Reviewerly/exHarmony huggingface dataset:
Improving Automatic Speech Recognition Utilizing Audio-codecs for Data Augmentation

To train end-to-end automatic speech recognition models, it requires a large amount of labeled speech data. This goal is challenging for languages with fewer resources. In contrast to the commonly used feature level data augmentation, we propose to expand the training set by using different audio codecs at the data level. The augmentation method consists of using different audio codecs with changed bit rate, sampling rate, and bit depth. The change reassures variation in the input data without drastically affecting the audio quality. Besides, we can ensure that humans still perceive the audio, and any feature extraction is possible later. To demonstrate the general applicability of the proposed augmentation technique, we evaluated it in an end-to-end automatic speech recognition architecture in four languages. After applying the method, on the Amharic, Dutch, Slovenian, and Turkish datasets, we achieved a 1.57 average improvement in the character error rates (CER) without integrating language models. The result is comparable to the baseline result, showing CER improvement of 2.78, 1.25, 1.21, and 1.05 for each language. On the Amharic dataset, we reached a syllable error rate reduction of 6.12 compared to the baseline result.
Health Care System for Home Quarantine People

Due to the ongoing COVID-19 crisis, many people who are arriving in Goa are home quarantined. Therefore, to continuously check their health status, we authors have proposed Internet of Things based electronic wireless communication system which is monitoring the health parameters continuously by using biosensors such as Respiration sensor, Body temperature sensor, Heart rate sensor, and Oxygen sensor. The received values will be transmitted to the nearby medical center or COVID Hospital using wireless technology. The same will be displayed on the Adafruit server. Also, the messages are sent on doctor's mobile devices dealing with quarantined people and Government authorities through server or GSM modem. If any of the health parameters such as the temperature of the human body, respiration rate, and Heartbeat rate are exceeding their normal rate or for health parameters such as oxygen level and respiration rate falls below the normal rate then the buzzer is ringing alarm and the preventive action is taken by receiving authorities. The above-proposed health monitoring system has analyzed using a soft computing technique called fuzzy logic.
Towards a Framework for Continuous Software Engineering

Characteristics and demands of the modern and digital society have transformed the software development scenario and presented new challenges to software developers and engineers, such as the need for faster deliveries, frequent changes in requirements, lower tolerance to failures and the need to adapt to contemporary business models. The adoption of agile practices has allowed organizations to shorten development cycles and increase customer collaboration. However, this has not been enough. Continuous actions of planning, construction, operation, deployment and evaluation are necessary to produce products that meet customers' needs and behaviors, to make well-informed decisions and identify business opportunities. Thus, organizations should evolve from traditional to continuous and data-driven development in a continuous software engineering approach. Continuous Software Engineering (CSE) consists of a set of practices and tools that support a holistic view of software development with the purpose of making it faster, iterative, integrated, continuous and aligned with business. It is a recent topic of Software Engineering, thus there are many open questions. This paper introduces a CSE framework that represents CSE processes, points out some research questions and discusses proposals to address them.
Spatiotemporal Guided Self-Supervised Depth Completion from LiDAR and Monocular Camera

Depth completion aims to estimate dense depth maps from sparse depth measurements. It has become increasingly important in autonomous driving and thus has drawn wide attention. In this paper, we introduce photometric losses in both spatial and time domains to jointly guide self-supervised depth completion. This method performs an accurate end-to-end depth completion of vision tasks by using LiDAR and a monocular camera. In particular, we full utilize the consistent information inside the temporally adjacent frames and the stereo vision to improve the accuracy of depth completion in the model training phase. We design a self-supervised framework to eliminate the negative effects of moving objects and the region with smooth gradients. Experiments are conducted on KITTI. Results indicate that our self-supervised method can attain competitive performance.
Optimally Displaced Threshold Detection for Discriminating Binary Coherent States Using Imperfect Devices

Because of the potential applications in quantum information processing tasks, discrimination of binary coherent states using generalized Kennedy receiver with maximum a posteriori probability (MAP) detection has attracted increasing attentions in recent years. In this paper, we analytically study the performance of the generalized Kennedy receiver having optimally displaced threshold detection (ODTD) in a realistic situation with noises and imperfect devices. We first prove that the MAP detection for a generalized Kennedy receiver is equivalent to a threshold detection in this realistic situation. Then we analyze the properties of the optimum threshold and the optimum displacement for ODTD, and propose a heuristic greedy search algorithm to design these parameters. We prove that the ODTD asymptotically approaches the Kennedy receiver with threshold detection when the signal power is large, and we also clarify the connection between the generalized Kennedy receiver with threshold detection and the one-port homodyne detection. Numerical results show that the proposed heuristic greedy search algorithm can obtain a lower and smoother error probability than the existing methods.
Spatiotemporal spectral histogramming analysis in hand gesture signature recognition

Dynamic signature recognition emerges to perfectly solve the hygiene concern due to its no-contact characteristic. Nevertheless, the recognition of dynamic texture is challenging compared with the static signature image due to their unknown spatial and temporal nature. In this work, we present a multi-view spatiotemporal approach based on spectral histogramming for hand gesture signature recognition. A Microsoft Kinect sensor is adopted to capture the motion of signing in a sequence of depth frames. The depth frame sequence is viewed from three directional sights to retrieve rich information, such as temporal changes at each spatial location, the signing motion flow of each vertical and horizontal spatial space in a temporal manner. Furthermore, the proposed approach performs feature description on different levels of locality. This function enables a multi-resolution analysis on this dynamic signature. The robustness of the proposed approach is reflected with the promising result by striking the state-of-the-art performance, as substantiated in the empirical results.
Investigating Case Learning Techniques for Agents to Play the Card Game of Truco

Truco is a popular game in many regions of South America; however, unlike worldwide games, Truco still requires a competitive Artificial Intelligence. Due to the limited availability of Truco data and the stochastic and imperfect information characteristics of the game, creating competitive models for a card game like Truco is a challenging task. To approach this problem, this work investigates the generation of concrete Truco problem-solving experiences through alternative techniques of automatic case generation and active learning, aiming to learn with the retention of cases in case bases. From this, these case bases guide the playing actions of the implemented Truco bots permitting to assess the capabilities of each bot, all implemented with Case-Based Reasoning (CBR) techniques.
Improved Key Recovery Attacks on Simplified Version of K2 Stream Cipher

Abstract The K2 stream cipher, designed for 32-bit words, is an ISO/IEC 18033 standard and is listed as a recommended algorithm used by the Japanese government in the CRYPTREC project. The main feature of the K2 algorithm is the use of a dynamic feedback control mechanism between the two linear feedback shift registers, which makes the analysis of the K2 algorithm more difficult. In this paper, for its simplified version algorithm, a key recovery attack is performed by using differential attacks. Firstly, for the unknown key, the same IV is fixed in two chosen IV differential attacks, and we use the input differences and the output differences of the S-box to recover the input of S-box; the internal state values can be uniquely determined by taking intersection of the input of S-box. This technology is used to improve the key recovery attack of seven-round algorithm proposed by Deike Priemuth-Schmid. Secondly, we find the constraint relationship between the keystream equations and the unknown differences by introducing the guess difference bit and eliminate the impossible differences by the constraint relationship. Thus, we expand the key recovery attack from seven to nine rounds. The time complexity of the attack is $\boldsymbol{O} \boldsymbol{(2^{113.93})}$, the data complexity is $\boldsymbol{O}\boldsymbol{(2^{8.71})}$ and the success rate is $\textbf{99.07\%}$.
Concept Drift Adaptation for Acoustic Scene Classifier Based on Gaussian Mixture Model

In non-stationary environments, data might change over time, leading to variations in the underlying data distributions. This phenomenon is called concept drift and it negatively impacts the performance of scene detection models due to them being trained and evaluated on data with different distributions. This paper presents a new algorithm for detecting and adapting to concept drifts based on combining the existing and new components Gaussian mixture model then merging it. The algorithm is equipped with a drift detector based on kernel density estimation enabling the algorithm to adapt to new data and generalize over old and new concepts well.
Low Resolution Facial Manipulation Detection

Detecting manipulated images and videos is an important aspect of digital media forensics. Due to severe discriminative information loss caused by resolution degradation, the performance of most existing methods is significantly reduced on low resolution manipulated images. To address this issue, we propose an Artifacts-Focus Super-Resolution (AFSR) module and a Two-stream Feature Extractor (TFE). The AFSR recovers facial cues and manipulation artifact details using an autoencoder learned with an artifacts focus training loss. The TFE adopts a two-stream feature extractor with key points-based fusion pooling to learn discriminative facial representations. These two complementary modules are jointly trained to recover and capture distinctive manipulation artifacts in low resolution images. Extensive experiments on two benchmarks including FaceForensics++ and DeepfakeTIMIT, evidence the favorable performance of our method against other state-of-the-art methods.
Quantum Maximum Likelihood Decoding for Linear Block Codes

While the maximum likelihood decoding (MLD) is optimal, it suffers from a high decoding complexity. In this work, we propose a quantum MLD (QMLD) for linear block codes, which provides an optimal decoding performance at reduced asymptotic complexity. To this end, we utilize the Diirr-Høyer Algorithm (DHA) to find out a codeword, c <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ML</sub> for a received signal vector y maximizing the conditional probability Pr(y|c) among codewords in a linear code C. Meanwhile, the DHA requires a quantum state representing the equiprobable superposition of all possible codewords as its input. To resolve the technical challenge, this work proposes a novel quantum circuit that produces the input quantum state to the DHA. Complexities of the proposed QMLD and classic MLD will be compared, which clearly demonstrates the computational superiority of the proposed QMLD.
Visual Action Recognition Using Deep Learning in Video Surveillance Systems

The skeleton tracking technique allows the usage of the skeleton information of human-like objects for action recognition. The major challenge in action recognition in a video surveillance system is the large variability across and within subjects. In this paper, we propose a deep-learning-based novel framework to recognize human actions using skeleton estimation. The main component of the framework consists of pose estimation using a stacked hourglass network (HGN). The pose estimation module provides the skeleton joint points of humans. Since the position of skeleton varies according to the point of view, we apply transformations on the skeleton points to make it invariable to rotation and position. The skeleton joint positions are identified using HGN-based deep neural networks (HGN-DNN), and the feature extraction and classification is carried out to obtain the action class. The skeleton action sequence is encoded using Fisher Vector before classification. The proposed system complies with Recommendation ITU-T H.626.5 "Architecture for intelligent visual surveillance systems", and has been evaluated over benchmarked human action recognition data sets. The evaluation results show that the system performance achieves a precision of 85% and the accuracy of 95.6% in recognizing actions like wave, punch, kick, etc. The HGN-DNN model meets the requirements and service description specified in Recommendation ITU-T F.743.
Accelerating End-to-End Deep Learning Workflow with Codesign of Data Preprocessing and Scheduling

In this article, we investigate the performance bottleneck of existing deep learning (DL) systems and propose DLBooster to improve the running efficiency of deploying DL applications on GPU clusters. At its core, DLBooster leverages two-level optimizations to boost the end-to-end DL workflow. On the one hand, DLBooster selectively offloads some key decoding workloads to FPGAs to provide high-performance online data preprocessing services to the computing engine. On the other hand, DLBooster reorganizes the computational workloads of training neural networks with the backpropagation algorithm and schedules them according to their dependencies to improve the utilization of GPUs at runtime. Based on our experiments, we demonstrate that compared with baselines, DLBooster can improve the image processing throughput by 1.4× - 2.5× and reduce the processing latency by 1/3 in several real-world DL applications and datasets. Moreover, DLBooster consumes less than 1 CPU core to manage FPGA devices at runtime, which is at least 90 percent less than the baselines in some cases. DLBooster shows its potential to accelerate DL workflows in the cloud.
Value Innovation with Crossover Services

Business process often contain many participants with different value orientations, and the fulfillment of the business value is achieved via cooperation and game among these participants in forms of services. Recently, the conceptual framework of crossover services has been constructed in pursuit of value innovation by breaking the traditional boundary and promoting convergence of service systems. Some value related factors include: 1) KPIs about activities, processes and collaborations; 2) Constraints on activities and resource utilization. The evaluation of these factors are highly dependent on participant's position, time and place of occurrence. However, in practice, such important information is not well-specified in business process models, let alone be evaluated, traced, and improved in applications. In this paper, we propose a temporal-spatial-domain ternary model to specify above information, and develop an annotation approach to attach it to standard BPMN 2.0 processes. We validate their effectiveness through case studies, and the experimental result reveals their potential advantages in pursuit of value innovation with crossover services.
Accumulators in (and Beyond) Generic Groups: Non-trivial Batch Verification Requires Interaction

We prove a tight lower bound on the number of group operations required for batch verification by any generic-group accumulator that stores a less-than-trivial amount of information. Specifically, we show that $$\varOmega (t \cdot (\lambda / \log \lambda ))$$ group operations are required for the batch verification of any subset of $$t \ge 1$$ elements, where $$\lambda \in \mathbb {N}$$ is the security parameter, thus ruling out non-trivial batch verification in the standard non-interactive manner. Our lower bound applies already to the most basic form of accumulators (i.e., static accumulators that support membership proofs), and holds both for known-order (and even multilinear) groups and for unknown-order groups, where it matches the asymptotic performance of the known bilinear and RSA accumulators, respectively. In addition, it complements the techniques underlying the generic-group accumulators of Boneh, Bünz and Fisch (CRYPTO ’19) and Thakur (ePrint ’19) by justifying their application of the Fiat-Shamir heuristic for transforming their interactive batch-verification protocols into non-interactive procedures. Moreover, motivated by a fundamental challenge introduced by Aggarwal and Maurer (EUROCRYPT ’09), we propose an extension of the generic-group model that enables us to capture a bounded amount of arbitrary non-generic information (e.g., least-significant bits or Jacobi symbols that are hard to compute generically but are easy to compute non-generically). We prove our lower bound within this extended model, which may be of independent interest for strengthening the implications of impossibility results in idealized models.
EventWarden: A Decentralized Event-driven Proxy Service for Outsourcing Arbitrary Transactions in Ethereum-like Blockchains

Transactions represent a fundamental component in blockchains as they are the primary means for users to change the blockchain state. Current blockchain systems such as Bitcoin and Ethereum require users to constantly observe the state changes of interest or the events taking place in a blockchain and requires the user to explicitly release the required transactions to respond to the observed events in the blockchain. This paper proposes EventWarden, a decentralized event-driven proxy service for users to outsource transactions in Ethereum-like blockchains. EventWarden employs a novel combination of smart contracts and blockchain logs. EventWarden allows a user to create a proxy smart contract that specifies an interested event and also reserves an arbitrary transaction to release. Upon observing the occurrence of the prescribed event, anyone in the Blockchain network can call the proxy contract to earn the service fee reserved in the contract by proving to the contract that the event has been recorded into blockchain logs, which then automatically triggers the proxy contract to release the reserved transaction. We show that the reserved transaction can only get released from the proxy contract when the prescribed event has taken place. We also demonstrate that as long as a single member in the Blockchain network is incentivized by the service fee to call the proxy contract after the prescribed event has taken place, the reserved transaction is guaranteed to get released. We implement EventWarden over the Ethereum official test network. The results demonstrate that EventWarden is effective and is ready-to-use in practice.
A Simple Speech Production System Based on Formant Estimation of a Tongue Articulatory System Using Human Tongue Orientation

An algorithm for a potentially non-obtrusive speech production system was developed and characterized.The algorithm is primarily based on the articulation of the human tongue referred as tongue articulatory system (TAS) and was cascaded with a previously developed laryngeal model.We developed and optimized statistical formulae for formants of vowels and consonants and studied the model for different ages and genders.The difference between the formant frequencies obtained using both the established vocal tract system and proposed cascaded system was found to be < 5%.The proposed model shows the significance of the articulatory nature of the tongue in human speech production.An algorithmic speech synthesizer was developed, and its output was matched with original speech signals for English vowels and consonants with an Normalized Root-Mean-Square deviation error (NRMSE) of < 0.15ms.Further, an experimental implementation of the developed algorithm was done, with flex-sensors emulating the tongue in an artificial oral cavity.The experimental test results further confirmed the effectiveness of the algorithm, revealing interesting features under tolerance analyses.This idea relates to a means for compensating for a whole or partial loss of speech.Such a model can be useful to interpret speech for tracheostomised patients who have undergone larynx surgery, speech-disabled due to accidents or voice disorders, medical rehabilitation and for robotics.
Assessment of Indonesian Short Essay using Transfer Learning Siamese Dependency Tree-LSTM

The automatic short essay assessment is considering many aspects, not only the student's answer, but also the teacher marking rubrics for getting precise answers. Sometimes the marking rubrics are not used, instead, the teacher only used a single precise answer so that condition raises scoring ambiguity. This paper presents some precise answers for a fair assessment and discovers the important representation of the answer using grammatical dependency. Grammatical used to overcome uncertainty relation word in answer. The precise answers as the correct answer tolerance and student answers are used as the new input pair. Due to the Indonesian short essay is a lack, transfer learning is carried out. In this paper, we present a transfer learning using Siamese dependency tree-LSTM for obtaining the assessment score. The evaluation of the proposed method using Quadratic Weighted Kappa and accuracy that is 85.74% and 70.80%, respectively.
Idenx: A Blockchain-based Identity Management System for Supply Chain Attacks Mitigation in Smart Grids

As supply chain attacks such as forged certificates and modified software updates to smart grid remain a major challenge, blockchain has emerged as an attractive solution for enabling supply chain security due to its transparent, immutability, and decentralisation characteristics. These attacks, which focus on compromising smart grid components and services, can have a cascading effect on smart grid operations. A large number of interdependencies between the components and services and reliance on trusted third parties hinders the ability to safely and securely identify and authenticate the components and services. We propose Idenx, a blockchain-based identity management system for mitigating supply chain attacks. Idenx uses the Elliptic Curve Digital Signature Algorithm (ECDSA) to construct the identity of every component or service and further uses a Secure Pseudo-Random Function (PRF) with the identity to derive an attribute identifier for the component or service. Our approach for identifying and authenticating a component is based on Idenx smart contracts that facilitate supply chain security agreements without trusted third parties. We presented the security analysis of Idenx and the results show that it is resilient to supply chain attacks. Furthermore, we implemented a prototype of Idenx on Ethereum blockchain and evaluated it with respect to two real-world smart grid supply chain attacks.