Reviewerly/exHarmony huggingface dataset:
Harnessing feedback region proposals for multi‐object tracking

In the tracking‐by‐detection approach of online multiple object tracking (MOT), a major challenge is how to associate object detections on the new video frame with previously tracked objects. Two important aspects that directly influence the performance of MOT are quality of detection and accuracy in data association. The authors propose an efficient and unified MOT framework for improved object detection, followed by enhanced object tracking. The object detection and tracking are considered as two independent functions in the tracking‐by‐detection paradigm. In this study, object detection accuracy has been increased by employing a faster region‐based convolutional neural network (Faster R‐CNN) modified with the feedback region proposals from the tracker. Target association is performed by the correlation filter‐based Siamese CNN model, which finds the similarity score between the input image patches. The Siamese CNN is trained using a supervised hard sample mining strategy. An optical flow‐based motion model is employed to predict the next probable location of the targets from the tracker and these region proposals are fed back to the classifier module of Faster R‐CNN. The authors’ extensive analysis of publicly available MOT benchmark datasets and comparison with the state‐of‐the‐art tracking methods demonstrate competitive tracking performance of the proposed MOT framework.
Third International Workshop on Languages for Modelling Variability (MODEVAR@SPLC 2020)

Feature models were invented in 1990 and have been recognised as one of the main contributions to the Software Product Line community. Although there have been several attempts to establish a sort of standard variability modelling language, there is still no consensus on a simple feature modelling language. There can be many motivations to have one but among others, there is one that is very important: information sharing among researchers, tools or developers. Following the spirit of the first two editions, this workshop is an interactive event where all participants shall share knowledge, but also first realizations about how to build up a simple feature modelling language that all the community can agree on.
The Secured AES designs against Fault Injection Attacks: A comparative Study

To protect the Advanced Encryption Standard (AES) against physical attacks known as fault injection attacks various fault detection schemes have been proposed. In this paper, a comparative study between the most well-known fault detection schemes in terms of fault detection capabilities and implementation cost has been proposed. In the considered study we implement, separately, the hardware, the temporal and the information redundancy for the 32-bit AES. These schemes are implemented on the Virtex-5 Xilinx FPGA board in order to evaluate their efficiency in terms of area, time costs and fault coverage.
A Compare-Aggregate Model with External Knowledge for Query-Focused Summarization

Query-focused extractive summarization aims to create a summary by selecting sentences from original document according to query relevance and redundancy. With recent advances of neural network models in natural language processing, attention mechanism is widely used to address text summarization task. However, existing methods are always based on a coarse-grained sentence-level attention, which likely to miss the intent of query and cause relatedness misalignment. To address the above problem, we introduce a fine-grained and interactive word-by-word attention to the query-focused extractive summarization system. In that way, we capture the real intent of query. We utilize a Compare-Aggregate model to implement the idea, and simulate the interactively attentive reading and thinking of human behavior. We also leverage external conceptual knowledge to enrich the model and fill the expression gap between query and document. In order to evaluate our method, we conduct experiments on DUC 2005–2007 query-focused summarization benchmark datasets. Experimental results demonstrate that our proposed approach achieves better performance than state-of-the-art.
Temporal Ontology-Mediated Queries and First-Order Rewritability: A Short Course

We discuss recent attempts to extend the ontology-based data access (aka virtual knowledge graph) paradigm to the temporal setting. Our main aim is to understand when answering temporal ontology-mediated queries can be reduced to evaluating standard first-order queries over timestamped data and what numeric predicates and operators are required in such reductions. We consider two ways of introducing a temporal dimension in ontologies and queries: using linear temporal logic LTL over discrete time and using metric temporal logic MTL over dense time.
Challenging Human Supremacy: Evaluating Monte Carlo Tree Search and Deep Learning for the Trick Taking Card Game Jass

Despite the recent successful application of Artificial Intelligence (AI) to games, the performance of cooperative agents in imperfect information games is still far from surpassing humans. Cooperating with teammates whose play-styles are not previously known poses additional challenges to current state-of-the-art algorithms. In the Swiss card game Jass, coordination within the two opposing teams is crucial for winning. Since verbal communication is forbidden, the only way to transmit information within the team is through a player’s play-style. This makes the game a particularly suitable candidate subject to continue the research on AI in cooperation games with hidden information. In this work, we analyse the effectiveness and shortcomings of several state-of-the-art algorithms (Monte Carlo Tree Search (MCTS) variants and Deep Neural Networks (DNNs)) at playing the Jass game. Our key contributions are two-fold. First, we provide a performance overview for state-of-the-art algorithms, thus, setting a strong foundation for further research on the subject. Second, we implement and open-source (https://gitlab.enterpriselab.ch/jass/info) a platform where different agents (both humans and AI) can play Jass in an automated fashion, effectively reducing the overhead for other researchers who want to perform further experiments.
Higher weight spectra of codes from Veronese threefolds

We study binary linear codes C obtained from the quadric Veronese embedding of P3 in P9 over F2. We show how one can find the higher weight spectra of these codes. Our method will be a study of the Stanley-Reisner rings of a series of matroids associated to each code C.
Apply Deep Reinforcement Learning to NS-SHAFT Game Control

Reinforcement Learning (RL) with both exploration and exploit abilities is applied to games. Related literature shows that it can surpass human performance, and some can even defeat human. This paper is mainly based on the combination of reinforcement learning and deep learning, called Deep Q-Network, to learn the action response of game NS-SHAFT autonomously. Based on a personal computer, we built an experimental learning environment that automatically captures the NS-SHAFT's frame which is provided to DQN to decide the action of moving left, moving right, or stay in same location, survey different parameters: such as the learning cycle, different reward settings, and exploration / exploit ratios etc., which affect the learning effectiveness. The experimental results show that moderate parameter settings have a certain degree of impact on the DQN learning effect.
A Case Study of Collaborative Mobile Learning in Large-size Classes

Class size has been considered as an important factor that influences the efficiency of language education. Research has found that collaborative learning and mobile technologies can help improve learning in large-size classes. Under this context, this paper reports an empirical study of integrating collaborative and mobile learning into English courses offered by a 4-year comprehensive university in China. It first introduces a team-based mobile learning model and then reports its implementation in language education among STEM students in large-size classes. The study reveals both challenges and opportunities for implementing collaborative mobile learning in higher educational classes in China.
Particle Classification Based on Movement Behavior in IPSO Stochastic Model

In Particle Swarm Optimization, the behavior of particles depends on the parameters of movement formulas. In our research, we identify types of particles based on their movement trajectories. Then, we propose new rules of particle classification based on the two attributes of the measure representing the minimum number of steps necessary for the expected particle location to obtain its stable state. The new classification clarifies the division into types of particles based on the observation of different shapes of their movement trajectories.
The hulls of matrix-product codes over commutative rings and applications

Given a commutative ring R with identity, a matrix $$A\in M_{s\times l}(R)$$ , and linear codes $$\mathcal {C}_1, \dots , \mathcal {C}_s$$ over R of the same length, this article considers the hull of the matrix-product code $$[\mathcal {C}_1 \dots \mathcal {C}_s]\,A$$ . Consequently, it introduces various sufficient conditions (as well as some necessary conditions in certain cases) under which $$[\mathcal {C}_1 \dots \mathcal {C}_s]\,A$$ is a complementary dual (LCD) code. As an application, LCD matrix-product codes arising from torsion codes over finite chain rings are considered. Moreover, we show the existence of asymptotically good sequences of LCD matrix-product codes over such rings.
Fast pore matching method based on core point alignment and orientation

Nowadays, high-resolution fingerprint images are more and more used in the fingerprint recognition systems thanks to the recognition accuracy that they provide. Indeed, they offer more sufficient details such as sweat pores, ridges, contours, and other details. Pores have been adopted to be one of the brilliant nominees in improving the efficiency of automated fingerprint identification systems to maintain a high level of security. However, the geometric transformations, that occur during the acquisition phase, can cause several defects on the result of the matching process, hence they decline the accuracy of the recognition. To overcome this problem, alignment is often needed. This image pretreatment is classically based on complex geometric operations that are time-consuming. Otherwise, for pore matching, the majority of approaches are based only on pore coordinates. In this paper, we propose a novel pore matching method based, firstly, on only one of the singular points, namely the core points for the alignment phase, and also the valuable features used for the score calculation namely position and the orientation of pores. We assess our proposed approach using the PolyU-HRF database and we compare it to some well-known approaches of level 3 fingerprint recognition. The experimental results demonstrate that the proposed method can achieve significant performance recognition accuracy across various qualities of fingerprint images.
FLeAC: A Human-Centered Associative Classifier Using the Validity Concept

Fuzzy associative classifiers (FACs) have recently received considerable attention in the data mining community due to their ability to address the imprecision and graduality of truth. Similar to their more traditional statistical peers, these classifiers, however, have remained largely data driven, not leveraging human knowledge to their advantage. This is while human expert opinion and intuition should be a unique vantage point for such systems. We introduce here, for the first time, a human-centered framework (FLeAC) for FACs based on extended fuzzy logic and f -transformation that uses experts' opinions and preferences along with statistical data to solve subjective real-world problems. In FLeAC, experts take part in both constructing and reasoning of the classifier by assigning linguistic validity to each item. These validities are then aggregated using collective intelligence that determines final item validity. To examine the proposed framework, we extend an efficient and well-known FAC, CFAR, and present an extended f -CFAR algorithm. Also, several variations of f -CFAR are implemented to examine the effect of rule validity and different f -transformation operators. We then run various nonparametric statistical tests, including Friedman, Nemenyi posthoc, and ROC tests on an actual medical dataset of burn patients from Ahwaz, Iran, to compare f -CFAR performance with those of the original and nine other rule-based classifiers. Statistical analysis shows that f -CFAR not only has a better overall diagnostic performance than CFAR but also it outperforms CFAR and the other rule-based classifiers in terms of the number of rules, the number of conditions, and the execution time, leading to a more compact and comprehensible classifier with comparable accuracy.
Hybrid Models of Factorization Machines with Neural Networks and Their Ensembles for Click-through Rate Prediction

Prediction of Click-through rate (CTR) is getting considerable attention of online campaign and advertising management systems as well as search and recommender engines. The reason behind these great interest in CTR prediction is: It facilitates exploring the interest of web/mobile users based on their interactions in online environments. With the rapid proliferation of deep learning in recent years, CTR prediction has also expanded its effects in terms of both prediction accuracy and widespread usage in Real-Time Bidding (RTB) services. This study examines the latest status of CTR prediction works particularly in the field of neural network extended models, and also aims to devise hybrid models of Factorization Machines with Neural Networks. Also, ensembles of these hybrid models are studied and how much difference ensemble models might make is presented. In the end, our best ensemble model is implemented on a real digital campaign to show both prediction performance and the financial outcome of the CTR prediction comparatively. Among the proposed implementations in this study, the best performing one is based on the serial ensem-bling of hybrid models via Gradient Boosting Algorithm. It achieves 32.4% reduction in mean-squared error compared to the baseline Matrix Factorization Model, and 76.1% reduction of cost (eCPC) on the campaign on the seventh day of it.
Face Recognition Speed Optimization Method for Embedded Environment

To solve the problem that it is difficult to combine performance and speed of face recognition in embedded environment, we design and implement a fast face recognition method based on deep learning. Firstly, face detection and quality selection are performed based on MTCNN algorithm to reduce the amount of feature extraction data needed for recognition. Secondly, the RKNN model is used to quantify the acceleration of feature extraction. Finally, fast face recognition is realized by feature matching. The experiment shows that the method is beneficial for face recognition processing on the RK3399Pro platform, and the average time is shortened by 80% compared with that before acceleration. In the case of ensuring the feature extraction accuracy and recognition accuracy, it basically meets the real-time requirements.
The Importance of Requirements Engineering for Teaching Large Visualization Courses

Teaching visualization courses typically requires some kind of small-scale software project in which the students collaboratively create a software product with the goal to practically apply visualization concepts. This software focuses on providing an interactive solution to a given dataset scenario supporting the detection of insights based on users' tasks at hand. However, while creating such a tool several software development stages have to be taken into account, with requirements engineering as one of the major repeating stages. In this paper we describe three different visualization project-based teaching strategies TS_H, TS_M, and TS_L depending on the degree of freedom for the student groups. Moreover, they differ in the way how requirements engineering is an inherent ingredient in order to design, implement, test, deploy, evaluate, maintain, and evolve a certain software product. We experienced with more than 1,000 students in a total of 9 courses running over 8 weeks each, in approximately 2 years of time. All of the three teaching strategies have their own benefits and drawbacks, however, requirements engineering is considered more or less important, mostly depending on the degree of freedom the student project groups got during the courses. As a major outcome we consider the courses successful, not only because we learned the importance of requirements engineering for teaching project-based visualization courses, but also because many students passed the courses, the student evaluation was quite positive, and the written reports showed that they had an immense learning effect.
On a Streaming Approach for Training Denoising Auto-encoders

Learning deep neural networks requires huge hardware resources and takes a long time. This is due to the need to process huge data sets multiple times. One type of neural networks that are particularly useful in practice are denoising autoencoders. It is, therefore, necessary to create new algorithms that reduce the training time for this type of networks. In this work, we propose a method that, in contrast to the classical approach, where each data element is repeatedly processed by the network, is focused on processing only the most difficult to analyze elements. In the learning process, subsequent data may lose their significance and others may become important. Therefore, an additional algorithm has been used to detect such changes. The method draws inspiration from boosting algorithms and drift detectors.
Prediction of Emotional Condition Through Dialog Narratives Using Deep Learning Approach

Human emotion recognition has gained significant importance in artificial intelligence. Several machine learning studies are also been conducted to emulate the human behavior. Emotion recognition has wide range of applications from opinion mining, understanding customer feedback and also in automation. In this paper, coversational text data is used to detect the emotion which is unnoticed or ignored most of the time during conversations. Recent advances in ML and AI have given impetus to detect emotion states in conversation like listener state and previous emotion which contributes to current, as they improve the accuracy of the emotion prediction. In the current approach, a modified version of recurrent neural network (RNN) is being used to out perform the exisisting state-of-the-art algorithm (SOTA) conversational memory network (CMN). Since the algorithm uses text-based technique, it eventually decreases the turnaround time of the algorithm. While our proposed algorithm outperforms the exisisting popular CNN algorithms with a significant accuracy and Fscore metrics using IEMOCAP Dataset.
Temporal Attention and Consistency Measuring for Video Question Answering

Social signal processing algorithms have become increasingly better at solving well-defined prediction and estimation problems in audiovisual recordings of group discussion. However, much human behavior and communication is less structured and more subtle. In this paper, we address the problem of generic question answering from diverse audiovisual recordings of human interaction. The goal is to select the correct free-text answer to a free-text question about human interaction in a video. We propose an RNN-based model with two novel ideas: a temporal attention module that highlights key words and phrases in the question and candidate answers, and a consistency measurement module that scores the similarity between the multimodal data, the question, and the candidate answers. This small set of consistency scores forms the input to the final question-answering stage, resulting in a lightweight model. We demonstrate that our model achieves state of the art accuracy on the Social-IQ dataset containing hundreds of videos and question/answer pairs.
Generalization support environment for understanding ways to use English words

Abstract When we translate Japanese sentence into English, sometimes several English words become the candidates. However, the usage situation of these candidate words is not the same. In order to choose appropriate words from them, we need to understand the usage situation for each candidate words. Usage situation of the words can be inferred by co-existing words in their example sentences. Co-existing words in example sentences are not always the same, so in order to understand usage situation, we need to generalize co-existing words from several example sentences. However, some of us who do not consciously generalize the co-existing words do not acquire the usage situation. This paper proposes the system which provides the environment where we can explicitly generalize co-existing words (keywords) in the example sentences to acquire the usage situation of the target words. This system also has a generalization support mechanism to provide concepts of words acquired through WordNet as hints. According to the experimental results, participants who used the system in learning English words reduced the number of incorrectly choosing the words and promoted to derive the own understanding of the usage situation.
Voice Recognition Using K-Means Clustering Based on Hidden Markov Model

Ortolan Bunting bird has various song-types. For time being, there are not so many systems which automatically cluster the song-type of Ortolan Bunting bird. Speech recognition is one example of technology development in the 20 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> Century which uses voice as the input. This research builds an automatic system to cluster the song-type of Ortolan Bunting bird automatically using K-means clustering algorithm based on Hidden Markov Models (HMM). Commonly, HMM is used on signal processing and for speech and speaker recognition on human voice. This research uses 5 song-types of bird, and every song-type is represented by 100 songs. The data will be the clustering using K-means based HMM with 3 feature extractions and the result validation employs index stability computation. The clustering parameter groups the data into 5 clusters with dissimilarity value of 8.2 %. It means that for several times of experiments there are 8.2 % data clustered into different group and 91.8% of cluster members which always gather in the same cluster. The highest similarity level was obtained using Greenwood function cepstral coefficients delta-acceleration (GFCC_D_A) feature with 36 parameters.
Structure of the Transfer Function Numerator Coefficients as One of the Factors of the Structural Precision of IIR Digital Filters

This work was carried out during the implementation of a project dedicated to the synthesis of recursive digital filters with a finite word length. The approach used in this project considers the finite word length already when calculating the zeros and poles. The calculated zeros and poles are not distorted by structural synthesis. The calculations are based on what the zeros and poles of a digital filter with finite length coefficients are algebraic numbers of the corresponding degree. Structural synthesis involves generating a topological matrix describing the structure. The purpose of the generation is to provide a given degree of algebraic numbers that are zeros and poles. The previously calculated values of zeros and poles are used to calculate the exact values of the coefficients of the generated structure. It is proposed to characterize various structures with structural precision that does not depend on the concrete values of the coefficients. This article discusses the issues of generating structures considering the structural precision of the transfer function numerator.