Reviewerly/exHarmony huggingface dataset:
Online Template Attack on ECDSA:

We retrieve the ephemeral private key from the power trace of a single scalar multiplication in an ECDSA signature generation and from that the signing private key using an online template attack. The innovation is that we generate the profiling traces using ECDSA signature verification on the same device. The attack can be prevented by randomization of the (projective) coordinates of the base point.
EDUGAME4CITY. A Gamification for Architecture Students. Viability Study Applied to Urban Design

Hereby we present the first results of a Financed Research Project that focuses on the use of (Serious games) SG for training future architects and urban planner in the urban design context. After the first viability studies and tests in the creation of urban scenarios at intermediate scale, virtual simulation, vegetative environment and lighting, as well as basic gamification, the complete development of the teacher's methodology proposal of the research project is outlined. First five urban scenarios have been selected in which the students of Máster y Grado de Arquitectura of the ETSABarcelona-UPC of landscape and urban design have developed of urban intervention projects from the local administration and neighborhood associations. These projects have been completed subsequently in different optional courses ICT, in which 3-D virtual scenarios have been created and rehearsed under a first basic gamification. Following with the project it has continued with other courses of Architectonic Representation degree, where final settings and designs have been created, according to the students´ interest in using Virtual Reality Technology. Afterwards the virtual models and scenarios were transferred to La Salle-Barcelona URL Architecture students in different courses of Representation tools, in order to continue with advanced gamification, HMD and analysis of its usage and their motivation. The results of these instructed processes have reached the administration, neighbors, professionals and general public.
Face Recognition in Complex Unconstrained Environment with An Enhanced WWN Algorithm

Abstract Face recognition is one of the core and challenging issues in computer vision field. Compared to computer vision, human visual system can identify a target from complex backgrounds quickly and accurately. This paper proposes a new network model deriving from Where-What Networks (WWNs), which can approximately simulate the information processing pathways (i.e., dorsal pathway and ventral pathway) of human visual cortex and recognize different types of faces with different locations and sizes in complex background. To enhance the recognition performance, synapse maintenance mechanism and neuron regenesis mechanism are both introduced. Synapse maintenance is used to reduce the background interference while neuron regenesis mechanism is introduced to regulate the neuron resource dynamically to improve the network usage efficiency. Experiments have been conducted on human face images of 5 types, 11 sizes, and 225 locations in complex backgrounds. Experiment results demonstrate that the proposed WWN model can basically learn three concepts (type, location and size) simultaneously. The experiment results also show the advantages of the enhanced WWN-7 model for face recognition in comparison with several existing methods.
Natural Language Processing: Challenges and Future Directions

Natural language processing (NLP) is a well-known sub-field of artificial intelligence that is having huge success and attention in recent years, its applications are also exploding in terms of innovation and consumer adoption, personal voice assistants and chatbots are two examples among many others, despite this recent success, NLP still has huge challenges and open issues. In this paper, we provide a short overview of NLP, then we dive into the different challenges that are facing it, finally, we conclude by presenting recent trends and future research directions that are speculated by the research community.
Indirect Deconvolution Algorithm

Neural network frameworks today commonly implement Deconvolution and closely related Convolution operator via a combination of GEMM (dense matrix-matrix multiplication) and a memory transformation. The recently proposed Indirect Convolution algorithm suggests a more efficient implementation of Convolution via the Indirect GEMM primitive - a modification of GEMM where pointers to rows are loaded from a buffer rather than being computed assuming constant stride. However, the algorithm is inefficient for Deconvolution with non-unit stride, which is typical in computer vision models. We describe a novel Indirect Deconvolution algorithm for efficient evaluation of the Deconvolution operator with nonunit stride by splitting Deconvolution with a large kernel into multiple subconvolutions with smaller, variable-size kernels, which can be efficiently implemented on top of the Indirect GEMM primitive.
Analyzing the GitHub Repositories of Research Papers

Linking to code repositories, such as on GitHub, in scientific papers becomes increasingly common in the field of computer science. The actual quality and usage of these repositories are, however, to a large degree unknown so far. In this paper, we present for the first time a thorough analysis of all GitHub code repositories linked in scientific papers using the Microsoft Academic Graph as a data source. We analyze the repositories and their associated papers with respect to various dimensions. We observe that the number of stars and forks, respectively, over all repositories follows a power-law distribution. In the majority of cases, only one person from the authors is contributing to the repository. The GitHub manuals are mostly kept rather short with few sentences. The source code is mostly provided in Python. The papers containing the repository URLs as well as the papers' authors are typically from the AI field.
Application of Role Playing Methods in Indonesian Language Subjects in Class 2 of Elementary Schools

Role playing can be called a drama or imitating certain behaviors or characters that differentiate each student's position. With this method, students are expected to be able to issue their imagination in playing a character or living up to the character of a predetermined character. The research was conducted on 2nd grade students at MI Muhammadiyah 2 Kedungbanteng Tanggulangin, Sidoarjo. Method of role play a method that can be one of the solutions in the learning process in the classroom that allows learners or students the spirit in learning. And it can increase students learning interest so that students can sharpen their thinking to learn well and try hard to get a satisfactory value.
Game-Like 3D Visualisation of Air Quality Data

The data produced by sensor networks for urban air quality monitoring is becoming a valuable asset for informed health-aware human activity planning. However, in order to properly explore and exploit these data, citizens need intuitive and effective ways of interacting with it. This paper presents CityOnStats, a visualisation tool developed to provide users, mainly adults and young adults, with a game-like 3D environment populated with air quality sensing data, as an alternative to the traditionally passive visualisation techniques. CityOnStats provides several visual cues of pollution presence with the purpose of meeting each user’s preferences. Usability tests with a sample of 30 participants have shown the value of air quality 3D game-based visualisation and have provided empirical support for which visual cues are most adequate for the task at hand.
Analysis on the Applicability of the Random Forest

Abstract Random forest is a flexible algorithm with a wide range of applications and performs well on a large number of data sets. Besides, Random forest is immune to statistical assumptions as well as preprocessing burden and can handle a large data set with high dimensionality and missing values. Nevertheless, random forest struggles with high-cardinality categorical variables, unbalanced data, time series forecasting, variables interpretation, and is sensitive to hyperparameter. Thus, random forest is relatively suitable for processing high-dimensional data and data with missing variables. Besides, random forest works well with a large amount of data, which is previously unprocessed. Moreover, random forest is an appropriate method, when there are prior statistical assumptions. However, random forest is non-ideal, when processing data with endogenous temporal effects or high-cardinality categorical variables, as well as when the interpretation is the primary goal. Despite the shortcomings of the random forest, there are still some improvements that can be made. It will be more convenient for users to screen methods, if there is a rating system to give an overall score towards all alternative algorithms depending on the input data and the users’ goals.
An Empirical Study on Recent Graph Database Systems

Graphs are widely used to model the intricate relationships among objects in a wide range of applications. The advance in graph data has brought significant value to artificial intelligence technologies. Recently, a number of graph database systems have been developed. In this paper, we present a comprehensive overview and empirical investigation on existing property graph database systems such as Neo4j, AgensGraph, TigerGraph and LightGraph (LightGraph has recently renamed to TuGraph.). These systems support declarative graph query languages. Our empirical studies are conducted in a single-machine environment against on the LDBC social network benchmark, consisting of three different large-scale datasets and a set of benchmark queries. This is the first empirical study to compare these graph database systems by evaluating data bulk importing and processing simple and complex queries. Experimental results provide insightful observations of various graph data systems and indicate that AgensGraph works well on SQL based workload and simple update queries, TigerGraph is powerful on complex business intelligence queries, Neo4j is user-friendly and suitable for small queries, and LightGraph is a more balanced product achieving good performance on different queries. The related code, scripts and data of this paper are available online ( https://github.com/UNSW-database/GraphDB-Benchmark ).
Software defect prediction based on non-linear manifold learning and hybrid deep learning techniques

Software defect prediction plays a very important role in software quality assurance, which aims to inspect as many potentially defect-prone software modules as possible. However, the performance of the prediction model i... | Find, read and cite all the research you need on Tech Science Press
Quantum Lower and Upper Bounds for 2D-Grid and Dyck Language

We study the quantum query complexity of two problems.First, we consider the problem of determining if a sequence of is a properly one (a Dyck word), with a depth of at most k. We call this the Dyck_{k,n} problem. We prove a lower bound of Ω(c^k √n), showing that the complexity of this problem increases exponentially in k. Here n is the length of the word. When k is a constant, this is interesting as a representative example of star-free languages for which a surprising O(√n) query quantum algorithm was recently constructed by Aaronson et al. [Scott Aaronson et al., 2018]. Their proof does not give rise to a general algorithm. When k is not a constant, Dyck_{k,n} is not context-free. We give an algorithm with O(√n(log n)^{0.5k}) quantum queries for Dyck_{k,n} for all k. This is better than the trival upper bound n for k = o({log(n)}/{log log n}). Second, we consider connectivity problems on grid graphs in 2 dimensions, if some of the edges of the grid may be missing. By embedding the balanced parentheses problem into the grid, we show a lower bound of Ω(n^{1.5-e}) for the directed 2D grid and Ω(n^{2-e}) for the undirected 2D grid. The directed problem is interesting as a black-box model for a class of classical dynamic programming strategies including the one that is usually used for the well-known edit distance problem. We also show a generalization of this result to more than 2 dimensions.
<i>MoneyMorph</i>: Censorship Resistant Rendezvous using Permissionless Cryptocurrencies

Abstract Cryptocurrencies play a major role in the global financial ecosystem. Their presence across different geopolitical corridors, including in repressive regimes, has been one of their striking features. In this work, we leverage this feature for bootstrapping Censorship Resistant communication. We conceptualize the notion of stego-bootstrapping scheme and its security in terms of rareness and security against chosencovertext attacks. We present MoneyMorph , a provably secure stego-bootstrapping scheme using cryptocurrencies. MoneyMorph allows a censored user to interact with a decoder entity outside the censored region, through blockchain transactions as rendezvous, to obtain bootstrapping information such as a censorshipresistant proxy and its public key. Unlike the usual bootstrapping approaches (e.g., emailing) with heuristic security, if any, MoneyMorph employs public-key steganography over blockchain transactions to ensure provable cryptographic security. We design rendezvous over Bitcoin, Zcash, Monero, and Ethereum, and analyze their effectiveness in terms of available bandwidth and transaction cost. With its highly cryptographic structure, we show that Zcash provides 1148 byte bandwidth per transaction costing less than 0.01 USD as fee.
Applying Improved Genetic Algorithm to Solve Travelling Salesman Problem

Travelling Salesman Problem is a very popular problem in graph theory and it is applicable to solve many problems of science and engineering. It has its applications in electronics, transportation, navigation, computer networks etc. As the number of cities increases the complexity of the algorithm to find the optimal solution increases. TSP is a maximization optimization problem. In this paper genetic algorithm is used to solve TSP problem. Genetic Algorithm is a nature inspired algorithm which is very suitable to solve optimization problems. An improved genetic algorithm is proposed and applied on standards TSP instances. The achieved results are found better as compared to other recent research works in this area.
Design space and usability of earable prototyping

Earable computing gains growing attention within research and becomes ubiquitous in society. However, there is an emerging need for prototyping devices as critical drivers of innovation. In our work, we reviewed the features of existing earable platforms. Based on 24 publications, we characterized the design space of earable prototyping. We used the open eSense platform (6-axis IMU, auditory I/O) to evaluate the problem-based learning usability of non-experts. We collected data from 79 undergraduate students who developed 39 projects. Our questionnaire-based results suggest that the platform creates interest in the subject matter and supports self-directed learning. The projects align with the research space, indicating ease of use, but lack contributions for more challenging topics. Additionally, many projects included games not present in current research. The average SUS score of the platform was 67.0. The majority of problems are technical issues (e.g., connecting, playing music).
ICTIR Tutorial: Modern Query Performance Prediction: Theory and Practice

Query performance prediction (QPP) is a core information retrieval (IR) task whose primary goal is to assess retrieval quality in the absence of relevance judgments. Applications of QPP are numerous, and include, among others, automatic query reformulation, fusion and ranker selection, distributed search and content analysis. The main objective of this tutorial is to introduce recent advances in the sub-research area of QPP in IR, covering both theory and applications. On the theoretical side, we will introduce modern QPP frameworks, which have advanced our understanding of the core QPP task. On the application side, the tutorial will set the connection between QPP theory and its usage in various modern IR applications, discussing the pros and cons, limitations, challenges and open research questions.
Assessing the Feasibility of Augmenting Fall Detection Systems by Relying on UWB-Based Position Tracking and a Home Robot

Falls in the home environment are a primary cause of injury in older adults. According to the U.S. Centers for Disease Control and Prevention, every year, one in four adults 65 years of age and older reports experiencing a fall. A variety of different technologies have been proposed to detect fall events. However, the need to detect all fall instances (i.e., to avoid false negatives) has led to the development of systems marked by high sensitivity and hence a significant number of false alarms. The occurrence of false alarms causes frequent and unnecessary calls to emergency response centers, which are critical resources that should be utilized only when necessary. Besides, false alarms decrease the level of confidence of end-users in the fall detection system with a negative impact on their compliance with using the system (e.g., wearing the sensor enabling the detection of fall events). Herein, we present a novel approach aimed to augment traditional fall detection systems that rely on wearable sensors and fall detection algorithms. The proposed approach utilizes a UWB-based tracking system and a home robot. When the fall detection system generates an alarm, the alarm is relayed to a base station that utilizes a UWB-based tracking system to identify where the older adult and the robot are so as to enable navigating the environment using the robot and reaching the older adult to check if he/she experienced a fall. This approach prevents unnecessary calls to emergency response centers while enabling a tele-presence using the robot when appropriate. In this paper, we report the results of a novel fall detection algorithm, the characteristics of the alarm notification system, and the accuracy of the UWB-based tracking system that we implemented. The fall detection algorithm displayed a sensitivity of 99.0% and a specificity of 97.8%. The alarm notification system relayed all simulated alarm notification instances with a maximum delay of 106 ms. The UWB-based tracking system was found to be suitable to locate radio tags both in line-of-sight and in no-line-of-sight conditions. This result was obtained by using a machine learning-based algorithm that we developed to detect and compensate for the multipath effect in no-line-of-sight conditions. When using this algorithm, the error affecting the estimated position of the radio tags was smaller than 0.2 m, which is satisfactory for the application at hand.
AI versus robot: in search of a domain for the new European civil law

In 2017, the European Parliament issued a Resolution calling on the Commission to elaborate new solutions based on civil law that could respond to the rapid present-day development of robotics and AI. The Resolution, pushing for the preparation of new tort law focusing on robots, postulates that a new definition of robot be prepared. Responding to the Resolution, this paper consists in a legal-cognitive-linguistic analysis which draws three conclusions: firstly, that the definitional method is not the best approach to determining the scope of the regulation of robotics and AI; secondly, that the Resolution is incorrect in assuming that a new civil law solution should turn on differentiating between AI and robots and that robots should be treated as focal in determining the scope of the regulation; and, thirdly, that any new norms should be rooted in the concept of AI and not, as proposed by the Resolution, in the concept of robot.
Grassmannian Diffusion Maps--Based Dimension Reduction and Classification for High-Dimensional Data

This work introduces the Grassmannian diffusion maps (GDMaps), a novel nonlinear dimensionality reduction technique that defines the affinity between points through their representation as low-dimensional subspaces corresponding to points on the Grassmann manifold. The method is designed for applications, such as image recognition and data-based classification of constrained high-dimensional data where each data point itself is a high-dimensional object (i.e., a large matrix) that can be compactly represented in a lower-dimensional subspace. The GDMaps is composed of two stages. The first is a pointwise linear dimensionality reduction wherein each high-dimensional object is mapped onto the Grassmann manifold representing the low-dimensional subspace on which it resides. The second stage is a multipoint nonlinear kernel-based dimension reduction using diffusion maps to identify the subspace structure of the points on the Grassmann manifold. To this end, an appropriate Grassmannian kernel is used to construct the transition matrix of a random walk on a graph connecting points on the Grassmann manifold. Spectral analysis of the transition matrix yields low-dimensional Grassmannian diffusion coordinates embedding the data into a low-dimensional reproducing kernel Hilbert space. Further, a novel data classification/recognition technique is developed based on the construction of an overcomplete dictionary of reduced dimension whose atoms are given by the Grassmannian diffusion coordinates. Three examples are considered. First, a “toy” example shows that the GDMaps can identify an appropriate parametrization of structured points on the unit sphere. The second example demonstrates the ability of the GDMaps to revealing the intrinsic subspace structure of high-dimensional random field data. In the last example, a face recognition problem is solved considering face images subject to varying illumination conditions, changes in face expressions, and occurrence of occlusions. The technique presented high recognition rates (i.e., 95% in the best case) using a fraction of the data required by conventional methods.
Overview of the EvaLatin 2020 Evaluation Campaign

This paper describes the first edition of EvaLatin, a campaign totally devoted to the evaluation of NLP tools for Latin. The two shared tasks proposed in EvaLatin 2020, i. e. Lemmatization and Part-of-Speech tagging, are aimed at fostering research in the field of language technologies for Classical languages. The shared dataset consists of texts taken from the Perseus Digital Library, processed with UDPipe models and then manually corrected by Latin experts. The training set includes only prose texts by Classical authors. The test set, alongside with prose texts by the same authors represented in the training set, also includes data relative to poetry and to the Medieval period. This also allows us to propose the Cross-genre and Cross-time subtasks for each task, in order to evaluate the portability of NLP tools for Latin across different genres and time periods. The results obtained by the participants for each task and subtask are presented and discussed.