Reviewerly/exHarmony huggingface dataset:
How (Not) to Measure Bias in Face Recognition Networks

Within the last years Face Recognition (FR) systems have achieved human-like (or better) performance, leading to extensive deployment in large-scale practical settings. Yet, especially for sensible domains such as FR we expect algorithms to work equally well for everyone, regardless of somebody’s age, gender, skin colour and/or origin. In this paper, we investigate a methodology to quantify the amount of bias in a trained Convolutional Neural Network (CNN) model for FR that is not only intuitively appealing, but also has already been used in the literature to argue for certain debiasing methods. It works by measuring the “blindness” of the model towards certain face characteristics in the embeddings of faces based on internal cluster validation measures. We conduct experiments on three openly available FR models to determine their bias regarding race, gender and age, and validate the computed scores by comparing their predictions against the actual drop in face recognition performance for minority cases. Interestingly, we could not link a crisp clustering in the embedding space to a strong bias in recognition rates—it is rather the opposite. We therefore offer arguments for the reasons behind this observation and argue for the need of a less naïve clustering approach to develop a working measure for bias in FR models.
USING MACHINE TRANSLATION ENGINES IN THE CLASSROOM: A SURVEY OF TRANSLATION STUDENTS’ PERFORMANCE

This paper outlines the results of the experimental study aiming to explore the impact of using machine translation engines on the performance of translation students. Machine translation engines refer to the software developed to translate source texts into target languages in a fully automatic mode which can be classified according to the algorithm (example-based, rule-based, statistical, pragmatics-based, neural, hybrid) and the level of customisation (generic, customised, adaptive). The study was carried out in the form of a translation test held during the first semester of 2019/2020 academic year (September). The subjects included 48 undergraduate students of the School of Foreign Languages of V. N. Karazin Kharkiv National University (42 women and 6 men aged from 19 to 22) majoring in translation. They were subdivided into two sample groups: sample group 1 performed translation from English into Ukrainian with the help of the MT engine while sample group 2 translated the same text from English into Ukrainian by hand. The MT engine chosen for conducting the research was Microsoft Translator for personal use. The research was arranged into the following stages: preliminary (designing the experiment), main (implementing the experiment into life), conclusive (processing the results and interpreting them). The comparison of the students’ average results allowed us to come to the conclusion that the hypothesis preconceived in the beginning of the study was confirmed: the quality of the text translated by the students who received no previous training in post-editing with the use of a modern machine translation engine was of a poorer quality compared to the translation of the same text made by the students without the use of a machine translation engine. The students using the machine translation engine showed the tendency not to treat critically the output of the machine translation engine, thus scoring more penal points than the students translating on their own. The evidence from this study implies the necessity of teaching students to use machine translation in their work and to post-edit texts by means of developing the appropriate cross-curricular methodology of teaching.
Fast Obstacle Detection Using 3D-to-2D LiDAR Point Cloud Segmentation for Collision-free Path Planning

Whereas many existing computer vision algorithms based on color images work well in robot navigation, most of them are sensitive to illumination and the reflectance of objects.Furthermore, according to the Oren-Nayar reflectance model, the reflectance depends on the material and surface of objects.Therefore, different sensors, passive sensors and active sensors, are used simultaneously to scan objects of different materials.In this paper, an integrated sensor system, including light detection and ranging (LiDAR), global positioning system (GPS), gyroscopes (Gyro), and a camera, is proposed for an autonomous vehicle.The GPS and Gyros are used for locating the robot position and identifying its orientation, which are used for global path planning to move toward a goal.The camera is used for remote video monitoring.LiDAR is used to capture the point clouds of the current environment for use in planning the local path.In this paper, a fast segmentation method is proposed for obstacle detection.The proposed method includes ground point removal, region of interest (ROI) detection, 3D-to-2D projection, and clustering by grids.The purpose of ROI detection is to determine whether the points are candidate obstacle points.The experimental results show that the proposed segmentation method can reduce the size of the point cloud and computation complexity significantly.The integrated multisensor system is expected to be practically used in the field.
Can neural networks acquire a structural bias from raw linguistic data?

We evaluate whether BERT, a widely used neural network for sentence processing, acquires an inductive bias towards forming structural generalizations through pretraining on raw data. We conduct four experiments testing its preference for structural vs. linear generalizations in different structure-dependent phenomena. We find that BERT makes a structural generalization in 3 out of 4 empirical domains---subject-auxiliary inversion, reflexive binding, and verb tense detection in embedded clauses---but makes a linear generalization when tested on NPI licensing. We argue that these results are the strongest evidence so far from artificial learners supporting the proposition that a structural bias can be acquired from raw data. If this conclusion is correct, it is tentative evidence that some linguistic universals can be acquired by learners without innate biases. However, the precise implications for human language acquisition are unclear, as humans learn language from significantly less data than BERT.
A Deep Learning Based Method to Discriminate Between Photorealistic Computer Generated Images and Photographic Images

The rapid development of multimedia tools has changed the digital world drastically. Consequently, several new technologies like virtual reality, 3D gaming, and VFX (Visual Effects) have emerged from the concept of computer graphics. These technologies have created a revolution in the entertainment world. However, photorealistic computer generated images can also play damaging roles in several ways. This paper proposes a deep learning based technique to differentiate computer generated images from photographic images. The idea of transfer learning is applied in which the weights of pre-trained deep convolutional neural network DenseNet-201 are transferred to train the SVM to classify the computer generated images and photographic images. The experimental results performed on the DSTok dataset show that the proposed technique outperforms other existing techniques.
Improve Neural Machine Translation by Building Word Vector with Part of Speech

Neural Machine Translation (NMT) based system is an important technology for translation applications. However, there is plenty of rooms for the improvement of NMT. In the process of NMT, traditional word vector cannot distinguish the same words under different parts of speech (POS). Aiming to alleviate this problem, this paper proposed a new word vector training method based on POS feature. It can efficiently improve the quality of translation by adding POS feature to the training process of word vectors. In the experiments, we conducted extensive experiments to evaluate our methods. The experimental result shows that the proposed method is beneficial to improve the quality of translation from English into Chinese.
Using Blockchain Technology to Implement Peer-to-Peer Network in Construction Industry

Collaboration is the intrinsic element of the construction industry since construction projects involve a dynamic grouping of several companies. [] studied the relationships among the companies and noted that some communication patterns among participants show a Peer-to-Peer nature. Digital technology which implements this Peer-to-Peer communication pattern is the Blockchain technology (BCT). BCT has gained significant attention across different industry sectors in the past years, such as finance, health care, supply chain management, etc. BTC can solve the trust and security problem of the Peer-to-Peer network. It enhances effectiveness, transparency, productivity, and cybersecurity in collaborative works. While BCT has been intensively researched in fields like the financial industry, healthcare, and supply chain management, the research in the construction industry is still in its early stages. Besides that, the majority of articles related to BCT in construction are still concepts. This paper gives an architecture of using BCT in collaboration with P2P nature and presents the proof of concept by implementing the BCT in the distributed product data platform.
Spectrum-Guided Adversarial Disparity Learning

It has been a significant challenge to portray intraclass disparity precisely in the area of activity recognition, as it requires a robust representation of the correlation between subject-specific variation for each activity class. In this work, we propose a novel end-to-end knowledge directed adversarial learning framework, which portrays the class-conditioned intraclass disparity using two competitive encoding distributions and learns the purified latent codes by denoising learned disparity. Furthermore, the domain knowledge is incorporated in an unsupervised manner to guide the optimization and further boosts the performance. The experiments on four HAR benchmark datasets demonstrate the robustness and generalization of our proposed methods over a set of state-of-the-art. We further prove the effectiveness of automatic domain knowledge incorporation in performance enhancement.
The Tactician

We present Tactician, a tactic learner and prover for the Coq Proof Assistant. Tactician helps users make tactical proof decisions while they retain control over the general proof strategy. To this end, Tactician learns from previously written tactic scripts and gives users either suggestions about the next tactic to be executed or altogether takes over the burden of proof synthesis. Tactician’s goal is to provide users with a seamless, interactive, and intuitive experience together with robust and adaptive proof automation.
Application of remote sensing to identify Copper–Lead–Zinc deposits in the Heiqia area of the West Kunlun Mountains, Chinas

Abstract The harsh natural environment and inaccessibility of the West Kunlun Mountains are barriers for their investigation via field geology. Remote sensing technology has the advantage of being efficient on a macroscale and not being restricted by terrain or road conditions in sparsely vegetated areas with exposed bedrock. This work focuses on copper–lead–zinc deposits in the Heiqia area in the West Kunlun Mountains as a case study to illustrate the application of IKONOS remote sensing images as major data sources to fabricate a standard image map, the extraction of information on ore-controlling factors and mineralization through the use of image enhancement methods, and the interpretation of remote sensing data to identify mineral resources. Alteration anomaly information was extracted from ASTER data, verified via field survey and sampling, and used to develop a remote sensing model for utilization in future prospecting efforts. The results of the survey showed that in IKONOS (band 3, 2, and 1 synthesis) images, the copper mineralization zone exhibits interlaced gray-white, blue-gray, and blue tones in a narrow strip-like pattern, while the lead–zinc mineralization zone shows gray-white, light gray-yellow, and yellowish-brown tones in a strip-like pattern. The main remotely sensed alteration anomalies are characteristic of hydroxyl. Six hydroxyl anomalies were delineated in the study area, of which five were found to be copper–lead–zinc deposits. The location of the ore bodies coincides well with the ASTER anomalies extracted. Two Cu–Pb–Zn mineralization belts are present in the study area. The ore-bearing rock series of belt No. I is phyllite interbedded with metasandstone, and the ore comprises mainly copper deposits supplemented by lead–zinc deposits. Belt No. II is in limestone and consists mainly of lead–zinc deposits supplemented by copper deposits. A remote sensing geological prospecting model for structurally altered Wenquangou Group copper–lead–zinc deposits with a genesis related to hot water basins is established. This provides a basis for future prospecting for similar minerals in the West Kunlun metallogenic belt.
Prospects of online education in Russia

&amp;nbsp; The subject of this article is the problems in the export of Russian education, particularly in online education. The author examines the prospects of demand for Russian online education in Russia and abroad. Such problematic questions of online education as its quality and control are raised in the course of this research. Attention is given to the export of education in the Russian Federation, namely online education in universities. The government conducts active import substitution policy since 2014, including in the sphere of education. The prospects of online education in Russia can be reflected in three vectors: 1) development of vocational education; 2) development of higher education; 3) development of business education. The scientific novelty consists in an attempt to answer the following question: is online education as an important element of import substitution policy and advancement of non-resource export is necessary for the government and educational organizations. The provided information testifies to the fact that online education is beneficial for flagman universities, as the grants are allocated to them. At the same time, it is an extremely rare case when nonprofit organizations gain advantage from it. Therefore, the question on the need for advancement of online education remains controversial. &amp;nbsp;
Privacy Preservation for Continuous Decremental Data Publishing

The privacy violation is a serious issue that must be considered when datasets are published to the outside scope of the data-collecting organization for gaining more data utility. To address this issue, a well-known privacy preservation model, (α, k)-Anonymity, is proposed. Unfortunately, this privacy preservation model is generally proposed to address privacy violation issues in datasets that are focused on performing one-time data publishing. Therefore, if published datasets are dynamic, i.e., the data of them can be always changed and performing multiple time data publishing such as continuous decremental datasets, (α, k)-Anonymity could be insufficient. For this reason, the vulnerabilities of (α, k)-Anonymity in continuous decremental datasets are identified in this work. Moreover, an exhaustive decremental privacy preservation algorithm for addressing privacy violation issues in continuous decremental datasets is also proposed. The aim of the proposed algorithm is that aside from privacy preservation constraints, the data utility of datasets is also maintained as much as possible. Furthermore, the experimental results are shown which indicate that the proposed algorithm is highly effective.
Practical operation extraction from electromagnetic leakage for side-channel analysis and reverse engineering

Determining which operations are being executed by a black-box device is an important challenge to tackle in reverse engineering. Furthermore, in order to perform a successful side-channel analysis (SCA) of said operations, their precise timing must be determined. In this paper, we tackle these two challenges in context of an electromagnetic (EM) analysis of a NodeMCU Amica IoT device. More specifically, we propose a convolutional neural network (CNN) architecture that is designed to classify operations performed by the NodeMCU out of a set of 8 possible operations, namely OpenSSL AES, native AES, TinyAES, OpenSSL DES, SHA1-PRF, HMAC-SHA1, SHA1, and SHA1Transform. In addition, we use the same architecture to predict the start and end times of the operation, thereby removing the need for firmware modifications or manual triggers in SCA. Our approach is evaluated using a 66 GB dataset containing 69,632 complex traces of EM leakage, captured with a USRP B210 software defined radio. The best variant of our methodology achieves a classification accuracy of 96.47%, and is able to predict the start and end times of the operation within 34 |is of the ground truth on average. We compare our methodology to classical template matching, and provide our open-source implementation and datasets to the community so that the achieved results can be reproduced.
An Open Simulation Strategy for Rapid Control Design in Aerial and Maritime Drone Teams: A Comprehensive Tutorial

The deployment of robot controllers into the real robotic platform is cumbersome and time consuming, especially when testing scenarios involve several robots or are sites not easily accessible. Besides this, most of the time, testing on the real platforms or real conditions provides little value in the early stages of controller design and prototype, phases where debugging and suitability of the controller are the main objectives. This paper proposes a simulation strategy for developing and testing controllers for Unmanned Aerial and Surface Vehicle coordination and interaction with the environment. The simulation strategy is based on V-REP and Matlab/Simulink which provide a large set of features, modularity and compatibility across platforms. Results show that this approach significantly reduces development and delivery times by providing an off-the-shelf simulation environment and a step-by-step implementation guidelines. The source code to deploy the simulations is available in an open-source repository.
Immersion Tools as a Strategy for Adolescents Mental Health: A Qualitative Study

Background: Information and communication technologies have revolutionized education, due to development of didactic strategies to take advantage of them for maximizing learning.The aim was to analyze the influence of immersion tools as a strategy to improve adolescents' mental health.Methods: Qualitative research was employed, and participants' experiences, opinions, values, behaviors, emotions, feelings were recorded.Twenty six students of both sexes, aged 13 to 15 years, from the three grades of secondary school were selected by convenience, and supported by the school's psychologist.A workshop on emotional intelligence (EI) based on educational technology was implemented using a video; students practiced a reflexive process to understand themselves and their surroundings, creating a video + audio as a product of the workshop.In it, students analyzed several problems they face and their relationship with their contextual situation.Data collection was through semi-structured interviews before and after the workshop on EI.Data analysis was according to Souza Minayo, and the theoretical framework according to Goleman, Tapia et al., Gorostieta et al., and Vigotsky.Results: Two categories emerged: Category I, EI as a risk factors transformer from living with addictions to protection factors in adolescents.Category II, EI development through immersion tools to improve mental health in adolescents.Conclusions: Immersion tools favor user to be closer, with a humanist focus where real necessities are studied after the vivid experience of the problem to solve or get a better situation.The goal is that students experience real situations inside controlled surroundings, in such a way that they learn from their experiences and acquire knowledge through self-learning.EI program through immersion tools favored to modulate feelings, and to get a better
The expressive power of kth-order invariant graph networks

The expressive power of graph neural network formalisms is commonly measured by their ability to distinguish graphs. For many formalisms, the k-dimensional Weisfeiler-Leman (k-WL) graph isomorphism test is used as a yardstick. In this paper we consider the expressive power of kth-order invariant (linear) graph networks (k-IGNs). It is known that k-IGNs are expressive enough to simulate k-WL. This means that for any two graphs that can be distinguished by k-WL, one can find a k-IGN which also distinguishes those graphs. The question remains whether k-IGNs can distinguish more graphs than k-WL. This was recently shown to be false for k=2. Here, we generalise this result to arbitrary k. In other words, we show that k-IGNs are bounded in expressive power by k-WL. This implies that k-IGNs and k-WL are equally powerful in distinguishing graphs.
Underwater Object Detection using Tensorflow

Object Detection is a popular technology that detects instances within an image. In order to eliminate the barriers in Computer Vision technology due to the dissolution of the BGR(Blue-Green-Red) constituents with the increase in depth, it has been a necessity that the accuracy and efficiency of detecting any object underwater is optimum. In this article, we conduct Underwater Object Detection using Machine Learning through Tensorflow and Image Processing along with Faster R-CNN (Regions with Convolution Neural Network) as an algorithm for implementation. A suitable environment will be created so that Machine Learning algorithm will be used to train different images of the object. Open source Computer Vision has various functions which can be used for the image processing needs when an image is captured.
Dreaming: Model-based Reinforcement Learning by Latent Imagination without Reconstruction

In the present paper, we propose a decoder-free extension of Dreamer, a leading model-based reinforcement learning (MBRL) method from pixels. Dreamer is a sample- and cost-efficient solution to robot learning, as it is used to train latent state-space models based on a variational autoencoder and to conduct policy optimization by latent trajectory imagination. However, this autoencoding based approach often causes object vanishing, in which the autoencoder fails to perceives key objects for solving control tasks, and thus significantly limiting Dreamer's potential. This work aims to relieve this Dreamer's bottleneck and enhance its performance by means of removing the decoder. For this purpose, we firstly derive a likelihood-free and InfoMax objective of contrastive learning from the evidence lower bound of Dreamer. Secondly, we incorporate two components, (i) independent linear dynamics and (ii) the random crop data augmentation, to the learning scheme so as to improve the training performance. In comparison to Dreamer and other recent model-free reinforcement learning methods, our newly devised Dreamer with InfoMax and without generative decoder (Dreaming) achieves the best scores on 5 difficult simulated robotics tasks, in which Dreamer suffers from object vanishing.