Reviewerly/exHarmony huggingface dataset:
Book Review: Schildermans, Hans. <i>Experiments in Decolonizing the University: Towards an Ecology of Study.</i> Radical Politics and Education Series. London: Bloomsbury Academic, 2021.

Time and place matter when we think about decolonization. Reading Hans Schildermans’ thought-provoking book, Experiments in Decolonizing the University: Towards an Ecology of Study , as a Pākehā (white) academic in Aotearoa New Zealand oriented me in a surprisingly obdurate way. He offers two divergent cases of decolonization. One is the plight of contemporary Palestinians displaced into refugee camps by the 1948 Nakba. The other is the ubiquitous predicament of present-day inhabitants of universities in Europe and America (and Australasia, I might add) who experience the occupation of their institutions, their work, their imagined selves, by “capitalist logics and mentalities” (Schildermans, 2021, p. 12). Both stand in contrast to my orientation towards decolonization as a movement to undo the racist legacy of 19th century (or so) imperialism over indigenous peoples, an attachment that tethers my thought to certain questions that are only addressed elliptically in Schildermans’ chapters. Despite (or even maybe because of) this ongoing disorientation, I found the book a rich and rewarding read and I discovered that many threads of his thought speak to my questions. Generally, Schildermans offers plenty of food for thinking about how to inhabit the university differently, or how the university might inhabit places outside institutional walls. As indicated in the book’s title, he pays particular attention to the ecology – the “interrelatedness of habits and habitats” (p. 113) – of study practices, a focus I find especially engaging, perhaps because of my earlier life as an academic study skills advisor. In this review, after describing the arc of Schildermans’ thinking in Experiments , I show how his thought is helpful for making sense of a troubling decolonization event that shared the time and place of my reading.
A Simple and Efficient Object-Capability Revocation Method

Revocation is important but difficult in object-capability based systems. As an essential mechanism of capability management, revocation provides the basis for canceling of granted access rights and reclaiming of shared resources. However, efficient revocation of capabilities is difficult to achieve due to the considerations for selective revocation and safety. In this paper, we propose an simple and efficient object-capability revocation method. We are inspired by the classic garbage collection algorithm Mark-and-Sweep algorithm, and decouple revocation process to two phases. In the first phase, capabilities are centrally set to be invalid or masked, by updating related fields in capability tap. In the second phase, this method trigger Rust's memory reclaim mechanism to sweep the disabled capabilities safely. Additionally, the sweep progress can be done as lazy sweeping to support fast revocation returning. Our revocation method can support selective revocation more efficiently than the existing methods, and the revocation process is concise and safe.
Extensive Simulation Of Human-Robot Interaction For Critical Care Telemedicine

This article proposes a new immersive digital-twin simulation framework, dubbed Extensive Simulation, aimed at high-precision analyses of human-robot interaction (HRI) for critical care telemedicine, or tele-ICU. The proposed approach first reproduces in a virtual ICU environment, the real-time digital replica of physical robot navigating under ICU care setting. Then, the digital twin constructed in the virtual environment is extended with immersive simulation technology, to elicit and collect interactive human behaviors from diverse group of tele-ICU users (including physicians, nurses, and technical staffs either onsite or remotely located, along with patients and guardians). The proposed Extensive Simulation framework advances digital twin concept by including humans into the loop via immersive simulation, which will help reliably capture diverse human behaviors and construct human model for tele-ICU work system.
Privacy-Aware Forecasting of Quality of Service in Mobile Edge Computing

We propose a novel privacy-aware Quality of Service (QoS) forecasting approach in the mobile edge environment – Edge-PMAM (Edge QoS forecasting with Public Model and Attention Mechanism). Edge-PMAM can make realtime, accurate and personalized QoS forecasting on the premise of user privacy preservation. Edge-PMAM comprises a public model for privacy-aware QoS forecasting in an edge region and a private model for personalized QoS forecasting for an individual user. An attention mechanism atop Long Short-Term Memory and an automated edge region division solution are devised to enhance the prediction accuracy of the public and private models. We conduct a series of experiments based on public and self-collected data sets. The results based on public and self-collected data sets demonstrate that our approach can effectively improve forecasting performance and protect user privacy.
Robot Global Path Planning Based on Improved Second-Order PSO Algorithm

When using particle swarm optimization (PSO) to realize the global path planning of the robot, premature convergence and falling into the local optimum often occur. Therefore, an improved second-order PSO is used to improve premature convergence and analyze its stability. The logistic function relationship is established between the inertia weight and the learning factors. Using linearly decreasing inertia weight and adaptive learning factors, making the inertia weight change together with the learning factors to change the global or local flight ability to improve the ability to jump out of the local optimum. Through simulation comparison, it is proved that the improved second-order PSO proposed in this paper has the ability to jump out of the local optimum in the global path planning, and at the same time avoids premature convergence, and can also improve the convergence speed of the algorithm, which verifies the effectiveness of the algorithm.
Leveraging the Symbiota Support Hub for Biodiversity Data Mobilization

Symbiota is an open source software for managing and mobilizing biodiversity data from physical and virtual collections. Over 700 natural history collections use Symbiota as their primary content management system, and over 600 additional collections use Symbiota portals to mobilize a copy or subsamples of their data for use by specific communities of expertise. For both "live-managed" and "snapshot" collections, Symbiota provides data import, export, and publishing tools to lower data mobilization barriers. For example, collections in Symbiota portals can publish their data directly to the Global Biodiversity Information Facility (GBIF) and Integrated Digitized Biocollections (iDigBio) using Darwin Core Archive protocols for data sharing, obviating the need to install or host local Integrated Publishing Toolkit (IPT) instances. Over 150 institutions currently use this workflow to publish datasets to GBIF. The strengths of Symbiota's approach to mobilization are part technical and part social. Once a collection has data in a portal, using the portal’s built-in data mobilization tools requires little technical expertise, and Symbiota portal managers are typically very accessible and helpful in assisting with the process. With sustained funding of the iDigBio Symbiota Support Hub, we are increasing the capacity of Symbiota to mobilize biodiversity through (1) improved documentation regarding the data mobilization process (https://symbiota.org/docs); (2) increased capacity for individualized attention to users through a larger service team and a robust help desk system; (3) the launch of "Portal Advancement Campaigns"—targeted efforts to promote data quality and mobilization (e.g., publishing to data aggregators); and (4) the development of an API infrastructure to enhance data interoperability and accessibility. We provide an overview of these new technologies and services for promoting and assisting with data mobilization, and we discuss future developments.
Implementasi Sistem Parental Control Pada Jaringan Client-Server Menggunakan Sistem Operasi Debian

The increasing number of internet users along with the development of technology is increasing, the current webiste service users have been used by anyone and in all fields. Various sites about education, entertainment, mystical even violent scenes and pornography are things that are impossible not to find on the internet. More and more sites that are not educating at this time make parents have to be more careful and wise in supervising their children who are still under 17 years old in using internet facilities. In this study, a parental control system will be designed and built that utilizes a proxy server on a network which can later be implemented on an internet network at home. The type of research in this research is research and development to carry out system development. With this system, it is hoped that parents can be calmer at work or outside the home and their children can monitor all their activities on the internet from those that are less suitable for the child to visit.
Global Semantic Descriptors for Zero-Shot Action Recognition

The success of Zero-shot Action Recognition (ZSAR) methods is intrinsically related to the nature of semantic side information used to transfer knowledge, although this aspect has not been primarily investigated in the literature. This work introduces a new ZSAR method based on the relationships of actions-objects and actions-descriptive sentences. We demonstrate that representing all object classes using descriptive sentences generates an accurate object-action affinity estimation when a paraphrase estimation method is used as an embedder. We also show how to estimate probabilities over the set of action classes based only on a set of sentences without hard human labeling. In our method, the probabilities from these two global classifiers (i.e., which use features computed over the entire video) are combined, producing an efficient transfer knowledge model for action classification. Our results are state-of-the-art in the Kinetics-400 dataset and are competitive on UCF-101 under the ZSAR evaluation. Our code is available at https://github.com/valterlej/objsentzsar
Enabling fast uncertainty estimation

Quantifying the uncertainty of neural networks (NNs) has been required by many safety-critical applications such as autonomous driving or medical diagnosis. Recently, Bayesian transformers have demonstrated their capabilities in providing high-quality uncertainty estimates paired with excellent accuracy. However, their real-time deployment is limited by the compute-intensive attention mechanism that is core to the transformer architecture, and the repeated Monte Carlo sampling to quantify the predictive uncertainty. To address these limitations, this paper accelerates Bayesian transformers via both algorithmic and hardware optimizations. On the algorithmic level, an evolutionary algorithm (EA)-based framework is proposed to exploit the sparsity in Bayesian transformers and ease their computational workload. On the hardware level, we demonstrate that the sparsity brings hardware performance improvement on our optimized CPU and GPU implementations. An adaptable hardware architecture is also proposed to accelerate Bayesian transformers on an FPGA. Extensive experiments demonstrate that the EA-based framework, together with hardware optimizations, reduce the latency of Bayesian transformers by up to 13, 12 and 20 times on CPU, GPU and FPGA platforms respectively, while achieving higher algorithmic performance.
PENGEMBANGAN SISTEM EVALUASI DOSEN MENGGUNAKAN LOGIKA FUZZY

Abstract&#x0D; In educational institutions, success is measured by the achievements of lecturers, or how many qualified lecturers meet the competency standards set by the Higher Education policy or campus quality assurance institution. This study tends to examine the superiority of the membership function in the process for fuzzy inference using the Mamdani method, to further accelerate and sharpen the accuracy of the lecturer's assessment by applying scientific techniques and also to avoid errors in the assessment that can be done manually.&#x0D; The results of our method for several real samples show that our approach can be used to evaluate the performance of Information Systems lecturers at Pamulang University.&#x0D; &#x0D; Keywords: Fuzzy logic, Lecturer Evaluation, Matlab, Mandani
Updating and using the EO4GEO Body of Knowledge for (AI) concept annotation

Abstract. The EO4GEO Body of Knowledge (BoK) serves as a vocabulary for the domain of geoinformation and earth observation, supporting the annotation of online resources. This paper presents how the BoK is designed, maintained and improved. We discuss how the BoK content can be extended, using the example of integrating artificial intelligence (AI) concepts and show how annotation is done by adding persistent concept identifiers in the metadata of training materials. This platform allows us to share online information with clarified semantics. A prolonged use necessitates the incentivisation of an active expert community and a further adoption of infrastructure standards.
A Novel Analysis of Readability Indices of MOOC Transcripts for e-Learner Retainment

e-learning has become an important part of the education system. This is more so in wake of the COVID-19 pandemic. There are a wide range of e-learning platforms available online and offering courses ranging from beginner to advanced levels. Such courses are accessible to nearly everyone around the world who has access to the internet. The readers and learners look for engaging content which will enhance their knowledge and skills in the respective field through e-learning. In order to provide the learners with effective learning, the content should be readable and easy to understand. The motives of the present study are multifold. The presented results will be helpful to the designers as well as creators of e-learning courses in maximizing the learner's attention span, content comprehension, ease of understanding and engagement with the e-course. To the best of our knowledge, this is first formal attempt for the analysis of the transcripts of the e-learning courses. Technically, we deploy five readability indices namely Flesch Readability Index (FRI), Gunning Fog Index (GFI), Flesch-Kincaid Grade Level (FKGL), Coleman-Liau Index (CLI), and Automated Readability Index (ARI) for the study. The calculations have been additionally fortified using Term Frequency-Inverse Document Frequency (TF-IDF). More than 1200 transcripts have been analyzed and vital statistics presented. It is concluded that the transcripts for the beginners should have a word range of 600- 900 while that for the intermediate and advanced learners should have an optimum word count value of around 1050.
A Wearable Smart Face Shield

The fourth industrial revolution mostly revolves around new techniques and concepts such as artificial intelligence (AI), machine learning (ML), internet of things (IoT), etc. The recent spurt of corona virus has wreaked havoc across the globe and led to huge loss of human lives. An intelligent system with innovative technologies can be implemented to address the rapid spread of the deadly virus. In this paper, we present our patented [1] idea of Smart Face Shield (SMAFS) that can not only help to maintain appropriate social distancing in a crowded place but also to identify a person with preliminary symptoms of corona virus.SMAFS is designed as a technically improved face shield to maintain social distancing by appropriate use of proximity sensor and to measure temperature of the wearer by using contact temperature sensor. LED's and buzzer are placed strategically to alert people via visual and audio signals respectively. Such precautionary detection and proximity alert prototype can prove instrumental in early diagnosis and isolation aiding in crowd management and free movement in places of social gathering.
The Optimized ORB Algorithm Based on Region Partition

Aiming at the problems of low matching accuracy and slow matching speed of ORB feature matching algorithm under complex environments, an improved ORB algorithm based on region division and RANSAC algorithm was proposed. By dividing the image into regions, the similarity algorithm is used to extract the regions with high similarity, then the ORB algorithm is used to extract the feature points of similar regions. The results can effectively improve the operation efficiency of image feature point extraction. The improved ORB algorithm combined with RANSAC algorithm to eliminate the wrong matching point pairs that get more accurate matching results. Experimental results show that compared with the original method, the improved algorithm has better accuracy and real-time performance.
Unraveling the Dark Web

Google is the most worshiped God currently and it is said that you can find everything and anything with a single google search. But the real question is does google tell you everything? The answer is simply a no. Google only tells what it's meant to i.e., only the content of the open public web. As a web developer or a blogger whatever content you publish, it's always on the public side of the internet, something we call indexed. But to sum it up you only see 1% of the internet. That's right 96-99% of the internet is something known as the “Deep Web.” It is the part of the internet that is not public. This chapter aims to not promote the usage of the Dark Web but to provide clarity on how vast it is and how it is laid out. We will be discussing the history and whereabouts of famous marketplaces like the “Silk Road” and try to explore can we create something in the Deep Web. Deep Web can be used by anyone for legal purposes while using Dark Web is illegal and is strictly restricted across nations bound by their IT laws.
Visual Perception using OpenCV

Visual Perception is related with detecting objects. Detection of the object is done by using YOLO network. There are multiple object detection algorithms like Fast- Convolutional Neural Network (Fast-CNN), Faster-Convolutional Neural Network(Faster- CNN), Regional- Convolutional Neural Network (R-CNN) that does not process the whole image at a time, but when compared to these, YOLO looks at the image completely and then passes it to the single network, which then predicts the bounding boxes using convolutional network and class probabilities for these bounding box and detects the image faster and accurately.
The Prospective Benefits of Using Machine Learning for the Prediction of Breast Cancer

Improving the percentage of patients diagnosed with early-stage cancer is a vital priority of the World Health Organization. Cancer is one of the most unsafe diseases for humans, yet no enduring cure has been developed. Breast cancer is one of the most common types of cancer in the middle east region. Early diagnosis and treatment of breast cancer can significantly improve the lives of millions of women across the globe. Due to the advancement in technology, artificial intelligence and machine learning have been used successfully to discover several dangerous diseases, and serving in early analysis and treatment. Thus, the integration of artificial intelligence and machine learning in the scientific field supports enhancing morbity and mortality rates. This research is a systematic review on breast cancer discovery and action using genetic sequencing or histopathological imaging with the help of deep learning and machine learning.
A location privacy protection method based on location semantics over road networks

Aiming at solving the problem that it is difficult for users to resist location semantic inference attacks when they consume location-based services (LBS) in a road network environment, a location privacy protection method based on the semantic similarity of road under user collaboration is proposed: For LBS consumption, in the roads provided by collaborative users, we use the semantic similarity between each road and the road where the requesting user is located to determine the most suitable road, build an anonymous road set, and pass the agent. The method is theoretically analysed, and experiments are carried out on the anonymous success rate and anonymous time based on the Brinkhoff road network data generator. Experimental results show that this method has better privacy protection and higher service quality.
The Effect of Low-Intensity Aerobic Exercise for Reducing Blood Glucose in Pre-Dialysis Diabetic Chronic Kidney Disease Patients: A Pilot Test

본 연구는 투석 전단계 당뇨병성 만성 신질환 환자의 혈당 감소를 위해 저강도 유산소 운동을 제공하고 연구 참여자들의 혈당 감소 정도를 파악하기 위한 단일집단 전후 비교 설계(one-group pretest-posttest design)의 실험연구이다. 연구는 2020년 8월부터 2020년 11월까지 진행되었으며, 연구 참여자는 20명이었다. 혈당 감소를 위한 저강도 유산소 운동의 효과는 Wilcoxon signed rank test 방법을 이용하여 분석하였다. 연구 결과 저강도 유산소 운동 적용 후 대상자들의 혈당은 164.88±27.10㎎/dL에서 138.08±31.52㎎/dL로 감소하였고(p＜.001), 당화혈색소(HbA1c)는 7.18±0.97㎎/dL에서 6.65±1.11㎎/dL로 감소하였다(p=.001). 따라서 본 연구에서 진행된 저강도 유산소 운동은 투석 전단계에 있는 당뇨병성 만성 신질환 환자의 혈당을 조절하고 더불어 혈압 강하에도 도움이 되는 것으로 확인되었다. 본 연구는 면역력이 약한 투석 전단계 당뇨병성 만성 신질환 환자가 COVID-19와 같은 팬데믹 시기에 집안에서 안전하고 쉽게 따라 함으로써 혈당조절에 도움이 될 수 있는 중재 방법을 제시하였기에 만성질환자 관리에 관한 보건의료적 관점에서 의미가 있을 것이다. The purpose of this study was to evaluate the effect of low-intensity aerobic exercise program provided to diabetic chronic renal failure patients before dialysis to reducing blood sugar. One-group pretest-posttest design was used and subjects for the present study were 20 (Male: 13, Female: 7). Data were collected from August to November in 2020 and analysed using Wilcoxon signed rank test. There was an improvement in participants’ blood sugar after applying low-intensity aerobic exercise (from 164.88±27.10㎎/dL to 138.08±31.52㎎/dL, p＜.001) and HbA1c (from 7.18±0.97㎎/dL to 6.65±1.11㎎/dL, p=.001). Therefore, this study is expected to be used as basic data for interventional studies for blood sugar management in diabetic chronic real failure patients. However, as this study is a pilot study, there are limitations in generalizing the results of the study, because the number of study subjects is small and the continuous effect of blood sugar management cannot be confirmed due to the short study period. Thus, the follow-up studies base on patient-control study design should be conducted to elucidate the effect of low-intensity aerobic exercise program for diabetic chronic renal failure patients.