Reviewerly/exHarmony huggingface dataset:
Research on Pre-training of Tibetan Natural Language Processing

In the field of natural language processing, pre-training can effectively improve the performance of downstream tasks. In recent years, pre-training has been continuously developed in Tibetan NLP. We built three pre-trained models of Tibetan Word2Vec, Tibetan ELMo, and Tibetan ALBERT, and applied them to the two downstream tasks of Tibetan text classification and Tibetan part-of-speech tagging. Comparing them with the baseline models of these two downstream tasks, it is found that the performance of the downstream tasks using the pre-training is significantly better than the baseline model. The three pre-trained models have also brought a gradual improvement in performance for Tibetan downstream tasks.
Unveiling patterns in xorshift128+ pseudorandom number generators

Xorshift128+ is a newly proposed pseudorandom number generator (PRNG), which is now the standard PRNG on a number of platforms. We demonstrate that three-dimensional plots of the random points generated by the generator have visible structures: they concentrate on particular planes in the cube. We provide a mathematical analysis of this phenomenon.
Simulating module anti-reflection coatings with SunSolve

Anti-reflection coatings are used on 92% of today’s module glass to reduce the front-surface reflection and increase the power output of the module. Currently, most anti-reflection coatings are designed to maximize transmittance at normal incidence to improve the power output at standard test conditions. However, in the field, where the meteorological conditions vary widely in time and space, it is not clear whether a maximum power gain at standard test conditions leads to maximum energy yield. Here, we use SunSolve Yield, a ray-tracing software, to investigate the optimum anti-reflection coating design by simulating the energy yield from a PV system under real-world conditions. We found that manufacturers could immediately provide a 0.3% increase in energy yield relative to their products today by simply increasing the ARC thickness by 20–40 nm thicker.
Development of Optimized Algorithm and Field Programmable Gate Array Implementation for Bio Medical Image Denoising for Health Informatics Applications

Denoising images is a most difficult task in applications for image processing. The image specifics are preserved and the additional sounds found in the images are removed. It is also a challenge to remove noise from medical and satellite images. It improves the diagnostic capacity of medical images and satellite images visual clarity. The noise in the images varies and its density varies depending on imaging techniques. The algorithms in the literature were suggested based on the noise density and the forms of noise. The aim of this paper is to eliminate the noise from ultrasound, magnetic resonance images and satellite images using an effective denoisation algorithm Hybrid Wiener Adaptive Weighted Median filter (HWAWMF) which is the combination of Wiener and Adaptive Centre Pixel Weighted Median Filter (ACPWMEF). In terms of performance parameters with an improved Peak to Signal Noise Ratio(PSNR), the hybrid filter shows better results than ACPWMEF. The Vienna filter takes out the additional noises in the images thus blurs the image’s optical perception. And also uses optimization approaches to enhance the image consistency. This paper proposes HWAWMF (PSO HWAWMF) based on particle swarm optimization and HWAWMF based on dragonfly optimization algorithms (DOAF HWAWMF). Visual vision and PSNR also have been improved by using the optimising algorithm at an average of 3.18 db, 4.83 db, and 3.14 db for lower noise (0.0% to 30%), medium noise (40% to 60%) as well as high noise density (70% to 90%). The efficacy of the algorithm using MATLAB R2013 is verified through both medical images, simulated and actual. In order to assess the computer complexity of the Altera algorithm for location, power and time using Cyclone II EP2C35F672C6, Cyclone II and Stratix III EP3SL150F1152C2, this algorithm is also implemented in the Altera Field Programable Gate Array (FPGA).
Distributing and Processing Data from the Edge. A Case Study with Ultrasound Sensor Modules

Currently, the proliferation of interconnected smart devices is related to smart urban management. These devices can process sensor data in order to obtain significant information. This information can be provided to the upper layers but also can be used by the devices to take some smart actions. This article shows the change from the classic hierarchical devices and data paradigm to a paradigm based on distributed intelligent devices. The distributed model has been used to create a system architecture with Arduino-based Control Nodes interconnected by means of an I2C-bus. Each module can read the distance to each vehicle, and process this data to provide the vehicle speed and length. A case has experimented where modules share raw data and another case where modules share processed data. Results show that it is possible to reduce processing load up to 22% in the case of sharing processed information instead of raw data.
Cognitive Continuous Tracking Algorithm for Centralized Multistatic Sonar Systems

This paper proposes a novel detection and tracking algorithm to improve the performance of continuous tracking of submarine for multistatic sonar systems. The algorithm focuses on a centralized fusion architecture and a cognitive closed loop. In the following trail of submarine, the future trajectory of the submarine and its echo intensity for different transmit-receive combinations are roughly predicted, where the target echo model is assumed to be a priori. These predicted echo intensity is fed back to the frontend detection and tracking processes. Then the proposed algorithm could adaptively adjust the key parameters of the centralized fusion rule. Moreover, the track management strategy is also adjusted based on the feedback information. At the beginning of another cycle after tracking, the future trajectory and the echo intensity of the target are predicted again. We use numerical simulations to evaluate the behavior of the proposed algorithm. It is demonstrated that the cognitive approach achieves a better performance of continuous tracking compared with the conventional non-cognitive method in terms of track probability of detection and track fragmentation rate.
28.6: 3D‐LUT Based Ultra‐Low Latency 4K Color Calibration System on FPGA

With the rapid development of 8K and 4K technology, consumers are enjoying more and more vivid pictures through ultra‐highresolution panels. Many technologies have been introduced to improve the video quality such as super resolution, HDR (High Dynamic Range), denoising and etc. Among all these techniques, “color” is one of the most important issues for getting a wonderful watching experience. A wide range of research has been done to fully illustrate different models and standards (CIE1931, CIE1976 and etc.) to better define colors on different devices. This work aims at achieving a color calibration system with ultra‐low latency delay on FPGA for 4K 60fps system based on 3D‐LUT. The “calibration” mean that the system would characterize the target panel and then correct each pixel for a better color performance. In the experiment, this system could process a single 4K frame within 16.67ms and the average Delta E is dropped from 5.52 to 1.69 which refer to a 69.38% improvement.
Cloud-based Robot Path Planning in Dynamic Environments

Most of the current path planning applications only focus on a single agent, without considering the surrounding robots or dynamic obstacles. In safety-critical environments, such as the crowded playground, the robot may have increased difficulty reaching the destination with crowds moving around, sometimes even cause damages. With the development of cloud computing and swarm intelligence, the concept of “cloud robotic” has been followed by more scholars. With collaboration and information sharing between robots, the cluster has better problem-solving skills than the individual. In this paper, we propose a cloud-based multi-agent navigation algorithm in dynamic environments. We propose the concept of pheromones of dynamic obstacles that represent congestion to enable clusters to plan collaboratively. Widespread static sensors are introduced to jointly estimate congestion in the environment with robots. When an agent needs route planning, the cloud provides safe and fast route with intermediate points. The robot uses a simple yet effective local planner based on artificial potential field (APF) to trace the trajectory. We demonstrate this approach's effectiveness compared to traditional A* global planner and APF local planner with traditional repulsion function. Experiments show that our cloud-based method reduces the number of collisions by 92.6%, with only 35% increase in path length. All code for reproducing the experiments is at https://github.com/Asber777/CDPP.
Blockchain: A New Era of Technology

The revolution with technologies happens very often; one such technology is called “blockchain” which will be a game-changer in many functional sectors of the society. Initially, this technology was developed to support cryptocurrencies (such as Bitcoin, Ethereum, and Litecoin). Blockchain is just a growing list of records that are termed as blocks that are linked using cryptographic algorithms. Every block in the Blockchain contains a hash of the previous block with timestamp along with the data. The rapid growth of web and usage of Internet resources have pervaded Blockchain over a broad range of applications which includes finance, healthcare, agriculture, insurance, government, and manufacturing with potential advantages contributing to society. This chapter is for educators, practitioners, and professionals who want an overview of Blockchain technology and provides insights of understanding of Blockchain technology and its impact on economy. This chapter walks through foundation of Blockchain technology by highlighting its development history, Blockchain framework, challenges, and so on. Finally, the future of Blockchain in industrial applications along with some case studies is discussed.
Trace data visualisation enquiry: a methodological coupling for studying information practices in relation to information systems

Purpose The purpose of this paper is to examine whether and how a methodological coupling of visualisations of trace data and interview methods can be utilised for information practices studies. Design/methodology/approach Trace data visualisation enquiry is suggested as the coupling of visualising exported data from an information system and using these visualisations as basis for interview guides and elicitation in information practices research. The methodology is illustrated and applied through a small-scale empirical study of a citizen science project. Findings The study found that trace data visualisation enquiry enabled fine-grained investigations of temporal aspects of information practices and to compare and explore temporal and geographical aspects of practices. Moreover, the methodology made possible inquiries for understanding information practices through trace data that were discussed through elicitation with participants. The study also found that it can aid a researcher of gaining a simultaneous overarching and close picture of information practices, which can lead to theoretical and methodological implications for information practices research. Originality/value Trace data visualisation enquiry extends current methods for investigating information practices as it enables focus to be placed on the traces of practices as recorded through interactions with information systems and study participants' accounts of activities.
Automatic Citation Contextualization Based Scientific Document Summarization Using Multi-objective Differential Evolution

The wide availability of research articles makes it harder for researchers to quickly learn about the progress made in their respective field’s. However, citation-based scientific document summarization, a process of forming summaries by distilling the most relevant information from a document, serves this purpose. In this paper to mitigate the summarization problem, a citation-based extractive scientific document summarization technique is developed utilizing the Multi-Objective Differential Evolution (MODE) approach. In this process, firstly the citation contextualization is exploited, and citation-related texts are extracted using a sentence similarity measure. Thereafter, the extracted-text is represented in vector-form considering sentence features like sentence length, citation-based sentence score, sentences cited score, and sentence weight based on TF-IDF score. The extracted-text is also represented in a matrix considering its sentence to sentence similarity/dissimilarity scores. Finally, the MODE approach is applied to generate a summary of the initial population by selecting a subset of feasible individuals. Each individual is represented in the binary form where each sentence is illustrated in (0,1). The text in vector and matrix form is further utilized to formulate objective functions that are optimized simultaneously. Modified crossover & mutation operator of DE and archive-based mating pool are used to generate offspring. Finally, the best individual based on crowding distance and non-dominating sorting is selected from the final population to create the summary. For evaluating purpose, CL-SciSumm-18 test data-set and ScisummNet data-set are used, and our best system achieves results that are on par with the state-of-the-art methods. We also demonstrated that functionalities, like semantic similarity measure, feature scaling, appropriate feature selection, and objective formulation, improved the summarization system’s performance.
Sparsity and Sentence Structure in Encoder-Decoder Attention of Summarization Systems

Transformer models have achieved state-ofthe-art results in a wide range of NLP tasks including summarization.Training and inference using large transformer models can be computationally expensive.Previous work has focused on one important bottleneck, the quadratic self-attention mechanism in the encoder.Modified encoder architectures such as LED or LoBART use local attention patterns to address this problem for summarization.In contrast, this work focuses on the transformer's encoder-decoder attention mechanism.The cost of this attention becomes more significant in inference or training approaches that require model-generated histories.First, we examine the complexity of the encoder-decoder attention.We demonstrate empirically that there is a sparse sentence structure in document summarization that can be exploited by constraining the attention mechanism to a subset of input sentences, whilst maintaining system performance.Second, we propose a modified architecture that selects the subset of sentences to constrain the encoder-decoder attention.Experiments are carried out on abstractive summarization tasks, including CNN/DailyMail, XSum, Spotify Podcast, and arXiv.
Foggy Image Enhancement Using Improved Histogram Equalization and Guided Filter

Due to some undesired particles in the atmosphere such as haze, fog, mist, etc., the image quality, taken outside, can suffer from poor visibility. To reestablish the original quality of the image, degraded by the presence of fog, the fog removal algorithm is of significance. Fog removal algorithm has huge application in various areas like tracking, navigation, surveillance, visual-based security, etc. In this work, we present CLAHE and guided-filtering-based image defogging algorithm.
Modelling Dynamics in Aspect-Oriented Programs

Aspect-oriented programming is an extension to the object-oriented programming paradigm that aims to provide greater modularization of source code through the identification of aspects. Aspects are the representation of cross-cutting concerns. These are design elements of code that are scattered or duplicated across multiple classes, and hence cannot be cleanly implemented in object-oriented languages. Though the use of aspects, cross cutting concerns are modularized and can be implemented in a single unit of code. However, aspects effectively allow the extension and modification of existing classes through the injection of new advising code. This ability to inject code provides a degree of dynamism not seen in object-oriented languages, and hence, cannot be modelled with standard object-oriented modelling techniques. In this paper an extension to UML for modelling the dynamics of aspect-oriented programs is proposed. This extension affords the modelling any introduced dynamic behavior and its effect on base object-oriented code.
Sepsis Detection Using Extreme Gradient Boost (XGB): A Supervised Learning Approach

Sepsis is one of the major trending topics in the field of Bio-Medical Sciences, Sepsis is a major disease, causing a lot of causalities, and a large amount of money is consumed for the diagnosis and treatment of sepsis found in patients. Early sepsis detection can decrease patients’ death rates and give a big economic relief to patients’ families. Many techniques have been used to detect sepsis earlier than clinical results are known, but machine learning approaches are leading one among all other techniques and tools. Many datasets are available which are used for more precise results, and some of the researchers use their own datasets, which are not easily available publicly. We used two datasets, Set A and Set B, for training and testing of our model, and these are publicly available at Physionet.org. We used Set A as a whole and a big proportion of Set B for the training of our algorithm and the remaining proportion of Set B for testing purpose [1]. We applied forward filling and backward filling on the dataset to fill the missing valuesand achieve the required training sets, then the extreme gradient boost (XGB) classifier is applied, which gives the result that sepsisis found. The sepsis is detection shows 92% accuracy. This algorithm would be used to increase patients’ chances of survival and would save the amount of money that is being used for diagnosis and treatment of sepsis annually.
Improved Power Decoding of Algebraic Geometry Codes

Power decoding is a partial decoding paradigm for arbitrary algebraic geometry codes for decoding beyond half the minimum distance, which usually returns the unique closest codeword, but in rare cases fails to return anything. The original version decodes roughly up to the Sudan radius, while an improved version decodes up to the Johnson radius, but has so far been described only for Reed-Solomon and one-point Hermitian codes. In this paper we show how the improved version can be applied to any algebraic geometry code.
An Experimental Study of SYCL Task Graph Parallelism for Large-Scale Machine Learning Workloads

Task graph parallelism has emerged as an important tool to efficiently execute large machine learning workloads on GPUs. Users describe a GPU workload in a task dependency graph rather than aggregated GPU operations and dependencies, allowing the runtime to run whole-graph scheduling optimization to significantly improve the performance. While the new CUDA graph execution model has demonstrated significant success on this front, the counterpart for SYCL, a general-purpose heterogeneous programming model using standard C++, remains nascent. Unlike CUDA graph, the SYCL runtime leverages out-of-order queues to implicitly create a task execution graph induced by data dependencies. For explicit task dependencies, users are responsible for creating SYCL events and synchronizing them at a non-negligible cost. Furthermore, there is no specialized graph execution model that allows users to offload a task graph directly onto a SYCL device in a similar way to CUDA graph. This paper conducts an experimental study of SYCL's default task graph parallelism by comparing it with CUDA graph on large-scale machine learning workloads in the recent HPEC Graph Challenge. Our result highlights the need for a new SYCL graph execution model in the standard.
Development of A Walking-Trajectory Measurement System

In recent years, robots have attracted attention to provide a supplemental workforce in Japan as the working population decreases. A reception and response control system using the interface robot ApriPoco&#x2122; has been studied, measuring human walking trajectories using a laser range finder, but accurate measurements have been impeded by overlapping people and trajectories leading to missing data. Therefore, in this paper, we propose a system that uses Gaussian process regression to predict human trajectories in addition to the conventional system. Three-directional walking experiments were conducted with groups of one, three, and five people, and the trajectories of the conventional and proposed systems were compared. The experimental results show that the proposed system has fewer missing data than the conventional system and can obtain more accurate trajectories. We also demonstrated human walking-trajectory measurement at a public museum to confirm the effectiveness of this system. In the future, we plan to build a reception system by linking this system with robots.
Privacy-Preserving Approximate Top-k Nearest Keyword Queries over Encrypted Graphs

With the prosperity of graph-based applications, it is increasingly popular for graph nodes to have labels in terms of a set of keywords. The top-k nearest keyword (k-NK) query can find a set of k nearest nodes containing a designated keyword to a given source node. In cloud computing era, graph owners prefer to outsource their graphs to cloud servers, leading to severe privacy risk for conducting k-NK queries. The current studies fail to support efficient and accurate k-NK query under the premise of privacy protection.In this paper, we propose a new graph encryption scheme Aton, which enables efficient and privacy-preserving k-NK querying. Based on the symmetric-key encryption and particular pseudo-random functions, we construct a secure k-NK query index. Aton is built on a ciphertext sum comparison scheme which can achieve approximate distance comparison with high accuracy. Rigorous security analysis proves that it is CQA-2 secure. Experiments with real-world datasets demonstrate that it can efficiently answer k-NK queries with more accurate results compared with the state-of-the-art.
Prevalence effects on the road: Rare hazards are often missed

Detecting hazards on the road is a complex perceptual task, requiring fast and accurate responses. Our ability to detect these dangers may depend on their prevalence, or how frequently they occur. Research in visual search has shown that rare events are often missed, but it is unknown whether these effects extend to dynamic real-world scenes like driving.