Reviewerly/exHarmony huggingface dataset:
School of Data and Shifting Forms of Political Subjectivity

Abstract Digital media are increasingly ‘data media’ and data media are involved in various forms of political activism. This chapter reconstructs political subjectivities around figurations of the ‘digital citizen’ within the field of (open) data activism. The authors draw on interviews, document analysis and concepts from modern and post-sovereign political theories of subjectivation to explore the transformative educational work of the Datenschule (School of Data) project, focusing on the intersection between open data and anti-discriminatory activism. The chapter suggests that although School of Data explicitly positions its work as supporting ‘skills’ acquisition (data literacy), indicating a modernist understanding of subjectivity, the project also generates an understanding of political subjectivation as a multiplicity of distributed transformative processes, entangling data literacy with power structures, data-related and organisational practices.
Supervised Machine Learning with Plausible Deniability

We study the question of how well machine learning (ML) models trained on a certain data set provide privacy for the training data, or equivalently, whether it is possible to reverse-engineer the training data from a given ML model. While this is easy to answer negatively in the most general case, it is interesting to note that the protection extends over non-recoverability towards plausible deniability: Given an ML model $f$, we show that one can take a set of purely random training data, and from this define a suitable ``learning rule'' that will produce a ML model that is exactly $f$. Thus, any speculation about which data has been used to train $f$ is deniable upon the claim that any other data could have led to the same results. We corroborate our theoretical finding with practical examples, and open source implementations of how to find the learning rules for a chosen set of raining data.
A family of mixture models for biclustering

Abstract Biclustering is used for simultaneous clustering of the observations and variables when there is no group structure known a priori. It is being increasingly used in bioinformatics, text analytics, and so on. Previously, biclustering has been introduced in a model‐based clustering framework by utilizing a structure similar to a mixture of factor analyzers. In such models, observed variables are modeled using a latent variable that is assumed to be from . Clustering of variables are introduced by imposing constraints on the entries of the factor loading matrix to be 0 and 1 that results in block diagonal covariance matrices. However, this approach is overly restrictive as off‐diagonal elements in the blocks of the covariance matrices can only be 1 which can lead to unsatisfactory model fit on complex data. Here, the latent variable is assumed to be from a where is a diagonal matrix. This ensures that the off‐diagonal terms in the block matrices within the covariance matrices are non‐zero and not restricted to be 1. This leads to a superior model fit on complex data. A family of models is developed by imposing constraints on the components of the covariance matrix. For parameter estimation, an alternating expectation conditional maximization (AECM) algorithm is used. Finally, the proposed method is illustrated using simulated and real datasets.
Research on Task Scheduling Based on Particle Swarm Optimization Simulated Annealing Algorithm in Hybrid Cloud Environment

In recent years, cloud computing has developed rapidly. Some problems exist in traditional schedule, such as inefficient task management and unreasonable resource allocation. To solve these problems, a particle swarm simulated annealing (PSO-SA) algorithm is proposed, which improves the inertia weight and learning factor, and redefines the adaptability function, thus effectively improving the task management in the hybrid cloud environment, and further improving the rational allocation of resources. The simulation experiments on the number of tasks and resources show that the performance of PSO-SA algorithm is enhanced after optimization.
Fast Graph Sampling for Short Video Summarization Using Gershgorin Disc Alignment

We study the problem of efficiently summarizing a short video into several keyframes, leveraging recent progress in fast graph sampling. Specifically, we first construct a similarity path graph (SPG) G, represented by graph Laplacian matrix L, where the similarities between adjacent frames are encoded as positive edge weights. We show that maximizing the smallest eigenvalue λ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">min</inf> (B) of a coefficient matrix B = diag(a) + µL, where a is the binary keyframe selection vector, is equivalent to minimizing a worst-case signal reconstruction error. We prove that, after partitioning $\mathcal{G}$ into Q sub-graphs $\left\{ {{\mathcal{G}^q}} \right\}_{q = 1}^Q$, the smallest Gershgorin circle theorem (GCT) lower bound of Q corresponding coefficient matrices—${\min _q}\lambda _{\min }^ - \left( {{{\mathbf{B}}^q}} \right)$—is a lower bound for λ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">min</inf> (B). This inspires a fast graph sampling algorithm to iteratively partition $\mathcal{G}$ into Q sub-graphs using Q samples (keyframes), while maximizing $\lambda _{\min }^ - \left( {{{\mathbf{B}}^q}} \right)$ for each sub-graph ${\mathcal{G}^q}$. Experimental results show that our algorithm achieves comparable video summarization performance as state-of-the-art methods, at a substantially reduced complexity.
Performance-Aware Interconnect Delay Insertion Against EM Side-Channel Attacks

Random Delay Insertion (RDI) has been shown to be an effective countermeasure to side-channel attacks (SCAs) on on-chip power networks. RDI effectively reduces the correlation between the power dissipation and the processed data. However, random delay insertion can degrade circuit performance. Considering the theoretical benefits of delay insertion, this paper proposes a novel methodology that adds delay to interconnect buses to mitigate electromagnetic (EM) SCAs without degrading bus latency. The methodology comprises an efficient delay insertion scheme that hinders electromagnetic attacks, where the delay is inserted into the boundary lines of the bus. As the worst-case bus latency is determined by the lines that drive the maximum cross-coupling capacitance, inserting delay at the boundary lines does not affect the circuit performance as these lines always drive a lower capacitance. The inserted delay improves the security strength of the bus to EM attacks due to the reduction of the correlation between EM emissions and transmitted data, making the methodology effective and directly applicable with negligible overhead. The technique is applied to interposer based off-chip memory buses due to the increasing adoption of 2.5-D integrated systems (although the method is effectively applicable to any interconnect bus). Simulation results show that the technique decreases SNR below 1, which makes EM attacks unsuccessful, and does not increase the (worst-case) bus latency, sustaining the overall circuit performance. Consequently, the proposed method provides a superior EM SCA mitigation method compared to the state-of-the-art. Indeed, theoretical analysis and simulation results demonstrate that the new technique can offer the same level of protection against SCAs with better performance than other hardware RDI countermeasures.
An Integrated Localization System with Fault Detection, Isolation and Recovery for Autonomous Vehicles

Since the effect of faulty measurements on the integrated localization of autonomous vehicle is catastrophic, the combination of Fault Detection, Isolation and Recovery (FDIR) and localization has become a major trend to improve the accuracy and reliability of localization for autonomous vehicles. This paper proposes new test variables on the basis of Extended Kalman Filter (EKF) and Gaussian distribution to detect faults. A new isolation method is designed to locate the faulty source and a two-way interaction mechanism is built to achieve fault recovery when there is a fault. Furthermore, we apply the FDIR in an integrated localization system. Due to the lack of public datasets, we collected multiple sets of data (with and without faults) with our autonomous driving platform “Pioneer”. Four novel metrics for evaluating FDIR algorithms are defined and corresponding comparisons are carried out. In the ablation experiment, we verify the importance and influence of modules of FDIR for localization. Experimental results demonstrate the effectiveness of the proposed framework. And “Pioneer” won the championship of the China Intelligent Vehicle Future Challenge in 2019 and 2020.
Privacy-preserving Secure Media Streaming for Multi-user Smart Environments

Over the last years, our lifestyle has been positively upset by the sudden advent of technology. The Internet of Things (IoT), offering universal and ubiquitous connectivity to both people and objects, revealed to be the silver bullet for enabling a vast number of previously unexpected applications. In particular, media streaming providers are growing in business and scope, and we can forecast that soon, video streaming will substitute TV broadcasting activities. With the increasing success of multi-user smart environments, empowered by new-generation smart devices and IoT architectures, multimedia contents (i.e., images and videos) need to be effectively accessed anytime and anywhere. Recent advances in computer vision technologies have made the development of intelligent monitoring systems for video surveillance and ambient-assisted living. Such a scenario permits better integration among technologies, multimedia content, and end-users. However, there are several challenges, and some are still open. More precisely, due to the sensitivity of some multimedia content (e.g., video-surveillance streams), it is paramount to preserve users’ privacy. Again, it is necessary to guarantee the integrity of usage rights during any multimedia transmission process, starting from the video encoding phase. In this way, the private content is disclosed only when the stream is decoded on the other endpoint, by the legitimate user. In this article, we present a secure video transmission strategy that can address the challenges mentioned above. The proposed strategy takes advantage of both watermarking and video scrambling techniques to make it possible for the secure and privacy-preserving transmission of multimedia streaming. Through our proposal, multimedia streaming is of low quality and thus unusable. However, it can be fully recovered and enjoyed only by authorized users. Finally, due to its low complexity and energy-efficiency, our proposal is particularly suitable for onboard implementations.
Cross-lingual Text Reuse Detection Using Translation Plus Monolingual Analysis for English-Urdu Language Pair

Cross-Lingual Text Reuse Detection (CLTRD) has recently attracted the attention of the research community due to a large amount of digital text readily available for reuse in multiple languages through online digital repositories. In addition, efficient machine translation systems are freely and readily available to translate text from one language into another, which makes it quite easy to reuse text across languages, and consequently difficult to detect it. In the literature, the most prominent and widely used approach for CLTRD is Translation plus Monolingual Analysis (T+MA). To detect CLTR for English-Urdu language pair, T+MA has been used with lexical approaches, namely, N-gram Overlap, Longest Common Subsequence, and Greedy String Tiling. This clearly shows that T+MA has not been thoroughly explored for the English-Urdu language pair. To fulfill this gap, this study presents an in-depth and detailed comparison of 26 approaches that are based on T+MA. These approaches include semantic similarity approaches (semantic tagger based approaches, WordNet-based approaches), probabilistic approach (Kullback-Leibler distance approach), monolingual word embedding-based approaches siamese recurrent architecture, and monolingual sentence transformer-based approaches for English-Urdu language pair. The evaluation was carried out using the CLEU benchmark corpus, both for the binary and the ternary classification tasks. Our extensive experimentation shows that our proposed approach that is a combination of 26 approaches obtained an F 1 score of 0.77 and 0.61 for the binary and ternary classification tasks, respectively, and outperformed the previously reported approaches [ 41 ] ( F 1 = 0.73) for the binary and ( F 1 = 0.55) for the ternary classification tasks) on the CLEU corpus.
Modified Pointwise Mutual Information-Based Feature Selection for Text Classification

Feature selection in text classification is applied to reduce the dimensionality of the vector space model. As a result, computational costs are reduced during model training and the quality of text classification is improved by eliminating noisy features. In the present paper, a modified pointwise mutual information-based method for feature selection (mPMI-based feature selection) in text classification is examined. The proposed approach overcomes the perceived shortcomings of PMI feature selection measure. The results of the experiments conducted are summarized and analyzed in order to compare the proposed approach with other approaches for feature selection across different classifiers and datasets. The obtained results confirm that mPMI-based feature selection is comparable or leads to a significant improvement in the performance of text classification for a small number of selected features.
The role of authorial context in mathematicians’ evaluations of proof

We report on a series of task-based interviews in which nine mathematicians were asked to evaluate a series of six mathematical arguments, purportedly produced either by fellow mathematicians or undergraduate students. In this paper, we attend to the role of context in mathematicians’ responses, leading to four themes in expectations when evaluating the proofs that they read. First, mathematicians’ evaluations of identical arguments were sensitive to researchers’ manipulation of authorship, with most accepting arguments purportedly produced by a colleague while taking a more critical view of that same argument if produced by an undergraduate student. Our thematic analysis of interview responses led to three context-based factors influencing mathematicians’ responses when evaluating student-produced texts: course goals, instructors’ expectations, and assessment type. In the final section, we consider implications for researchers focused on understanding common practice amongst mathematicians as well as the pedagogic consequences of our findings for practice in the classroom.
Developing a Two-Hour Design Thinking Workshop to Examine the Potentials of Age-Divers Co-creation: Why Product Design Teams Should Invite Users Aged 50+, When Designing for the Demographic Change

In light of demographic change as a driver of innovation, products need to align with requirements of an aging society. Based on evidence from organizational psychology, which has identified key benefits of high age diversity in teams, and the benefits of co-creation, we expected positive outcomes in terms of innovative ideas, when product designers integrate elderly users on eye-level in co-creation processes. Therefore, we developed a design thinking field-experiment with six groups of product designers and six co-creation groups (mixed teams consisting of users aged 50–65 and design experts). With each of the 12 groups (n = 75), a two-hour formalized design thinking workshop was conducted. The workshop was designed to meet requirements of both designers and non-designers (users). As a result, 75 ideas as provided by both groups, were prepared and presented to two juries (n = 10, n = 13), one jury of interdisciplinary experts and one of non-experts. They rated all ideas in the categories “creativity”, “impact on life-long autonomy”, “contribution to social coherence” and “market potential”. Our results show that ideas of age-divers co-creation groups were rated significantly more creative and had higher impact on life-long autonomy as compared to the age-homogeneous product design groups. Thus, co-creation with elderly people facilitates innovation processes, which results in products allowing to face the demographic change. Finally, the statistics show that all 75 ideas made an above-average contribution to “social coherence”. We therefore discuss our workshop as a contribution to social innovation.
Violence Detection from CCTV Footage Using Optical Flow and Deep Learning in Inconsistent Weather and Lighting Conditions

Physical assault detection in surveillance systems plays an extremely significant role in the safety of our city. The current methods include a primitive approach for video-based pre-processing methods, which includes grayscale conversion, image augmentation, etc. The models include 3D Convolutional Neural Networks (3DCNN) and Support Vector Machines (SVMs). The method which we propose consists of a preprocessing method that involves isolating the pixels into a video (or a group of videos) relevant to the action that is taking place in the original video. In addition, a deep learning model which includes a time-distributed Convolutional Neural Network (CNN) + Long Short Term Memory (LSTM) model extracts the relevant spatial and temporal information or features that can help us predicting violence from an input CCTV footage. Given an input CCTV footage, our model will be able to extract the relevant features to distinguish a violent event from a non-violent one, preferably independent of the lighting. In the current approaches, it was observed that the number of relevant pixels dedicated to the action is low (due to naive cropping). The present system doesn't take into account the inconsistent weather and lighting conditions and its accuracy is expected to drop under those conditions.
Simplified Robot Programming Framework for a Gearbox Assembly Application

In this paper, we are presenting a framework for multi-modal human-robot interaction (HRI), where complex robotic tasks can be programmed using a skill-based approach and intuitive HRI modalities. This approach is demonstrated using a gearbox assembly application in a realistic industrial environment. Our system includes mobile and static robots for actuation, 2D and 3D cameras for sensing, and GUIs, spatial- and see-through- Augmented Reality for HRI.
Hybrid Perspective Mapping: Align Method for Cross-View Image-Based Geo-Localization

Cross view image-based geo-localization aims to estimate global position of an image by matching the query image with the images in a geo-referenced image database. Cross-view image-based geo-localization is a potential supplement of GPS, but it&#x0027;s hard to matching cross-view image pairs because of the tremendous appearance differences. However, most of deep learning approaches match cross-view image pairs directly and ignore the inner relation between them. In this paper, we introduce our novel hybrid perspective mapping method to align ground-level image to aerial image by considering projection relation between them. Unlike other learning based method which generate corresponding aerial image by traning, our approach is totally geometry based and can be plugged to other network conveniently. And we propose our network based on hybrid perspective mapping. our network shows higher retrieval accuracy and powerful generalization ability on several public dataset. In addition, we also conduct cross-view image matching experiments on our own dataset and analyse the influence of spatial resolution and seasonal variation of aerial image on image matching.
An analysis of the data efficiency in Tacotron2 speech synthesis system

This paper introduces an evaluation of the amount of data required by the Tacotron2 speech synthesis model in order to achieve a good quality output synthesis. We evaluate the capabilities of the model to adapt to new speakers in very limited data scenarios. We use three Romanian speakers for which we gathered at most 5 minutes of speech, and use this data to fine tune a large pre-trained model over a few training epochs. We look at the performance of the system by evaluating the intelligibility, naturalness and speaker similarity measures, as well as performing an analysis of the trade-off between speech quality and overfitting of the network.The results show that the Tacotron2 network can replicate the identity of a speaker from as little as one speech sample. Also it inherently learns individual grapheme representations, such that if the training data is carefully selected to present all the common graphemes in the language, the adaptation data requirements can be significantly lowered.
Construction of Digital Management Courses in the Big Data Era

With the advent of the era of big data, the digital teaching model has developed into a general trend, and the country has further increased its efforts to promote digital teaching. At present, the construction and application of digital courses is no longer an educational development trend, but a normalized teaching method. Although the state has invested heavily in the construction of digital teaching resources for basic education and higher education in recent years and has achieved certain results, compared with this, the construction of digital courses in universities still started late, and there are still some problems in course construction. Therefore, this article aims to study the construction of digital management courses in the era of big data. Based on the analysis of the common theoretical support of digital course design, the principles of digital course construction and the problems existing in management courses, a questionnaire survey will be conducted on a certain city in this city. Teachers and students of management majors in colleges and universities conduct a questionnaire survey on the “status of digital courses” and find out the existing problems and then give suggestions on the construction of relevant digital courses. The survey results show that only 20.2% of teachers believe that digital courses can enhance students’ interest in learning; 23.4% of teachers believe that digital courses can enhance the status of students as the subject of learning; 26.2% of teachers believe that digital courses can complement and inspire students’ imagination, can help students solve difficult problems and improve learning efficiency.
Augmented Reality Geometrical Optics (AR-GiOs) for Physics Learning in High Schools

Abstract This study aims to produce “Augmented Reality Geometrical Optics (AR-GiOs)” as a medium for physics learning for geometrical optics in high school. The method used in this study is the Research and Development method with the ADDIE model (Analyze, Design, Develop, Implement, and Evaluate). AR-GiOs was developed to facilitate students in learning the concept of image formation and image properties of mirrors and lenses. AR-GiOs can display four 3D simulations, including 3D simulations of the image formation process by a concave mirror, a convex mirror, a concave lens, and a convex lens. In addition to AR, in this study, a worksheet was also developed to guide student learning activities and valuable as a marker. The results of product validation covering the material and media aspects got a very good average score (84% and 90%). Based on the results of the expert validation test, it can be concluded that AR-GiOs is suitable for use as a medium for learning physics in high school.