Reviewerly/exHarmony huggingface dataset:
Negative string tension of a higher-charge Schwinger model via digital quantum simulation

We study some properties of generalized global symmetry for the charge-$q$ Schwinger model in the Hamiltonian formalism, which is the $(1+1)$-dimensional quantum electrodynamics with a charge-$q$ Dirac fermion. This model has the $\mathbb{Z}_q$ $1$-form symmetry, which is a remnant of the electric $U(1)$ $1$-form symmetry in the pure Maxwell theory. It is known that if we put the theory on closed space, then the Hilbert space is decomposed into $q$ distinct sectors, called universes and some states with higher energy density do not decay to the ground state due to the selection rule of the $1$-form symmetry. Even with open boundaries, we can observe the stability of such states by seeing a negative string tension behavior, meaning that opposite charges repel with each other. We develop a method based on the adiabatic state preparation to see this feature with digital quantum simulation and confirm it using a classical simulator of quantum devices. Especially, we measure local energy density and see how it jumps between inside and outside of insertion of the probe charges. We explicitly see that the energy density inside is lower than the outside one. This is a clear signature of the negative string tension.
Pirouette: higher-order typed functional choreographies

We present Pirouette, a language for typed higher-order functional choreographic programming. Pirouette offers programmers the ability to write a centralized functional program and compile it via endpoint projection into programs for each node in a distributed system. Moreover, Pirouette is defined generically over a (local) language of messages, and lifts guarantees about the message type system to its own. Message type soundness also guarantees deadlock freedom. All of our results are verified in Coq.
Use of Optical Emission Spectroscopy Data for Fault Detection of Mass Flow Controller in Plasma Etch Equipment

To minimize wafer yield losses by misprocessing during semiconductor manufacturing, faster and more accurate fault detection during the plasma process are desired to increase production yields. Process faults can be caused by abnormal equipment conditions, and the performance drifts of the parts or components of complicated semiconductor fabrication equipment are some of the most unnoticed factors that eventually change the plasma conditions. In this work, we propose improved stability and accuracy of process fault detection using optical emission spectroscopy (OES) data. Under a controlled experimental setup of arbitrarily induced fault scenarios, the extended isolation forest (EIF) approach was used to detect anomalies in OES data compared with the conventional isolation forest method in terms of accuracy and speed. We also used the OES data to generate features related to electron temperature and found that using the electron temperature features together with equipment status variable identification data (SVID) and OES data improved the prediction accuracy of process/equipment fault detection by a maximum of 0.84%.
Mobile Learning as a Solution for Restricted Learning during the COVID-19 Pandemic

The proliferation of smartphones and web technology 2.0 raises the possibilities for supporting mobile learning during the COVID-19 outbreak. This quantitative research attempted to justify the use of mobile learning as a potential tool to fill the gap of restricted learning in higher education during this COVID-19 crisis. Respondents were chosen through multi-stage random sampling. A total of 116 teachers and 291 students from a state university in the Philippines answered the online survey. As results indicated, mobile internet access through smartphones was the primary educational gadget used by the students nowadays. They were familiar with mobile learning however they had little experience in the utilization of it before the pandemic crisis. Most of the teachers and students viewed mobile learning as an indispensable learning tool and agreed about its significance during this time of restricted learning despite some difficulties in implementation. However, some teachers and the majority of the students resisted using m-learning after the crisis. Such reasons must be addressed by the institution to ensure the effective implementation of m-learning for learning continuity nowadays and for the possible transition into m-learning in the future. It is also crucial to consider the qualitative part of this study to shed more light on the perception of the respondents and the veracity of the results.
The Representation Theory of Neural Networks

In this work, we show that neural networks can be represented via the mathematical theory of quiver representations. More specifically, we prove that a neural network is a quiver representation with activation functions, a mathematical object that we represent using a network quiver. Furthermore, we show that network quivers gently adapt to common neural network concepts such as fully connected layers, convolution operations, residual connections, batch normalization, pooling operations and even randomly wired neural networks. We show that this mathematical representation is by no means an approximation of what neural networks are as it exactly matches reality. This interpretation is algebraic and can be studied with algebraic methods. We also provide a quiver representation model to understand how a neural network creates representations from the data. We show that a neural network saves the data as quiver representations, and maps it to a geometrical space called the moduli space, which is given in terms of the underlying oriented graph of the network, i.e., its quiver. This results as a consequence of our defined objects and of understanding how the neural network computes a prediction in a combinatorial and algebraic way. Overall, representing neural networks through the quiver representation theory leads to 9 consequences and 4 inquiries for future research that we believe are of great interest to better understand what neural networks are and how they work.
A core ontology on the Human–Computer Interaction phenomenon

Human–Computer Interaction (HCI) is a complex communication phenomenon involving human beings and computer systems that gained large attention from industry and academia with the advent of new types of interactive systems (mobile applications, smart cities, smart homes, ubiquitous systems and so on). Despite of its importance, there is still a lack of formal and explicit representations of what the HCI phenomenon is. In this paper, we intend to clarify the main notions involved in the HCI phenomenon, by establishing an explicit conceptualization of it. To do so, we need to understand what interactive computer systems are, which types of actions users perform when interacting with an interactive computer system, and finally what human–computer interaction itself is. The conceptualization is presented as a core reference ontology, called HCIO (HCI Ontology), which is grounded in the Unified Foundational Ontology (UFO). HCIO was evaluated using ontology verification and validation techniques and has been used as core ontology of an HCI ontology network.
Real time Face Detection and Optimal Face Mapping for Online Classes

Abstract The main objective of this paper is to provide a web-based tool for identifying faces in a real-time environment, such as Online Classes. Face recognition in real-time is now a fascinating field with an ever-increasing challenge such as light variations, occlusion, variation in facial expressions, etc. During the current pandemic scenario of COVID-19, the demand for online classrooms has rapidly increased. This has escalated the need for a real-time, economic, simple, and convenient way to track the attendance of the students in a live classroom. This paper addresses the aforementioned issue by proposing a real-time online attendance system. Two alternative face recognition algorithms are perceived in order to develop the tool for realtime face detection and recognition with improved accuracy. The algorithms adopted are Local Binary Pattern Histogram(LBPH) and Convolutional Neural Network (CNN) for face recognition as well as Haar cascade classifier with boosting for face detection. Experimental results show that CNN with an accuracy of 95% is better in this context than LBPH that yields an accuracy of 78%.
Comparative Analysis of Machine Learning based Filtering Techniques using MovieLens dataset

The primary objective of the present work is two-fold. First, to compare the different filtering techniques namely BayesNet, Decision Table, Logistic, k-NN, JRip, LibSVM, Randomized Filtered Classifier, Random Forest, Random Tree and OneR on the basis of six evaluation metrics including Kappa Statistic, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), F- Measure, ROC Area and Accuracy. Second, to improve the accuracy with the resampling mechanism. To determine the most appropriate algorithm, we have compared them using WEKA tool on a large sample of MovieLens dataset. The performance of ten different training algorithms has been analyzed and presented in this paper. It is found that random forest has received better results in comparison with other techniques with 99.39% accuracy. In addition to this, we have also incorporated the discussion of on-going researches and related works carried in the domain of recommendation systems. The present research shall help the researchers in exploring the best classification techniques for improving accuracy.
A multi-objective algorithm for virtual machine placement in cloud environments using a hybrid of particle swarm optimization and flower pollination optimization

The demand for virtual machine requests has increased recently due to the growing number of users and applications. Therefore, virtual machine placement (VMP) is now critical for the provision of efficient resource management in cloud data centers. The VMP process considers the placement of a set of virtual machines onto a set of physical machines, in accordance with a set of criteria. The optimal solution for multi-objective VMP can be determined by using a fitness function that combines the objectives. This paper proposes a novel model to enhance the performance of the VMP decision-making process. Placement decisions are made based on a fitness function that combines three criteria: placement time, power consumption, and resource wastage. The proposed model aims to satisfy minimum values for the three objectives for placement onto all available physical machines. To optimize the VMP solution, the proposed fitness function was implemented using three optimization algorithms: particle swarm optimization with Lévy flight (PSOLF), flower pollination optimization (FPO), and a proposed hybrid algorithm (HPSOLF-FPO). Each algorithm was tested experimentally. The results of the comparative study between the three algorithms show that the hybrid algorithm has the strongest performance. Moreover, the proposed algorithm was tested against the bin packing best fit strategy. The results show that the proposed algorithm outperforms the best fit strategy in total server utilization.
A Wearable Sensor Network With Embedded Machine Learning for Real-Time Motion Analysis and Complex Posture Detection

This work presents an embedded system driven by a wearable sensor network and machine learning to perform complex posture detection and high-precision <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">human activity recognition</i> (HAR) in real time. The presented prototype performs real-time HAR using raw data collected from three wireless wearable motion sensor nodes in parallel. The sensors communicate the measured inertial data to a Raspberry Pi 3 running a pre-trained classifier, which performs motion detection and classification in real-time. Our approach based on raw data and machine learning provides more efficiency and simplicity by decreasing the computation cost and the latency. Our detection and classification algorithm utilizes a new custom preconditioning method called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Multi-Mapping Spherical Normalization (MMSN)</i> , in combination with a Support Vector Machine with Radial Basis Function Kernel (RBF-SVM). This new preconditioning algorithm allows to sparse the raw inertial data to increase successful classification results without adding any computational burden. The presented approach achieves a motion classification accuracy of 98.28% for 12 body motions, while allowing for real-time prediction with low latency output (< 20 ms which is 50% less than some studies) for preconditioning and processing thanks to the new MMSN preconditioning method and the use of raw data. We tested our approach with 10 able-bodied subjects.
Extracting and Composing Robust Features With Broad Learning System

With effective performance and fast training speed, broad learning system (BLS) has been widely developed in recent years, which provides a new way for network training. However, the randomly generated feature nodes and enhancement nodes in the BLS network may have redundant and inefficient features, which will affect the subsequent classification performance. In response to the above issues, we propose a series of self-encoding networks based on BLS from the perspective of unsupervised feature extraction. These include the single hidden layer autoencoder built on the basis of BLS(BLS-AE), the stacked BLS-based autoencoder (ST-BLS), the sparse BLS-based autoencoder (SP-BLS), and the stacked sparse BLS-based autoencoder(SS-BLS). The proposed BLS-based self-encoding networks retain the advantage of efficient BLS model training, and overcome the time-consuming defect of iterative parameter optimization in traditional self-encoding networks. In addition, the higher-level abstract features of the input data can be learned through the progressive encoding and decoding process. Combining <inline-formula><tex-math notation="LaTeX">$L_1$</tex-math></inline-formula> regularization to train the parameters can further enhance the robustness of the extracted features. Extensive comparative experiments on real-world data sets demonstrate the superiority of the proposed methods in terms of both effectiveness and efficiency.
Compositional Programming

Modularity is a key concern in programming. However, programming languages remain limited in terms of modularity and extensibility. Small canonical problems, such as the Expression Problem (EP), illustrate some of the basic issues: the dilemma between choosing one kind of extensibility over another one in most programming languages. Other problems, such as how to express dependencies in a modular way, add up to the basic issues and remain a significant challenge. This article presents a new statically typed modular programming style called Compositional Programming . In Compositional Programming, there is no EP: It is easy to get extensibility in multiple dimensions (i.e., it is easy to add new variants as well as new operations). Compositional Programming offers an alternative way to model data structures that differs from both algebraic datatypes in functional programming and conventional OOP class hierarchies. We introduce four key concepts for Compositional Programming: compositional interfaces , compositional traits , method patterns , and nested trait composition . Altogether, these concepts allow us to naturally solve challenges such as the Expression Problem, model attribute-grammar-like programs, and generally deal with modular programs with complex dependencies . We present a language design, called CP , which is proved to be type-safe, together with several examples and three case studies.
ExGen: Cross-platform, Automated Exploit Generation for Smart Contract Vulnerabilities

Smart contracts, just like other computer programs, are prone to a variety of vulnerabilities, which lead to severe consequences including massive token and coin losses. Prior works have explored automated exploit generation for vulnerable Ethereum contracts. However, the scopes of prior works are limited in both vulnerability types and contract platforms. In this paper, we propose a cross-platform framework, called <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ExGen</small> , to generate multiple transactions as exploits to given vulnerable smart contracts. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ExGen</small> first translates either Ethereum or EOS contracts to an intermediate representation (IR). Then, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ExGen</small> generates symbolic attack contracts with transactions in a partial order and then symbolically executes the attack contracts together with the target to find and solve all the constraints. Lastly, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ExGen</small> concretizes all the symbols, generates attack contracts with multiple transactions, and verifies the generated contracts’ exploitability on a private chain with values crawled from the public chain. We implemented a prototype of <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ExGen</small> and evaluated it on Ethereum and EOS benchmarks. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ExGen</small> successfully exploits 1,258/1,399 (89.9%) Ethereum and 126/130 (96.9%) EOS vulnerabilities. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ExGen</small> is also able to exploit zero-day vulnerabilities on EOS.
A Survey of Synthetic Data Generation for Machine Learning

Data is the fuel of machine learning algorithms, therefore data generation in machine learning is becoming an important topic. The problem is that finding enough data for machine learning algorithms in some domains or situations is difficult. For example, some data may invade the privacy of people or some other datasets can be related to national security and difficult to be unveiled. This paper reviews the related work in synthetic data generation in terms of available methods for data generation (augmentation) and how the generated data helps in improving the performance of machine learning algorithms. The main focus of this paper is data synthetic methods in the healthcare domain.
User Requirements Analysis on Augmented Reality-Based Maintenance in Manufacturing

Abstract Maintenance of technical equipment in manufacturing is inevitable for sustained productivity with minimal downtimes. Elimination of unscheduled interruptions as well as real-time monitoring of equipment health can potentially benefit from adopting augmented reality (AR) technology. How best to employ this technology in maintenance demands a fundamental comprehension of user requirements for production planners. Despite AR applications being developed to assist various manufacturing operations, no previous study has examined how these user requirements in maintenance have been fulfilled and the potential opportunities that exist for further development. Reviews on maintenance have been general on all industrial fields rather than focusing on a specific industry. In this regard, a systematic literature review was performed on previous studies on AR applications in the maintenance of manufacturing entities from 2017 to 2021. Specifically, the review examines how user requirements have been addressed by these studies and identifies gaps for future research. The user requirements are drawn from the challenges encountered during AR-based maintenance in manufacturing following a similar approach to usability engineering methodologies. The needs are identified as ergonomics, communication, situational awareness, intelligence sources, feedback, safety, motivation, and performance assessment. Contributing factors to those needs are cross-tabulated with the requirements and their results presented as trends, prior to drawing insights and providing possible future suggestions for the made observations.
CRUSOE: A toolset for cyber situational awareness and decision support in incident handling

The growing size and complexity of today's computer network make it hard to achieve and maintain so-called cyber situational awareness, i.e., the ability to perceive and comprehend the cyber environment and be able to project the situation in the near future. Namely, the personnel of cybersecurity incident response teams or security operation centers should be aware of the security situation in the network to effectively prevent or mitigate cyber attacks and avoid mistakes in the process. In this paper, we present a toolset for achieving cyber situational awareness in a large and heterogeneous environment. Our goal is to support cybersecurity teams in iterating through the OODA loop (Observe, Orient, Decide, Act). We designed tools to help the operator make informed decisions in incident handling and response for each phase of the cycle. The Observe phase builds on common tools for active and passive network monitoring and vulnerability assessment. In the Orient phase, the data on the network are structured and presented in a comprehensible and visually appealing manner. The Decide phase opens opportunities for decision-support systems, in our case, a recommender system that suggests the most resilient configuration of the critical infrastructure. Finally, the Act phase is supported by a service that orchestrates network security tools and allows for prompt mitigation actions. Finally, we present lessons learned from the deployment of the toolset in the campus network and the results of a user evaluation study.
Sequence-based mapping for probabilistic visual loop-closure detection

During simultaneous localization and mapping, the robot should build a map of its surroundings and simultaneously estimate its pose in the generated map. However, a fundamental task is to detect loops, i.e., previously visited areas, allowing consistent map generation. Moreover, within long-term mapping, every autonomous system needs to address its scalability in terms of storage requirements and database search. In this paper, we present a low-complexity sequence-based visual loop-closure detection pipeline. Our system dynamically segments the traversed route through a feature matching technique in order to define sub-maps. In addition, visual words are generated incrementally for the corresponding sub-maps representation. Comparisons among these sequences-of-images are performed thanks to probabilistic scores originated from a voting scheme. When a candidate sub-map is indicated, global descriptors are utilized for image-to-image pairing. Our evaluation took place on several publicly-available datasets exhibiting the system&#x2019;s low complexity and high recall compared to other state-of-the-art approaches.