Reviewerly/exHarmony huggingface dataset:
Textronic Glove Translating Polish Sign Language

Communication between people is a basic social skill used to exchange information. It is often used for self-express and to meet basic human needs, such as the need for closeness, belonging, and security. This process takes place at different levels, using different means, with specific effects. It generally means a two-way flow of information in the immediate area of contact with another person. When people are communicating using the same language, the flow of information is much easier compared to the situation when two people use two different languages from different language families. The process of social communication with the deaf is difficult as well. It is therefore essential to use modern technologies to facilitate communication with deaf and non-speaking people. This article presents the results of work on a prototype of a glove using textronic elements produced using a physical vacuum deposition process. The signal from the sensors, in the form of resistance changes, is read by the microcontroller, and then it is processed and displayed on a smartphone screen in the form of single letters. During the experiment, 520 letters were signed by each author. The correctness of interpreting the signs was 86.5%. Each letter was recognized within approximately 3 s. One of the main results of the article was also the selection of an appropriate material (Velostat, membrane) that can be used as a sensor for the proposed application solution. The proposed solution can enable communication with the deaf using the finger alphabet, which can be used to spell single words or the most important key words.
Radar Signal Recognition Based on CNN with a Hybrid Attention Mechanism and Skip Feature Aggregation

Radar signal recognition is of great significance for threat analysis and intelligence support in radar electronic warfare. However it remains a challenging task due to (1) the variety of radar signal modulation types, (2)the increasingly saturated electromagnetic environment and the more serious influence of noise interference, and (3)the intrinsic real-time requirement of this application. In this paper, a novel automatic modulation recognition algorithm based on the joint architecture of convolution neural network (CNN) and the support vector machine (SVM) is proposed to comprehensively address these challenges. In particular, this algorithm first employs time-frequency analysis to better show the pulse representation in the time-frequency domain. Then a well-designed DCNN equipped with a hybrid attention (HA) mechanism and skip feature aggregation (SFA) is used to automatically learn the feature representations of distinctiveness from time frequency images. HA is designed to quickly capture the important information of local parts in sight and enhance the ability of the backbone network to mine high-level features. SFA is applied to selectively aggregate features among channels for self-monitoring, which improves the expression and generalization ability. Sixteen types of radar signals are used to verify the feasibility and effectiveness of this algorithm. The experimental evaluation shows that the proposed method not only achieve competitive accuracy with a better timeliness than state-of-the-art methods, but also achieves effective identification of most of the signal modulation types.
Defending Substitution-Based Profile Pollution Attacks on Sequential Recommenders

While sequential recommender systems achieve significant improvements on capturing user dynamics, we argue that sequential recommenders are vulnerable against substitution-based profile pollution attacks. To demonstrate our hypothesis, we propose a substitution-based adversarial attack algorithm, which modifies the input sequence by selecting certain vulnerable elements and substituting them with adversarial items. In both untargeted and targeted attack scenarios, we observe significant performance deterioration using the proposed profile pollution algorithm. Motivated by such observations, we design an efficient adversarial defense method called Dirichlet neighborhood sampling. Specifically, we sample item embeddings from a convex hull constructed by multi-hop neighbors to replace the original items in input sequences. During sampling, a Dirichlet distribution is used to approximate the probability distribution in the neighborhood such that the recommender learns to combat local perturbations. Additionally, we design an adversarial training method tailored for sequential recommender systems. In particular, we represent selected items with one-hot encodings and perform gradient ascent on the encodings to search for the worst case linear combination of item embeddings in training. As such, the embedding function learns robust item representations and the trained recommender is resistant to test-time adversarial examples. Extensive experiments show the effectiveness of both our attack and defense methods, which consistently outperform baselines by a significant margin across model architectures and datasets.
On H1–H2 as an acoustic measure of linguistic phonation type

The measure H1–H2, the difference in amplitude between the first and second harmonics, is frequently used to distinguish phonation types and to characterize differences across voices and genders. While H1–H2 can differentiate voices and is used by listeners to perceive changes in voice quality, its relation to voice articulation is less straightforward. Its calculation also involves practical issues with error propagation. This paper highlights some developments in the use of H1–H2 and proposes a new measure that we call “residual H1.” In residual H1, the amplitude of the first harmonic is normalized against the overall sound energy (as measured by root mean square energy) instead of against H2. Residual H1 may mitigate some of the issues with using H1–H2. The current study tests the correlation between residual H1 and electroglottographic contact quotient (CQ) and compares the ability of residual H1 vs H1–H2 to differentiate statistically across phonation types in !Xóõ and utterance-level changes in phonatory quality in Mandarin. The results show that residual H1 has a stronger correlation with CQ and differentiates contrastive and allophonic phonatory quality better than H1–H2, particularly for more constricted phonation types.
Digital Forensic Case Studies for In-Vehicle Infotainment Systems Using Android Auto and Apple CarPlay

Vehicle systems have been one of the fastest-growing fields in recent years. Vehicles are extremely helpful for understanding driver behaviors and have received significant attention from a forensic perspective. Extensive forensic research was previously conducted on on-board vehicle systems, such as an event data recorders, located in the electronic control unit or manufacturer-based infotainment systems. However, unlike previous vehicles that used only manufacturer-based infotainment systems, most vehicles today are equipped with infotainment systems such as Android Auto and Apple CarPlay. These in-vehicle infotainment (IVI) systems connect to mobile devices such as smartphones and tablets. The vehicle can periodically communicate with a smartphone and thus a network outside the vehicle. Drivers can use more services in their vehicles than ever before. Accordingly, an increasing number of diverse data are being stored in vehicles, with mobile devices connected to both the vehicle and the cloud. Such data include information that can be of significant help to investigators in solving problems during forensic investigations. Therefore, forensics of IVI systems such as Android Auto and Apple CarPlay are becoming increasingly important. We analyzed various forensic studies conducted on Android Auto and Apple CarPlay. Most of the research was mainly focused on mobile devices connected through a wired USB connection. The use of wireless-based IVI systems has recently been increasing. However, the analysis of Android Auto and Apple CarPlay from this point of view is insufficient. Therefore, we proposed a forensic methodology that fully considers such limitations. A forensic analysis was conducted on various IVI systems. We also developed an IVI system forensics tool that works based on the proposed methodology.
Research on Path Planning Method of Unmanned Boat Based on Improved DWA Algorithm

Aiming at the problem of local path planning for unmanned surface vehicles, the traditional dynamic window algorithm (DWA) has the problems of uneven obstacle avoidance path and poor adaptability to complex environments. This paper proposes a local path planning method based on improved DWA. The evaluation item that replaces the direction angle difference with the target distance is used to avoid the path from being unsmooth due to vibration. And the fuzzy logic control algorithm is introduced to dynamically adjust the weight value of the trajectory evaluation function to complete the path optimization. The introduction of the A <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mo>∗</mo> </math> algorithm is designed to preplan the map environment and extract key turning points as subtarget points of the local obstacle avoidance algorithm to enhance the path optimization ability. The simulation results show that the improved DWA algorithm can shorten the planned path length by 23.4% and the path smoothness by 34.8%, enabling USV to find a reasonable, efficient, and smooth path in complex environments.
Chronological Progress of Blockchain in Science, Technology, Engineering and Math (STEM): A Systematic Analysis for Emerging Future Directions

The emergence of Industry 4.0 has awoken the adoption of blockchain as a key factor to enhance the industrial supply chain across the globe, enabling cost-effective and fast-paced delivery of products and services, ownership of products with privacy, and high security as well as traceability. This new digital horizon is underpinning the future direction of humankind, aligning with the Sustainable Development Goal themes of Good Health and Well-being (SDG3) and Sustainable Cities and Communities (SDG 11). Thus, the main objective of this paper is to elucidate the adoption of blockchain technology in Science, Technology, Engineering, and Math (STEM) disciplines by determining of the key academic research players and the evolution of blockchain in different fields. It begins by clarifying the definition of these concepts, followed by a discussion regarding the chronological progress of blockchain over time, an evaluation of the adoption of blockchain technologies in different key research areas, and lastly, providing comments on several directions to guide practitioners in developing a sustainable global blockchain roadmap in education science.
The Impact of Crypto Currencies on the Economy and The Financial Industry

This paper is based on the discussion about the cryptocurrencies and its impact on the financial industry. However, the adoption of cryptocurrencies is a different matter because right now the Bitcoins is considered as for wealthy, developed and tech-savvy cultures. Based on the analysis about the points of differences of the currency a countries profile can be developed who are more likely to adopt the Bitcoin. The cryptocurrencies are considered a potential solution against the accessibility, exchange, fraud, and inflation. Moreover, the prices are considerably increasing because the demand for core good is growing rapidly relative to supply. As per the analysis the fiat currency system is considered as the main reason for the future inflation however it will not cause any significant problem in the overall financial industry. The growing demand for housing is also increasing the price of lumber that also increased in the prices of wood. Consequently, all the related goodwill becomes more expensive following the trickle through the economy. Moreover, the asset market also contributes considerably to the inflation. An increase in the price of certain commodity incentivizes the people who are producing those commodities. In this regard, the producers withhold the supply of those commodities that eventually results in increased prices. Furthermore, the inflation can also be caused by the supply shock like the interruption in oil supply to Southern US caused by Katrina devastated. It caused fluctuation dramatically in the real exchange rates instead of producing desired results. The fluctuation also triggers the development of three asymmetries in the exchange market. The first factors that come into play is the loss aversion which means that the losing side will suffer more loss vocally and acutely as compare to the less vocal winning side during the time of depreciation or appreciation. Hence, the fiscal policy will experience more disturbances.&#x0D;
Facial Emotion Recognition Using a Novel Fusion of Convolutional Neural Network and Local Binary Pattern in Crime Investigation

The exploration of facial emotion recognition aims to analyze psychological characteristics of juveniles involved in crimes and promote the application of deep learning to psychological feature extraction. First, the relationship between facial emotion recognition and psychological characteristics is discussed. On this basis, a facial emotion recognition model is constructed by increasing the layers of the convolutional neural network (CNN) and integrating CNN with several neural networks such as VGGNet, AlexNet, and LeNet-5. Second, based on the feature fusion, an optimized Central Local Binary Pattern (CLBP) algorithm is introduced into the CNN to construct a CNN-CLBP algorithm for facial emotion recognition. Finally, the validity analysis is conducted on the algorithm after the preprocessing of face images and the optimization of relevant parameters. Compared with other methods, the CNN-CLBP algorithm has higher accuracy in facial expression recognition, with an average recognition rate of 88.16%. Besides, the recognition accuracy of this algorithm is improved by image preprocessing and parameter optimization, and there is no poor-fitting. Moreover, the CNN-CLBP algorithm can recognize 97% of the happy expressions and surprised expressions, but the misidentification rate of sad expressions is 22.54%. The research result provides data reference and direction for analyzing psychological characteristics of juveniles involved in crimes.
Fully Automated End-to-End Fake Audio Detection

The existing fake audio detection systems often rely on expert experience to design the acoustic features or manually design the hyperparameters of the network structure. However, artificial adjustment of the parameters can have a relatively obvious influence on the results. It is almost impossible to manually set the best set of parameters. Therefore this paper proposes a fully automated end-to-end fake audio detection method. We first use wav2vec pre-trained model to obtain a high-level representation of the speech. Furthermore, for the network structure, we use a modified version of the differentiable architecture search (DARTS) named light-DARTS. It learns deep speech representations while automatically learning and optimizing complex neural structures consisting of convolutional operations and residual blocks. The experimental results on the ASVspoof 2019 LA dataset show that our proposed system achieves an equal error rate (EER) of 1.08%, which outperforms the state-of-the-art single system.
SDRTV-to-HDRTV via Hierarchical Dynamic Context Feature Mapping

In this work, we address the task of SDR videos to HDR videos(SDRTV-to-HDRTV conversion). Previous approaches use global feature modulation for SDRTV-to-HDRTV conversion. Feature modulation scales and shifts the features in the original feature space, which has limited mapping capability. In addition, the global image mapping cannot restore detail in HDR frames due to the luminance differences in different regions of SDR frames. To resolve the appeal, we propose a two-stage solution. The first stage is a hierarchical Dynamic Context feature mapping (HDCFM) model. HDCFM learns the SDR frame to HDR frame mapping function via hierarchical feature modulation (HME and HM ) module and a dynamic context feature transformation (DYCT) module. The HME estimates the feature modulation vector, HM is capable of hierarchical feature modulation, consisting of global feature modulation in series with local feature modulation, and is capable of adaptive mapping of local image features. The DYCT module constructs a feature transformation module in conjunction with the context, which is capable of adaptively generating a feature transformation matrix for feature mapping. Compared with simple feature scaling and shifting, the DYCT module can map features into a new feature space and thus has a more excellent feature mapping capability. In the second stage, we introduce a patch discriminator-based context generation model PDCG to obtain subjective quality enhancement of over-exposed regions. The proposed method can achieve state-of-the-art objective and subjective quality results. Specifically, HDCFM achieves a PSNR gain of 0.81 dB at about 100K parameters. The number of parameters is 1/14th of the previous state-of-the-art methods. The test code will be released on https://github.com/cooperlike/HDCFM.
Cross-Compatible Embedding and Semantic Consistent Feature Construction for Sketch Re-identification

Sketch re-identification (Re-ID) refers to using sketches of pedestrians to retrieve their corresponding photos from surveillance videos. It can track pedestrians according to the sketches drawn based on eyewitnesses without querying pedestrian photos. Although the Sketch Re-ID concept has been proposed, the gap between the sketch and the photo still greatly hinders pedestrian identity matching. Based on the idea of transplantation without rejection, we propose a Cross-Compatible Embedding (CCE) approach to narrow the gap. A Semantic Consistent Feature Construction (SCFC) scheme is simultaneously presented to enhance feature discrimination. Under the guidance of identity consistency, the CCE performs cross modal interchange at the local token level in the Transformer framework, enabling the model to extract modal-compatible features. The SCFC improves the representation ability of features by handling the inconsistency of information in the same location of the sketch and the corresponding pedestrian photo. The SCFC scheme divides the local tokens of pedestrian images with different modes into different groups and assigns specific semantic information to each group for constructing a semantic consistent global feature representation. Experiments on the public Sketch Re-ID dataset confirm the effectiveness of the proposed method and its superiority over existing methods. Experiments on Sketch-based image retrieval datasets QMUL-Shoe-v2 and QMUL-Chair-v2 are conducted to assess the method's generalization. The results show that the proposed method outperforms the state-of-the-art works compared. The source code of our method is available at: https://github.com/lhf12278/CCSC.
Review and analysis of secure energy efficient resource optimization approaches for virtual machine migration in cloud computing

Computing operations such as databases, networks, hardware, programs, analytics, and so on are all part of the cloud computing service. As such, it may serve as an option to in-house hardware and software. However, dynamic consolidation of VMs is required to improve power consumption, load balance, the frequency of migrations, Quality of Service (QoS), and the rate at which SLA violations are addressed, all of which contribute to better resource use. VM technology has quickly become a pillar of data centers and cluster systems because to its utility in partitioning, consolidating, and moving workloads. VM placement is another thing that affects the quality of consolidation. It is important to design a system that improves energy efficiency by allocating resources to applications in a smart way while still meeting QoS requirements for applications. Moreover, the security of information that is being processed during the migration process is also one of the important tasks. Hence, in this paper, review and analysis based on parameters and metrics for the VM migration under the influence of energy optimization, consolidation, and security have been investigated in a wider manner. Moreover, some open issues that are still prevalent in the current field have also been highlighted.
Comparing conventional statistical models and machine learning in a small cohort of South African cardiac patients

Machine learning is used to process big data volumes with complex non-linear relationships between predictive variables and predictions. Research into the usefulness of machine learning in small data volumes remains limited. To compare conventional statistical methods and machine learning to predict angiogram outcomes in a small cohort of South African cardiac patients. This is a retrospective study on patients with cardiac risk factors at Inkosi Albert Luthuli Central Hospital, Durban, South Africa, from 2002 to 2008. Models were designed using predictive risk factors to forecast a binary angiogram outcome (normal or abnormal) by applying conventional statistical models (binary logistic and log binomial) and stacking ensemble machine learning. The outcome prevalence of abnormal angiograms was 99/173 (57%). Predictive data was used to model this outcome. The binary logistic regression model, which estimates odds ratio, was unsuitable. The log binomial model, which estimates relative risk, did not converge after various stepwise modelling attempts. Thereafter, machine learning models were used. These included logistic regression, k-nearest neighbour, decision tree, support vector machine, and naïve Bayes. The ensemble model amalgamated all algorithms and showed accuracy >70% and excellent performance at different thresholds with an area under the curve (AUC) > 80%. The logistic regression model was unsuitable because an odds ratio would have been unreliable and overestimated the true effect since the outcome prevalence was >10%. A log binomial model with relative risk estimates did not converge, possibly owing to the multiple predictive variables. Overall, conventional statistical models were unsuccessful in this instance. Machine learning models had limitations from a small dataset. However, the combined modelling with the stacking ensemble method produced good results in the small, homogenous database by exploiting the strengths of each contributing algorithm. Researchers may apply machine learning when conventional statistical models are inconclusive in homogenous small databases with multiple variables and a complex relationship to the outcome. Machine learning is a viable option even with relatively small cohorts if the number of predictive variables is also small.
Unification of coherence and quantum correlations in tripartite systems

In quantum resource theories (QRTs), evidence of intrinsic connections between coherence and quantum correlations, including Bell nonlocality, entanglement, quantum steering, and so on, exists. However, building these relationships is a vital yet challenging task in multipartite quantum systems. Here, we focus on a unified framework of interpreting the interconversions among coherence and different quantum correlations in tripartite systems. In particular, an exact relation between the generalized geometric measure and the genuinely multipartite concurrence is derived for tripartite entanglement states. Then we obtain the trade-off relation between the first-order coherence and the genuine tripartite entanglement by the genuinely multipartite concurrence and concurrence fill. Furthermore, the trade-off relation between the maximum steering inequality violation and concurrence fill for arbitrary three-qubit states is found. In addition, we investigate the close relation between the maximum steering inequality violation and the first-order coherence. The results show that coherence and quantum correlations are intrinsically related and can be converted to one another in the framework of QRTs.