Reviewerly/exHarmony huggingface dataset:
Shoppers Detection Analysis in an Intelligent Retail Environment

Mainly in the last years the analysis of the behaviour of shoppers inside a store is becoming a very attracting issue. Thanks to the use of technologies and artificial intelligence based approaches novel methods are applied to automatically evaluate the movement of shopper in the store, the interactions with the products on the shelves, the time spent inside the store, and more, in a passive mode without interviewing the consumers and preserving their privacy. The aim of this paper is to propose a method based on a Support Vector Machine classifier that classifies the interactions of the shopper with products solving the problem of unclassifiable interactions, when carts, baskets, or other objects are temporarily placed in front of the shelves. The implemented system is also able to solve the overcrowding problem that emerges when several shoppers are close to each other, and entering the analysis area of the camera together, were detected as a single person and not as distinct persons.
LEGION: Visually compare modeling techniques for regression

People construct machine learning (ML) models for various use cases in varied domains such as in healthcare, finance, public-policy, etc. In doing so they aim to improve a models' performance by adopting various strategies, such as changing input data (data augmentation), tuning model hyperparameters, performing feature engineering that includes feature extraction, feature augmentation or feature transformation. However, how would users know which of these model construction strategies to adopt for their problem? Following any or all of these approaches allows the construction of a gigantic set of models, from which users may select model(s) suited to their data analytic task. This problem of model selection is non-trivial because in real-world use cases many of the best performing models (in relation to a specified metric) may appear to serve users' goal but often exhibits nuances and tradeoffs (e.g, may weight features differently, varying compute times to train, or may predict relevant data instances differently etc.). This paper aims to solve the problem of how to construct models and how to select a preferred modeling strategy by allowing users to compare the differences and similarities between multiple regression models, and then learn not only about the model but also about their data. This learning further empowers them to select model(s) that more precisely suit their analysis goals. We present LEGION, a visual analytic tool that helps users to compare and select regression models constructed either by tuning their hyperparameters or by feature engineering. We also present two use cases on real world datasets validating the utility and effectiveness of our tool.
The Practice and Thinking of Computer Virtual Reality Technology in the Teaching Mode of Physical Education in Colleges and Universities

Abstract Today, with the rapid development of computer technology, it can achieve good results to form a set of professional computer virtual technology system and apply it to the PE courses in colleges and universities. This study analyzes the current situation and characteristics of computer virtual technology, the necessity of introducing this technology in the field of college physical education courses, as well as the specific role that this technology can play in the curriculum. The results show that computer virtual reality technology is able to realize the modernization, automation and programming of physical education to some extent.
Investigating Local Differential Privacy and Generative Adversarial Network in Collecting Data

For recent decades there has been a great demand for data collection to build machine learning models. But collecting data has to do with the privacy of its owners. Therefore, it is important to protect data privacy. There are many methods for protecting the privacy of user data. The differential privacy approach is often applied to machine learning models that protect data privacy from queries to the model. Furthermore, local differential privacy is recently used to protect privacy in data collection. Besides that, the machine learning model that wants to have high accuracy, and requires a lot of data to train. One of the ways to generate data is to use a generative model, specifically generative adversarial network. In this paper, we combine the generative adversarial network model with local differential privacy to protect privacy during data collection. The experimental results show that the generative adversarial networks method can be used to perturb the data in local setting for data collection, and experiment on MNIST dataset with ε parameter in local differential privacy.
Blockchain Enabled Data Trading with User Consent

Data trading is ubiquitous in today's Internet. User data is often sensitive in nature, for example, medical records and sensitive health related information. Present data trade often does not capture the owner's consent or allow tracking of the sale of her data. Even during a sale, fairness is not guaranteed and both the data owner and the data buyer remains at risk of being cheated. In this paper, we propose an end-to-end protocol to trade sensitive information addressing the key issues of consent, traceability and fairness. We use blockchain as the arbitrator and tamper-resistant log. We implement a prototype of our protocol on Ethereum and show that our proposed solution is practical.
Always On:

Insulin pumps are effective tools for the precise control of glucose levels, for type 1 diabetes (T1D) patients. Unfortunately, many design and usability challenges still exist with these technologies. We investigated current shortcomings and limitations through survey (N=105), interview (N=7), and participatory workshop (N=3) data collection methods. Our findings revealed issues with current technolog y including wear-ability and accessibility in public, operation while performing demanding tasks, interruptions during social activities, continuity of maintenance, and interface operations. Using the data from our investigative work, we produced design criteria to develop a novel wrist-worn interface and separate pump design for a closed loop system. We then evaluated the design through remote usability testing sessions (N=7) with insulin pump users. Our study aspires to inform the future design of novel insulin pumps that enable people with T1D to maintain better control of their glucose through consistent interactions with these tools, during their everyday activities.
Design of Data Acquisition System for Highway Bridge Health Monitoring Based on Internet of Things

With the continuous development of the transportation industry, highway bridge health has become one of the important factors affecting the development of the transportation industry. The traditional highway bridge health monitoring data collection system is used to monitor the bridge, and the system's data operation ability is poor. The main reason is that the traditional system data collection module setting technology is poor, and the data collection hardware and the system adaptability price difference. Therefore, the highway bridge health monitoring data acquisition system based on the Internet of things is designed. Use IoT technology to optimize the sensors and data acquisition hardware in the hardware part of the traditional system. Frequency method is used as the main algorithm of data acquisition module. According to the characteristics of bridge data, the logical relationship of database is set. Choose Modbus protocol as the system transmission protocol to realize data collection and transmission. So far, the design of the road and bridge health monitoring data collection system based on the Internet of Things is completed. Compared with the traditional system, the system has larger data throughput and better performance than the traditional system. It can be seen that the system has high data collection capabilities and is suitable as the main data source for highway and bridge health monitoring.
Lossless chain code compression with an improved Binary Adaptive Sequential Coding of zero-runs

A new method for encoding a sequence of integers, named Binary Adaptive Sequential Coding with Return to Bias, is proposed in this paper. It extends the compressing pipeline for chain codes’ compression consisting of Burrows Wheeler Transform, Move-To-Front Transform, and Adaptive Arithmetic Coding. We also explain when to include the Zero-Run Transform into the above-mentioned pipeline. The Zero-Run Transform generates a sequence of integers corresponding to the number of zero-runs. This sequence is encoded by Golomb coding, Binary Adaptive Sequential Coding, and the new Binary Adaptive Sequential Coding with Return to Bias. Finally, a comparison is performed with the two state-of-the-art methods. The proposed method achieved similar compression efficiency for the Freeman chain code in eight directions. However, for the chain codes with shorter alphabets (Freeman chain code in four directions, Vertex Chain Code, and Three-OrThogonal chain code), the introduced method outperforms the referenced ones.
Graph Representation Learning for Multi-Task Settings: a Meta-Learning Approach

Graph Neural Networks (GNNs) have become the state-of-the-art method for many applications on graph structured data. GNNs are a model for graph representation learning, which aims at learning to generate low dimensional node embeddings that encapsulate structural and feature-related information. GNNs are usually trained in an end-to-end fashion, leading to highly specialized node embeddings. While this approach achieves great results in the single-task setting, the generation of node embeddings that can be used to perform multiple tasks (with performance comparable to single-task models) is still an open problem. We propose the use of meta-learning to allow the training of a GNN model capable of producing multi-task node embeddings. In particular, we exploit the properties of optimization-based meta-learning to learn GNNs that can produce general node representations by learning parameters that can quickly (i.e. with a few steps of gradient descent) adapt to multiple tasks. Our experiments show that the embeddings produced by a model trained with our purposely designed meta-learning procedure can be used to perform multiple tasks with comparable or, surprisingly, even higher performance than both single-task and multi-task end-to-end models.
Preventing a Fork in a Blockchain - David Fighting Goliath

A recent CSCML'2020 paper by Dolev and Liber presents a low cost blockchain based on cryptographic mechanism. Its main feature is a mechanism discouraging a user to fork a blockchain - if this happens, then a user's secret is leaked and thereby a proof of his misbehavior emerges. This approach is very attractive as long as we can guarantee that breaking the underlying cryptographic problem is infeasible for all parties. The situation changes dramatically in case of a partial compromise of a cryptographic scheme: when a powerful adversary can break the underlying scheme, while for the regular users the scheme is still resistant to attacks. In this case the blockchain techniques proposed becomes a dangerous trap allowing discrediting the users by powerful parties hiding own cryptanalytic power. To address this problem we introduce a David and Goliath adversary model, where a powerful adversary (Goliath) can break the underlying cryptographic assumption, but for a regular user (David) this is infeasible. For almost all cryptographic schemes, the situation of David is hopeless in this model: e.g., Goliath can forge standard signatures of David. Such an advantage of Goliath in case of a blockchain would be disastrous. We show that one can create a blockchain such that the advantage of Goliath is limited: if he creates a fork in a blockchain mimicking a double-spending attempt by David, then David can prove to be innocent.
Geometric moment invariants to spatial transform and N-fold symmetric blur

In this paper, we focus on the derivation of blur moment invariants. Blur moment invariants are image moment-based features, which preserve their values when the image is convolved by a point-spread function (PSF). Suppose a PSF has N-fold rotational symmetry, we prove its geometric moments of the same order are linearly dependent. Depending on this property, a new approach is proposed to determine whether an existing similarity or affine moment invariant also has invariance to N-fold symmetric blur. Unlike earlier work, this method is not based on complicated operators and construction formulas. We use it to analyse classical moment-based features, and surprisingly find that five of Hu moment invariants are naturally invariant to N-fold symmetric blur. Meanwhile, we first prove the existence of moment invariants to both affine transform and N-fold symmetric blur. The experiments using synthetic and real blur image datasets are carried out to test these expectations. And the results show that five Hu moment invariants outperform some widely used blur moment invariants and non-moment image features in image retrieval, classification and template matching.
An Innovative Spacecube Application for Atmospheric Science

StereoBit is an embedded application for the SpaceCube family of hybrid onboard processors developed by NASA's Goddard Space Flight Center under sponsorship of the Earth Science Technology Office. StereoBit applies structure from motion algorithms to measure disparities between clouds being tracked from multiple angles to enable 3D resolution of atmospheric motion, which is an important Decadal Survey objective. Our Advanced Information Systems Technology project has the objectives of developing and demonstrating StereoBit for a future mission architecture employing a constellation of CubeSats and to mature the capabilities of the community to program the SpaceCube- family of processors for science applications.
Arabic voice system to help illiterate or blind for using computer

Abstract Speech recognition is one type of technology, which make a computer to recognize the voice of words that an individual speaks through a microphone and convert it into the written text. In this paper, the proposed system for helping illiterate and blind peoples to open applications with them voices. The proposed system includes two parts; the first part is the training part while the second part is used for the testing. The system contains seven-steps; the first step is the recording of voices and the second step is voice pre-processing. The third step is the feature extraction using MFCC (Mel Frequency Cepstrum Coefficient) method that involves seven steps. The fourth step is for classification voices, there are 1400 voices samples used in training by using a Naïve Bayesian method as a classifier. The fifth step is the matching step using the Correlation Coefficient, there are 200 voices samples in testing. The sixth step was to convert voice into text and the seventh step for execution one of 20 commands. The accuracy of results from using the Naïve Bayesian algorithm in the training phase gives (100 %) while the accuracy of results in the testing phase using Correlation Coefficient gives (98%).
Building and Checking Suffix Array Simultaneously by Induced Sorting Method

Many efficient open-source suffix sorters using the induced sorting (IS) method to build the fundamental data structure suffix array (SA) for compressing and indexing data have been proposed. To avoid potential faults caused by possible implementation bugs, checking the output SA from any IS sorter without engineering warranty for correctness is a de-facto process. The existing SA checkers commonly perform checking after an SA is built completely, with significant time and space complexities compared with that of builders. This article proposes an efficient solution for building and checking SA simultaneously by enhancing the original IS method with a checking scheme using hash computations to on-the-fly verify the results produced by the last induction phase of IS method. Given an input of constant alphabet, this checking scheme requires linear time and constant RAM space when running on external memory, and its time and space overheads are negligible compared with that for building SA. In our experiments on real-world data, the proposed methods take advantages over the counterparts of existing SA checkers by running faster with less space. This work can help provide a value-added bonus feature for open-source IS sorters to guarantee the correctness of a built SA, and such a feature should be desirable for applications using these sorters.
Hybrid Recommender for Condensed Sinhala News with Grey Sheep User Identification

With the present explosion of news circulating the digital space, which consists mostly of unstructured textual data, there is a need to absorb the content of news easily and effectively. While there are many Sinhala news sites out there, no site facilitates recommendation despite the popularity of recommender systems in the current age and day. Therefore, it is effective if the news were presented in a summarized version which tallies with the user preferences as well. Our research aims to fill these gaps by providing a centralized news platform that recommends news to its users clearly and concisely. The news articles were collected using web scraping and after performing categorization it will be presented in a summarized context. Also, we expect to detect the grey sheep users and to provide separate recommendations to them in order to minimize errors in the recommendation. By implementing the proposed system, we provide a user-friendly Sinhala news platform.
Performance Analysis of Quantum Repeaters Based Hybrid Communications Networks

Abstract Towards offering efficient optical networks with regards to the cost and delay, the adaptation of quantum and classical communications can offer a solution to reduce high generated cost of implementing the bare quantum repeaters-based networks. In this paper, the performance of hybrid (classical-Quantum) networks that uses quantum repeaters is analysed and compared with bare quantum repeater network. In a distance, these repeaters are removed and replaced with classical base station. Several network metrics have been evaluated after modelling both quantum and hybrid networks, such as the latency, data rate, energy efficiency, scalability, power consumption and cost of implementation. The results show that the hybrid networks can offer faster connectivity, reduced cost, less required cost of operation and higher scalability. In contrary, the bare quantum repeater network showed higher data rate and higher energy efficiency.
Collaborative game model for teaching physics using smartphone sensors

Physics is a science that takes a conclusion for granted through real experiments. However, many educational institutions do not have specialized equipment or they do have, but their teachers are not trained to use this equipment. So, the teaching-learning process in this field requires continuous innovation by using new technologies. Mobile devices like smartphones incorporate sensors, which makes a powerful tool for accurate measurement of physical phenomena and collaborative learning is a growing trend that promotes students to enhance critical thinking and individual responsibility in a cooperative environment. Our research proposes a model for teaching-learning physics in a virtual collaborative learning environment using smartphone sensors. The model takes into account gamification and national curriculum competencies. The results suggest that students who used our model achieved better on physics tests.
On The Distributed Determinization Of Large NFAs

Large NFAs are automata that cannot fit in a single computer, or the computations would not fit within the computer RAM, or may take a long time. We describe and implement a distributed solution of such NFAs determinization. Our method drastically reduces required iterations, and avoids producing useless states of the determinized automaton, contrary to some previous implementations of well-known powerset construction. A running example is provided with execution details. Finally, complexity analysis is given. This work will help in bringing out relevant programming artifacts for our long term goal that consists of a high level distributed graph language.
Domain adaptive object detection with generative adversarial network

Domain adaptive object detection has emerged to alleviate the lack of massive amounts of labeled data in the target field. Currently, Generative Adversarial Network (GAN) has been exhibited its benefits to the Faster R-CNN for some specific application. However, it brings up an open question that GAN still has flexibility and robustness in more generic scenarios. Thus, this paper aims to extensively evaluate its efficiency of the GAN on more generic domain-adaptive object detection. More specifically, it presents four generic domain-adaptive object detection with GAN architectures, containing Faster R-CNN with CycleGAN, Faster R-CNN with DualGAN, RetinaNet with CycleGAN, and RetinaNet with DualGAN. Extensive experiment results demonstrate that GAN-derived model makes significant contribution and consistent improvements in two domain shift scenarios for domain-adaptive object detection. Additionally, comparing with the state-of-the-art methods, domain-adaptive object detection with GAN has been validated its ability of generalization and robustness in more challenging scenarios.
Part-level Car Parsing and Reconstruction in Single Street View Images

Part information has been proven to be resistant to occlusions and viewpoint changes, which are main difficulties in car parsing and reconstruction. However, in the absence of datasets and approaches incorporating car parts, there are limited works that benefit from it. In this paper, we propose the first part-aware approach for joint part-level car parsing and reconstruction in single street view images. Without labor-intensive part annotations on real images, our approach simultaneously estimates pose, shape, and semantic parts of cars. There are two contributions in this paper. First, our network introduces dense part information to facilitate pose and shape estimation, which is further optimized with a novel 3D loss. To obtain part information in real images, a class-consistent method is introduced to implicitly transfer part knowledge from synthesized images. Second, we construct the first high-quality dataset containing 348 car models with physical dimensions and part annotations. Given these models, 60K synthesized images with randomized configurations are generated. Experimental results demonstrate that part knowledge can be effectively transferred with our class-consistent method, which significantly improves part segmentation performance on real street views. By fusing dense part information, our pose and shape estimation results achieve the state-of-the-art performance on the ApolloCar3D and outperform previous approaches by large margins in terms of both A3DP-Abs and A3DP-Rel.
Convergence of Online Gradient Method with Momentum for BP Neural Network

Abstract The BP neural network, which uses the steepest descent method (gradient descent method) as the basic idea for learning, is often used to deal with approximation problems because of its strong nonlinear mapping ability. The gradient method with added momentum can improve the learning speed of BP neural network. We study the convergence of the online gradient method with momentum for two-layer BP neural network when the training samples are randomly arranged in each iteration. Choosing appropriate learning rates, and selecting momentum coefficients in an adaptive manner, we prove the weak and strong convergence theorems of the algorithm.