Reviewerly/exHarmony huggingface dataset:
Bayesian Generative Adversarial Nets with Dropout Inference

Generative adversarial networks are one of the most popular approaches to generate new data from complex high-dimensional data distributions. They have revolutionized the area of generative models by creating quality samples that highly resemble the true data distribution. However, these samples often cover only few high density areas of the true data distribution. As some of the modes are missing in the generated data, this issue is referred to as mode collapse. Bayesian GANs (BGANs) can address this to a great extend by considering Bayesian learning principles. Instead of learning point estimates of parameters in the network, BGANs learn a probability distribution over these parameters and make use of the posterior distribution over parameters to make prediction. As these models are huge neural networks, analytical inference is not feasible due to the intractable likelihood and evidence terms. Hence, BGANs perform an approximate inference based on stochastic gradient Hamiltonian Monte Carlo (SGHMC) sampling which is computationally expensive and displays convergence problems. We propose a simple and effective Bayesian GAN model based on Monte Carlo dropout based inference (BDGAN). We establish theoretical connection between variational inference in Bayesian GANs and Monte Carlo dropout in GANs. The effectiveness of the proposed model in overcoming mode collapse is demonstrated on various synthetic and real-world data sets. Additionally, we analyse the training time and memory usage to show case the proposed method's advantages over Bayesian GAN.
Revisiting the Security of DbHtS MACs: Beyond-Birthday-Bound in the Multi-user Setting

Double-block Hash-then-Sum (DbHtS) MACs are a class of MACs that aim for achieving beyond-birthday-bound security, including SUM-ECBC, PMAC_Plus, 3kf9 and LightMAC_Plus. Recently Datta et al. (FSE'19), and then Kim et al. (Eurocrypt'20) prove that DbHtS constructions are secure beyond the birthday bound in the single-user setting. However, by a generic reduction, their results degrade to (or even worse than) the birthday bound in the multi-user setting. In this work, we revisit the security of DbHtS MACs in the multi-user setting. We propose a generic framework to prove beyond-birthday-bound security for DbHtS constructions. We demonstrate the usability of this framework with applications to key-reduced variants of DbHtS MACs, including 2k-SUM-ECBC, 2k-PMAC_Plus and 2k-LightMAC_Plus. Our results show that the security of these constructions will not degrade as the number of users grows. On the other hand, our results also indicate that these constructions are secure beyond the birthday bound in both single-user and multi-user setting without additional domain separation, which is used in the prior work to simplify the analysis. Moreover, we find a critical flaw in 2kf9, which is proved to be secure beyond the birthday bound by Datta et al. (FSE'19). We can successfully forge a tag with probability 1 without making any queries. We go further to show attacks with birthday-bound complexity on several variants of 2kf9.
Towards Enforcing Social Distancing Regulations with Occlusion-Aware Crowd Detection

In this paper, we present a video analysis method that automatically detects crowds violating social distancing regulations in public spaces, which is widely accepted to be essential to minimise the spreading of COVID-19. While various approaches have been published online to tackle this problem, our work presents a systematic study with comprehensive quantitative analysis of different deep learning models on multiple datasets. We experimented with two types of one-stage pedestrian detection models and further optimised their performance with a repulsion loss to address occlusions in crowds. We also propose a distance computation technique with locally adaptive threshold to approximate the actual spatial distance between pedestrians in the real world. In addition, since there is no existing dataset providing ground truth annotations of distances, we manually annotated three public datasets with such information to perform quantitative evaluation of our crowd detection method. Our comprehensive evaluation shows that our method achieves good detection performance with improvement provided by repulsion loss. Our code and ground truth annotations can be obtained from https://github.com/thomascong121/SocialDistance.
Prioritized multi-criteria federated learning

In Machine Learning scenarios, privacy is a crucial concern when models have to be trained with private data coming from users of a service, such as a recommender system, a location-based mobile service, a mobile phone text messaging service providing next word prediction, or a face image classification system. The main issue is that, often, data are collected, transferred, and processed by third parties. These transactions violate new regulations, such as GDPR. Furthermore, users usually are not willing to share private data such as their visited locations, the text messages they wrote, or the photo they took with a third party. On the other hand, users appreciate services that work based on their behaviors and preferences. In order to address these issues, Federated Learning (FL) has been recently proposed as a means to build ML models based on private datasets distributed over a large number of clients, while preventing data leakage. A federation of users is asked to train a same global model on their private data, while a central coordinating server receives locally computed updates by clients and aggregate them to obtain a better global model, without the need to use clients’ actual data. In this work, we extend the FL approach by pushing forward the state-of-the-art approaches in the aggregation step of FL, which we deem crucial for building a high-quality global model. Specifically, we propose an approach that takes into account a suite of client-specific criteria that constitute the basis for assigning a score to each client based on a priority of criteria defined by the service provider. Extensive experiments on two publicly available datasets indicate the merits of the proposed approach compared to standard FL baseline.
Agglomerative Neural Networks for Multiview Clustering

Conventional multiview clustering methods seek a view consensus through minimizing the pairwise discrepancy between the consensus and subviews. However, pairwise comparison cannot portray the interview relationship precisely if some of the subviews can be further agglomerated. To address the above challenge, we propose the agglomerative analysis to approximate the optimal consensus view, thereby describing the subview relationship within a view structure. We present an agglomerative neural network (ANN) based on constrained Laplacian rank to cluster multiview data directly without a dedicated postprocessing step (e.g., using K -means). We further extend ANN with a learnable data space to handle data of complex scenarios. Our evaluations against several state-of-the-art multiview clustering approaches on four popular data sets show the promising view-consensus analysis ability of ANN. We further demonstrate ANN's capability in analyzing complex view structures, extensibility through our case study and robustness and effectiveness of data-driven modifications.
Fuzzy Approach for Evaluation of Student’s Performance

A series of works on fuzzy logic application in educational systems exist. Student’s performance is a basic attribute in this realm and it depends on level of education. Student’s performance evaluation includes analysis of skills and ability which are characterized by uncertainty. Always student’s results are evaluated by using linguistic terms instead of numerical values. This paper uses fuzzy logic-based approach to find a student with the highest performance. The purpose of the paper is to make university give full consideration of the grades of students to define their performance.
Improved integer/fractional order partial differential equation-based thresholding

This study presents an improved partial differential equation (PDE)-based binarization model, which utilizes a modified structure tensor-based edge enhancement, diffusion and binarization terms in its formulation. This enables simultaneous stain removal and faded text restoration during the binarization process. Comparisons of the proposed scheme with recent state-of-the-art binarization approaches show comparable or superior results based on visual and objective analysis.
Regularized Estimation with Variable Exponential Forgetting

The real-time estimation of normal regression-type models with unknown time-varying parameters is considered and discussed from the Bayesian perspective. A novel tracking technique combining the variable regularization approach with the forgetting operation is derived and elaborated into algorithmic details. The regularization of the parameter covariance is accomplished by incorporating soft equality constraints on the regression parameters into the learning procedure. The resultant procedure is designed to smooth the parameter estimate, preventing it from changing too rapidly. Moreover, the form of the constraints guarantees a minimal amount of information about the parameter estimate, which makes the estimator robust with respect to poor system excitation. The forgetting of obsolete information is provided in two different parameterization options and is performed automatically in a way that complies with the degree of the process nonstationarity. The whole concept preserves the self-reproducibility of the statistics of the normal-Wishart distribution.
Application of improved algorithm based on sphere OBB hybrid hierarchical bounding box in Teaching

Abstract In this paper, on the premise of analyzing the theoretical knowledge of collision detection algorithm, the advantages and disadvantages of several commonly used hierarchical bounding box methods are compared and analyzed, and then the traditional collision detection algorithm is studied. This paper presents a new collision detection algorithm. After preprocessing, the first order collision detection based on the separation axis theorem is used to exclude a large number of disjoint spatial model objects through preprocessing. The purpose of adding primary collision detection is to further investigate disjoint object pairs, and prepare for the final detailed detection stage. The pre-determined OBB bounding box is used for fast collision detection, and its storage structure and mode are optimized quickly. After preprocessing, it effectively eliminates the obviously disjoint space object pairs, reduces a large number of operations, and improves the detection efficiency.
Software Risk Estimation Through Bug Reports Analysis and Bug-fix Time Predictions

Categorizing the level of software risk components is very important for software developers. This categorization allows the developers to increase software availability, security, and provide better project management process. This research proposes a novel approach risk estimation system that aims to help software internal stakeholders to evaluate the currently existing software risk by predicting a quantitative software risk value. This risk value is estimated using the earlier software bugs reports based on a comparison between current and upcoming bug-fix time, duplicated bugs records, and the software component priority level. The risk value is retrieved by using a machine learning on a Mozilla Core dataset (Networking: HTTP software component) using Tensorflow tool to predict a risk level value for specific software bugs. The total risk results ranged from 27.4% to 84% with maximum bug-fix time prediction accuracy of 35%. Also, the result showed a strong relationship for the risk values obtained from the bug-fix time prediction and showed a low relationship with the risk values from the duplicated bug records.
Comparative Analysis of Nine Arabic Stemmers on Microblog Information Retrieval

Stemming has shown to be effective in many natural language processing (NLP) applications such as in document classification, machine translation, and information retrieval (IR). This paper compares the performance of nine stemmers for Arabic language on microblog IR. These stemmers include: Information Science Research Institute (ISRI), Tashaphyne, Khoja, AL-stem, Light10, Motaz, Assem, Farasa, and ARLStem. Each stemmer was studied independently using the EveTAR dataset on a specific information retrieval task to obtain relevant query tweets. The performance of the nine stemmers was evaluated using BM25, precision at 30, and Mean Average Precision (MAP). The results show that root-based stemmers (i.e. ISRI and Khoja) outperformed others.
Computation of CNN’s Sensitivity to Input Perturbation

Although Convolutional Neural Networks (CNNs) are considered as being "approximately invariant" to nuisance perturbations such as image transformation, shift, scaling, and other small deformations, some existing studies show that intense noises can cause noticeable variation to CNNs' outputs. This paper focuses on exploring a method of measuring sensitivity by observing corresponding output variation to input perturbation on CNNs. The sensitivity is statistically defined in a bottom-up way from neuron to layer, and finally to the entire CNN network. An iterative algorithm is proposed for approximating the defined sensitivity. On the basic architecture of CNNs, the theoretically computed sensitivity is verified on the MNIST database with four types of commonly used noise distributions: Gaussian, Uniform, Salt and Pepper, and Rayleigh. Experimental results show the theoretical sensitivity is on the one hand in agreement with the actual output variation what on the maps, layers or entire networks are, and on the other hand an applicable quantitative measure for robust network selection.
Cratus: A Lightweight and Robust Approach for Mobile Live Streaming

Live video applications are getting popular, and content providers widely use adaptive bitrate (ABR) streaming to improve QoE while maintaining low latency. However, users’ increasing preference to watch videos on mobile devices poses great challenges for ABR algorithm due to the dramatically varying cellular network. Existing learn-based ABR algorithms face difficulties to generalize to various network conditions because of their reliance on training traces, and model/rule-based ABR schemes suffer from rebuffering under low latency constraint since they cannot robustly control the buffer occupancy within a small range. To address it, this work proposes Cratus, a lightweight and robust ABR algorithm for mobile live streaming, which achieves high QoE and low latency by accurately regulating the buffer at a small level. To enhance the control ability, Cratus controls the buffer dynamic behavior rather than the buffer occupancy. By using sliding mode control approach, Cratus robustly controls the buffer dynamic and ensures that the buffer occupancy is bounded around the target level regardless of network uncertainties. Trace-driven experiments show that Cratus outperforms existing ABRs: average QoE is increased by 12.3 to 28.6 percent, and rebuffering time is limited within 0.8 <inline-formula><tex-math notation="LaTeX">$s$</tex-math></inline-formula> on average, which is reduced by 53.5 to 92.3 percent.
Objective Comparison and Selection in Mono- and Multi-Objective Evolutionary Neurocontrollers

Often in multi-objective problems, several elemental objectives are combined into compound objectives by using auxiliary equations to reduce these problems to just one or two objectives. Reducing the number of objectives simplifies the problem into a more easily optimized mono-objective problem, or for multi-objective problems, reduces the Pareto front to a few dimensions for easy analysis. Here, multi-objective evolutionary neurocontrollers with both compound and elemental objectives are compared to a mono-objective evolutionary neurocontroller. The goal of this research is to compare the effectiveness of individual elemental and compound objective effectiveness, and not directly compare mono- and multi-objectivity. The effectiveness of each of the objectives is determined through a series of experiments using a previously demonstrated Lamarckian-inherited neuromodulated evolutionary neurocontroller. The evolved neurocontrollers operate a simulated vehicle pursuing a basic evader vehicle in the pursuit-evasion game. Both vehicles are subject to the effects of mass and drag. It is shown that under certain circumstances, binary objectives can be unsuitable choices as objectives, and that it can be more effective to use multi-objective solutions than to combine elemental objective problems into mono-objective problems by auxiliary functions. It is also shown that the obvious choice of objective may not be the most effective choice.
Reliable Image Transmission in Wireless Sensor Networks for Smart Grid Applications

With the rapid development of Smart Grids over wireless sensor networks, the reliable image and video transmission has become more imperative and faces many challenges in wireless harsh environment. Transmission of the image data may experience distortions that decrease the quality of the image due to noise, interference and path loss. In this paper, we show the effect of two error correction algorithms (Static and Adaptive RS) in order to increase the perceptual quality of the transmitted images. Here, as Static Reed-Solomon (RS) coding utilizes additional bits to the information transmitted to correct errors at the receiver side. Unlike Static RS, Adaptive RS is a derived version of the Reed-Solomon that sets the error correction codes of the algorithm based on the distance between the nodes adaptively. Performance results obtained from numerous simulations indicate that the Adaptive RS exhibits much better performance results in terms of PSNR, BER, and Throughput than Static RS. It is good candidate for providing application layer perceptual quality requirements for Smart Grid applications.
Effective Hamiltonian of the Jaynes–Cummings model beyond rotating-wave approximation*

The Jaynes–Cummings model with or without rotating-wave approximation plays a major role to study the interaction between atom and light. We investigate the Jaynes–Cummings model beyond the rotating-wave approximation. Treating the counter-rotating terms as periodic drivings, we solve the model in the extended Floquet space. It is found that the full energy spectrum folded in the quasi-energy bands can be described by an effective Hamiltonian derived in the high-frequency regime. In contrast to the Z 2 symmetry of the original model, the effective Hamiltonian bears an enlarged U (1) symmetry with a unique photon-dependent atom-light detuning and coupling strength. We further analyze the energy spectrum, eigenstate fidelity and mean photon number of the resultant polaritons, which are shown to be in accordance with the numerical simulations in the extended Floquet space up to an ultra-strong coupling regime and are not altered significantly for a finite atom-light detuning. Our results suggest that the effective model provides a good starting point to investigate the rich physics brought by counter-rotating terms in the frame of Floquet theory.
Machine Learning for Supplementing Behavioral Assessment

The Questions About Behavioral Function (QABF) has a high degree of convergent validity, but there is still a lack of agreement between the results of the assessment and the results of experimental function analysis. Machine learning (ML) may improve the validity of assessments by using data to build a mathematical model for more accurate predictions. We used published QABF and subsequent functional analyses to train ML models to identify the function of behavior. With ML models, predictions can be made from indirect assessment results based on learning from results of past experimental functional analyses. In Experiment 1, we compared the results of five algorithms to the QABF criteria using a leave-one-out cross-validation approach. All five outperformed the QABF assessment on multilabel accuracy (i.e., percentage of predictions with the presence or absence of each function indicated correctly), but false negatives remained an issue. In Experiment 2, we augmented the data with 1,000 artificial samples to train and test an artificial neural network. The artificial network outperformed other models on all measures of accuracy. The results indicated that ML could be used to inform conditions that should be present in a functional analysis. Therefore, this study represents a proof-of-concept for the application of machine learning to functional assessment.
Cluster load based content distribution and speculative execution for geographically distributed cloud environment

The scale of big data has shown an explosive growth, which makes the processing of big data put forward higher requirements on data centers, and a single data center can no longer meet the needs of big data processing. To deal with this situation, a geographically distributed cloud system needs to be built. However, in the geographically distributed cloud system, each data center is distributed in different geographic locations, which makes the data placement operations in the geographically distributed cloud system lead to greater overhead. To solve this problem, this paper proposes a data placement strategy. This strategy comprehensively considers the data transmission latency, bandwidth cost, cloud server storage capacity, and load capacity during the data placement process, and formulates a data placement problem that minimizes the energy consumption of data transmission. Then the minimum set cover method based on Lagrangian relaxation is used to solve this problem and obtain the optimal data placement scheme. On the other hand, in a geographically distributed cloud data center, the execution progress of the job submitted by the user will be affected by the straggler task. To solve this problem, this paper proposes a speculative execution strategy for the geographically distributed cloud system. This strategy performs different speculative execution operations according to the state of the cluster load, and then calculates the load capacity of the nodes in the cluster. The node with the strongest load capacity in the cluster is used to perform speculative execution operations. Experimental results show that the proposed data placement strategy can effectively improve the performance of the energy consumption, the data storage cost, the network transmission cost and the data transmission time. The proposed speculative execution strategy can effectively improve the performance of the job completion time, cluster throughput and QoS satisfaction rate.
Economic Implications of Scaling Blockchains: Why the Consensus Protocol Matters

This paper examines the economic implications of scaling blockchains under two different consensus protocols: Proof-of-Work (PoW) and Proof-of-Stake (PoS). We study an economic model whereby agents can store wealth through the blockchain's cryptocurrency but may face a costly delay when liquidating due to the blockchain's finite transaction rate. Agents may expedite processing by paying fees to the blockchain's validators. Within such a model, we study the ability of a malicious agent to compromise the security of the blockchain. We show how improved scaling alleviates congestion, leading to a decrease in equilibrium fees. Under a PoW protocol, this leads validators to earn less revenue and thus spend less on computational power, lowering the cost of a successful attack and therefore the security of the PoW blockchain. Scaling has the opposite effect for the PoS protocol as alleviating congestion increases the demand and therefore the market value of the blockchain's cryptocurrency. That increased market value increases the cost of acquiring enough cryptocurrency necessary for a successful attack and thereby improves PoS blockchain security.