Reviewerly/exHarmony huggingface dataset:
Quantum distance to uncontrollability and quantum speed limits

Distance to Uncontrollability is a crucial concept in classical control theory. Here, we introduce Quantum Distance to Uncontrollability as a measure how close a universal quantum system is to a non-universal one. This allows us to provide a quantitative version of the Quantum Speed Limit, decomposing the bound into a geometric and dynamical component. We consider several physical examples including globally controlled solid state qubits and a cross-Kerr system, showing that the Quantum Distance to Uncontrollability provides a precise meaning to spectral crowding, weak interactions and other bottlenecks to universality. We suggest that this measure should be taken into consideration in the design of quantum technology.
The Evolution of the Notion of Overlap Functions

In this chapter we make a review of the notion of overlap function. Although originally developed in order to determine up to what extent a given element belongs to two sets, overlap functions have widely developed in the last years for very different problems. We recall here the motivation that led to the introduction of this new notion and we discuss further theoretical developments that have appeared to deal with other types of problems.
Controllable and accelerated generation of entangled states between two superconducting qubits in circuit QED

Optimal generation of entangled states is of significance to quantum information processing. Here an efficient scheme is proposed for performing controllable and accelerated generation of entangled states between two superconducting qubits. Two three-level artificial atoms of Cooper-pair box circuits, coupling to a quantized field of transmission-line resonator, are individually driven by classical microwaves. In the two-photon resonance with a large detuning, each atom is reduced to an effective two-level qubit. Within a composite qubit-photon-qubit system, two types of entangled state can be controllably created using the technique of invariant-based shortcuts to adiabaticity. Compared with an adiabatic case, the entanglement generations in the shortcut manner need much shorter times. Numerical simulations show that the operations are robust against decoherence effects, and the interatomic cross resonance is safely negligible. The proposed scheme could pave a promising avenue towards optimized preparation of entangled states with superconducting qubits in circuit QED.
Towards Transforming Variability Models

A plethora of variability modeling approaches has been developed in the last 30 years, e.g., feature modeling, decision modeling, Orthogonal Variability Modeling (OVM), and UML-based variability modeling. While feature modeling approaches are probably the most common and well-known group of variability modeling approaches, even within that group multiple variants have been developed, i.e., there is not just one type of feature model. Many variability modeling approaches have been demonstrated as useful for a certain purpose, e.g., domain analysis or configuration of products derived from a software product line. Nevertheless, industry frequently develops their own custom solutions to manage variability. The (still growing) number of modeling approaches simply makes it difficult to find, understand, and eventually pick an approach for a specific (set of) systems or context. In this paper, we discuss usage scenarios, required capabilities and challenges for an approach for (semi-)automatically transforming variability models. Such an approach would support researchers and practitioners experimenting with and comparing different variability models and switching from one modeling approach to another. We present the key components of our envisioned approach and conclude with a research agenda.
Detecting SQL Injection On Web Application Using Deep Learning Techniques: A Systematic Literature Review

Based on OWASP, code injection is one of the top lists of security risks. Structured Query Language (SQL) Injection is one of these types of attacks. SQL injection attack is an attack by spoofing the server to execute malicious code. The main object of this paper is to identify relevant works about deep learning methods to detect SQL-Injection on web applications. To achieve that, we conduct a survey review of the literature. In this study, we provide a review of 14 studies using deep learning algorithms to detect SQL Injection on web applications and it provides a comparison between them. Deep learning has great potential in threat intelligence detection.
Multiscale Optical PM2.5 Particles Recognition and Sorting System in Dust Probes

Authors propose the novel approach for optical PM2.5 and PM10 particles recognition and sorting based on Super-resolution neural networks in the research on dust emissions from industrial enterprises. The objective of the dust emissions analysis is to determine their component composition and the fine particle size distribution (PM10 and PM2.5). The scanning electronic microscope of high resolution was used to obtain the set of large-scale images of dust particles. We use the images of particles in different scales as the entire imagery data to create the high quality image suitable for reliable particles recognition and use quality metrics PSNR, MSE, SSIM to ensure that the created image is close to ground truth.
A Mixed Method with Effective Color Reduction

This article presents a color quantization technique that combines two previously proposed approaches: the Binary splitting method and the Iterative ant-tree for color quantization method. The resulting algorithm can obtain good quality images with low time consumption. In addition, the iterative nature of the proposed method allows the quality of the quantized image to improve as the iterations progress, although it also allows a good initial image to be quickly obtained. The proposed method was compared to 13 other color quantization techniques and the results showed that it could generate better quantized images than most of the techniques assessed. The statistical significance of the improvement obtained using the new method is confirmed by applying a statistical test to the results of all the methods compared.
Fast Decoding with Cryptcodes for Burst Errors

Random Codes Based on Quasigroups (RCBQ) are cryptcodes that provide a correction of transmission errors and an information security, all with one algorithm. Standard algorithm, Cut-Decoding and 4-Sets-Cut-Decoding algorithms are different versions of RCBQ and they are proposed elsewhere. The decoding in all these algorithms is list decoding, so the speed of the decoding depends on the list size. In order to decrease the list size, Fast-Cut-Decoding and Fast-4-Sets-Cut-Decoding algorithms are proposed elsewhere and they improve performances of these codes for transmission through a Gaussian channel. Here, we propose a new modification of these algorithms to improve their properties for transmission through a burst channels.
Heterogeneous Decentralized Fusion Using Conditionally Factorized Channel Filters

This paper studies a family of heterogeneous Bayesian decentralized data fusion problems. Heterogeneous fusion considers the set of problems in which either the communicated or the estimated distributions describe different, but overlapping, states of interest which are subsets of a larger full global joint state. On the other hand, in homogeneous decentralized fusion, each agent is required to process and communicate the full global joint distribution. This might lead to high computation and communication costs irrespective of relevancy to an agent's particular mission, for example, in autonomous multi-platform multi-target tracking problems, since the number of states scales with the number of targets and agent platforms, not with each agent’s specific local mission. In this paper, we exploit the conditional independence structure of such problems and provide a rigorous derivation for a family of exact and approximate, heterogeneous, conditionally factorized channel filter methods. Numerical examples show more than 95% potential communication reduction for heterogeneous channel filter fusion, and a multi-target tracking simulation shows that these methods provide consistent estimates.
Spending Time: Co-Designing a Personalized Calendar at the Care Center

This paper reports on a full iteration from a project with an older adult, P, residing at a 24hour Danish care center for adults with acquired brain damage. P has due to a cognitive impairment severe challenges in remembering planned activities, which requires a substantial need for staff to remind him round the clock, thus leading to further dependency on staff and lack of self-determination. Grounded in Participatory Design, thereby taking into account P's and staff members' situated problems, a planning system was designed together with P and prototyped with the MEAN-stack implemented on a Raspberry Pi. The prototype was then deployed over two periods (one week and three months) to be evaluated with P and staff members in its real context. Findings demonstrate how reciprocity ensured commitment and additional knowledge about the problem-field. The evaluations of the prototype revealed the importance of staff cooperation for system integration but also showed how the prototype enabled new meaningful social routines to form for both the co-designing resident and staff. Thus, the paper sheds light on the many mechanics embedded when embarking on design work, which goes full circle within a Scandinavian healthcare context.
Maximum approximate likelihood estimation of general continuous-time state-space models

Continuous-time state-space models (SSMs) are flexible tools for analysing irregularly sampled sequential observations that are driven by an underlying state process. Corresponding applications typically involve restrictive assumptions concerning linearity and Gaussianity to facilitate inference on the model parameters via the Kalman filter. In this contribution, we provide a general continuous-time SSM framework, allowing both the observation and the state process to be non-linear and non-Gaussian. Statistical inference is carried out by maximum approximate likelihood estimation, where multiple numerical integration within the likelihood evaluation is performed via a fine discretization of the state process. The corresponding reframing of the SSM as a continuous-time hidden Markov model, with structured state transitions, enables us to apply the associated efficient algorithms for parameter estimation and state decoding. We illustrate the modelling approach in a case study using data from a longitudinal study on delinquent behaviour of adolescents in Germany, revealing temporal persistence in the deviation of an individual's delinquency level from the population mean.
Inconsistencies of Presence Questionnaires in Virtual Reality

Presence in virtual reality (VR) is typically assessed through questionnaires in the real world and after leaving an immersive experience. Previous research suggests that questionnaires in VR reduce biases caused by the real-world setup. However, it remains unclear whether presence questionnaires still provide valid results when subjects are being surveyed while the construct is perceived. In a user study with 36 participants, two standardized presence questionnaires (IPQ, SUSa) were either completed in the real lab, in a virtual lab scene, or in the actual scene after a virtual gaming experience. Our results show inconsistencies between the measurements and that main scores, as well as subscales of the presence measures are significantly affected by the subjects' environment. As presence questionnaires have been designed to be answered after an immersive experience, we recommend revising those tools for measuring presence in VR.
Two-Step FORCE Learning Algorithm for Fast Convergence in Reservoir Computing

Reservoir computing devices are promising as energy-efficient machine learning hardware for real-time information processing. However, some online algorithms for reservoir computing are not simple enough for hardware implementation. In this study, we focus on the first order reduced and controlled error (FORCE) algorithm for online learning with reservoir computing models. We propose a two-step FORCE algorithm by simplifying the operations in the FORCE algorithm, which can reduce necessary memories. We analytically and numerically show that the proposed algorithm can converge faster than the original FORCE algorithm.
Tuning Deep Neural Network’s Hyperparameters Constrained to Deployability on Tiny Systems

Deep Neural Networks are increasingly deployed on tiny systems such as microcontrollers or embedded systems. Notwithstanding the recent success of Deep Learning, also enabled by the availability of Automated Machine Learning and Neural Architecture Search solutions, the computational requirements of the optimization of the structure and the hyperparameters of Deep Neural Networks usually far exceed what is available on tiny systems. Therefore, the deployability becomes critical when the learned model must be deployed on a tiny system. To overcome this critical issue, we propose a framework, based on Bayesian Optimization, to optimize the hyperparameters of a Deep Neural Network by dealing with black-box deployability constraints. Encouraging results obtained on a classification benchmark problem on a real microcontroller by STMicroelectronics are presented.
Mechanical Shells

In this paper, I introduce a concept of mechanical shells, which are physical add-ons that can adaptively augment, extend, and reconfigure the interactivities of self-actuated tangible user interfaces (TUIs). While a variety of research explores actuated and shape-changing interfaces for providing dynamic physical affordance and tangible displays, the concept of mechanical shell intends to overcome the constraint of existing generic actuated TUI hardware thereby enabling greater versatility and expression. This paper overviews the mechanical shell concept, describes project examples, outlines a research framework, and suggests open space for future research.
A market basket analysis of the US auto-repair industry

Market basket analysis (MBA), or the mining of transactional data to uncover association rules, is a popular methodology used in managerial decision making. MBA is centered around three key parameters: support, confidence, and lift, and the choice of starting values for these parameters can have a significant impact on the results of the analysis. We develop a procedure in R around the Apriori algorithm to help in identifying lift maximising rules when the support covers a specified proportion. The procedure facilitates the choice of minimum parameters, eliminates redundancies, and organizes the resulting association rules into actionable formats. When applied to the US auto repair data, we find un-exploited bundling packages that can be added to the scheduled maintenance services of traditional marketing campaigns.
Machine Learning and Natural Language Processing: Review of Models and Optimization Problems

The purpose of this paper is to consider one of the most important modern technologies, namely: Natural Language Processing (NLP) and the machine learning algorithms related to it. The aim of the authors is to present the machine learning models in this interdisciplinary scientific field, which represents an intersection point of computer science, artificial intelligence, and linguistics. The machine learning techniques are classified and the corresponding models are briefly discussed. Different optimization approaches and problems for machine learning are considered. In this regard, some conclusions are drawn about the development trends in the area and the directions for future research.
LiDAR-Camera-Based Deep Dense Fusion for Robust 3D Object Detection

For the camera-LiDAR-based three-dimensional (3D) object detection, image features have rich texture descriptions and LiDAR features possess objects' 3D information. To fully fuse view-specific feature maps, this paper aims to explore the two-directional fusion of arbitrary size camera feature maps and LiDAR feature maps in the early feature extraction stage. Towards this target, a deep dense fusion 3D object detection framework is proposed for autonomous driving. This is a two stage end-to-end learnable architecture, which takes 2D images and raw LiDAR point clouds as inputs and fully fuses view-specific features to achieve high-precision oriented 3D detection. To fuse the arbitrary-size features from different views, a multi-view resizes layer (MVRL) is born. Massive experiments evaluated on the KITTI benchmark suite show that the proposed approach outperforms most state-of-the-art multi-sensor-based methods on all three classes on moderate difficulty (3D/BEV): Car (75.60%/88.65%), Pedestrian (64.36%/66.98%), Cyclist (57.53%/57.30%). Specifically, the DDF3D greatly improves the detection accuracy of hard difficulty in 2D detection with an 88.19% accuracy for the car class.
WITHDRAWN: Enterprise human resource management system based on 5G network and internet of things system

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn at the request of the Editor in Chief. Subsequent to acceptance of this special issue paper by the responsible Guest Editor Sundhararajan Mahalingam, the integrity and rigor of the peer-review process was investigated and confirmed to fall beneath the high standards expected by Microprocessors & Microsystems. There are also indications that much of the Special Issue includes unoriginal and heavily paraphrased content. Due to a configuration error in the editorial system, unfortunately the Editor in Chief did not receive these papers for approval as per the journal's standard workflow.
Classification of Big Five Personality Behavior Tendencies Based On Study Field with Twitter Analysis Using Support Vector Machine

Behavioral tendencies can be seen from the characteristics or personality. A person's reaction to something can be seen through Twitter, from words written in tweets, so that their character or personality can be known. Personality can change which is influenced by several factors, one of which is the field of study. The field of study is occupied influencing environmental factors during the study process, for example in the study environment during the lecture. Therefore, this study conducted a Twitter analysis for the classification of Big Five Personality behavioral tendencies based on the field of study. Fields of study are grouped into seven groups namely art/humanities, law, economics, medicine, political science, psychology, and science. The classification process is carried out in several scenarios for dividing the amount of tweets data. The results of the classification with the highest accuracy is in the data of 300 tweets, with an accuracy value of 80.5% SVM and 82% MNB. Comparison of the Big Five Traits with previous research has in common for each field of study in addition to economic.
OctCNN: An Energy-Efficient FPGA Accelerator for CNNs using Octave Convolution Algorithm

Recently, embedded FPGAs have been explored as a potential platform for deploying machine learning on edge-devices due to their high energy efficiency and low cost. However, the lack of resources also makes the deployment of CNN on FPGAs more challenging. In this paper, we present OctCNN, which utilizes the octave convolution (OctConv) algorithm to optimize the FPGA-based CNN accelerator. We first propose a novel architecture for deploying OctConv on FPGAs and then present a resource and performance analysis model to guide a fast design space exploration. As a case study, we implement a classic CNN model, VGG16, on Xilinx ZC702. Results show, compared to the mobile-class CPU and GPU, OctCNN achieves <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{16.88}\times$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{2.43}\times$</tex> energy efficiency, respectively. Besides, it has a promising energy efficiency compared to previous FPGA accelerators.
Generating Datasets for Software Requirements Prioritization Research

Software requirements are the need of the stakeholders which are identified during requirements elicitation process. There are different types of the software requirements like functional requirements, non-functional requirements, testing requirements, etc. After requirements elicitation process, a system may have hundreds or thousands of requirements; and during the development process it is not possible to implement all the software requirements because of the several limitations of an organization as well as the stakeholders. To develop the successful software product, the elicited software requirements must be prioritized before the development of software so that high ranked software requirements can be identified. Computation of the ranking values of the software requirements is a difficult task because it involves lot of computational steps like evaluation of software requirements, computation of group preferences from the individual preferences, computation of the ranking values after normalizing the preference matrices, etc. Among these steps, in this paper, we mainly focus on the evaluation of the software requirements using linguistic variables because in real life applications people use linguistic variables to specify their preferences on software requirements. The objective of this paper is to generate a dataset of 32 functional requirements after evaluating it on the basis of 3 non-functional requirements so that the generated dataset can be used as an input into the software requirements prioritization methods. The institute examination system is used as case study to explain the steps for the generation of the dataset.
Improving NER Performance by Applying Text Summarization on Pharmaceutical Articles

Analyzing long text articles in the pharmaceutical domain, for the purpose of knowledge extraction and recognizing entities of interest, is a tedious task. In our previous research efforts, we were able to develop a platform which successfully extracts entities and facts from pharmaceutical texts and populates a knowledge graph with the extracted knowledge. However, one drawback of our approach was the processing time; the analysis of a single text source was not interactive enough, and the batch processing of entire article datasets took too long. In this paper, we propose a modified pipeline where the texts are summarized before the analysis begins. With this, the source articles is reduced significantly, to a compact version which contains only the most commonly encountered entities. We show that by reducing the text size, we get knowledge extraction results comparable to the full text analysis approach and, at the same time, we significantly reduce the processing time, which is essential for getting both real-time results on single text sources, and faster results when analyzing entire batches of collected articles from the domain.