Reviewerly/exHarmony huggingface dataset:
Research on Green Management Effect Evaluation of Power Generation Enterprises in China Based on Dynamic Hesitation and Improved Extreme Learning Machine

Carbon emissions and environmental protection issues have become the pressure from the international community during the current transitional stage of China’s energy transformation. China has set a macro carbon emission target, which will reduce carbon emissions per unit of Gross Domestic Product (GDP) by 40% in 2020 and 60–65% in 2030 than that in 2005. To achieve the emission reduction target, the industrial structure must be adjusted and upgraded. Furthermore, it must start from a high-pollution and high-emission industry. Therefore, it is of practical significance to construct a low-carbon sustainability and green operation benefits of power generation enterprises to save energy and reduce emissions. In this paper, an intuitionistic fuzzy comprehensive analytic hierarchy process based on improved dynamic hesitation degree (D-IFAHP) and an improved extreme learning machine algorithm optimized by RBF kernel function (RELM) are proposed. Firstly, we construct the evaluation indicator system of low-carbon sustainability and green operation benefits of power generation enterprises. Moreover, during the non-dimensional processing, the evaluation index system is determined. Secondly, we apply the evaluation indicator system by an empirical analysis. It is proved that the D-IFAHP evaluation model proposed in this paper has higher accuracy performance. Finally, the RELM is applied to D-IFAHP to construct a combined evaluation model named D-IFAHP-RELM evaluation model. The D-IFAHP evaluation results are used as the input of the training sets of the RELM algorithm, which simplifies the comprehensive evaluation process and can be directly applied to similar projects.
Quantifying the Breakability of Voice Assistants

In this paper, we present a thorough study of voice impersonation attacks that can compromise the security of voice authentication technology deployed in several popular, state-of-the-art Android and iOS apps. Our study is based on our formulated Sneakers attack system that comprises a variety of well-known as well as newly designed attacks: (1) recorded and replayed voice of the authorized user (replay attack); (2) reordered and played-back voice of the authorized user (reorder attack); and (3) synthesized voice generated - based on voice conversion techniques - using an unauthorized user's voice (standard conversion attack), or using a noise-free recording from a text-to-speech engine (TTS conversion attack). Taking Sneakers as a basis, we report on a carefully designed study to examine a variety of real-world voice authentication apps for their vulnerability against malicious authentication. Our study follows a two-phase methodology. In the preliminary phase, we analyze 8 popular mobile apps against standard simplistic attack setups. Our results show that, while the tested apps seem to resist the reorder attack and the standard conversion attack, they are highly vulnerable to the replay attack. In the main phase of the study, we comprehensively assess 5 of the above apps against more advanced newly designed attack setups. Like in the preliminary phase, the apps prove to be highly vulnerable to the replay attack. More seriously, the apps also turn out to be highly insecure against our advanced attack setups, i.e., the reorder attack with coordinated timing and the TTS conversion attack, yielding success rates of 82%-98%. These malicious authentication measurement results are highly pertinent in practice because, we demonstrate that the apps generally work well in the benign authentication scenario to reliably "accept" an authorized user and "reject" an unauthorized user. Our work shows that many standard attacks that prior work demonstrated to be effective against standalone voice authentication algorithms do not work against current voice authentication apps. Yet, our new attack designs could still compromise these apps. Overall, our work highlights a serious vulnerability of real-world voice authentication apps, which seems very challenging to mitigate at a fundamental level.
Improving Short Job Latency Performance in Hybrid Job Schedulers with Dice

It is common to find a mixture of both long batch jobs and latency-sensitive short jobs in enterprise data centers. Recently hybrid job schedulers emerge as attractive alternatives of conventional centralized job schedulers.
On the Transmission Probabilities in Quantum Key Distribution Systems Over FSO Links

In this paper, we investigate the transmission probabilities in three cases (depending only on the legitimate receiver, depending only the eavesdropper, and depending on both legitimate receiver and eavesdropper) in quantum key distribution (QKD) systems over free-space optical links. To be more realistic, we consider a generalized pointing error scenario, where the azimuth and elevation pointing error angles caused by stochastic jitters and vibrations in the legitimate receiver platform are independently distributed according to a non-identical normal distribution. Taking these assumptions into account, we derive approximate expressions of transmission probabilities by using the Gaussian quadrature method. To simplify the expressions and get some physical insights, some asymptotic analysis on the transmission probabilities is presented based on asymptotic expression for the generalized Marcum Q-function when the telescope gain at the legitimate receiver approaches to infinity. Moreover, from the asymptotic expression for the generalized Marcum Q-function, the asymptotic outage probability over Beckmann fading channels (a general channel model including Rayleigh, Rice, and Hoyt fading channels) can be also easily derived when the average signal-to-noise ratio is sufficiently large, which shows the diversity order and array gain.
Modeling the Performance of Atomic Primitives on Modern Architectures

Utilizing the atomic primitives of a processor to access a memory location atomically is key to the correctness and feasibility of parallel software systems. The performance of atomics plays a significant role in the scalability and overall performance of parallel software systems.
Primal-dual algorithm to solve the constrained second-order total generalized variational model for image denoising

The variational method, which is a popular approach for image denoising, aims to estimate the original image from a noisy or corrupted image. To consider the constraints of image pixel values fully, our study investigates a constrained second-order total generalized variational (TGV) model, which includes non-negative and bounded constraints as a special case. By adopting an equivalent definition of the second-order TGV, we transform the proposed constrained minimization problem into a minimization of the sum of two convex functions, where one is composed of a linear transformation. Subsequently, we employ the relaxed primal-dual proximity algorithm to solve it. The advantage of the obtained algorithm is that it is matrix-inversion free and does not involve any subproblem. Numerical results demonstrate that the performance of the constrained TGV model is slightly better than that of the unconstrained model.
Political Marketing Strategy: Soundbites and Voting Behaviour in Contemporary Ghana

This study examines the inﬂuence of soundbites, political media, rationality, and emotion on voter behavior utilizing the Rational Choice Theory in the context of political marketing. Data were collected through questionnaire self-administered to 557 voters. Structural equation modeling was applied to examine the significant effects among the studied constructs. The findings revealed that soundbites, political media, rationality, and emotion positively influence voter behavior in Ghana. Again, the finding showed that the language used in communicating the soundbites significantly moderates the relationship between soundbites and voter behavior. Overall, the study establishes that soundbites and emotion relatively contribute higher to voter behavior. The study suggests that soundbite is gaining recognition in Ghana’s politics as a political marketing strategy to disseminate information to voters.
No-reference artifacts measurements based video quality metric

In multimedia delivery, perceived quality of video signal has a significant role in overall users Quality of Experience (QoE). Therefore, multimedia service providers must constantly measure and monitor perceived video quality, which is usually performed using no-reference (NR) video quality metrics. In this paper, a novel NR objective video quality metric named Artifacts Measurements Based Video Quality Metric (AMB-VQM) is proposed. In addition to artifacts measures computed by blocking (BL), packet-loss (PL), and freezing (FZ) artifacts detection algorithms, the metric incorporates artifacts masking based on spatial and temporal video content complexity. Furthermore, a newly created FERIT-RTRK-2 video quality database, which contains 486 Full HD test video sequences impaired by video compression (MPEG-2, H.264/AVC, and H.265), packet-loss, frame freezing, and combinations of these procedures, is presented in this paper. FERIT-RTRK-2 database is publicly available for scientific community at http://www.rt-rk.com/other/VideoDBReadme.html. Additionally, newly created user-friendly subjective video quality assessment tool with graphical user interface, which can be used for conducting of subjective video quality experiments, is presented. In experimental part the performance of the proposed AMB-VQM is compared to this of 10 other objective video quality metrics (PSNR, SSIM, VSNR, PSNRHVS, PSNRHVSM, VIFP, ViS3, ST-MAD, BRISQUE, VIIDEO) using distorted video sequences from four different video quality databases: CSIQ, LIVE, FERIT-RTRK and FERIT-RTRK-2. The results show that AMB-VQM achieves high performance when predicting the video quality for videos distorted in different manners. AMB-VQM results outperform the results of most of the analyzed popular and very often used video quality metrics.
Cellular Genetic Algorithms: Understanding the Behavior of Using Neighborhoods

In this paper, we analyze the neighborhood effect in the selection of parents on an evolutionary algorithm. In this line, we compare a cellular genetic algorithm (cGA), which intrinsically uses the neighbor notion in the mating process, with a modified genetic algorithm including the concept of neighborhood in the selection of parents. Additionally, we analyze the neighborhood size considered for the selection of parent, trying to discover if a quasi-optimal size exists. All the analysis is carried out from a traditional analytic sense to a theoretical point of view regarding evolvability measures. The experimental results suggest that the neighbor effect is important in the performance of an evolutionary algorithm and could provide the cGA with higher chances of success in well-known optimization problems. Regarding the neighborhood size, there is an evidence that a range of neighbors of six, plus/minus two, individuals leads to the cGA to perform more efficiently than other considered sizes.
Probabilistic Bisection Converges Almost as Quickly as Stochastic Approximation

The probabilistic bisection algorithm (PBA) solves a class of stochastic root-finding problems in one dimension by successively updating a prior belief on the location of the root based on noisy responses to queries at chosen points. The responses indicate the direction of the root from the queried point and are incorrect with a fixed probability. The fixed-probability assumption is problematic in applications, and so we extend the PBA to apply when this assumption is relaxed. The extension involves the use of a power-one test at each queried point. We explore the convergence behavior of the extended PBA, showing that it converges at a rate arbitrarily close to, but slower than, the canonical “square root” rate of stochastic approximation.
Discriminatively Re-trained I-vector Extractor for Speaker Recognition

In this work we revisit discriminative training of the i-vector extractor component in the standard speaker verification (SV) system. The motivation of our research lies in the robustness and stability of this large generative model, which we want to preserve, and focus its power towards any intended SV task. We show that after generative initialization of the i-vector extractor, we can further refine it with discriminative training and obtain i-vectors that lead to better performance on various benchmarks representing different acoustic domains.
12 lead electrocardiography signals compression by a new genetic programming based mathematical modeling algorithm

Telemedicine refers to a group of modern medical services that are provided on the platform of advanced telecommunication technologies. One of these services is the screening for heart diseases, which are the leading cause of mortality across the world. But the development of telemedicine systems for cardiac screening faces multiple challenges. One of these challenges is the large volume of ECG signals, which makes them difficult to store and transfer. Of the many algorithms proposed for the compression of ECG signals, most rely on the processing of data as discrete numerical values. The alternative approach followed in this study is to model the signal compression problem into a regression problem and then convert it into a text compression problem. Using this approach, the paper presents a new genetic programming based method for the compression of ECG signals. The proposed method starts with denoising and smoothing the ECG signal with discrete wavelet transform and then constructing its mathematical model with a genetic programming based algorithm. This model is a piecewise mathematical function where each sub-function models one part of the signal. Next, the model is converted to a character string and regular expressions are used to extract the function coefficients and encode the symbols contained in the string. Finally, the strings and coefficients are compressed using the LZW and arithmetic encoding methods, respectively. The efficiency of the algorithm is evaluated through compression ratio (CR), percent root-mean-square difference (PRD), root-mean-square-error (RMSE) and quality score (QS) on MIT-BIH Arrhythmia Database records. The evaluation results demonstrate the good performance of the proposed method in comparison with other state-of-the-art techniques.
Scaling Simulation-to-Real Transfer by Learning Composable Robot Skills

We present a novel solution to the problem of simulation-to-real transfer, which builds on recent advances in robot skill decomposition. Rather than focusing on minimizing the simulation-reality gap, we learn a set of diverse policies that are parameterized in a way that makes them easily reusable. This diversity and parameterization of low-level skills allows us to find a transferable policy that is able to use combinations and variations of different skills to solve more complex, high-level tasks. In particular, we first use simulation to jointly learn a policy for a set of low-level skills, and a “skill embedding” parameterization which can be used to compose them. Later, we learn high-level policies which actuate the low-level policies via this skill embedding parameterization. The high-level policies encode how and when to reuse the low-level skills together to achieve specific high-level tasks. Importantly, our method learns to control a real robot in joint-space to achieve these high-level tasks with little or no on-robot time, despite the fact that the low-level policies may not be perfectly transferable from simulation to real, and that the low-level skills were not trained on any examples of high-level tasks. We illustrate the principles of our method using informative simulation experiments. We then verify its usefulness for real robotics problems by learning, transferring, and composing free-space and contact motion skills on a Sawyer robot using only joint-space control. We experiment with several techniques for composing pre-learned skills, and find that our method allows us to use both learning-based approaches and efficient search-based planning to achieve high-level tasks using only pre-learned skills.
Efficient Time-Evolving Stream Processing at Scale

Time-evolving stream datasets exist ubiquitously in many real-world applications where their inherent hot keys often evolve over times. Nevertheless, few existing solutions can provide efficient load balancing on these time-evolving datasets while preserving low memory overhead. In this paper, we present a novel load balancing mechanism (named FISH), which can provide the efficient time-evolving stream processing at scale through recent hot keys identification and worker assignment. The key insight of this work is that the keys of time-evolving stream data can have a skewed distribution within the bounded distance of time interval. This enables to accurately identify the recent hot keys for the real-time load balancing within a bounded scope. We therefore propose an epoch-based recent hot key identification with specialized intra-epoch frequency counting (for maintaining low memory overhead) and inter-epoch hotness decaying (for suppressing superfluous computation). We also propose to heuristically infer the accurate information of remote workers through computation rather than communication for cost-efficient worker assignment. We have integrated our approach into Apache Storm. Our results on a cluster of 128 nodes for both synthetic and real-world stream datasets show that FISH significantly outperforms state-of-the-arts with the average and the 99th percentile latency reduction by 87.12 and 76.34 percent (versus W-Choices), and memory overhead reduction by 96.66 percent (versus Shuffle Grouping).
Explainable Sequential Recommendation using Knowledge Graphs

Knowledge Graphs have proven to be extremely valuable to recommender systems in recent years. By exploring the links within a knowledge graph, the connectivity between users and items can be discovered as paths, which provide rich and complementary information to user-item interactions. Leveraging this wealth of heterogeneous information for sequential recommendation is a challenging task, as it requires the ability to effectively encoding a diversity of semantic relations and connectivity patterns. To address the limitations of existing embedding-based and path-based methods for KG-aware recommendation, our work proposes a novel hybrid framework that naturally incorporates path representations with attentive weights derived from the knowledge graphs and sequential preference which links items with existing knowledge base into recommender systems to effectively recommend next item to a user. Our proposed model further employs a deep neural network to predict the interaction probabilities of a user and unseen items. Extensive experiments on real-world datasets illustrate that our approaches can give large performance improvements in a variety of scenarios, including movie, music and book recommendation.
Region-filtering correlation tracking

Correlation filters (CF) have demonstrated a good performance in visual tracking. However, the base training sample region is larger than the object region, including the interference region (IR). IRs in training samples from cyclic shifts of the base training sample severely degrade the quality of the tracking model. In this paper, a region-filtering correlation tracking (RFCT) algorithm is proposed to address this problem. In this algorithm, we filter training samples by introducing a spatial map into the standard CF formulation. Compared with the existing correlation filter trackers, the proposed tracker has the following advantages. (1) Using a spatial map, the correlation filter can be learned on a larger search region without the interference of IR. (2) Due to processing training samples by a spatial map, it is a more general way to control background information and target information in training samples. In addition, a better spatial map can be explored, the values of which are not restricted. Quantitative evaluations are performed on four benchmark datasets: OTB-2013, OTB-2015, VOT2015, and VOT2016. Experimental results demonstrate that the proposed RFCT algorithm performs favorably against several state-of-the-art methods.
Integrating four-dimensional ontology and systems requirements modelling

(2019). Integrating four-dimensional ontology and systems requirements modelling. Journal of Engineering Design: Vol. 30, Ontological Engineering for Supporting Semantic Reasoning in Design, pp. 477-522.
RazorNet: Adversarial Training and Noise Training on a Deep Neural Network Fooled by a Shallow Neural Network

In this work, we propose ShallowDeepNet, a novel system architecture that includes a shallow and a deep neural network. The shallow neural network has the duty of data preprocessing and generating adversarial samples. The deep neural network has the duty of understanding data and information as well as detecting adversarial samples. The deep neural network gets its weights from transfer learning, adversarial training, and noise training. The system is examined on the biometric (fingerprint and iris) and the pharmaceutical data (pill image). According to the simulation results, the system is capable of improving the detection accuracy of the biometric data from 1.31% to 80.65% when the adversarial data is used and to 93.4% when the adversarial data as well as the noisy data are given to the network. The system performance on the pill image data is increased from 34.55% to 96.03% and then to 98.2%, respectively. Training on different types of noise can benefit us in detecting samples from unknown and unseen adversarial attacks. Meanwhile, the system training on the adversarial data as well as noisy data occurs only once. In fact, retraining the system may improve the performance further. Furthermore, training the system on new types of attacks and noise can help in enhancing the system performance.
TeeVR

Conventional image-based rendering has limited applicability for large-scale spaces. In this study, we demonstrate an efficient alternative to conventional image-based rendering. Our key approach is based on a spatial template (ST), which solely includes architectural geometric primitives. The predictability of ST improves the efficiency of acquisition, storage, and rendering. Thereby, our system can be applied to the modeling and rendering of larger indoor spaces.
Incrementally Building Topology Graphs via Distance Maps

Mapping is an essential task for mobile robots and topological representation often works as a basis for the various applications. In this paper, a novel framework that can build topological maps incrementally is proposed. The algorithm is using a distance map, and in our framework the topological map can grow as we append more sensor data to the map. To demonstrate our algorithm, we show the result of the distance map based method on several popular maps and run the incremental framework with raw sensor data to have a growing topological map, as an example of a robot exploring the environment.
Iterative first-order reverse image filtering

In this paper, we study an unconventional but practically meaningful problem reverse image filtering, which aims at removing the filtering effects with the given image filter. We propose an iterative first-order reverse image filtering algorithm based on the Newton-Raphson method. The convergence of the proposed iterative procedure is verified via extensive experiments. The experimental results show that our method could effectively reverse many prevalent image filters. Our method finds its practical usage in many applications, such as non-blind image deblurring and image details recovery. Furthermore, our method could probe the behaviors of black-box image filtering systems, which poses security concerns on the protection for the privately commercial image filters.