techniques focused on the mitigation of these problems, that are used in combination with interpretable models. Our results indicate that the use of oversampling along with sliding windows can improve the resulting AUC of models (up to reaching a mean AUC of 0.80 in our dataset), and FCBF can be used to drastically reduce the number of predictors, obtaining simpler models with a slight AUC reduction (from a mean number of predictors of 69.78 to 16.28, achieving a mean AUC of 0.76). According to our results, we show that the combination of multiple techniques for dealing with the aforementioned data-mining problems can clearly improve the performance of prediction models for antimicrobial resistance.
TIR-MS: Thermal Infrared Mean-Shift for Robust Pedestrian Head Tracking in Dynamic Target and Background Variations

Thermal infrared (TIR) pedestrian tracking is one of the major issues in computer vision. Mean-shift is a powerful and versatile non-parametric iterative algorithm for finding local maxima in probability distributions. In existing infrared data, and mean-shift-based tracking is generally based on the brightness feature values. Unfortunately, the brightness is distorted by the target and background variations. This paper proposes a novel pedestrian tracking algorithm, thermal infrared mean-shift (TIR-MS), by introducing radiometric temperature data in mean-shift tracking. The thermal brightness image (eight-bits) was distorted by the automatic contrast enhancement of the scene such as hot objects in the background. On the other hand, the temperature data was unaffected directly by the background change, except for variations by the seasonal effect, which is more stable than the brightness. The experimental results showed that the TIR-MS outperformed the original mean-shift-based brightness when tracking a pedestrian head with successive background variations.
The Cross-Country Diffusion of New Entrepreneurial Practices: The Case of Initial Coin Offerings

We examine the cross-country diffusion of a new entrepreneurial practice, namely Initial Coin Offerings (ICOs). Drawing on institutional theory, we argue that governments (including their financial regulators), the media and academic institutions affect the institutional legitimacy of ICOs, thereby influencing their cross-country diffusion. Further, we hypothesize that ICO bans in one country have internationally mobile governance implications that affect the diffusion of ICOs in other countries. We introduce new quarterly data on ICO activity from 73 countries between 2013 and 2017 (inclusive), and present evidence consistent with our new hypotheses and institutional theory. Overall, we offer novel insights on the cross-country diffusion of ICOs, a new entrepreneurial practice, in an increasingly global and decentralized world.
Cognitive Module Networks for Grounded Reasoning

The necessity for neural-symbolic integration becomes evident as more complex problems like visual question answering are beginning to be addressed, which go beyond such limited-domain tasks as classification. Many existing state-of-the-art models are designed for a particular task or even benchmark, while general-purpose approaches are rarely applied to a wide variety of tasks demonstrating high performance. We propose a hybrid neural-symbolic framework, which tightly integrates the knowledge representation and symbolic reasoning mechanisms of the OpenCog cognitive architecture and one of the contemporary deep learning libraries, PyTorch, and show how to implement some existing particular models in our general framework.
Development and future prospects of quantitative mineral assessment in China

Mineral potential assessment at the Earth’s surface has been an important research for geoscientists around the world in the past five decades. The fundamental aspects of mineral assessment at different scales can be associated with the following tasks, e.g., mineral potential mapping and estimation of mineral resources. This paper summarized the history and development in terms of theories, methods technologies and software platforms for quantitative assessment of mineral resources in China, e.g. comprehensive information methodology, geological anomaly, three-component quantitative prediction method, 5P ore-finding area, integrated information assessment method, nonlinear process modeling and fractals, three dimensional mineral potential mapping, etc. At last, to discuss the future of quantitative mineral assessment in an era of big data including platform for 3D visualization, analysis and sharing, new methods and protocols for data cleaning, information enhancement, information integration, and uncertainties and multiple explanations of multi-information.
Enhanced Data Sharing with Blockchain in Healthcare

With the age of modernization the technologies are built at rapid speed with highly advanced features in it. Similar to this there are issues related to personal healthcare. In this paper we proposed blockchain technology to secure data of every individual and to have properscalable, reliable data records. Our system provides patient comprehensive, unassailable log of leaseback admittance to their medical related statistics. Our proposal of innovative health data sharing solution by usage of permissioned blockchain to provide security and concealment using network formation organization and boost the distinctiveness management by using the participation service provided and braced by blockchain. It preserves the top score, a proof of veracity and authentication to retrieve facts from the blockchain. It gives highly ascendable, trusted, liable and genuine data. It paybacks the future scope in IT and Investigation by supplying apposite and expedient data with veracity and interoperability.
Resetting-Label Network Based on Fast Group Loss for Person Re-Identification

In this paper, we propose a Resetting-Label Network based on Fast Group Loss for Person Re-Identification (RLFGL-ReID). The major challenge of Re-ID lies in how to preserve the similarity of the same person against large variations caused by complex background, different illuminations, and various view angles while discriminating different individuals. To address the above-mentioned problems, we propose the RLFGL-ReID that includes resetting-label (RL) and fast group loss (FGL). Two main contributions of our network are as follows. First, a new method, the resetting-label method, which resets the ID labels, is proposed for the Re-ID network. Resetting the ID labels of each pedestrian is beneficial in maximizing the inter-group distances between each people, achieving better performance on classifying different individuals. Second, a fast group loss, i.e., an advanced version of variance group loss (VGL), is proposed to simplify the training process and accelerate the loss computation. By doing so, the network can eliminate the restriction of inputting the whole group of data when training the network. To confirm the effectiveness of our method, we extensively conduct our method on several widely used person Re-ID benchmark datasets. As the result shows, our method achieves rank@1 accuracy of 98.38% on CUHK03, 95.46% on Market1501, and 91.2% on DukeMTMC-reID, outperforming the state-of-the-art methods and confirming the advantage of our method.
Semi-Autonomous Tongue Control of an Assistive Robotic Arm for Individuals with Quadriplegia

Individuals suffering from quadriplegia can achieve increased independence by using an assistive robotic manipulator (ARM). However, due to their disability, the interfaces that can be used to operate such devices become limited. A versatile intraoral tongue control interface (ITCI) has previously been develop for this user group, as the tongue is usually spared from disability. A previous study has shown that the ITCI can provide direct and continuous control of 6-7 degrees of freedom (DoF) of an ARM, due to a high number of provided inputs (18). In the present pilot study we investigated whether semi-automation might further improve the efficiency of the ITCI, when controlling an ARM. This was achieved by adding a camera to the end effector of the ARM and using computer vision algorithms to guide the ARM to grasp a target object. Three ITCI and one joystick control scheme were tested and compared: 1) manual Cartesian control with a base frame reference point, 2) manual Cartesian control with an end effector reference point 3) manual Cartesian control with an end effector reference point and an autonomous grasp function 4) regular JACO2 joystick control. The results indicated that end effector control was superior to the base frame control in total task time, number of commands issued and path efficiency. The addition of the automatic grasp function did not improve the performance, but resulted in fewer collisions/displacements of the target object when grasping.
Semi-Supervised Fine-Grained Image Categorization Using Transfer Learning With Hierarchical Multi-Scale Adversarial Networks

Fine-grained image categorization is still a challenging computer vision problem in recent years. Most of existing methods highly rely on massive labeled data which are scarce in many real world applications. It should also be noticed that progressive learning demands of existing data is very common today. That is, we may pay attention to more fine-grained information (like arctic tern, black tern, buttercup or tulip) in an existing data set with labels like “bird” and “flower”. It is reasonable to believe that the existing labels and model with transferable knowledge would be helpful to another related but different, fine-grained recognition task. In this context, an improved transfer deep learning approach with hierarchical multi-adversarial networks is proposed in this paper. With this approach, cross domain features are extracted by advanced deep encoders coarsely. After that, we annotate a small amount of images in the target domain, thereby creating the “active labels” which can provide instructions for adversarial learning. Then, the GAN-based hierarchical model is utilized to select cross domain categories and enhance related features so as to facilitate an effective transfer. In order to exploit useful local features, a novel adaptive attention mechanism, Region Adversarial Network (RAN) which can select attention regions during adversarial learning and generate valuable fine-grained features, is introduced in the article. We call the proposed hierarchical framework “Attentional Multi-Adversarial Networks (AMAN)”. Experimental results show that AMAN is able to augment cross domain features well-directly and build an effective classifier for fine-grained categorization in the target domain with fewer training samples and higher accuracies.
RUSLAN: Russian Spoken Language Corpus for Speech Synthesis

We present RUSLAN – a new open Russian spoken language corpus for the text-to-speech task. RUSLAN contains 22200 audio samples with text annotations – more than 31 h of high-quality speech of one person – being the largest annotated Russian corpus in terms of speech duration for a single speaker. We trained an end-to-end neural network for the text-to-speech task on our corpus and evaluated the quality of the synthesized speech using Mean Opinion Score test. Synthesized speech achieves 4.05 score for naturalness and 3.78 score for intelligibility on a 5-point MOS scale.
Borçlar Hukuku ile Veri Koruma Hukuku Açısından Blockchain Teknolojisi ve Akıllı Sözleşmeler: Hukuk Düzenimizde Bir Paradigma Değişimine Gerek Var Mı?

Blockchain technology is essentially a registry that records data in encrypted form, which is not kept by a central organization, and all information in encrypted form is shared with every participant in the system. The transactions carried out in this register are recorded in blocks, these blocks are linked to each other and each new transaction must be kept unchanged with the information of the previous transactions in order to be approved. For this reason, blockchain technology is considered to be a technology that eliminates intermediary institutions, offers equal opportunities to every participant and is almost impossible to manipulate. In particular, the digital currency, referred to as co Bitcoin, is based&nbsp;on this technology, but blockchain technology is not limited to this. There is no uniform appearance of the blockchain technology. Smart contracts, which provide a framework for transactions on the blockchain network, allow system participants to make many different transactions among them by simply going beyond crypto currency transactions. However, blockchain technology based on the principle that the transaction history cannot be changed and the smart contracts associated with this technology also bring many problems in terms of data protection law and law of obligations. The aim of this study is to evaluate block-chain technology in terms of our current legal order, especially in terms of data protection law and law of obligations.
Digitalization in Modern University: Realities and Development Prospects

Digitalization is an actual issue and the main trend of Russian higher education and a widely discussed topic in the professional community. A number of government decrees and programs set a goal for universities — highquality and affordable online education using digital technologies. The article presents the study’s results of digitalization of education in Russia and abroad, indicating the initial stage of digitalization of higher education in Russia and the creation of a digital educational environment. Researches confirm the high need for digital education on the part of students and teachers, therefore, de facto, a lot of universities use digital technology in practice. However, de jure digitalization in higher education is not supported by conceptual provisions, uniform standards and management approaches. Exploring the opinions of target audiences confirms that a blended learning is the best option in the current situation. The advantages, disadvantages and conditions of digitalization in modern higher education are discussed in this article. The problems and main trends in the development of digital education in universities are pointed out too. Possibilities of micro and macro learning, popular online platforms are determined. The author concludes that the transformation of higher education in the context of digitalization is inevitable, fundamental changes are assumed in the content of education, teaching methods, technological approaches, resource support, in the system of knowledge quality control.
DNA Based Cloud Storage Security Framework Using Fuzzy Decision Making Technique

In recent years, a cloud environment with the ability to detect illegal behaviours along with a secured data storage capability is much needed.This study presents a cloud storage framework, wherein a 128-bit encryption key has been generated by combining deoxyribonucleic acid (DNA) cryptography and the Hill Cipher algorithm to make the framework unbreakable and ensure a better and secured distributed cloud storage environment.Moreover, the study proposes a DNA-based encryption technique, followed by a 256-bit secure socket layer (SSL) to secure data storage.The 256-bit SSL provides secured connections during data transmission.The data herein are classified based on different qualitative security parameters obtained using a specialized fuzzy-based classification technique.The model also has an additional advantage of being able to decide on selecting suitable storage servers from an existing pool of storage servers.A fuzzy-based technique for order of preference by similarity to ideal solution (TOPSIS) multi-criteria decision-making (MCDM) model has been employed for this, which can decide on the set of suitable storage servers on which the data must be stored and results in a reduction in execution time by keeping up the level of security to an improved grade.
Primate Face Identification in the Wild

Ecological imbalance owing to rapid urbanization and deforestation has adversely affected the population of several wild animals. This loss of habitat has skewed the population of several non-human primate species like chimpanzees and macaques and has constrained them to co-exist in close proximity of human settlements, often leading to human-wildlife conflicts while competing for resources. For effective wildlife conservation and conflict management, regular monitoring of population and of conflicted regions is necessary. However, existing approaches like field visits for data collection and manual analysis by experts is resource intensive, tedious and time consuming, thus necessitating an automated, non-invasive, more efficient alternative like image based facial recognition. The challenge in individual identification arises due to unrelated factors like pose, lighting variations and occlusions due to the uncontrolled environments, that is further exacerbated by limited training data. Inspired by human perception, we propose to learn representations that are robust to such nuisance factors and capture the notion of similarity over the individual identity sub-manifolds. The proposed approach, Primate Face Identification (PFID), achieves this by training the network to distinguish between positive and negative pairs of images. The PFID loss augments the standard cross entropy loss with a pairwise loss to learn more discriminative and generalizable features, thus making it appropriate for other related identification tasks like open-set, closed set and verification. We report state-of-the-art accuracy on facial recognition of two primate species, rhesus macaques and chimpanzees under the four protocols of classification, verification, closed-set identification and open-set recognition.
A Compact Light Field Camera for Real-Time Depth Estimation

Depth cameras are utilized in many applications. Recently light field approaches are increasingly being used for depth computation. While these approaches demonstrate the technical feasibility, they can not be brought into real-world application, since they have both a high computation time as well as a large design. Exactly these two drawbacks are overcome in this paper. For the first time, we present a depth camera based on the light field principle, which provides real-time depth information as well as a compact design.