Reviewerly/exHarmony huggingface dataset:
Scalable CNN Synthesis for Resource-Constrained Embedded Platforms

State-of-the-art convolutional neural networks are designed to identify numerous object classes. Inference using such complex networks is fairly resource intensive, prohibiting their deployment on resource-constrained edge devices. In this context, we make two observations: First, the ability to classify an exhaustive list of categories is excessive for the demands of most IoT applications. Furthermore, designing a new custom-designed CNN for each new IoT application is inefficient. The observations motivate us to consider if one can utilize an existing optimized CNN model to automatically construct a competitive CNN for a given IoT application whose objects of interest are a fraction of categories that the original CNN was designed to classify, such that the model&#x2019;s inference resource requirement is proportionally scaled down. We use the term <i>resource scalability</i> to refer to this concept, and develop a methodology for automated synthesis of resource scalable CNNs from an optimized baseline CNN. The synthesized CNN has sufficient learning capacity for handling the given IoT application requirements, and yields competitive accuracy. The proposed approach is fast, and unlike the presently common practice of neural network design, does not require iterative rounds of training trial and error to find an optimal architecture. Experimental results showcase the efficacy of the approach, and highlight its complementary nature with respect to existing model compression techniques, such as pruning and quantization.
How Do Agile Practitioners Interpret and Foster “Technical Excellence”?

“Technical excellence” is a nebulous term in agile software development. This vagueness is risky, as it creates a gap in the understanding of agile that may have consequences on how software development practitioners operate. Technical excellence is the only reference to quality in the agile manifesto. Hence, it is fundamental to understand how agile software development practitioners both interpret and implement it. We conducted interviews with twenty agile practitioners about their understanding of the term “technical excellence” and how they approach the task of fostering it. To validate the ﬁndings, two focus group meetings were conducted after the interviews and the analysis of the data were completed. We found that the conﬁguration of technical excellence is made of four traits: (1) software craftsmanship; (2) software quality (3) mindset for excellence; and (4) consistency with good software engineering practices. Fostering technical excellence is a continuous endeavor. Further, we identified three key principles that were commonly cited as essential to implementing technical excellence, namely: 1) continuous learning; 2) continuous improvement; and 3) control of excellence. Based on our findings, we present several recommendations for software development teams seeking to better realize the goal of technical excellence in their agile implementation.
A Comparative Analysis on Sensor-Based Human Activity Recognition Using Various Deep Learning Techniques

To forecast conditions of action or actions during physical activity, the issue of classifying body gestures and reactions is referred to as human activity recognition (HAR). As the main technique to determine the range of motion, speed, velocity, and magnetic field orientation during these physical exercises, inertial measurement units (IMUs) prevail. Inertial sensors on the body can be used to produce signals tracking body motion and vital signs that can develop models efficiently and identify physical activity correctly. Extreme gradient boosting, multilayer perceptron, convolutional neural network, and long short-term memory network methods are contrasted in this paper to distinguish human behaviors on the HEALTH datasets. The efficiency of machine learning models is often compared to studies that better fit the multisensory fusion analysis paradigm. The experimental findings of this article on the MHEALTH dataset are strongly promising and reliably outperform current baseline models, comprising of 12 physical activities obtained from four separate inertial sensors. The best efficiency metrics were obtained by MLP and XGBoost with accuracy (92.85%, 90.97%), precision (94.66%, 92.09%), recall (91.59%, 89.99%), and F1-score (92.7%, 90.78%), respectively.
Creating Ensembles of Generative Adversarial Network Discriminators for One-Class Classification

We introduce an algorithm for one-class classification based on binary classification of the target class against synthetic samples. We use a process inspired by Generative Adversarial Networks (GANs) in order to both acquire synthetic samples and to build the one-class classifier. The first objective is achieved by leading the generator's output into close vicinities of the target class region. For the second objective, we obtain a one-class classifier by generating an ensemble of discriminators obtained from the GAN's training process. Our approach is tested on publicly available datasets producing promising results when compared to other methods.
Learning to Discover Task-Relevant Features for Interpretable Reinforcement Learning

Reinforcement Learning (RL) agents are often fed with large-dimensional observations to achieve the ideal performance in complex environments. Unfortunately, the massive observation space usually contains useless or even adverse features, which leads to low sample efficiency. Existing methods rely on domain knowledge and cross-validation to discover efficient features which are informative for decision-making. To minimize the impact of prior knowledge, we propose a temporal-adaptive feature attention algorithm (TAFA). We adopt a non-linear attention module, automatically choosing task-relevant components of hand-crafted state features without any domain knowledge. Our experiments on MuJoCo and TORCS tasks show that the agent achieves competitive performance with state-of-the-art methods while successfully identifying the most task-relevant features for free. We believe our work takes a step towards the interpretability of RL. Our code is available at https://github.com/QiyuanZhang19/Temporal-Adaptive-Feature-Attention/tree/master.
Defining Data Literacy Communities by Their Objectives: A Text Mining Analysis

Data literacy is a multidimensional concept that attracts the attention of a variety of communities of practice, from different angles. The authors grouped these communities of practice in three categories: education, fields and professions, and citizenship. The meaning of data literacy varies depending on who uses it, and its concept is often conveyed in terms other than data literacy. This paper addresses the problematization of data literacy as a term by examining academic literature around it. To this end, a desk study was carried out to gather sources where the term is used. After an extensive search in the main academic databases and a subsequent PRISMA selection process, automated content analysis was applied to the gathered sources. The findings suggest that the concept of data literacy has a different treatment in different communities of practice. For example, librarians and citizen scientists have a different understanding of the concept of data literacy.
Covid-19 Face Mask Detection Using Convolutional Neural Network and Image Processing

Object detection and image classifications are the popular research work in the rapid growth of advanced technology to detect and identify real-time problems in the governmental core-areas such as business shops, airlines, universities, and army place with the help of webcams surveillance cameras, and open-source platforms. The objective of this research work is to propose Convolutional Neural Network (CNN) and Open Source Computer Vision (OpenCV) to detect COVID-19 face mask detection from real-time (live streaming video) and image datasets. We have employed data argumentation pre-processing techniques to maximize our dataset and improved the performance of the proposed models, and in addition to this, we have used MobileNet CNN architecture to reducing the size and model complexity. This research work's experimental result has been shown 96.50%, 97.07%, 99.27% accuracies obtained with the help of CNN, CNN-data augmentation, MobileNet2V, respectively.
Evaluating the translation of speech to virtually-performed sign language on AR glasses

This paper describes the proof-of-concept evaluation for a system that provides translation of speech to virtually performed sign language on augmented reality (AR) glasses. The discovery phase via interviews confirmed the idea for a signing avatar displayed within the users field of vision through AR glasses. In the evaluation of the first prototype through a wizard-of-Oz-experiment, the presented AR solution received a high acceptance rate among deaf and hard-of-hearing persons. However, the machine learning based method used to generate sign language from video still lacks the required accuracy for fully preserving comprehensibility. Signed sentences with large recognisable arm movements were understood better than sentences relying mainly on finger movements, where only a small interaction space is visible.
Amazon echo dot or the reverberating secrets of IoT devices

Smart speakers, such as the Amazon Echo Dot, are very popular and routinely trusted with private and sensitive information. Yet, little is known about their security and potential attack vectors. We develop and synthesize a set of IoT forensics techniques, apply them to reverse engineer the hardware and software of the Amazon Echo Dot, and demonstrate its lacking protections of private user data. An adversary with physical access to such devices (e.g., purchasing a used one) can retrieve sensitive information such as Wi-Fi credentials, the physical location of (previous) owners, and cyber-physical devices (e.g., cameras, door locks). We show that such information, including all previous passwords and tokens, remains on the flash memory, even after a factory reset. This is due to the wear-leveling algorithms of the flash memory and lack of encryption. We identify and discuss the design flaws in the storage of sensitive information and the process of de-provisioning used devices. We demonstrate the practical feasibility of such attacks on 86 used devices purchased on eBay and flea markets. Finally, we propose secure design alternatives and mitigation techniques.
A dataset for the detection of fake profiles on social networking services

The use of multiple social media platforms is a common practice on more than two-third of all Internet users, according to OurWorld In Data. From this perspective, the verification of a real profile is a matter of growing interest, because false virtual identity could trigger problems such as spoofing, bots, grooming, sextortion, just to name a few. This paper presents a method to detect fake profiles on social media platforms by deploying some machine learning detection methods over a novel dataset. The dataset was designed with 17 metadata features from real and fake profiles and it was tested on Instagram profiles. After deploying different machine learning algorithms, the obtained detection rate was near to 96% with good false positive rates.
Deep Learning Image Recognition for Non-images

Powerful deep learning algorithms open an opportunity for solving non-image Machine Learning (ML) problems by transforming these problems into the image recognition problems. The CPC-R algorithm presented in this chapter converts non-image data into images by visualizing non-image data. Then deep learning CNN algorithms solve the learning problems on these images. The design of the CPC-R algorithm allows preserving all high-dimensional information in 2-D images. The use of pair values mapping instead of single value mapping used in the alternative approaches allows encoding each n-D point with 2 times fewer visual elements. The attributes of an n-D point are divided into pairs of its values and each pair is visualized as 2-D points in the same 2-D Cartesian coordinates. Next, grey scale or color intensity values are assigned to each pair to encode the order of pairs. This is resulted in the heatmap image. The computational experiments with CPC-R are conducted for different CNN architectures, and methods to optimize the CPC-R images showing that the combined CPC-R and deep learning CNN algorithms are able to solve non-image ML problems reaching high accuracy on the benchmark datasets. This chapter expands our prior work by adding more experiments to test accuracy of classification, exploring saliency and informativeness of discovered features to test their interpretability, and generalizing the approach.
Leveraging Static Models for Link Prediction in Temporal Knowledge Graphs

Including temporal scopes of facts in knowledge graph embedding (KGE) presents significant opportunities for improving the resulting embeddings, and consequently for increased performance in downstream applications. Yet, little research effort has focussed on this area and much of the carried out research reports only marginally improved results compared to models trained without temporal scopes (static models). Furthermore, rather than leveraging existing work on static models, they introduce new models specific to temporal knowledge graphs. We propose a novel perspective that takes advantage of the power of existing static embedding models by focussing effort on manipulating the data instead. Our method, SPLIME, draws inspiration from the field of signal processing and early work in graph embedding. We show that SPLIME competes with or outperforms the current state of the art in temporal KGE. Additionally, we uncover issues with the procedure currently used to assess the performance of static models on temporal graphs and introduce two ways to counteract them.
Quantum Computing for high school: an approach to interdisciplinary in STEM for teaching

Abstract The paper focuses on a Quantum Computing teaching module for high school students that was designed and implemented within the I SEE Erasmus+ project ( https://iseeproject.eu ) The module is discussed as an example of how the S-T-E-M disciplines can be integrated to stress the conceptual, epistemological, and social relevance of quantum computing. We implemented a three-level approach to introduce quantum technologies without getting lost in the technicalities. The approach has allowed us to highlight the difference between classical and quantum computers and to bring out the interdisciplinary character that characterises the new technologies.
What Can Style Transfer and Paintings Do For Model Robustness?

A common strategy for improving model robustness is through data augmentations. Data augmentations encourage models to learn desired invariances, such as invariance to horizontal flipping or small changes in color. Recent work has shown that arbitrary style transfer can be used as a form of data augmentation to encourage invariance to textures by creating painting-like images from photographs. However, a stylized photograph is not quite the same as an artist-created painting. Artists depict perceptually meaningful cues in paintings so that humans can recognize salient components in scenes, an emphasis which is not enforced in style transfer. Therefore, we study how style transfer and paintings differ in their impact on model robustness. First, we investigate the role of paintings as style images for stylization-based data augmentation. We find that style transfer functions well even without paintings as style images. Second, we show that learning from paintings as a form of perceptual data augmentation can improve model robustness. Finally, we investigate the invariances learned from stylization and from paintings, and show that models learn different invariances from these differing forms of data. Our results provide insights into how stylization improves model robustness, and provide evidence that artist-created paintings can be a valuable source of data for model robustness. Code and data are available at: https://github.com/hubertsgithub/style_painting_robustness
Learning Graphs for Knowledge Transfer with Limited Labels

Fixed input graphs are a mainstay in approaches that utilize Graph Convolution Networks (GCNs) for knowledge transfer. The standard paradigm is to utilize relationships in the input graph to transfer information using GCNs from training to testing nodes in the graph; for example, the semi-supervised, zero-shot, and few-shot learning setups. We propose a generalized framework for learning and improving the input graph as part of the standard GCN-based learning setup. Moreover, we use additional constraints between similar and dissimilar neighbors for each node in the graph by applying triplet loss on the intermediate layer output. We present results of semi-supervised learning on Citeseer, Cora, and Pubmed benchmarking datasets, and zero/few-shot action recognition on UCF101 and HMDB51 datasets, significantly outperforming current approaches. We also present qualitative results visualizing the graph connections that our approach learns to update.
Content Based Bot Detection using Bot Language Model and BERT Embeddings

Micro-blogging sites such as Twitter, with 187 million active users daily, have become an essential source for getting news and trends, sharing information, and gathering various insights. Many user accounts on Twitter are automated with software to share content automatically without human intervention. Such accounts are called bots. However, the open nature of Twitter has resulted in people misusing it for changing the information dynamics, as seen in the US 2016 elections. This research proposes a neural network ensemble of Text CNN and LSTM model with BERT embeddings to classify tweets as bot tweets or not based on the tweets' textual content. Along with this, we handle the problem of data imbalance present in the Twitter dataset, using the Language Model Oversampling Technique to create a bot language model that will mimic bot tweets over the traditional techniques like Synthetic Minority Oversampling technique. A comparison and analysis of the tweets generated by the above two methods is made. The comparison with the various state-of-the-art model shows that the proposed model outperforms the previous models.
Accelerated RISC-V for Post-Quantum SIKE

In this work, we present a fast and area-efficient software-hardware implementation of the supersingular isogeny key encapsulation (SIKE) mechanism. Our software-hardware design achieves both the flexibility of software as well as the efficient performance of intense computations of hardware. In particular, our implementation takes advantage of new and highly optimized hardware modules for addition, multiplication, and hardware-software control, targeted at Xilinx FPGAs. In conjunction with a small RISC-V processor, we can support all four SIKE parameter sets. On a Virtex-7 FPGA, this implementation occupies 3,492 slices, 78 DSPs, and 29 BRAMs, to perform encapsulation and decapsulation over SIKEp434, SIKEp503, SIKEp610, and SIKEp751 in 14.5, 19.2, 29.8, and 42.7 ms, respectively. Despite supporting all four parameter sets, this design has the best area-time product of all isogeny accelerators in the literature.
A Context-Aware Middleware for Context Modeling and Reasoning: A Case-Study in Smart Cultural Spaces

The proliferation of smart things and the subsequent emergence of the Internet of Things has motivated the deployment of intelligent spaces that provide automated services to users. Context-awareness refers to the ability of the system to be aware of the virtual and physical environment, allowing more efficient personalization. Context modeling and reasoning are two important aspects of context-aware computing, since they enable the representation of contextual data and inference of high-level, meaningful information. Context-awareness middleware systems integrate context modeling and reasoning, providing abstraction and supporting heterogeneous context streams. In this work, such a context-awareness middleware system is presented, which integrates a proposed context model based on the adaptation and combination of the most prominent context categorization schemata. A hybrid reasoning procedure, which combines multiple techniques, is also proposed and integrated. The proposed system was evaluated in a real-case-scenario cultural space, which supports preventive conservation. The evaluation showed that the proposed system efficiently addressed both conceptual aspects, through means of representation and reasoning, and implementation aspects, through means of performance.
Who will receive the ball? Predicting pass recipient in soccer videos

The game of soccer involves an act of one team trying to score a goal against the other. During the game, defending players constantly try to predict the pass of the attacking player to prevent a goal. So, pass prediction is an important facet to anticipate the game strategy of participating teams. Here we present a probabilistic framework for pass prediction. Aberrating the state-of-the-art notion of mutually independent decision models, the proposed framework predicts pass recipients by integrating two dependent models, designed from the coordinates of the players in abstract top-view visualization. To evaluate the real time efficacy of the proposed pass prediction framework, a soccer data set has been introduced. The proposed pass prediction algorithm is compared against recent methods and the ground truth available in the soccer data set. The proposed method outperforms the existing approaches by a noticeable margin.
Program analysis for reversible languages

Reversible computing is a technique to “let computations run forwards and backwards” and thus extends the traditional model of computation. As an example, consider a function to compute the factorial of a given number, say 5, yielding 120. Running this program backwards inverses the function by taking 120 as an input and giving 5 as result. Reversible programming languages allow the creation of programs that can be executed backwards as well as forwards and have been a focus of research over the last decade mostly due to the work of Glück, Yokoyama, Mogensen, and many others. In this paper, we report our recent activities to perform program analysis for reversible static-single-assignment form (RSSA) and using them to perform local and global optimizations. This work is based on our compiler translating from the reversible language Janus to RSSA. As far as we know, this is the first compiler from Janus to RSSA, and no results on optimization of reversible intermediate code or programming languages are known to us either. Optimization techniques in “traditional” compilers are always based on the understanding that programs are executed forwards - in reversible languages that assumption is no longer true and program analysis becomes much more difficult. Our first results on applying our analysis methods for common-subexpression-elimination and constant propagation are nevertheless promising and have been implemented successfully.
Relational Boosted Bandits

Contextual bandits algorithms have become essential in real-world user interaction problems in recent years. However, these algorithms represent context as attribute value representation, which makes them infeasible for real world domains like social networks, which are inherently relational. We propose Relational Boosted Bandits (RB2), a contextual bandits algorithm for relational domains based on (relational) boosted trees. RB2 enables us to learn interpretable and explainable models due to the more descriptive nature of the relational representation. We empirically demonstrate the effectiveness and interpretability of RB2 on tasks such as link prediction, relational classification, and recommendation.