Reviewerly/exHarmony huggingface dataset:
Incompleteness in Networks

Most network analysis is conducted on existing incomplete samples of much larger complete, fully observed graphs. For example, many researchers obtain graphs from online data repositories without knowing how these graphs were collected. Thus, these graphs can be poor representations of the fully observed networks. More complete data would lead to more accurate analyses, but data acquisition can be at best costly and at worst error-prone. For example, think of an adversary that deliberately poisons the answer to a query. Given a query budget for identifying additional nodes and edges, how can one improve the observed graph sample so that it is a more accurate representation of the complete, fully observed network? How does the approach change if one is interested in learning the best function (e.g. node classifier) on the network for a down-stream task? This is a novel problem that is related to, but distinct from, topics such as graph sampling and crawling. Given the prevailing use of graph samples in the research literature, this problem is of considerable importance, even though it has been ignored. In this tutorial, we discuss latent biases in incomplete networks and present methods for enriching such networks through active probing of nodes and edges. We focus on active learning and sequential decision-making formulations of this problem (a.k.a. the network discovery problem). We present distinctions between learning to grow the network (a.k.a. active exploration) vs. learning the "best" function on the network (a.k.a. active learning). In addition, we will discuss issues surrounding adversarial machine learning when querying for more data to reduce incompleteness.
Real-time, single camera, digital human development

We have built a real-time (60 fps) photo-realistic facial motion capture system which uses a single camera, proprietary deep learning software, and Unreal Engine 4 to create photo-real digital humans and creatures. Our system uses thousands of frames of realistic captured 3D facial performance of an actor (generated from automated offline systems) instead of a traditional FACS-based facial rig to produce an accurate model of how an actor's face moves. This 3D data is used to create a real-time machine learning model which uses a single image to accurately describe the exact facial pose in under 17 milliseconds. The motion of the face is highly realistic and includes region based blood flow, wrinkle activation, and pore structure changes, driven by geometry deformations in real-time. The facial performance of the actor can be transferred to a character with extremely high fidelity, and switching the machine learning models is instantaneous. We consider this a significant advancement over other real-time avatar projects in development. Building on top of our real-time facial animation technology, we seek to make interaction with our avatars more immersive and emotive. We built an AR system for the actor who is driving the human / character to see and interact with people in VR or others viewing in AR. With this technique, the character you are interacting with in VR can make correct eye contact, walk around you, and interact as if you were together all while still achieving the highest quality capture. This process allows for a much more tangible VR / AR experience than any other system. Another goal of ours is to achieve photo-real avatar telepresence with minimal latency. We have been able to successfully live-drive our digital humans from our office in Los Angeles to our office in Vancouver.
Transformation of the Banking System as a Way to Minimize Information Asymmetry

The banking market is not an exception among other markets that are subjects to cases of inefficient operation of the market mechanism. In this regard, this article is focused on addressing the issues of prevention and finding ways to deal with failures in the banking segment, most of which are related to the digitalization process. The system approach was the methodological research tool that allowed studying the problem of combating imperfections in the functioning of the banking market in a comprehensive manner, affecting not only individual problems of modernizing the banking system due to the introduction of individual IT technologies in the business processes of these structures, but also the transformation of the banking system in an ever-increasing competition with information investment platforms. The article examines the genesis of market fiasco, which made it possible to identify not only the factors contributing to the reproduction of failures, but also to see the tools with which these failures can be eliminated; The analysis of theoretical approaches to the classification of market failures was carried out, which made it possible to identify the most characteristic fiasco; A number of recommendations have been proposed for eliminating and minimizing the socioeconomic consequences of failures of the Russian banking market, taking into account the process of transformation of the banking system in modern conditions. The materials of the article are of practical value for the banking business community, offering a number of tools to improve the competitiveness of this financial segment.
Vocabulary Learning Strategies: Learning Engineering Terminology among Engineering Majors for Industry 4.0 Readiness

It is imperative in the era of industry 4.0 for scholars including engineers and practitioners to communicate effectively through mastering technical vocabulary in their specializations.The use of strategies in learning vocabulary has been proven to have a significant impact on the process of learning vocabulary.Nevertheless, it was found that advance learning is insufficient, and the digitization era requires more words to be mastered to have clear communication.Hence, this research aims to discover the strategies used among engineering majors in learning engineering terminology.This research employed mixed methods consisting of both quantitative and qualitative approaches.The instruments of this research are, a vocabulary learning strategies questionnaire and semi-structured interviews.The questionnaires were administered to 150 students majoring in engineering from five engineering clusters.This research found that engineering students employed determination and metacognitive strategies most frequently.The most frequently used strategy was found to be listening and watching English media while the least frequently used strategy was keeping a vocabulary notebook.Findings show that future communication should focus on interactivity of the media used to increase the proficiency of technical vocabulary and the Malaysian government can drive industry 4.0 though strategies identified in this study.
A method of smart phone original video identification by using unique compression ratio pattern

Due to enhanced camera performance and easy using convenience, most videos are taken and distributed by smart phones. However, smart phone video file as an evidence needs to be verified. We analyzed digital videos with basic information (resolution, shooting time, frame rate, bit depth, sample rate, and so on) and compressed patterns with unique compression algorithm adjusted to certain models. We also classified them based on their original characters. For analysis, we first estimated the virtual data quantity of a real digital video EXIF information without compression. We then analyzed the correlation between real recorded data and virtual data to calculate the original unique compression pattern. We then proposed a technical standard to identify the origin by comparing original compression ratio and remade video's compression ratio through a video editing software.
Inferring Hints for Defect Fixing Order from Requirements-to-test-case Mappings

In what order should we debug defects reported in regression testing to minimize the overall effort of repairing a program? Other than prioritizing defects from business and project managerial perspectives, technical considerations such as dependencies among defects have also to do with answering this question. Errors ripple through code - on one hand, we may want to fix defects that have wide impact on other program failures revealed by testing. But some of such root causes of failures that are affected by other defects may not be ready for correction. Without systematic impact analysis among functional program units, programmers determine the order of fixing defects mostly manually, relying on intuition. We propose a semi-automated method with filtering, visualizationsJarzabek, Stan and heuristics-based computations to solve the problem. Instead of dependency graphs for impact analysis, the sourceLiszewski, Konrad of information for our method are easier to build and often used mappings between test cases and relevant software requirements. We derived and validatedBoldak, Cezary our method over 15 years of teaching a project course in which test cases are tagged with relevant requirements.
Bank Form Classification using Document Layout Analysis and Image Processing Techniques

Every day thousands of forms are filled out and submitted across the world, in banks, post offices, government organizations, educational institutions etc. These include electronic forms as well as physical forms. All of these forms irrespective of their origin are at some stage made digital and stored electronically to address issues of physical storage, form degradation and data accessibility. Document layout analysis is a basic step in converting document images into electronic form. This conversion is laborious and can be made more efficient (in terms of throughput and human resource) by automating most of the conversion process using document layout analysis techniques. Document classification is an important step in Office Automation, Digital Libraries, and other document image analysis applications. Physical forms require human supervision for any operations done on the form. Digitization of these forms reduces human resources, also reduces any human redundancy involved with the operation on the physical forms. This paper addresses the initial stage of this automation, namely, bank form classification and decipherment of fields. The former recognizes the type of the bank form and the latter extracts regions of useful data from the classified bank form. The proposed work aims to provide accurate bank form classification along with noise removal, skew detection and correction, finally layout analysis is carried out to extract fields like name, address, signature from the classified forms.
BiLSTM-based Approach to the Natural Language Text Dependencies Analysis

This article discusses the idea of conducting a multistage process of building a search image of a query in natural language for use in the semantic search system. Modern methods and tools for processing natural language are widely used in the field of machine translation. Research on search engines and semantic search mainly focuses on data storage and further analysis. Most search engines use a huge amount of previously accumulated user queries to predict search results, without taking into account the user’s intention through quality query processing. The proposed approach is based on the selection of the maximum amount of information from the original request by means of syntactic and semantic analysis, as well as the use of synonymous extension techniques. This article describes the first step in the process of building a search query model, based on extracting syntactic dependencies from the original sentence.
Circumsolar irradiance modelling using libRadtran and AERONET data

The aperture angle of pyrheliometers is greater than the limit angle of the solar disk, which leads to a contribution of circumsolar radiation to the measurements of direct normal irradiance (DNI). On the other hand, since Concentrating Solar Power (CSP) technologies have, generally, a lower aperture angle with respect to pyrheliometers, it is important to determine the fraction of circumsolar irradiance present in the DNI measurements as well as its angular distribution to provide accurate information for sizing and operation of CSP systems. In this work, the monochromatic circumsolar irradiance (at 675 nm) is modelled using libRadtran and AERONET data at Évora, Portugal for selected cases representing the 1st, 25th, 50th and 75th percentiles of the aerosol optical depth (AOD) measurements distribution at 675 nm and a maximum value of AOD registered during a Saharan dust outbreak in February 2017 in Portugal. The AERONET products used were the columnar values of water vapor content, aerosol optical depth (AOD) and aerosol phase function. Since there are no measurements of circumsolar irradiance at Évora, the assessment of the model output accuracy was done through a comparison of both the modelled DNI from the solar disk only and the DNI including the circumsolar contribution against measurements from a pyrheliometer included in a meteorological station installed at the same location. The mean bias error and the root mean square error were found to be lower for the DNI with the circumsolar contribution, thus validating the modelling approach of circumsolar irradiance.
Erythrocyte Morphology of Tuberculosis Patients

Tuberculosis is one of the causes of anemia in chronic diseases in addition to rheumatoid arthritis, urserative colitis, Crohn's disease, ovarian malignancy and Hodgkin's lymphoma. Chronic disease anemia also known as anemia due to inflammation, occurs in patients with chronic infections. This anemia also occurs in some patients with malignancy. The causes of anemia are based on cell size, namely iron deficiency (often), anemia of chronic diseases (often), Thalassemia (often in certain ethnicities) causing the size of cells to become microsytic. Cells turn macrocytic due to liver disease, excess alcohol, megaloblastic anemia (vitamin B12 deficiency or folate or exposure to certain drugs), hemolysis, and aplastic anemia. Whereas the normal cell factors are blood loss, the initial phase of iron deficiency, anemia, chronic disease, and kidney failure. The aim of this study was to determine the type of anemia based on the morphological description of erythrocytes in the form, color, and size of erythrocytes seen in the smear blood supply of tuberculosis patients based on treatment duration of 0-6 months. The type of research used was a descriptive survey with a population of 15 people taken by accidental sampling. Data collection is done by examining the blood smear smears. Based on the examination of peripheral blood smear preparations in pulmonary tuberculosis patients, the morphology of erythrocytes in the form of Normocytic normochrome was 74% and microsocytic normochrome was 26%. Suggestions in the research for further research can be carried out with support other than the examination of peripheral blood smear.
An Automatic Real Time Speech-Speaker Recognition System: A Real Time Approach

In this paper, an automatic speech-speaker recognition system is implemented in real time noisy environment. The database creation with personalized voice in noisy environment is done with microphone arrangement. Various techniques in preprocessing step can be used to remove noise from sampled voice signal. Mel Frequency Cepstral Coefficient (MFCC) technique is used to extract Mel Cepstral Coefficients from each speech sample and thus database is created during training phase. For testing purpose, each input sampled speech signal is mapped with stored database using Vector Quantization (VQ) and Dynamic Time Warping (DTW) techniques. Output of mapped VQ is Speaker Recognition and output of mapped DTW is Speech Recognition. Using single sampled voice, real time Speech and Speaker can be recognized. This system is very useful for various applications such as Forensic, Banking where security is at highest priority.
The disappearing hand: vestibular stimulation does not improve hand localisation

Bodily self-consciousness depends on the coherent integration of sensory information. In addition to visual and somatosensory information processing, vestibular contributions have been proposed and investigated. Vestibular information seems especially important for self-location, but remains difficult to study.This randomised controlled experiment used the MIRAGE multisensory illusion box to induce a conflict between the visually- and proprioceptively-encoded position of one hand. Over time, the perceived location of the hand slowly shifts, due to the fact that proprioceptive input is progressively weighted more heavily than the visual input. We hypothesised that left cold caloric vestibular stimulation (CVS) augments this shift in hand localisation.The results from 24 healthy participants do not support our hypothesis: CVS had no effect on the estimations with which the perceived position of the hand shifted from the visually- to the proprioceptively-encoded position. Participants were more likely to report that their hand was 'no longer there' after CVS. Taken together, neither the physical nor the subjective data provide evidence for vestibular enhanced self-location.
Speech Driven Backchannel Generation using Deep Q-Network for Enhancing Engagement in Human-Robot Interaction

We present a novel method for training a social robot to generate backchannels during human-robot interaction. We address the problem within an off-policy reinforcement learning framework, and show how a robot may learn to produce non-verbal backchannels like laughs, when trained to maximize the engagement and attention of the user. A major contribution of this work is the formulation of the problem as a Markov decision process (MDP) with states defined by the speech activity of the user and rewards generated by quantified engagement levels. The problem that we address falls into the class of applications where unlimited interaction with the environment is not possible (our environment being a human) because it may be time-consuming, costly, impracticable or even dangerous in case a bad policy is executed. Therefore, we introduce deep Q-network (DQN) in a batch reinforcement learning framework, where an optimal policy is learned from a batch data collected using a more controlled policy. We suggest the use of human-to-human dyadic interaction datasets as a batch of trajectories to train an agent for engaging interactions. Our experiments demonstrate the potential of our method to train a robot for engaging behaviors in an offline manner.
DeepCABAC: A Universal Compression Algorithm for Deep Neural Networks

The field of video compression has developed some of the most sophisticated and efficient compression algorithms known in the literature, enabling very high compressibility for little loss of information. Whilst some of these techniques are domain specific, many of their underlying principles are universal in that they can be adapted and applied for compressing different types of data. In this work we present DeepCABAC, a compression algorithm for deep neural networks that is based on one of the state-of-the-art video coding techniques. Concretely, it applies a Context-based Adaptive Binary Arithmetic Coder (CABAC) to the network's parameters, which was originally designed for the H.264/AVC video coding standard and became the state-of-the-art for lossless compression. Moreover, DeepCABAC employs a novel quantization scheme that minimizes the rate-distortion function while simultaneously taking the impact of quantization onto the accuracy of the network into account. Experimental results show that DeepCABAC consistently attains higher compression rates than previously proposed coding techniques for neural network compression. For instance, it is able to compress the VGG16 ImageNet model by x63.6 with no loss of accuracy, thus being able to represent the entire network with merely 8.7MB. The source code for encoding and decoding can be found at this https URL.
Knowledge Management in Software Process Improvement: A Systematic Literature Review

The development of software products and services within organizations generates a large amount of information that is not recorded and is generally known as tacit knowledge, that is the most valuable information for an organization, since knowledge helps to improve and avoid repetitions of errors. In this context, the implementation of Software Process Improvement within this type of organizations is increasingly important, due to the constant demand for quality in their products and services. However, many of these organizations lose this knowledge and it is not reflected in their software development processes. Therefore, this article establishes the current state of the techniques, tools and strategies for good knowledge management that allow the extraction of tacit knowledge, and its importance within the software processes improvement. The analysis carried out had made it possible to identify the importance of knowledge management and the case studies where knowledge management has been implemented in the software processes improvement. The analysis of the 16 primary studies has identified the importance of knowledge management showing that 15% of the selected primary studies propose a technique, another 15% a tool, while 23% propose a strategy equal to 20% a new domain, with this 27% present a case study.
Leverage Implicit Feedback for Context-aware Product Search

Product search serves as an important entry point for online shopping. In contrast to web search, the retrieved results in product search not only need to be relevant but also should satisfy customers' preferences in order to elicit purchases. Previous work has shown the efficacy of purchase history in personalized product search. However, customers with little or no purchase history do not benefit from personalized product search. Furthermore, preferences extracted from a customer's purchase history are usually long-term and may not always align with her short-term interests. Hence, in this paper, we leverage clicks within a query session, as implicit feedback, to represent users' hidden intents, which further act as the basis for re-ranking subsequent result pages for the query. It has been studied extensively to model user preference with implicit feedback in recommendation tasks. However, there has been little research on modeling users' short-term interest in product search. We study whether short-term context could help promote users' ideal item in the following result pages for a query. Furthermore, we propose an end-to-end context-aware embedding model which can capture long-term and short-term context dependencies. Our experimental results on the datasets collected from the search log of a commercial product search engine show that short-term context leads to much better performance compared with long-term and no context. Our results also show that our proposed model is more effective than word-based context-aware models.
Optimizing in the Dark: Learning an Optimal Solution through a Simple Request Interface

Network resource reservation systems are being developed and deployed, driven by the demand and substantial benefits of providing performance predictability for modern distributed applications. However, existing systems suffer limitations: They either are inefficient in finding the optimal resource reservation, or cause private information (e.g., from the network infrastructure) to be exposed (e.g., to the user). In this paper, we design BoxOpt, a novel system that leverages efficient oracle construction techniques in optimization and learning theory to automatically, and swiftly learn the optimal resource reservations without exchanging any private information between the network and the user. We implement a prototype of BoxOpt and demonstrate its efficiency and efficacy via extensive experiments using real network topology and trace. Results show that (1) BoxOpt has a 100% correctness ratio, and (2) for 95% of requests, BoxOpt learns the optimal resource reservation within 13 seconds.