Reviewerly/exHarmony huggingface dataset:
The use of Voice Source Features for Sung Speech Recognition

In this paper, we ask whether vocal source features (pitch, shimmer, jitter, etc) can improve the performance of automatic sung speech recognition, arguing that conclusions previously drawn from spoken speech studies may not be valid in the sung speech domain. We first use a parallel singing/speaking corpus (NUS-48E) to illustrate differences in sung vs spoken voicing characteristics including pitch range, syllables duration, vibrato, jitter and shimmer. We then use this analysis to inform speech recognition experiments on the sung speech DSing corpus, using a state of the art acoustic model and augmenting conventional features with various voice source parameters. Experiments are run with three standard (increasingly large) training sets, DSing1 (15.1 hours), DSing3 (44.7 hours) and DS-ing30 (149.1 hours). Pitch combined with degree of voicing produces a significant decrease in WER from 38.1% to 36.7% when training with DSing1 however smaller decreases in WER observed when training with the larger more varied DSing3 and DSing30 sets were not seen to be statistically significant. Voicing quality characteristics did not improve recognition performance although analysis suggests that they do contribute to an improved discrimination between voiced/unvoiced phoneme pairs.
Graph Attention and Interaction Network With Multi-Task Learning for Fact Verification

Fact verification is a challenging task which requires to retrieve relevant sentences from plain texts and then take these sentences as evidences to verify given claims. Conventional methods treat sentence selection and claim verification as separate subtasks in a pipeline. Claim verification models usually analyzes the inference relationship between each retrieved sentence and the claim, and then aggregates the claim-sentence representations by graph-based reasoning methods, such as graph attention networks (GAT). In this paper, we propose a graph attention and interaction network (GAIN) for claim verification. In addition to GAT, this model includes a graph interaction network (GIN), which considers the comparative relationships among all claim-sentence representations. More importantly, a multi-task learning strategy, which combines the objectives of both sentence selection and claim verification, is designed to train the GAIN model in order to utilize the supervision information of both subtasks. Experimental results on the FEVER dataset show that the GAIN model with multi-task learning achieves a FEVER score of 73.04%, which outperforms other published models.
Interpolation of Large and Noisy Scatter Data

One often deals with a large number of measurements of natural phenomena in environmental sciences. These data can be frequently irregularly distributed and affected by different type of errors. The problem of interest is to recover the variable field with certain regular properties and a reasonable computational effort. To solve this problem, we use block interpolation followed by a projection onto the wavelet space of cubic splines. In this article, we focus our attention on the block interpolation using Thin Plate and Kriging methods.
Unsupervised and Cost-Effective Learning: Dynamically Expose Anomaly Devices

The defect detection may be crucial issues when monitoring a large number of IoT devices, such as webcams, home appliances, and computers. The malfunctioned issues, disconnection or powerless, are obvious to detect intuitively, but slightly abnormal devices are ambiguous to define and possibly leads to critical sabotage. In the worst situation, abnormal behavior may be caused by device hijacking and authentication breaking. Our research utilizes unsupervised learning approach to distinguish those ambitious behavior with dynamical algorithm. The result shows how to quickly explore suspicious targets within large numbers of devices with cost-effectiveness computation.
Opportunities of the discipline of «Training professional communication» in the formation of students' universal competences

The indication of the need for the formation of universal competencies in students in the current FSES HE 3++ has activated the scientific and pedagogical community to search for ways to solve this problem. The article reveals the possibilities of the «Training professional communication» discipline in the formation of universal competencies in the process of professional training of bachelors at a university in the direction of 44.03.02 «Psychological and pedagogical education», «Psychology and Social Pedagogy» direction. The purpose of the research is to reveal the experience of the formation of universal competencies in students as a factor in increasing the effectiveness of the educational process. The research methods are represented by a set of theoretical methods of cognition, such as generalization, design, comparison, forecasting. The research results. The category «universal competences» and its indicators have been clarified; the planned learning outcomes in the discipline of «Training professional communication» in the context of a competence-oriented approach have been proposed, a criterion scale for assessing UC-3 universal competence formation in the field of training of 44.03.02 «Psychological and pedagogical education» in the framework of the discipline of «Training professional communication» has been developed. Key findings: universal competencies are certain qualities, types of behavior that people tend to exhibit, striving to achieve work goals; the list of planned learning outcomes in accordance with the Federal State Educational Standard of Higher Education 3++ correlates with indicators of achievement of each universal competence through the triad «knows; knows how; owns» for each indicator of achievement of competencies. The criterion scale for assessing UK-3 universal competence formation in the corresponding direction of training includes four levels: unsatisfactory, satisfactory, good and excellent, which are revealed in the process of current and intermediate control of knowledge among students in an academic discipline.
Reclaiming the Habitat: Food, Fire and Affordance in Designing and Living the Urban

This essay is a critical and radical proposition to reclaim habitats of life, investigating ecological and pedagogical models of critical and spatial design practices. We examine these in relation to commons which we associate to urbanism and life. We do so via politics of food, fire, and affordance, which we explore as agents shaping the assemblage of sapiens life through design, and reposition as cues for methods of thinking, learning, designing, and constructing (conditions of life). The vilification of fire (when associated to fear) and food (when associated to desire) has a tremendous effect on how we think of and practice life, and presents ramifications to how we design, make, and consume objects and environments. We must affirm such phenomena as part of advanced capitalist networks and societies, and reconsider commoning and its affinity with social capital in practice. We attempt in response to propose ecologies and pedagogies of food and fire (as methods for urban commoning) in urban design practice, promoting larval affordances between bodies, buildings, and the commons as urban conflict. We approach conflict as affordance and the collective shaping agent allowing public sharing to form through dynamic and non-stagnant networks and patterns. As with urban commoning practices, these propositions may diverge from dominant institutions and can open up to opportunities that allow us to rethink life and live differently. The proposal will therefore offer a space for theorization, interrogating contemporary citizenship in learning to design for and by such experiences and events, and highlight pathologies of urban living.
Domain Adaptation for Learning Generator From Paired Few-Shot Data

We propose a Paired Few-shot GAN (PFS-GAN) model for learning generators with sufficient source data and a few target data. While generative model learning typically needs large-scale training data, our PFS-GAN not only uses the concept of few-shot learning but also domain shift to transfer the knowledge across domains, which alleviates the issue of obtaining low-quality generator when only trained with target domain data. The cross-domain datasets are assumed to have two properties: (1) each target-domain sample has its source-domain correspondence and (2) two domains share similar content information but different appearance. Our PFS-GAN aims to learn the disentangled representation from images, which composed of domain-invariant content features and domain-specific appearance features. Furthermore, a relation loss is introduced on the content features while shifting the appearance features to increase the structural diversity. Extensive experiments show that our method has better quantitative and qualitative results on the generated target-domain data with higher diversity in comparison to several baselines.
Efficient Training of Object Detection Models for Automated Target Recognition

GATR™ (Globally-scalable Automated Target Recognition) is a software system for object detection and classification on a worldwide basis developed by Lockheed Martin. One of the targets it detects are oil/gas fracking wells. In this work we explore the following techniques for efficiently training a fracking well detector in WorldView satellite imagery: determining the optimal ground sample distance (GSD) of data to use for training/inference, leveraging the use of auto-generated training labels, training on different sized subsets of the training data, and using active learning to determine the best subsets of data to train with. We compare the mean average precision (mAP) for models trained at different GSDs from 0.3 to 12 m and find optimal performance at 1.2 m GSD (mAP = 0.91), but acceptable performance all the way to 6 m GSD (mAP = 0.80). We also show that using auto-generated training labels from fracking well permits results in a model with mAP reduced by less than 4% (IoU threshold = 0.2) compared to a model trained on the human annotated dataset. We show that training on just 20% of the full dataset results in a mAP drop of less than 6% (mAP = 0.86). Finally, we compare multiple active learning selection methods.
A Comparison of Sentiment Analysis Techniques on Movie Reviews

Applying sentiment analysis to mine the huge amount of unstructured data in social media has become an important field of research. Businesses and organizations have shown growing interests. Sentiment analysis can help understand the end user’s opinion about a given topic. Movie reviews represent a large amount of opinionated data which reflect feelings and emotions about a particular movie. The objective of this work is to develop a domain-specific sentiment classifier which is capable of identifying sentiment expressed in these reviews. We developed a sentiment analysis model based on movie reviews which achieved a 90% accuracy. Logistic regression, support vector machines, and multinomial naive Bayes learning algorithms along with various text preprocessing algorithms were used. Support vector machine algorithm gave the best results achieving 90% accuracy.
Improving Explainability of Integrated Gradients with Guided Non-Linearity

Along with the performance improvements of neural network models, developing methods that enable the explanation of their behavior is a significant research topic. For convolutional neural networks, the explainability is usually achieved with attribution (heatmap) that visualizes pixel-level importance or contribution of input to its corresponding result. This attribution should reflect the relation (dependency) between inputs and outputs, which has been studied with a variety of methods, e.g., derivative of an output with respect to an input pixel value, a weighted sum of gradients, amount of output changes to input perturbations, and so on. In this paper, we present a new method that improves the measure of attribution, and incorporates it into the integrated gradients method. To be precise, rather than using the conventional chain-rule, we propose a method called guided non-linearity that propagates gradients more effectively through non-linear units (e.g., ReLU and max-pool) so that only positive gradients backpropagate through nonlinear units. Our method is inspired by the mechanism of action potential generation in postsynaptic neurons, where the firing of action potentials depends on the sum of excitatory (EPSP) and inhibitory postsynaptic potentials (IPSP). We believe that paths consisting of EPSP-giving-neurons faithfully reflect the contribution of inputs to the output, and we make gradients flow only along those paths (i.e., paths of positive chain reactions). Experiments with 5 deep neural networks have shown that the proposed method outperforms others in terms of the deletion metrics, and yields fine-grained and more human-interpretable attribution.
Transmittance Regularizer for Binary coded Aperture Design in a Computational Imaging end-to-end Approach

Deep learning End-to-End (E2E) approaches have emerged as alternative optical design models, which jointly train the optical parameters of the sensing protocol, and the parameters of the deep neural network to achieve a specific task. This E2E model is particularly useful in the design of coding optical systems to address relevant constraints of the coded aperture (CA) design. To name, recent works address the binary constraint by incorporating regularization functions in the E2E optimization problem to promote binary value entries. How-ever, they do not consider other important CA assembling properties as the transmittance level, which plays a crucial role in implementable setups. Therefore, this work proposes two transmittance regularizers that jointly induce binary en-tries and adjust the transmittance level to be incorporated in an E2E approach. In particular, one of the regularizers allows achieving an exact value of the transmittance level when required for specific applications.
A Functional Paradigm for Capacity Planning of Cloud Computing Workloads

Cloud computing allows the shared use of computational resources with its cost based on operational cycles. Given the technical and financial constraints of cloud computing projects, capacity planning processes help to identify the need for resources in a given workload in order for it to operate properly in the face of different contexts. The current industrial paradigm considers historical usage precedents for the capacity planning of cloud computing workloads. In addition, current practices of cloud capacity planning do not establish how accurate these plans are. This work aims to establish a functional paradigm for capacity planning of cloud computing workloads, which include: an architectural meta-model; a method for functional and architectural classification of workloads; a sizing equation and confidence index.
Analysis on the Thinking Innovation of Ideological and Political Education Based on the Theory of Blockchain in the Information Age

Blockchain is the systematic integration innovation of distributed accounting, consensus mechanism, point-to-point transmission, encryption algorithm, intelligent contract and other technologies, providing a solution for information security and social trust in the Information Age. The principles, characteristics and deep thinking of Blockchain technology can promote the thinking innovation and solve the existing obstacles including the weak effectiveness of education activities, the burnout of education subjects and the lack of mutual trust in the field of ideological and political education. This paper focuses on analyzing the expanded application of Blockchain theory of ideological and political education. It is emphasized that we should build Learn Ledger, promote the participation degree of education subject, establish a new interactive trust mechanism and intensify endogenous incentives to promote the deep transformation of ideological and political education, enhance the effectiveness of educational content, expand the participation in educational activities, and strengthen the multi-subject trust-relationship reconstruction of ideological and political education.
Design of Hardware Accelerators for Fractal based Machine Learning Applications

Natural processes are non-linear dynamical systems. Samples extracted from such processes are often estimated through non-Euclidean geometry like fractals. Provided such a structure exists in a given data distribution, it becomes imperative to use non-Euclidean space like graphs and manifolds to represent it. Traditional Euclidean machine learning or deep learning techniques may not be sufficient for such spaces. This work provides some insights on how learning or inference for such classes of problems may be accomplished by following some novel hardware software co-design techniques. This work proposes some novel techniques to design hardware accelerators for realizing fractal based machine learning applications.
Efficiently Merging r-indexes

Large sequencing projects, such as GenomeTrakr and MetaSub, are updated frequently (sometimes daily, in the case of GenomeTrakr) with new data. Therefore, it is imperative that any data structure indexing such data supports efficient updates. Toward this goal, Bannai et al. (TCS, 2020) proposed a data structure named dynamic r-index which is suitable for large genome collections and supports incremental construction; however, it is still not powerful enough to support substantial updates. Here, we develop a novel algorithm for updating the r-index, which we refer to as RIMERGE. Fundamental to our algorithm is the combination of the basics of the dynamic r-index with a known algorithm for merging Burrows-Wheeler Transforms (BWTs). As a result, RIMERGE is capable of performing batch updates in a manner that exploits parallelism while keeping the memory overhead small. We compare our method to the dynamic r-index of Bannai et al. using two different datasets, and show that RIMERGE is between 1.88 to 5.34 times faster on reasonably large inputs.
An Improved Authentication Scheme for Wireless Sensor Network Using User Biometrics

Sensors are tiny devices deployed in an unsupervised environment. These devices monitor the readings, process them, and transmit them to the predefined destination. The internet has also availed the users to query the sensors and get the values directly. Users are to register themselves with the gateway node. After registration, they increase flexibility to query the sensors. The sensors are to authenticate the users for their legitimacy before dispatching the requested information. The proposal increases security by minimizing the replay attacks and enhance reliability in sensor-user communication. The proposed work hikes reliability as well as conserves energy substantially. Also, it minimizes replay attacks that are vulnerable in WSN.
Learning Hawaiian Open Ocean Navigation Methods with Kilo Hōkū VR

Kilo Hoku VR (t: "to observe and study the stars") is a virtual reality simulation of the Hokulea, a Polynesian double-hulled sailing canoe, and a simulation of the practice of Modern Hawaiian wayfinding, or non-instrument open ocean navigation. It was developed to assist in the cultural preservation of the celestial navigation portion of Modern Hawaiian wayfinding, and to expand the availability of learning opportunities. We here introduce new features added to the simulation for teacher and student interaction and learning. We performed a user study and observed the potential viability of using Kilo Hoku VR with students who are currently learning wayfinding in a classroom setting.
EcDEALS: Adaptive Local Search Strategies in Differential Evolution for Escalating Convergence

The differential evaluation (DE) algorithm has shown the capability to solve complex optimization problems that are difficult to solve using available deterministic methods. An improved version of DE has been introduced in this paper by including three search strategies to balance convergence and diversification. After each DE search cycle, two local search techniques are based on probability, namely classical uni-dimensional local search (CULS) and levy flight local search (LFLS), and a cogent search, namely stochastic diffusion scout search (SDSS) is included in DE. New local search approach enhance the population convergence potential, while SDSS perform diversification of solutions. The new algorithm’s competition is established for the 15 standard test problems. The comparison of the DE and its most recent variants through experimental results shows the proposed version’s competence.
Member spotlight

In this feature of the Bulletin, we highlight members of the SIGCSE community. In this issue, Bulletin co-editor Leo Porter interviewed Mark Guzdial. Mark Guzdial is a Professor of Electrical Engineering and Computer Science at the University of Michigan and an ACM Fellow. He was awarded the 2019 SIGCSE Award for Outstanding Contribution to Computer Science Education. He will receive the award and offer a keynote address at the upcoming SIGCSE symposium.
Enhancing Depth Quality of Stereo Vision using Deep Learning-based Prior Information of the Driving Environment

Generation of high density depth values of the driving environment is indispensable for autonomous driving. Stereo vision is one of the practical and effective methods to generate these depth values. However, the accuracy of the stereo vision is limited by texture-less regions, such as sky and road areas, and repeated patterns in the image. To overcome these problems, we propose to enhance the stereo generated depth by incorporating prior information of the driving environment. Prior information, generated by deep learning-based U-Net model, is utilized in a novel post-processing mathematical framework to refine the stereo generated depth. The proposed mathematical framework is formulated as an optimization problem, which refines the errors due to texture-less regions and repeated patterns. Owing to its mathematical formulation, the post-processing framework is not a black-box and is explainable, and can be readily utilized for depth maps generated by any stereo vision algorithm. The proposed framework is qualitatively validated on the acquired dataset and KITTI dataset. The results obtained show that the proposed framework improves the stereo depth generation accuracy.
Electromagnetic Fields as a Sign of Side-Channel Attacks in GSM Module

The article offers an overview of various methods of detecting unauthorized access in mobile communication devices. Describes the most common ways to access the transmitted information and hardware using vulnerability in the GSM standard. Gives some techniques for identifying threats. By studying the methods, the weaknesses and strengths of each are identified, which, in the future, allows us to determine the most effective method of searching for threats, which is electromagnetic analysis. Observing the data obtained in the course of measuring the intensity of electromagnetic fields, the main advantages are formulated, as a result of which it becomes possible to automate the analysis and determine the legitimacy of the radio transmitting modules.
Entanglement between two qubits with two-photon transitions interacting with a slightly detuned thermal field

In this paper, we investigate entanglement between two qubits when they simultaneously not-resonantly interact with a single-mode thermal field through the degenerate two-photon transitions. We obtain the exact solution of the quantum Liouville equation for density matrix of the considered systems in the "dressed" states representation. On its basis the calculate the qubit-qubit reduced density matrix and negativity. We show that a slight detuning between the qubit transition frequency and the twice field frequency might cause high entanglement between the qubits.