Reviewerly/exHarmony huggingface dataset:
Forecasting the Export and Import Volume of Crude Oil, Oil Products and Gas Using ANN

The purpose of this study is to see the development of the volume (value) of exports and Imports of oil and gas in Indonesia in the form of estimated results for the coming years. Research data was taken from the Central Statistics Agency and the Indonesian Customs Service. Data is divided into 7 variables, namely; In the year, crude oil exports, crude oil Imports, oil exports, oil Imports, gas exports and gas Imports. The application of the method for estimating the volume of Crude Oil, Oil Products and Gas export Imports is the ANN backpropagation algorithm with 4 network architectural models namely; 12-5-1, 12-8-1, 12-10-1 and 12-14-1. The best network architectural model is 12-5-1 with an accuracy of 83% and MSE 0.0281641257. The minimum error used is 0.001-0.05 with a learning rate of 0.01. While the activation function used is bipolar and linear sigmoid with gradient descent training function.
Digital Competence Assessment Across Generations

Based on the European framework (DigComp), a self-assessment tool digital competency wheel is used for this quantitative study to measure the individuals' perceptions toward digital competence. With a sample of 197 individuals from different generations in Finland, this study aims to provide empirical evidence that generational technological abilities are diverse. The data in this study show that “Net Generation,” also coined as “digital natives,” has obtained the highest level of digital competence. Nevertheless, when looking at the performance of all the investigated groups, the slight inter-generational difference was found in the case of problem-solving, whereas programming was found as the least developed competency among these groups. Based on the results, the study concludes that digital competence is very much distributed across generations. This also contributes to intergenerational learning that may enhance technological skills across generations.
High-dimensional unsupervised classification via parsimonious contaminated mixtures

The contaminated Gaussian distribution represents a simple heavy-tailed elliptical generalization of the Gaussian distribution; unlike the often-considered t-distribution, it also allows for automatic detection of mild outlying or “bad” points in the same way that observations are typically assigned to the groups in the finite mixture model context. Starting from this distribution, we propose the contaminated factor analysis model as a method for dimensionality reduction and detection of bad points in higher dimensions. A mixture of contaminated Gaussian factor analyzers (MCGFA) model follows therefrom, and extends the recently proposed mixture of contaminated Gaussian distributions to high-dimensional data. We introduce a family of 32 parsimonious models formed by introducing constraints on the covariance and contamination structures of the general MCGFA model. We outline a variant of the expectation-maximization algorithm for parameter estimation. Various implementation issues are discussed, and the novel family of models is compared to well-established approaches on both simulated and real data.
SizeNet: Weakly Supervised Learning of Visual Size and Fit in Fashion Images

Finding clothes that fit is a hot topic in the e-commerce fashion industry. Most approaches addressing this problem are based on statistical methods relying on historical data of articles purchased and returned to the store. Such approaches suffer from the cold start problem for the thousands of articles appearing on the shopping platforms everyday, for which no prior purchase history is available. We propose to employ visual data to infer size and fit characteristics of fashion articles. We introduce SizeNet, a weakly supervised teacher-student training framework that leverages the power of statistical models combined with the rich visual information from article images to learn visual cues for size and fit characteristics, capable of tackling the challenging cold start problem. Detailed experiments are performed on thousands of textile garments, including dresses, trousers, knitwear, tops, etc. from hundreds of different brands.
Energy-Efficient LSTM Networks for Online Learning

We investigate variable-length data regression in an online setting and introduce an energy-efficient regression structure build on long short-term memory (LSTM) networks. For this structure, we also introduce highly effective online training algorithms. We first provide a generic LSTM-based regression structure for variable-length input sequences. To reduce the complexity of this structure, we then replace the regular multiplication operations with an energy-efficient operator, i.e., the ef-operator. To further reduce the complexity, we apply factorizations to the weight matrices in the LSTM network so that the total number of parameters to be trained is significantly reduced. We then introduce online training algorithms based on the stochastic gradient descent (SGD) and exponentiated gradient (EG) algorithms to learn the parameters of the introduced network. Thus, we obtain highly efficient and effective online learning algorithms based on the LSTM network. Thanks to our generic approach, we also provide and simulate an energy-efficient gated recurrent unit (GRU) network in our experiments. Through an extensive set of experiments, we illustrate significant performance gains and complexity reductions achieved by the introduced algorithms with respect to the conventional methods.
Experimental performance of graph neural networks on random instances of max-cut

This note explores the applicability of unsupervised machine learning techniques towards hard optimization problems on random inputs. In particular we consider Graph Neural Networks (GNNs) -- a class of neural networks designed to learn functions on graphs -- and we apply them to the max-cut problem on random regular graphs. We focus on the max-cut problem on random regular graphs because it is a fundamental problem that has been widely studied. In particular, even though there is no known explicit solution to compare the output of our algorithm to, we can leverage the known asymptotics of the optimal max-cut value in order to evaluate the performance of the GNNs. In order to put the performance of the GNNs in context, we compare it with the classical semidefinite relaxation approach by Goemans and Williamson~(SDP), and with extremal optimization, which is a local optimization heuristic from the statistical physics literature. The numerical results we obtain indicate that, surprisingly, Graph Neural Networks attain comparable performance to the Goemans and Williamson SDP. We also observe that extremal optimization consistently outperforms the other two methods. Furthermore, the performances of the three methods present similar patterns, that is, for sparser, and for larger graphs, the size of the found cuts are closer to the asymptotic optimal max-cut value.
City-scale vehicle tracking and traffic flow estimation using low frame-rate traffic cameras

Vehicle flow estimation has many potential smart cities and transportation applications. Many cities have existing camera networks which broadcast image feeds; however, the resolution and frame-rate are too low for existing computer vision algorithms to accurately estimate flow. In this work, we present a computer vision and deep learning framework for vehicle tracking. We demonstrate a novel tracking pipeline which enables accurate flow estimates in a range of environments under low resolution and frame-rate constraints. We demonstrate that our system is able to track vehicles in New York City's traffic camera video feeds at 1 Hz or lower frame-rate, and produces higher traffic flow accuracy than popular open source tracking frameworks.
A Low-Cost and High-Throughput FPGA Implementation of the Retinex Algorithm for Real-Time Video Enhancement

For video applications in a special environment such as medical imaging, space exploration, and underwater exploration, the video captured by an image sensor is often deteriorated because of low lighting conditions. Therefore, it is necessary to enhance the part of the image that is too dark to distinguish details while maintaining the remaining part with the same brightness. The retinex algorithm is widely used to restore naturalness of a video, especially exhibiting outstanding performance in the enhancement of a dark area. However, it demands large computational complexity because of its intricate structure, such as the Gaussian filter and exponentiation operations, and consequently, it is difficult to process in real time. This article presents a low-cost and high-throughput design of the retinex video enhancement algorithm. The hardware (HW) design is implemented using a field-programmable gate array (FPGA), and it supports a throughput of 60 frames/s for a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1920 \times 1080$ </tex-math></inline-formula> image with negligible latency. The proposed FPGA design minimizes HW resources while maintaining the quality and the performance by using a small line buffer instead of a frame buffer, by applying the concept of approximate computing for the complex Gaussian filter, and by designing a new and nontrivial exponentiation operation. The proposed design makes it possible to significantly reduce HW resources (up to 79.22% of total resources) compared to existing systems and is compatible with commercialized devices through the standard HDMI/DVI video ports.
Resource-utilization-aware task scheduling in cloud platform using three-way clustering

Task clustering is an effective approach of improving cloud computing resource utilization, which includes other benefits such as better QoS, load balance and low energy consumption. Different existing clustering methods have sharp boundaries, three-way clustering as an application of three-way dec ision, uses core region and fringe region to represent a cluster. In this paper, we propose a novel idea of clustering weight algorithm called TWCW algorithm(Three-way clustering weight) based on three-way decision to overcome the low utilization aiming at improving energy-efficient. The algorithm encompasses two steps, the identified tasks are assigned into the core region and the uncertain tasks are assigned into the fringe region based on diversity of cloud tasks and the dynamic nature of resources using the three-way K-means clustering firstly. The cluster center of CSi, centroidi = {mips, ram, bw} is obtained from the result of three-way clustering. In the second step is to score clusters and schedule tasks. We define a scoring matrix to record scores of the weight between clusters and the preference of attributes within clusters according to the cluster center, and then schedule tasks based on scoring matrix. We validate the high utilization of resources of the proposed algorithm by using simulation of CloudSim. The experiment shows the proposed algorithms significantly reduce energy consumption while significant improving response time of tasks comparing with K-means algorithm and FCM algorithm.
Why Visualize? Untangling a Large Network of Arguments

Visualization has been deemed a useful technique by researchers and practitioners, alike, leaving a trail of arguments behind that reason why visualization works. In addition, examples of misleading usages of visualizations in information communication have occasionally been pointed out. Thus, to contribute to the fundamental understanding of our discipline, we require a comprehensive collection of arguments on “why visualize?” (or “why not?”), untangling the rationale behind positive and negative viewpoints. In this paper, we report a theoretical study to understand the underlying reasons of various arguments; their relationships (e.g., built-on, and conflict); and their respective dependencies on tasks, users, and data. We curated an argumentative network based on a collection of arguments from various fields, including information visualization, cognitive science, psychology, statistics, philosophy, and others. Our work proposes several categorizations for the arguments, and makes their relations explicit. We contribute the first comprehensive and systematic theoretical study of the arguments on visualization. Thereby, we provide a roadmap towards building a foundation for visualization theory and empirical research as well as for practical application in the critique and design of visualizations. In addition, we provide our argumentation network and argument collection online at https://whyvis.dbvis.de, supported by an interactive visualization.
A comparison of shape-based matching with deep-learning-based object detection

Abstract Matching, i. e. determining the exact 2D pose (e. g., position and orientation) of objects, is still one of the key tasks in machine vision applications like robot navigation, measuring, or grasping an object. There are many classic approaches for matching, based on edges or on the pure gray values of the template. In recent years, deep learning has been utilized mainly for more difficult tasks where the objects of interest are from many different categories with high intra-class variations and classic algorithms are failing. In this work, we compare one of the latest deep-learning-based object detectors with classic shape-based matching. We evaluate the methods both on a matching dataset as well as an object detection dataset that contains rigid objects and is thus also suitable for shape-based matching. We show that for datasets of this type, where rigid objects appear with rigid transformations, shape-based matching still outperforms recent object detectors regarding runtime, robustness, and precision if only a single template image per object is used. On the other hand, we show that for the application of object detection, the deep-learning-based approach outperforms the classic approach if annotated data is used for training. Ultimately, the choice of the best suited approach depends on the conditions and requirements of the application.
Motion2Vector

With the increasing popularity of consumer wearable devices augmented with sensing capabilities (smart bands, smart watches), there is a significant focus on extracting meaningful information about human behaviour through large scale real-world wearable sensor data. The focus of this work is to develop techniques to detect human activities, utilising a large dataset of wearable data where no ground truth has been produced on the actual activities performed. We propose a deep learning variational auto encoder activity recognition model - Motion2Vector. The model is trained using large amounts of unlabelled human activity data to learn a representation of a time period of activity data. The learned activity representations can be mapped into an embedded activity space and grouped with regards to the nature of the activity type. In order to evaluate the proposed model, we have applied our method on public dataset - The Heterogeneity Human Activity Recognition (HHAR) dataset. The results showed that our method can achieve improved result over the HHAR dataset. In addition, we have collected our own lab-based activity dataset. Our experimental results show that our system achieves good accuracy in detecting such activities, and has the potential to provide additional insights in understanding the real-world activity in the situations where there is no ground truth available.
Analysis of Dogs’ Sleep Patterns Using Convolutional Neural Networks

Video-based analysis is one of the most important tools of animal behavior and animal welfare scientists. While automatic analysis systems exist for many species, this problem has not yet been adequately addressed for one of the most studied species in animal science—dogs. In this paper we describe a system developed for analyzing sleeping patterns of kenneled dogs, which may serve as indicator of their welfare. The system combines convolutional neural networks with classical data processing methods, and works with very low quality video from cameras installed in dogs shelters.
Towards pixel-to-pixel deep nucleus detection in microscopy images

Nucleus is a fundamental task in microscopy image analysis and supports many other quantitative studies such as object counting, segmentation, tracking, etc. Deep neural networks are emerging as a powerful tool for biomedical image computing; in particular, convolutional neural networks have been widely applied to nucleus/cell detection in microscopy images. However, almost all models are tailored for specific datasets and their applicability to other microscopy image data remains unknown. Some existing studies casually learn and evaluate deep neural networks on multiple microscopy datasets, but there are still several critical, open questions to be addressed.We analyze the applicability of deep models specifically for nucleus detection across a wide variety of microscopy image data. More specifically, we present a fully convolutional network-based regression model and extensively evaluate it on large-scale digital pathology and microscopy image datasets, which consist of 23 organs (or cancer diseases) and come from multiple institutions. We demonstrate that for a specific target dataset, training with images from the same types of organs might be usually necessary for nucleus detection. Although the images can be visually similar due to the same staining technique and imaging protocol, deep models learned with images from different organs might not deliver desirable results and would require model fine-tuning to be on a par with those trained with target data. We also observe that training with a mixture of target and other/non-target data does not always mean a higher accuracy of nucleus detection, and it might require proper data manipulation during model training to achieve good performance.We conduct a systematic case study on deep models for nucleus detection in a wide variety of microscopy images, aiming to address several important but previously understudied questions. We present and extensively evaluate an end-to-end, pixel-to-pixel fully convolutional regression network and report a few significant findings, some of which might have not been reported in previous studies. The model performance analysis and observations would be helpful to nucleus detection in microscopy images.
Investigating Gesture Typing for Indirect Touch

With the development of ubiquitous computing, entering text on HMDs and smart TVs using handheld touchscreen devices (e.g., smartphone and controller) is becoming more and more attractive. In these indirect touch scenarios, the touch input surface is decoupled from the visual display. Compared with direct touch input, entering text using a keyboard in indirect touch is more challenging because before the finger touch, no visual feedback is available for locating the touch finger. Aiming at this problem, in this paper, we investigate the feasibility of gesture typing for indirect touch since keeping the finger in touch with the screen during typing makes it possible to provide continuous visual feedback, which is beneficial for increasing the input performance. We first examine users' gesture typing ability in terms of the appropriate keyboard size and location in motor space and then compare the typing performance in direct and indirect touch mode. We then propose an improved design to address the uncertainty and inaccuracy of the first touch. Our evaluation result shows that users can quickly acquire indirect gesture typing, and type 22.3 words per minute after 30 phases, which significantly outperforms previous numbers in literature. Our work provides the empirical support for leveraging gesture typing for indirect touch.
Networked Cameras Are the New Big Data Clusters

The increasing complexity of deep learning and massive deployment of cameras at the edge have drastically increased the resource demand of edge data analytics. Compared to traditional Internet web applications, such resource demand (in computing, storage and networking) is not limited by millions of human users, but rather the continuous activities of billions of sensors. This paper presents the abstraction of camera cluster as an attempt to address this challenge in the context of video analytics. We envision a novel analytics stack that orchestrates the computing resource of massive networked cameras to enable efficient edge video analytics.
Malicious Domain Names Detection Algorithm Based on Lexical Analysis and Feature Quantification

Malicious domain names usually refer to a series of illegal activities, posing threats to people's privacy and property.Therefore, the problem of detecting malicious domain names has aroused widespread concerns.In this study, a malicious domain names detection algorithm based on lexical analysis and feature quantification is proposed.To achieve efficient and accurate detection, the method includes two phases.The first phase checks an observed domain name against a blacklist of known malicious uniform resource locator (URLs).The observed domain name is classified as being definitely malicious or potentially malicious based on its edit distances to the domain names on the blacklist.The second phase further evaluates a potential malicious domain name by its reputation value that represents its lexical feature and is calculated based on an N-gram model.The top 100,000 normal domain names in Alexa are used to obtain a whitelist substring set using the N-gram method in which each domain name excluding the top-level domain is segmented into substrings with the length of 3, 4, 5, 6 and 7.The weighted values of the substrings are calculated according to their occurrence counts in the whitelist substring set.A potential malicious domain name is segmented by the N-gram method and its reputation value is calculated based on the weighted values of its substrings.Finally, the potential malicious domain name is determined to be malicious or normal based on its reputation value.The effectiveness of the proposed detection method has been demonstrated by experiments on public available data.
Physical Rehabilitation based on Smart Wearable and Virtual Reality Serious Game

The paper describes a solution for physical rehabilitation of upper limbs based on Virtual Reality (VR) therapeutic serious games and IoT wearable devices expressed by smart sensors embedded in a headband and two gloves. The implemented smart sensors are based in Arduino Nano platform connected to an IMU 9DOF module. In the case of smart gloves, a set of force and flexion sensors (FlexiForce & FlexSensors 2.2) were considered to provide information about the finger flexion and finger force contact. The communication between the wearable modules and the virtual reality computation platform is performed using Bluetooth communication protocol. A software was developed and includes the VR serious games for hands and fingers rehabilitation. A Web API was used to store and process the data acquired by the sensors and to extract information about user physical capabilities and limitations during the training. A cross-platform mobile application was developed for user electronic record including training data visualization purposes. Experimental data obtained during the rehabilitation sessions, including metrics evolution that characterize upper limb and finger motion, are included in the article.