Reviewerly/exHarmony huggingface dataset:
Nonequilibrium cavity QED model accounting for dipole-dipole interaction in strong-, ultrastrong-, and deep-strong-coupling regimes

We propose a generalized system of nonequilibrium cavity QED with interacting dipoles coupled to a single-mode of an electromagnetic field in strong-, ultrastrong-, and deep-strong-coupling regimes. To illustrate the applicability of the system, an extended Dicke model is developed for atoms undergoing Raman transitions between the ground states in the presence of laser fields and considering dipole-dipole interactions; the latter has been neglected in many previous works. We have studied the effect of a ferroelectric and an antiferroelectric arrangement on the phase transition for both a finite and an infinite number of dipoles. An additional superradiant phase is observed in the deep-strong-coupling regime due to influence of the dipole-dipole interaction term. A high degree of dipole-dipole entanglement occurs for the antiferroelectric arrangement in the deep-strong regime, whereas it gets disentangled quite rapidly for the ferroelectric arrangement. A sharp transition of system parameters is observed in the ultrastrong-coupling regime and beyond. The dipole-dipole interaction also influences the spectra of the system, inducing a significant shift in the peaks, and modifies the average number of photons emitted.
Personalized system for human gym activity recognition using an RGB camera

Human Activity Recognition is one of the most researched topics in the field of computer vision. It is a powerful tool mainly used to aid medical systems, smart homes, surveillance, and many more areas. In this paper, an RGB camera was used to record gym activities such as push-up, squat, plank, forward lunge, and sit-up. Features were extracted from the recorded videos and were fed into classification algorithms such as Support Vector Machines, Decision Tree classifier, K-Nearest Neighbor classifier, and Random Forest classifier. The developed models were evaluated using metrics such as accuracy, balanced accuracy, precision score, recall score, and F1 score. The Random Forest Classifier outperformed all the other attempted methods with an accuracy of 98.98%. A repetition counter was developed, which splits workouts based on local minima analysis, and correctness of the workout was calculated for each skeletal point using dynamic time warping. An interactive android application was built for the user to gain insights on the performed workouts.
Twitter text mining for sentiment analysis on government’s response to forest fires with vader lexicon polarity detection and k-nearest neighbor algorithm

Abstract Opinions about the government’s response to forest fires have drawn many opinions from the community. One way for people to express their opinions is to use social media Twitter. This study conducted a sentiment analysis process on the government’s response to handling forest fires in Indonesia in 2019 with data sources from Twitter. The analysis was carried out on 6325 datasets written on Twitter on September 20, 2019, and then through the process of pre-processing, automating labeling and classification. The automate labeling process uses a Vader that automatically detects the negative or positive polarity of each data and then goes through the classification process using the KNN algorithm. The test results that were built using rapidminer tools showed an accuracy level of the KNN algorithm of 79.45%, the highest if compared to other classifier algorithms such as decision trees, naïve Bayes and random forests. The sentiment analysis process can almost run automatically without human touch because there is already automated labeling using Vader. Testing sentiment analysis related to the government’s response to forest fires can be analyzed using the KNN algorithm and lexicon polarity detection Vader can be done properly.
Ask Less, Get More: Side-Channel Signal Hiding, Revisited

Signal hiding countermeasures have been extensively investigated in the early side-channel attacks' literature. Due to design and physical imperfections, their stand-alone use only leads to a limited reduction of the attacks' complexity. As a result, more algorithmic countermeasures providing a more formal cost vs. security tradeoff (e.g., shuffling and masking) have gained more attention. Yet, since the cost associated with these is high, designers aim at combining countermeasures, leveraging the strength of each. In this manuscript, we demonstrate that by asking less to both signal hiding and algorithmic countermeasures (as stand-alone), we can develop combined countermeasures that indeed provide higher security at lower cost. For this purpose, we show how we can stack signal reduction and amplitude randomization techniques with ultra low cost automatic design flows and standard tools, and reach attractive security levels in combination with masking. Concretely, we examine two natural strategies for signal hiding and their combination: namely WDDL and a simple, local, scalable and easy-to-implement noise generation engine. A 65nm technology ASIC is evaluated with multiple isolated AES cores, leveraging recent information theoretic bounds which are connected to masking security proofs, significantly reducing the side-channel information leakage. We further quantify performance gains for masked designs.
Parkinson’s Disease Diagnosis: Towards Grammar-based Explainable Artificial Intelligence

Machine Learning (ML) approaches are vastly used for supporting humans in decision-making processes. However, the poor explainability associated to their behavior hampers their application in fields were the impact of the decision is critical, as it is the case for medical application, since physicians cannot simply use the predictions of the model but they must trust the results it provides. This work focuses on the automatic detection of Parkinson’s disease (PD), whose impact on both the individual’s quality of life and social well-being is constantly increasing with the aging of the population. To this end, we propose an explainable approach based on Genetic Programming, called Grammar Evolution (GE). This technique uses context-free grammar to describe the language of the programs to be generated and evolved. In this case, the generated programs are the explicit classification rules for the diagnosis of the subjects. The results of the experiments obtained on the publicly available HandPD data set show GE’s high expressive power and performance comparable to those of several ML models that have been proposed in the literature.
Computational fuzzy extractors

Fuzzy extractors derive strong keys from noisy sources. Their security is usually defined information-theoretically, with gaps between known negative results, existential constructions, and polynomial-time constructions. We ask whether using computational security can close these gaps. We show the following: Negative result: Noise tolerance in fuzzy extractors is usually achieved using an information reconciliation component called a secure sketch. We show that secure sketches defined using pseudoentropy (Håstad et al., SIAM J. Comput. 1999) instead of information-theoretic security are still subject to upper bounds from coding theory. Positive result: We show that our negative result can be avoided by constructing and analyzing a computational fuzzy extractor directly. We modify the code-offset construction (Juels and Wattenberg, CCS 1999) to use random linear codes. Security is based on the Learning with Errors problem and holds when the noisy source is uniform or symbol-fixing (that is, each dimension is either uniform or fixed).
An Automatic Advisor for Refactoring Software Clones Based on Machine Learning

To assist developers refactored code and to enable improvements to software quality when numbers of clones are found in software programs, we require an approach to advise developers on what a clone needs to refactor and what type of refactoring is needed. This paper suggests a unique learning method that automatically extracts features from the detected code clones and trains models to advise developers on what type needs to be refactored. Our approach differs from others, which specifies types of refactored clones as classes and creates a model for detecting the types of refactored clones and the clones which are anonymous. We introduce a new method by which to convert refactoring clone type outliers into Unknown clone set to improve classification results. We present an extensive comparative study and an evaluation of the efficacy of our suggested idea by using state-of-the-art classification models.
Digital media steganalysis

Steganography is the process of hiding messages inside an object known as a carrier. The idea is establishing a covert communication channel where messages go unnoticed by observers having access to that channel. Steganalysis is dedicated to the detection of such hidden messages; these messages can be embedded in several different types of digital media, such as images, video, audio, plain text files, and covert channels. Traditional steganalysis schemes are divided into two stages; the first one involves manual extraction of high-end features, and the second one is classification using machine learning (ML) methods. Nowadays, deep learning (DL) can unify both traditional stages into an end-to-end scheme with promising results. In this chapter, we show the most relevant steganalysis techniques using statistical, ML, and DL methods on digital media.
Entanglement of bipartite partly non-orthogonal $\frac{1}{2}$-spin coherent states

We investigate the entanglement of a bipartite partly non-orthogonal state of the form through -spin coherent states by adopting the concurrence as a measure to quantify the degree of entanglement of the two-qubit system. After, we study the amount of entanglement of mixed states represented by a statistical mixture of partly non-orthogonal bipartite pure states. We also express the lower and upper bounds of the squared concurrence of mixed states in terms of the amplitudes of spin coherent states.
Exploring Automated Question Answering Methods for Teaching Assistance

One important aspect of learning is through verbal interactions with teachers or teaching assistants (TAs), which requires significant effort and puts a heavy burden on teachers. Artificial intelligence has the potential to reduce their burden by automatically addressing the routine part of this interaction, which will free them up to focus on more important aspects of learning. We explore the use of automated question answering methods to power virtual TAs in online course discussion forums, which are heavily relied on during the COVID-19 pandemic as classes transition online. First, we focus on answering frequent and repetitive logistical questions and adopt a question answering framework that consists of two steps: retrieving relevant documents from a repository and extracting answers from retrieved documents. The document repository consists of course materials that contain information on course logistics, e.g., the syllabus, lecture slides, course emails, and prior discussion forum posts. This question answering framework can help virtual TAs decide whether a question is answerable and how to answer it. Second, we analyze the timing of student posts in discussion threads and develop a classifier to predict the timing of follow-up posts. This classifier can help virtual TAs decide whether to respond to a question and when to do so. We conduct experiments on data collected from an introductory physics course and discuss both the utility and limitations of our approach .
Learning to Acquire the Quality of Human Pose Estimation

Making human poses serve high-level computer vision tasks such as action recognition, recognizing the quality of estimated poses is of critical importance. Conventionally, the mean confidence of each keypoint is used as pose quality in most human pose estimation frameworks. However, because different types of keypoint are not identical in visibility and size, they should not contribute equally, which produces biased quality scores. In the paper, we propose end-to-end human pose quality learning, which adds a quality prediction block alongside pose regression. The proposed block learns the object keypoint similarity (OKS) between the estimated pose and its corresponding ground truth by sharing the pose features with heatmap regression. The predicted OKS correlates well with pose quality, making the selection of reliable poses straightforward. Moreover, utilizing the learned quality as pose score improves pose estimation performance during COCO AP evaluation, because it ranks more accurate ones high among all pose detections. We conduct extensive experiments based on the three most popular human pose estimation frameworks, including Hourglass, SimpleBaseline and HRNet. Adding the proposed quality learning block is able to consistently bring nearly 1 percent AP improvement on all the frameworks.
Taking DDL online

Abstract This paper describes the rationale, design and implementation of a short private online course (SPOC) on data-driven learning (DDL) ( Johns, 1991 ), focusing on L2 error correction in postgraduate academic writing and involving over 300 registered users. I discuss the affordances of using a SPOC platform (namely EdX ) for online DDL training, describing activities that cover a range of useful strategies for DDL-led error detection and correction. Learners’ usage of the SPOC platform and their quantitative and qualitative perceptions of the course are described for the reader. The paper also outlines certain conceptual and methodological challenges involved in taking DDL instruction online.
‘OpenNARS for Applications’: Architecture and Control

A pragmatic design for a general purpose reasoner incorporating the Non-Axiomatic Logic (NAL) and Non-Axiomatic Reasoning System (NARS) theory. The architecture and attentional control differ in many respects to the OpenNARS implementation. Key changes include; an event driven control process, separation of sensorimotor from semantic inference and a different handling of resource constraints.
Supporting Student-Teacher Interaction Through a Chatbot

Nowadays, an important part of academic education comes from online information, such as class topics, homework, and practices. In this sector, chatbots can be a valuable tool in the teaching/learning process by providing information about courses, procedures, and school services. Thus, the deployment of chatbots in schools and universities starts having a high priority for the academic staff. Nevertheless, most chatbots lack mechanisms to adequately integrate into the educational activities, since they should be useful for teachers, students, and educative assistant personnel (e.g., social workers, psychologists, pedagogues, and prefects). In this article, we propose a chatbot whose primary objective is to serve as an extra-school tool and as an intermediary between teachers and students. In this way, students could express themselves more freely, since the chatbot acts as a bridge with their teachers and other involved personnel. Our prototype was developed as a Web application that provides a text-based user interface and defines several interaction profiles to encompass the specific functions of the different actors in the teaching/learning process. We evaluated the proposed chatbot with the participation of third-year middle school students and teachers. In particular, we measured the workload perceived by our participants after performing a series of tasks. According to our results, our chatbot mostly obtained positive impressions from the test participants.
Wasserstein autoencoders for collaborative filtering

The recommender systems have long been studied in the literature. The collaborative filtering is one of the most widely adopted recommendation techniques which is usually applied to the explicit data, e.g., rating scores. However, the implicit data, e.g., click data, is believed to be able to discover user's latent preferences. Consequently, a number of research attempts have been made toward this issue. To the best of our knowledge, this paper is the first attempt to adapt the Wasserstein autoencoders to collaborative filtering problem. Particularly, we propose a new loss function by introducing an $$L_1$$ regularization term to learn a sparse low-rank representation form to represent latent variables. Then, we carefully design (1) a new cost function to minimize the data reconstruction error, and (2) the appropriate distance metrics for the calculation of KL divergence between the learned distribution of latent variables and the underlying true data distribution. Rigorous experiments are performed on three widely adopted datasets. Both the state-of-the-art approaches, e.g., Mult-VAE and Mult-DAE, and the baseline models are evaluated on these datasets. The promising experimental results demonstrate that the proposed approach is superior to the compared approaches with respect to evaluation criteria Recall@R and NDCG@R.
Orchestration from the Cloud to the Edge

The effective management of complex and heterogeneous computing environments is one of the biggest challenges that service and infrastructure providers are facing in the cloud-to-thing continuum era. Advanced orchestration systems are required to support the resource management of large-scale cloud data centres integrated with the big data generation of IoT devices. The orchestration system should be aware about all available resources and their current status in order to perform dynamic allocations and enable short time deployment of applications. This chapter will review the state of the art with regards to orchestration along the cloud-to-thing continuum with a specific emphasis on container-based orchestration (e.g. Docker Swarm and Kubernetes) and fog-specific orchestration architectures (e.g. SORTS, SOAFI, ETSI IGS MEC, and CONCERT).
Identifying redundant features using unsupervised learning for high-dimensional data

In the digital era, classifiers play a vital role in various machine learning applications such as medical diagnosis, weather prediction and pattern recognition. The classifiers are built by classification algorithms using data. Nowadays, the data are high dimensional in nature since the data are massively generated due to advancements in information and communication technology. The high-dimensional space contains irrelevant and redundant features; both of them reduce the classification accuracy and increase space and building time of the classifiers. Redundancy and relevancy analysis mechanisms of the feature selection process remove the irrelevant and redundant features. Identifying the irrelevant features is a simple task since that only considers the relevancy between each feature and the target class of a dataset using any one of the statistical or information theoretic measures. Identifying the redundant features from a dataset is quite difficult, especially in high-dimensional space since it needs to consider the relevancy among the features. This leads to more computational complexity and an inappropriate relevancy measure that can degrade the classification accuracy. In order to overcome these problems, this paper presents an unsupervised learning-based redundancy analysis mechanism for feature selection by evaluating various clustering techniques in terms of average redundancy rate and runtime.
Commanding and Re-Dictation

Existing voice-based interfaces have limited support for text editing, especially when seeing the text is difficult, e.g., while walking or cooking. This research develops voice interaction techniques for eyes-free text editing. First, with a Wizard-of-Oz study, we identified two primary user strategies: using commands, e.g., “&lt;scps&gt;replace&lt;/scps&gt; go with goes ” and re-dictating over an erroneous portion, e.g., correcting “he go there” by saying “he goes there.” To support these user strategies with an actual system implementation, we developed two eyes-free voice interaction techniques, Commanding and Re-dictation , and evaluated them with a controlled experiment. Results showed that while Re-dictation performs significantly better for more semantically complex edits, Commanding is more suitable for making one-word edits, especially deletions. We developed VoiceRev to combine both the techniques in the same interface and evaluated it with realistic tasks. Results showed improved usability of the combined techniques over either of the two techniques used individually.
Multi-objective evolutionary GAN

Generative Adversarial Network (GAN) is a generative model proposed to imitate real data distributions. The original GAN algorithm has been found to be able to achieve excellent results for the image generation task, but it suffers from problems such as instability and mode collapse. To tackle these problems, many variants of the original model have been proposed; one of them is the Evolutionary GAN (EGAN), where a population of generators is evolved.
Exploring Revisions in Academic Text: Closing the Gap Between Process and Product Approaches in Digital Writing

To date, research into dynamic descriptions of text has focused mainly on the spoken mode; and while writing process research has examined language structures, it has largely ignored the functionality (meaning) inherent in them. Therefore, drawing on systemic functional linguistics (SFL) and keystroke logging software, this article takes a further step toward an interdisciplinary dialogue by outlining a new schematic for coding and analyzing revisions. More specifically, we show how revision activity can be tracked within functional components, across functional components, and across clauses in terms of forward and backward movements. By exploring three digitally constructed texts, which were produced and observed unobtrusively in a natural setting, we have attempted to illustrate how one writer’s revising process can be operationalized in terms of (a) chronological movement (sequence) and (b) spatial movement (location). Findings showed how activity was relatively consistent across datasets with regard to session management, revision frequency, and distribution of revision types. Moreover, results also showed how most revision activity occurred at, or ahead of, the point of inscription, particularly with regard to revising the end of clauses. However, findings also indicated that revising the start of clauses was equally important when considering the size of functional components.
Knowledge-infused Deep Learning

Deep Learning has shown remarkable success during the last decade for essential tasks in computer vision and natural language processing. Yet, challenges remain in the development and deployment of artificial intelligence (AI) models in real-world cases, such as dependence on extensive data and trust, explainability, traceability, and interactivity. These challenges are amplified in high-risk fields, including healthcare, cyber threats, crisis response, autonomous driving, and future manufacturing. On the other hand, symbolic computing with knowledge graphs has shown significant growth in specific tasks with reliable performance. This tutorial (a) discusses the novel paradigm of knowledge-infused deep learning to synthesize neural computing with symbolic computing (b) describes different forms of knowledge and infusion methods in deep learning, and (c) discusses application-specific evaluation methods to assure explainability and reasoning using benchmark datasets and knowledge-resources. The resulting paradigm of "knowledge-infused learning'' combines knowledge from both domain expertise and physical models. A wide variety of techniques involving shallow, semi-deep, and deep infusion will be discussed along with the corresponding intuitions, limitations, use cases, and applications. More details can be found \urlhttp://kidl2020.aiisc.ai/.