Reviewerly/exHarmony huggingface dataset:
Solving Fraud Detection Tasks Based on Wavelet-Neuro Autoencoder

The basis of any business is customer databases, which provide information on customer relations with the company. For example, in the field of banking services, the database stores information about the client, account number, data on financial transactions in online trading, purchased goods, their quantity, time of purchase, etc. A fraud detection is a field of data mining, which includes a set of methods for detecting fraudulent activities in the credit and financial sector, telecommunications, and other areas where illegal manipulations with customer accounts, tariff changes, etc. are possible. Typically, a fraud detection technique is based on the detection of events that do not fit into a specific pattern or behavioral pattern specific to a given business process or client that does not correspond to its patterns and trends. Analytical methods of Data Mining are widely used to build fraud detection systems: neural networks, decision trees, associative rules, sequential patterns, etc.To solve this problem, the architecture of wavelet-neuro autoencoder and its learning algorithm are proposed. Such wavelet-neuro autoencoder has simple architecture, which is based on wavelet neuron. The tuning parameters of this architecture are not only synaptic weights and also parameters of adaptive wavelet activation functions. The tuning parameters of adaptive wavelet activation function are width, center and shape. Proposed wavelet-neuro autoencoder allows solving the fraud detection problem based on both big dataset and small dataset cases.
Identity-of-Things Model using Composite Identity on Permissioned Blockchain Network

The growing prevalence of Internet-of-Things (IoT) technology has led to an increase in the development of heterogeneous smart applications. Smart applications may involve a collaborative participation between IoT devices. Participation of IoT devices for specific application requires a tamper-proof identity to be generated and stored, in order to completely represent the device, as well as to eliminate the possibility of identity spoofing and presence of rogue devices in a network. In this paper, we present a composite Identity-of-Things (IDoT) approach on IoT devices with permissioned blockchain implementation for distributed identity management model. Our proposed approach considers both application and device domains in generating the composite identity. In addition, the use of permissioned blockchain for identity storage and verification allows the identity to be immutable. A simulation has been carried out to demonstrate the application of the proposed identity management model.
Quantum algorithm for hyperparameters estimation

Hyperparameters play an important role in machine learning algorithms, such as linear regression and support vector machines. In this paper, we present a quantum hyperparameters estimation (QHE) algorithm and design the corresponding quantum circuit to accomplish HE effectively. Then we analyze the complexity, probability, and fidelity of the whole algorithm. Finally, we deploy a numerical simulation of a small-scale QHE circuit on the ibmqx4 quantum processor. Importantly, our algorithm and circuit may inspire new investigations in the field of secure quantum machine learning.
A Geometrical Perspective on Image Style Transfer with Adversarial Learning

Recent years witness the booming trend of applying generative adversarial nets (GAN) and its variants to image style transfer. Although many reported results strongly demonstrate the power of GAN on this task, there is still little known about neither the interpretations of several fundamental phenomenons of image style transfer by generative adversarial learning, nor its underlying mechanism. To bridge this gap, this paper presents a general framework for analyzing style transfer with adversarial learning through the lens of differential geometry. To demonstrate the utility of our proposed framework, we provide an in-depth analysis of Isola et al.'s pioneering style transfer model pix2pix [1] and reach a comprehensive interpretation on their major experimental phenomena. Furthermore, we extend the notion of generalization to conditional GAN and derive a condition to control the generalization capability of the pix2pix model. From a higher viewpoint, we further prove a learning-free condition to guarantee the existence of infinitely many perfect style transfer mappings. Besides, we also provide a number of practical suggestions on model design and dataset construction based on these derived theoretical results to facilitate further researches.
Economic resilience dataset in facing physical distancing during COVID-19 global pandemic

The dataset was collected using the survey method with an electronic questionnaire. The use of electronic questionnaires is intended to reach many respondents during the physical distancing caused by the COVID-19 global pandemic. The instrument used in data collection was adapted from Alinovi, Mane, and Romano (2008). The instrument was designed anonymously to ensure the privacy and independence of respondents in giving their responses. Data that is captured includes several categories either nominal, ordinal, or interval refers to the information that needs to be captured. Determination of the type of data also refers to the recommendation of previous research. The data was collected using the Google form platform. Questionnaire distribution was conducted on April 12–20, 2020, and 1096 responses were collected. The date is two weeks after the government call of the Republic of Indonesia to carry out Physical Distancing to anticipate the COVID-19 Global Pandemic. The entire data is then screened and filtered so that it only leaves the data with respondents who are domiciled in Medan City. After filtered, there is remained 846 data that is ready for analysis. In order to make more informative data, researchers conducted a descriptive statistical analysis, ANOVA, Kruskal Wallis, and the Spearman's rank correlation. Analysis of the data provides valuable information related to the interrelation of each item and the pattern of economic resilience that the urban city community has as a data of the COVID-19 global pandemic. Researchers can then further analyzed the data with more advanced analytical tools to produce more valuable information in the development of science and in developing strategic policies related to anticipating the economic vulnerability of the household due to the global pandemic disaster.
Surface charges from a sensing pixel perspective

A new technique is presented for understanding how charges distribute themselves on the surface of a conductor during current flow. The technique uses a set of three-dimensional calculation cells (“pixels”) that cover the conductor's surface and contain internal charge. The pixels have two faces separated by an infinitesimal, but finite, distance, with one face being conductive and the other non-conductive. Each pixel acts as a sensor by responding to (“sensing”) the net Coulomb electric field at its conductive face due to charges in other pixels and charges at the current source and sink (collectively, the external charges). Through a feedback process implemented as a series of time steps, the pixels' internal charges adjust themselves until, at each pixel, a balance is achieved between the electric flux at the conductive face due to the external charges and that due to the pixel's internal charge. Specifically, at each time step, for each pixel at which there is flux imbalance, charge will move into or out of the pixel's conductive face in the direction that reduces the imbalance. The charge distribution for the set of pixels that gives balanced flux for each of the pixels is the system's steady state and for systems where retardation effects are not significant, e.g., biological systems, the time series is the path by which the system reaches that state. Fluxes are calculated using solid angles and because solid angles do not vary with a change in scale, the charge distribution, when expressed in terms of charges/pixel, as opposed to charges/area, depends only on the system shape.
CAFT: Congestion-Aware Fault-Tolerant Load Balancing for Three-Tier Clos Data Centers

Production data centers operate under various workload sizes ranging from latency-sensitive mice flows to long-lived elephant flows. However, the predominant load balancing scheme in data center networks, equal-cost multi-path (ECMP), is agnostic to path conditions and performs poorly in asymmetric topologies, resulting in low throughput and high latencies. In this paper, we propose CAFT, a distributed congestion-aware fault-tolerant load balancing protocol for 3-tier data center networks. It first collects, in real time, the complete congestion information of two subsets from the set of all possible paths between any two hosts. Then, the best path congestion information from each subset is carried across the switches, during the Transport Control Protocol (TCP) connection process, to make path selection decision. Having two candidate paths improve the robustness of CAFT to asymmetries caused by link failures. Large-scale ns-3 simulations show that CAFT outperforms Expeditus in mean flow completion time (FCT) and network throughput for both symmetric and asymmetric scenarios.
Generalized monoidal effects and handlers

Abstract Algebraic effects and handlers are a convenient method for structuring monadic effects with primitive effectful operations and separating the syntax from the interpretation of these operations. However, the scope of conventional handlers is limited as not all side effects are monadic in nature. This paper generalizes the notion of algebraic effects and handlers from monads to generalized monoids, which notably covers applicative functors and arrows as well as monads. For this purpose, we switch the category theoretical basis from free algebras to free monoids. In addition, we show how lax monoidal functors enable the reuse of handlers and programs across different computation classes, for example, handling applicative computations with monadic handlers. We motivate and present these handler interfaces in the context of build systems. Tasks in a build system are represented by a free computation and their interpretation as a handler. This use case is based on the work of Mokhov et al. [(2018). PACMPL 2 (ICFP), 79:1–79:29.].
Video Compression Estimating Recognition Accuracy for Remote Site Object Detection

Current video compression algorithms are designed to achieve a smaller data size and enable higher human perception for the real time streaming of video applications. However, with the recent explosive technical progress of deep neural network (DNNs), video surveillance is increasingly being performed not by humans but by computer vision systems. In this work, we propose a video compression method for object detection by computer vision algorithms. Our method detects the ROI in an image and differentiates the image quality between the ROI and other areas. It also optimizes the image quality in the ROI by estimating the recognition accuracy of the object detection model. The results of an experimental evaluation demonstrate that our proposed method can achieve high-quality encoding in terms of data size and successfully estimate the recognition accuracy of the object detection model.
A Literature Review of Current Vision Based Fall Detection Methods

Falls, especially among the elderly, is a serious issue. With the increase in the number of nuclear families, very often the elderly are left alone at home. Personal caregiver is not always affordable. In such cases, a home installed automatic fall detection system could prove to be very useful. A lot of research has been going on in this field and a large number of work has been published especially over the last five years. In this paper, we have comprehensively summarized the latest research going on in this field and the shortcomings that are there which need to be overcome.
ESP-VDCE: Energy, SLA, and Price-driven Virtual Data Center Embedding

In this work, we present a multi-objective Virtual Data Center Embedding (VDCE) scheme for multi-domain cloud computing setups. The primary focus of the proposed scheme is on -Energy minimization, SLA assurance, and reduced energy Prices; and is named as ESP-driven VDCE. In the preliminary phase of this work, we formulate the proposed scheme as an optimization problem. However, due to the intractability of the formulated problem, its is remodelled and divided into three sub-problems (SP), i.e., data center identification, virtual machine mapping, and virtual link embedding. The output of one SP serves as an input to the next SP, such that the search space can be significantly narrowed. Finally, the proposed approach for VDCE is extensively validated against other algorithms. The obtained results indicate that the proposed ESP-driven VDCE approach achieves almost 7.6% more energy-aware embeddings with 11.5% higher SLA levels and approximately 23% lower energy expenses.
Work in progress - Use of immersive videos of virtual reality in education

Immersive videos, also known as 360° videos or spherical videos, are videos created with a panorama that records in all directions at the same time. In this work of educational innovation, we measure the effectiveness of the use of 360° videos in education. We apply a series of learning activities so that students relate real-life actions with the contents of the course. The students watched 360° videos with virtual reality lenses in the classroom and made the didactic sequence prepared for each activity. We measure the degree of acceptance of this innovative didactic resource by students as a learning tool. The students who used the immersive technology of the 360° videos recorded in scenarios related to their professional environment obtain a greater knowledge of their future professional life through the content of the video, they have a greater feeling of illusion of place, positive affection and a greater enjoyment of the topic that is covered in the 360° video. As a result of the activity, we conclude that the use of 360° videos in education are very effective and its use will be an important trend in the coming years.
Blockchain-Based Digital Rights Management Techniques

The usage of information is essential for data-driven capabilities in artificial intelligence. The data-driven AI techniques lead to several security and privacy concerns. Among various digital techniques, digital rights management is required as one of collaboration scheme that ensures the security and privacy of intellectual rights. Though a number of researchers have proposed various security techniques, none of them have proposed an efficient and effective privacy procedure for digital rights. Recently, blockchain technique is considered as one of the major security methods to ensure a transparent communication among individuals. It can be used by various applications such as industries, marketing, transportation systems, etc. The aim of this chapter is to propose an ensured resource allocation algorithm that validates the scheme by comparing various security measures against previous approaches. Further, the proposed phenomenon ensures the transparency on security and privacy due to its integration.
Learning and Optimization with Bayesian Hybrid Models

Bayesian hybrid models fuse physics-based insights with machine learning constructs to correct for systematic bias. In this paper, we compare Bayesian hybrid models against physics-based glass-box and Gaussian process black-box surrogate models. We consider ballistic firing as an illustrative case study for a Bayesian decision-making workflow. First, Bayesian calibration is performed to estimate model parameters. We then use the posterior distribution from Bayesian analysis to compute optimal firing conditions to hit a target via a single-stage stochastic program. The case study demonstrates the ability of Bayesian hybrid models to overcome systematic bias from missing physics with fewer data than the pure machine learning approach. Ultimately, we argue Bayesian hybrid models are an emerging paradigm for data-informed decision-making under parametric and epistemic uncertainty.
Copy-Move Image Forgery Detection Using Shannon Entropy

Forgery detection plays a very important role in today’s fast-moving world of technology. Videos and images are having a wide range of applications in authentication, communication, military, banking, and so on. There are many tools available in the market to tamper an original image. In this technique of copy and paste forgery, the required part of digital image is copied and then pasted on the same image in order to hide the targeted image. And the tampered image looks similar to the original image. The operator does many pre- and post-operation on the original image such as rotation, flipping, resizing, compressing, and many more. Thus, it is very difficult to detect such kind of unauthorized alterations. In this article, the authors will be discussing the forgery detection of an image using Shannon’s entropy method and similarity and dissimilarity measurements. The proposed method gives higher accuracy than the methods which are mentioned in the comparative study.
Energy-Optimal Tours for Quadrotors to Scan Moth-Infested Trees in Densely-Packed Forests

In this paper, optimal control theory is used to generate energy-optimal tree-to-tree routes by taking into account the map of the forest environment and the quadrotor's dynamics. After computing the energy consumption between all pairs of trees, a Travelling Salesman Problem is formulated and solved using Integer Linear Programming along with Sub-Tour Elimination Constraints to determine the optimal global route. The first scan is not only time-optimal but is also complete. Thus, an infection map is established after the first scan. For subsequent scans, an online solution is proposed to update the path-planner and only include a subset of the trees by considering the probability of infection of a tree based on neighboring trees.
A tool for efficient and accurate segmentation of speech data: announcing POnSS

Abstract Despite advances in automatic speech recognition (ASR), human input is still essential for producing research-grade segmentations of speech data. Conventional approaches to manual segmentation are very labor-intensive. We introduce POnSS, a browser-based system that is specialized for the task of segmenting the onsets and offsets of words, which combines aspects of ASR with limited human input. In developing POnSS, we identified several sub-tasks of segmentation, and implemented each of these as separate interfaces for the annotators to interact with to streamline their task as much as possible. We evaluated segmentations made with POnSS against a baseline of segmentations of the same data made conventionally in Praat. We observed that POnSS achieved comparable reliability to segmentation using Praat, but required 23% less annotator time investment. Because of its greater efficiency without sacrificing reliability, POnSS represents a distinct methodological advance for the segmentation of speech data.
A note on the separability of items in knowledge structures delineated by skill multimaps

This paper introduces the bi-separability of items in knowledge structures as a bidirectional separability of items. Let (Q,K) be the knowledge structure delineated by the skill multimap (Q,S,μ). This paper gives some necessary and sufficient conditions, expressed in terms of competencies of μ, ensuring that (Q,K) is discriminative (resp. bi-discriminative), which generalizes the discussion of the separability of items in the delineated knowledge structure from skill maps to skill multimaps. As applications of these results, the separability and the bi-separability are discussed around distributed skill functions and the meshing of the delineated knowledge structures.
Differentially Private Interval Observer Design with Bounded Input Perturbation

Real-time data processing for emerging systems such as intelligent transportation systems requires estimating variables based on privacy-sensitive data gathered from individuals, e.g., their location traces. In this paper, we present a privacy-preserving interval observer architecture for a multiagent system, where a bounded privacy-preserving noise is added to each participant's data and is subsequently taken into account by the observer. The estimates published by the observer guarantee differential privacy for the agents' data, which means that their statistical distribution is not too sensitive to certain variations in any single agent's signal. A numerical simulation illustrates the behavior of the proposed architecture.
Micromanagement in StarCraft Game AI: a case study

Among Game AI, in macromanegement level, applying of deep learning algorithms like reinforcement learning has been proven to be useful. In this paper, we researched the development of Game AI and chose Starcraft II as our testing game. We compare various deep learning algorithms to normal algorithms like boids algorithms, potential field pathfinding etc., in the micromanegment level, to see their difference in run-time performance, training time and CPU utilization level.
Detecting GAN-based Privacy Attack in Distributed Learning

Distributed learning unleashes the power of training collaboration among multiple parties who have different training data. While participants enjoy mutually beneficial outcomes of the distributed learning, which cannot be achieved by single party, they also worry about the risk of privacy leaking. In fact, recent work shows that a malicious participant is able to leverage a Generative Adversarial Network (GAN) to steal sensitive information of training data owned by others through shared gradient updates. However, existed countermeasures, such as the differential privacy or cryptographic methods, could disturb the training in terms of model accuracy or computation overhead. In this paper, we seek to mitigate this privacy issue in a non-intrusive manner. Instead of passive protection, we propose to actively detect such GAN-based attackers at the very beginning of training. Our detection only utilizes the gradient updates uploaded by participants during the training, so it is transparent to participants and does not require protocol changes. We demonstrate the effectiveness of our detection through extensive experiments in different settings and attack scenarios.
A New Hybrid Brain MR Image Segmentation Algorithm With Super-Resolution, Spatial Constraint-Based Clustering and Fine Tuning

Tissue segmentation from a single brain MR image is of paramount importance for brain reconstruction and analysis. In this paper, we propose a new hybrid algorithm for brain MR image segmentation, combining super-resolution, spatial constraint based clustering and fine-tuning. To smooth noise and improve image clarity, we first amplify the brain MR image by using a super-resolution algorithm - cubic surface fitting with edges in the image as constraints. Then an improved fuzzy c-means clustering algorithm is performed on the amplified image for the global segmentation, in which a shape parameter and an anomaly detection parameter are introduced. With the introduction of these two parameters, the robustness of the clustering is enhanced, and the trade-off between noise smoothing and detail preservation can be controlled more accurately. Furthermore, the local regions around boundaries of different brain tissues (e.g., gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF)) are re-segmented in a fine-tuning process, and a soft voting strategy is adopted for adjusting the incorrect pixels, which makes full use of the boundary details of different tissues. Experimental results show the new algorithm can preserve major brain tissue structures and smooth out noise.