Reviewerly/exHarmony huggingface dataset:
A Universal Metric for Robust Evaluation of Synthetic Tabular Data

Synthetic tabular data generation becomes crucial when real data is limited, expensive to collect, or simply cannot be used due to privacy concerns. However, producing good quality synthetic data is challenging. Several probabilistic, statistical, generative adversarial networks (GANs), and variational auto-encoder (VAEs) based approaches have been presented for synthetic tabular data generation. Once generated, evaluating the quality of the synthetic data is quite challenging. Some of the traditional metrics have been used in the literature but there is lack of a common, robust, and single metric. This makes it difficult to properly compare the effectiveness of different synthetic tabular data generation methods. In this paper we propose a new universal metric, TabSynDex, for robust evaluation of synthetic data. The proposed metric assesses the similarity of synthetic data with real data through different component scores which evaluate the characteristics that are desirable for “high quality” synthetic data. Being a single score metric and having an implicit bound, TabSynDex can also be used to observe and evaluate the training of neural network based approaches. This would help in obtaining insights that was not possible earlier. We present several baseline models for comparative analysis of the proposed evaluation metric with existing generative models. We also give a comparative analysis between TabSynDex and existing synthetic tabular data evaluation metrics. This shows the effectiveness and universality of our metric over the existing metrics. Source Code: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/vikram2000b/tabsyndex</uri>
Accesibilidad de los estudiantes universitarios a los entornos virtuales de aprendizaje implementados por la Universidad Laica Eloy Alfaro Extensión El Carmen

The objective of this study is to describe the level of accessibility of university students to the virtual learning environments implemented by La Universidad Laica Eloy Alfaro de Manabí, El Carmen extension, in the 2020-2021 academic period due to the COVID-19 pandemic. This work has a qualitative approach, descriptive level and field modality. Therefore, for data collection, a nominal scale survey was applied to 105 students, they were organized in statistical tables; obtaining the following results: students did not have adequate internet due to the limited coverage available, they depended on prepaid mobile data and data plans, which ran out very quickly due to the demands of the platforms, in addition, they did not have sufficient technological resources to enter to classes, since some accessed through cell phones, laptops and tablets; I did not have a desktop computer with an internet connection. Hence, it is concluded that the university students investigated did not have access facilities to virtual learning environments, that is, the University investigated did not guarantee the availability of technological resources so that its students had access, permanence and completion of their studies. In a virtual face-to-face manner with quality and excellence, as stipulated in the LOES in article 4 y 5, in agreement Constitution of the Republic of Ecuador in Section Eight, specifically in article 26, 28 in 57. This situation violated the right to education with equal opportunities.
Demystifying Unsupervised Semantic Correspondence Estimation

We explore semantic correspondence estimation through the lens of unsupervised learning. We thoroughly evaluate several recently proposed unsupervised methods across multiple challenging datasets using a standardized evaluation protocol where we vary factors such as the backbone architecture, the pre-training strategy, and the pre-training and finetuning datasets. To better understand the failure modes of these methods, and in order to provide a clearer path for improvement, we provide a new diagnostic framework along with a new performance metric that is better suited to the semantic matching task. Finally, we introduce a new unsupervised correspondence approach which utilizes the strength of pre-trained features while encouraging better matches during training. This results in significantly better matching performance compared to current state-of-the-art methods.
On the Impossibility of Algebraic Vector Commitments in Pairing-Free Groups

Vector Commitments allow one to (concisely) commit to a vector of messages so that one can later (concisely) open the commitment at selected locations. In the state of the art of vector commitments, algebraic constructions have emerged as a particularly useful class, as they enable advanced properties, such as stateless updates, subvector openings and aggregation, that are for example unknown in Merkle-tree-based schemes. In spite of their popularity, algebraic vector commitments remain poorly understood objects. In particular, no construction in standard prime order groups (without pairing) is known. In this paper, we shed light on this state of affairs by showing that a large class of concise algebraic vector commitments in pairing-free, prime order groups are impossible to realize. Our results also preclude any cryptographic primitive that implies the algebraic vector commitments we rule out, as special cases. This means that we also show the impossibility, for instance, of succinct polynomial commitments and functional commitments (for all classes of functions including linear forms) in pairing-free groups of prime order.
A Global and Local Surrogate-Assisted Genetic Programming Approach to Image Classification

Genetic programming (GP) has achieved promising performance in image classification. However, GP-based methods usually require a long computation time for fitness evaluations, posing a challenge to real-world applications. Surrogate models can be efficiently computable approximations of expensive fitness evaluations. However, most existing surrogate methods are designed for evolutionary computation techniques with a vector-based representation consisting of numerical values, thus cannot be directly used for GP with a tree-based representation consisting of functions/operators. The variable sizes of GP trees further increase the difficulty of building the surrogate model for fitness approximations. To address these limitations, we propose a new surrogate-assisted GP approach including global and local surrogate models, which can accelerate the evolutionary learning process and achieve competitive classification performance simultaneously. The global surrogate model can assist GP in exploring the entire search space, while the local surrogate model can speed up convergence and further improve performance. Furthermore, a new surrogate training set is constructed to assist in establishing the relationship between the GP tree and its fitness, and effective surrogate models can be built accordingly. Experimental results on ten datasets of varying difficulty show that the new approach significantly reduces the computational cost of the GP-based method without sacrificing the classification accuracy. The comparisons with other state-of-the-art methods also demonstrate the effectiveness of the new approach. Further analysis reveals the significance of the global and local surrogates and the new surrogate training set on improving or maintaining the performance of the proposed approach while reducing the computational cost.
A Moroccan Sign Language Recognition Algorithm Using a Convolution Neural Network

Gesture recognition is an open phenomenon in computer vision, and one of the topics of current interest. Gesture recognition has many applications in the interpretation of sign language in deaf-mutes, one is in human-computer interaction, and the other is in immersive game technology. For this purpose, we have developed a model of image processing recognition of gestures, based on Artificial Neural Networks, starting from data collection, identification, tracking and classification of gestures, to the display of the obtained results. We propose an approach to contribute to the translation of sign language into voice/text format. In this paper, we present a Moroccan sign language recognition system using a Convolutional Neural Network (CNN). This system includes an important data set of more than 20 files. Each file contains 1,000 static images of each signal from several different angles that we collected with the camera. Different Sign Language models were evaluated and compared with the proposed CNN model. The proposed system achieved 99.33% and achieved the best performance with an accuracy rate of 98.7%.
Automatic Terminology Extraction and Ranking for Feature Modeling

Requirements terminology defines and unifies key specialized and/or technical concepts of the software system, which is significant for understanding the application domain in requirements engineering (RE). However, manual terminology extraction from natural language requirements is laborious and expensive, especially with large scale requirements specifications. In this paper, we aim to employ natural language processing (NLP) techniques and machine learning (ML) algorithms to automatically extract and rank the requirements terms to support high-level feature modeling. To this end, we propose an automatic framework composed of noun phrase identification technique for requirements terms extraction and TextRank combined with semantic similarity for terms ranking. The final ranked terms are organized as a hierarchy, which can be used to help name elements when performing feature modeling. In the quantitative evaluation, our extraction method performs better than three baseline methods in recall with comparable precision. Moreover, our adapted TextRank algorithm can rank more relevant terms at the top positions in terms of average precision compared with most baselines. An illustrative example on the smart home domain further shows the usefulness of our framework in aiding elements naming during feature modeling. The research results suggest that proper adoption and adaption of NLP and ML techniques according to the characteristics of specific RE task could provide automation support for problem domain understanding.
Theseus

The detection of anomalies in time series has gained ample academic and industrial attention, yet, no comprehensive benchmark exists to evaluate time-series anomaly detection methods. Therefore, there is no final verdict on which method performs the best (and under what conditions). Consequently, we often observe methods performing exceptionally well on one dataset but surprisingly poorly on another, creating an illusion of progress. To address these issues, we thoroughly studied over one hundred papers, and summarized our effort in TSB-UAD, a new benchmark to evaluate univariate time series anomaly detection methods. In this paper, we describe Theseus, a modular and extensible web application that helps users navigate through the benchmark, and reason about the merits and drawbacks of both anomaly detection methods and accuracy measures, under different conditions. Overall, our system enables users to compare 12 anomaly detection methods on 1980 time series, using 13 accuracy measures, and decide on the most suitable method and measure for some application.
Improving Determinization in Hindsight for On-line Probabilistic Planning

Recently, "determinization in hindsight" has enjoyed surprising success in on-line probabilistic planning. This technique evaluates the actions available in the current state by using non-probabilistic planning in deterministic approximations of the original domain. Although the approach has proven itself effective in many challenging domains, it is computationally very expensive. In this paper, we present three significant improvements to help mitigate this expense. First, we use a method for detecting potentially useful actions, allowing us to avoid estimating the values of unnecessary ones. Second, we exploit determinism in the domain by reusing relevant plans rather than computing new ones. Third, we improve action evaluation by increasing the chance that at least one determin- istic plan reaches a goal. Taken together, these improvements allow determinization in hindsight to scale significantly better on large or mostly-deterministic problems.
Deep fake Image Detection based on Modified minimized Xception Net and DenseNet

This paper deals with the problem of image forgery detection because of the problems it causes. Where The Fake im-ages can lead to social problems, for example, misleading the public opinion on political or religious personages, de-faming celebrities and people, and Presenting them in a law court as evidence, may Doing mislead the court. This work proposes a deep learning approach based on Deep CNN (Convolutional Neural Network) Architecture, to detect fake images. The network is based on a modified structure of Xception net, CNN based on depthwise separable convolution layers. After extracting the feature maps, pooling layers are used with dense connection with Xception output, to in-crease feature maps. Inspired by the idea of a densenet network. On the other hand, the work uses the YCbCr color system for images, which gave better Accuracy of %99.93, more than RGB, HSV, and Lab or other color systems.
Digital Contact Tracing Solutions: Promises, Pitfalls and Challenges

The COVID-19 pandemic has caused many countries to deploy novel digital contact tracing (DCT) systems to boost the efficiency of manual tracing of infection chains. In this paper, we systematically analyze DCT solutions and categorize them based on their design approaches and architectures. We analyze them with regard to effectiveness, security, privacy and ethical aspects and compare prominent solutions based on these requirements. In particular, we discuss shortcomings of the Google and Apple Exposure Notification API (GAEN) that is currently widely adopted all over the world. We find that the security and privacy of GAEN has considerable deficiencies as it can be compromised by severe large-scale attacks. We also discuss other proposed approaches for contact tracing, including our proposal <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TraceCORONA</small> , that are based on Diffie-Hellman (DH) key exchange and aim at tackling shortcomings of existing solutions. Our extensive analysis shows that <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TraceCORONA</small> fulfills the above security requirements better than deployed state-of-the-art approaches. We have implemented <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TraceCORONA</small> and its beta test version has been used by more than 2000 users without any major functional problems <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://tracecorona.net/download-tracecorona/</uri> , demonstrating that there are no technical reasons requiring to make compromises with regard to the requirements of DCT approaches.
VINCENT

Exploratory search is a search paradigm that plays a vital role in databases, data mining, and information retrieval to assist users to get familiar with the underlying databases. It supports iterative query formulation to explore the data space. Despite its growing importance, exploratory search on graph-structured data has not received adequate attention in the literature. In this paper, we demonstrate a novel system called Vincent that facilitates an efficient exploratory subgraph search in a graph database containing a large collection of small or medium-sized graphs. By automatically generating the content for panels in GUI and diversified patterns from databases and providing a visual result explorer, Vincent supports data-driven visual query formulation, incremental subgraph processing, and efficient query result summarization.
Research and Application of Improved Pure Pursuit Algorithm in Low-Speed Driverless Vehicle System

Low-speed driverless vehicle with automatic driving as the operating system came into being with the development of vehicle networking technology and the construction of new infrastructure such as 5G base stations. However, low-speed driverless vehicle does not have to face the complex high-speed environment, compared with driverless passenger vehicles. Especially, driverless vehicle accurately tracks the path to ensure the safe and stable arrival of vehicles at their destination. In this paper, a improved pure pursuit path tracking algorithm based on reference path curvature adaptive adjustment of looking-forward distance is proposed. Using this method, the non single looking-forward distance is adopted on the whole reference path to ensure that the looking-forward distance is changed according to the curvature change of the reference path within the preview distance during driving, so as to ensure the tracking effect of vehicles in curves, turns and other sections, reduce cutting-corner and overshoot problems. With many tests, the improved pure pursuit path tracking algorithm which adaptively adjusts the looking-forward distance according to the path curvature can ensure the good path tracking performance of unmanned vehicles in curves and so on.
The Content Improvement Service: An Adaptive System for Continuous Improvement at Scale

Advances in artificial intelligence and automatic question generation have made it possible to create millions of questions to apply an evidence-based learn by doing method to thousands of e-textbooks, an unprecedented scale. Yet the scaling of this learning method presents a new challenge: how to monitor the quality of these automatically generated questions and take action as needed when human review is not feasible. To address this issue, an adaptive system called the Content Improvement Service was developed to become an automated part of the platform architecture. Rather than adapting content or a learning path based on student mastery, this adaptive system uses student data to evaluate question quality to optimize the learning environment in real time. In this paper, we will address the theoretical context for a platform-level adaptive system, describe the methods by which the Content Improvement Service functions, and provide examples of questions identified and removed through these methods. Future research applications are also discussed.
Federated Edge for Tracking Mobile Targets on Video Surveillance Streams in Smart Cities

Nowadays, video surveillance is a very common practice in Smart Cities. There are public and private video surveillance systems, and very often different systems or single devices frame the same area. However, when a target needs to be identified or needs to be tracked in real-time, such solutions typically require human intervention to configure the devices in the best possible way (e.g., choosing the optimal cameras, setting up their focus, and so on). To address such a problem, in this paper, we define a new interrogation method based on a Federated Edge approach. This approach addresses the problem from the point of view of both camera hardware and shooting angle associated with it. According to the presented approach, it is possible to understand which the best camera to identify a target and possibly tracking it in a specific area is. A case study is defined in the context of urban mobility management.
LidarNAS: Unifying and Searching Neural Architectures for 3D Point Clouds

Developing neural models that accurately understand objects in 3D point clouds is essential for the success of robotics and autonomous driving. However, arguably due to the higher-dimensional nature of the data (as compared to images), existing neural architectures exhibit a large variety in their designs, including but not limited to the views considered, the format of the neural features, and the neural operations used. Lack of a unified framework and interpretation makes it hard to put these designs in perspective, as well as systematically explore new ones. In this paper, we begin by proposing a unified framework of such, with the key idea being factorizing the neural networks into a series of view transforms and neural layers. We demonstrate that this modular framework can reproduce a variety of existing works while allowing a fair comparison of backbone designs. Then, we show how this framework can easily materialize into a concrete neural architecture search (NAS) space, allowing a principled NAS-for-3D exploration. In performing evolutionary NAS on the 3D object detection task on the Waymo Open Dataset, not only do we outperform the state-of-the-art models, but also report the interesting finding that NAS tends to discover the same macro-level architecture concept for both the vehicle and pedestrian classes.
COCO Annotator

COCO Annotator is an image annotation tool that allows the labelling of images to create training data for object detection and localization. It provides many features, including the ability to label an image segment by drawing, label objects with disconnected visible parts, efficiently store and export annotations in the well-known COCO format as well as importing existing publicly available datasets in COCO format. Once installed, or started with Docker, the interface is web-based and customizable, and provides different tools for creating datasets. The exported annotations can be used for the training of modern deep learning algorithms (Mask R-CNN, YOLO, etc.). Import of the COCO format is supported by several machine learning frameworks. COCO Annotator is developed by Justin Brooks and is supported by a lively community on Github [1].
Transfer Without Forgetting

This work investigates the entanglement between Continual Learning (CL) and Transfer Learning (TL). In particular, we shed light on the widespread application of network pretraining, highlighting that it is itself subject to catastrophic forgetting. Unfortunately, this issue leads to the under-exploitation of knowledge transfer during later tasks. On this ground, we propose Transfer without Forgetting (TwF), a hybrid approach building upon a fixed pretrained sibling network, which continuously propagates the knowledge inherent in the source domain through a layer-wise loss term. Our experiments indicate that TwF steadily outperforms other CL methods across a variety of settings, averaging a 4.81% gain in Class-Incremental accuracy over a variety of datasets and different buffer sizes. Our code is available at https://github.com/mbosc/twf .
ON THE ISSUE OF SYNCHRONIZING THE EDUCATIONAL ACTIVITIES OF A MODERN SECONDARY VOCATIONAL INSTITUTION WITH THE REQUIREMENTS OF BUSINESS

The article describes a technology of studying the requirements of companies and employers expeditiously in order to synchronize educational activities with the requirements, expectations and opportunities for the effective personnel training in the regions. The article presents the original method of involving students of 1–2 courses in research and project work related to the specialization of professional education as effective organizers of business networking and career interaction with potential employers. A model for setting up effective cooperation between institutions of secondary vocational education and a large number of specialized companies and employers is presented and defined by the author as personnel partnership based on updated business requests for the training of young professionals.