Reviewerly/exHarmony huggingface dataset:
GreenDRL

Managing datacenters to maximize efficiency and sustain-ability is a complex and challenging problem. In this work, we explore the use of deep reinforcement learning (RL) to manage "green" datacenters, bringing a robust approach for designing efficient management systems that account for specific workload, datacenter, and environmental characteristics. We design and evaluate GreenDRL, a system that combines a deep RL agent with simple heuristics to manage workload, energy consumption, and cooling in the presence of onsite generation of renewable energy to minimize brown energy consumption and cost. Our design addresses several important challenges, including adaptability, robustness, and effective learning in an environment comprising an enormous state/action space and multiple stochastic processes. Evaluation results (using simulation) show that GreenDRL is able to learn important principles such as delaying deferrable jobs to leverage variable generation of renewable (solar) energy, and avoiding the use of power-intensive cooling settings even at the expense of leaving some renewable energy unused. In an environment where a fraction of the workload is deferrable by up to 12 hours, GreenDRL can reduce grid electricity consumption for days with different solar energy generation and temperature characteristics by 32--54% compared to a FIFO baseline approach. GreenDRL also matches or outperforms a management approach that uses linear programming together with oracular future knowledge to manage workload and server energy consumption, but leaves the management of the cooling system to a separate (and independent) controller. Overall, our work shows that deep RL is a promising technique for building efficient management systems for green datacenters.
Understanding Hate Group Videos on YouTube

Hate content warning: This paper contains hate ideology content that may cause discomfort.
Decolonial and Postcolonial Computing Research: A Scientometric Exploration

Decolonial and postcolonial computing scholars focus on the relationship between coloniality and technology. While many recent empirical and design studies have adopted these theoretical lenses, these conversations are often disconnected. Through a systematic literature review, we seek to understand patterns within and between decolonial computing and postcolonial computing. As an early step toward that objective, this poster presents results from our preliminary scientometric exploration of 115 papers’ metadata and discusses research trends and popular publication venues in these areas. Using citation network analysis, we found smaller communities in decolonial and postcolonial computing scholarship based on their use of theoretical frameworks, objectives, types of papers, authors’ collaboration and affiliations, and research sites and populations. We conclude by discussing future research directions to bring these communities into conversations with each other.
See it to believe it?

A common fixture of computer systems research are Visualisation-in-the-Loop Tools: tools that produce complex output data and require a human user to interpret the data visually. However, systems research frequently omits or sidelines details of the visualisation components that were necessary for the tool. In a survey of 1,274 recent systems papers we find that at least 7.7% (98) of them present a visualisation-in-the-loop tool. We also find that the majority of these publications pay no attention and give little explanation to implemented visualisations. We propose that the impact and reach of visualisation-in-the-loop systems research can be greatly enhanced when exposition is given to visualisation, and propose a concrete checklist of steps for authors to realise this opportunity.
Towards Situation Awareness and Attention Guidance in a Multiplayer Environment using Augmented Reality and Carcassonne

Augmented reality (AR) games are a rich environment for researching and testing computational systems that provide subtle user guidance and training. In particular computer systems that aim to augment a user's situation awareness benefit from the range of sensors and computing power available in AR headsets. The main focus of this work-in-progress paper is the introduction of the concept of the individualized Situation Awareness-based Attention Guidance (SAAG) system used to increase humans' situating awareness and the augmented reality version of the board game Carcassonne for validation and evaluation of SAAG. Furthermore, we present our initial work in developing the SAAG pipeline, the generation of game state encodings, the development and training of a game AI, and the design of situation modeling and eye-tracking processes.
CoSpot

Most large cloud operators offer a lower-priced, lower-priority alternative to regular (on-demand or reserved) virtual machines, commonly referred to as spot instances. Spot instances are opportunistically allocated to servers in order to utilize any residual cloud capacity, but are evicted whenever regular virtual machines need to use that capacity. This paper proposes CoSpot, a lightweight framework for cooperative allocation of regular virtual machines and spot instances, which allows for easy integration of arbitrary virtual machine and spot allocators. In our experiments, employing the framework achieves up to 245% improvement (average 34% improvement) in spot revenue, with no loss in virtual machine revenue, compared to the baseline VM and spot allocation without using our framework. We also derive and release a reusable workload with both virtual machines and spot instances, based on data previously shared by Microsoft Azure.
LockNKey: Improvised Cloud Storage System using Threshold Cryptography Approach

In light of the current cloud security issues, this research focuses on developing a secure mechanism to improve the security of data stored in the cloud for computing service providers using threshold cryptography and encryption. While the data is being uploaded to cloud storage, it is vulnerable to attacks. Recent studies include distribution of keys for encrypted files to enhance the security. This research proposes a system which secures the data with the concept of threshold cryptography on a modified version of the RSA algorithm before uploading it to the cloud storage. The private key generated by the RSA algorithm is split up into N smaller parts or shares which can be stored at different places. The user would require a minimum k number of shares (where k is less than N) to recreate the private key and decrypt the data. The key is regenerated using the Lagrange interpolation algorithm which allows to reconstruct any polynomial of degree K minus 1 from K points on its curve. Thus, the system is even more protected since an attacker will not be able to decode the data even if he has as many as k minus 1 shares. Also, it is more secure in the manner that even if the legitimate user loses N minus k shares, they would still be able to decrypt the data by recreating the private key using the remaining k keys. Thus, in this manner by splitting the private key into a larger number of smaller shares, there is an increase in the security of the data being stored, added to this based on different input sizes there has been a decrease in the encryption time by 1.42 percent on the original RSA algorithm. A Graphical User Interface application is also built to make it easier for the user to secure their documents they want to store on cloud.
Achieving low latency in public edges by hiding workloads mutual interference

On multi-tenant platforms, such as public clouds and edges, workloads interfere with each other through shared resources. The performance degradation caused by such interference is a notoriously challenging problem. Though many solutions have been proposed for clouds, they can hardly help the application in edges, where workloads are mostly latency-critical, highly dynamic, and more sensitive to interference. Aggressive resource over-provisioning looks to be the only practical solution, albeit it causes significant resource waste.
Detection of Prostate Cancer Using MRI Images Classification with Deep Learning Techniques

Prostate Cancer (PCa) is considered as one of the widely cancer diseases affect men around the globe. Research found about 16.67% of men affects by PCa in their life specially after 40 ages. Studies reported that one of every six men are suffering from Prostate Cancer approximately. The traditional methods of PCa diagnosis are considered a tedious process and subjected to human errors. Therefore, research tried to develop an efficient diagnostic technique for early detection of PCa which can make a great clinical treatment impact. This paper presents an intelligent system for automated the process of detecting prostate cancer by using MRI images with convolution neural network approaches. Five pre-trained transfer-learning models are used here including: Inception-v3, Inception-v4, Inception-Resent-v2, Xception, and PolyNet. The dataset consisted of 1524 prostate MRIs, which were divided into three parts: 1067 MRI for training, 304 MRI for validation, and 153 MRI for test purposes. Initially, the dimension of MRI images was resized to reduce the complexity and computation processing. Then, the training phase conducted by forwarding MRI images into transfer learning models individually for feature extractions and PCa classification. The transfer learning models were used to classify MRI prostate images into two sets: positive (significant) and negative (nonsignificant) results. Finally, experiments were conducted to evaluate the pre-trained models using both validation and testing datasets. The experimental results showed a robust and high accuracy recognition rate of detecting PCa for each model where Inception-v3 = 98.69, Inception-v4= 96.73%, Inception-Resent-v2= 96.73%, Xception=95.42%, and PolyNet=99.34%. We found here the ability of proposed transfer learning models to detect prostate cancer using MRI images successfully with a high accuracy recognition rate.
Cyber Security in Businesses: Challenges and Recovery Modes

Cyber security is one of the necessities of today’s business world as their systems consist of valuable as well as sensitive information and it is used by businesses to protect their relevant data from theft or damage. With the evolution in technology, there are inherent risks taking place in the business world in the form of cyber threats or cyber attacks and security can be attained by adopting the right means through multilayered protection using cyber security. Cyber security is the strategic process that can be used in the critical protection of business systems from data breaches. The study is conducted with the purpose to determine the relevance of cyber security in businesses along with the future instance of cyber security. The research also aims to study the challenges faced by businesses related to cyber security and to evaluate the recovery modes from a revival of cyber crimes. The study is based on an exploratory research design as the objectives are developed to find the conclusion on that basis. The study has been conducted using secondary data from the available literature. Cyber crime has become quite sophisticated these days and various solutions can be used in securing business-related data. There are various modes of recovery from a data breach even if it is hacked. Cyber security plays a major role in protecting business information by keeping it digitally secure and safe. Cyber security must be part of business culture. Security is the responsibility of each and every individual in the organization. With the implementation of the Mobile Device Management tool, the data can be protected, managed as well as backed up regularly. The data of the businesses can be controlled through virtual as well as physical access using strong passwords and multi-factor authentication. There is a need for a comprehensive approach to determine better solutions for security potential.
Arabic Sentiment Analysis on Mobile Applications Using Levenshtein Distance Algorithm and Naive Bayes

Due to the specificity of the characteristics and features of the Arabic language and its complexity, the field of sentiment analysis in Arabic texts still represents a major challenge. Few studies have been conducted for Arabic sentiment analysis (ASA) compared to English or other Latin languages. In addition, most of the current studies on ASA have been conducted on data sets collected from Twitter and very few studies on user comments in Mobile applications (apps) reviews in Google Play Store. Therefore, this paper presents a new approach to sentiment analysis in Arabic text based on the mobile app comments dataset of Google Play Store. The proposed approach uses algorithms such as the Levenshtein distance (LD) algorithm for preprocessing the data. Thereafter, it applies various classification models to identify the mobile applications(apps) reviews in the Google Play Store of Arabic text. The results of the experiment show that the proposed approach is effective for sentiment analysis of Arabic text. The experiments were carried out by comparing the accuracy using the Naive Bayes (NB) algorithm, and we obtained an accuracy of 95.80% and compared it with the use of the LD Algorithm, where we obtained a better accuracy of 96.40% to identify the reviews of the sentence when k=9.
Minimizing packet retransmission for real-time video analytics

In smart-city and video analytics (VA) applications, high-quality data streams (video frames) must be accurately analyzed with a low delay. Since maintaining high accuracy requires compute-intensive deep neural nets (DNNs), these applications often stream massive video data to remote, more powerful cloud servers, giving rise to a strong need for low streaming delay between video sensors and cloud servers while still delivering enough data for accurate DNN inference. In response, many recent efforts have proposed distributed VA systems that aggressively compress/prune video frames deemed less important to DNN inference, with the underlying assumptions being that (1) without increasing available bandwidth, reducing delays means sending fewer bits, and (2) the most important frames can be precisely determined before streaming. This short paper challenges both views. First, in high-bandwidth networks, the delay of real-time videos is primarily bounded by packet losses and delay jitters, so reducing bitrate is not always as effective as reducing packet retransmissions. Second, for many DNNs, the impact of missing a video frame depends not only on itself but also on which other frames have been received or lost. We argue that some changes must be made in the transport layer, to determine whether to resend a packet based on the packet's impact on DNN's inference dependent on which packets have been received. While much research is needed toward an optimal design of DNN-driven transport layer, we believe that we have taken the first step in reducing streaming delay while maintaining a high inference accuracy.
A Continuous Collision Checking Method for Non-holonomic Path Planning in Narrow Environments

This paper presents a new method of collision checking for non-holonomic dynamical systems in environments with narrow passages. This new method is compared with alternative approaches for collision checking, which are widely used in conjunction with different path planners. All methods are evaluated in a systematic simulation study with numerous scenarios, using two of the most popular path planning methods, Hybrid A* and RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> . On the one hand, the results show that the new collision checking approach can manage the narrow passages better than the compared methods. On the other hand, its computation time can be longer or shorter, depending on the particular scenario.
Visual Feedback Control using CNN Based Architecture with Input Data Fusion

Visual servoing systems are designed to solve pose alignment problems by providing the necessary linear and angular velocities using data extracted from images. Among the difficulties encountered by the traditional visual servoing approaches, there are feature detection and tracking, camera calibration, scene complexity, and robotic system constraints. Part of these problems can be solved if Convolutional Neural Networks (CNNs) are added to a visual servoing architecture. The main advantage of CNNs is the capability of understanding both the overall structure and specific details of the images corresponding to the current and desired layouts. To take a step further the state-of-the-art architectures, in this paper, we show how extra input data can improve the visual servoing behaviour. The extra data result from maps of regions induced by the feature points' positions, without the necessity of employing tracking. The results obtained on relevant data sets show that the proposed input fusion-based CNN provides an improved approximation of the linear and angular visual servoing velocities.
Object Detection in Invoices

Key field information extraction from documents is an increasingly covetable task. Previous related work has touched upon the subject through the lens of rule-based systems or through natural language processing methods. In this paper we see the task of information extraction from invoices as an object detection task. To this end, we used three different models YOLOv5, Scaled YOLOv4 and Faster R-CNN to detect key field information in invoices. Additionally, we propose a data preprocessing method that helps to better generalize the learning. All of the experiments were performed on a custom made dataset with a very high variety of invoice layouts. This decision comes in part from the lack of any suitable public dataset and from the need of finding the best procedure for annotating data pertaining to this task. The obtained results were encouraging, leading us to the conclusion that object detection is a viable method for information extraction.
A Case Study for Designing a Secure Communication Protocol over a Controller Area Network

This paper presents a case study for designing and implementing a secure communication protocol over a Controller Area Network (CAN). The CAN based protocol uses a hybrid encryption method on a relatively simple hardware / software environment. Moreover, the blockchain technology is proposed as a working solution to provide an extra secure level of the proposed system.
Characterization of Fractal Basins Using Deep Convolutional Neural Networks

Neural network models have recently demonstrated impressive prediction performance in complex systems where chaos and unpredictability appear. In spite of the research efforts carried out on predicting future trajectories or improving their accuracy compared to numerical methods, not sufficient work has been done on using deep learning techniques in which the unpredictability can be characterized of chaotic systems or give a general view of the global unpredictability of a system. In this work, we propose a novel approach based on deep learning techniques to measure the fractal dimension of the basins of attraction of the Duffing oscillator for a variety of parameters. As a consequence, we provide an algorithm capable of predicting fractal dimension measures as accurately as the box-counting algorithm, but with a computation speed about ten times faster.
Ensemble Deep Learning Model for Breast Histopathology Image Classification

Breast cancer is a life-threatening disease that affects individuals all over the world. As a consequence, effective and precise breast cancer screening is crucial. Early detection of breast cancer allows patients to obtain the best treatment available, boosting their probability of surviving. Several studies have resulted in the development of computational algorithms for predicting breast cancer progression using a variety of diagnostic imaging modalities. In this paper, a deep learning approach for the classification of breast cancer histopathology images is carried out. The proposed model is hybrid combination of Inspection-ResNetv2 and EfficientNetV2-S with pretrained weights as ImageNet. The proposed model was validated on BreakHis and Breast Cancer Histology (BACH) dataset. For concatenation of both networks, top layer was removed and global average pooling was added, followed by dense layer, dropout and final classification layer. The proposed model was evaluated in comparison with individual results obtained by Inspection-ResNetv2 and EfficientNetV2 model results. The final classification layer consists of dense layer of four neurons of BACH dataset classification and for BreakHis 8 neurons. The experimentation of proposed model showed good results by achieving overall accuracy of 98.15% for the BACH dataset and 99.036% for BreakHis dataset.
Breast Cancer Detection Using Bag of Visual Words

Breast cancer is the cause of most frequent cancer-related death in women around the globe, with a five-year survival rate that is significantly lower than that of many other cancers. Much research has been done on identifying and spotting breast cancer using various image processing and classification approaches. Despite this, the disease remains one of the most lethal. Because the root of breast cancer remains unknown, avoidance is impossible. Breast cancer can only be cured if tumours in the breast are detected early. We can detect breast cancer using bag of deep visual words on histopathological images. The rate of mortality can be effectively reduced if real discovery is made. Our model can aid in the early identification of breast cancer. Here, we quantitatively depict the study methods used for feature detection of cancer by using VGG19, K-means, and histogram generation. These features are compared and processed via an SVM classifier to better comprehend the cancer pattern in the histopathology image.
Analysis of Public Perception of Autonomous Vehicles Based on Unlabelled Data from Twitter

Many innovations are frequently rejected by the general public owing to controversies, which has a detrimental impact on their acceptance and their commercialization. Recently, there is an increase in the use of microblogging sites such as Twitter, Instagram, and Reddit, and thanks to these, the general public may convey their views and thoughts on any issue more easily than ever before. We aim to explore unlabelled Twitter data and use the sentiment analysis tool VADER on it to determine the general public’s perception of autonomous vehicles. The data from Twitter is pre-processed using the tools provided by the NLTK library, and then a sentiment intensity value is calculated for each tweet using VADER’s sentiment intensity analyser. We count the number of tweets that have positive, negative, or neutral impressions and identify why. We collected a total of 35,476 tweets and finally analysed 32,976 tweets after pre-processing.