Reviewerly/exHarmony huggingface dataset:
Hierarchical knowledge amalgamation with dual discriminative feature alignment

Heterogeneous Knowledge Amalgamation (HKA) algorithms attempt to learn a versatile and lightweight student neural network from multiple pre-trained heterogeneous teachers. They encourage the student not only to produce the same prediction as the teachers but also to imitate each teacher’s features separately in a learned Common Feature Space (CFS) by using Maximum Mean Discrepancy (MMD). However, there is no theoretical guarantee of the Out-of-Distribution robustness of teacher models in CFS, which can cause an overlap of feature representations when mapping unknown category samples. Furthermore, global alignment MMD can easily result in a negative transfer without considering class-level alignment and the relationships among all teachers. To overcome these issues, we propose a Dual Discriminative Feature Alignment (DDFA) framework, consisting of a Discriminative Centroid Clustering Strategy (DCCS) and a Joint Group Feature Alignment method (JGFA). DCCS promotes the class-separability of the teachers’ features to alleviate the overlap issue. Meanwhile, JGFA decouples the complex discrepancy among teachers and the student at both category and group levels, extending MMD to align the features discriminatively. We test our model on a list of benchmarks and demonstrate that the learned student is robust and even outperforms its teachers in most cases.
An improved CycleGAN-based emotional voice conversion model by augmenting temporal dependency with a transformer

Emotional voice conversion (EVC) is a task that converts an utterance’s emotional features into a target one while retaining semantic information and speaker identity. Recently, some researchers leverage deep learning methods to improve the performance of EVC, such as deep neural network (DNN), sequence-to-sequence model (seq2seq), long-short-term memory network (LSTM), and convolutional neural network (CNN), as well as their combinations with an attention mechanism. However, their methods always suffer from some instability problems (e.g., mispronunciations and skipped phonemes) because these models fail to capture temporal intra-relationships among a wide range of frames, resulting in unnatural speech and discontinuous emotional expression. To enhance the ability to capture intra-relations among frames by augmenting the temporal dependency of models, we explored the power of a transformer in this study. Specifically, we proposed a CycleGAN-based model with the transformer and investigated its ability in the EVC task. In the training procedure, we adopted curriculum learning to gradually increase the frame length to ensure that the model can monitor from short segments throughout the entire speech. The proposed method was evaluated on a Japanese emotional speech dataset and Emotional Speech Dataset (ESD, contains English and Chinese speech). Then, it was compared to widely used EVC baselines (ACVAE, CycleGAN) involving objective and subjective evaluations. The results indicate that our proposed model can convert emotion with higher emotional similarity, quality, and naturalness. • A CycleTransGAN is proposed to improve its performance on the emotional voice conversion (EVC) task. • Curriculum learning was adopted to gradually increase the input length during training. • A fine-grained level discriminator was designed to enhance the model’s ability to convert emotional voices. • The proposed method was evaluated on a Japanese emotional speech dataset and Emotional Speech Dataset (ESD, containing English and Chinese speech). • The transformer enhanced the model’s temporal dependency with a wider range, which improved the quality of converted speech.
Optimal Federation Method for Embedding Internet of Things in Software-Defined Perimeter

In the digital transformation era, the Internet is rapidly evolving, as represented by the Internet of Things (IoT) and artificial intelligence. Accordingly, information security incidents are increasing and diversifying. An example of this diversification is the emergence of internal fraud in telework and other scenarios. As a countermeasure, the software-defined perimeter (SDP), a zero-trust model, is attracting attention. SDP ensures that users are always secure by authenticating and authorizing them each time a service is provided. SDP is expected to be integrated with the IoT in order to expand its application. However, it is difficult to ensure the security of the IoT itself due to the lack of resources, such as CPU power and storage. Therefore, when embedding IoT devices into SDP, the main issue is how to ensure the reliability of the IoT itself, but this has not been sufficiently studied. In this article, we propose a method to compensate for the lack of IoT resources so that it can be securely embedded into SDP. We investigated several federation methods and found through qualitative evaluation that the identity provider is the most effective and can efficiently achieve authentication and authorization in SDP.
Toward Accountable and Explainable Artificial Intelligence Part One: Theory and Examples

Like other Artificial Intelligence (AI) systems, Machine Learning (ML) applications cannot explain decisions, are marred with training-caused biases, and suffer from algorithmic limitations. Their eXplainable Artificial Intelligence (XAI) capabilities are typically measured in a two-dimensional space of explainability and accuracy ignoring the accountability aspects. During system evaluations, measures of comprehensibility, predictive accuracy and accountability remain inseparable. We propose an Accountable eXplainable Artificial Intelligence (AXAI) capability framework for facilitating separation and measurement of predictive accuracy, comprehensibility and accountability. The proposed framework, in its current form, allows assessing embedded levels of AXAI for delineating ML systems in a three-dimensional space. The AXAI framework quantifies comprehensibility in terms of the readiness of users to apply the acquired knowledge and assesses predictive accuracy in terms of the ratio of test and training data, training data size and the number of false-positive inferences. For establishing a chain of responsibility, accountability is measured in terms of the inspectability of input cues, data being processed and the output information. We demonstrate applying the framework for assessing the AXAI capabilities of three ML systems. The reported work provides bases for building AXAI capability frameworks for other genres of AI systems.
Generic and Sensitive Anomaly Detection of Network Covert Timing Channels

Network covert timing channels can be maliciously used to exfiltrate secrets, coordinate attacks and propagate malwares, posing serious threats to cybersecurity. Current covert timing channels normally conduct small-volume transmission under the covers of various disguising techniques, making them hard to detect especially when a detector has little priori knowledge of their traffic features. In this article, we propose a generic and sensitive detection approach, which can simultaneously (i) identify various types of channels without their traffic knowledge and (ii) maintain reasonable performance on small traffic samples. The basis of our approach is the finding that the short-term timing behavior of covert and legitimate traffic is significantly different from the perspective of inter-packet delays’ variation. This phenomenon can be a generic reference to detect various channels because it is resistant to major channel disguising techniques which only mimic long-term traffic features, while it is also a sensitive reference to spot small-volume covert transmission since it can capture traffic anomalies in a fine-grained manner. To obtain the inner patterns of inter-packet delays’ variation, we design a context-sensitive feature-extraction technique. This technique transforms each raw inter-packet delay into a discrete counterpart based on its contextual properties, thus extracting its variation features and reducing traffic data complexity. Then we learn legitimate variation patterns using a neural network model, and identify samples showing anomalous variation as covert. The experimental results show that our approach effectively detects all currently representative channels in the absence of their knowledge, presenting once to twice higher sensitivity than the state-of-the-art solutions.
Adversarial Attacks and Defenses in Image Classification: A Practical Perspective

The rapid and steady development of machine learning, especially deep learning, has promoted significant progress in the field of image classification. However, Machine learning models are demonstrated to be vulnerable to adversarial examples, which pose serious threats in security-critical applications. This paper summarizes the adversarial attacks and defenses from a practical perspective, facing the field of image classification. We further analyze and evaluate the characteristics and performance of various defense techniques from four aspects: gradient masking, adversarial training, adversarial examples detection and input transformations. We discuss the advantages and disadvantages of different defenses. Finally, the future development trend of defense techniques against adversarial examples is discussed. We hope our study will advance the research in machine learning security.
A Target-region-based SAR ATR Adversarial Deception Method

In order to attack the hostile deep learning based Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) models, a lot of algorithms have been proposed to create adversarial examples by adding perturbations to random positions in the images. Practical applications prefer that these perturbations locate in the target regions, so that the adversarial examples can guide us to camouflage the targets by adding strong scattering objects or absorbing material on the targets. Regarding this weakness, a new target-region-based SAR ATR adversarial deception method is proposed in this paper. We first utilize the Gabor feature-based texture segmentation (GFTS) method to extract the mask of targets in SAR images. Then the mask parameters are introduced into the loss function of perturbation generator to aggregate the perturbations of SAR adversarial samples into the target regions. The experimental results show that our method can not only generate the adversarial samples effectively fooling the SAR target recognition networks but also the perturbation only appears in part of the target regions. It provides a theoretical basis for the disguise of SAR targets in the physical world.
SegWit Extension and Improvement of the BlockSim Bitcoin Simulator

Fourteen years after its inception, the Bitcoin market capitalization exceeds $700 trillion. As Bitcoin blockchain continues gaining tremendous interest, it is vital evaluating Bitcoin protocol performance. In this context, the BlockSim simulation framework is among the current state-of-the-art tools. Despite that, the BlockSim model of the Bitcoin protocol shows two main limitations: i) at the consensus layer, the model does not account for Segregated Witness (SegWit) upgrade, which sensibly improves the Bitcoin throughput; and ii) at the network layer, the simulated block propagation process is oversimplified. Those limitations affect several blockchain performance metrics, like throughput, orphan block rate and mining reward per block. In this work we improve the existing BlockSim model of the Bitcoin protocol. Namely, we introduce SegWit support, and we update the simulated block propagation process. Block propagation delay is now estimated via linear regression on a per-simulated-block basis. We also extensively validate the proposed model within the light simulation technique of BlockSim. The results show that the model correctly simulates the current Bitcoin blockchain. As to the full simulation technique of BlockSim, it actually turns out to be so inefficient to be unpractical. We experimentally prove the performance limitations of the full technique, thereby confirming the original claims regarding its inefficiency. Yet, we provide suggestions to improve execution time and memory footprint, corroborated by profiling results.
Privacy-Preserving Energy Trading Using Blockchain and Zero Knowledge Proof

Microgrid research and construction effectively use distributed energy resources to stimulate clean energy devel-opment. Peer-to-peer (P2P) energy trading in microgrids helps create a fully competitive and autonomous energy trading market. Blockchain technology is employed to realize a P2P energy trading framework. However, while public blockchain is generally transparent, energy trading information is sensitive and thus requires a privacy-preserving mechanism. In this paper, we design a privacy-preserving energy trading mechanism by using blockchain and zero-knowledge proofs. A user uploads a commitment to the blockchain instead of the original bid amount, and the zero-knowledge proof of commitment is uploaded into the blockchain. Other participants can verify the correctness of energy trading in each auction match. Our experiments show the design is feasible and efficient in Ethereum although there are more gas consumptions.
Block Interval Adjustment Based on Block Propagation Time in a Blockchain

Transaction confirmation throughput is one of the most important performance metrics of public blockchains. Throughput can be improved by shortening the block generation interval, but that approach could sacrifice security because shorter intervals increase the fork rate. Our proposed method improves the throughput by adjusting the block generation interval to be as short as possible. Part of the nodes observe the block propagation times over the blockchain network, and the interval is determined so as not to increase the fork rate. Our simulation showed the proposed method works as it is supposed to.
Human Pose Estimation for Fitness Exercise Movement Correction

Based on computer vision technology, this research suggests an application to identify and assess a fitness practitioner’s movements. Several fitness movements such as lifting weights, squat jumps, and pull-ups that are very beneficial for health and body fitness become the main movement for body building. However, those kinds of activities may be very dangerous if done incorrectly. Based on the problem, we developed an application based on computer vision to recognize and correct the pose accuracy of fitness practitioners by using input in the form of videos that record the movements of fitness practitioners continuously. To categorize the many forms of fitness sport movements, this system uses the support vector machine (SVM) method. On the monitor screen, the classification results will be visible. The result shows that the accuracy of the system is 96.87% by using SVM with the Radial Basis Function (RBF) kernel type and can make corrections to four types of fitness movements with a testing accuracy of 90.62%.
IoT Based Climate Prediction System Using Long Short-Term Memory (LSTM) Algorithm as Part of Smart Farming 4.0

Climate is an important element in human life. Climate change can cause many impacts in various fields such as industry, agriculture, livestock, and others. Agriculture is one sector that takes advantage of climatic conditions to maintain the quality of crop production. In this paper, a climate prediction system is proposed to support smart farming 4.0 to produce good quality and quantity of crop production and reduce losses. This system provides climate prediction information using Long Short-Term Memory (LSTM) method. The weather elements used in this research are temperature, humidity, rainfall, duration of sunlight, and wind speed with sensors data retrieval and processing supported by the Internet of Things. Predictions of the amount of monthly rainfall are processed to produce climate types and crop planning in a certain area. The results of the prediction model training test using the LSTM algorithm with filtering obtained the best model with a monthly data resample scenario with a value of n or time steps is 2 months, distribution of 80% train data and 20% test data, 48 batch size usage, amount of LSTM units 120 with 256 hidden layer neurons. Produce an RMSE value of 30.54, R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> score of 0.74, a loss of 0.0247, a validation loss of 0.0282, a prediction computation time of 1.746 seconds on the train data, and 0.068 seconds on the test data.
Register variation remains stable across 60 languages

Abstract This paper measures the stability of cross-linguistic register variation. A register is a variety of a language that is associated with extra-linguistic context. The relationship between a register and its context is functional: the linguistic features that make up a register are motivated by the needs and constraints of the communicative situation. This view hypothesizes that register should be universal, so that we expect a stable relationship between the extra-linguistic context that defines a register and the sets of linguistic features which the register contains. In this paper, the universality and robustness of register variation is tested by comparing variation within versus between register-specific corpora in 60 languages using corpora produced in comparable communicative situations: tweets and Wikipedia articles. Our findings confirm the prediction that register variation is, in fact, universal.
NEW REQUIREMENTS FOR HUMAN RESOURCES IN THE INFORMATION SOCIETY

The article is devoted to the study of new requirements for human resources in the information society. The efficiency of the modern economy is directly influenced by information databases and the creative intellectual potential of employees. Consequently, employees of organizations should have new competencies to solve modern development problems. Increasing the complexity and diversity of the services provided affects the formation of new requirements for the qualification of employees, their adaptability, mastering new knowledge and skills. Special attention is paid to the consideration of the importance of such quality as the productivity of employees. The purpose of the research presented in the article is to study the need for the formation of new qualities of employees in the information society environment. The scientific novelty of the research consists in substantiating the prerequisites for the emergence of new requirements for personnel in the conditions of the information economy and post-industrial society. The practical value of the research consists in the development of applied recommendations for improving the process of interaction between educational institutions and the labor market based on a competence-based approach and the study of new requirements for employees.
CLAIRE—Parallelized Diffeomorphic Image Registration for Large-Scale Biomedical Imaging Applications

We study the performance of CLAIRE-a diffeomorphic multi-node, multi-GPU image-registration algorithm and software-in large-scale biomedical imaging applications with billions of voxels. At such resolutions, most existing software packages for diffeomorphic image registration are prohibitively expensive. As a result, practitioners first significantly downsample the original images and then register them using existing tools. Our main contribution is an extensive analysis of the impact of downsampling on registration performance. We study this impact by comparing full-resolution registrations obtained with CLAIRE to lower resolution registrations for synthetic and real-world imaging datasets. Our results suggest that registration at full resolution can yield a superior registration quality-but not always. For example, downsampling a synthetic image from 10243 to 2563 decreases the Dice coefficient from 92% to 79%. However, the differences are less pronounced for noisy or low contrast high resolution images. CLAIRE allows us not only to register images of clinically relevant size in a few seconds but also to register images at unprecedented resolution in reasonable time. The highest resolution considered are CLARITY images of size 2816×3016×1162. To the best of our knowledge, this is the first study on image registration quality at such resolutions.
Multi-Scale Attention-Guided Non-Local Network for HDR Image Reconstruction

High-dynamic-range (HDR) image reconstruction methods are designed to fuse multiple Low-dynamic-range (LDR) images captured with different exposure values into a single HDR image. Recent CNN-based methods mostly perform local attention- or alignment-based fusion of multiple LDR images to create HDR contents. Depending on a single attention mechanism or alignment causes failure in compensating ghosting artifacts, which can arise in the synthesized HDR images due to the motion of objects or camera movement across different LDR image inputs. In this study, we propose a multi-scale attention-guided non-local network called MSANLnet for efficient HDR image reconstruction. To mitigate the ghosting artifacts, the proposed MSANLnet performs implicit alignment of LDR image features with multi-scale spatial attention modules and then reconstructs pixel intensity values using long-range dependencies through non-local means-based fusion. These modules adaptively select useful information that is not damaged by an object's movement or unfavorable lighting conditions for image pixel fusion. Quantitative evaluations against several current state-of-the-art methods show that the proposed approach achieves higher performance than the existing methods. Moreover, comparative visual results show the effectiveness of the proposed method in restoring saturated information from original input images and mitigating ghosting artifacts caused by large movement of objects. Ablation studies show the effectiveness of the proposed method, architectural choices, and modules for efficient HDR reconstruction.
The demographic and economic features: the nexus with internet use

The goal of this study was to examine the nexus between demographic dividend type and economic features with internet use. The data source was from the World Development Indicator of the World Bank. The unit analysis was country. The panel data analysis method were used for the examination, employing fixed effects regression models using country income level, country regional group, and year as identifiers. The random effects regression model, pooled least square model, and static generalized method of moments were utilized for robustness checks. The dependent variable was the percentage of population using the internet. The independent variables consisted of demographic and economic variables. The demographic variable was the demographic dividend type, while the economic variables were access to electricity, GDP, inflation rate, and foreign direct investment. The results of fixed effects regression indicate that after controlling for the economic features, higher internet use in a country was associated with late- and post-demographic dividend type. Higher internet use was also associated with higher access to electricity, higher GDP, lower inflation rate, and higher foreign direct investment inflow. Robustness checks using random-effects and pooled least square models, using fixed-effects model by country income level, using two-stage least square, and using second stage regression by G20 and non-G20 country group division and year, similarly gave consistent results. The association of internet use with the demographic and economic features may imply that population-based and economic development program should be enhanced toward the favorable ones that increase internet usage among the population.