Reviewerly/exHarmony huggingface dataset:
Speaker recognition using PCA-based feature transformation

This paper introduces a Weighted-Correlation Principal Component Analysis (WCR-PCA) for efficient transformation of speech features in speaker recognition. A Recurrent Neural Network (RNN) technique is also introduced to perform the weighted PCA. The weights are taken as the log-likelihood values from a fitted Single Gaussian-Background Model (SG-BM). For speech features, we show that there are large differences between feature variances which makes covariance based PCA less optimal. A comparative study of the performance of speaker recognition is presented using weighted and unweighted correlation and covariance based PCA. Extensions to improve the extraction of MFCC and LPCC features of speech are also proposed. These are Odd Even filter banks MFCC (OE-MFCC) and Multitaper-Fitted LPCC. The methodologies are evaluated for the i-vector speaker recognition system. A subset of the 2010 NIST speaker recognition evaluation set is used in the performance testing in addition to evaluations on the VoxCeleb1 dataset. A relative improvement of 44% in terms of EER is found in the system performance using the NIST data and 18% using the VoxCeleb1 dataset.
Bitcoin and market-(in)efficiency: a systematic time series approach

Recently, cryptocurrencies have received substantial attention by investors given their innovative features, simplicity and transparency. We here analyze the increasingly popular Bitcoin and verify pertinence of the efficient market hypothesis. Recent research suggests that Bitcoin markets, while inefficient in their early days, transitioned into efficient markets recently. We challenge this claim by proposing simple trading strategies based on moving average filters, on classic time series models as well as on non-linear neural nets. Our findings suggest that trading performances of our designs are significantly positive; moreover, linear and non-linear approaches perform similarly except at singular time periods of the Bitcoin; finally, our results suggest that markets are becoming less rather than more efficient towards the sample end of the data.
Selecting component sourcing options: A survey of software engineering’s broader make-or-buy decisions

Component-based software engineering (CBSE) is a common approach to develop and evolve contemporary software systems. When evolving a system based on components, make-or-buy decisions are frequent, i.e., whether to develop components internally or to acquire them from external sources. In CBSE, several different sourcing options are available: (1) developing software in-house, (2) outsourcing development, (3) buying commercial-off-the-shelf software, and (4) integrating open source software components. Unfortunately, there is little available research on how organizations select component sourcing options (CSO) in industry practice. In this work, we seek to contribute empirical evidence to CSO selection. We conduct a cross-domain survey on CSO selection in industry, implemented as an online questionnaire. Based on 188 responses, we find that most organizations consider multiple CSOs during software evolution, and that the CSO decisions in industry are dominated by expert judgment. When choosing between candidate components, functional suitability acts as an initial filter, then reliability is the most important quality. We stress that future solution-oriented work on decision support has to account for the dominance of expert judgment in industry. Moreover, we identify considerable variation in CSO decision processes in industry. Finally, we encourage software development organizations to reflect on their decision processes when choosing whether to make or buy components, and we recommend using our survey for a first benchmarking.
LARES: An AI-based teleassistance system for emergency home monitoring

The latest progresses in medicine are helping people live longer and better. An ageing population is a sign of a developed society with an advanced health care system. Improved life expectancy should be welcomed as a major achievement, but it should not cause a financial or social burden. In this scenario, it is critical to support older and handicap adults to continue living independently and retaining their current lifestyle. New technological advances in Wireless Sensors Networks (WSN) and Artificial Intelligence (AI) can facilitate this task. In this direction we present lares, an AI-based system that integrates a (i) WSN for receiving information of the environment and the dependent person, (ii) an autonomous robot able to take decisions based on the received information, and (iii) a Web-based system to provide telecare assistance. lares has been tried in two dependent elderly home environments during several weeks, and the experiments show that is able to detect anomalies and generate alarms in abnormal situations.
Tool Support for Correctness-by-Construction

Correctness-by-Construction (CbC) is an approach to incrementally create formally correct programs guided by pre- and postcondition specifications. A program is created using refinement rules that guarantee the resulting implementation is correct with respect to the specification. Although CbC is supposed to lead to code with a low defect rate, it is not prevalent, especially because appropriate tool support is missing. To promote CbC, we provide tool support for CbC-based program development. We present CorC, a graphical and textual IDE to create programs in a simple while-language following the CbC approach. Starting with a specification, our open source tool supports CbC developers in refining a program by a sequence of refinement steps and in verifying the correctness of these refinement steps using the theorem prover KeY. We evaluated the tool with a set of standard examples on CbC where we reveal errors in the provided specification. The evaluation shows that our tool reduces the verification time in comparison to post-hoc verification.
Hermitian Self-Dual, MDS, and Generalized Reed–Solomon Codes

Both self-dual codes and maximum distance separable (MDS) codes have nice algebraic structures, theoretical significance, and practical implications. We present two classes of the Hermitian self-dual, MDS, and generalized Reed–Solomon (GRS) codes. Conversely, we prove that the Hermitian self-dual, MDS, and GRS codes must be above two classes for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${n =4}$ </tex-math></inline-formula> and conjecture that this result also holds for each even length <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ {n}$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4\le {n}\le \text {q+1}$ </tex-math></inline-formula> .
A Mobile Application for Cat Detection and Breed Recognition Based on Deep Learning

Deep learning is one of the latest technologies in computer science. It allows using machines to solve problems in a manner similar to the human brain. Deep learning approaches have significantly improved the performance of visual recognition applications including image classification and image detection. In this paper we benchmark different deep learning models and present an Android application used to predict the location and breed of a given cat using a mobile phone camera. The average accuracy of the finalized model was 81.74%.
How can geologic decision-making under uncertainty be improved?

Abstract. In the geosciences, recent attention has been paid to the influence of uncertainty on expert decision-making. When making decisions under conditions of uncertainty, people tend to employ heuristics (rules of thumb) based on experience, relying on their prior knowledge and beliefs to intuitively guide choice. Over 50 years of decision-making research in cognitive psychology demonstrates that heuristics can lead to less-than-optimal decisions, collectively referred to as biases. For example, the availability bias occurs when people make judgments based on what is most dominant or accessible in memory; geoscientists who have spent the past several months studying strike-slip faults will have this terrain most readily available in their mind when interpreting new seismic data. Given the important social and commercial implications of many geoscience decisions, there is a need to develop effective interventions for removing or mitigating decision bias. In this paper, we outline the key insights from decision-making research about how to reduce bias and review the literature on debiasing strategies. First, we define an optimal decision, since improving decision-making requires having a standard to work towards. Next, we discuss the cognitive mechanisms underlying decision biases and describe three biases that have been shown to influence geoscientists' decision-making (availability bias, framing bias, anchoring bias). Finally, we review existing debiasing strategies that have applicability in the geosciences, with special attention given to strategies that make use of information technology and artificial intelligence (AI). We present two case studies illustrating different applications of intelligent systems for the debiasing of geoscientific decision-making, wherein debiased decision-making is an emergent property of the coordinated and integrated processing of human–AI collaborative teams.
A Forensic Audit of the Tor Browser Bundle

The increasing use of encrypted data within file storage and in network communications leaves investigators with many challenges. One of the most challenging is the Tor protocol, as its main focus is to protect the privacy of the user, in both its local footprint within a host and over a network connection. The Tor browser, though, can leave behind digital artefacts which can be used by an investigator. This paper outlines an experimental methodology and provides results for evidence trails which can be used within real-life investigations.
Integrating a Path Planner and an Adaptive Motion Controller for Navigation in Dynamic Environments

Since an individual approach can hardly navigate robots through complex environments, we present a novel two-level hierarchical framework called JPS-IA3C (Jump Point Search improved Asynchronous Advantage Actor-Critic) in this paper for robot navigation in dynamic environments through continuous controlling signals. Its global planner JPS+ (P) is a variant of JPS (Jump Point Search), which efficiently computes an abstract path of neighboring jump points. These nodes, which are seen as subgoals, completely rid Deep Reinforcement Learning (DRL)-based controllers of notorious local minima. To satisfy the kinetic constraints and be adaptive to changing environments, we propose an improved A3C (IA3C) algorithm to learn the control policies of the robots’ local motion. Moreover, the combination of modified curriculum learning and reward shaping helps IA3C build a novel reward function framework to avoid learning inefficiency because of sparse reward. We additionally strengthen the robots’ temporal reasoning of the environments by a memory-based network. These improvements make the IA3C controller converge faster and become more adaptive to incomplete, noisy information caused by partial observability. Simulated experiments show that compared with existing methods, this JPS-IA3C hierarchy successfully outputs continuous commands to accomplish large-range navigation tasks at shorter paths and less time through reasonable subgoal selection and rational motions.
Improving DIC Accuracy in Experimental Setups

Digital image correlation (DIC) is a common software tool to measure displacement and strain fields on the surface of a specimen during mechanical testing. Due to its low requirements on the experimental setup and the simple usability it is widely spread in the community of applied mechanics. Nevertheless, the real noise of the method often stays uninvestigated. Therefore, this work shows different approaches to measure errors in DIC. The present contribution proves lighting as the main error source and it shows that a series of static images allows to estimate the error level of the DIC before performing the experiment. To give a proof of concept of the improved DIC setup, three‐point bending tests on single struts of aluminum foam are performed and evaluated.
A Self-tuning Multiobjective Genetic Algorithm with Application in the SVM Classification

In this paper we suggest the self-tuning multiobjective genetic algorithm (STMGA) based on the NSGA-II. This new algorithm is aimed to improve the SVM classification quality. The quality classification indicators such as overall accuracy, specificity, sensitivity and a number of support vectors represent the objective functions in the STMGA. The ways for realizing the self-tuning of the such STMGA parameters as the crossover probability, the crossover distribution index and the mutation distribution index have been proposed and investigated. The considered STMGA is more flexible in the context of selecting its parameters’ values and allows to refuse from the use of the parameters’ values which are set manually. In the case of the radial basis kernel function used for the SVM classifier development, the STMGA finds the Pareto-front of such parameters values as the regularization parameter value and the Gaussian kernel parameter value which give the best values in the chosen set of the classification quality indicators. The experimental results obtained on the basis of the model and real datasets of loan scoring, medical and technical diagnostics, etc. confirm the efficiency of the proposed STMGA.
A New Prediction Structure for Efficient MV-HEVC based Light Field Video Compression

Light Field imaging has emerged as a technology that enables the capture of images and video with richer information. Captured content is composed of numerous views aligned in both horizontal and vertical directions providing full parallax, offering light intensity and directional information, but at the same time significantly increasing bandwidth requirements. Several multi-view coding methods have attempted to tackle this problem. However, these approaches do not fully assess the intricacies that are found in light field content. This paper proposes a prediction structure for coding light field content using the MV-HEVC standard, exploiting the inter-view correlations in two directions along with the high similarity between views around the center of each frame. Experimental results show BD-rate gains up to 38% compared to an existing state-of-the-art method.
Using the Shapes of Clinical Data Trajectories to Predict Mortality in ICUs

1) To show how to exploit the information contained in the trajectories of time-varying patient clinical data for dynamic predictions of mortality in the ICU; and 2) to demonstrate the additional predictive value that can be achieved by incorporating this trajectory information.Observational, retrospective study of patient medical records for training and testing of statistical learning models using different sets of predictor variables.Medical ICU at the Yale-New Haven Hospital.Electronic health records of 3,763 patients admitted to the medical ICU between January 2013 and January 2015.None.Six-hour mortality predictions for ICU patients were generated and updated every 6 hours by applying the random forest classifier to patient time series data from the prior 24 hours. The time series were processed in different ways to create two main models: 1) manual extraction of the summary statistics used in the literature (min/max/median/first/last/number of measurements) and 2) automated extraction of trajectory features using machine learning. Out-of-sample area under the receiver operating characteristics curve and area under the precision-recall curve ("precision" refers to positive predictive value and "recall" to sensitivity) were used to evaluate the predictive performance of the two models. For 6-hour prediction and updating, the second model achieved area under the receiver operating characteristics curve and area under the precision-recall curve of 0.905 (95% CI, 0.900-0.910) and 0.381 (95% CI, 0.368-0.394), respectively, which are statistically significantly higher than those achieved by the first model, with area under the receiver operating characteristics curve and area under the precision-recall curve of 0.896 (95% CI, 0.892-0.900) and 0.905 (95% CI, 0.353-0.379). The superiority of the second model held true for 12-hour prediction/updating as well as for 24-hour prediction/updating.We show that statistical learning techniques can be used to automatically extract all relevant shape features for use in predictive modeling. The approach requires no additional data and can potentially be used to improve any risk model that uses some form of trajectory information. In this single-center study, the shapes of the clinical data trajectories convey information about ICU mortality risk beyond what is already captured by the summary statistics currently used in the literature.
A logical approach to context-specific independence

Directed acyclic graphs (DAGs) constitute a qualitative representation for conditional independence (CI) properties of a probability distribution. It is known that every CI statement implied by the topology of a DAG is witnessed over it under a graph-theoretic criterion of d-separation. Alternatively, all such implied CI statements are derivable from the local independencies encoded by a DAG using the so-called semi-graphoid axioms. We consider Labeled Directed Acyclic Graphs (LDAGs) modeling graphically scenarios exhibiting context-specific independence (CSI). Such CSI statements are modeled by labeled edges, where labels encode contexts in which the edge vanishes. We study the problem of identifying all independence statements implied by the structure and the labels of an LDAG. We show that this problem is coNP-hard for LDAGs and formulate a sound extension of the semi-graphoid axioms for the derivation of such implied independencies. Finally we connect our study to certain qualitative versions of independence ubiquitous in database theory and teams semantics.
An Investigation of Multilingual ASR Using End-to-end LF-MMI

The end-to-end lattice-free maximum mutual information (LF-MMI) approach has recently been shown to be beneficial for automatic speech recognition (ASR) in general. More specifically, its end-to-end nature and use of context independent phone labels make it attractive for multilingual ASR. We show that end-to-end LF-MMI is indeed competitive on a low-resourced multilingual task, comfortably outperforming a connectionist temporal classification (CTC) baseline. We further investigate the feasibility of biphone contexts, being a candidate compromise between the context independent approach and the triphone contexts that usually perform well. We show that biphones do not initially perform well, but can do so after language adaptive training, concluding that biphones carry language variability but are promising for multilingual ASR.
Performance Analysis of 256-bit AES Encryption Algorithm on Android Smartphone

Encryption Techniques are one of the most important and very useful things to secure and protect the messages against such threads. To develop a highly secure android application, the application developer needs to take consideration various things furthermore the security after performance. Because of Android smartphone have limited resources, therefore effort to improve the performance of applications for the Android platform need to be done. The questions about which algorithms are performing better on Android are frequently arise in various developer forum such us Stack Overflow and Quora. To answer the questions this paper will show performance comparison of the most common safe encryption algorithm specifically Rijndael, Serpent and Twofish in order to establish which one of these is the most optimum to be implemented in Android smartphone. The test is carried out over devices which measure the performance and computational cost from the CPU and memory consumptions of the device and execution time when they run each algorithm on 256-bit key length. The test result shows that the Serpent has better encryption and decryption performance than Rijndael and Twofish. Serpent has the faster time for encryption and decryption. While Serpent and Twofish surpass Rijndael on efficiency use of memory for encryption and decryption. However, the most efficient percentage of CPU usage is done by Rijndael.
Securing and Hiding Secret Message in Image using XOR Transposition Encryption and LSB Method

The popular LSB technique is used in steganography techniques on spatial domains. This technique has many advantages in terms of imperceptibility and payload. Except in the case of security, the LSB technique is very weak because it is very simple and predictable. This study combines LSB steganography techniques and transposition XOR encryption techniques with the aim of increasing the security of embedded messages. Before the embedded message is encrypted with a transposition encryption algorithm and embedded with an XOR operation based on the key on the largest bit of the Host image. In the process of testing text messages with a size of 1 kB, 2 kB, and 4 kB pinned. The test results indicate that the security of the message is getting stronger without reducing the advantages of LSB techniques, namely imperceptibility and payload. All messages can also be extracted and decrypted perfectly, as evidenced by the CER value = 0.
Adaptive and Maladaptive Coping with an It Threat

Numerous studies have investigated employees' decisions to adopt IS security measures by focusing on adaptive responses. Using the Extended Parallel Processing model (EPPM), this study evaluates subjects' adaptive and maladaptive intentions with respect to antispyware software. Our results show that both adaptive and maladaptive coping occur, and we identify antecedents of each. We advance IS security theory by considering maladaptive coping along with adaptive coping and by modeling threat and efficacy as contingent effects.
Enhanced Digital Image Correlation Analysis of Ruptures with Enforced Traction Continuity Conditions Across Interfaces

Accurate measurements of displacements around opening or interfacial shear cracks (shear ruptures) are challenging when digital image correlation (DIC) is used to quantify strain and stress fields around such cracks. This study presents an algorithm to locally adjust the displacements computed by DIC near frictional interfaces of shear ruptures, in order for the local stress fields to satisfy the continuity of tractions across the interface. In the algorithm, the stresses near the interface are extrapolated by local polynomials that are constructed using a constrained inversion. This inversion is such that the traction continuity (TC) conditions are satisfied at the interface while simultaneously matching the displacements produced by the DIC solution at the pixels closest to the center of the subset, where the DIC fields are more accurate. We apply the algorithm to displacement fields of experimental shear ruptures obtained using a local DIC approach and show that the algorithm produces the desired continuous traction field across the interface. The experimental data are also used to examine the sensitivity of the algorithm against different geometrical parameters related to construction of the polynomials in order to avoid artifacts in the stress field.