Reviewerly/exHarmony huggingface dataset:
An Image Encryption using Intertwining Logistic map and Enhanced Logistic Map

Chaos is used for encryption of data because of its characteristics such as sensitiveness to the initial conditions and high unpredictability. Even though some of the image encryption schemes based on chaos are having security issues. To overcome the limitation an encryption technique that uses the combination of 3D Intertwining Logistic Map (ILM) and Enhanced Logistic Map (ELM) with Cosine based transformation is used for generating chaotic sequences for image encryption. This work is composed of 3 parts, in the first part by using scrambling the correlation between the image pixels are reduced followed by shuffling of all the remaining pixels of an image by rotating the image by 90° anti-clockwise and in the last part by using random order substitution changes done till now are diffused in the entire matrix row-wise and column-wise. The effectiveness of this paper has been tested by Entropy(randomness) Correlation Coefficient (CC determines the relation among neighboring pixels) - vertical, horizontal and diagonal, Unified Average Change in Intensity (UACI). The significance of this paper is that it takes less time to encrypt the image.
Augmented Instructions: Analysis of Performance and Efficiency of Assembly Tasks

Augmented Reality (AR) technology makes it possible to present information in the user’s line of sight, right at the point of use. This brings the capability to visualise complex information for industrial maintenance applications in an effective manner, which typically rely on paper instructions and tacit knowledge developed over time. Existing research in AR instruction manuals has already shown its potential to reduce the time taken to complete assembly tasks, as well as improving accuracy [1–3]. In this study, the outcomes of several aspects of AR instructions are explored and their effects on the chosen Key Performance Indicators (KPIs) of task completion time, error rate, cognitive effort and usability are assessed. A standardised AR assembly task is also described for performance comparison, and a novel AR experimental tool is presented, which takes advantage of the flexibility of internet connected peripherals, to explore various different aspects of AR app design to isolate their effects. Results of the experiments are given here, providing insight into the most effective way of delivering information and promoting interaction between user and computer, in terms of user performance and acceptance.
HandiText

The Internet of Things (IoT) is a new manifestation of data science. To ensure the credibility of data about IoT devices, authentication has gradually become an important research topic in the IoT ecosystem. However, traditional graphical passwords and text passwords can cause user’s serious memory burdens. Therefore, a convenient method for determining user identity is needed. In this article, we propose a handwriting recognition authentication scheme named HandiText based on behavior and biometrics features. When people write a word by hand, HandiText captures their static biological features and dynamic behavior features during the writing process (writing speed, pressure, etc.). The features are related to habits, which make it difficult for attackers to imitate. We also carry out algorithms comparisons and experiments evaluation to prove the reliability of our scheme. The experiment results show that the Long Short-Term Memory has the best classification accuracy, reaching 99% while keeping relatively low false-positive rate and false-negative rate. We also test other datasets, the average accuracy of HandiText reach 98%, with strong generalization ability. Besides, the 324 users we investigated indicated that they are willing to use this scheme on IoT devices.
Simulating the Impact of Recommender Systems on the Evolution of Collective Users' Choices

The major focus of recommender systems (RSs) research is on improving the goodness of the generated recommendations. Less attention has been dedicated to understand the effect of an RS on the actual users' choices. Hence, in this paper, we propose a novel simulation model of users' choices under the influence of an RS. The model leverages real rating/choice data observed up to a point in time in order to simulate next, month-by-month, choices of the users. We have analysed choice diversity, popularity and utility and found that: RSs have different effects on the users' choices; the behaviour of new users is particularly important to understand collective choices; and the users' previous knowledge, i.e., their "awareness" of the item catalogue greatly affects choice diversity.
EncoDeep

This article proposes EncoDeep, an end-to-end framework that facilitates encoding, bitwidth customization, fine-tuning, and implementation of neural networks on FPGA platforms. EncoDeep incorporates nonlinear encoding to the computation flow of neural networks to save memory. The encoded features demand significantly lower storage compared to the raw full-precision activation values; therefore, the execution flow of EncoDeep hardware engine is completely performed within the FPGA using on-chip streaming buffers with no access to the off-chip DRAM. We further propose a fully automated optimization algorithm that determines the flexible encoding bitwidths across network layers. EncoDeep full-stack framework comprises a compiler that takes a high-level Python description of an arbitrary neural network. The compiler then instantiates the corresponding elements from EncoDeep Hardware library for FPGA implementation. Our evaluations on MNIST, SVHN, and CIFAR-10 datasets demonstrate an average of 4.65× throughput improvement compared to stand-alone weight encoding. We further compare EncoDeep with six FPGA accelerators on ImageNet, showing an average of 3.6× and 2.54× improvement in throughput and performance-per-watt, respectively.
Large-Alphabet Semi-Static Entropy Coding Via Asymmetric Numeral Systems

An entropy coder takes as input a sequence of symbol identifiers over some specified alphabet and represents that sequence as a bitstring using as few bits as possible, typically assuming that the elements of the sequence are independent of each other. Previous entropy coding methods include the well-known Huffman and arithmetic approaches. Here we examine the newer asymmetric numeral systems (ANS) technique for entropy coding and develop mechanisms that allow it to be efficiently used when the size of the source alphabet is large—thousands or millions of symbols. In particular, we examine different ways in which probability distributions over large alphabets can be approximated and in doing so infer techniques that allow the ANS mechanism to be extended to support large-alphabet entropy coding. As well as providing a full description of ANS, we also present detailed experiments using several different types of input, including data streams arising as typical output from the modeling stages of text compression software, and compare theproposed ANS variants with Huffman and arithmetic coding baselines, measuring both compression effectiveness and also encoding and decoding throughput. We demonstrate that in applications in which semi-static compression is appropriate, ANS-based coders can provide an excellent balance between compression effectiveness and speed, even when the alphabet is large.
SoCNNet: An Optimized Sobel Filter Based Convolutional Neural Network for SEM Images Classification of Nanomaterials

In this paper an optimized deep Convolutional Neural Network (CNN) for the automatic classification of Scanning Electron Microscope (SEM) images of homogeneous (HNF) and nonhomogeneous nanofibers (NHNF) produced by electrospinnig process is presented. Specifically, SEM images are used as input of a Deep Learning (DL) framework consisting of: a Sobel filter based pre-processing stage followed by a CNN classifier. Here, such DL architecture is denoted as SoCNNet. The Polyvinylacetate (PVAc) SEM image of NHNF and HNF dataset collected at the Materials for Environmental and Energy Sustainability Laboratory of the University Mediterranea of Reggio Calabria (Italy) is used to evaluate the performance of the developed system. Experimental results (average accuracy rate up to $$80.27\% \pm 0.0048$$ ) demonstrate the potential effectiveness of the proposed SoCNNet in the industrial chain of nanofibers production.
The Impact of Adaptive Activities in Acrobatiq Courseware - Investigating the Efficacy of Formative Adaptive Activities on Learning Estimates and Summative Assessment Scores

The purpose of this paper is to explain the learning methodologies behind the adaptive activities within Acrobatiq’s courseware, and to investigate the impact of these adaptive activities on learning estimates and summative assessment scores using real course data. The adaptive activities used for this analysis were part of a Probability and Statistics course, which was delivered to college students at a public four-year institution as part of an educational grant. The data were analyzed to identify if the adaptive activities had an impact on learning estimates as well as on summative assessment scores. Results showed that the adaptive activities had a net positive effect on learning estimates. Results also showed that not only did learning estimate states correlate to mean summative assessment scores, but improving learning estimates after completing the adaptive activity practice yielded higher mean summative assessment scores. The implications of this analysis and future research are discussed.
An Efficient Particle Swarm Optimization with Multidimensional Mean Learning

Particle swarm optimization (PSO) algorithm is a stochastic and population-based optimization algorithm. Its traditional learning strategy is implemented by updating the best position using the particle’s own historical best experience and its neighborhood’s best experience to find the optimal solution of the problem. However, the learning strategy is ineffective when dealing with highly complex problems. In this paper, a particle swarm optimization algorithm based on a multidimensional mean learning strategy is proposed. In this algorithm, an opposition-based learning strategy is utilized to initialize the population to enhance the exploitation capability. Furthermore, the historical best positions of all the particles are reconstructed in a vertical crossover manner that is based on the mean information of multiple optimal dimensions to generate the guiding particles. Additionally, an improved inertia weight is used to further guide all the particle movements to balance the capability of the proposed algorithm for global exploration and local exploitation. The proposed algorithm is tested on 12 benchmark functions and is compared with some well-known PSO algorithms. The experimental results show that the proposed algorithm obtains more competitive optimal solution compared with other PSO algorithms when solving high-dimensional complex problems.
Detection of Reading Movement from EOG Signals

In this paper, it is aimed to analysis of Electrooculography (EOG) signals recorded during the back to eye movement (retrieving words/re-reading) and skipping lines while reading. Two situations are characterized by large amplitude fluctuations in EOG signals. For this aim, EOG signals were recorded simultaneously while reading a text from 10 volunteers and changes in EOG signals caused by jumping a bottom line and back movements as reading were analyzed. The classification of these signals may allow the development of a new method for early and rapid diagnosis of various reading disorders (for example dyslexia). This study consists of two main processes; feature extraction and classification. Firstly, two features were determined from the recorded EOG signals for determination of retrieving words/re-reading from EOG signal. Then these signals were applied as input to various classifiers. The classifier performances were evaluated by calculating accuracy, sensitivity, specificity, precision and F measure. Overall classification results were obtained with high performance from all classifiers, and the highest accuracy of the classifiers used was 98% for each of the Random Forest and k-NN classifiers. The results show that this proposed method has an important performance for classification of eye movements from EOG signals.
Telemedicine DSS-AI Multi Level Platform for Monoclonal Gammopathy Assistance

The proposed work describes preliminary results of a research project based on the realization of a Decision Support System -DSS- platform embedding medical and artificial intelligence -AI- algorithms. Specifically the telemedicine platform is suitable for the optimization of assistance processes of patients affected by Monoclonal Gammopaty. The results are related to the whole design of the platform implementing a DSS based on a multi-level decision making process. Starting from the main architecture specifications, is formulated a flowchart based on different alerting levels of patient risk including artificial intelligence -AI- decision supporting facilities. Finally, the perspectives of the performed research are discussed.
A Robust Feature Descriptor for Biomedical Image Retrieval

Biomedical image retrieval is a crucial side of computer-aided diagnosis. It helps the radiologist and medical specialist to spot and perceive the specific disease. This paper proposed an efficient approach for retrieving similar biomedical images based on the Zernike moment features, curvelet features and histogram of oriented gradients (HOG) feature. The Zernike polynomials based moments set defines the Zernike moment which is a global descriptor and it is capable of extracting both texture and shape information with minimum redundant data. The curvelet transformation is used to compute the edge-based shape information in form of curvelet histogram for the curves with discontinuity and the HOG features calculate the happenings of gradient orientation in the local areas of an image. The experiments were conducted on four benchmark biomedical image databases: HRCT dataset, Emphysema CT database, OASIS MRI database and NEMA MRI database respectively. The performance of the proposed approach was compared with many existing methods and achieved a better retrieval rate on all the four databases.
Digitally Enhancing Society Through Structuralism: Virtualizing Collective Human Eyesight and Hearing Capabilities as a Case Study

Information technologies have dramatically changed our real world. There are two major concepts have played a role to change the real world. Through the computation concept, our various tasks can be automated. Also, the informatization concept makes us possible to quantify our daily activities. These concepts offer infinite possibilities for our future. However, another concept named the digital enhancement through virtualization concept has not been well discussed previously for changing the real world, but the concept has strong power to refine our real world. In this paper, we use the concept to virtualize collective human eyesight capabilities. We have developed a prototype implementation of the distributed platform to virtualize collective human eyesight and hearing capabilities and extracted some insights from the current platform. The paper concludes by exploring several future challenges to enhancing our current approach.
Flatpack ML: How to Support Designers in Creating a New Generation of Customizable Machine Learning Applications

This paper examines how designers can be supported in creating a new generation of interactive machine learning (ML) applications that run locally on consumer-level hardware and can be trained and customized by end-users for their specific context and use case. It delineates the proposed applications against research into Interactive Machine Learning and Machine Teaching, examines the challenges designers face in these contexts and their relevance for designing the proposed new applications, and reports on the findings of a survey exploring designers' interest in ML, their understanding of ML capabilities and concepts, and their preferences for learning about ML. Based on findings from the literature and the survey, the paper identifies three overlapping research challenges in supporting designers to ideate, design and prototype the proposed ML applications.
Text Classification Based on Convolutional Neural Network and Attention Model

Using the traditional convolutional neural network (CNN) model for text classification, it is difficult to effectively capture the important local features in the text and the correlation between the feature words in the input text and the text. In order to solve this problem, this paper introduces the attention mechanism into the basic CNN model and establishes three CNN-based architectures: ATCNN-1, ATCNN-2 and ATCNN-3. ATCNN-1 increases the attention mechanism layer after the word embedding layer to obtain important local feature words, thus improving the features of convolution calculation. ATCNN-2 introduces the attention mechanism after the convolutional layer, and uses the attention mechanism to calculate the weights of each convolutional output vector to distinguish the importance degree, so that the model can extract features selectively. ATCNN-3 superimposes ATCNN-1 and ATCNN-2 together, combining the advantages of ATCNN1 and ATCNN-2. The experimental results in the two tasks of emotion classification and Chinese news text classification show that the effects of ATCNN-1, ATCNN-2 and ATCNN-3 are obviously better than the traditional CNN model, and the classification effect is also improved to some extent compared with the classical text classification model.
Visually-Aware Video Recommendation in the Cold Start

Recommender Systems have become essential tools in any modern video-sharing platform. Although, recommender systems have shown to be effective in generating personalized suggestions in video-sharing platforms, however, they suffer from the so-called New Item problem. New item problem, as part of Cold Start problem, happens when a new item is added to the system catalogue and the recommender system has no or little data available for that new item. In such a case, the system may fail to meaningfully recommend the new item to the users.
Variability-Aware Process Extension for Updating Cyber Physical Systems Over the Air

Considering the rising amount of software applications in Cyber Physical Systems (CPS) and their shortening life cylces, updates may be required to fix bugs, enhance safety and security or add new features. For this purpose, Over The Air (OTA) communication can be used as an efficient deployment method. However, since CPS are often used to monitor and control safety-critical processes, it is necessary to thoroughly test the updated software before its distribution. This testing process is preceded by elicitation of the variants of devices that are eligible to receive the update and choosing representative configurations for testing. We propose an approach to select representative variants based on feature models and device configuration identifiers. Using this methodology, it is possible to manage OTA updates from the first design stages to the deployment to the maintained systems. A possible application could be a large fleet of intelligent vehicles. In addition, we explain how it is possible to use the introduced variant management to reduce the verification and validation effort for safety-critical software releases. The proposed procedure is instrumental for a novel update process covering the phases of design, deployment and run-time monitoring.
Change Detection in Multitemporal Monitoring Images Under Low Illumination

Video surveillance may involve the simultaneous monitoring of a large number of areas. Real-time automatic change detection of a monitoring area (such as involving the movement of people or vehicles) can reduce risks incurred in negligent manual observation. However, the low signal-to-noise ratio (SNR) of dark environments can significantly corrupt camera images, making it difficult for machine learning surveillance systems to detect small changes in monitored images. In addition, in the absence of changes between two multitemporal monitoring images, sensor noise can lead to false alarms. The objective of this paper is to reduce the effect of sensor noise on change detection of monitored images and the run time of change detection algorithms. For these purposes, we proposed a novel multitemporal monitoring image change detection algorithm based on morphological structure filtering and normalized fusion difference image. First, the random noise in two surveillance images was removed using a multidirectional weighted multiscale series of a morphological filter. Next, two difference images were obtained by using the compression log-ratio operator and the mean ratio operator, and a fusion difference image was obtained by equal-weight fusion of the two difference images. Then, the sigmoid function was used to compress the fusion difference map to obtain a normalized fusion difference image, and a median filter was used to obtain a final difference image. Finally, the k-means clustering algorithm was utilized to obtain the change detection results. The experimental results demonstrate that the proposed method can accurately detect changes in a night monitoring scene in real time. Subjective and objective evaluation of the experimental results demonstrate that the proposed method is superior to reference algorithms in terms of change detection accuracy, time and robustness.
“Boundaries Do Not Sit Still” from Interaction to Agential Intra-action in HCI

We live in a world that is increasingly shaped through computational technology. The expansion and pervasiveness of computing brings along opportunities and challenges for the field of HCI. New application domains evolve and technological advances pose novel interaction possibilities. But with computing’s shaping power comes also the need for increased responsibility and attention to the societal, political and ecological impact of technology. In this conceptual paper, new materialist thinking is employed to locate these societal etc. aspects as inextricably enmeshed within computing research and development. New materialism is a multi-vocal, cross-disciplinary field of doing-thinking. In contrast to Western dichotomic tradition, a monistic, post-anthropocentric perspective is provided and matter as an active agent in world-making is acknowledged. Central to this paper is the questioning of the human and computer dichotomy at the core of the field of HCI. For this, a material-semiotic perspective on human-computer interaction is elaborated upon. Furthermore, the paradigm of interaction is revisited and reworked as a conception of agential intra-action, a term coined by physicist Karen Barad. From this interrogation of dichotomies and the notion of intra-action follow further conceptual shifts that are discussed as productive for the future of HCI, namely responsibility as specification of ethical agency and performativity in contrast to representationalism.