Reviewerly/exHarmony huggingface dataset:
Automatic genetic algorithm in clustering for discrete elements

This article proposes an automatic genetic algorithm in clustering for discrete elements (AGAD). In this algorithm, the suitable number of clusters is determined by a new measure called the similar index of cluster and the steps of traditional genetic algorithm are improved. The convergence of AGAD is considered by theory and illustrated by numerical examples. The proposed algorithm is tested by many numerical examples and performed by Matlab procedure. These examples illustrate the superiority of the proposed algorithm over some existing algorithms. They also demonstrate the feasibility and applicability of studied problem.
A Decade of Shared Tasks in Digital Text Forensics at PAN

Digital text forensics aims at examining the originality and credibility of information in electronic documents and, in this regard, to extract and analyze information about the authors of these documents. The research field has been substantially developed during the last decade. PAN is a series of shared tasks that started in 2009 and significantly contributed to attract the attention of the research community in well-defined digital text forensics tasks. Several benchmark datasets have been developed to assess the state-of-the-art performance in a wide range of tasks. In this paper, we present the evolution of both the examined tasks and the developed datasets during the last decade. We also briefly introduce the upcoming PAN 2019 shared tasks.
User Experience and Recommender Systems

Since the advent of recommender systems (RS), majority of the published work aim to propose new algorithms that are more accurate in predicting the users' need. Recently, researchers have acknowledged that embedding User eXperience (UX) into recommender systems increase the effectiveness of recommendations dramatically for the end users. However, UX is essentially a subjective matter and it is not easy to be measured without direct user's feedback on the recommendation. There are few studies investigating the role of UX in the field of RSs but research on UX of RS is relatively less explored. Therefore, in this study, we review the existing studies and discuss definition of UX especially in the field of RS. Our review showed that despite the recent attempts on UX evaluation of RS, this area is still new and needs further investigations.
Gaze behaviour on interacted objects during hand interaction in virtual reality for eye tracking calibration

In this paper, we investigate the probability and timing of attaining gaze fixations on interacted objects during hand interaction in virtual reality, with the main purpose for implicit and continuous eye tracking re-calibration. We conducted an evaluation with 15 participants in which their gaze was recorded while interacting with virtual objects. The data was analysed to find factors influencing the probability of fixations at different phases of interaction for different object types. The results indicate that 1) interacting with stationary objects may be favourable in attaining fixations to moving objects, 2) prolonged and precision-demanding interactions positively influences the probability to attain fixations, 3) performing multiple interactions simultaneously can negatively impact the probability of fixations, and 4) feedback can initiate and end fixations on objects.
Rethinking Planar Homography Estimation Using Perspective Fields

Planar homography estimation refers to the problem of computing a bijective linear mapping of pixels between two images. While this problem has been studied with convolutional neural networks (CNNs), existing methods simply regress the location of the four corners using a dense layer preceded by a fully-connected layer. This vector representation damages the spatial structure of the corners since they have a clear spatial order. Moreover, four points are the minimum required to compute the homography, and so such an approach is susceptible to perturbation. In this paper, we propose a conceptually simple, reliable, and general framework for homography estimation. In contrast to previous works, we formulate this problem as a perspective field (PF), which models the essence of the homography - pixel-to-pixel bijection. The PF is naturally learned by the proposed fully convolutional residual network, PFNet, to keep the spatial order of each pixel. Moreover, since every pixels’ displacement can be obtained from the PF, it enables robust homography estimation by utilizing dense correspondences. Our experiments demonstrate the proposed method outperforms traditional correspondence-based approaches and state-of-the-art CNN approaches in terms of accuracy while also having a smaller network size. In addition, the new parameterization of this task is general and can be implemented by any fully convolutional network (FCN) architecture.
Tight Time-Memory Trade-Offs for Symmetric Encryption

Concrete security proofs give upper bounds on the attacker’s advantage as a function of its time/query complexity. Cryptanalysis suggests however that other resource limitations – most notably, the attacker’s memory – could make the achievable advantage smaller, and thus these proven bounds too pessimistic. Yet, handling memory limitations has eluded existing security proofs. This paper initiates the study of time-memory trade-offs for basic symmetric cryptography. We show that schemes like counter-mode encryption, which are affected by the Birthday Bound, become more secure (in terms of time complexity) as the attacker’s memory is reduced. One key step of this work is a generalization of the Switching Lemma: For adversaries with S bits of memory issuing q distinct queries, we prove an n-to-n bit random function indistinguishable from a permutation as long as $$S \times q \ll 2^n$$ . This result assumes a combinatorial conjecture, which we discuss, and implies right away trade-offs for deterministic, stateful versions of CTR and OFB encryption. We also show an unconditional time-memory trade-off for the security of randomized CTR based on a secure PRF. Via the aforementioned conjecture, we extend the result to assuming a PRP instead, assuming only one-block messages are encrypted. Our results solely rely on standard PRF/PRP security of an underlying block cipher. We frame the core of our proofs within a general framework of indistinguishability for streaming algorithms which may be of independent interest.
Tracking the Consumption of Home Essentials

Predictions of people's behaviour increasingly drive interactions with a new generation of IoT services designed to support everyday life in the home, from shopping to heating. Based on the premise that such automation is difficult due to the contingent nature of people's practices, in this work we explore the nature of these contingencies in depth. We have designed and conducted a technology probe that made use of simple linear predictions as a provocation, and invited people to track the life of their household essentials over a two-month period. Through a mixed-method approach we demonstrate the challenges of simple predictions, and in turn identify eight categories of contingencies that influenced prediction accuracy. We discuss strategies for how designers of future predictive IoT systems may take the contingencies into account by removing, hiding, revealing, managing, or exploiting the system uncertainty at the core of the issue.
A New Symmetric Key Encryption Algorithm With Higher Performance

In this era of information technology, information security is a major concern. To address the challenges of data security, cryptography is used for storage as well as for the communication of data. A cryptographic algorithm has two main performance characteristics: the ability to secure data against different attacks and the processing speed. An algorithm is considered secure if no attack exists to reveal the original contents without knowing the key. The strength of any secure encryption algorithm is, therefore, generally measured based on the difficulty to obtain the encryption key through cyber-attacks such as brute force. It is presumed that the bigger the key size, the more difficult it is for the attacker to compute the key. Consequently, increasing the key size generally increases the computational complexity and processing time of algorithms. As a result, an established assumption is the existence of a tradeoff and computational complexity. In this paper, we are presenting an alternate symmetric key encryption algorithm, which can easily avoid long and complex computation of conventional popular symmetric key encryption algorithms such as the Data Encryption Standards and the Advance Encryption Standard. We have shown that despite providing a higher level of security, one simplest software implementation of our algorithm is faster than some previously implemented conventional algorithms. Additionally, our algorithm also provides other advantages such as inherent data compression option and flexibility of choosing different complexity levels according to the need for different applications.
A human-centered wearable sensing platform with intelligent automated data annotation capabilities

Wearable computers provide significant opportunities for sensing and data collection in user's natural environment (NE). However, they require both raw data and annotations to train their respective signal processing algorithms. Collecting these annotations is often burdensome for the users. Our proposed methodology leverages the notion of location from nearable sensors in Internet of Things (IoT) platforms and learns users' patterns of behavior without any prior knowledge. It also requests users for annotations and labels only when the algorithms are unable to automatically annotate the data. We validate our proposed approach in the context of diet monitoring, a significant application that often requires considerable user compliance. Our approach improves eating detection accuracy by 2.4% with requested annotations restricted to 20 per day.
Fish-eye camera and image processing for commanding a solar tracker

The design and implementation of a solar tracker based on panoramic images captured by a fisheye camera are proposed. Such images receive a digital treatment to estimate the sun azimuth and the elevation angles. These angles are fed to a microcontroller, handling an accelerometer with a gyroscope, that positions the solar tracker to the angle of solar elevation and a compass to set the azimuth angle concerning the north, either magnetic or geographic. The proposed system works satisfactorily to guide the solar tracker regardless of whether it is a sunny or cloudy day.
Decentralization: The Failed Promise of Cryptocurrencies

Cryptocurrencies promise to revolutionize the financial market due to two main features: security and decentralization. In this article, the authors analyze whether cryptocurrencies are fully decentralized—in other words, whether the transaction processing is distributed among different entities. In addition, they present the consequences that a possible centralization entails.
Automatic Facial Expression Recognition Using Combined Geometric Features

This study presents a geometric feature based automatic facial expression recognition system. The proposed system utilises the facial landmark points to determine the relative distances between the facial features in order to capture deformities caused by the movement of facial muscles due to different expressions. Three feature sets are generated by using landmark coordinates, relative distances between the facial points and a combination of both. Discriminating power of each feature set is determined by training different classification models for classifying an image into six basic emotions or neutral state. The proposed system is validated on two publically available facial expression databases. Experimental results show good accuracy of 95.5% for MUG database on the combined features by using ensemble neural network.
Adaptive Image-Based Visual Servoing for Hovering Control of Quad-Rotor

Image-based visual servoing (IBVS) achieves precise positioning and motion control for a relatively stationary target by visual feedback, but problems persist with convergence and stability. Appropriate servoing gains for the IBVS are critical to the convergence and stability, but this control gain is heuristically a constant for most IBVS applications. This paper proposes an integrated method that allows adaptive adjustment of the servoing gain by reinforcement learning (RL) for IBVS control. The proposed method learns a policy to determine the value of the servoing gain on the fly. To ensure rapid convergence for the RL, truncating Q-learning (TQL) with faster convergence is used as learning algorithm, which uses truncated temporal differences (TDs) to update the TD. A nonuniform state space partitioning as a state encoder for RL allows more efficient policy. A strategy that uses the Metropolis derived from the simulated annealing is introduced for selecting the action, in order to balance exploration and exploitation so as to accelerate the learning speed. The integrated IBVS control system is tested using experiments involving a quad-rotor helicopter hovering control. The results of simulation and experiment show that the integrated IBVS method increases stability and ensures more rapid convergence than other methods.
Transfer Learning and Supervised Classifier Based Prediction Model for Breast Cancer

Breast cancer is one of the most common types of cancer among women. More than 1.5 million women each year are affected by breast cancer, which causes approximately 15% of all cancer deaths among women and the rate is increasing each year. Automatic detection of breast cancer could help to decrease the mortality rate by making diagnosis less time consuming and more accurate. Analysis of a breast cancer histopathology image is a challenging task. There have been many research studies on breast cancer tissue image classification. In this paper, we propose a machine learning model to automate the classification of benign and malignant tissue images. The best validation accuracy using this model for each of the magnification factor ranges from 92% to 94% and the best test accuracy ranges from 92% to 96%. The performance for each of the combination of feature extractor and classifier is also analyzed in terms of precision, recall, and f1-score, both in tabular form and graphically.
Hyper-bent Boolean Functions and Evolutionary Algorithms

Bent Boolean functions play an important role in the design of secure symmetric ciphers, since they achieve the maximum distance from affine functions allowed by Parseval's relation. Hyper-bent functions, in turn, are those bent functions which additionally reach maximum distance from all bijective monomial functions, and provide further security towards approximation attacks. Being characterized by a stricter definition, hyper-bent functions are rarer than bent functions, and much more difficult to construct. In this paper, we employ several evolutionary algorithms in order to evolve hyper-bent Boolean functions of various sizes. Our results show that hyper-bent functions are extremely difficult to evolve, since we manage to find such functions only for the smallest investigated size. Interestingly, we are able to identify this difficulty as not lying in the evolution of hyper-bent functions itself, but rather in evolving some of their components, i.e. bent functions. Finally, we present an additional parameter to evaluate the performance of evolutionary algorithms when evolving Boolean functions: the diversity of the obtained solutions.
Subjectively interesting connecting trees and forests

Consider a large graph or network, and a user-provided set of query vertices between which the user wishes to explore relations. For example, a researcher may want to connect research papers in a citation network, an analyst may wish to connect organized crime suspects in a communication network, or an internet user may want to organize their bookmarks given their location in the world wide web. A natural way to do this is to connect the vertices in the form of a tree structure that is present in the graph. However, in sufficiently dense graphs, most such trees will be large or somehow trivial (e.g. involving high degree vertices) and thus not insightful. Extending previous research, we define and investigate the new problem of mining subjectively interesting trees connecting a set of query vertices in a graph, i.e., trees that are highly surprising to the specific user at hand. Using information theoretic principles, we formalize the notion of interestingness of such trees mathematically, taking in account certain prior beliefs the user has specified about the graph. A remaining problem is efficiently fitting a prior belief model. We show how this can be done for a large class of prior beliefs. Given a specified prior belief model, we then propose heuristic algorithms to find the best trees efficiently. An empirical validation of our methods on a large real graphs evaluates the different heuristics and validates the interestingness of the given trees.
Voice Conversion with Cyclic Recurrent Neural Network and Fine-tuned Wavenet Vocoder

This paper presents a novel framework for providing high-quality parallel voice conversion (VC) using a cyclic recurrent neural network (RNN) and a finely tuned WaveNet vocoder. Using the proposed system, we are tackling the quality degradation issue faced by WaveNet when it is fed with estimated (oversmoothed) speech features, such as mel-cepstrum parameters predicted from a statistical model. In VC, providing predicted features to fine-tune a pretrained WaveNet model is not straightforward owing to the difference in time-sequence alignment. To overcome this problem, we propose the use of a cyclic spectral conversion network that is capable of performing a conversion flow, i.e., source-to-target, and a cyclic flow, i.e., generate self-predicted target speaker features, and is trained by using both the conversion and cyclic losses. The experimental results demonstrate that, overall, the proposed system significantly improves the converted speech, resulting in a mean opinion score of 3.79 and a speaker similarity score of 73.86%.
Talking About Security with Professional Developers

This paper describes materials developed to engage professional developers in discussions about security. First, the work is framed in the context of ethnographic studies of software development, highlighting how the method is used to explore and investigate research aims for the Motivating Jenny research project. A description is given of a series of practitioner engagements, that were used to develop a reflection and discussion tool using security stories taken from media and internet sources. An explanation is given for how the tool has been used to collect data within field sites, offering a way to clarify and member check findings, and to provide a different view on practice and process. The report concludes with observations and notes about future aims for supporting and encouraging professionals to engage with security in practice.
Implementation of reversible Peres gate using electro-optic effect inside lithium-niobate based Mach-Zehnder interferometers

Power dissipation is an important consideration in digital design. Reversible logic is an unconventional form of computing in which the computational process is bijective and can efficiently reduce power dissipation problem. Peres gate is a basic reversible logic gate used in various reversible circuit. In this paper, design of Peres gate using electro-optic effect inside lithium-niobate based Mach-Zehnder interferometer is proposed. The various applications of Peres gate such as full-adder circuit and flip-flop is also explained. A theoretical description along with mathematical formulation of device is elaborated. The device is analyzed through Beam propagation method and MATLAB simulations.
Nonlocal low-rank regularization for human motion recovery based on similarity analysis

Human motion capture(mocap) data records movement information from markers attached to human joints and has been widely used in animation productions. However, due to lack of accuracy of the motion capture system and the occlusion of the markers, the original mocap data may be missing over a period of time. Low-rank matrix completion(LRMC) has recently provided remarkable results in the human motion recovery. These LRMC methods assume that the entire data is of low-rank, ignoring the rich similarity among human poses in the space-time dimension, which may result in an inability to recover the details of human motion with complex and diverse structures.Therefore, we propose a novel nonlocal low-rank regularization(NLR) method to model the structured sparsity according to the similarity of human motion, and we explore its application in human motion recovery tasks. Moreover, in order to better exploit the low-rank properties, weighted Schatten p-norm(WSN) is proposed to treat each rank component differently. Then, the recovery process also effectively utilizes kinematic information(i.e., bone-length constraint) to maintain the visual naturalness of the recovery results. Extensive experiments compared with other state-of-the-art methods demonstrate the superiority of the proposed model in terms of recovery accuracy and visualization results.
Rapid and Simultaneous Detection of Hazardous Heavy Metals Contamination in Agricultural Soil Using Infrared Reflectance Spectroscopy

Soil function can be disrupted by highly accumulated contaminations in soils, due to several human activities such as building construction, mining, sewage disposal, transportation, and fertilizing. These activities may cause contaminations especially by heavy metals accumulated in soil and surely will affects growing plants on it. A real time, quick, robust and simultaneous method is therefore required to rapidly detect heavy metal contamination in agricultural soil. Infrared technology has been widely employed and applied in many field areas including in agriculture and soil science. The main aim of this research is to study the feasibility of infrared spectroscopy as a fast and robust method in detecting hazardous contaminations caused by accumulated heavy metals namely Zn and Pb in agricultural soil. Diffuse reflectance spectral data were acquired for a total of 8 soil samples from four different site locations in Aceh province. Spectra data, in wavelength range of 1000-2500 nm, were enhanced using multiplicative scatter correction (MSC). Hazardous detection was performed using principal component analysis (PCA) and accumulated heavy metals were predicted using partial least square regression (PLSR) method. The results showed that IR spectroscopy can clearly distinguish contaminated soil and un-contaminated ones, with total explained variance derived from PCA was 97%. Both accumulated heavy metals (Zn and Pb) can be predicted simultaneously with coefficient correlation (r) were 0.98 for Zn and 0.98 for Pb, whereas residual predictive deviation (RPD) indexed were 3.85 for Zn and 3.91 for Pb. It may conclude that infrared spectroscopy can be used as a fast, robust and accurate method in detecting and predicting heavy metal contaminations in agricultural soil.
Problems with Statistical Practice in Human-Centric Software Engineering Experiments

Background Examples of questionable statistical practice, when published in high quality software engineering (SE) journals, may lead to novice researchers adopting incorrect statistical practices.