Reviewerly/exHarmony huggingface dataset:
Educational Communication as a Tool to Forming General Cultural Competencies of Students

Using the example of the implementation of the Digital Exhibition project the article shows the learning process scheme using information technologies [1] and based on a socio-cultural and professionally-oriented environment with the aim of achievement of personhood and professional development of students. (Abstract).
Cyber forensic framework for big data analytics using Sunflower Jaya optimization‐based Deep stacked autoencoder

Abstract The skills of forensic analysts are at risk to process the increasing data in the Internet of Things‐based environment platforms. However, the technical issues like anti‐forensics, variety of traffic formats, steganography or encrypted data, and real‐time live investigation degrades the performance of the cyber forensic framework. Therefore, an effective method named Sunflower Jaya Optimization‐based Deep stacked autoencoder (SFJO‐based Deep stacked autoencoder) is proposed to perform the cyber forensic framework. The finite element model of Sunflower optimization is integrated with the control parameters of Jaya optimization to solve the issues in the cyber forensic framework. The proposed SFJO‐based Deep stacked autoencoder uses the pollination and the peculiar behaviors to enable the cyber forensic framework based on the error value in the big data analytics model. Accordingly, the solution with the minimal value of error is accepted as the best optimal solution by computing the orientation vector. However, the proposed model is illustrated based on the unconstrained benchmark function, which in turn results in the fitness function to reveal the best candidate solution. The proposed SFJO‐based Deep stacked autoencoder attained better performance using metrics like precision, sensitivity, and specificity with the values of 0.9053, 0.8865, and 0.8839 using dataset‐1.
Diagramming Working Field Theories for Design in the HCI Classroom

HCI has historically provided little support for moving from fieldwork insights or theories to design outcomes. Having witnessed many students struggle and then justify their designs with a form of marketing hype, we developed a supporting approach of “field theories”. A field theory is a working theory about salient interactions in a particular domain and sensitizing concepts in order to frame design investigations. It is presented visually in a field theory diagram to support succinct communication and critique. Studying use of design prototypes that have been informed by a field theory helps to reflect upon and refine the theory. In this paper we present examples from our HCI classes and reflections based on interviews with students. We discuss how field theories offer an orientation in the spirit of a ‘bricoleur’ who harnesses elements of theory and practice to produce deeper understandings and more fitting outcomes for the task at hand.
Real-Time Control Using Convolution Neural Network for Self-Driving Cars

In this paper, we perform an Autonomous deep learning robot using an end-to-end system. The system operates as the controller for navigating and driving automatically. The deep learning robot used Convolution Neural Network (CNN). The CNN architecture is Mobile net with Softmax activation function. The Softmax activation function predicts the probability of steering angles. In the training phase, the CNN model learns from images and steering angles that are collected during the driving. In the testing phase, we apply the diversified environment to the trained CNN model. The CNN model accuracy is up to 85.03%. The results showed that the CNN is able to learn the diversified tasks of lanes and roads following with and without lane marking, direction planning and automatically control. Also, the CNN can replace the conventional PID controller.
Galois/Monodromy Groups for Decomposing Minimal Problems in 3D Reconstruction

We consider Galois/monodromy groups arising in computer vision applications, with a view toward building more efficient polynomial solvers. The Galois/monodromy group allows us to decide when a given problem decomposes into algebraic subproblems, and whether or not it has any symmetries. Tools from numerical algebraic geometry and computational group theory allow us to apply this framework to classical and novel reconstruction problems. We consider three classical cases—three-point absolute pose, five-point relative pose, and four-point homography estimation for calibrated cameras—where the decomposition and symmetries may be naturally understood in terms of the Galois/monodromy group. We then show how our framework can be applied to novel problems from absolute and relative pose estimation. For instance, we discover new symmetries for absolute pose problems involving mixtures of point and line features. We also describe a problem of estimating a pair of calibrated homographies between three images. For this problem of degree 64, we can reduce the degree to 16, the latter better reflecting the intrinsic difficulty of algebraically solving the problem. As a byproduct, we obtain new constraints on compatible homographies, which may be of independent interest.
TUI as Social Entity: a Study of Joint-actuation and Turn-taking-actuation in Actuated-interfaces

We present an actuated-interface that is not only a tangible interface but also an autonomous object, designed as an independent entity that takes a similar role to the user’s role in an anagram word game. We highlight two leading interaction paradigms: Turn-taking-actuation and Joint-actuation, and evaluate both in a qualitative interaction study with the autonomous actuated-interface. Our findings reveal that all participants perceived the interaction as a social experience. The different interaction paradigms led to different interpretations: Turn-taking-actuation was interpreted as a competitive experience, while Joint-actuation was interpreted as a collaborative experience. The interaction paradigms also influenced the intensity of emotions and perception of control, with Joint-actuation leading to more intense emotions and higher sensitivity to control in the interaction. To conclude, our findings show that it is possible to design an actuated-interface that users perceive both as a tangible interface and as a social entity with its own intent.
NeuSE: A Neural Snapshot Ensemble Method for Collaborative Filtering

In collaborative filtering (CF) algorithms, the optimal models are usually learned by globally minimizing the empirical risks averaged over all the observed data. However, the global models are often obtained via a performance tradeoff among users/items, i.e., not all users/items are perfectly fitted by the global models due to the hard non-convex optimization problems in CF algorithms. Ensemble learning can address this issue by learning multiple diverse models but usually suffer from efficiency issue on large datasets or complex algorithms. In this article, we keep the intermediate models obtained during global model learning as the snapshot models, and then adaptively combine the snapshot models for individual user-item pairs using a memory network-based method. Empirical studies on three real-world datasets show that the proposed method can extensively and significantly improve the accuracy (up to 15.9% relatively) when applied to a variety of existing collaborative filtering methods.
KAPPA as Drift Detector in Data Stream Mining

Concept Drift is considered a challenging problem that appears in data streaming. The classifier's error rate and the ensemble are used in most of the previous works to manage classification accuracy as a criterion for judging whether concept drift is happening or not. KAPPA is an effective way to measure the level of agreement, and it may be suitable to detect concept drift in a reliable, fast, and computationally efficient way. In this paper, we propose a new concept drift detector, called KAPPA, which aims at reacting to detect concept drift in a reliable, fast, and computationally efficient way. Contrary the disagreement measure that we have already considered in our preliminary work (DMDDM), KAPPA would measure the level of agreement when different classifiers access data items is suitable to detect concept drifts. The performance of KAPPA has been experimentally compared with DMDDM on synthetic dataset streams, considering different performance measures, e.g., delay detection, true positives and the mean accuracy.
Modeling Class Diagram using NLP in Object-Oriented Designing

Requirement's analysis and design is a multifaceted and time-consuming process. The success of software projects critically relies on careful & timely analysis and modeling of system requirements. Mostly, the requirements gathered from the stakeholders are written in some language (probably English). In this regard, significant manual efforts are required for the formation of good class model which unfortunately results in time delays in the software industry. The problems associated with the requirement analysis and class modeling can be overcome by the appropriate employment of machine learning. In this paper, we propose a system, requirement engineering analysis & design (READ) to generate unified modeling language (UML) class diagram using natural language processing (NLP) and domain ontology techniques. We have implemented the READ system in Python and it successfully generates the UML class diagram i.e., class name, attributes methods, and relationships from the textual requirements written in English. To assess the performance of the proposed system, we have evaluated it on publicly available standards and the experimental results show that it outperforms the existing techniques for object-oriented based software designing.
Mobile Application Recommendation System for Mobile Data Plan Research in progress

This paper presents a research in progress mobile application recommendation system for mobile data plan. With the vast amount of mobile data plan available, students must put a lot of effort in finding a mobile data plan that is the best value for money or the best for their heavy usage needs. Thus, paper proposes a recommendation system to aid students in the selection process of a mobile data plan. Further, hybrid filtering will be applied in the development process of this system to recommend a few suitable mobile data plans.
DPD-InfoGAN

Generative Adversarial Networks (GANs) are deep learning architectures capable of generating synthetic datasets. Despite producing high-quality synthetic images, the default GAN has no control over the kinds of images it generates. The Information Maximizing GAN (InfoGAN) is a variant of the default GAN that introduces feature-control variables that are automatically learned by the framework, hence providing greater control over the different kinds of images produced. Due to the high model complexity of InfoGAN, the generative distribution tends to be concentrated around the training data points. This is a critical problem as the models may inadvertently expose the sensitive and private information present in the dataset. To address this problem, we propose a differentially private version of InfoGAN (DP-InfoGAN). We also extend our framework to a distributed setting (DPD-InfoGAN) to allow clients to learn different attributes present in other clients' datasets in a privacy-preserving manner. In our experiments, we show that both DP-InfoGAN and DPD-InfoGAN can synthesize high-quality images with flexible control over image attributes while preserving privacy.
Project Design and Implementation for Digital Forensics Education

As with other disciplines in cybersecurity, hands-on activities are an important component in digital forensics education to help students gain better understanding of basic concepts and knowledge presented in class lectures. While these lab activities are helpful for students to learn how to use software and hardware forensic tools, it is hard to help students gain problem-solving and analytic skills and other experiences that are needed to conduct digital forensic investigation in real-world. In our digital forensic courses, we have been using course projects as a means to help students develop their skills for identifying, locating, preserving, recovering, examining, analyzing and presenting electronic evidence associated with a case of digital forensic investigation. Student's feedback is positive and the educational outcome is promising. In this paper, we present the idea to design and implement a course project to achieve specified educational objectives for a digital forensic course. Example projects finished by students are introduced to show the major activities to complete a project. Experience, lessons and feedback from students are discussed. Our results will provide a point of reference for those who teach a digital forensics course at a college or university, or are developing a digital forensic curriculum.
Classifying Eye-Tracking Data Using Saliency Maps

A plethora of research in the literature shows how human eye fixation pattern varies depending on different factors, including genetics, age, social functioning, cognitive functioning, and so on. Analysis of these variations in visual attention has already elicited two potential research avenues: 1) determining the physiological or psychological state of the subject and 2) predicting the tasks associated with the act of viewing from the recorded eye-fixation data. To this end, this paper proposes a visual saliency based novel feature extraction method for automatic and quantitative classification of eye-tracking data, which is applicable to both of the research directions. Instead of directly extracting features from the fixation data, this method employs several well-known computational models of visual attention to predict eye fixation locations as saliency maps. Comparing the saliency amplitudes, similarity and dissimilarity of saliency maps with the corresponding eye fixations maps gives an extra dimension of information which is effectively utilized to generate discriminative features to classify the eye-tracking data. Extensive experimentation using Saliency4ASD [1], Age Prediction [2], and Visual Perceptual Task [3] dataset show that our saliency-based feature can achieve superior performance, outperforming the previous state-of-the-art methods [2], [4], [5] by a considerable margin. Moreover, unlike the existing application-specific solutions, our method demonstrates performance improvement across three distinct problems from the real-life domain: Autism Spectrum Disorder screening, toddler age prediction, and human visual perceptual task classification, providing a general paradigm that utilizes the extra-information inherent in saliency maps for a more accurate classification.
Research on Intelligent Campus System Design Based on BIM &amp; VR/AR Technology

Abstract With the new form of social development dominated by Internet technology innovation, more and more domestic colleges and universities are applying virtual reality technology to the construction of smart campuses. This method of combining virtual reality and augmented reality (VR &amp; AR) technology with smart campuses has realized campus The goal of informatization, intelligence and visualization.
A multi-objective optimization algorithm based on subgroup stratified coarse-grained model and its application

Low efficiency and the tendency to fall into local optimum are the major obstacles to web service composition optimization. In this paper, we propose a multi-objective optimization algorithm based on the subgroup stratified coarse-grained model to improve the performance of web service composition optimization. Compared to the general particle swarm optimization algorithms, the proposed algorithm improves population structure, dynamically adjusts evolution strategy and increases the local extremum's perturbation. We demonstrate a solution to the constrained multi-objective web service composition optimization problem based on the proposed algorithm. Theoretical analysis and experimental results show that it improves the performance of web service composition optimization.
Implementation of Lightweight Convolutional Neural Networks via Layer-Wise Differentiable Compression

Convolutional neural networks (CNNs) have achieved significant breakthroughs in various domains, such as natural language processing (NLP), and computer vision. However, performance improvement is often accompanied by large model size and computation costs, which make it not suitable for resource-constrained devices. Consequently, there is an urgent need to compress CNNs, so as to reduce model size and computation costs. This paper proposes a layer-wise differentiable compression (LWDC) algorithm for compressing CNNs structurally. A differentiable selection operator OS is embedded in the model to compress and train the model simultaneously by gradient descent in one go. Instead of pruning parameters from redundant operators by contrast to most of the existing methods, our method replaces the original bulky operators with more lightweight ones directly, which only needs to specify the set of lightweight operators and the regularization factor in advance, rather than the compression rate for each layer. The compressed model produced by our method is generic and does not need any special hardware/software support. Experimental results on CIFAR-10, CIFAR-100 and ImageNet have demonstrated the effectiveness of our method. LWDC obtains more significant compression than state-of-the-art methods in most cases, while having lower performance degradation. The impact of lightweight operators and regularization factor on the compression rate and accuracy also is evaluated.
Similarity Embedding Networks for Robust Human Activity Recognition

Deep learning models for human activity recognition (HAR) based on sensor data have been heavily studied recently. However, the generalization ability of deep models on complex real-world HAR data is limited by the availability of high-quality labeled activity data, which are hard to obtain. In this article, we design a similarity embedding neural network that maps input sensor signals onto real vectors through carefully designed convolutional and Long Short-Term Memory (LSTM) layers. The embedding network is trained with a pairwise similarity loss, encouraging the clustering of samples from the same class in the embedded real space, and can be effectively trained on a small dataset and even on a noisy dataset with mislabeled samples. Based on the learned embeddings, we further propose both nonparametric and parametric approaches for activity recognition. Extensive evaluation based on two public datasets has shown that the proposed similarity embedding network significantly outperforms state-of-the-art deep models on HAR classification tasks, is robust to mislabeled samples in the training set, and can also be used to effectively denoise a noisy dataset.
SIGHInt: Special Interest Group for Human-Computer Integration

Human-Computer Integration (HInt) is a growing paradigm within HCI which seeks to understand how humans can, and already are, merging with computational machines. HInt's recent inception and evolution has seen much discussion in a variety of symposiums, workshops, and publications for HCI. This has enabled a democratized and decentralised emergence of its core concepts. While this has allowed for rapid growth in our understanding of HInt, there is some discrepancy in how the proponents of this movement might describe its principles, motivations, definitions, and ultimate goals, with many offshoot concepts of HInt beginning to emerge. SIGHint aims to provide a platform to facilitate high level discussion and collation of information between researchers and designers seeking to learn from and contribute to the development of Human-Computer Integration. It is our intention that through this SIG we may better understand how new and emerging, diverging ideas, and perspectives within Human-Computer Integration relate to each other, ultimately facilitating a mapping of the paradigm and a synthesis of its concepts.
Secure and Efficient Data Deduplication in JointCloud Storage

Data deduplication can efficiently eliminate data redundancies in cloud storage and reduce the bandwidth requirement of users. However, most previous schemes depending on the help of a trusted key server (KS) are vulnerable and limited because they suffer from revealing information, poor resistance to attacks, great computational overhead, etc. In particular, if the trusted KS fails, the whole system stops working, i.e., single-point-of-failure. In this article, we propose a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u> ecure and <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</u> fficient data <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</u> eduplication scheme (named SED) in a JointCloud storage system which provides the global services via collaboration with various clouds. SED also supports dynamic data update and sharing without the help of the trusted KS. Moreover, SED can overcome the single-point-of-failure that commonly occurs in the classic cloud storage system. According to the theoretical analyses, our SED ensures the semantic security in the random oracle model and has strong anti-attack ability such as the brute-force attack resistance and the collusion attack resistance. Besides, SED can effectively eliminate data redundancies with low computational complexity and communication and storage overhead. The efficiency and functionality of SED improves the usability in client-side. Finally, the comparing results show that the performance of our scheme is superior to that of the existing schemes
Pause-Encoded Language Models for Recognition of Alzheimer’s Disease and Emotion

We propose enhancing Transformer language models (BERT, RoBERTa) to take advantage of pauses. Pauses play an important role in speech. In previous work we developed a method to encode pauses in transcripts for recognition of Alzheimer's disease. In this study, we extend this idea to language models. We re-train BERT and RoBERTa using a large collection of pause-encoded transcripts, and conduct fine- tuning for two downstream tasks, recognition of Alzheimer's disease and emotion. Pause-encoded language models outperform text-only language models on these tasks. Pause augmentation by duration perturbation for training is shown to improve pause-encoded language models.
A humanist university in a posthuman world: relations, responsibilities, and rights

This paper explores the entanglement of humanism and posthumanism within the Western European University, focusing on ethical implications and the role of the university in protecting the marginalised. To illustrate its arguments, it draws on a small study conducted in an ancient Italian university which specialises in Humanities and Social Sciences. The paper focuses on two key knotty issues: relationships between university aspirations to posthumanism and the colonial legacies of humanism, and the implications of holding on to humanism as a guarantor of human rights for marginalised people. It concludes by discussing the limitations of a posthuman ethics of responsibility and proposes instead a posthuman ethics of rights for the university.
Steel: Composable Hardware-Based Stateful and Randomised Functional Encryption

Trusted execution environments (TEEs) enable secure execution of programs on untrusted hosts and cryptographically attest the correctness of outputs. As these are complex systems, it is essential to formally capture the exact security achieved by protocols employing TEEs, and ultimately, prove their security under composition, as TEEs are typically employed in multiple protocols, simultaneously.