Reviewerly/exHarmony huggingface dataset:
Diversified Teaching Evaluation of Python Programming Based on OBE Concept

Python language programming is a general education course for college students, which cultivates students' computational thinking ability and the ability to use computers to solve problems. Teaching design based on OBE concept for different majors, using diversified teaching evaluation methods based on process evaluation. Effectively combine the Python curriculum framework system designed based on the OBE concept with a diversified teaching evaluation system, and use fuzzy clustering to process and analyze evaluation data. It is expected to provide a certain reference for the OBE-based curriculum system and evaluation system.
Optimised Privacy Model for Cloud Data

Data security in the cloud has a been major issue upon the inception of cloud computing, frameworks have been proposed and implemented yet data breach prevails. With encryption being the dominant method of cloud data security, the advent of quantum computing implies that there is an urgent need to come up with an improved system that will provide adequate data security for both cloud and quantum computing. However, some cryptosystems will be rendered susceptible and obsolete although, some cryptosystems will stand the test of quantum computing. This paper proposes an optimised system for cloud data security using McEliece and NTRU cryptosystems for data security in cloud and quantum computing. While a of McEliece cryptosystem will be used for securing user credentials, a variant of NTRU will also be used for securing user data.
Post-Science in a Post-Modern World

The destruction of grand narratives has been followed by a wary attitude towards tying scientific knowledge to a particular epistemic authority. However, science retained its status because it underlies both modernity and post-modernity. As an experimental practice of discovery, science facilitated the formation of cognitive diversity. Cognitive diversity produces new scientific knowledge and progress. Furthermore, diversity and organized skepticism facilitated the ‘open society’. Simultaneously, the development of the education system led to a large-scale spread of skepticism in the scientific community and educated people. Consequently, an increasing number of people began to question the social order and the right of the elite to determine the intellectual and social ‘agenda’. This initiated the emergence of a post-truth situation, where social groups are challenging each other’s standpoints. What matters now is the ability to defend one’s views. A similar situation is forming in science. Scientists are paying less attention to criticism. The promotion of one’s own results is becoming more important than the search for truth. In this situation, intellectuals begin to play a particular role, they can take part in discussions related to various domains. This can facilitate the renewal of mutual criticism and maintain the formation of cognitive diversity.
Adversarial Perturbation Attacks on Nested Dichotomies Classification Systems

The study of robustness of deep classifiers has exposed their vulnerability to perturbation attacks. Prior work has largely focused on adversarial attacks targeting one-stage-classifiers. By contrast, here we investigate the susceptibility of Nested Dichotomies Classifiers (NDCs), which decompose a multiclass problem into a collection of binary ones, to such types of individual attacks. First, we show that the overall regret of an NDC is the sum of regrets of the binary classifiers along the path from the root to the leaf nodes of these dichotomies. Then, we formulate an optimization program to generate perturbations fooling NDCs and propose an algorithmic solution based on a convex relaxation. A solution is obtained by developing an ADMM-based solver to the convex programs. The experiments show that NDCs are more robust than their single stage counterpart in that the optimal perturbations inducing misclassifications are more perceptible.
Edge-based Realtime Image Object Detection for UAV Missions

Unmanned Aerial Vehicle (UAV) has limited computing power, but requires high accuracy and low latency in the visual object detection for critical UAV missions, such as infrastructure inspection. It may need highly complex machine learning algorithms with the demand of extensive computing power. With the rising edge computing technology, the heavily-loaded object detection tasks can be offloaded to edge computing systems. To enable such edge-based object detection with low overhead, we discover that it is critical to minimize the response time of the detection while maximizing the frequency of detected image frames. In this paper, we identify three key research challenges, conduct an experimental case study to show that current edge-based naive solutions cannot achieve the above goal, and finally point out major ideas for potential solutions.
(Don't) Mind the Step

It is well established that the presence of other people and the physical traces of their activities impact our navigation decisions, e.g. following a path in a park where trails are worn by frequent passage. Here we investigate such effect within a digital setup where social cues are displayed on the floor. We contribute the first lab study (n=24) investigating how social cues, presented as footprints on an interactive floor display, can impact navigation decisions. We particularly explore two task-based scenarios: exploration and search. A direct comparison of path choices did not reveal significant effects in both tasks but a further qualitative analysis showed that social cues affected path choices depending on how participants interpreted them. Additionally, results revealed a pattern of direct decision-making behaviours when social cues were present. We discuss how these insights can contribute to designing engaging floor interfaces that guide navigation decisions using social cues.
Vector Quantization Image Compression Algorithm Based on Bat Algorithm of Adaptive Separation Search

Vector quantization image compression algorithm is an effective data compression technique for image reconstruction by codebook and index, effectively reducing the amount of transmitted data. However, the initial codebook can affect the quality of reconstructed images. This paper proposes a modified bat algorithm based on the adaptive separation search mode to optimize the design of the codebook using the mean square error as the adaptation value. The bat algorithm uses the pulse emission rate to switch the search mode and the loudness to determine the search range, but it still suffers from a shortage of search capability. Therefore, adaptive separation rules are introduced to avoid early convergence and to improve the global exploration capability. The update formulas of some parameters are modified to improve the search performance. Finally, the experimental data show that the initial codebook based on the modified bat algorithm can effectively improve the image reconstruction performance.
Quantum Circuit Learning to Compute Option Prices and Their Sensitivities

The application of quantum computing to finance has recently become an active area of research, but how quantum computing can make computations in finance more efficient in practice remains unclear. In this paper, quantum circuit learning (QCL) is applied to computing option prices and their sensitivities. The advantage of this method is that a suitable choice of quantum circuit architecture makes it possible to compute the sensitivities analytically by applying parameter-shift rules. QCL is the quantum analogue of classical deep learning; the neural networks are replaced by a parameterized quantum circuit. The motivation is that the superposition principle could makes it possible to enhance learning option prices and sensitivities. We consider European options and Bermudan swaptions as example and our numerical result to pave the way for using quantum machine learning for option pricing.
A lightweight semantic-location system for indoor and outdoor behavior modelling

In intelligent environments, one of the most common available input data is location. It can be easily captured with little additional infrastructure thanks to the ever-present smartphones or smartwatches that enable new opportunities and services in the field of pervasive computing and sensing. However, in some cases, such as in an elderly care context, it is useful to infer additional information, such as identifying unusual activities or abnormal behaviors of monitored users. In this paper, a system that uses location data to infer additional semantic information about a user's behavior is presented. The semantic location data can then be transformed into behavioral indicators that can be used to analyze the user's activities. In order to infer user activities, the proposed system requires a minimal infrastructure.
Accessible Self-Care and Automated Indoor Navigation for COVID-19 Vaccination Centre

The motive behind conceptualizing and implementing the project is to leverage the availability of technological advances to cater to the needs of a COVID-19 vaccination center. The built cross-stage system assists users to navigate through the waiting, vaccination and monitoring room using the indoor navigation map of the vaccination center based on the availability of the rooms and live location of other people. The approach of using the application also helps us maintain the social distancing norms and corroborates the compulsory use of masks in the center.
On the Cross-lingual Transferability of Contextualized Sense Embeddings

In this paper we analyze the extent to which contextualized sense embeddings, i.e., sense embeddings that are computed based on contextualized word embeddings, are transferable across languages.To this end, we compiled a unified cross-lingual benchmark for Word Sense Disambiguation.We then propose two simple strategies to transfer sensespecific knowledge across languages and test them on the benchmark.Experimental results show that this contextualized knowledge can be effectively transferred to similar languages through pre-trained multilingual language models, to the extent that they can outperform monolingual representations learned from existing language-specific data.
Online Handwritten Mathematical Expression Solver Using Artificial Neural Network

Mathematics plays a predominant role in each of our lives. When it comes to solving a mathematical expression, we are highly dependent on the calculators that are available in almost every electronic gadget. Since all these gadgets are touchscreen-based nowadays, building a system that recognizes and solves online handwritten mathematical expressions is the potential area of this research. Recognition of online handwritten mathematical expressions is a complicated task. In this paper, an Artificial Neural Network model is built for the recognition of handwritten digits, operators, and symbols. Tkinter GUI interface is built for the users to type in their expressions and image processing is done by capturing an image from the canvas and converting it into a NumPy array and then applying the thresholding technique to convert it into a binary array. Connected component labeling is done to separate every number and symbol on the canvas. These numbers and symbols are then sent to the artificial neural network for predictions. The model gave a training accuracy of 98.97% and a test accuracy of 98.95%. Finally, the expression is evaluated, and the translated expression and output are shown on the Tkinter GUI interface.
Conceptualising a Model for Meaningful Digital Pedagogy

This action research aims to develop practices for the teacher education practicum that can support and encourage the collaborative planning of digital technology integration in teaching by university teacher educators and student teachers and supervising in-service teachers. More specifically, this study seeks to identify the features that make the pedagogical use of digital technologies meaningful. The background arises from the need to find ways to develop technology-enhanced pedagogy that encourages student teachers to engage in lifelong collaborative learning. We use the technology integrated matrix (TIM) as a theoretical frame-work and in the content analysis. The data collection took place during a primary school teacher education practicum period and involved teacher educators (N = 5), student teachers (N = 10), and classroom teachers (N = 5) in Finland. The results indicate that the pedagogical use of digital technologies needs to be considered throughout the teaching process: in the planning phase, action phase, and evaluation phase. Based on the results, we identify the key features of meaningful digital pedagogy to form a model that supports the codevelopment of digital pedagogical solutions.
A Typed Programmatic Interface to Contracts on the Blockchain

Smart contract applications on the blockchain can only reach their full potential if they integrate seamlessly with traditional software systems via a programmatic interface. This interface should provide for originating and invoking contracts as well as observing the state of the blockchain. We propose a typed API for this purpose and establish some properties of the combined system. Specifically, we provide an execution model that enables us to prove type-safe interaction between programs and the blockchain. We establish further properties of the model that give rise to requirements on the API. A prototype of the interface is implemented in OCaml for the Tezos blockchain.
Experiencing the communication advantage of the Superposition of Causal Orders

Many recent studies deal with the Superposition of Causal Orders, a quantum operation with promising advantages in both communication or computing. To experience the advantages, there are several way of implementing it. In literature, most of the set-ups are photonic-based. Instead, our interest is witnessing the Superpositon of Causal Orders within a programmable technology, based on superconductors. To do that, we focus on a specific case of the subject operation, which could be useful for the future of quantum communication.
Multinomial Naive Bayes Categorization for Semantic Web Services

The significant existence of web services is challenging to the researchers regarding their diversity of types and their diffusion. It may lead to difficulty in identifying the relevant service during the discovery or composition process. To tackle this problem, we propose a new method to categorize semantic web services based on the Naive Bayes algorithm using a weighting method (TF-IDF), which binds a service according to its description importance offered by the service provider to be categorized in a relevant class. It enhances the performance by proposing a compatible combination of the preprocessing techniques (Natural language processing) to achieve a better classification result. This method has been tested on the OWLS-TC dataset, categorized into seven classes, and its accuracy is 93%.
Physical Distancing Detection using YOLO v3 and Bird's Eye View Transform

One regulation that has been established by governments in most countries to curb the spread of Covid-19 is physical distancing. However, many people still ignore the importance of this regulation. Thus, it is important to develop a system that can help enforcing this regulation. In this paper, we propose a system that can automatically detect the presence of humans in a video frame and measure their distances from each other. Object detection is performed using YOLO v3 and the accuracy of distance measurement is enhanced using Bird's Eye View Transformation. Our experiments show that using this transformation yields an accuracy improvement of up to 20.93% compared to the performance of the system without transformation (i.e., from 74.42% to 95, 35% accuracy).
Balanced Graph Partitioning based I/O Intensive Virtual Cluster Placement in Cloud Data Center

Distributed application deployment in a cloud environment has emerged as a new area of research in cloud computing. I/O intensive virtual machines form a virtual cluster, which requires a large volume of data transfer for communication among themselves. Cloud service providers manage huge infrastructural resources using standard hierarchical network topologies, like FatTree, VL2, etc. To achieve high throughput communication cost needs to be reduced. In this regard, efficient allocation of the cooperating virtual machines may reduce communication costs and delay without compromising total energy consumption by the active hosts of the data center. However, the objectives are conflicting with each other. This research proposes a novel balanced graph partitioning-based heuristic that reduces communication costs by allocating the cooperating VMs at proximity and energy consumption by consolidating the virtual machines in a minimum number of active hosts for reducing wastage of the active hosts, which in turn reduces the energy consumption of the data center.
Particle Swarm Optimization Algorithm in Improved Electrical Control System

Abstract In today’s social background where high-tech emerges endlessly, various production fields in our country have fully entered the era of mechanical automation and electrical automation, and electrical control systems have been widely used in our country’s electrical appliance manufacturing industry. This paper is based on the theoretical analysis of the particle swarm optimization algorithm. Based on this optimization algorithm, a brand-new particle swarm optimization algorithm is obtained. It is applied to the electrical control system to improve it and makes full use of the improved particle swarm optimization algorithm. The existing electrical control system is optimized. This article firstly analyzes the types of common electrical control systems, puts forward some basic methods to improve the control system, and then explains the effective techniques for improvement, hoping to make reference to the improvement of electrical control systems later in this article. This article first improves the particle swarm optimization algorithm, adding the ability to adjust the control system and dynamic learning factors, focusing on strengthening the later stage of the optimization of the particle swarm algorithm and the ability to converge to improve the efficiency of the calculation. The second is to improve the traditional particle swarm optimization algorithm and update the calculation method of the formula to reduce the possibility of selecting undesirable particles and affecting the optimization results. Finally, through MATLAB and reverse simulation analysis, compared with the traditional electrical control system algorithm, the improved particle swarm optimization algorithm has a faster convergence speed and high control system efficiency. The experimental research results show that the particle swarm optimization algorithm proposed in this paper has a huge advantage compared with other algorithms, and its parameter optimization gives full play to the powerful performance of the electrical control system.
Digital Image Correlation (DIC)

Digital image correlation (DIC) isDigital image correlation (DIC)image correlation a full-field non-contacting optical method that can capture the shape, motion, and deformation of solid objectsObject. The basis of the method is the matching of the gray values of points from an image of the surface of an object before and after deformation. The gray values of points of images of an object are acquired, stored, digitized, and correlated (matched) to compute shape and surface displacements. A matching process based on gray intensity levels is performed, hence the name of the method digital image correlation. DIC techniques can be applied to macro-, micro-, and nano-scale mechanical testing under static and dynamic loading. The development of DIC is due to the advances in computer technology and digital cameras.
The High Precision Real-Time Facial Landmark Detection Technique Based on ShufflenetV2

Although in the past few decades, many methods such as heatmap, 3D morphable model (3DMM), and generative adversarial network (GAN), have been used to assist facial landmarks extraction, there is a lack of research on balancing the models’ size and accuracy. Therefore, this paper proposes a landmark detection model based on the ShufflenetV2 module and the Wingloss function. The model achieves 86% accuracy on the WFLW extended testing set when its size is only 789.9KB (×0.5), and its speed can reach 60 FPS on a single Intel i5-8250U CPU. We also use pruning to further compress the model. After pruning, the size of the model is reduced by about 30%, the accuracy is reduced by 4%, and the speed is increased by 17%.
Personalized privacy assistant for digital voice assistants: Case study on Amazon Alexa

The advancements in modern technologies permit the invention of various digital devices which are controlled and activated by people’s gestures, touch and even by one’s voice. Google Assistant, iPhone Siri, Amazon Alexa etc., are most popular voice enabled devices which have grabbed the attention of digital gadget users. Their usage definitely makes the life easier and comfortable. The other side of these smart enabled devices is incredible violation of the privacy. This happens due to their continuous listening to the user and data transmission over a public network to the third-party services. The work proposed in this paper attempts to overcome the existing privacy violation problem with the voice enabled devices. The main idea is to incorporate an intelligent privacy assistant that works based on the user preferences over their data.
Topic Model based Ensemble Learning for Rating Prediction and Its Application

User rating prediction is an important problem in recommendation system, this task has been plagued by data sparsity for a long time. Especially, the rating prediction based on product features, user behavior and other background data is not only troubled by cold start, but also difficult to find the migration of user interest. In order to solve above problems, this paper proposes an integrated learning method based on topic model. In this method, the interest points of user comments are obtained by topic model questions, and then the score is predicted based on the improved CatBoost. Through this strategy, we can use the semantic information of the comment text more effectively for rating prediction. The proposed method is implemented in an online travel data analysis platform, experiment result shows it has ideal performance in rating prediction of online travel community.