Reviewerly/exHarmony huggingface dataset:
An Adjustable Variant of Round Robin Algorithm Based on Clustering Technique

CPU scheduling is the basic task within any time-shared operating system.One of the main goals of the researchers interested in CPU scheduling is minimizing time cost.Comparing between CPU scheduling algorithms is subject to some scheduling criteria (e.g., turnaround time, waiting time and number of context switches (NCS)).Scheduling policy is divided into preemptive and non-preemptive.Round Robin (RR) algorithm is the most common preemptive scheduling algorithm used in the time-shared operating systems.In this paper, the authors proposed a modified version of the RR algorithm, called dynamic time slice (DTS), to combine the advantageous of the low scheduling overhead of the RR and favor short process for the sake of minimizing time cost.Each process has a weight proportional to the weights of all processes.The process's weight determines its time slice within the current period.The authors benefit from the clustering technique in grouping the processes that are similar in their attributes (e.g., CPU service time, weight, allowed time slice (ATS), proportional burst time (PBT) and NCS).Each process in a cluster is assigned the average of the processes' time slices in this cluster.A comparative study of six popular scheduling algorithms and the proposed approach on nine groups of processes vary in their attributes was performed and the evaluation was measured in terms of waiting and turnaround times, and NCS.The experiments showed that the proposed algorithm gives better results.
Automated Attendance Management Using Hybrid Approach in Image Processing

Image Processing, which is a significant field of Machine Learning and falls under the adage of Artificial Intelligence, is used worldwide to achieve face detection and recognition. Image Processing focuses on two tasks, namely the enhancement of image for our interpretation and reading image data to store, transmit, and represent image perception via machines. The project focuses on the implementation of Machine Learning and Image Processing to automate attendance management in a corporate/personal space. In the current scenario, most corporate spaces make use of biometric systems to mark attendance, which is both time-consuming and costly. Setting up a working space with the biometric system also requires an individual with professional knowledge of the same. Our primary focus is to build a robust and automated security system capable of monitoring attendance without much human interference.
Development of Feature Extraction-Based Currency Recognition System Using Artificial Neural Network

Development of paper currency detection system is one kind of smart framework which is of significant requirement in today’s modern world. Currency recognition means denomination classification and counterfeit detection. There are many currencies all over the world. Every currency appears unique based on features such as variation in size, texture, colour etc. In the present study, a system is developed to reduce counterfeit detection time. Image processing and feature extraction technique in the presence of UV light are implemented for currency identification. Some of the features of real banknote are only visible in the presence of UV light. In case of Indian currencies, security thread looks green in presence of UV light. This study trains a feedforward backpropagation neural network with 20 samples of each denomination. The validation resulted 100% accuracy with reduced processing time for currency recognition and counterfeit detection.
Enclose a Target with Multiple Nonholonomic Agents

In this paper, an algorithm on circular circumnavigation of nonholonomic agents is proposed. The agents are required to enclose a static target with predefined radius and circumferential speed. The algorithm completely relies on local bearing angle measurement. Cyclic pursuit is adapted to coordinate the agents and generate an even formation at the circle. Theoretical analysis on the stability of algorithm is given. The applicability and effectiveness of the algorithm is testified with simulations on the unmanned surface vessel (USV) platform.
Design Space for Voice-Based Professional Reporting

Speech technology has matured so that voice-based reporting utilizing speech-to-text can be applied in various domains. Speech has two major benefits: it enables efficient reporting and speech input improves the quality of the reports since reporting can be done as a part of the workflow without delays between work and reporting. However, designing reporting voice user interfaces (VUIs) for professional use is challenging, as there are numerous aspects from technology to organization and language that need to be considered. Based on our experience in developing professional reporting VUIs with different stakeholders representing both commercial and public sector, we define a design space for voice-based reporting systems. The design space consists of 28 dimensions grouped into five categories: Language Processing, Structure of Reporting, Technical Limitations in the Work Domain, Interaction Related Aspects in the Work Domain, and Organization. We illustrate the design space by discussing four voice-based reporting systems, designed and implemented by us, and describing a design process that utilizes it. The design space enables designers to identify critical aspects of professional reporting VUIs and optimize those for their target domain. The design space can be used as a practical tool especially by designers with limited experience on speech technologies.
Improving exploratory information retrieval for neophytes

Digital libraries have become an essential tool for researchers in all scientific domains. With almost unlimited storage capacities, current digital libraries hold a tremendous number of documents. Though some efforts have been made to facilitate access to documents relevant to a specific information need, such a task remains a real challenge for a new researcher. Indeed neophytes do not necessarily use appropriate keywords to express their information need and they might not be qualified enough to evaluate correctly the relevance of documents retrieved by the system. In this study, we suppose that to better meet the needs of neophytes, the information retrieval system in a digital library should take into consideration features other than content-based relevance. To test this hypothesis, we use machine learning methods and build new features from several metadata related to documents. More precisely, we propose to consider as features for machine learning: content-based scores, scores based on the citation graph and scores based on metadata extracted from external resources. As acquiring such features is not a trivial task, we analyze their usefulness and their capacity to detect relevant documents. Our analysis concludes that the use of these additional features improves the performance of the system for a neophyte. In fact, by adding the new features we find more documents suitable for neophytes within the results returned by the system than when using content-based features alone.
Visualizing Parameter Adaptation in Differential Evolution with Expected Fitness Improvement

In this paper the expected fitness improvement metric is proposed to visualize the parameter search space in Differential Evolution. The expected fitness improvement is estimated at every generation of the algorithm and plotted in a heatmap profile. The spread of promising scaling factor values is analyzed for the SHADE and jDE algorithms with two different mutation strategies. In addition, the distance between the individuals in the population is considered, and the connection between distance and scaling factor values is observed. The performed experiments reveal important properties of Differential Evolution mutation operators, as well as widely used parameter adaptation techniques.
Actor vs Critic: Learning the Policy or Learning the Value

The widespread use of value-based, policy gradient, and actor-critic methods for solving problems in the area of Reinforcement Learning raises the question whether one of these methods is superior to the others in general or at least whether it is more appropriate to use a particular one under certain circumstances. In this paper, we will compare these methods and identify their advantages and disadvantages. Moreover, we will illustrate the insights obtained using the examples of REINFORCE, DQN and DDPG for a better understanding. Finally, we will give brief suggestions about which approach to use under certain conditions.
Survey of Code Reuse Attacks and Comparison of Mitigation Techniques

Code-Reuse Attacks (CRAs) are solid mechanisms to bypass advanced software and hardware defenses. Due to vulnerabilities found in software which allows attackers to corrupt the memory space of the vulnerable software to modify maliciously the contents of the memory; hence controlling the software to be able to run arbitrary code. The CRAs defenses either prevents the attacker from reading program code, controlling program memory space directly or indirectly through the usage of pointers. This paper provides a thorough evaluation of the current mitigation techniques against CRAs with regards to impact on performance, coverage, and efficiency of those techniques.
Sign Lang age Recogni ion sing Openc and HSV color Model

Communication is the process of exchanging information, views and expressions between two or more person. The two types of communication are verbal and nonverbal. Hand gestures are nonverbal method of communication. A more organized form of hand gestures is known as sign. The physically disabled person uses these gestures to communicate with everyone. To design a system that can recognize sign language accurately so that physically impaired person can have communication with normal people is the main aim for this paper. System follows the general steps like capturing the image from webcam, compare the image with stored images in data set and convert the gesture into text which is readable by disabled person. It is done by using HSV color model and Opencv libraries.
A Literature Review of Critical Success Factors in Agile Testing Method of Software Development

Software development process is a collaborative effort to fulfil multidimensional product requirements. The development methodology plays an important role to meet the expectations of all the stakeholders especially the client. So, agile teams are constituted having experts level of knowledge and versatility in software development. The team tests the software in errands and takes regular feedback to develop a final product. For this, researchers have suggested numerous approaches in their research works. This paper is a literature review of those earlier research works suggesting different approaches of software development using agile testing method to increase efficacy of software as compared to traditional methods. The purpose is to list the objectives considered which help in deciding the development approach to be undertaken. It brings forth the related agile attributes which influence the software development task in terms of quality, scope, timeliness and cost effectiveness. Further, a framework has been presented for use by any of the stakeholders involved in agile software development methodology.
Unsupervised Task Clustering for Multi-task Reinforcement Learning

Meta-learning, transfer learning and multi-task learning have recently laid a path towards more generally applicable reinforcement learning agents that are not limited to a single task. However, most existing approaches implicitly assume a uniform similarity between tasks. We argue that this assumption is limiting in settings where the relationship between tasks is unknown a-priori. In this work, we propose a general approach to automatically cluster together similar tasks during training. Our method, inspired by the expectation-maximization algorithm, succeeds at finding clusters of related tasks and uses these to improve sample complexity. We achieve this by designing an agent with multiple policies. In the expectation step, we evaluate the performance of the policies on all tasks and assign each task to the best performing policy. In the maximization step, each policy trains by sampling tasks from its assigned set. This method is intuitive, simple to implement and orthogonal to other multi-task learning algorithms. We show the generality of our approach by evaluating on simple discrete and continuous control tasks, as well as complex bipedal walker tasks and Atari games. Results show improvements in sample complexity as well as a more general applicability when compared to other approaches.
Intrusion detection of railway clearance from infrared images using generative adversarial networks

The intrusion detection of railway clearance is crucial for avoiding railway accidents caused by the invasion of abnormal objects, such as pedestrians, falling rocks, and animals. However, detecting intrusions using deep learning methods from infrared images captured at night remains a challenging task because of the lack of sufficient training samples. To address this issue, a transfer strategy that migrates daytime RGB images to the nighttime style of infrared images is proposed in this study. The proposed method consists of two stages. In the first stage, a data generation model is trained on the basis of generative adversarial networks using RGB images and a small number of infrared images, and then, synthetic samples are generated using a well-trained model. In the second stage, a single shot multibox detector (SSD) model is trained using synthetic data and utilized to detect abnormal objects from infrared images at nighttime. To validate the effectiveness of the proposed method, two groups of experiments, namely, railway and non-railway scenes, are conducted. Experimental results demonstrate the effectiveness of the proposed method, and an improvement of 17.8% is achieved for object detection at nighttime.
Forming Cognitive Maps of Ontologies Using Interactive Visualizations

Ontology datasets, which encode the expert-defined complex objects mapping the entities, relations, and structures of a domain ontology, are increasingly being integrated into the performance of challenging knowledge-based tasks. Yet, it is hard to use ontology datasets within our tasks without first understanding the ontology which it describes. Using visual representation and interaction design, interactive visualization tools can help us learn and develop our understanding of unfamiliar ontologies. After a review of existing tools which visualize ontology datasets, we find that current design practices struggle to support learning tasks when attempting to build understanding of the ontological spaces within ontology datasets. During encounters with unfamiliar spaces, our cognitive processes align with the theoretical framework of cognitive map formation. Furthermore, designing encounters to promote cognitive map formation can improve our performance during learning tasks. In this paper, we examine related work on cognitive load, cognitive map formation, and the use of interactive visualizations during learning tasks. From these findings, we formalize a set of high-level design criteria for visualizing ontology datasets to promote cognitive map formation during learning tasks. We then perform a review of existing tools which visualize ontology datasets and assess their interface design towards their alignment with the cognitive map framework. We then present PRONTOVISE (PRogressive ONTOlogy VISualization Explorer), an interactive visualization tool which applies the high-level criteria within its design. We perform a task-based usage scenario to illustrate the design of PRONTOVISE. We conclude with a discussion of the implications of PRONTOVISE and its use of the criteria towards the design of interactive visualization tools which help us develop understanding of the ontological space within ontology datasets.
When Size Matters: Markov Blanket with Limited Bit Depth Conditional Mutual Information

Due to the proliferation of mobile computing and Internet of Things devices, there is an urgent need to push the machine learning frontiers to the network edge so as to fully unleash the potential of the edge big data. Since feature selection becomes a fundamental step in the data analysis process, the need to perform this preprocessing task in a reduced precision environment arises as well. To achieve this, limited bit depth conditioned mutual information is proposed within a Markov Blanket procedure. This work also shows the process of generating approximate tables and obtaining the values required to test the independence of the variables involved in the algorithm. Finally, it compares the results obtained during the whole process, from preprocessing to classification, using different numbers of bits.
Evaluation of Deep Learning Based Human Expression Recognition on Noisy Images

Several engineers are currently attempting to recognize the emotions of an individual based on facial expressions. Previously, the images used for human expression recognition had a high resolution and were of good quality. In practice, due to factors such as the camera sensor being sensitive to temperature and illumination and noise added to the transmission of images from a camera to a computer system, noise is added to pictures. It changes the pixel values. Past research has not studied the impact of these factors in great detail. We have proposed a Convolutional Neural Network (CNN) based human expression recognition system and evaluated its performance in the presence of different types of noise. We have also compared the CNN-based model with the Support Vector machine algorithm that has been used in the past to explain why CNN-based human expression recognition is more robust than other face recognition methods. Based on the findings from the experiments, we offer suggestions for developers using human expression recognition technologies.
MongoDB Versus MySQL: A Comparative Study of Two Python Login Systems Based on Data Fetching Time

A database refers to data structure used for systematical and organized collection of data. Now-a-days, Database system follows mostly two different algorithms i.e. SQL and NoSQL. In SQL, databases schema is predefined, fixed and vertically scalable whereas in NoSQL database, schema is dynamic and horizontally scalable. Also, SQL database are table-based database such as MySQL, Oracle, and MS SQL whereas NoSQL databases are document based, key-value based, and graph databases such as MongoDB, CouchDB, and so on. Here, A login system project developed using Python programming language is used to analyze performance of MongoDB and MySQL based on their data fetching speed from databases. A login system can be used where it is required that user should be verified before accessing confidential information or data. This paper will also help us to decide which type of database system should be used as backend database for storing and retrieving user credential information for any login-based system.
A step edge detector based on bilinear transformation

Nowadays, Canny edge detector is considered to be one of the best edge detection approaches for the images with step form. Various overgeneralized versions of these edge detectors have been offered up to now, e.g. Saryazdi edge detector. This paper proposes a new discrete version of edge detection which is obtained from Shen-Castan and Saryazdi filters by using bilinear transformation. Different experimentations are conducted to decide the suitable parameters of the proposed edge detector and to examine its validity. To evaluate the strength of the proposed model, the results are compared to Canny, Sobel, Prewitt, LOG and Saryazdi methods. Finally, by calculation of mean square error (MSE) and peak signal-to-noise ratio (PSNR), the value of PSNR is always equal to or greater than the PSNR value of suggested methods. Moreover, by calculation of Baddeley’s error metric (BEM) on ten test images from the Berkeley Segmentation DataSet (BSDS), we show that the proposed method outperforms the other methods. Therefore, visual and quantitative comparison shows the efficiency and strength of proposed method.
Coevolutionary Deep Reinforcement Learning

The ability to learn without instruction is a powerful enabler for learning systems. A mechanism for this, selfplay, allows reinforcement learning to develop high performing policies without large datasets or expert knowledge. Despite these benefits, self-play is known to be less sample efficient and suffer unstable learning dynamics. This is in part due to a nonstationary learning problem where an agent's actions influence their opponents and as a consequence the training data they receive. In this paper we demonstrate that competitive pressures can be utilised to improve self-play. This paper leverages coevolution, an evolutionary inspired process in which individuals are compelled to innovate and adapt, to optimise the training of a population of reinforcement learning agents. We demonstrate that our algorithm improves the final performance of a Rainbow DQN trained in the game Connect Four, achieving a 15% higher win percentage over the next leading self-play algorithm. Furthermore, our algorithm exhibits more stable training with less variation in evaluation performance.
Quantization of double enhanced charge phase-slip qubits and quantum entanglement control

The quantization scheme between double enhanced charge phase-slip (ECPS) qubits is given. In this system, there exist quantum entanglement phenomenona. The entanglement characteristic is discussed by employing the concurrence. An interesting conclusion is obtained, i.e., if one desire to change the entanglement between two ECPS qubits, the mutual inductance should be considered first, but it is also a good choice to tune symmetric and asymmetric combination of two gate voltages.
Habitar: A Collaborative Tool to Visualize, Distribute, Organize and Share Domestic Tasks Towards Reducing the Gender Gap in Household Labor

Household work and domestic labor are an integral part of keeping up and maintaining a home. Nevertheless, traditional gender roles are still prevalent in a way that women often spend significantly more time and effort than men doing household chores as a form of unpaid labor. Habitar is a tool proposed to take on this complicated subject, developed through User-Centered Design, it distributes and organizes domestic chores between the inhabitants of a house while promoting active and continuous participation of men and children at home. The interactive and collective platform would allow its users to organize, distribute, visualize and share domestic work with each other, as it frames individual effort in a larger context of collective well-being for those involved.
Universal basic income revisited: COVID-19, biopolitical trade-offs, and the expropriation of digital academic labour

Due to the COVID-19 pandemic, the biopolitical trade-offs inherent to contemporary capitalism are cascading down to higher education. Based on insights derived from theories of digitalized capitalism, this article argues that the emergency shift of educational activities online has much potential to heighten the expropriation of digital academic labour. The net result is an intensification of the master process of digitally driven academic proletarianization. At the same time, the reopening of campuses in countries and regions with high infection rates demonstrably puts academics and others at risk. Both of these developments provide reasons, the article maintains, to support the introduction of universal basic income (UBI). After drawing the crucial distinction between UBI as an emergency response and UBI as an institutionally frame-breaking initiative, the latter – non-emergency UBI – is advocated as a solution to the increasingly binaristic choice between work and life in the neoliberal university and beyond.