Reviewerly/exHarmony huggingface dataset:
The effect of sampling variability on systems and individual speakers in likelihood ratio-based forensic voice comparison

The likelihood ratio (LR) framework has been widely adopted in voice (and other forensic) evidence evaluation. However, in developing any forensic comparison system, it is necessary to make subjective and pragmatic decisions, which in turn may affect the results that system produces. One such decision relates to not only the size of the samples used for training and testing the system, but also which specific individuals are used in the samples. The current study explores the relationship between sampling variability (i.e. the choice of speakers used for training and testing systems, rather than sample size) and the choice of linguistic features used. The first three formants and f0 from the vocalic portion of the filled pause um were used as input, as well as both vowel and nasal durations. 25 speakers were used in test, training and reference sets respectively. Experiments were carried out using all 31 logically possible combinations of features, and replicated 100 times using different configurations of 25 training and reference speakers. The results show that (a) overall, Cllr mean reduces with more features involved and no clear pattern is observed in Cllr range; meanwhile, considerable fluctuation is observed within individual speakers; (b) while the majority of speakers yield stronger mean LLRs in systems with three or more features, a few speakers can be well-separated using one or two features; (c) sampling variability in the training and reference speakers has limited effect on individual test speakers' LR outputs in same-speaker (SS) comparisons, but a marked effect on different-speaker (DS) LRs.
Siamese reciprocal classification and residual regression for robust object tracking

Recently, Siamese trackers have received more attention in visual tracking due to their satisfactory balance between performance and efficiency. However, most Siamese trackers neglect the misalignment between classification confidence and regression accuracy, which limits their best performance. In this paper, we proposed a novel reciprocal localization-aware classification and residual regression architecture with Siamese network named SiamRCRR. The proposed SiamRCRR consists of three subnetworks: a residual regression subnetwork to fully exploit the accuracy of object localization through the refinement of the bounding box, a localization-aware classification subnetwork to predict comprehensive scores of classification confidence and regression accuracy, and a Siamese subnetwork to extract features for classification and regression. The interaction of the residual regression subnetwork and localization-aware classification subnetwork forms a closed-loop structure to take advantage of the mutual benefit between classification and regression tasks. Therefore, the consistency between classification confidence and regression accuracy is guaranteed during tracking. Experiments on five challenging benchmarks including GOT-10k, OTB-100, LaSOT, VOT2019, and NFS show that SiamRCRR significantly outperforms its well-behaved counterparts in tracking accuracy and execution effectiveness.
The Legal Complexities of Processing and Protecting Personal Data in the Electricity Sector

The use of smart meters enables the emergence of innovations in the electricity sector, such as smart grids, prosumers and Peer-to-Peer trading, which can play an important role in realizing the energy transition. These developments rely on the processing of personal data, triggering the application of data protection legislation in addition to the legislation organizing the electricity markets. This article examines the interaction between the General Data Protection Regulation (GDPR) and the Directive (EU) 2019/944 on common rules for the internal market for electricity in the European Union, following the method of doctrinal legal research. Concretely, this article investigates what kinds of tensions may arise from the coexistence of these two legal regimes and whether there are mechanisms in place to prevent or mitigate such tensions. Three main tensions are identified. The first tension lies in the fact that some of the innovations facilitated by smart metering in the energy sector rely on technologies that might not be entirely compatible with the GDPR. A second tension follows from the existence of separate but interrelated regimes for access to data of the consumer/data subject in the two legal instruments here analysed. The third tension relates to a possible overlap of competences between the supervisory authorities of both regimes. This article is a contribution to the still scarce legal scholarship on the interplay between the GDPR the Recast Electricity Directive. The findings of this research are of interest not only for academics but also for practitioners, policymakers and supervisory authorities that have to deal with the issues here identified.
Kernel naïve Bayes classifier-based cyber-risk assessment and mitigation framework for online gaming platforms

Recently, the number and intensity of cyberattacks against massively multiplayer online (MMO) gaming platforms have increased; up to 74% of distributed denial-of-service (DDoS) attacks on MMO gaming (MMOG) firms have been launched by hackers. These malicious attacks affect gamers’ experience and MMOG firms’ revenue model. Along with financial losses, MMOG firms’ reputation also suffers from these attacks. Therefore, in this study, we devised a framework to quantify and mitigate cyber-risk for MMOG firms using a hybrid learning method, namely, a kernel naïve Bayes classifier. Our kernel naïve Bayes classifier-based cyber-risk assessment and mitigation (KB-CRAM) framework included the DDoS attack traits. Subsequently, it outputs (i) the probability of DDoS attacks; (ii) the expected financial losses; and (iii) cyber-risk mitigation strategies, such as self-protection (technology, compliance, and legal deterrence), self-insurance, or cyber-insurance. Our study contributes to field-relevant literature by providing managers with a tool to improve game performance. This framework also suggests ways in which MMOG firms can hedge losses against repeated attacks from unethical hackers.
High dimensional quantum key distribution with temporal and polarization hybrid encoding

High dimensional quantum key distribution is regarded as one of the most promising ways to boost the performance of quantum communication. In this paper, we propose a new scheme of high dimensional quantum key distribution in advantage of improving the efficiency by hybridly encoding the temporal and polarization degrees of freedom. The experimental setup consisting of merely off-the-shelf components is also discussed. Only two time slots are used to achieve 4-dimensional encoding, which can enhance the system frequency or reduce the requirement for the time resolution of the detector. The interference efficiency is increased to 100% during the phase basis decoding, which improves the secure key rate and the working distance. The performance of the proposed scheme is analyzed with finite-size effect. Numerical results show that the secure key rate is much higher than the previous protocol or the 2-dimensional version.
Defense Against Adversarial Attacks Based on Stochastic Descent Sign Activation Networks on Medical Images

Machine learning techniques in medical imaging systems are accurate, but minor perturbations in the data known as adversarial attacks can fool them. These attacks make the systems vulnerable to fraud and deception, and thus a significant challenge has been posed in practice. We present the gradient-free trained sign activation networks to detect and deter adversarial attacks on medical imaging AI systems. Experimental results show that a higher distortion value is required to attack our proposed model than the other existing state-of-the-art models on MRI, Chest X-ray, and Histopathology image datasets, where our model outperforms the best and is even twice superior. The average accuracy of our model in classifying the adversarial examples is 88.89%, whereas those for MLP and LeNet are 81.48%, and that of ResNet18 is 38.89%. It is concluded that the sign network is a solution to defend adversarial attacks due to high distortion and high accuracy on transferability. Our work is a significant step towards safe and secure medical AI systems.
Stochastic stability analysis of composite dynamic system for particle swarm optimization

The stochastic stability analysis for particle swarm optimization (PSO) has met formidable challenges due to the intricate influences on particles’ motion from collaboration capability, cognitive competence, selective randomness and environmental complexity. This paper firstly regards the above-mentioned factors as forces acting on particles of PSO: interaction, damping, randomness and external forces. Based on this mechanical analogy, a novel stochastic composite dynamic model (SCDM) for PSO is proposed which can be classified into two parts: diffusion process and drift process. The diffusion process can not be easily obtained by analytical analysis, but its approximate distribution can be experimentally justified to obey Gaussian model, and the drift process is partly determined by its optimization function.To analyze the influence on PSO from the external environment, linear and quadratic optimization functions are chose to simplify the analysis. And the SCMD’s stability is discussed via constructing the responding Lyapunov functions. We conclude that the gradient of linear function only decides the uniform ultimate bound while the gradient of quadratic function affects the stable conditions besides the uniform ultimate bound. Furthermore, the stable point of PSO is proved to be the optimum of quadratic function. The convergence time is also discussed to measure PSO’s performance.
Analysis of double aperture hololens imaging system through simulation and experimentation for its application in speckle metrology

In-plane displacement measurement using Double exposure Speckle photography involves illumination of a rough surface by coherent source of light and formation of image of the object containing speckles on a high-resolution photosensitive film using imaging lenses corresponding to undeformed and deformed state of object. When doubly exposed and properly processed specklegram is illuminated with an unexpended laser beam, two slightly displaced speckles behave as two-point scatterer giving rise to young's type of speckle correlation fringes. From the measured value of fringe spacing, in-plane displacement occurred between the two exposures is measured. Monochromic aberrations of the imaging system lead to formation of deteriorated curved fringes which introduces error in measurement. Further, to measure in-plane displacement component with enhanced sensitivity of measurement, Duffy's double aperture interferometer is used where diffraction limited performance almost free from all monochromatic aberration is required over the two apertures. Sensitivity of measurement depends on separation between the two apertures. For achieving higher sensitivity of measurement, separation between the twin apertures should be large requiring low f-number imaging lens of large diameter for given focal length of the imaging lens. Conventional diffraction limited and almost aberration free lenses are costly and require tedious processing for their production. Present work presents detailed analysis of a four-hololens imaging system through simulation and experimentation to show that properly recorded and played back hololens imaging system may be used advantageously to achieve diffraction limited imaging performance almost free from all monochromatic aberrations. Further, it is shown that to minimize aberration for extended object field, a two-hololens imaging configuration corresponding to each aperture has to be utilized and to obtain appreciable illumination over the image plane the two lenses of each aperture should be kept in contact making the system compact. The experimentally obtained values of in-plane displacement using the compact version of the low f- number four-hololens imaging system are found to be in good agreement with theoretical predictions as well as results obtained for similar experimentation where Duffy's double aperture imaging configuration comprising of conventional imaging lens was used.
Multitask Generative Adversarial Imitation Learning for Multi-Domain Dialogue System

In the task-oriented dialogue system, dialog policy plays an important role since it determines the suitable actions based on the user's goals. However, in real situations, user's goals are varying so that the system needs to deal with the complex optimization problem for dialog policy. This paper presents a novel approach to build the multi-domain dialog system based on the multitask generative adversarial imitation learning (MGAIL). MGAIL combines hierarchical reinforcement learning and generative adversarial imitation learning where a mixture of generators are represented for multitask learning. Unlike the traditional imitation learning, this method decomposes each of complex tasks into several subtasks and builds the policy in a hierarchical way to relax the agent in handling multiple complex tasks. Experiments on a multi-domain dialogue system using MultiWOZ 2.1 under ConvLab-2 frame-work show that the proposed method outperforms the other reinforcement learning methods in system-wise evaluation in terms of complete rate, success rate and book rate.
On Automating Hyperparameter Optimization for Deep Learning Applications

Given a large amount of data and appropriate hyperparameters, deep learning techniques can deliver impressive performance if several challenging issues with training, such as vanishing gradients, can be overcome. Often, deep learning training techniques produce suboptimal results because the parameter search space is large and populated with many less-than-ideal solutions. Automatic hyperparameter tuning algorithms, known as autotuners, offer an attractive alternative for automating the training process, though they can be computationally expensive. Additionally, autotuners democratize state-of-the-art machine learning approaches and increase the accessibility of deep learning technology to different scientific communities and novice users. In this paper, we investigate the efficacy of autotuning using Keras Tuner on both synthetic and real-world datasets. We show that autotuning performed well on synthetic datasets but was inadequate on real data. As we increase model complexity, autotuning produces errors that are tedious to resolve for those with limited experience in machine learning. Avoiding overfitting, for example, requires extensive knowledge of an algorithm's unique characteristics (e.g., adding dropout layers). Autotuning tools are excellent for creating baseline models on new datasets, but they need more attention to formulate optimal solutions for end-users with less background in deep learning. Because of this, manual tuning based on domain knowledge and experience is still preferred in machine learning because it produces better performance, even though it requires extensive machine learning expertise.
Multi-Person Gaze-Following with Numerical Coordinate Regression

Gaze-Following is a complex task that needs to combine the gaze with the scene. Previous works performed well on predicting single-person gaze-following but expensive computations are impractical to the real-world project. Moreover, when there are multiple people appearing at the same time, previous works will excecute repeated scene feature extraction. In addition, obtaining gaze target point through the heatmap argmax method seems to be a convention for gaze-following while the quantization error of the heatmap is ignored. In this paper, a simple but efficient network structure is proposed to provide shared scene features for the multi-person gaze-following, and a numerical coordinate regression is firstly introduced to calculate the gaze target point and regression loss. Our experiments show that the accuracy of our method can achieve SOTA on both GazeFollow dataset and VideoAttentionTarget dataset. At the same time, by using the ghostnet, the FLOPs of our method is only about 1/18 of other methods with the same accuracy. Further, sharing scene features saves nearly 40% of inference time in multi-person gaze-following task when more than 6 people in the frame.
Multicast-based <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>N</mml:mi></mml:math> -party remote-state preparation of arbitrary Greenberger-Horne-Zeilinger–class states

As an important resource, the multiqubit entangled state is widely concerned in quantum computing and quantum communication. Based on the multiqubit partially entangled state, we propose a quantum multicast scheme. In the proposed scheme, arbitrary $N$-qubit Greenberger-Horne-Zeilinger state could be sent to $N$ receivers via a $2N$-qubit partially entangled state. Specially, the proposed scheme is accomplished with controlled-not gates, which ensure the scheme is more efficient and easier to be implemented. In addition, we construct the quantum circuit simulations and the partially entangled channel on IBMQ to prove the feasibility of the scheme. Finally, the performance of the proposed scheme is analyzed to show that the scheme owns the higher efficiency.
Principal Component Analysis and Entropy-based Selection for the Improvement of Bug Triage

With ever-larger scales of modern technology companies, it is necessary to be able to assign bugs found within organizations in a manner that is automated, accurate, and time efficient. Hence, an implementation of a bug triage, the process by which you assign bug cases, feature requests, and improvements to appropriate individuals and/or teams, has become a focus of many organizations. In this paper, we present, to the best of our knowledge, a novel data processing routine that has empirically shown to improve the baseline accuracy of various elementary classifiers. In most bug reports, there are discrete features, such as the nature of the bug and software environment details, and there are non-discrete features such as fluid error messages, comments, requests, etc. We perform principal component analysis on the one-hot encoded discrete features. Whereas, for the non-discrete features, we first use standard Natural Language Processing techniques to parse and tokenize the text and then employ a greedy variant of entropy-based keyword extraction. Moreover, we make use of a heuristic combination of the contributors (contribution weighted labeling) which is converted into a probability mass function, signifying the probability of assigning a case to a given individual. The results of such processing are profound, achieving state-of-the-art top-k accuracy. With the application of such data processing, our experiments show an average increase of 15% and 14%-points for team and developer assignments, respectively. Overall, we achieve Top-1 accuracy of 79%, Top-5 accuracy of 87%, and Top-10 accuracy of 90% for team assignments. As for developer assignments, we observe Top-1 accuracy of 54%, Top-5 accuracy of 63%, and Top-10 accuracy of 67%. These results were validated on a proprietary data set containing 257 teams and 2204 developers and show significant improvement in comparison to a previous study by our research group [1].
LSTM-CNN Deep Learning Model for French Online Product Reviews Classification

Sentiment analysis (SA) is one of the most popular areas for analyzing and discovering insights from text data from various sources, including Facebook, Twitter, and Amazon. It plays a pivotal role in helping enterprises actively improve their business strategies and better understand customers’ feedback on products. In this work, the dataset has been taken from Amazon, which contains French reviews. After preprocessing and extracting the features using contextualized word embedding for French-language including ELMO, ULMFiT, and CamemBERT, we applied deep learning algorithms including CNN, LSTM, and combined CNN and LSTM to classify reviews as positive or negative. The results show that the combined model (LSTM+CNN) using CamemBERT achieved the best performance to classify the French reviews with an accuracy of 93.7%.
Intelligent Video Surveillance Based on YOLO: A Comparative Study

Number of Research study show that handheld gun is the primary weapon used for various crimes such as shoplifting, robbery, and rape. We can reduce these crimes by identifying the behavior at early stage which can significantly reduce collateral damage. Object detection using Deep neural networks is difficult when the objects are small such as handheld-Weapons. The object detection is different from classification, which recognize objects but doesn&#x2019;t localize object in image. classification don&#x2019;t work on multiple objects in single images. To address this problem in this study, we aim to detect small objects which is a handheld pistol using different Recent state of art model i.e YOLO and compare them Specifically for Handheld weapon Detection. The Experimental Results shows that The YOLOV4 model Achieves mAP@0.5 of 98.79%, Precision 91%, Recall 99% when detecting Handheld weapon (pistol). YOLOv3 Model Achieves mAP@0.5 of 90.37%,Precision 93% and Recall 80% demonstrating that it can accurately detect Handheld Weapon (Pistol) insurveillance.
BYOD Security Risks and Mitigation Strategies: Insights from IT Security Experts

Bring Your Own Device (BYOD) is considered one of the top security risks organizations face today as these devices are very much part of the working culture of today's employees. However, there is still a lack of understanding of BYOD security risks and their impact on both information security and service delivery, particularly in the government sector, nor are there any strategies to reduce these risks. To examine this problem, interviews and BYOD risk assessments were conducted with eight IT security experts from selected public sector organizations to furnish in-depth insights into BYOD risks and its impact on organizations, and to recommend mitigation strategies to overcome them. Security risks that emanate from the security behavior of employees using their personal devices are identified and categorized into people, process, and technology risks. The risk assessment resulted in 16 critical risks for public sector organizations and strategies such as security training and awareness (SETA), policy, top management commitment, and technical countermeasures to overcome critical BYOD risks.