Reviewerly/exHarmony huggingface dataset:
Improving the Performance of MapReduce for Small-Scale Cloud Processes Using a Dynamic Task Adjustment Mechanism

The MapReduce architecture can reliably distribute massive datasets to cloud worker nodes for processing. When each worker node processes the input data, the Map program generates intermediate data that are used by the Reduce program for integration. However, as the worker nodes process the MapReduce tasks, there are differences in the number of intermediate data created, due to variation in the operating-system environments and the input data, which results in the phenomenon of laggard nodes and affects the completion time for each small-scale cloud application task. In this paper, we propose a dynamic task adjustment mechanism for an intermediate-data processing cycle prediction algorithm, with the aim of improving the execution performance of small-scale cloud applications. Our mechanism dynamically adjusts the number of Map and Reduce program tasks based on the intermediate-data processing capabilities of each cloud worker node, in order to mitigate the problem of performance degradation caused by the limitations on the Google Cloud platform (Hadoop cluster) due to the phenomenon of laggards. The proposed dynamic task adjustment mechanism was compared with a simulated Hadoop system in a performance analysis, and an improvement of at least 5% in the processing efficiency was found for a small-scale cloud application.
Design and Analysis of a Novel Concept-Based Unmanned Aerial Vehicle with Ground Traversing Capability

Abstract Unmanned aerial vehicle (UAV) is a typical aircraft that is operated remotely by a human operator or autonomously by an on-board microcontroller. The UAV typically carries offensive ordnance, target designators, sensors or electronic transmitters designed for one or more applications. Such application can be in the field of defence surveillance, border patrol, search, bomb disposals, logistics and so forth. These UAVs are also being used in some other areas, such as medical purposes including for medicine delivery, rescue operations, agricultural applications and so on. However, these UAVs can only fly in the sky, and they cannot travel on the ground for other applications. Therefore, in this paper, we design and present the novel concept-based UAV, which can also travel on the ground and rough terrain as an unmanned ground vehicle (UGV). This means that according to our requirement, we can use this as a quadcopter and caterpillar wheel–based UGV using a single remote control unit. Further, the current study also briefly discusses the two-dimensional (2D) and three-dimensional (3D) SolidWorks models of the novel concept-based combined vehicle (UAV + UGV), together with a physical model of a combined vehicle (UAV + UGV) and its various components. Moreover, the kinematic analysis of a combined vehicle (UAV + UGV) has been studied, and the motion controlling kinematic equations have been derived. Then, the real-time aerial and ground motions and orientations and control-based experimental results of a combined vehicle (UAV + UGV) are presented to demonstrate the robustness and effectiveness of the proposed vehicle.
Activity recognition on smartphones using an AKNN based support vectors

Purpose Mobile phone-based human activity recognition (HAR) consists of inferring user’s activity type from the analysis of the inertial mobile sensor data. This paper aims to mainly introduce a new classification approach called adaptive k-nearest neighbors (AKNN) for intelligent HAR using smartphone inertial sensors with a potential real-time implementation on smartphone platform. Design/methodology/approach The proposed method puts forward several modification on AKNN baseline by using kernel discriminant analysis for feature reduction and hybridizing weighted support vector machines and KNN to tackle imbalanced class data set. Findings Extensive experiments on a five large scale daily activity recognition data set have been performed to demonstrate the effectiveness of the method in terms of error rate, recall, precision, F1-score and computational/memory resources, with several comparison with state-of-the art methods and other hybridization modes. The results showed that the proposed method can achieve more than 50% improvement in error rate metric and up to 5.6% in F1-score. The training phase is also shown to be reduced by a factor of six compared to baseline, which provides solid assets for smartphone implementation. Practical implications This work builds a bridge to already growing work in machine learning related to learning with small data set. Besides, the availability of systems that are able to perform on flight activity recognition on smartphone will have a significant impact in the field of pervasive health care, supporting a variety of practical applications such as elderly care, ambient assisted living and remote monitoring. Originality/value The purpose of this study is to build and test an accurate offline model by using only a compact training data that can reduce the computational and memory complexity of the system. This provides grounds for developing new innovative hybridization modes in the context of daily activity recognition and smartphone-based implementation. This study demonstrates that the new AKNN is able to classify the data without any training step because it does not use any model for fitting and only uses memory resources to store the corresponding support vectors.
State Classification via a Random-Walk-Based Quantum Neural Network

In quantum information technology, crucial information is regularly encoded in different quantum states. To extract information, the identification of one state from the others is inevitable. However, if the states are non-orthogonal and unknown, this task will become awesomely tricky, especially when our resources are also limited. Here, we introduce the quantum stochastic neural network (QSNN), and show its capability to accomplish the binary discrimination of quantum states. After a handful of optimizing iterations, the QSNN achieves a success probability close to the theoretical optimum, no matter whether the states are pure or mixed. Other than binary discrimination, the QSNN is also applied to classify an unknown set of states into two types: entangled ones and separable ones. After training with four samples, it can classify a number of states with acceptable accuracy. Our results suggest that the QSNN has the great potential to process unknown quantum states in quantum information.
A Case Study of the Design and Operation of a Flipped Learning Class using Edu tech in SW Basic Liberal Arts Edu cation

In a world that is changing with digital technology, SW and AI are no longer necessary only for those students majoring in these fields. The Corona pandemic has accelerated these changes even more. A University also reorganized its information technology utilization competency curriculum to keep pace with these changes and opened SW basic compulsory liberal arts courses from 2021. Due to the nature of SW education, learning effects can be expected only when learning motivation and continuous learning are maintained. However, for learners who need to receive basic SW liberal arts education in a non-face-to-face class environment, the burden of learning has increased. Therefore, as a way to increase the learning effect of SW basic liberal arts education, Edutech was used as a relatively efficient flipped-learning teaching method during the Corona pandemic and the class was designed and operated. After operating a flipped learning class using Edutech in SW basic liberal arts education, students were asked to participate in the class through a questionnaire involving their level of understanding, interest, benefit, recommendation, satisfaction, appropriateness of learning materials, assignments and quizzes. Appropriateness of learning support was checked. As a result of the questionnaire analysis, it was possible to confirm the positive aspects of the effectiveness of the flipped learning class using Edutech in the SW basic liberal arts education, and also in the area concerning the need for improvement measures. Even in the post-Corona era, the use of Edutech is expected to develop more and more. In this respect, the flipped learning class design using Edutech in SW basic liberal arts education can be said to be a meaningful class design and mode of operation in preparation for the post-Corona era. In future research, by conducting in-depth research on improvement measures, we intend to develop a systematization plan for SW basic liberal arts education that can be helpful in strengthening information technology capabilities of the students and nurturing their talents.
Empirical Analysis of Support Vector Machine and Multinomial Naive Bayes

Abstract: As everyone is free to give their opinions about people or products they buy on social media and E-Commerce platforms respectively So, Opinion Mining is extensively used for classifying the opinions into different polarities- positive and negative. Its use has significant overlap with the domain of Machine Learning. The objective of this paper is to compare two Supervised Learning Algorithms- Support Vector Machine (SVM) and Multinomial Naïve Bayes (MNB) and Opinion Mining on twitter dataset. Opinion Mining is a Natural Language Processing (NLP) task which aims to determine the views of people by identifying and extracting the data. The performances of both the models are evaluated and compared their accuracy, precisions, recall values and f scores. The measurement accuracy is measured by Confusion Matrix and ROC curve. In results, it is observed that SVM and MNB both show almost same performance when compared. Keywords: Confusion Matrix, Multinomial Naïve Bayes, Natural Language Processing, Opinion Mining ROC curve, Support Vector Machine
Critical Jugaad Ontologies: Practices for Just Future

Design, however critical, does not emerge exclusively in closed studios of designers or classrooms. Instead, it happens concurrently at the sites of the everyday struggle to survive, resist, and subvert. Yet, very few designers and researchers have emphasized the importance of designerly practices within socially situated contexts that illustrate the underlying critical and political imperatives. To address such perspectives, the author has investigated critical making practices through a conceptual framework of Critical Jugaad that represents designerly making practices from the Global South. In this article, the author further examines an ontology of Critical Jugaad from a futures perspective, through a conversation with the designer and researcher Ernesto Oroza discussing his theory of Technological Disobedience. This inquiry aims to provoke the imaginaries of how we move beyond a western logic of criticality in design through practices that engage with deeper social, cultural, political, and economic conditions of designerly praxis.
Gold and aurostibite from the metaturbidite-hosted Au–Zn–Pb–Ag Hera deposit, southern Cobar Basin, central NSW, Australia: geochemical and textural evidence for gold remobilisation

Abstract The Devonian Hera metaturbidite-hosted polymetallic Au–Zn–Pb–Ag deposit of central NSW, Australia, contained a total undepleted resource of 3.6 Mt @ 3.3 g/t Au, 25 g/t Ag, 2.6% Pb and 3.8% Zn. The deposit comprises a number of distinctive lodes with each containing a distinctive ore and alteration/gangue mineralogy, though generally the sulfide ore comprises various mixtures of sphalerite, galena, chalcopyrite, pyrrhotite and relatively common visible gold–electrum. The North Pod and Far West lodes are distinctly Sb rich and contain a more diverse ore mineralogy with arsenopyrite, native silver, native antimony, gudmundite, tetrahedrite-(Fe), argentotetrahedrite-(Fe), acanthite, dyscrasite, nisbite and breithauptite. From analysis of 52,760 assays from across the deposit it was found that there was a very poor correlation between gold and each of Fe, Zn, S, Pb, Cu, As and Ag, whereas Ag correlated reasonably well with both Pb and Zn. Results from EPMA shows that gold varies widely in composition from host-rock associated gold (96 wt.% Au) through more intermediate compositions (88–73 wt.% Au) to electrum (46–27 wt.% Au), commonly associated with Sb-phases and containing significant Sb within the gold itself (1.05–2.58 wt.% Sb). From the Far West lense, aurostibite occurs as distinctive rims around gold. Although aurostibite associated with gold contains no silver, the gold itself contains constant moderate amounts (10.87–12.27 wt.% Ag). We suggest that the aurostibite and other Sb phases formed from a late-stage Sb-rich hydrothermal during low-temperature retrograde skarn alteration. There is abundant evidence for both chemical and physical remobilisation at Hera and this remobilisation is largely responsible for the spectrum of gold compositions observed. The source for these fluids may be an underlying magmatic body, evidence for which occurs as granite pegmatite dykes in various locations throughout the deposit. Furthermore, gold with a moderate to high Sb content may be indicative of a low temperature of formation.
Multi-robot Autonomous Exploration and Map Merging in Unknown Environments

This work aims to develop and implement Simultaneous Localization and Mapping (SLAM) on multiple mobile robots, to accomplish that, each robot must autonomously explore and merge their individual estimated maps of an environment into one full global map. Robots must obey rules of coordination to not fall into “blind” multi-robot SLAM, where each robot is unaware of each other and can’t take in consideration the advantage of multi-robot systems. In order to validate the proposed approach, experiments using simulated robots were applied to autonomously explore and map indoor environments with the objective of complete a percentage of the total available area, in a task called coverage.
The university perception of the teaching-learning process: online education vs faceto face education

The university perception of the teaching-learning process: online education vs face-to-face education, once again shows that students want to be present in the classroom again or resort to hybrid classes, since they consider that online learning it does not pay them substantially in their professional training. The present study is quantitative, descriptive, and explanatory, cross-sectional and with a sample by availability. Three axes’ variables are analyzed: demographic, socio-educational descriptors and the one that refers to the teaching-learning process online vs face-to-face. The general objective is to analyze the perceived differences between online education situations and face-to-face education in the current pandemic situations conditions. Attendance or hybrid classes is already an observable fact in the different HEIs in our country, it is necessary to be prepared in this new contingency in the teaching-learning process to incorporate leveling strategies among the population that has been left behind by the situation pandemic in which it has lived.
VIRTUAL DATA SPHERE: INVERSE STEREOGRAPHIC PROJECTION FOR IMMERSIVE MULTI-PERSPECTIVE GEOVISUALIZATION

Abstract. Immersive geospatial visualization finds increasing application for navigation, exploration, and analysis. Many such require the display of data at different scales, often in views with three-dimensional geometry. Multi-view solutions, such as focus+context, overview+detail, and distorted projections can show different scales at the same time, and help place an area of interest within its surroundings. By inverting the principle of stereographic projection – projecting spatial features from a map onto a virtual sphere which surrounds the viewer – we present a novel technique for immersive geospatial focus+context that aims to mitigate problems with existing solutions. This sphere can intersect the map, dividing it into two parts: the inside of the sphere, which stays unchanged, and the outside, which gets projected to the surface, resulting in an inversion of the lens metaphor by distorting the context instead of the focus. This detail-in-context visualization maximizes the amount of context that can be legibly shown by the smooth compression inherent to the stereographic projection, and by utilizing otherwise unused screen space in the sky. The projection method allows for easy control over the projection and distortion characteristics by varying only two main parameters – the sphere’s radius and its position. The omnidirectional nature of our system makes it particularly well-suited for immersive displays by accommodating typical immersive exploration and fully utilizing the additional visual space available. Applying our system to an urban environment, we were able to solicit positive reactions during feedback sessions with experts from urbanism.
IVMCT: Image Visualization based Multiclass Malware Classification using Transfer Learning

Computer systems have made it possible to transfer human life from the real world to virtual reality. This process has been accelerated by the Covid-19 virus. Cybercriminals have also switched from a real-life to a virtual one. Online, committing a crime is far easier than in real life. Cybercriminals often use malicious software (malware), to launch cyber-attacks. Apart from this polymorphic and metamorphic malware are used that use obfuscation techniques to create new malware variants. To effectively battle new malware types, you'll need to employ creative approaches that depart from the conventional. Traditionally signature-based techniques are used with machine learning algorithms to detect malware that is unable to catch its variants. Deep learning (DL), which differs from typical machine learning methods, might be a potential approach to the challenge of identifying all varieties of malware. In the present study, an IVMCT framework is introduced which classifies malware using transfer learning. For this purpose, the MalImg dataset is used which is based on grayscale images converted from binaries of malware. The comparison of IVMCT is done with existing techniques which shows that our technique is better than existing techniques.
DisCoPy for the quantum computer scientist

DisCoPy (Distributional Compositional Python) is an open source toolbox for computing with string diagrams and functors. In particular, the diagram data structure allows to encode various kinds of quantum processes, with functors for classical simulation and optimisation, as well as compilation and evaluation on quantum hardware. This includes the ZX calculus and its many variants, the parameterised circuits used in quantum machine learning, but also linear optical quantum computing. We review the recent developments of the library in this direction, making DisCoPy a toolbox for the quantum computer scientist.
From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective

Neural retrievers based on dense representations combined with Approximate Nearest Neighbors search have recently received a lot of attention, owing their success to distillation and/or better sampling of examples for training -- while still relying on the same backbone architecture. In the meantime, sparse representation learning fueled by traditional inverted indexing techniques has seen a growing interest, inheriting from desirable IR priors such as explicit lexical matching. While some architectural variants have been proposed, a lesser effort has been put in the training of such models. In this work, we build on SPLADE -- a sparse expansion-based retriever -- and show to which extent it is able to benefit from the same training improvements as dense models, by studying the effect of distillation, hard-negative mining as well as the Pre-trained Language Model initialization. We furthermore study the link between effectiveness and efficiency, on in-domain and zero-shot settings, leading to state-of-the-art results in both scenarios for sufficiently expressive models.
Practical security of continuous-variable quantum key distribution involving saturation attack with finite-size analysis

Abstract Saturation attack of imperfect detectors compromises the practical security of continuous-variable quantum key distribution (CVQKD) in implementations. In this paper, we demonstrate the practical security of the CVQKD system with finite-size analysis when considering effects of saturation attack on imperfect detectors at the receiver. We suggest an approach to resisting this attack by embedding an adjustable optical filter in the detector, motivated by characteristics of saturation attack. Numerical simulations illustrate the practical security of the system with finite-size analysis in terms of the achieved secret key rate and maximal transmission distance. It indicates that this approach can be elegantly used for defeating the potential saturation attack in practical quantum networks.
LANDSCAPE OF NEURAL ARCHITECTURE SEARCH ACROSS SENSORS: HOW MUCH DO THEY DIFFER ?

Abstract. With the rapid rise of neural architecture search, the ability to understand its complexity from the perspective of a search algorithm is desirable. Recently, Traoré et al. have proposed the framework of Fitness Landscape Footprint to help describe and compare neural architecture search problems. It attempts at describing why a search strategy might be successful, struggle or fail on a target task. Our study leverages this methodology in the context of searching across sensors, including sensor data fusion. In particular, we apply the Fitness Landscape Footprint to the real-world image classification problem of So2Sat LCZ42, in order to identify the most beneficial sensor to our neural network hyper-parameter optimization problem. From the perspective of distributions of fitness, our findings indicate a similar behaviour of the CNN search space for all sensors: the longer the training time, the larger the overall fitness, and more flatness in the landscapes (less ruggedness and deviation). Regarding sensors, the better the fitness they enable (Sentinel-2), the better the search trajectories (smoother, higher persistence). Results also indicate very similar search behaviour for sensors that can be decently fitted by the search space (Sentinel-2 and fusion).
Reframing research impact

Amy Nelson, Senior Research Associate at University College London, and her team proposed a suite of deep learning models for scientific research evaluation that goes beyond citation-based features in impact analysis of biomedical research. In this People of Data, she talks about the future of medicine and patient care from the perspective of data science.