Reviewerly/exHarmony huggingface dataset:
Computer Vision Models to Categorize Art Collections According to the Visual Content: A New Approach to the Abstract Art of Antoni Tàpies

This study uses computer vision models, which to some extent simulate the initial stages of human visual perception, to help categorize data in large sets of images of artworks by the artist Antoni Tàpies. The images have been analyzed on the basis of their compositional, chromatic and organizational characteristics, without textual notes, so that the analogies found may take us closer to, and help us to understand, the creator’s original values. The system as programmed can assist the specialist by establishing analogies between different artists or periods using the same criteria.
FPGA-Based Car-Like Robot Path Follower with Obstacle Avoidance

This project is about the design and implementation of an System on Programmable Chip (SoPC) based automated path following with obstacle avoidance using infrared sensors and ultrasonic sensor.The Line follower robot is a mobile machine that can detect and follow the line drawn on the floor using infrared sensors which are installed under the robot. The path is predefined as a black line on a white surface with a high contrasted color.This robot is provided with ultrasonic sensor to avoid an obstacle either from the right or the and it will stop if an obstacle is detected on the path or if the path is ended.The digital controller is designed and implemented using both the integrated circuits and the Field Programmable Gate Array (FPGA).
Training Of Learning Strategies in Writing Essay

Writing essay is still considered difficult for English students to master. The students are expected to be able to express their ideas correctly without making any chance of messages to be misinterpreted by the readers; therefore, they have to know and use the best or the most appropriate ways to write. The purpose of writing this article is to introduce learning strategies and the possibility of training of learning strategies in writing essay to English students. The first strategy is integration of strategy with the process of writing essay: pre-writing, writing draft, and post-writing activities. The second is training learning strategies, such as memory, cognitive, compensation, metacognitive, affective, and social strategies to students to make writing essay easier, faster, more enjoyable, more effective, and more self-directed to achieve the desired result in their writing. To get better result of improvement, the model of training of learning strategies in writing essay should be applied and developed based on the appropriateness of teachers, learners, subjects, and situations.
Simultaneous dimension reduction and clustering via the NMF-EM algorithm

Mixture models are among the most popular tools for clustering. However, when the dimension and the number of clusters is large, the estimation of the clusters become challenging, as well as their interpretation. Restriction on the parameters can be used to reduce the dimension. An example is given by mixture of factor analyzers for Gaussian mixtures. The extension of MFA to non-Gaussian mixtures is not straightforward. We propose a new constraint for parameters in non-Gaussian mixture model: the $K$ components parameters are combinations of elements from a small dictionary, say $H$ elements, with $H \ll K$. Including a nonnegative matrix factorization (NMF) in the EM algorithm allows us to simultaneously estimate the dictionary and the parameters of the mixture. We propose the acronym NMF-EM for this algorithm, implemented in the R package {\tt nmfem}. This original approach is motivated by passengers clustering from ticketing data: we apply NMF-EM to data from two Transdev public transport networks. In this case, the words are easily interpreted as typical slots in a timetable.
Dynamic Classifier Chains for Multi-label Learning

In this paper, we deal with the task of building a dynamic ensemble of chain classifiers for multi-label classification. To do so, we proposed two concepts of classifier chains algorithms that are able to change label order of the chain without rebuilding the entire model. Such modes allows anticipating the instance-specific chain order without a significant increase in computational burden. The proposed chain models are built using the Naive Bayes classifier and nearest neighbour approach as a base single-label classifiers. To take the benefits of the proposed algorithms, we developed a simple heuristic that allows the system to find relatively good label order. The heuristic sort labels according to the label-specific classification quality gained during the validation phase. The heuristic tries to minimise the phenomenon of error propagation in the chain. The experimental results showed that the proposed model based on Naive Bayes classifier the above-mentioned heuristic is an efficient tool for building dynamic chain classifiers.
Enhancing Accuracy in a Touch Operation Biometric System: A Case on the Android Pattern Lock Scheme

The main objective of this research study is to enhance the functionality of an Android pattern lock application by determining whether the time elements of a touch operation, in particular time on dot (TOD) and time between dot (TBD), can be accurately used as a biometric identifier. The three hypotheses that were tested through this study were the following–H 1 : there is a correlation between the number of touch stroke features used and the accuracy of the touch operation biometric system; H 2 : there is a correlation between pattern complexity and accuracy of the touch operation biometric system; H 3 : there is a correlation between user training and accuracy of the touch operation biometric system. Convenience sampling and a within-subjects design involving repeated measures were incorporated when testing an overall sample size of 12 subjects drawn from a university population who gave a total of 2,096 feature extracted data. Analysis was done using the Dynamic Time Warping (DTW) Algorithm. Through this study, it was shown that the extraction of one-touch stroke biometric feature coupled with user training was able to yield high average accuracy levels of up to 82%. This helps build a case for the introduction of biometrics into smart devices with average processing capabilities as they would be able to handle a biometric system without it compromising on the overall system performance. For future work, it is recommended that more work be done by applying other classification algorithms to the existing data set and comparing their results with those obtained with DTW.
Session Types with Linearity in Haskell

Type systems with parametric polymorphism can encode a significant proportion of the information contained in session types.This allows concurrent programming with session-type-like guarantees in languages like ML and Java.However, statically enforcing the linearity properties of session types, in a way that is also natural to program with, is more challenging.Haskell provides various language features that can capture concurrent programming with session types, with full linearity invariants and in a mostly idiomatic style.This chapter overviews various approaches in the literature for session typed programming in Haskell.As a starting point, we use polymorphic types and simple type-level functions to provide session-typed communication in Haskell without linearity.We then overview and compare the varying approaches to implementing session types with static linearity checks.We conclude with a discussion of the remaining open problems.The code associated with this chapter can be found at http://github.com/dorchard/betty-book-haskell-sessions.
Improved method for finding optimal formulas for bilinear maps in a finite field

In 2012, Barbulescu, Detrey, Estibals and Zimmermann proposed a new framework to exhaustively search for optimal formulas for evaluating bilinear maps over finite fields, such as Strassen or Karatsuba formulas. The main contribution of this work is a new criterion to aggressively prune useless branches in the exhaustive search, thus leading to the computation of new optimal formulas. We apply in particular our approach to the short product modulo X5 and the circulant product modulo (X5−1). Moreover, we are able to prove that there is essentially only one optimal decomposition of the product of 3×2 by 2×3 matrices up to the action of some group of automorphisms.
The Transition from Classical to Post-Quantum Cryptography

Quantum computing is the study of computers that use quantum featuresin calculations. For over 20 years, it has been known that if verylarge, specialized quantum computers could be built, they could have adevastating effect on asymmetric classical cryptographic algorithmssuch as RSA and elliptic curve signatures and key exchange, as well as(but in smaller scale) on symmetric cryptographic algorithms such asblock ciphers, MACs, and hash functions. There has already been agreat deal of study on how to create algorithms that will resistlarge, specialized quantum computers, but so far, the properties ofthose algorithms make them onerous to adopt before they are needed.Small quantum computers are being built today, but it is still farfrom clear when large, specialized quantum computers will be builtthat can recover private or secret keys in classical algorithms at thekey sizes commonly used today. It is important to be able to predictwhen large, specialized quantum computers usable for cryptanalysiswill be possible so that organization can change to post-quantumcryptographic algorithms well before they are needed. This documentdescribes quantum computing, how it might be used to attack classicalcryptographic algorithms, and possibly how to predict when large,specialized quantum computers will become feasible.
Quantum walks and the size of the graph

A continuous-time quantum walk is modelled using a graph. In this short paper, we provide lower bounds on the size of a graph that would allow for some quantum phenomena to occur. Among other things, we show that, in the adjacency matrix quantum walk model, the number of edges is bounded below by a cubic function on the eccentricity of a periodic vertex. This gives some idea on the shape of a graph that would admit periodicity or perfect state transfer. We also raise some extremal type of questions in the end that could lead to future research.
Exploiting long-term temporal dynamics for video captioning

Automatically describing videos with natural language is a fundamental challenge for computer vision and natural language processing. Recently, progress in this problem has been achieved through two steps: 1) employing 2-D and/or 3-D Convolutional Neural Networks (CNNs) (e.g. VGG, ResNet or C3D) to extract spatial and/or temporal features to encode video contents; and 2) applying Recurrent Neural Networks (RNNs) to generate sentences to describe events in videos. Temporal attention-based model has gained much progress by considering the importance of each video frame. However, for a long video, especially for a video which consists of a set of sub-events, we should discover and leverage the importance of each sub-shot instead of each frame. In this paper, we propose a novel approach, namely temporal and spatial LSTM (TS-LSTM), which systematically exploits spatial and temporal dynamics within video sequences. In TS-LSTM, a temporal pooling LSTM (TP-LSTM) is designed to incorporate both spatial and temporal information to extract long-term temporal dynamics within video sub-shots; and a stacked LSTM is introduced to generate a list of words to describe the video. Experimental results obtained in two public video captioning benchmarks indicate that our TS-LSTM outperforms the state-of-the-art methods.
Wrong Warping, Sequence Breaking, and Running through Code

This article focuses on Nintendo's influential and much-celebrated 1998 videogame The Legend of Zelda: Ocarina of Time (hereafter OoT). In particular, it explores the often unexpectedly creative and wholly transformative ways in the game is played by 'speedrunners.' Seeking to race through the game as quickly as possible, speedrunners play in a distinctive way that combines mastery of performance execution with a deep knowledge of the game's operation and how its systems may be exploited. Speedrunning performances are creative because they involve astonishingly detailed investigations of the minutiae of game behaviors. They are transformative because they disrupt and even invert much-vaunted aspects of the game as sequences that slow down progress are circumvented. As such, what would otherwise class as crucial moments in the storyline, key character development and even locales, are not only raced through but are actively skipped. Compared with the game's narrative, the connectivity of its spaces, and the complex but clearly mapped passage of time as set out in Nintendo's officially-endorsed 'Strategy Guides' (see Buchanan et al 1998; Hollinger et al 1998; Loe and Guess 1998), the OoT speedrun constructs an altogether different game with vastly altered priorities. And so, the offer to, 'Join legendary hero Link as he journeys across Hyrule, and even through time, to thwart the plans of Ganondorf.' (Nintendo 2017), is recast as a breakneck dash to the closing credits sequence evading and avoiding all but the essential moments of gameplay.
KS(conf): A Light-Weight Test if a ConvNet Operates Outside of Its Specifications

Computer vision systems for automatic image categorization have become accurate and reliable enough that they can run continuously for days or even years as components of real-world commercial applications. A major open problem in this context, however, is quality control. Good classification performance can only be expected if systems run under the specific conditions, in particular data distributions, that they were trained for. Surprisingly, none of the currently used deep network architectures have a built-in functionality that could detect if a network operates on data from a distribution it was not trained for, such that potentially a warning to the human users could be triggered. In this work, we describe KS(conf), a procedure for detecting such outside of specifications (out-of-specs) operation, based on statistical testing of the network outputs. We show by extensive experiments using the ImageNet, AwA2 and DAVIS datasets on a variety of ConvNets architectures that KS(conf) reliably detects out-of-specs situations. It furthermore has a number of properties that make it a promising candidate for practical deployment: it is easy to implement, adds almost no overhead to the system, works with all networks, including pretrained ones, and requires no a priori knowledge of how the data distribution could change.
Improved particle swarm optimization and neighborhood field optimization by introducing the re-sampling step of particle filter

<p style='text-indent:20px;'>A technique of introducing the re-sampling step of particle filter is proposed to improve the particle swarm optimization (PSO) algorithm, a typical global search algorithm. The re-sampling step can decrease particles with low weights and duplicate particles with high weights, given that we define a type of suitable weights for the particles. To prevent the identity of particles, the re-sampling step is followed by the existing method of particle variation. Through this technique, the local search capability is enhanced greatly in the later searching stage of PSO algorithm. More interesting, this technique can also be employed to improve another algorithm of which the philosophy is "learning from neighbors", i.e., the neighborhood field optimization (NFO) algorithm. The improved algorithms (PSO-resample and NFO-resample) are compared with other metaheuristic algorithms through extensive simulations. The experiments show that the improved algorithms are superior in terms of convergence rate, search accuracy and robustness. Our results also suggest that the proposed technique can be general in the sense that it can probably improve other particle-based intelligent algorithms.
Object tracking via dual fuzzy low-rank approximation

For the object loss problem in the tracking process caused by illumination, occlusion, pose variation, and motion blur, the tracking method based on dual fuzzy low-rank approximation in a particle filter framework is proposed in this paper. Firstly, multiple constraint regions are built to filter insignificant samples, and more distinguished candidate samples are selected. Secondly, dual fuzzy observation function of each candidate sample is created based on the designed low-rank approximation representations of object and background. Then the generalized tracking results are obtained by computing membership degrees of dual fuzzy observation functions. Finally, based on the spatial coherency principle, the final tracking result is determined from the generalized results by measuring similarities of consecutive objects. The proposed method shows good performance as compared with several state-of-the-art trackers on challenging benchmark sequences.
Wrist detection based on a minimum bounding box and geometric features

Wrist detection is a crucial element in the hand-pose estimation and hand-gesture recognition processes in Human-Computer Interaction applications. Most methods use horizontal parallel lines to scan for the location of a wrist line. The challenging problems in wrist detection are determining the orientation and localising the horizontal parallel lines that scan for various hand poses. The proposed method automatically detects a wrist, based on a minimum bounding box and geometric features. It also determines the start and stop points to localise the scanning. The evaluation used a set of 1240 hand images with ground-truth data taken from three sets of data. The hand images contained several gestures and individuals to prove that the method is robust against various gestures. The evaluation shows that the method successfully detects the image orientation and the wrist points with high accuracy.
Using Whats App for Developing Collaborative Writing among English Majors at MUST

The aim of this study was to develop collaborative writing using the Whats Apps among 3rd year English majors at Misr University for Science and Technology (MUST) in Fall 2017 semester. The problem of the study was that 3rd year English majors were not motivated enough in the writing class and had difficulty in communicating through writing with each other during the limited time of the class. So, the idea of using Whats Apps for developing writing skills was necessary especially that there is Wi Fi available at MUST. The researcher used the quasi- experimental design with two groups, experimental and control. Two instruments were used; a pre/ post collaborative writing skills test, and a reflection sheet. The results of the reflection sheet showed that 3rd year English majors did not use the Whats App in learning any language skill before and that they were satisfied with using it in the writing class. The writing skills test revealed that     using Whats App was effective in developing collaborative writing among the participants in the study.  Also, the results of the reflection sheet proved that the students of the experimental group were satisfied with using Whats App for developing collaborative writing.
Full Representation Data Embedding via Nonoverlapping Historical Features

Data recycling, which reuses the historical data to assist the present data to achieve better performance, is an emerging and important research topic. A common case is that historical examples only have features from one source while presently have more data collection ways and extract different types of features simultaneously for new examples. Previous studies assume that either historical data appear in all sources, or at least there is one type of representations for all data. In this paper, we study the challenging problem in the above common case and propose a novel semisupervised approach by leveraging nonoverlapping historical features (NHFs). It learns full representations of both historical features and present features in a latent subspace. We utilize the intrinsic geometrical structure of all data and add the label information of historical data as a hard constraint to discover a latent subspace. Then, the classification will be performed with these new representations. Moreover, we provide an efficient algorithm to solve the formulated optimization problem with proved convergence behavior, together with some insightful discussions about parameter determination. Experimental results on real-world data sets are provided to examine the effectiveness of our algorithm. Furthermore, we have also evaluated our method in face recognition. They all demonstrate the effectiveness of our proposed approach on recycling NHFs.
Dynamics of entanglement creation between two spins coupled to a chain

We study the dynamics of entanglement between two spins which is created by the coupling to a common thermal reservoir. The reservoir is a spin-$$\frac{1}{2}$$ Ising transverse field chain thermally excited; the two defect spins couple to two spins of the chain which can be at a macroscopic distance. In the weak-coupling and low-temperature limit, the spin chain is mapped onto a bath of linearly interacting oscillators using the Holstein–Primakoff transformation. We analyze the time evolution of the density matrix of the two defect spins for transient times and deduce the entanglement which is generated by the common reservoir. We discuss several scenarios for different initial states of the two spins and for varying distances.
Semisupervised Regression With Optimized Rank for Matrix Data Classification

There has been growing interest in developing more effective algorithms for matrix data classification. At present, most of the existing vector-based classifications involve vectorization process, which results in two main problems. First, the underlying structural information is disregarded. Second, the vectorization of a matrix incurs the creation of a vector with potentially very high dimensionality, which may lead to over-fitting when the number of training data is small. To avoid such problems, we propose a new matrix-based regression algorithm for classification, in which the input matrices to be classified are directly used to learn two regression matrices for each order of the input matrix. To further explore the discrimination information, we add a joint l2,1 -norm on two regression matrices, which endows the algorithm optimized regression rank by uncovering common sparse columns in the two regression matrices. To further boost the classification performance, we incorporate a semisupervised learning process, which leverages both labeled and unlabeled data to enhance the training process. Experiments on public benchmark datasets show that our method outperforms a number of the existing state-of-the-art classification methods even when only few labeled training samples are provided.
Detecting Potential Local Adversarial Examples for Human-Interpretable Defense

Machine learning models are increasingly used in the industry to make decisions such as credit insurance approval. Some people may be tempted to manipulate specific variables, such as the age or the salary, in order to get better chances of approval. In this ongoing work, we propose to discuss, with a first proposition, the issue of detecting a potential local adversarial example on classical tabular data by providing to a human expert the locally critical features for the classifier’s decision, in order to control the provided information and avoid a fraud.