Reviewerly/exHarmony huggingface dataset:
Speaker Adaptation on Articulation and Acoustics for Articulation-to-Speech Synthesis

Silent speech interfaces (SSIs) convert non-audio bio-signals, such as articulatory movement, to speech. This technology has the potential to recover the speech ability of individuals who have lost their voice but can still articulate (e.g., laryngectomees). Articulation-to-speech (ATS) synthesis is an algorithm design of SSI that has the advantages of easy-implementation and low-latency, and therefore is becoming more popular. Current ATS studies focus on speaker-dependent (SD) models to avoid large variations of articulatory patterns and acoustic features across speakers. However, these designs are limited by the small data size from individual speakers. Speaker adaptation designs that include multiple speakers' data have the potential to address the issue of limited data size from single speakers; however, few prior studies have investigated their performance in ATS. In this paper, we investigated speaker adaptation on both the input articulation and the output acoustic signals (with or without direct inclusion of data from test speakers) using the publicly available electromagnetic articulatory (EMA) dataset. We used Procrustes matching and voice conversion for articulation and voice adaptation, respectively. The performance of the ATS models was measured objectively by the mel-cepstral distortions (MCDs). The synthetic speech samples were generated and are provided in the supplementary material. The results demonstrated the improvement brought by both Procrustes matching and voice conversion on speaker-independent ATS. With the direct inclusion of target speaker data in the training process, the speaker-adaptive ATS achieved a comparable performance to speaker-dependent ATS. To our knowledge, this is the first study that has demonstrated that speaker-adaptive ATS can achieve a non-statistically different performance to speaker-dependent ATS.
A PNU-Based Methodology to Improve the Reliability of Biometric Systems

Face recognition is an important application of pattern recognition and image analysis in biometric security systems. The COVID-19 outbreak has introduced several issues that can negatively affect the reliability of the facial recognition systems currently available: on the one hand, wearing a face mask/covering has led to growth in failure cases, while on the other, the restrictions on direct contact between people can prevent any biometric data being acquired in controlled environments. To effectively address these issues, we designed a hybrid methodology that improves the reliability of facial recognition systems. A well-known Source Camera Identification (SCI) technique, based on Pixel Non-Uniformity (PNU), was applied to analyze the integrity of the input video stream as well as to detect any tampered/fake frames. To examine the behavior of this methodology in real-life use cases, we implemented a prototype that showed two novel properties compared to the current state-of-the-art of biometric systems: (a) high accuracy even when subjects are wearing a face mask; (b) whenever the input video is produced by deep fake techniques (replacing the face of the main subject) the system can recognize that it has been altered providing more than one alert message. This methodology proved not only to be simultaneously more robust to mask induced occlusions but also even more reliable in preventing forgery attacks on the input video stream.
LSTM-GTCC based Approach for Audio Spoof Detection

With the recent advancement in the internet and technology, the user’s private information is vulnerable to different threats. Similar to any biometric system such as: finger print, face recognition Automatic Speaker Verification (ASV) are also exposed to different spoofing attacks. In order to tackle different spoofing attacks, the ASV system has two main components. One is front end feature extraction part and other one is backend classification model. The main focus of the paper is to develop the less complex ASV system that is robust against voice spoofing attacks. The proposed work uses Gammatone Cepstral Coefficients (GTCC) feature extraction technique at front end and Long-Short Term Memory (LSTM) technique at back end. The proposed work is trained and developed in Voice Spoofing Detection Corpus (VSDC). This dataset contains single point replay attacks (OPR) and multi point replay attacks (1PR). The proposed work provides 2.38 %,2.72 % Equal Error Rate (EER) values in 1PR and 2PR attacks. Also, it provides 98%, 97.26% accuracy under same attacks. The work is also evaluated using validation accuracy and Area Under the Curve (AUC) values.
Dual Regression for Efficient Hand Pose Estimation

Hand pose estimation constitutes prime attainment for human-machine interaction-based applications. Real-time operation is vital in such tasks. Thus, a reliable estimator should exhibit low computational complexity and high precision at the same time. Previous works have explored the regression techniques, including the coordinate regression and heatmap regression methods. Primarily incorporating ideas from them, in this paper, we propose a novel, fast and accurate method for hand pose estimation, which adopts a lightweight network architecture and a post-processing scheme. Hence, our architecture uses a Dual Regression strategy, consisting of two regression branches, namely the coordinate and the heatmap ones, and we refer to the proposed method as DRHand. By carefully selecting the branches' characteristics, the proposed structure has been designed to exploit the benefits of the two methods mentioned above while impoverishing their weaknesses to some extent. The two branches are supervised separately during training, and a post-processing module estimates their outputs to boost reliability. This way, our novel pipeline is considerably faster, reaching 44.39 frames-per-second on an NVIDIA Jetson TX2 graphics processing unit, offering a beyond real-time performance for any custom robotics application. Lastly, extensive experiments conducted on two publicly-available datasets demonstrate that the proposed framework outperforms previous state-of-the-art techniques and can generalize on various hand pose scenarios.
Mixed Reality Human–Robot Interface With Adaptive Communications Congestion Control for the Teleoperation of Mobile Redundant Manipulators in Hazardous Environments

Robotic interventions with redundant mobile manipulators pose a challenge for telerobotics in hazardous environments, such as underwater, underground, nuclear facilities, particle accelerators, aerial or space. Communication issues can lead to critical consequences, such as imprecise manipulation resulting in collisions, breakdowns and mission failures. The research presented in this paper was driven by the needs of a real robotic intervention scenario in the Large Hadron Collider (LHC) at the European Organization for Nuclear Research (CERN). The goal of the work was to develop a framework for network optimisation in order to help facilitate Mixed Reality techniques such as 3D collision detection and avoidance, trajectories planning, real-time control, and automatized target approach. The teleoperator was provided with immersive interactions while preserving precise positioning of the robot. These techniques had to be adapted to delays, bandwidth limitation and their volatility in the 4G shared network of the real underground particle accelerator environment. The novel application-layer congestion control with automatic settings was applied for video and point cloud feedback. Twelve automatic setting modes were proposed with algorithms based on the camera frame rate, resolution, point cloud subsampling, network round-trip time and throughput to bandwidth ratio. Each mode was thoroughly characterized to present its specific use-case scenarios and the improvements it brings to the adaptive camera feedback control in teleoperation. Finally, the framework was presented according to which designers can optimize their Human-Robot Interfaces and sensor feedback depending on the network characteristics and task.
An Improved Attention-Guided Network for Arbitrary-Oriented Ship Detection in Optical Remote Sensing Images

Existing ship detection approaches in optical remote sensing images often suffer from bottlenecks in inshore scenarios due to the substantial interference. In addition, the ship targets with different orientation angles and large aspect ratios increase the difficulty to accurately profile and locate them in optical remote sensing images. To address the aforementioned issues, a novel dual separation attention network (DSA-Net) based on the skew complete intersection-over-union (SkewCIoU) loss is proposed in this letter. In our DSA-Net, we construct a contextual location module (CLM) as the spatial attention in the backbone stage and a global channel module (GCM) as the channel attention in the neck stage, respectively. The two separated attention modules enhance the discrimination between ship targets and complex inshore interferences. Moreover, a SkewCIoU loss considering both the angles and aspect ratios of ship targets is introduced to obtain a well-trained neural network with more accurate detection performance of slender ships. Experiments on the dataset of high-resolution ship collection 2016 (HRSC2016) manifest the superiority of the proposed algorithm in comparison to the existing state-of-the-art methods.
A New Concept of Digital Transformation, Institutional Research, and Information and Communication Technology in Higher Education Based on Eduinformatics

We propose a novel concept of digital transformation, institutional research, and information and communication technology based on Eduinformatics. Digital transformation was proposed by Prof. E. Stolterman and Prof. A. C. Fors in 2004. Eduinformatics is a new interdisciplinary field that includes informatics and education. Eduinformatics analyze data of students and develop novel analytical methods. In our article, we set research questions on how to link digital transformation and education to refine higher education. We indicate some examples in a Japanese university. From these examples, we conclude that Eduinformatics is the key to linking digital transformation and information and communication technology and institutional research in higher education.
Sailfish Optimization with Deep Learning Based Oral Cancer Classification Model

Recently, computer aided diagnosis (CAD) model becomes an effective tool for decision making in healthcare sector.The advances in computer vision and artificial intelligence (AI) techniques have resulted in the effective design of CAD models, which enables to detection of the existence of diseases using various imaging modalities.Oral cancer (OC) has commonly occurred in head and neck globally.Earlier identification of OC enables to improve survival rate and reduce mortality rate.Therefore, the design of CAD model for OC detection and classification becomes essential.Therefore, this study introduces a novel Computer Aided Diagnosis for OC using Sailfish Optimization with Fusion based Classification (CADOC-SFOFC) model.The proposed CADOC-SFOFC model determines the existence of OC on the medical images.To accomplish this, a fusion based feature extraction process is carried out by the use of VGGNet-16 and Residual Network (ResNet) model.Besides, feature vectors are fused and passed into the extreme learning machine (ELM) model for classification process.Moreover, SFO algorithm is utilized for effective parameter selection of the ELM model, consequently resulting in enhanced performance.The experimental analysis of the CADOC-SFOFC model was tested on Kaggle dataset and the results reported the betterment of the CADOC-SFOFC model over the compared methods with maximum accuracy of 98.11%.Therefore, the CADOC-SFOFC model has maximum potential as an inexpensive and non-invasive tool which supports screening process and enhances the detection efficiency.
Investigating the point of view of project management practitioners on technical debt

Context: Technical debt (TD) can bring short-term benefits to software projects, but its presence is also associated with issues such as decreasing product quality. Recent literature has proposed indicator-based strategies for TD identification and different approaches for TD management, but most of them focused on the point of view of software developers. Little is still known about how project management practitioners actually discuss, experience, and manage TD. Goal: This work investigates, from the point of view of project management practitioners, how they commonly discuss, experience, and manage TD. Method: We mined, curated, and selected a total of 42 TD discussions on Stack Exchange Project Management (SEPM), totaling 583 messages. We analyzed this data set quantitatively and qualitatively. Results: The most commonly discussed types of debt are process and people, revealing 47 indicators for recognizing debt items. We also found 72 practices related to TD management. Conclusion: The perspective considered by project management practitioners to analyze the TD phenomenon is different from the one considered by other roles in the software development process. This work organizes the TD indicators and management practices identified in SEPM into a Sankey diagram, which may assist practitioners and serve as guidance for future research.
Implementation of a 32-Bit RISC-V Processor with Cryptography Accelerators on FPGA and ASIC

This paper describes the use of a combination of hardware construction languages SpinalHDL and Verilog HDL to implement a 32-bit Linux-capable RISC-V processor with cryptography accelerators on an FPGA. LiteX and SpinalHDL are two intertwined frameworks in the design flow. The CPU core was created with SpinalHDL, while the integration of IP and CPU cores was performed with LiteX. Verilog source code was generated with the configured 32-bit RISC-V architecture after the design was completed on the high-level framework. This 32-bit RISC-V architecture was successfully built on a Nexys4DDR FPGA and ASIC using a 65nm CMOS process operating at 50MHz. It incorporated Verilog HDL-based hardware accelerators with customized assembly instructions for conventional cryptographic functions such as SHA-1, AES-128, and RSA-2048 cores. The functions of the accelerators were tested using a modified OpenSSL and LibreSSL library on Linux.
Latent Space Representational Learning of Deep Features for Acute Lymphoblastic Leukemia Diagnosis

Acute Lymphoblastic Leukemia (ALL) is a fatal malignancy that is featured by the abnormal increase of immature lymphocytes in blood or bone marrow.Early prognosis of ALL is indispensable for the effectual remediation of this disease.Initial screening of ALL is conducted through manual examination of stained blood smear microscopic images, a process which is time-consuming and prone to errors.Therefore, many deep learning-based computer-aided diagnosis (CAD) systems have been established to automatically diagnose ALL.This paper proposes a novel hybrid deep learning system for ALL diagnosis in blood smear images.The introduced system integrates the proficiency of autoencoder networks in feature representational learning in latent space with the superior feature extraction capability of standard pretrained convolutional neural networks (CNNs) to identify the existence of ALL in blood smears.An augmented set of deep image features are formed from the features extracted by GoogleNet and Inception-v3 CNNs from a hybrid dataset of microscopic blood smear images.A sparse autoencoder network is designed to create an abstract set of significant latent features from the enlarged image feature set.The latent features are used to perform image classification using Support Vector Machine (SVM) classifier.The obtained results show that the latent features improve the classification performance of the proposed ALL diagnosis system over the original image features.Moreover, the classification performance of the system with various sizes of the latent feature set is evaluated.The retrieved results reveal that the introduced ALL diagnosis system superiorly compete the state of the art.
Emotion recognition by web-shaped model

Abstract Emotions recognition is widely applied for many tasks in different fields, from human-computer and human-robot interaction to learning platforms. Also, it can be used as an intrinsic approach for face recognition tasks, in which an expression-independent face classifier is developed. Most approaches face the problem by designing deeper and deeper neural networks that consider an expression as a still image or, in some cases, a sequence of consecutive frames depicting the temporal component of the expression. However, these suffer the training phase’s computational burden, which can take hours or days to be completed. In this work, a Web Shaped Model is proposed, which consists of a geometrical approach for extracting discriminant features from a face, depicting the characteristics of an expression. The model does not need to be trained since it is applied on a face and centred on the nose tip, resulting in image size and face size independence. Experiments on publicly available datasets show that this approach reaches comparable and even better results than those obtained applying DNN-based approaches.
Temporal Characterization of XR Traffic with Application to Predictive Network Slicing

Over the past few years, eXtended Reality (XR) has attracted increasing interest thanks to its extensive industrial and commercial applications, and its popularity is expected to rise exponentially over the next decade. However, the stringent Quality of Service (QoS) constraints imposed by XR’s interactive nature require Network Slicing (NS) solutions to support its use over wireless connections: in this context, quasi-Constant Bit Rate (CBR) encoding is a promising solution, as it can increase the predictability of the stream, making the network resource allocation easier. However, traffic characterization of XR streams is still a largely unexplored subject, particularly with this encoding. In this work, we characterize XR streams from more than 4 hours of traces captured in a real setup, analyzing their temporal correlation and proposing two prediction models for future frame size. Our results show that even the state-of-the-art H.264 CBR mode can have significant frame size fluctuations, which can impact the NS optimization. Our proposed prediction models can be applied to different traces, and even to different contents, achieving very similar performance. We also show the trade-off between network resource efficiency and XR QoS in a simple NS use case.
Implementing Biometric or Graphical Password Authentication in a Universal Three-Factor Authentication System

There are three critical aspects of cyber security: authentication, safety, and secrecy. Consumers have access to a wide range of alternatives for improving the safety of password-based login systems. With two-factor authentication, the majority of this was done. Two-factor authentication combines single-factor authentication processes. Two-factor authentication is becoming increasingly common and widely accepted in today’s technological age due to the growing need for privacy and security. Customized security measures are more effective and bought if they are easy to use and implement. For increased website and mobile app security, this study examines the consequences of using a three-factor authentication scheme. This post will present an app we built that might provide a good three-factor authentication approach without losing the convenience.
Gaussian Map to Improve Firefly Algorithm Performance

Firefly Algorithm (FA) mimics firefly behavior by flashing and attracts them. Firefly's global search mobility is improved for dependable global optimization using chaotic maps in this work. Investigations of benchmark problems with chaotic maps are carried out in depth. The system uses eight separate chaotic maps to fine-tune the firefly's enticing movements. By using planned chaotic transmissions instead of fixed values, the new method beats classic firefly methods. According to statistical data and the success rates of FA, the new algorithms improve the solution's performance and the reliability of global optimality.
Efficient and Side-Channel Resistant Design of High-Security Ed448 on ARM Cortex-M4

The demand for classical cryptography schemes continues to increase due to the exhaustive studies on their security. Thus, constant improvement of timing, power consumption, and memory requirements are needed for the most widely used classical Elliptic Curve Cryptography (ECC) primitives, suiting high-as well as low-end devices. In this work, we present the first implementation of the Edwards Curve Digital Signature Algorithm (EdDSA) based on the Ed448 targeting the ARM Cortex-M4-based STM32F407VG microcontroller, which forms a large part of the Internet of Things (IoT) world. We report timing and memory consumption results based on portable C and target-specific hand-crafted assembly code implementations of the low-level finite filed arithmetics. We optimize the high-level group operations by implementing the efficient scalar multiplication over the Ed448 isogenous map to reduce the computation complexity. Furthermore, we provide a side-channel analysis (SCA) and fault attack protected design by developing point randomization, scalar blinding techniques, and repeated signature, and evaluate the performance. Our optimized architecture performs a signature and verification in 39.88ms and 51.54ms, respectively, where SCA protection can be achieved at less than 6.4% cost of performance overhead.
A semantic matching approach addressing multidimensional representations for web service discovery

In recent years, discovering appropriate web services has become increasingly difficult as the number of services has grown rapidly. With the goal of improving discovery performance through accurate text matching, this study developed a service discovery method that constructs a neural matching network based on multidimensional service representations. Specifically, we performed data processing and adopted three methods called term frequency-inverse document frequency, Word2Vec, and ELMo to generate multidimensional representations for capturing the word frequency, static context features, and dynamic context features of each keyword. Based on these features, we calculated the cosine similarity of pairs of keywords to construct a multidimensional similarity matrix. We then implemented convolution, pooling, and optimization operations to construct a neural matching network that has a direct impact on the accuracy of service discovery. Finally, for a given query, target services are retrieved by ranking candidate services according to the scores predicted by the matching network. The proposed method was evaluated through multiple comparisons and the experimental results demonstrate the effectiveness of optimal web service retrieval.