Reviewerly/exHarmony huggingface dataset:
Repetitive Activity Monitoring from Multivariate Time Series: A Generic and Efficient Approach

Repetitive activities like breathing and walking account for a large fraction of human activities. Monitoring these activities with sensing technology plays a vital role in numerous applications ranging from health monitoring to manufacturing management. Over the last decade, traditional machine learning approaches and recent end-to-end deep learning paradigms have achieved massive successes in human activity recognition. However, these approaches are mostly scenario dependent and computationally expensive. Moreover, real-world repetitive activities may have varying time intervals between each repetition, which invalidate existing sliding window methods. In this paper, we propose STEM, a Scalable Template Extraction Method for scenario independent monitoring of repetitive activities with varying intervals. Instead of using sliding windows, we detect and locate the appearance of repeating patterns based on the Matrix Profile. Distributional features are then extracted from the identified patterns such that domain knowledge can be avoided. The approach is efficient and robust as shown by the evaluation on three public datasets, in which around 95% of the undesired computation were eliminated with up to 4% accuracy improvement. It is also generic as demonstrated by a use case of respiration rate estimation using wireless signals.
Self-Fusion Convolutional Neural Networks

Efficiency is an important concern for practical applications, therefore, it is of great importance to build effective lightweight networks. This paper proposes a novel lightweight feature self-fusion convolutional (SFC) module, which consists of self-fusion and point-wise convolution. The core of SFC is a three-step self-fusion. First, each input feature map is expanded to a high dimensional space individually, prohibiting connections with other input channels. Then, in the second step, we fuse all features from the same input in the high dimensional space to enhance the representation ability. Finally, we compress high dimensional features to a low dimensional space. After self-fusion, we connect all features by one point-wise convolution. Compared to inverted bottleneck, SFC module decreases the number of parameters by replacing the dense connections among channels with self-fusion. To the best of our knowledge, SFC is the first method to build lightweight networks by feature self-fusion. We then build a new network namely SFC-Net, by stacking SFC modules. Experimental results on the CIFAR and downsampled ImageNet datasets demonstrate our SFC-Net achieves better performance to some previous popular CNNs with fewer number of parameters and achieves comparable performance compared to other previous lightweight architectures. The code is available at https://github.com/Yankeegsj/Self-fusion.
Leadership of place in virtual environments

Place and space concepts help to illuminate how the place an organization inhabits and related beliefs have a significant impact on leadership processes. While places often have a physical presence, a sense of place is socially constructed by those who interact in it. This article offers analysis of how virtual environments can be seen as socially constructed places and how that conceptualization impacts leadership, both in the environment acting as a leadership substitute and how people engage in virtual leadership. This conceptual analysis occurs by integrating existing literature on space, place, technology affordances, and virtual leadership, as well as analyzing current virtual work environments and virtual leaders. We illustrate how virtual places can offer affordances for leadership sensemaking of political leaders, virtual place-making by social media influencers, algorithmic leadership, and shared leadership in the gig economy. We close the article by discussing how current leaders can consider the affordances of virtual environments and needed future needed research.
An objective metric for Explainable AI: How and why to estimate the degree of explainability

This paper presents a new method for objectively measuring the explainability of textual information, such as the outputs of Explainable AI (XAI). We introduce a metric called Degree of Explainability (DoX), drawing inspiration from Ordinary Language Philosophy and Achinstein's theory of explanations. It assumes that the degree of explainability is directly proportional to the number of relevant questions that a piece of information can correctly answer. We have operationalized this concept by formalizing the DoX metric through a mathematical formula, which we have integrated into a software tool named DoXpy. DoXpy relies on pre-trained deep language models for knowledge extraction and answer retrieval in order to estimate the DoX, transforming our theoretical insights into a practical tool for real-world applications. To confirm the effectiveness and consistency of our approach, we conducted comprehensive experiments and user studies with over 190 participants. These studies evaluated the quality of explanations by healthcare and finance XAI-based software systems. Our results demonstrate a correlation between increases in objective explanation usability and increments in the DoX score. These findings suggest that the DoX metric is congruent with other mainstream explainability measures. It provides a more objective and cost-effective alternative to non-deterministic user studies. Thus, we discuss the potential of DoX as a tool to evaluate the legal compliance of XAI systems. By bridging the gap between theory and practice in Explainable AI, our work fosters transparency, understandability, and legal compliance. DoXpy and related materials have been made available online to ensure reproducibility.
Usability and Security of Arabic Text-based CAPTCHA Using Visual Cryptography

Recently, with the spread of online services involving websites, attackers have the opportunity to expose these services to malicious actions.To protect these services, A Completely Automated Public Turing Test to Tell Computers and Humans Apart (CAPTCHA) is a proposed technique.Since many Arabic countries have developed their online services in Arabic, Arabic text-based CAPTCHA has been introduced to improve the usability for their users.Moreover, there exist a visual cryptography (VC) technique which can be exploited in order to enhance the security of text-based CAPTCHA by encrypting a CAPTCHA image into two shares and decrypting it by asking the user to stack them on each other.However, as yet, the implementation of this technique with regard to Arabic text-based CAPTCHA has not been carried out.Therefore, this paper aims to implement an Arabic printed and handwritten text-based CAPTCHA scheme based on the VC technique.To evaluate this scheme, experimental studies are conducted, and the results show that the implemented scheme offers a reasonable security and usability levels with text-based CAPTCHA itself.
Analysis of Technical, Economic and Social Information Features to Predict the Bitcoin Price Direction for Day-Trade Operations

Cryptocurrencies are one of the most important financial and technological innovations of recent years. Currently, the interest in Bitcoin has grown, for traders and the general public. However, its high volatility represents a challenge in terms of prediction models for day-trade operations. In this way, recent studies have been proposed to predict the Bitcoin price direction for day-trade operations, but the maximum accuracy obtained was around 57.5%. In order to contribute and advance the state-of-the-art, this article experiences the impact of use Blockchain data, international economic indices, social trends information and technical indicators to overcome the predictions of the Bitcoin price direction. Thus, it is proposed a methodology based on data collection and processing, where weighted moving averages (for the Blockchain data, economic indices and social media trends) and technical indicators (considering the Bitcoin exchange rate data) were extracted/calculated from the original databases. The attributes were submitted to the Information Gain algorithm to select the most relevant ones. In the sequence, Support Vector Machines and Artificial Neural Networks models were used to predict the Bitcoin price direction for day-trade purposes. As a result, it was possible to obtain an average accuracy of 63.84% in a 1-year prediction period, overcoming other related studies.
Analysis of Five Techniques for the Internal Representation of a Digital Image Inside a Quantum Processor

In this paper, five techniques of representing a digital image inside a quantum processor are compared. The techniques are: flexible representation of quantum images (FRQI), novel enhanced quantum representation (NEQR), generalized quantum image representation (GQIR), multi-channel representation for quantum images (MCQI), and quantum Boolean image processing (QBIP). The comparison will be based on implementations on the Quirk simulator, and on the IBM Q Experience processors, from the point of view of performance, robustness (noise immunity), deterioration of the outcomes due to decoherence, and technical viability.
Saturated Visual-Servoing Control Strategy for Nonholonomic Mobile Robots With Experimental Evaluations

A novel saturated visual control strategy for nonholonomic mobile robots is presented. The principal focus is to deal with the stabilization problem. The saturation bounds for the control actions avoid exceeding the physical restrictions of the actuators. This feature is the main difference concerning related works. The closed-loop system is analyzed using the theory of non-autonomous cascade systems, and global uniform asymptotic stability is guaranteed. Experimental validation is carried out by using a unicycle-type mobile robot with an onboard camera. The experiment consists of stabilizing the robot in a preset pose using visual information. The experimental results are compared with a literature unsaturated visual servoing strategy. The results confirm the effectiveness of the proposed approach, completing the assigned task and bounding the control inputs.
Private Data Exfiltration from Cyber-Physical Systems Using Channel State Information

Data exfiltration methods aim to extract data without authorization from a network or device without detection. In this paper, we present a novel data exfiltration method using Channel State Information (CSI) from ambient WiFi signals. Modulation is performed by modifying the environment by moving a physically actuated machine resulting in a change to the channel response that is measurable by a distant receiver capable of collecting CSI samples. An attacker can use this to exfiltrate data when transmission using conventional methods is impossible, yet the attacker controls a moving mechanism. We discuss the design of the covert channel in detail and produce a proof of concept implementation to evaluate the performance in terms of communication quality. We find that even a simple implementation provides robust communication in an office environment. Additionally, we present several countermeasures against an attack of this type.
Complex-valued U-Net for PolSAR Image Semantic Segmentation

Abstract As an image semantic segmentation network, U-Net has the advantage of simple structure which is suitable for semantic segmentation of PolSAR images with small datasets. However, the original U-Net is a real-valued (RV) network, whose input must be RV. If it is directly used in the segmentation of PolSAR image, the complex-valued (CV) input must be converted into RV, which results in the loss of information. In this paper, a CV U-Net, which is mathematically strict, is proposed for semantic segmentation of PolSAR images. Considering that the PolSAR dataset is small, the structure and parameters of CV U-Net are furtherly simplified based on the original U-Net to prevent overfitting. Experimental results on the Flevoland dataset show that the proposed CV U-Net has better segmentation performance than the original RV U-Net and some other semantic segmentation networks.
A novel intrinsic measure of data separability

In machine learning, the performance of a classifier depends on both the classifier model and the separability/complexity of datasets. To quantitatively measure the separability of datasets, in this study, we propose an intrinsic measure – the Distance-based Separability Index (DSI), which is independent of the classifier model. We then formally show that the DSI can indicate whether the distributions of datasets are identical for any dimensionality. DSI can measure separability of datasets because we consider the situation in which different classes of data are mixed in the same distribution to be the most difficult for classifiers to separate. And, DSI is verified to be an effective separability measure by comparing it to state-of-the-art separability/complexity measures using synthetic datasets and real datasets (CIFAR-10/100). Having demonstrated the DSI’s ability to compare distributions of samples, our other studies show that it can be used in other separability-based applications, such as measuring the performance of generative adversarial networks (GANs) and evaluating the results of clustering methods.
Graph Based Answer Set Programming Solver Systems

Answer set programming (ASP) is a popular nonmonotonic-logic based paradigm for knowledge representation and solving combinatorial problems. Computing the answer set of an ASP program is NP-hard in general, and researchers have been investing significant effort to speed it up. The majority of current ASP solvers employ SAT solver-like technology to find these answer sets. As a result, justification for why a literal is in the answer set is hard to produce. There are dependency graph based approaches to find answer sets, but due to the representational limitations of dependency graphs, such approaches are limited. This paper proposes a novel dependency graph-based approach for finding answer sets in which conjunction of goals is explicitly represented as a node which allows arbitrary answer set programs to be uniformly represented. Our representation preserves causal relationships allowing for justification for each literal in the answer set to be elegantly found. In this paper, we explore two different approaches based on the graph representation: bottom-up and top-down. The bottom-up approach finds models by assigning truth values along with the topological order, while the top-down approach generates models starting from the constraints.
Ensuring a Robust Multimodal Conversational User Interface During Maintenance Work

It has been shown that the provision of a conversational user interface proves beneficial in many domains. But, there are still many challenges when applied in production areas, e.g. as part of a virtual assistant to support workers in knowledge-intensive maintenance work. Regarding input modalities, touchscreens are failure-prone in wet environments and the quality of voice recognition is negatively affected by ambient noise. Augmenting a symmetric text- and voice-based user interface with gestural input poses a good solution to provide both efficiency and a robust communication. This paper contributes to this research area by providing results on the application of appropriate head and one-hand gestures during maintenance work. We conducted an elicitation study with 20 participants and present a gesture set as its outcome. To facilitate the gesture development and integration for application designers, a classification model for head gestures and one for one-hand gestures were developed. Additionally, a proof-of-concept for operators' acceptance regarding a multimodal conversational user interface with support of gestural input during maintenance work was demonstrated. It encompasses two usability testings with 18 participants in different realistic, but controlled settings: notebook repair (SUS: 82.1) and cutter head maintenance (SUS: 82.7).
The Digitalization of Pakistan's Universities?

Higher educational institutes today need to focus on identifying the requirements of industry as well as the market, so that they can help students develop the necessary skills and enable them to work with intelligent machines in today's era of the 4th industrial revolution which is also termed digitalization. Digitalization has increased pressure on educational institutions to update their existing curricula and course contents. It is important to note that, while industry as well as educational institutions in the developed world are rather quick on embracing such trends, developing economies often lag behind. Universities in developed countries are mostly on the path towards a hybrid way of teaching, while those in developing countries, such as Pakistan, frequently struggle to make these changes. This chapter seeks to provide suggestions and recommendations for the higher education sector, including universities and policymakers. It identifies the role that the higher education sector must play in preparing and upskilling future employees for Pakistan's digital future.
Streaming Decision Trees for Lifelong Learning

Lifelong learning models should be able to efficiently aggregate knowledge over a long-term time horizon. Comprehensive studies focused on incremental neural networks have shown that these models tend to struggle with remembering previously learned patterns. This issue known as catastrophic forgetting has been widely studied and addressed by several different approaches. At the same time, almost no research has been conducted on online decision trees in the same setting. In this work, we identify the problem by showing that streaming decision trees (i.e., Hoeffding Trees) fail at providing reliable long-term learning in class-incremental scenarios, which can be further generalized to learning under temporal imbalance. By proposing a streaming class-conditional attribute estimation, we attempt to solve this vital problem at its root, which, ironically, lies in leaves. Through a detailed experimental study we show that, in the given scenario, even a rough estimate based on previous conditional statistics and current class priors can significantly improve the performance of streaming decision trees, preventing them from catastrophically forgetting earlier concepts, which do not appear for a long time or even ever again.
Image-Based Multi-UAV Tracking System in a Cluttered Environment

A tracking controller for unmanned aerial vehicles (UAVs) is developed to track moving targets undergoing unknown translational and rotational motions. The main challenges are to control both the relative positions and angles between the target and the UAVs to within desired values, and to guarantee that the generated control inputs to the UAVs are feasible (i.e., within their motion capabilities). Moreover, the UAVs are controlled to ensure that the target always remains within the fields of view of their onboard cameras. To the best of our knowledge, this is the first work to apply multiple UAVs to cooperatively track a dynamic target while ensuring that the UAVs remain connected and that both occlusion and collisions are avoided. To achieve these control objectives, a designed controller solved based on the aforementioned tracking controller using quadratic programming can generate minimally invasive control actions to achieve occlusion avoidance and collision avoidance. Furthermore, control barrier functions (CBFs) with a distributed design are developed in order to reduce the amount of inter-UAV communication. Simulations were performed to assess the efficacy and performance of the developed CBF-based controller for the multi-UAV system in tracking a target.
Feature and Application in Smart Campus: A Systematic Literature Review

Right now, a smart campus has become one of the most important things that every university must consider it. This could happen because a smart campus can measure credibility from the university related to facilities and innovations that have been implemented by the university. However, in Indonesia, not all the universities can apply for using the smart campus. Therefore, if the universities use a smart campus, both students and employees can be facilitated in teaching and learning activities including assisting students in finding their identity so that they can create innovations and ideas that can be implemented for the country and the nation.To get the right results and according to the purpose, this journal uses a research method, namely Systematic Literature Review (SLR). This study will use a qualitative data collection method from the previous research paper as a reference to be observed. The results of this research are a paper that can help the final project later also will be a reference for another journal that talked about the same object. Then, for the purpose of this study is to give an explanation about the benefit of applying for a smart campus for teaching and learning activities.
Light-Weight Document Image Cleanup Using Perceptual Loss

Smartphones have enabled effortless capturing and sharing of documents in digital form. The documents, however, often undergo various types of degradation due to aging, stains, or shortcoming of capturing environment such as shadow, non-uniform lighting, etc., which reduces the comprehensibility of the document images. In this work, we consider the problem of document image cleanup on embedded applications such as smartphone apps, which usually have memory, energy, and latency limitations due to the device and/or for best human user experience. We propose a light-weight encoder decoder based convolutional neural network architecture for removing the noisy elements from document images. To compensate for generalization performance with a low network capacity, we incorporate the perceptual loss for knowledge transfer from pre-trained deep CNN network in our loss function. In terms of the number of parameters and product-sum operations, our models are 65-1030 and 3-27 times, respectively, smaller than existing state-of-the-art document enhancement models. Overall, the proposed models offer a favorable resource versus accuracy trade-off and we empirically illustrate the efficacy of our approach on several real-world benchmark datasets.
Constructions of &lt;i&gt;ℓ&lt;/i&gt;-Adic &lt;i&gt;t&lt;/i&gt;-Deletion-Correcting Quantum Codes

We propose two systematic constructions of deletion-correcting codes for protecting quantum information. The first one works with qudits of any dimension, but only one deletion is corrected and the constructed codes are asymptotically bad. The second one corrects multiple deletions and can construct asymptotically good codes. The second one also allows conversion of stabilizer-based quantum codes to deletion-correcting codes, and entanglement assistance.
On the Role of Context Length for Feature Extraction and Sequence Modeling in Human Activity Recognition

At the core of human activity recognition (HAR) lies a time-series analysis problem. Given the sequential nature of the data, sensor readings are analyzed in their temporal contexts thereby focusing on two modeling components: feature extraction and sequence modeling for activity classification. Many HAR approaches utilize identical context lengths for both model components. In this paper we show that the consideration of such identical temporal contexts is not ideal. Motivated by the fact that features should capture temporally local characteristics of the data whereas sequence modeling should focus on longer ranging relationships, we modify a state-of-the-art HAR model (DeepConvLSTM) and experiment with different temporal contexts. Our evaluation on seven benchmark datasets demonstrates the benefit of separately optimizing temporal contexts for feature extraction and sequence modeling in HAR.