Reviewerly/exHarmony huggingface dataset:
An automatic and parameter-free information-based method for sparse representation in wavelet bases

In this article an information-based method for the selection of expansion coefficients of functions in a Hilbert basis is presented. An information-based measure, namely Entropic NID (ENID), is presented; the optimal separation point between more informative coefficients and less informative ones is selected by evaluating the information contribution of two competing sets of expansion coefficients. A consistent numerical scheme is given to approximate ENID and the numerical error is studied. Numerical simulations are provided to test the behaviour of ENID in different wavelet bases, as well as to perform comparative studies.
Zero Correlation Zone Sequences from a Unified Construction of Perfect Polyphase Sequences

Sequence families with Zero Correlation Zone (ZCZ) can be used in quasi-synchronous code-division multiple-access (QS-CDMA) communication systems. This paper proposes two methods for constructing polyphase ZCZ sequence sets by using a unified construction of perfect polyphase sequences. The first method generates sets of perfect sequences with zero cross-correlation zone, i.e., sets such that the out-of-phase autocorrelation of each sequence is zero and the cross-correlation of any two sequences in a set is zero in some zone around the origin. The second method produces sets of so-called interference-free sequences with zero autocorrelation zone, i.e., sets such that the cross-correlation of any two sequences in a set is zero at any shift and the autocorrelation of each sequence is zero in some zone around the origin. Moreover, each generated ZCZ sequence set is optimal with respect to the Tang-Fan-Matsufuji bound.
Patching Deep Neural Networks for Nonstationary Environments

In this work we present neural network patching, an approach for adapting deep neural network models to nonstationary environments. Instead of creating or updating a network to accommodate concept drift, neural network patching leverages the inner layers of a previously trained network as well as its output to learn a patch that enhances the classification. It learns (i) a predictor that estimates whether the original network will misclassify an instance, and (ii) a patching network that fixes the misclassification. Neural network patching is based on the idea that the original network can still classify a majority of instances well, and that the inner feature representations encoded in the deep network aid the classifier to cope with unseen or changed inputs. We evaluated this technique on several datasets, comparing it to similar methods. Our finding is that neural network patching is adapting quickly to concept shifts, while also maintaining long-term learning capabilities similar to more complex methods that update the whole network.
Information Theoretic Causal Effect Quantification

Modelling causal relationships has become popular across various disciplines. Most common frameworks for causality are the Pearlian causal directed acyclic graphs (DAGs) and the Neyman-Rubin potential outcome framework. In this paper, we propose an information theoretic framework for causal effect quantification. To this end, we formulate a two step causal deduction procedure in the Pearl and Rubin frameworks and introduce its equivalent which uses information theoretic terms only. The first step of the procedure consists of ensuring no confounding or finding an adjustment set with directed information. In the second step, the causal effect is quantified. We subsequently unify previous definitions of directed information present in the literature and clarify the confusion surrounding them. We also motivate using chain graphs for directed information in time series and extend our approach to chain graphs. The proposed approach serves as a translation between causality modelling and information theory.
Dimensionality Reduction in Multilabel Classification with Neural Networks

A new neural network method for Dimensionality Reduction (DR) of the input feature space in Multilabel Classification (MC) problems is proposed and experimentally evaluated in this paper. The method (abbreviated as TCART-MR) can be used in two possible scenarios: either as a stand-alone DR pre-processing phase, preceding subsequent application of any particular MC algorithm, or as a compact MC approach in which TCART-MR is applied twice - first to DR task and then to MC problem with reduced input space. Extensive experimental results proved statistically relevant advantage of TCART-MR over three state-of-the-art approaches in DR domain (in the context of MC), as well as its superiority over 10 state-of-the-art MC algorithms listed in a recent MC survey paper. The MC tests were performed on a set of 9 benchmark problems and 16 evaluation measures (leading to 144 experimental cases in total).
An Evolutionary Approach to Optimize Data Center Profit in Smart Grid Environment

Overwhelming energy-related costs mar data center profits. In a smart grid, the price of electricity may change with real-time demand, geographic area, and time-of-use. Data centers with flexible request dispatch and resource allocation capabilities can cooperatively avail these price variations to reduce expenditures and maximize profit. In this paper, we model the data center profit maximization as a constrained multi-objective optimization problem. Our proposed scheme optimizes data center revenue and expense objectives simultaneously and to the best of our knowledge, is the first scheme that provides trade-off solutions for use in varied operational scenarios. The approach utilizes the Strength Pareto Evolutionary Algorithm (SPEA-II) as the base framework and adapts it to devise an algorithm. Our technique finds Pareto optimal solutions for data center profit maximization problem in a smart grid environment. The simulation results prove the efficacy of the proposed technique.
Applying Face Recognition in Video Surveillance Security Systems

Face Detection and Recognition is an important surveillance problem to provide citizens' security. Nowadays, many citizen service areas as airports, railways, security services are starting to use face detection and recognition services because of their practicality and reliability. In our research, we explored face recognition algorithms and described facial recognition process applying Fisherface face recognition algorithm. This process is theoretically justified and tested with real-world outdoor video. The experimental results demonstrate practically applying of face detection from several foreshortenings and recognition results. The given system can be used in building a smart city as a smart city application, also in different organization to ensure security of people.
Detection of Typical Pronunciation Errors in Non-native English Speech Using Convolutional Recurrent Neural Networks

A machine learning method for the automatic detection of pronunciation errors made by non-native speakers of English is proposed. It consists of training word-specific binary classifiers on a collected dataset of isolated words with possible pronunciation errors, typical for Finnish native speakers. The classifiers predict whether the typical error is present in the given word utterance. They operate on sequences of acoustic features, extracted from consecutive frames of an audio recording of a word utterance. The proposed architecture includes a convolutional neural network, a recurrent neural network, or a combination of the two. The optimal topology and hyperparameters are obtained in a Bayesian optimisation setting using a tree-structured Parzen estimator. A dataset of 80 words uttered naturally by 120 speakers is collected. The performance of the proposed system, evaluated on a well-represented subset of the dataset, shows that it is capable of detecting pronunciation errors in most of the words (46/49) with high accuracy (mean accuracy gain over the zero rule 12.21 percent points).
On evaluating the online local pool generation method for imbalance learning

Imbalanced problems are characterized by a disproportion between the number of samples from the classes in a classification problem. This difference in amount of examples may lead to a bias toward the majority class, hindering the recognition of the underrepresented minority class. Ensemble methods have been widely used for dealing with such problems, and have been shown to perform well on them. In this context, Dynamic Selection (DS) approaches, which perform the classification task on a local level, have been receiving some attention for their promising results. More specifically, the Frienemy Indecision Region Dynamic Ensemble Selection++ (FIRE-DES++) framework, which has yielded state-of-the-art results on imbalanced problems, use a data preprocessing technique for noise removal and a class-balanced neighborhood definition for coping with imbalanced datasets. A different DS-based approach proposed in a previous work, an online local pool generation method, generates on the fly locally accurate classifiers for labelling samples near the class borders. Though the local generation of the classifiers may reduce the impact of class imbalance on the performance of the technique, its suitability for imbalance learning was not yet evaluated. Thus, in this work we evaluate how well the online local pool generation method deals with imbalanced problems. We perform a comparative analysis with a baseline technique using three Dynamic Classifier Selection (DCS) techniques over 64 imbalanced datasets and four performance measures. We also evaluate the use of the preprocessing and balanced neighborhood definition steps from the FIRE-DES++ on the online scheme to assess their impact on the performance of the method. Moreover, we evaluate the online technique and its variants against seven state-of-the-art ensemble methods, including both static and DS approaches. Experimental results show that the approach of locally generating the classifiers is advantageous for imbalance learning, providing an improvement to the DCS techniques and yielding state-of-the-art results. Furthermore, the addition of the noise removal and the balanced neighborhood definition steps to the online scheme improved the overall results of the technique, which indicates the advantage of including such steps in DS-based techniques.
CARL: Aggregated Search with Context-Aware Module Embedding Learning

Aggregated search aims to construct search result pages (SERPs) from blue-links and heterogeneous modules (such as news, images, and videos). Existing studies have largely ignored the correlations between blue-links and heterogeneous modules when selecting the heterogeneous modules to be presented. We observe that the top ranked blue-links, which we refer to as the context, can provide important information about query intent and helps identify the relevant heterogeneous modules. For example, informative terms like "streamed" and "recorded" in the context imply that a video module may better satisfy the query. To model and utilize the context information for aggregated search, we propose a model with context attention and representation learning (CARL). Our model applies a recurrent neural network with attention mechanism to encode the context, and incorporates the encoded context information into module embeddings. The context-aware module embeddings together with the ranking policy are jointly optimized under the Markov decision process (MDP) formulation. To achieve a more effective joint learning, we further propose an optimization function with self-supervision loss to provide auxiliary supervision signals. Experimental results based on two public datasets demonstrate the superiority of CARL over multiple baseline approaches, and confirm the effectiveness of the proposed optimization function in boosting the joint learning process.
Using Character-Grams to Automatically Generate Pseudowords and How to Evaluate Them

Abstract This paper provides a practical solution to the problem of generating (good) pseudowords, which are commonly used in vocabulary testing and experimental research in applied linguistics, and introduces an empirically founded solution to evaluating the suitability of pseudowords for different tasks. In the first part of the paper, we propose a novel way of generating pseudowords—a character-gram chaining algorithm. A major advantage of the algorithm is that it does not require any knowledge of the language, thereby facilitating the generation of pseudowords in any language. Secondly, there is currently a lack of formal criteria for evaluating pseudowords, both in terms of (i) their orthographic fit in the target language they are intended for and (ii) their suitability for use in various lexical processing and language teaching tasks. In the second part of the paper, we argue for the need to evaluate pseudowords, propose a set of linguistic criteria for evaluating the generated pseudowords, and provide a comparison with other current pseudoword lists using this criteria.
Branch Use in Practice: A Large-Scale Empirical Study of 2,923 Projects on GitHub

Branching is often used to help developers work in parallel during distributed software development. Previous studies have examined branch usage in practice. However, most studies perform branch analysis on industrial projects or only a small number of open source software (OSS) systems. There are no broad examinations of how branches are used across OSS communities. Due to the rapidly increasing popularity of collaboration in OSS projects, it is important to gain insights into the practice of branch usage in these communities. In this paper, we performed an empirical study on branch usage for 2,923 projects developed on GitHub. Our work mainly studies the way developers use branches and the effects of branching on the overall productivity of these projects. Our results show that: 1) Most projects use a few branches (<;5) during development; 2) Large scale projects tend to use more branches than small scale projects. 3) Branches are mainly used to implement new features, conduct version iteration, and fix bugs. 4) Almost all master branches have been requested by contributors to merge their contributions; 5) There always exists a branch playing a more important role in merging contributions than other branches; 6) Almost all commits of more than 75% branches are included in the master branches; 7) The number of branches used in a project has a positive effect on a project's productivity but the effect size is small, and there is no statistically significantly difference between personal projects and organizational projects.
Eye Gesture Based Communication for People with Motor Disabilities in Developing Nations

Current eye tracking systems for people with motor disabilities are expensive, restricted to only four eye movements, require constant re-calibration, assume literacy in English and offer no easy method to contact people during emergencies. Even though the above systems are flawed, they are crucial for the aforementioned individuals to communicate. Despite this, our surveys show that no such systems are in use for people in developing countries and there is a heavy dependence on simple yes or no answers. To overcome these obstacles we created E-ACE (Eye-based Alternative Communication Exchange) : A smartphone based system that allows the user to utilize nine eye movements rather than the traditional four. Our surveys show that many people with motor disabilities have difficulties in performing the conventional four eye movements. To counter this, E-ACE allows each user to create a personalized system which consists of their preferred four eye movements from a set of nine (four traditional, five new). To overcome the language barrier that many people face while using the current systems, E-ACE provides easy to understand pictures for a list of relevant English words and phrases which was curated from the doctors we surveyed. E-ACE also lets the user access an SOS Board that can call, message and alert the concerned people with just two eye movements. Thus, E-ACE offers a robust, portable and low-cost solution which solves the problems faced with current systems. Our Evaluations and User studies show that E-ACE has good user experience, is easily adopted by non-English speaking individuals, and is cost-effective, making it accessible to people in a developing nation.
Graph structure optimization of Genetic Network Programming with ant colony mechanism in deterministic and stochastic environments

Evolutionary Algorithms are of the most successful algorithms in solving various optimization problems. Genetic network programming is one of the Evolutionary Algorithms with good capabilities in agent control problems. In this algorithm, the individuals' structure is a directed graph. Using this structure, it is possible to model the solution of many complex problems. However, in this algorithm, crossover and mutation operators repeatedly break the structures of individuals and make new ones. Although this can lead to better structures, it may break suitable structures in elite individuals. Meanwhile, in stochastic environments, each time an individual is evaluated, it leads to different fitness values. So, calculating the fitness value of individuals requires evaluating each individual several times. This extremely decreases the evolution process speed. In this paper, inspired by mechanisms of ant colony algorithm, a new method is proposed to prevent the algorithm from iteratively breaking down the structures of individuals. This method improves the performance of individuals from one generation to the next using a constructive process. Unlike generative process that the individuals are generated by combination of some others, in constructive process they are produced according to the experience of previous generations. Using this mechanism, we not only prevent breaking suitable structures but also can manage uncertainty in stochastic environments. Our proposed method is used to solve two agent control problems when the environment is deterministic or stochastic. The results show that the proposed algorithm has very high ability in creating an efficient decision making strategies especially in stochastic environments.
A Deep Investigation into fastText

The researches on text classification range from traditional machine learning methods which depend on handcrafted features to recent deep learning models which learn complex and non-linear relationships automatically. While neural models have been proven to be effective for text classification, they operate like a black box and offer no interpretability for humans. In this paper, we study the state-of-the-art text classification model fastText and propose a mathematical approach to interpret the behavior of fastText into a score matrix. By decomposing the output of fastText into the sum of word-level importance scores, we can observe how each word contributes to the final prediction and understand the inner implementation of fastText. What is more, inspired by the idea of score matrix, we have also proposed two frequency matrix based methods which are totally obtained from simple statistics but achieve reasonable performance under certain conditions. At last, we perform extensive experiments on various benchmarking text classification datasets to offer clear explanations about how fastText makes predictions with the score matrix, and we provide a comprehensive analysis on frequency matrix based methods. The deep investigation into fastText helps us understand the decision making process as well as provides heuristic ideas on how to design more effective text classification models in the future.
Do Humans Look Where Deep Convolutional Neural Networks “Attend”?

Deep Convolutional Neural Networks (CNNs) have recently begun to exhibit human level performance on some visual perception tasks. Performance remains relatively poor, however, on some vision tasks, such as object detection: specifying the location and object class for all objects in a still image. We hypothesized that this gap in performance may be largely due to the fact that humans exhibit selective attention, while most object detection CNNs have no corresponding mechanism. In examining this question, we investigated some well-known attention mechanisms in the deep learning literature, identifying their weaknesses and leading us to propose a novel attention algorithm called the Densely Connected Attention Model. We then measured human spatial attention, in the form of eye tracking data, during the performance of an analogous object detection task. By comparing the learned representations produced by various CNN architectures with that exhibited by human viewers, we identified some relative strengths and weaknesses of the examined computational attention mechanisms. Some CNNs produced attentional patterns somewhat similar to those of humans. Others focused processing on objects in the foreground. Still other CNN attentional mechanisms produced usefully interpretable internal representations. The resulting comparisons provide insights into the relationship between CNN attention algorithms and the human visual system.
Analyzing Eye Movements in Interview Communication with Virtual Reality Agents

In human-agent interactions, human emotions and gestures expressed when interacting with agents is a high-level personally trait that quantifies human attitudes, intentions, motivations, and behaviors. The virtual reality space provides a chance to interact with virtual agents in a more immersive way. In this paper, we present a computational framework to analyze human eye movements by using a virtual reality system in a job interview scene. First, we developed a remote interview system using virtual agents and implemented the system into a virtual reality headset. Second, by tracking eye movements and collecting other multimodal data, the system could better analyze human personality traits in interview communication with virtual agents, and it could better support training in people's communication skills. In experiments, we analyzed the relationship between eye gaze feature and interview performance annotated by human experts. Experimental results showed acceptable accuracy value for the single modality of eye movement in the prediction of eye contact and total performance in job interviews.
Fiber optic white light interferometer for areal surface measurement

White light interferometers (WLI) are extremely powerful tools in the context of areal surface topography instrumentation. With lateral scanning mechanisms, larger objects with trans-scale structures are also measurable. However, the existing commercial WLIs are usually only applicable for off-line measurements in the laboratory. This paper presents a flexible WLI, in which the light source and the vertical scanning mechanism are separated from the interference probe using fiber-optic components. As a result, the interference probe is more compact, and portable for on-machine measurement. Meanwhile, a comprehensive algorithm that combines the improved gravity center method and phase-shifting technique is proposed for signal processing, which can balance computation time and noise robustness. The simulation results show the effectiveness by comparing with the traditional white light scanning interferometry methods. A 1000 nm step height standard was used as a test specimen to evaluate the proposed system and algorithm. The experimental data show that the measurement results are basically in good agreement with the calibrated value.