Reviewerly/exHarmony huggingface dataset:
Distributed supervision of an edge micro datacenter

This paper presents novel hierarchical management architecture of heterogeneous edge data center. The management architecture was implemented in BRAINE (Big data pRocessing and Artificial Intelligence at the Network Edge) project. One of the key objectives of this project was to develop an energy efficient heterogeneous 3U Edge Micro Data Center (EMDC) including all PCBs, thermal subsystem and mechanics. The BRAINE’s Edge framework is designed to be flexible and support many types of data processing and storage cards. Because of this flexibility and the wide range of heterogeneous cards a sophisticated and hierarchical board/card management functionality is needed. The hierarchical system management consists of a central board management node (BMC: Board Management Controller), and a distributed low-level board management hardware present on all the nodes or cards (BMMC: Board Management MicroController). The goal of this paper is to describe the functionalities and present implementation details of this multi-fold low level interconnection and the services being provided.
Efficiency Analysis of Tandem Substring Search Methods

Strings and lists play a fundamental role in data structures. Considering string operations, one of the key functions is the search for matching parts within a given host string. The location of repeating substring pattern is a special type of general substring search. This task is of outstanding importance, among other in biology (examining genes problem) or in process modeling (searching for cycles). This article presents a specific alternative method for tandem substring search which unlike the traditional, direct pattern search methods presents, is based on the application of neural networks. The performed comparative analyzes carried out show that a neural network-based methods is a good alternative, especially in the case of approximate searches.
FPGA accelerated DeepSORT object tracking

Object detection and tracking is one of the most important vision algorithms in intelligent environments, applications based on it are the driving force behind the growing deployment of edge devices. As FPGAs can implement application specific hardware accelerators, they not only offer outstanding performance but also increased energy efficiency which is a very important factor in edge devices and the most recent FPGA families include processing elements which greatly increase the efficiency and performance of AI inference. In this paper we present a heterogeneous FPGA based implementation of the popular DeepSORT tracking algorithm.
Benchmark example for the Heuristically accelerated FRIQ-learning

Heuristically accelerated FRIQ-learning (HFRIQ-learning) is a Fuzzy Rule-Interpolation based Q-learning method which is able to embed external expert knowledge into the learning system. It usually speeds up the learning process, but in case if the expert knowledge describes an incorrect solution, it could also have a negative effect on the learning performance. In HFRIQ-learning, the Q-function knowledge representation is a state-action-value sparse (fuzzy rule interpolated) rule-base. The expert knowledge usually expressed by production (state-action) rules. This paper briefly introduces the way how the HFRIQ-learning optimises the Q-function rule-base and how it locates and corrects the incorrect expert rules. The paper also demonstrates the effect of the expert knowledge quality on the HFRIQ-learning performance on a popular reinforcement learning benchmark example.
Fast Joint Image Compression-Encryption Algorithm used for 3D Reconstruction

This paper presents a JPEG-improved algorithm for compression and encryption of 2D structured light images applied in 3D reconstruction. The proposed method incorporates compression and encryption simultaneously to achieve a high compression ratio while adding security without affecting 3D reconstruction. The 2D structured light image is divided into 8×8 blocks, then DCT is applied for each block followed by an optimized JPEG-based quantization matrix. The resulting blocks are merged in a single matrix, and the DC coefficients in each column are subjected to first-order difference to reduce the Min-Max span for fast decoding. The decompression is accomplished reversely, and a fast sequential search method is proposed based on a conventional searching approach. The results showed achieving up to a 98% compression ratio of 2D structured light images while maintaining successful and accurate 3D reconstruction. Moreover, the execution time decreased significantly by (61%-80%).
Design and research of news recommendation system based on perceptron model in big data era

Abstract With the continuous development of information technology, we are now in an era of information explosion, and we are also facing the problem of information overload, i.e., it is difficult to find the news that interests us from the huge news sources on the Internet. Based on this problem, this paper proposes a perceptron model-based news recommendation system, which uses the perceptron model to extract news keywords and realize the classification of the news; for user interest preferences, we analyze users’ news browsing preferences by collecting user behavior logs and use a multi-layer perceptron model to aggregate users’ interest features. The perceptron model is compared with traditional recommendation methods in terms of accuracy and recall, and one user is selected from 50 users who have been using the system for three months to analyze their news browsing preferences by single-day, short-term and long-term news data to verify the effectiveness of the perceptron model in aggregating users’ interest features. In terms of accuracy and recall, the accuracy of the perceptron model is higher than that of the traditional method for each news type, improving the accuracy by at least 5%-10%, e.g., 10% for real estate news and 9% for financial news. The recall rate of the perceptron model is improved by more than 6% for all news types except for social and entertainment news types. In terms of user interest preference mining, the whole change process of user browsing preference trend is in line with the human forgetting curve, which verifies that the perceptron model in the system is better used and meets the design requirements. Therefore, the applicability and reliability of the model proposed in this paper for news recommendation systems are verified in terms of accuracy, recall, and user interest preference mining.
Automatic Question Generation based on Sentence Structure Analysis

Automated question generation is a key problem in computational linguistics and knowledge engineering. There are different approaches and methodologies like template-based, syntax-based, or machine learning approaches. The paper investigates the efficiency and analysis of the role of the embedding module in sentence parsing. Embedding language models are important modules and the performed tests show that embeddings can provide a high accuracy in semantic-level similarity methods.
The Impact of College English Teaching on Improving Students’ Writing Effectiveness in the Context of Big Data

Abstract In order to improve the English writing ability of college students and help them lay a good foundation for written communication in English when they graduate from school or continue their studies abroad. This paper collects data on college students’ English writing on the Internet through a clustering algorithm with the support of big data technology background and defines the scoring criteria for essays to target the deficiencies in college students’ English writing nowadays and urge students to correct them. With the support of a search algorithm, we obtained the demand for college students’ English from employers in China over the past 10 years and reflected on the aggregated demand data to link classroom teaching with workplace English. The study found that the English language skills valued by employers in order were speaking 80.5%, writing 86.3%, reading 57.9%, listening 40.2%, and translation 21.7%. A demand questionnaire survey was conducted randomly among non-English majored first- to fourth-year undergraduate students in five universities with tiered teaching. The results showed that 72% of the students thought they needed to improve their English speaking skills, 70.3% thought they needed to improve their English writing skills, and 40% thought they needed to improve their grammar skills, which shows that college students’ needs for improving their English speaking and writing skills are particularly prominent. The emphasis on English writing classroom education for college students can solve the urgent needs of students for English learning and improve college students’ English writing skills through big data science.
A Tweedie Markov process and its application in fisheries stock assessment

Abstract The Tweedie distribution is a useful tool to model zero-inflated non-negative continuous data. However, the Tweedie dispersion relationship (DR) is not general enough to cover some important forms such as quadratic dispersion, and an easy and fast-to-implement Tweedie AR(1) model (first-order autoregressive model) needs to be developed for spatio-temporal modelling. In this research we extend the Tweedie distribution to accommodate flexible DRs, and propose a Tweedie Markov process (TMP) with the AR(1) autocorrelation structure. This TMP is simple to implement and requires only the Tweedie probability density function. Simulation studies and real data analysis are conducted to validate our new approach.
FPGA-based Intelligent Logistics Management for Universities

Abstract With the strong drive for economic and cultural development, China’s higher education has flourished and formed a higher education system and scale with typical Chinese characteristics, and the healthy development of higher education cannot be separated from the strong support and guarantee of university logistics. Based on FPGA technology, this paper explains in detail the design method and computing steps of FPGA technology to build a university intelligent logistics management data analysis system. The performance evaluation is carried out for the system designed in this paper, and the data analysis of the indicator examples is carried out by using five examples of logistics management service indicators of the University of W, namely campus environment, property service, commercial service, residence management service, and utility maintenance. From the performance evaluation data of the FPGA university intelligent logistics management system, the average accuracy, precision, recall, and AUC values of the FPGA-based university logistics management system are 91.90%, 95.78%, 97.28%, and 97.41%, respectively. From the index instance analysis, the average evaluation values of the five services of very satisfied, satisfied, generally satisfied, and dissatisfied are 24.89%, 25.16%, 40.97%, and 8.98%, compared with the previous average values of very satisfied and satisfied evaluations increased by 18.36% and 4.61%, respectively, and the average values of generally satisfied and unsatisfied evaluations decreased by 7.65% and 15.31%, respectively. The FPGA-based intelligent management system of colleges and universities has higher stability and stronger data analysis ability, which can more effectively propose corresponding reform directions for the problems and provide technical support to promote more intelligent logistics management of colleges and universities. At the same time, it enables university teachers and students to carry out relevant academic research and study life without any worries, contributes technical power to promote the overall healthy development of universities, and provides new research directions to broaden the development field of FPGA technology.
Low-Cost Navigation System for UAV

The paper considers the small-sized navigation system NV-micro by the company Integral Ltd. The article presents the design of the navigation system, the algorithm, and the results of testing the navigation system on a light-engine airplane, demonstrating the accuracy of the proposed navigation system.
Request for a Surveillance Tower: Evasive Tactics in Cyber Defense Exercises

The cyber defense exercise (CDX) is an emerging live-fire exercise that enables diverse teams with different roles to train in one game. To evaluate the cyber defense capabilities of the training audience, organizers prepare various scores using different scoring methods ranging from technical to non-technical. The technical scores in Locked Shields, for example, consist of an availability check, a usability check, the success of the red team (RT) attack, and forensics.Immersed in scores due to excessive competition, a blue team (BT) may unnecessarily focus on the scoring process, aiming to perform evasive tactics (ET), which boosts scores unfairly by abusing the weaknesses of the scoring system. ET has occurred in various forms in existing CDXs, and similar cases have been found in the recent iteration of CDXs, meaning that ET is becoming BT’s selectable strategy.Such a phenomenon is undesirable since it will reduce the reliability of the evaluation and the effectiveness of the training. In this paper, we provide an overview of an availability check and examine ET that appeared in both the availability check and RT’s evidence-obtaining process, followed by several mitigations to them. We also discuss evidence and usability issues of ET in CDX and conclude by emphasizing the importance of supporting the green team (GT) in researching and implementing a robust scoring system.
Zero-Day Operational Cyber Readiness

As we move all our business practices into cyber terrain, the unique characteristics of cyberspace assets and threats require a different perspective to define and implement the concept of cyberspace readiness. The connected and dependent nature of functional and core services in and through cyberspace has created a nondeterministic security environment with unpredictable, ubiquitous and ambiguous threat perceptions. Building, increasing and sustaining cyber readiness requires producing, training, equipping, deploying and sustaining cyber warriors with competent capabilities against a continuously mutating threat landscape in a timely manner. Traditional military readiness approaches geared for kinetic services do not suit the unique requirements of cyber warfare readiness. A unit at “60 days notice to move” has 60 days to get ready to act. If the average time to detect a cyber attack is 200 days, cyber defenders must be ready for cyber attacks on average 200 days before they start. Hence, we propose the term “zero-day readiness” to describe agile and vigilant cyber readiness. In this paper, we offer a novel cyberspace readiness model based on principles, resources, activities, capabilities and benefits. While resource-demanding to build, improve and sustain, the proposed Zero-Day Readiness model has the potential to significantly increase the assessment and visibility of gaps as well as support judgment on the allocation of limited resources. The added value of this research is in developing a more revisionist readiness perspective for cyberspace operational readiness than the traditional kinetic operational domains, particularly for organizational and military cyber defense perspectives.
Specification and Scaffolding in Project-Based Learning of Systems Architecture

Project-based learning (PBL) through open-ended group projects is praised for fostering technical communication, collaboration, and leadership skills. We examine PBL in the group project element of Web Systems Architecture, an upper-level undergraduate systems course. We investigate learning outcomes, team dynamics, technical communication, and confidence-building. Our observations suggest that while learning outcomes are similarly achieved with and without additional specification and scaffolding, when given a choice for receiving further specification and scaffolding, students are inclined towards more specification and avoid taking risks in open-ended projects. Specifically, 86% of our students decided to choose the scaffolded project stream, and by the end of the project, 41% of the students (58% of survey respondents) indicated their preference for even a more specified project. We explore the factors influencing this choice and discuss design alternatives to further motivate risk-taking, and our initial results using them.
Scalable, High-Quality Scheduling of Data Center Workloads

Data center schedulers must make complex tradeoffs and optimizations to achieve their scalability and high-quality scheduling goals. Scalability refers to the scheduler’s ability to support both the increasing scale of the workloads (in terms of arrival rate and resource demand) and the infrastructure required by these workloads. Scheduling quality refers to the scheduler’s ability to meet the user’s performance requirements (such as latency guarantees and placement constraints) without compromising the data center’s resource utilization.We propose two solutions to achieve scalability and high-quality scheduling. The first is Megha, a decentralized, federated scheduling framework that uses an eventually-consistent global state to make fast, high-quality scheduling decisions for data centers with tens of thousands of nodes. The second is an intra-node scheduling technique called Niyama, which provides robust CPU bandwidth isolation to latency-sensitive tasks - protecting them from interference from co-located tasks. The two frameworks have been evaluated using workloads generated from publicly available cluster traces, and the results show significant improvements over existing state-of-the-art solutions.
Pan-Institutional Applied Research within Undergraduate and Post-Degree Diploma Teaching Programs

Many undergraduate students’ applied research projects (projects) are conducted within the computer and computing science departments of colleges and universities [24, 36]. Every department has its own experiences, successes and failures in projects and related subjects teaching, which would be helpful to share and discuss with the education community. In this paper, we discuss the pan-institutional applied research projects’ experiences and results of teaching capstone project courses since 2005, upper-level courses in Data Mining, Data Warehousing, Object Analysis and Design, Special Topic Courses in Databases, in the Computer Information Systems (CIS) Diploma, the Bachelor of Computer Information Systems (BCIS) Degree programs at Okanagan College (OC), and the Post Degree Diploma in Data Analytics (PDDDA) program at Langara College (LC), and similar courses at UBC Okanagan and the University of the Fraser Valley [2, 3, 4, 5, 6, 10, 11, 12, 17, 23, 24, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 46, 47, 48, 49, 50, 52, 53, 58]. Teaching a new computing science topic was synchronized through practical software development and software engineering (SEng) projects with industrial sponsors and by small groups of students. In addition to industrial projects, we introduced several student-centred applied research projects from academia and industry in British Columbia (BC) and across Canada. In the last 5 years, we initiated collaboration in these student-centred projects between several post-secondary institutions (OC and LC, UBC Okanagan (UBC O), and the University of the Fraser Valley (UFV)) in Canada and internationally in Université Paris-Est Créteil (UPEC), France. Managers or executives from the sponsoring companies and instructors and professors from the educational institutions supervised and supported students as sponsors, advisors, or mediators. Many student project teams were able to develop impressive, high-quality engineering and research applications and systems [42]. The sponsors provided positive feedback and references for most of the projects. The results of some of these industrial projects were turned into products by the sponsoring companies.
Efficient Design for Smart Environment Using Raspberry Pi with Blockchain and IoT (BRIoT)

Internet of Things (IoT) is reshaping digital world day by day by integrating several technologies to provide smart services. However, intrinsic features of IoT resulting in a number of challenges, such as decentralization, poor interoperability, privacy, confidentiality, and security vulnerabilities. Several security techniques like encryption, third-party software’s are in use currently to protect users data. Blockchain was initially established for digital crypto currencies with a Proof of Work (PoW) consensus process and the advantage of smart contracts, which enabled distributed trust without the involvement of a third party. Its distributed trust concept paved the way for many other developments, such as the development of new consensus mechanisms such as Proof of Stake (PoS) and Proof of Authority (PoA), which aided in the adoption of Blockchain with low computation machines into sectors such as smart industry and smart transportation. Blockchain implementation in IoT can address the security issue, here we proposed a design using Raspberry Pi as edge node (BRIoT).
Web Services Relocation and Reallocation for Data Residency Compliance

Compliance with data residency regulations is a huge challenge for most enterprises and cloud service providers. With the emerging data regulations, enterprises need to intermittently review their web service deployment decisions. To be compliant, enterprises need to relocate and reallocate some of the non-compliant users and web services. It can be obtained by minimal or relatively large migrations; however, it has implications over cost.In this paper, we propose an optimization model for web services relocation and reallocation problem (referred to as WSLAP-Repair). The goal is to achieve minimal reallocations with lower additional operational costs while meeting the latency requirements of the concerned web services. We propose a novel heuristic for solving WSLAP-Repair problem. We demonstrate our results on small problem instances using an integer programming open-source solver.
AggFirstJoin: Optimizing Geo-Distributed Joins using Aggregation-Based Transformations*

Today, data is generated in a geographically distributed manner in a wide variety of domains such as social networks, e-commerce, search engines, online advertisements, audio and video streaming, energy, smart cities, IoT sensors etc. Consequently, this data is stored across geographically distributed edges and data centers (DCs) near to the end-users and end-devices, the very sources of this data. Analyzing this geographically distributed data is challenging primarily due to two reasons: 1) constrained and costly WAN bandwidth links which connect the geo-distributed edges and DCs (henceforth collectively called as sites) [1], and 2) limited compute availability at each site (especially the edges) [2].
Transformer Inference Acceleration in Edge Computing Environment

The rapid development of deep neural networks (DNNs) has provided a strong foundation for the popularization of intelligent applications. However, the limited computing power of IoT devices cannot support the massive computing load of DNNs. Traditional cloud computing solutions suffer from distance and bandwidth constraints, and cannot meet latency-sensitive requirements. Prior research on DNN acceleration has predominantly focused on convolutional neural networks (CNNs), Transformer has recently gained significant popularity due to its outstanding performance in natural language processing, image processing, and other domains. In this context, we have explored the acceleration of Transformer in edge environments. To model Vision Transformer, we have employed the design concept of a multi-branch network and proposed an optimization strategy to accelerate Transformer inference in the edge environment. We have evaluated our approach on three publicly available datasets and demonstrated its superior performance in challenging network conditions when compared to existing mainstream DNN collaborative acceleration inference techniques.