Reviewerly/exHarmony huggingface dataset:
Impact of User Playback Interactions on In-Network Estimation of Video Streaming Performance

With significant growth in video streaming services, coupled with widespread use of traffic encryption, network operators are faced with the challenge of monitoring Key Performance Indicators (KPI) needed to detect quality impairments and drive Quality of Experience (QoE) management mechanisms. Though literature suggests that QoE/KPIs can be inferred from encrypted network traffic using machine learning (ML) methods, most studies published thus far fail to account for frequent viewer interactions, such as pauses, seeking forward/backward, or video abandonment. Such playback-related interactions inherently impact traffic patterns used as input for ML–based KPI estimation models. In this paper, we investigate to what extent network operators can monitor application-layer KPIs considering realistic user interactions, focusing on the use-case of a popular streaming platform with videos streamed to mobile devices. We first investigate the impact of user interactions (pause, seek forward, and abandonment) on the performance of both session-based and real-time KPI classification models trained on datasets that do not contain interactions. Secondly, we systematically evaluate the performance of KPI estimation models trained on datasets including specific sets of interactions to determine which types of interactions need to be included in the model training procedure in order to be applicable for realistic streaming sessions.
Directly Recognize Who a Specific Pedestrian is with a Surveillance Camera

In recent years, with the rapid development of large-scale network camera-based data mining and face recognition, the demand and technical feasibility of face recognition with surveillance cameras are increasing. In practical applications such as violation detection, we often have to not only find the violation but also recognize who the violator is directly from the surveillance video. However, in the monitoring scene, the occlusion of other pedestrians, unclear monitoring videos, poor monitoring angle, and excessive distance between target and camera are still pose a challenge to accurate recognition. For the sake of convenience, we refer to the above-mentioned task of recognizing a specific target from surveillance video as specific pedestrian recognition. In order to realize competitive real-time recognition in real-world scenarios, we divide the specific pedestrian recognition into three sub-problems, pedestrian tracking, face detection and face recognition. We first use the Kernelized Correlation Filters to perform pedestrian tracking, which eliminates the occlusion problem and effectively obtains the target image for face detection. Then, for the comprehensive consideration of speed and accuracy, we chose Yolov4 as the face detector based on the concept of cross-dataset evaluation. And in order to make the face detection module more convenient and effective to deploy to various scenarios, we modified Yolov4 to be anchor-free. Finally, we used ResNet-50 and Arcface loss function to extract facial features for cluster analysis of face recognition. Through the above methods, we have achieved a competitive recognition performance in the surveillance video. And through the practice in a real project, it is shown that our system can be effectively deployed in real-time specific pedestrian recognition tasks with the surveillance cameras.
Two-Agent Self-Play

Previous chapters were concerned with how a single agent can learn optimal behavior for its environment. This chapter is different. We turn to problems where two agents operate whose behavior will both be modeled (and, in the next chapter, more than two).
Robust Explainable Prescriptive Analytics

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Learning from Imbalanced Healthcare Data Using Overlap Pattern Synthesis

Diagnosis in healthcare has become a daunting task owing to the exponential growth of diseases and the complexity it brings along with it. Most diagnostic data are heavily imbalanced. An evident fall out of the traditional learning techniques is that they are mostly subdued by the well-represented class than the under-represented class. Such fall out needs rectification without compromising the accuracy of all the classes within the imbalanced data. Our novel technique “Pattern_Syn” reduces the skew distribution of the imbalanced healthcare data solely relying on the properties of the data. The technique is works on the principle of overlap pattern synthesis to generate new similar patterns for the minority class. The technique is, thus, classifier-independent, data-driven and can produce effective results for advanced machine learning techniques. The implementation ran across various imbalanced healthcare datasets showing a considerable improvement of up to 12% increase in efficiency concerning relevant performance measures. The superiority of the method also is showcased in comparison with SMOTE technique.
Review of Few-Shot Learning in the Text Domain and the Image Domain

Classical machine learning works ineffectively when the data set is small. Recently, few-shot learning is proposed to solve this problem. Few-shot learning models a few samples through the prior knowledge. We could divide few-shot learning into various categories depending on where the prior knowledge is extracted from. There are mainly three classes in this paper: (i) the prior knowledge extracted from the labeled data; (ii) the prior knowledge extracted from a weakly labeled or unlabeled data set; (iii) the prior knowledge extracted from similar data sets. For the convenience of searching corresponding few-shot learning methods in a certain domain, based on the above classification, we further classify few-shot learning models into ones which are applied to the image domain and the other which are applied to the text domain. With this taxonomy, we review the previous works on few-shot learning and discuss them according to these categories. Finally, present challenges and promising directions, in the aspect of few-shot learning, are also proposed.
An Essence of Domain Engineering

Before software can be designed one must have a reasonable grasp of its requirements. Before requirements can be prescribed one must have a reasonable grasp of the domain in which the software is to serve. So we must study, analyse and describe the application domain. We shall argue that domain science & engineering is a necessary prerequisite for requirements engineering, and hence software design. We survey elements of domain science & engineering – and exemplify some elements of domain descriptions. We finally speculate on the relevance of domain engineering in the context of and aeronautics and space.
The Lightning Network: Turning Bitcoin into Money

The Lightning Network (LN) is a means of netting Bitcoin payments outside the blockchain. We find a significant association between LN adoption and reduced blockchain congestion, suggesting that the LN has helped improve the efficiency of Bitcoin as a means of payment. This improvement cannot be explained by other factors, such as changes in demand or the adoption of SegWit. We find mixed evidence on whether increased centralization in the Lightning Network has improved its efficiency. Our findings have implications for the future of cryptocurrencies as a means of payment and their environmental footprint.
Composite Fault Diagnosis of Analog Circuit System Using Chaotic Game Optimization-Assisted Deep Elm-Ae

When multiple electronic components in analog circuit fail simultaneously, it is arduous to establish an accurate model and diagnose the composite faults precisely. The rapid development of deep learning provides a new idea for it. In this study, an improved strategy based on deep extreme learning machine with autoencoder is proposed, which possesses more prominent capability on feature extraction. The proposed method first utilizes multiple extreme learning machine with autoencoder to extract hidden features from original signals adaptively . Subsequently, efficient classification will be conducted based on extreme learning machine algorithm. All parameter values of the proposed network are determined at one time, eliminating the iterative updating process. To further enhance the performance of DELM-AE, a novel chaos game optimization algorithm is introduced to optimize the initial network parameters. The experimental results on two typical circuits demonstrate that the proposed method can effectively diagnose the composite faults of analog circuit.
The Aesthetics of Liminality: Augmentation as an Art Form

Since its emergence as an art medium, augmented reality (AR) has developed as a number of evidential sites. As an extension of virtual media, it merges real-time pattern recognition with media, finally realizing the fantasies of William Gibson through goggles or handheld devices. This creates a welding of a form of perceptual vision and virtual reality, or optically registered simulation overlaid upon actual spatial environments. And even though AR-based works can be traced back into the late 1990s, much of this work required at least an intermediate understanding of coding and tethered imaging equipment from webcams to goggles. It is not until the advent of marker-based AR possessing lower entries to usage, as well as geolocational AR-based media through handheld devices and tablets that augmented reality as an art medium would begin to propagate. While one can make arguments that much AR-based art is a convergence between handheld device art and virtual reality, there are gestures that are specific to augmented reality that allow for its specificity as a genre. In this chapter, we will look at some historical examples of AR and critical issues of the AR-based gesture, such as compounding of the gaze, problematizing the retinal, and the representational issues of informatics overlays. This also generates four gestural vectors analogous to those defined in The Translation of Virtual Art (Lichty P. The Translation of Art in Virtual Worlds. In: The Oxford Handbook of Virtuality. Oxford University Press, 2014), which we will examine through case studies. Through these case studies, historical and recent to the time of this publication, we may determine the issues of the gestures and aesthetics of AR.
A Revealed Imperfection in Concept Drift Correction in Metabolomics Modeling

Prediction models that rely on time series data are often affected by diminished predictive accuracy. This occurs from the causal relationships of the data that shift over time. Thus, the changing weights that are used to create prediction models lose their informational value. One way to correct this change is by using concept drift information. That is exactly what prediction models in biomedical applications need. Currently, metabolomics is at the forefront in modeling analysis for phenotype prediction, making it one of the most interesting candidates for biomedical prediction diagnosis. However, metabolomics datasets include dynamic information that can harm prediction modeling. The study presents concept drift correction methods to account for dynamic changes that occur in metabolomics data for better prediction outcomes of phenotypes in a biomedical setting.
What Makes IoT Secure? A Maturity Analysis of Industrial Product Manufacturers’ Approaches to IoT Security

The Internet of Things (IoT) carries enormous potential but also exposes products to new security threats. Even though recent years have seen several costly breaches and security experts advocate for a more proactive approach, security is often not up to par with technological innovations. But why is this so? Whereas a lot of research has been dedicated to describing technical security issues, there is a lack of research into product manufacturers' practices of securing IoT; what challenges do they face in developing, manufacturing, and selling secure IoT products, and what resources do they have for overcoming them? Without knowledge of these empirical perspectives, initiatives to further IoT security grope in the dark. Employing a theory of change to unfold organizational aspects of IoT security, this paper seeks to explore the socio-technical factors that shape IoT security in practice. Based on a qualitative interview study with 52 informants from 26 companies making products for industrial enterprises and critical infrastructures, this paper not only offers insights into the real-world challenges in working with IoT security, but also presents a maturity model based on three necessary conditions for companies' ability to handle IoT security.
Aerial Photograph Categorization by Cross-Resolution Deep Human Gaze Behavior Learning

Accurately recognizing aerial photographs is a useful technique in many domains like autonomous driving and environmental evaluation. In practice, both low-resolution and high-resolution aerial photos are captured asynchronistically for each region, as there are hundreds of earth observation satellites orbitting the earth. Realizing such multi-resolution-based region recognition is a difficult task due to three challenges: 1) mimicking human visual perception when they actively viewing the semantic objects inside each aerial photo; 2) deeply modeling the visually/semantically salient objects sequentially perceived by human visual system; 3) developing a cross-resolution knowledge transferal module to enhance the feature representation for an area. To solve these challenges, we propose a cross-domain aerial photograph recognition system by leveraging the low-resolution spatial composition to enhance the deep encoding of human gaze shifting path (GSP) with a high-resolution. More specifically, we first use an active learning algorithm to discover multiple visually/semantically salient object patches for constructing GSP from a high-resolution aerial photo. Then, an aggregation-based deep model is formulated to sequentially link the deep features learned from the object patches inside each GSP. Subsequently, a novel knowledge transferal algorithm leverages the global spatial composition from low-resolution counterparts to upgrade the deeply-learned GSP feature of the high-resolution aerial photo. Using the upgraded deep GSP feature, a multi-label SVM classifier is trained for categorizing aerial photographs. Comparative studies on our million-scale aerial photograph set have demonstrated the competitiveness of our approach.
Acute Leukemia Subtype Prediction Using EODClassifier

Leukemia is a type of blood cancer having two major subtypes—acute lymphoblastic leukemia and acute myeloid leukemia. A possible cause of leukemia is the genetic factors of a person. Machine learning techniques are being increasingly applied in analyzing the relation between gene expression and genetic diseases such as leukemia. In this paper, we report prediction of leukemia subtypes from microarray gene expression samples using a recently reported ensemble classifier called EODClassifier. Across multiple cross-validation experiments, classification accuracy of over 96% is obtained which reveals consistent performance and robustness. It is also demonstrated that like other popular classifiers, the EODClassifier is also performing well in leukemia prediction.
Knowing Through Hearing, Towards an Epistemology of Auditory Perception

This paper proposes some guidelines for the undeveloped discussion of auditory epistemology. Auditory epistemology is an approach concerned with the perceptual basis for knowledge and belief, specifically around audition. The article pursues two goals. Firstly, it claims that addressing auditory perception from the viewpoint of epistemology is more fruitful than the discussion on phenomenology which has thus far dominated the debates in the literature on sound. Secondly, it elaborates a concrete proposal pertaining to the cooperation of sense-modalities. In so doing, a model for “Perceptual Coherence” is suggested. The first section of the paper critically reviews the problem of perception as it is usually understood in traditional epistemology. An ensuing section outlines the model of perceptual coherence.
Blockchain for digital rights management

A majority of digital media in today's age is consumed over the internet. Any digital content uploaded online is extremely vulnerable to leakage through piracy, illegal spread, and sharing of digital copies of files to users who have not paid for the content's consumption. Many times, authentication of content to trace its source of spread fails due to the anonymity of said source. The current system of centralized content distribution networks working in conjunction with digital rights management systems to provide and authenticate users consuming digital content is faulty and can be replaced with a far more robust peer-to-peer system that operates on blockchain technology. Such a system would benefit consumers since content will no longer be held by a centralized server but rather be decentralized geographically, ensuring availability through physical hindrances such as natural disasters or digital blockages such as distributed denial of service attacks, etc. The system will also help digital content creators of various fields such as photography and music by providing the ability to detect exact points of content breaches as well as maintaining a chain of transactions in case needed in a future investigation.
Frankenhead - Exploring Participatory Engagement and Play as a Cultural Event

Frankenhead is a playable sculpture inspired by Mary Shelley's 1818 novel “Frankenstein; or, the Modern Prometheus.” It explores the idea of constructing an artificial creature from the bits and pieces of existing beings. Just as the imaginary Dr. Frankenstein, in the 19th century, used electricity to build a creature and bring it to live, players use digital media to puzzle together body parts drawn from a database containing a large collection of representations of Frankenstein’s monster, ranging from the first illustrations of the novel to contemporary depictions of the Frankenstein myth. The public installation allows multiple players to collaboratively piece together the monster. Players can use a face detection software that takes their own picture, decomposes it into its parts, and inserts it into the Frankenhead database to become part of the monster-puzzle. Players insert internally coded, blank modules into sockets in the sculpture; once inserted, the modules appear as the body parts they are. Players can move them around to piece together a monster of their liking. The project realizes a contemporary take on the topic of the novel and remaps aspects of the techno- and gender-criticism of Shelley’s writing to today’s techno-social landscape. The paper presents the design decisions and implementation of the installation and discusses the findings made observing players during the event.
Setting Up an Anonymous Gesture Database as Well as Enhancing It with a Verbal Script Simulator for Rehabilitation Applications

Physical therapy patients are rehabilitated by performing exercises at home that do not consider proper movement and can be detrimental to the healing process. Maintaining patient anonymity is an important aspect of collecting patient data. Using our method, we are able to collect information about limb movements in a completely anonymous manner by taking a picture of the patient in the clinic and immediately converting the picture into an anatomical skeleton. A human gesture database accompanied by a verbal script simulator and anonymous tagging was created with the intention of tagging, measuring, and inferring human gestures using neural networks. We have developed a system that utilizes neural network autoencoder architecture to classify the quality and accuracy of patients' movements in videos. Since there is a lack of videos of tagged physiotherapy exercises, we simulate patients' movements to enhance the database. The purpose of this paper is to describe a simulator that mimics the output of OpenPose software so that synthetic human skeletal movements can be computed without utilizing OpenPose. As inputs, these vectors are fed to the autoencoder which, after compressing them into low dimension vectors, classifies them according to their movement using the Dynamic Time Warping (DTW) distance algorithm. Validation of the research was performed on a dataset of 7 different physiotherapy exercises, and 91.8% accuracy was achieved.
Generational Exclusion Particle Swarm Optimization with Central Aggregation Mechanism

This paper proposes Generational Exclusion Particle Swarm Optimization with Central Aggregation Mechanism (GEPSO-CAM) to improve the global searching ability of the standard particle swarm optimization (SPSO). First, in the generational exclusion strategy, particles will not only approach the optimal particle, but also stay away from the worst particle with a certain probability when updating positions. Then, in the central aggregation mechanism, particles learn from both their historical optimal positions and the central positions in the swarm. Finally, the proposed algorithm is compared with SPSO and bacterial foraging optimization (BFO) algorithms based on four benchmark functions. The experimental results show that the GEPSO-CAM can effectively alleviate falling into local optimal solutions and improve the accuracy of optimal solutions.
Behavior Analysis of Photovoltaic-Storage-Use Value Chain Game Evolution in Blockchain Environment

In view of the stability of Pv utilization and trust in transactions, this paper constructed a photovoltaic-storage-use value chain（PSUVC） in the block chain environment, and studied the behavioral strategies of photovoltaic power generators(PvG), energy storage providers(ESP) and users in the value chain by using a three-party evolutionary game model. We analyzes the influence of the initial willingness of game players, the cost and utilization degree of blockchain technology, government subsidies, the price reduction coefficient of users, and the loss of photovoltaic-storage being wholesaled on the results of evolutionary equilibrium. The results show the cooperation between PvG and ESP will become one of the trends of Pv development in the future, and users will greatly enhance the utilization degree of Pv and realize the value co-creation of the three parties. The decrease of blockchain technology cost, the increase of blockchain utilization, the increase of government subsidies, the increase of user price reduction coefficient, the decrease of photovoltaic-storage loss coefficient of being wholesaled and so on encourage the increase of cooperation of photovoltaic power generators and energy storage providers, and users' behavior of using Pv. These results can provide some reference for the development of Pv and energy storage industry.
One-Hot Coding Similarity of Vehicle Routing Particle Swarm Optimizer

Aiming at the real-time problem of vehicle routing planning, an improved particle swarm optimization method, called AVSPSO, is proposed in this paper. In the method, the path similarity of vehicle routing is calculated by adopting based on the One-Hot coding vector and by improving the constraint processing through the combination of coding mode and fixed penalty, in which dynamically adjust the inertia weight through the evolution state is to balance the ability of local mining and global development of particles. Experimental results on two practical cases of vehicle routing optimization problems and comparisons with other methods have shown that the proposed method is feasible and stable with better performances.