Reviewerly/exHarmony huggingface dataset:
The parallelism motifs of genomic data analysis

Genomic data sets are growing dramatically as the cost of sequencing continues to decline and small sequencing devices become available. Enormous community databases store and share this data with the research community, but some of these genomic data analysis problems require large scale computational platforms to meet both the memory and computational requirements. These applications differ from scientific simulations that dominate the workload on high end parallel systems today and place different requirements on programming support, software libraries, and parallel architectural design. For example, they involve irregular communication patterns such as asynchronous updates to shared data structures. We consider several problems in high performance genomics analysis, including alignment, profiling, clustering, and assembly for both single genomes and metagenomes. We identify some of the common computational patterns or motifs that help inform parallelization strategies and compare our motifs to some of the established lists, arguing that at least two key patterns, sorting and hashing, are missing.
Toward Variable-Rate Generative Compression by Reducing the Channel Redundancy

Compressing large images with a generative model goes beyond typical image encoding standards under a notably low bitrate. In this paper, we step toward practical generative compression systems based on recent advances. Specifically, we show that the channel redundancy of the latent representation produced by an autoencoder network can be effectively compressed via mask compression. The mask compression performs quantization on the channel variance of latent representation instead of original values. Instead of training multiple models, changing the mask leads to a simple and efficient variable rate compression scheme. Then, we estimate the relative bitrate by measuring the L1 norm of the channel variance and hence obtain the rate-distortion formulation. The L1 regularizer assumes a Laplacian prior on the channel variance, through which model we develop corresponding methods to produce approximate images at a target bitrate. This eliminates the need for manually searching hyperparameters for our variable-rate compression. We conduct exhaustive experiments to demonstrate the advanced performance of the proposed method in preserving image quality and semantics.
Lorenz Curve-Based Entropy Thresholding on Circular Histogram

Circular histogram thresholding is a new threshold selection method in color image segmentation. However, the method of the existing circular histogram thresholding based on the Otsu Criteria lacks the generality of using the circular histogram. In order to improve the effectiveness and reduce the complexity of thresholding on circular histogram, this paper firstly introduces the Lorenz curve into circular histogram. Then the circular histogram is expanded into the linearized histogram in clockwise or anti-clockwise direction by the optimal index of the Lorenz curve. In the end, the entropy thresholding of the linearized circular histogram is adopted to choose the optimal threshold to obtain the object of color images. Many experimental results show that the proposed method has better effectiveness and adaptability than the existing circular thresholding utilizing Otsu Criteria.
P for Politics D for Dialogue

Participatory Design strives to open up the decision-making process and empower all those who may be affected by design. This is opposed to Design as a non-participatory process, in which the power to make decisions is vested in the hands of one group to the possible detriment of others. In this paper we interrogate the nature, possibilities and limitations of Participatory Design through the perspective of Child Computer Interaction (CCI) and Animal Computer Interaction (ACI). Due to the cognitive and communication characteristics, and to the social and legal status of their participants, researchers in these communities have to contend with and challenge existing notions of participation and design. Thus, their theories and practices provide a lens through which the nature and goals of Participatory Design can be examined with a view to facilitating the development of more inclusive participatory models and practices.
Anomaly Detection in Time Series using Generative Adversarial Networks

Generative Adversarial Networks (GANs) have been applied to an increasing amount of tasks, especially related to image data. A comparably recent advance was their application to the domain of anomaly detection in images and, even more recently, on spatiotemporal data. In this work, a recurrent GAN (RGAN) is applied on cardiovascular data from the MIT-BIH dataset to learn the natural variety of normal sinus rhythms in a healthy individual. The generator is used to reconstruct samples using differently parameterized levels of similarity and thresholds. We find that solely using the generator already allows a surprisingly good anomaly detection performance. Furthermore, we discuss adding the discriminator, which might significantly improve the performance. Future work also includes only using the discriminator, minimizing the time required for inference, which is important for streaming data.
Multisensor Fusion Method Based on the Belief Entropy and DS Evidence Theory

The Dempster–Shafer evidence theory has been widely applied in multisensor information fusion. Nevertheless, illogical results may occur when fusing highly conflicting evidence. To solve this problem, a new method of the grouping of evidence is proposed in this paper. This method uses a combination of the belief entropy and the degree of conflict of the evidence as the judgment rule and divides the entire body of evidence into two separate groups. For the grouped evidence, both the credibility weighted factor based on the belief entropy function and the support weighted factor based on the Jousselme distance function are taken into consideration. The two determined weighted factors are integrated to adjust the evidence before applying the DS combination rule. Numerical examples are provided to demonstrate the theoretical feasibility and rationality of the proposed method. The fusion results indicate that the proposed method is more accurate than the compared algorithms in handling the paradoxes. A decision-making case analysis of the biological system is performed to validate the practical applicability of the proposed method. The results confirm that the proposed method has the highest belief degree of the target concentration (50.98%) and has superior accuracy compared to other related methods.
High-speed videogrammetric measurement of the deformation of shaking table multi-layer structures

This paper proposes a practical videogrammetric technique to measure shaking table tests of multi-layer structure models using two CMOS cameras. Three major issues, including high-speed videogrammetric hardware system configuration and network configuration, image sequence processing, and deformation information retrieval, are studied. The implementation of the proposed method is illustrated with a shaking table experiment for a multi-layer reinforced concrete structure in Tongji University, Shanghai. Sub-millimeter-level accuracy for the deformation measurements was achieved with the image resolution of 1280 (H) by 1024 (V) pixels. The velocity and acceleration of the shaking table structure are obtained accordingly. The results from the videogrammetric technique are compared with those from the traditional gauging and accelerometer methods. The experimental results show that, when compared with the traditional methods, the proposed videogrammetric method measures the deformation with sub-millimeter accuracy and can obtain more detailed peak values at the critical points on the shaking table structure.
The Carabattola—Vibroacoustical Analysis and Intensity of Acoustic Radiation (IAR)

Among the studies of musical instruments, one important, sometime underestimated discipline, is represented by ethnomusicology. The acoustic analyses on ethnic musical instruments (M.I.) are much more infrequent if compared to those on classical M.I. This article deals with the vibro-acoustic analysis on one of the most unknown ethnic, Italian M.I., i.e., the carabattola (also called battola), which used to be played in Italy until the late 1960s during the Holy Thursday before Easter. The study includes modal analysis and Intensity of Acoustic Radiation measured on an original carabattola, which was played in the Romagna area until the early twentieth century. After a brief overview about the theory of acoustic and vibrational analysis on musical instruments, the Intensity of acoustic radiation and its correlation with modal analysis are recalled, based on previous studies. In the experimental part of the article, the measurements conducted on the carabattola are described. Afterwards, the results obtained both from modal analysis and IAR measurements are analyzed and compared with other measurements previously conducted on musical (particularly percussion) instruments and commented.
Knowledge Graph based Automated Generation of Test Cases in Software Engineering

Knowledge Graph (KG) is extremely efficient in storing and retrieving information from data that contains complex relationships between entities. Such a representation is relevant in software engineering projects, which contain large amounts of inter-dependencies between classes, modules, functions etc. In this paper, we propose a methodology to create a KG from software engineering documents that will be used for automated generation of test cases from natural (domain) language requirement statements. We propose a KG creation tool that includes a novel Constituency Parse Tree (CPT) based path finding algorithm for test intent extraction, Conditional Random field (CRF) based Named Entity Recognition (NER) model with automatic feature engineering and a Sentence vector embedding based signal extraction. This paper demonstrates the contributions on an automotive domain software project.
Efficient convolution pooling on the GPU

The main contribution of this paper is to show efficient implementations of the convolution-pooling in the GPU, in which the pooling follows the multiple convolution. Since the multiple convolution and the pooling operations are performed alternately in earlier stages of many Convolutional Neural Networks (CNNs), it is very important to accelerate the convolution-pooling. Our new GPU implementation uses two techniques, (1) convolution interchange with direct sum, and (2) conversion to matrix multiplication. By these techniques, the computational and memory access cost are reduced. Further the convolution interchange is converted to matrix multiplication, which can be computed by cuBLAS very efficiently. Experimental results using Tesla V100 GPU show that our new GPU implementation compatible with cuDNN for the convolution-pooling is expected 2.90 times and 1.43 times faster for fp32 and fp16 than the multiple convolution and then the pooling by cuDNN, respectively. the most popular library of primitives to implement the CNNs in the GPU.
Method of Depression Classification Based on Behavioral and Physiological Signals of Eye Movement

This paper presents a method of depression recognition based on direct measurement of affective disorder. Firstly, visual emotional stimuli are used to obtain eye movement behavior signals and physiological signals directly related to mood. Then, in order to eliminate noise and redundant information and obtain better classification features, statistical methods (FDR corrected t -test) and principal component analysis (PCA) are used to select features of eye movement behavior and physiological signals. Finally, based on feature extraction, we use kernel extreme learning machine (KELM) to recognize depression based on PCA features. The results show that, on the one hand, the classification performance based on the fusion features of eye movement behavior and physiological signals is better than using a single behavior feature and a single physiological feature; on the other hand, compared with previous methods, the proposed method for depression recognition achieves better classification results. This study is of great value for the establishment of an automatic depression diagnosis system for clinical use.
Solving the ruin probabilities of some risk models with Legendre neural network algorithm

This paper studies a numerical method based on Legendre polynomials and extreme learning machine algorithm to solve the ruin probabilities in the classical risk model and the Erlang(2) risk model. In our method, the hidden layer is eliminated by expanding the input pattern using Legendre polynomials. The network parameters are obtained by solving a system of linear equations using extreme learning machine algorithm. The numerical experiments of some risk models under exponential distribution and Pareto distribution have been performed to validate the accuracy and reliability of our proposed Legendre neural network algorithm. Compared with the existing method, the results obtained by our proposed Legendre neural network model can achieve very high accuracy. Legendre neural network algorithm is well suited for solving the ruin probabilities of the risk models.
Activity Recognition with Wearable Accelerometers using Deep Convolutional Neural Network and the Effect of Sensor Placement

Human activity recognition (HAR) has become ubiquitous in modern daily life, and thus requires robust classification algorithms. Accelerometers are the most commonly used sensor for HAR, but often provide an incomplete picture of activities due to their locality. Given a sensor network of accelerometers, we believe there are two main issues that need to be addressed: (i) developing a robust, end-to-end classification framework, and (ii) identifying the optimum number and placement of sensors. To address these issues, a convolutional neural network (CNN) is implemented, tuned, and tested for activity classification. Our evaluation shows that the proposed system outperforms a number of other classifiers with a perfect classification accuracy (100%). Next, we utilize the developed pipeline to investigate the impact of different combinations of sensors and analyze HAR accuracy with respect to location and number of sensors. Our results show that at least two accelerometers are needed to achieve perfect classification for daily activities, while an accelerometer placed on the ankle is most informative for near-perfect performance.
Object Reidentification via Joint Quadruple Decorrelation Directional Deep Networks in Smart Transportation

Object reidentification with the goal of matching pedestrian or vehicle images captured from different camera viewpoints is of considerable significance to public security. Quadruple directional deep learning features (QD-DLFs) can comprehensively describe object images. However, the correlation among QD-DLFs is an unavoidable problem, since QD-DLFs are learned with quadruple independent directional deep networks (QIDDNs) driven with the same training data, and each network holds the same basic deep feature learning architecture (BDFLA). The correlation among QD-DLFs is harmful to the complementarity of QD-DLFs, restricting the object reidentification performance. For that, we propose joint quadruple decorrelation directional deep networks (JQD <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> Ns) to reduce the correlation among the learned QD-DLFs. In order to jointly train JQD <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> Ns, besides the softmax loss functions, a parameter correlation cost function is proposed to indirectly reduce the correlation among QD-DLFs by enlarging the dissimilarity among the parameters of JQD <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> Ns. Extensive experiments on three publicly available large-scale data sets demonstrate that the proposed JQD <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> Ns approach is superior to multiple state-of-the-art object reidentification methods.
Kernel-Ridge Regression-Based Quality Measure and Enhancement of Three-Dimensional-Synthesized Images

In this article, we propose an efficient joint image quality assessment and enhancement algorithm for the 3-D-synthesized images using a global predictor, namely, kernel ridge regression (KRR). Recently, a few prediction-based image quality assessment (IQA) algorithms have been proposed for 3-D-synthesized images. These algorithms use efficient prediction algorithms and try to predict all the regions efficiently, except the boundaries of the regions with geometric distortions. Unfortunately, these algorithms only count the number of pixels along the boundaries of the regions with geometric distortions and subsequently, calculate the quality scores. With this view, we propose a new algorithm for 3-D-synthesized images based upon the global KRR-based predictor, which estimates the complete distortion surface with geometric distortions. Further, it uses the distortion surface to estimate the perceptual quality of the 3-D-synthesized images. Also, the joint quality assessment and enhancement algorithms for 3-D-synthesized images are missing in literature. With this view, we propose to estimate the distortion map of the geometric distortions via the same predictor used in quality estimation and it subsequently enhances the perceptual quality of the 3-D-synthesized images. The performance of the proposed quality assessment algorithm is better than the existing IQA algorithms. Also, the proposed quality enhancement algorithm is promising, significantly enhancing the perceptual quality of 3-D-synthesized images.
Design and Evaluation of a Hybrid Feature Descriptor based Handwritten Character Inference Technique

Handwritten character inference refers to the task of converting the character images into the corresponding digital characters. A hybrid descriptor based character recognition algorithm is proposed in this paper. The novel hybrid descriptor model exhibits a better character inference rate when empirically assessed against the standard Chars74K dataset and the self curated Telugu handwritten character dataset. In order for the computational complexity of the recognition task to reduce, the number of comparisons required to recognize the character is reduced using an effective pruning technique. Experimentation of the proposed technique depicted that the proposed hybrid model recognizes the characters at a better rate compared to the techniques using HMM model, HoG, run length features and transform based feature extraction techniques.
Axiomatic scale theory

Scales are a fundamental concept of musical practice around the world. They commonly exhibit symmetry properties that are formally studied using cyclic groups in the field of mathematical scale theory. This paper proposes an axiomatic framework for mathematical scale theory, embeds previous research, and presents the theory of maximally even scales and well-formed scales in a uniform and compact manner. All theorems and lemmata are completely proven in a modern and consistent notation. In particular, new simplified proofs of existing theorems such as the equivalence of non-degenerate well-formedness and Myhill's property are presented. This model of musical scales explicitly formalizes and utilizes the cyclic order relation of pitch classes.
Secure Accelerometer-Based Pairing of Mobile Devices in Multi-Modal Transport

We address the secure pairing of mobile devices based on accelerometer data under various transportation environments, e.g., train, tram, car, bike, walking, etc. As users commonly commute by several transportation modes, extracting session keys from various scenarios to secure the private network of user's devices or even the public network formed by devices belonging to distinct users that share the same location is crucial. The main goal of our work is to establish the amount of entropy that can be collected from these environments in order to determine concrete security bounds for each environment. We test several signal processing techniques on the extracted data, e.g., low-pass and high-pass filters, then apply sigma-delta modulation in order to expand the size of the feature vectors and increase both the pairing success rate and security level. Further, we bootstrap secure session keys by the use of existing cryptographic building blocks EKE (Encrypted Key Exchange) and SPEKE (Simple Password Exponential Key Exchange). We implement our proof-of-concept application on Android smart-phones and take benefit from numerical processing environments for the off-line analysis of the collected datasets.
Active Hotspot: An Issue-Oriented Model to Monitor Software Evolution and Degradation

Architecture degradation has a strong negative impact on software quality and can result in significant losses. Severe software degradation does not happen overnight. Software evolves continuously, through numerous issues, fixing bugs and adding new features, and architecture flaws emerge quietly and largely unnoticed until they grow in scope and significance when the system becomes difficult to maintain. Developers are largely unaware of these flaws or the accumulating debt as they are focused on their immediate tasks of address individual issues. As a consequence, the cumulative impacts of their activities, as they affect the architecture, go unnoticed. To detect these problems early and prevent them from accumulating into severe ones we propose to monitor software evolution by tracking the interactions among files revised to address issues. In particular, we propose and show how we can automatically detect active hotspots, to reveal architecture problems. We have studied hundreds of hotspots along the evolution timelines of 21 open source projects and showed that there exist just a few dominating active hotspots per project at any given time. Moreover, these dominating active hotspots persist over long time periods, and thus deserve special attention. Compared with state-of-the-art design and code smell detection tools we report that, using active hotspots, it is possible to detect signs of software degradation both earlier and more precisely.
On the Complexity of Sequence to Graph Alignment

Availability of extensive genetics data across multiple individuals and populations is driving the growing importance of graph based reference representations. Aligning sequences to graphs is a fundamental operation on several types of sequence graphs (variation graphs, assembly graphs, pan-genomes, etc.) and their biological applications. Though research on sequence to graph alignments is nascent, it can draw from related work on pattern matching in hypertext. In this paper, we study sequence to graph alignment problems under Hamming and edit distance models, and linear and affine gap penalty functions, for multiple variants of the problem that allow changes in query alone, graph alone, or in both. We prove that when changes are permitted in graphs either standalone or in conjunction with changes in the query, the sequence to graph alignment problem is $$\mathcal {NP}$$ -complete under both Hamming and edit distance models for alphabets of size $${\ge }2$$ . For the case where only changes to the sequence are permitted, we present an $$O(|V|+m|E|)$$ time algorithm, where m denotes the query size, and V and E denote the vertex and edge sets of the graph, respectively. Our result is generalizable to both linear and affine gap penalty functions, and improves upon the run-time complexity of existing algorithms.