Reviewerly/exHarmony huggingface dataset:
Four Classes of Optimal Quinary Cyclic Codes

The construction of optimal ternary cyclic codes has received a lot of attention and much progress has been made. However, little is known about optimal quinary cyclic codes. In this letter, four classes of infinite families of optimal quinary cyclic codes with parameters [5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> -1, 5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> -2m-2, 4] are presented.
Implementation of a Proof of Concept for a Blockchain-based Smart Contract for the Automotive Industry in Mauritius

In recent years, there has been a growth of interest in the blockchain technology across a wide range of industries.Blockchain technology has the potential to transform the way businesses operate especially in the automotive industry.The distributed infrastructure and the secure nature of the blockchain technology encourages trust among businesses and consumers.In Mauritius, the automotive industry is facing challenges such as tampering of vehicle information, falsification of mileage and poor traceability which leads to a lack of trust from customers.In this work, an implementation of a proof of concept (POC) for a blockchain-based smart contract application has been proposed and implemented to mitigate these challenges.The automotives use cases: (a) vehicle importation; and (b) vehicle sale and registration have been implemented in the IBM blockchain platform which provides a secure and transparent way to invoke transactions.Finally, the performance and benefits of the Hyperledger Fabric vehicle application have been assessed based on transparency, security, traceability and efficiency.
Research and Study of the Hybrid Algorithms Based on the Collective Behavior of Fish Schools and Classical Optimization Methods

Inspired by biological systems, swarm intelligence algorithms are widely used to solve multimodal optimization problems. In this study, we consider the hybridization problem of an algorithm based on the collective behavior of fish schools. The algorithm is computationally inexpensive compared to other population-based algorithms. Accuracy of fish school search increases with the increase of predefined iteration count, but this also affects computation time required to find a suboptimal solution. We propose two hybrid approaches, intending to improve the evolutionary-inspired algorithm accuracy by using classical optimization methods, such as gradient descent and Newton’s optimization method. The study shows the effectiveness of the proposed hybrid algorithms, and the strong advantage of the hybrid algorithm based on fish school search and gradient descent. We provide a solution for the linearly inseparable exclusive disjunction problem using the developed algorithm and a perceptron with one hidden layer. To demonstrate the effectiveness of the algorithms, we visualize high dimensional loss surfaces near global extreme points. In addition, we apply the distributed version of the most effective hybrid algorithm to the hyperparameter optimization problem of a neural network.
Privacy-Aware UAV Flights through Self-Configuring Motion Planning

During flights, an unmanned aerial vehicle (UAV) may not be allowed to move across certain areas due to soft constraints such as privacy restrictions. Current methods on self-adaption focus mostly on motion planning such that the trajectory does not trespass predetermined restricted areas. When the environment is cluttered with uncertain obstacles, however, these motion planning algorithms are not flexible enough to find a trajectory that satisfies additional privacy-preserving requirements within a tight time budget during the flights. In this paper, we propose a privacy risk aware motion planning method through the reconfiguration of privacy-sensitive sensors. It minimises environmental impact by re-configuring the sensor during flight, while still guaranteeing the safety and energy hard constraints such as collision avoidance and timeliness. First, we formulate a model for assessing privacy risks of dynamically detected restricted areas. In case the UAV cannot find a feasible solution to satisfy both hard and soft constraints from the current configuration, our decision making method can then produce an optimal reconfiguration of the privacy-sensitive sensor with a more efficient trajectory. We evaluate the proposal through various simulations with different settings in a virtual environment and also validate the approach through real test flights on DJI Matrice 100 UAV.
Efficient Color Artifact Removal Algorithm Based on High-Efficiency Video Coding (HEVC) for High-Dynamic Range Video Sequences

High efficiency video coding (HEVC) has been developed rapidly to support new generation display devices and their ultra high definition (UHD) with high dynamic range (HDR) and wide color gamut (WCG).To support HDR/WCG sequences on the HEVC standard, pre-/post-processing technique has been designed.After an HDR video is compressed, a reconstructed frame exhibits chromatic distortions that resemble color smearing.To remove this color artifact, we herein propose a block-level quantization parameter (QP) offset-control-based efficient compression algorithm for the HDR sequence.First, we extract the candidate coding units (CUs) with the annoying area to the human eye based on the just noticeable distortion (JND) model.Subsequently, the chromatic distorted blocks are verified by the activity function as the chromatic artifact is observed at the nearby strong edge.For the verified artifact blocks, we reassign the QPs for the Cb and Cr chroma components.Our experimental results show that the proposed method yields an average gain in BD-rate of 3.3% for U, and 3.4% for V with a negligible bitrate increase of 0.3% on average.
High-temperature stereo-digital image correlation using a single polarization camera

We propose a new, to the best of our knowledge, full-frame, single-camera stereo-digital image correlation (stereo-DIC) technique that is capable of measuring surface profile and deformation over a wide temperature range. This technique uses a single polarization camera and a polarizing beam splitter to synchronously capture two full-frame sub-images with orthogonal polarization directions. By further adopting the idea of active imaging that combines monochromatic blue-light active lighting and narrow bandpass filter imaging, this system can capture stable and high-contrast speckle images of specimens at extremely high temperatures. From the captured sub-image pairs, profile and deformation fields can be retrieved using a regular stereo-DIC algorithm. Compared with the existing full-frame single-camera stereo-DIC systems implemented on a color camera, the established system offers wider applicability, since it is insensitive to serious variations in ambient light in non-laboratory environments and to the thermal radiation of hot objects in extreme high-temperature environments. The effectiveness and accuracy of the proposed technique are validated by room-temperature tests and high-temperature tests at up to 1000°C. The advantages of a simple configuration, free synchronization, and full-frame high-temperature deformation measurement ability offer the proposed technique potential in both high-speed and high-temperature 3D measurement applications.
Gaining Situation Awareness through a Vibrotactile Display to Mitigate Motion Sickness in Fully-Automated Driving Cars

Many previous studies mention that passive drivers or passengers of fully-automated driving cars have less awareness of the surrounding and more experience to motion sickness symptoms when engaging in non-driving tasks. This occurrence is especially magnified when riding in an urban area with lots of junctions and corners. The aim of the current study is to investigate the effects of peripheral information about upcoming manoeuvres through a vibrotactile display in increasing the fully-automated driving car passengers’ awareness of situations and mitigating their motion sickness level. Twenty participants took part in the experiment which used a Wizard of Oz method to simulate autonomous driving, and the experiment was conducted in an instrumented car on a real road environment. Objective and subjective measurements were gathered. The results show that the implementation of the vibrotactile display increased situation awareness but failed to reduce the motion sickness. This study concludes that in order to mitigate motion sickness inside a fully-automated driving car, more specific information need to be included in the peripheral information. In addition, a device that can actively help in controlling the posture movements should also be implemented in the vehicle.
A Noninvasive Method to Detect Diabetes Mellitus and Lung Cancer Using the Stacked Sparse autoencoder

Diabetes mellitus and lung cancer are two of the most common fatal diseases in the world, causing considerable deaths every year. However, it is not easy to detect diabetes mellitus and lung cancer efficiently--needing professional medical instruments such as a CT and a qualified individual to perform the Fasting Plasma Glucose test. Considering the risks and various inconveniences with conventional diagnosis methods, noninvasive approaches based on computerized analysis are desired. The aim of this paper is to distinguish patients with diabetes mellitus, lung cancer from healthy people simultaneously by analyzing facial images through the stacked sparse autoencoder. Experimental results on a dataset containing 450 healthy samples, 284 diabetes and 175 lung cancer patients produced the F1-score of 93.57%, 97.54%, 81.56% for detecting healthy, diabetes and lung cancer, respectively, validating the effectiveness of our proposed method.
Neural Embedding-Based Metrics for Pre-retrieval Query Performance Prediction

Query Performance Prediction (QPP) is concerned with estimating the effectiveness of a query within the context of a retrieval model. It allows for operations such as query routing and segmentation, leading to improved retrieval performance. Pre-retrieval QPP methods are oblivious to the performance of the retrieval model as they predict query difficulty prior to observing the set of documents retrieved for the query. Since neural embedding-based models are showing wider adoption in the Information Retrieval (IR) community, we propose a set of pre-retrieval QPP metrics based on the properties of pre-trained neural embeddings and show that such metrics are more effective for query performance prediction compared to the widely known QPP metrics such as SCQ, PMI and SCS. We report our findings based on Robust04, ClueWeb09 and Gov2 corpora and their associated TREC topics.
Novel Technique for Robust Optimal Algorithmic Cooling

Heat-bath algorithmic cooling provides algorithmic ways to improve the purity of quantum states. These techniques are complex iterative processes that change from each iteration to the next and this poses a significant challenge to implementing these algorithms. Here, we introduce a new technique that on a fundamental level, shows that it is possible to do algorithmic cooling and even reach the cooling limit without any knowledge of the state and using only a single fixed operation, and on a practical level, presents a more feasible and robust alternative for implementing heat-bath algorithmic cooling. We also show that our new technique converges to the asymptotic state of heat-bath algorithmic cooling and that the cooling algorithm can be efficiently implemented; however, the saturation could require exponentially many iterations and remains impractical. This brings heat-bath algorithmic cooling to the realm of feasibility and makes it a viable option for realistic application in quantum technologies.
Segmentation of River Scenes Based on Water Surface Reflection Mechanism

Segmentation of a river scene is a representative case of complex image segmentation. Different from road segmentation, river scenes often have unstructured boundaries and contain complex light and shadow on the water’s surface. According to the imaging mechanism of water pixels, this paper designed a water description feature based on a multi-block local binary pattern (MB-LBP) and Hue variance in HSI color space to detect the water region in the image. The improved Local Binary Pattern (LBP) feature was used to recognize the water region and the local texture descriptor in HSI color space using Hue variance was used to detect the shadow area of the river surface. Tested on two data sets including simple and complex river scenes, the proposed method has better segmentation performance and consumes less time than those of two other widely used methods.
Facial Feature Embedded Cyclegan For Vis-Nir Translation

Visible and near-infrared (VIS-NIR) face recognition remains a challenging task due to distinctions between spectral components of two modalities. Inspired by the CycleGAN, this paper presents a method aiming to translate between VIS and NIR face images. To achieve this, we propose a new facial feature embedded CycleGAN. Firstly, to learn the particular feature while preserving common facial representation between VIS and NIR domains, we employ a general facial feature extractor (FFE) to extract effective features. Herein the MobileFaceNet is pre-trained on a VIS face database and serves as the FFE. Secondly, the domain-invariant feature learning is enhanced by proposing a new pixel consistency loss. Lastly, we establish a new WHU VIS-NIR database including varies in face rotation and expressions to enrich the training data. Experimental results on the Oulu-CASIA and our WHU VIS-NIR databases show that the proposed FFE-based CycleGAN (FFE-CycleGAN) outperforms some state-of-the-art methods and achieves 96.5% accuracy.
Demonstrative Reference and Semantic Space: A Large-Scale Demonstrative Choice Task Study

Spatial demonstratives (words like this and that) have been thought to primarily be used for carving up space into a peripersonal and extrapersonal domain. However, when given a noun out of context and asked to couple it with a demonstrative, speakers tend to choose this for words denoting manipulable objects (small, harmless, and inanimate), while non-manipulable objects (large, harmful, and animate) are more likely to be coupled with that. Here, we extend these findings using the Demonstrative Choice Task (DCT) procedure and map demonstrative use along a wide spectrum of semantic features. We conducted a large-scale (N = 2197) DCT experiment eliciting demonstratives for 506 words, rated across 65 + 11 perceptually and cognitively relevant semantic dimensions. We replicated the finding that demonstrative choice is influenced by object manipulability. Demonstrative choice was furthermore found to be related to a set of additional semantic factors, including valence, arousal, loudness, motion, time and more generally, the self. Importantly, demonstrative choices were highly structured across participants, as shown by a strong correlation detected in a split-sample comparison of by-word demonstrative choices. We argue that the DCT may be used to map a generalized semantic space anchored in the self of the speaker, the self being an extension of the body beyond physical space into a multidimensional semantic space.
Parallelizing Adam Optimizer with Blockwise Model-Update Filtering

Recently Adam has become a popular stochastic optimization method in deep learning area. To parallelize Adam in a distributed system, synchronous stochastic gradient (SSG) technique is widely used, which is inefficient due to heavy communication cost. In this paper, we attempt to parallelize Adam with blockwise model-update filtering (BMUF) instead. BMUF synchronizes model-update periodically and introduces a block momentum to improve performance. We propose a novel way to modify the estimated moment buffers of Adam and figure out a simple yet effective trick for hyper-parameter setting under BMUF framework. Experimental results on large scale English optical character recognition (OCR) task and large vocabulary continuous speech recognition (LVCSR) task show that BMUF-Adam achieves almost a linear speedup without recognition accuracy degradation and outperforms SSG-based method in terms of speedup, scalability and recognition accuracy.
Game theoretical approach for load balancing using SGMLB model in cloud environment

On-demand cloud computing is one of the rapidly evolving technologies that is being widely used in the industries now. With the increase in IoT devices and real-time business analytics requirements, enterprises that ought to scale up and scale down their services have started coming towards on-demand cloud computing service providers. In a cloud data center, a high volume of continuous incoming task requests to physical hosts makes an imbalance in the cloud data center load. Most existing works balance the load by optimizing the algorithm in selecting the optimal host and achieves instantaneous load balancing but with execution inefficiency for tasks when carried out in the long run. Considering the long-term perspective of load balancing, the research paper proposes Stackelberg (leader-follower) game-theoretical model reinforced with the satisfaction factor for selecting the optimal physical host for deploying the tasks arriving at the data center in a balanced way. Stackelberg Game Theoretical Model for Load Balancing (SGMLB) algorithm deploys the tasks on the host in the data center by considering the utilization factor of every individual host, which helps in achieving high resource utilization on an average of 60%. Experimental results show that the Stackelberg equilibrium incorporated with a satisfaction index has been very useful in balancing the loading across the cluster by choosing the optimal hosts. The results show better execution efficiency in terms of the reduced number of task failures by 47%, decreased 'makespan' value by 17%, increased throughput by 6%, and a decreased front-end error rate as compared to the traditional random allocation algorithms and flow-shop scheduling algorithm.
An Empirical Comparison of Unsupervised Constituency Parsing Methods

Unsupervised constituency parsing aims to learn a constituency parser from a training corpus without parse tree annotations.While many methods have been proposed to tackle the problem, including statistical and neural methods, their experimental results are often not directly comparable due to discrepancies in datasets, data preprocessing, lexicalization, and evaluation metrics.In this paper, we first examine experimental settings used in previous work and propose to standardize the settings for better comparability between methods.We then empirically compare several existing methods, including decade-old and newly proposed ones, under the standardized settings on English and Japanese, two languages with different branching tendencies.We find that recent models do not show a clear advantage over decade-old models in our experiments.We hope our work can provide new insights into existing methods and facilitate future empirical evaluation of unsupervised constituency parsing.
Mobile Assisted Learning for Self-Directed Learning Development at Technical University: SWOT Analysis

The aim of the paper is to identify strengths, weaknesses, opportunities, and threats (SWOT) of self-directed learning (SDL) supported by mobile-assisted technologies integrated into English for Specific Purposes course at university.Teachers cannot prepare students for all challenges of the modern world; therefore, students need skills of self-education to keep updated.SDL skills are crucial for today's learners but, as they include such features as ownership for learn ing, self-managing, self-reflection and extension of learning, SDL skills should be explained and trained by teachers -facilitators.The goal of our study was to carry out a SWOT analysis of the technology-enhanced SDL p rocess fro m teachers' and students' points of view.This kind of analysis will allow identifying co mmon and different issues that participants of the study encounter in the process of SDL.Therefore, we carried out a qualitative study with questionnaires to perform a SW OT analysis and to identify problematic issues.Co mparing the results, we found out that students and teachers identified mostly the same strengths and opportunities, whereas weaknesses and threats were different and required immed iate mean ingful solutions.Regarding educational technologies' impact, we observed that their integration also caused challenges for teachers to cope with.This study evidenced that technologies are a powerful tool to develop SDL skills and we developed some practical reco mmendations on how to avoid threats and eliminate weaknesses of SDL skills formation assisted by technologies.We also suggest some mobile-assisted technologies for the enhancement of SDL skills.
Specific Emitter Identification Based on Synchrosqueezing Transform for Civil Radar

Time-frequency (TF) signal features are widely used in specific emitter identification (SEI) which commonly arises in many applications, especially for radar signals. Due to data scale and algorithm complexity, it is difficult to obtain an informative representation for SEI with existing TF features. In this paper, a feature extraction method is proposed based on synchrosqueezing transform (SST). The SST feature has an equivalent dimension to Fourier transform, and retains the most relevant information of the signal, leading to on average approximately 20 percent improvement in SEI for complex frequency modulation signals compared with existing handcrafted features. Numerous results demonstrate that the synchrosqueezing TF feature can offer a better recognition accuracy, especially for the signals with intricate time-varying information. Moreover, a linear relevance propagation algorithm is employed to attest to the SST feature importance from the perspective of deep learning.
A New Approach to Determine the Optimal Number of Clusters Based on the Gap Statistic

Data clustering is one of the most important unsupervised classification method. It aims at organizing objects into groups (or clusters), in such a way that members in the same cluster are similar in some way and members belonging to different cluster are distinctive. Among other general clustering method, k-means is arguably the most popular one. However, it still has some inherent weaknesses. One of the biggest challenges when using k-means is to determine the optimal number of clusters, k. Although many approaches have been suggested in the literature, this is still considered as an unsolved problem. In this study, we propose a new technique to improve the gap statistic approach for selecting k. It has been tested on different datasets, on which it yields superior results compared to the original gap statistic. We expect our new method to also work well on other clustering algorithms where the number k is required. This is because our new approach, like the gap statistic, can work with any clustering method.