Reviewerly/exHarmony huggingface dataset:
Automatic Grammatical Error Detection of Non-native Spoken Learner English

Automatic language assessment and learning systems are required to support the global growth in English language learning. They need to be able to provide reliable and meaningful feedback to help learners develop their skills. This paper considers the question of detecting "grammatical" errors in non-native spoken English as a first step to providing feedback on a learner's use of the language. A state-of-the-art deep learning based grammatical error detection (GED) system designed for written texts is investigated on free speaking tasks across the full range of proficiency grades with a mix of first languages (L1s). This presents a number of challenges. Free speech contains disfluencies that disrupt the spoken language flow but are not grammatical errors. The lower the level of the learner the more these both will occur which makes the underlying task of automatic transcription harder. The baseline written GED system is seen to perform less well on manually transcribed spoken language. When the GED model is fine-tuned to free speech data from the target domain the spoken system is able to match the written performance. Given the current state-of-the-art in ASR, however, and the ability to detect disfluencies grammatical error feedback from automated transcriptions remains a challenge.
A methodical FHE-based cloud computing model

Attacks such as Meldown and Spectre have shown that traditional cloud computing isolation mechanisms are not sufficient to guarantee the confidentiality of processed data. With Fully Homomorphic Encryption (FHE), data may be processed encrypted in the cloud, making any leaked information look random to an attacker. Furthermore, a client might also be interested in protecting the processing algorithm. While there has been research on ensuring the confidentiality of the processing algorithm, the resulting systems are impractical. Herein, we propose an automatic and methodical technique to approximate a wide range of functions homomorphically. As the approximations are all evaluated in the same manner, a homomorphic evaluator has no way to distinguish them. Since the derivation of the FHE circuit is decoupled from the function development process, users benefit from traditional programming and debugging tools. The proposed tools may exploit different kinds of number representations during the homomorphic evaluation of functions, namely stochastic number representations and fixed-point arithmetic, each with its own characteristics. Additionally, an implementation of the system is presented, its applicability is verified in practice for commonly used applications, including image processing and machine learning, and the two number representations are thoroughly compared.
Single- and two-qubit universal quantum gates in photonic Ti:LiNbO3 circuits

We report on the photonic Ti:LiNbO3 circuit design of single-qubit Hadamard, polarization and spatially encoded single-photon two-qubit CSIGN and CNOT quantum gates, using continuous-wave pump source and 812nm photon-pairs. The proposed circuits aim at optimizing the overall performance in terms of optical power dissipation management and accurately realizing the desired functionalities. By utilizing S-bend structures, through simulations we have obtained Electrical-field amplitudes with approximate values of ± (1/2)1/2 at the output branches of the Hadamard circuit. CSIGN modified circuit exhibits a reduction of 0.618 dB and 0.4288 dB in polarization-odd and -even modes dispersion, respectively. The modified CNOT circuit is 128.7 μm less wide than the initially designed circuit and the unwanted TM ⇄ TE polarization conversion at the applied voltage of V = 53 V is addressed.
TS-WRSVM: twin structural weighted relaxed support vector machine

Classification of data with imbalanced class distributions is a major problem in the data mining community. Imbalanced classification is a challenging task in the presence of outliers. In this paper, we propose a new cost-sensitive learning method with regard to the structure of data distribution for classifying imbalanced data and diminishing the effect of outliers. The proposed method combines the benefits of “structured” learning models (such as structural support vector machine) with the advantages of “cost-sensitive” learning models (such as weighted relaxed support vector machine). We call our method twin structural weighted relaxed support vector machine (TS-WRSVM). A TS-WRSVM uses two nonparallel hyperplanes to determine the class label of new data so that each model only considers the structural information of one class. We allocate a weight and a limited amount of penalty-free slack to each model by considering the size of each class. Results of experiments indicate that a TS-WRSVM is superior to other current algorithms based on cost-sensitive learning in the areas of classification accuracy and computational time.
Business Process Modeling and Analysis Based on Queue Logical Petri Nets

In the present modeling tools, there is no proper model to describe the fairness of resource scheduling for sharing systems. In order to solve this problem well, we propose a new sharing system and Queue Logical Petri nets. In this paper, a complete process of entering and leaving a garage is modeled by a Queue Logical Petri nets. Based on random arrival and exponential arrival, the average waiting time of vehicles, the average stay time in the queue and the average number of waiting vehicles in the queue are analyzed. According to reachable sequence of Queue Logical Petri net, the Invariant, the utilization of garage and the number of waiting vehicles are analyzed. And the validity of the sharing system with queue is proved by compared with other methods in the experiment.
Change-Proneness of Object-Oriented Software Using Combination of Feature Selection Techniques and Ensemble Learning Techniques

Change-prone modules are characterized as the programming parts in the source code which have high probability to alter in the future. Change-proneness prediction helps software testers to streamline and concentrate their testing assets on the modules which have a higher probability of alteration. In this work, we perform an empirical study of 11 feature selection techniques to identify the suitable set of source code metrics, out of 21 metrics, for change-proneness prediction. We first proposed a source code validation framework that includes Wilcoxon signed rank test, univariate logistic regression analysis, cross correlation analysis, multivariate linear regression stepwise forward selection. We then compared the results of proposed software metrics validation framework (PFST) with 10 feature selection techniques. The selected features are then used for predicting change-proneness using 18 machine learning algorithms. Experimental results show that the prediction model built using source code metrics obtained from PFST outperforms other techniques.
Mandarin Chinese Mispronunciation Detection and Diagnosis Leveraging Deep Neural Network Based Acoustic Modeling and Training Techniques

Automatic mispronunciation detection and diagnosis are two critical and integral components of a computer-assisted pronunciation training (CAPT) system, collectively facilitating second-language (L2) learners to pinpoint erroneous pronunciations in a given utterance so as to improve their spoken proficiency. In this chapter, we will first briefly introduce the latest trends and developments in mispronunciation detection and diagnosis with state-of-the-art automatic speech recognition (ASR) methodologies, especially those using deep neural network based acoustic models. Afterward, we present an effective training approach that estimates the deep neural network based acoustic models involved in the mispronunciation detection process by optimizing an objective directly linked to the ultimate performance evaluation metric. We also investigate the extent to which the subsequent mispronunciation diagnosis process can benefit from the use of these specifically trained acoustic models. For this purpose, we recast mispronunciation diagnosis as a classification problem and a set of indicative features are derived. A series of experiments on a Mandarin Chinese mispronunciation detection and diagnosis task are conducted to evaluate the performance merits of such an approach.
Improving relevant subjective testing for validation: Comparing machine learning algorithms for finding similarities in VQA datasets using objective measures

Subjective quality assessment is a necessary activity to validate objective measures or to assess the performance of innovative video processing technologies. However, designing and performing comprehensive tests requires expertise and a large effort especially for the execution part. In this work we propose a methodology that, given a set of processed video sequences prepared by video quality experts, attempts to reduce the number of subjective tests by selecting a subset with minimum size which is expected to yield the same conclusions of the larger set. To this aim, we combine information coming from different types of objective quality metrics with clustering and machine learning algorithms that perform the actual selection, therefore reducing the required subjective assessment effort while trying to preserve the variety of content and conditions needed to ensure the validity of the conclusions. Experiments are conducted on one of the largest publicly available subjectively annotated video sequence dataset. As performance criterion, we chose the validation criteria for video quality measurement algorithms established by the International Telecommunication Union.
Dense matching for wide baseline images based on equal proportion of triangulation

The dense matching of pairs is the basis of advanced image processing applications, such as 3D reconstruction and simultaneous localisation and mapping (SLAM). However, it is difficult to obtain efficient dense matching results in wide baseline image pairs due to various conditions. For this reason, the authors propose a dense matching method for wide baseline images based on an equal proportion of triangulation. Firstly, this Letter proves the mathematical property of equal proportion of triangulation affine transformation, and then builds a triangulation based on the set of sparse matching points. By using this property, the positions of equal proportion points in the corresponding triangulation of the two images to be matched are calculated, respectively. Secondly, to eliminate the points and obtain the inner point set, these proportional points are used to check the similarity of the matching triangle regions. Finally, the inner point set is used to calculate the positions of the dense matching points as the final dense matching result. The experimental results verified the validity and accuracy of the proposed method and this method is suitable for tilt photography, multispectral and multidate remote sensing image dense registration, and more.
EasyStego: Robust Steganography Based on Quick-Response Barcodes for Crossing Domains

Despite greater attention being paid to sensitive-information leakage in the cyberdomain, the sensitive-information problem of the physical domain remains neglected. Anonymous users can easily access the sensitive information of other users, such as transaction information, health status, and addresses, without any advanced technologies. Ideally, secret messages should be protected not only in the cyberdomain but also in the complex physical domain. However, popular steganography schemes only work in the traditional cyberdomain and are useless when physical distortions of messages are unavoidable. This paper first defines the concept of cross-domain steganography, and then proposes EasyStego, a novel cross-domain steganography scheme. EasyStego is based on the use of QR barcodes as carriers; therefore, it is robust to physical distortions in the complex physical domain. Moreover, EasyStego has a large capacity for embeddable secrets and strong scalability in various scenarios. EasyStego uses an AES encryption algorithm to control the permissions of secret messages, which is more effective in reducing the possibility of sensitive-information leakage. Experiments show that EasyStego has perfect robustness and good efficiency. Compared with the best current steganography scheme based on barcodes, EasyStego has greater steganographic capacity and less impact on barcode data. In robustness tests, EasyStego successfully extracts secret messages at different angles and distances. In the case of adding natural textures and importing quantitative error bits, other related steganography techniques fail, whereas EasyStego can extract secret messages with a success rate of nearly 100%.
Towards Automated Multiscale Imaging and Analysis in TEM: Glomerulus Detection by Fusion of CNN and LBP Maps

Glomerulal structures in kidney tissue have to be analysed at a nanometer scale for several medical diagnoses. They are therefore commonly imaged using Transmission Electron Microscopy. The high resolution produces large amounts of data and requires long acquisition time, which makes automated imaging and glomerulus detection a desired option. This paper presents a deep learning approach for Glomerulus detection, using two architectures, VGG16 (with batch normalization) and ResNet50. To enhance the performance over training based only on intensity images, multiple approaches to fuse the input with texture information encoded in local binary patterns of different scales have been evaluated. The results show a consistent improvement in Glomerulus detection when fusing texture-based trained networks with intensity-based ones at a late classification stage.
InfoSec Process Action Model (IPAM): Targeting Insiders' Weak Password Behavior

ABSTRACT The possibility of noncompliant behavior is a challenge for cybersecurity professionals and their auditors as they try to estimate residual control risk. Building on the recently proposed InfoSec Process Action Model (IPAM), this work explores how nontechnical assessments and interventions can indicate and reduce the likelihood of risky individual behavior. The multi-stage approach seeks to bridge the well-known gap between intent and action. In a strong password creation experiment involving 229 participants, IPAM constructs resulted in a marked increase in R2 for initiating compliance behavior with control expectations from 47 percent to 60 percent. Importantly, the model constructs offer measurable indications despite practical limitations on organizations' ability to assess problematic individual password behavior. A threefold increase in one measure of strong password behavior suggested the process positively impacted individual cybersecurity behavior. The results suggest that the process-nuanced IPAM approach is promising both for assessing and impacting security compliance behavior.
A Method for Order/Degree Problem Based on Graph Symmetry and Simulated Annealing with MPI/OpenMP Parallelization

The network topology in various systems, such as large-scale data centers, high-performance computing systems, and Network on Chip, is strongly related to network latency. Designing a network topology with low latency can be defined as an order/degree problem (ODP) in graph theory by modeling the network topology as an undirected graph. This study proposes a method for efficiently solving ODPs based on graph symmetry and simulated annealing (SA). This method makes the network topology symmetrical, thereby improving the solution search performance of SA and drastically reducing the calculation time. The proposed method is applied to several problems from an international competition for ODPs called Graph Golf to find network topologies with sufficiently low latency. The symmetry-based calculation achieves a speed up of 31.76 times for one of the problems. Furthermore, to reduce calculation time, the proposed method is extended to use hybrid parallelization with MPI and OpenMP. As a result, a maximum speed up of 209.80 times was achieved on 20 compute nodes consisting of 400 CPU cores. Even faster performance was achieved by combining the symmetry-based calculation and hybrid parallelization.
Interoperability in semantic Web of Things: Design issues and solutions

Summary The significant improvement in processing power, communication, energy consumption, and the size of computational devices has led to the emergence of the Internet of Things (IoT). IoT projects raise many challenges, such as the interoperability between IoT applications because of the high number of sensors, actuators, services, protocols, and data associated with these systems. Semantics solves this problem by using annotations that define the role of each IoT element and reduces the ambiguity of information exchanged between the devices. This work presents SWoTPAD, a semantic framework that helps in the development of IoT projects. The framework is designer oriented and provides a semantic language that is more user‐friendly than OWL‐S and WSML and allows the IoT designer to specify devices, services, environment, and requests. Following this, it makes use of these specifications and maps them for RESTful services. Additionally, it generates an automatic service composition engine that is able to combine services needed to handle complex user requests. We validated this approach with two case studies. The former concerns a residential security system and the latter, the cloud application deployment. The average time required for service discovery and automatic service composition corresponds to 72.9% of the service execution time in the case study 1 and 64.4% in the case study 2.
COCHIS: Stable and coherent implicits

Abstract Implicit programming (IP) mechanisms infer values by type-directed resolution, making programs more compact and easier to read. Examples of IP mechanisms include Haskell’s type classes, Scala’s implicits, Agda’s instance arguments, Coq’s type classes and Rust’s traits. The design of IP mechanisms has led to heated debate: proponents of one school argue for the desirability of strong reasoning properties, while proponents of another school argue for the power and flexibility of local scoping or overlapping instances. The current state of affairs seems to indicate that the two goals are at odds with one another and cannot easily be reconciled. This paper presents COCHIS, the Calculus Of CoHerent ImplicitS, an improved variant of the implicit calculus that offers flexibility while preserving two key properties: coherence and stability of type substitutions . COCHIS supports polymorphism, local scoping, overlapping instances, first-class instances and higher-order rules, while remaining type-safe, coherent and stable under type substitution. We introduce a logical formulation of how to resolve implicits, which is simple but ambiguous and incoherent, and a second formulation, which is less simple but unambiguous, coherent and stable. Every resolution of the second formulation is also a resolution of the first, but not conversely. Parts of the second formulation bear a close resemblance to a standard technique for proof search called focusing. Moreover, key for its coherence is a rigorous enforcement of determinism.
How People Visually Represent Discrete Constraint Problems

Problems such as timetabling or personnel allocation can be modeled and solved using discrete constraint programming languages. However, while existing constraint solving software solves such problems quickly in many cases, these systems involve specialized languages that require significant time and effort to learn and apply. These languages are typically text-based and often difficult to interpret and understand quickly, especially for people without engineering or mathematics backgrounds. Visualization could provide an alternative way to model and understand such problems. Although many visual programming languages exist for procedural languages, visual encoding of problem specifications has not received much attention. Future problem visualization languages could represent problem elements and their constraints unambiguously, but without unnecessary cognitive burdens for those needing to translate their problem's mental representation into diagrams. As a first step towards such languages, we executed a study that catalogs how people represent constraint problems graphically. We studied three groups with different expertise: non-computer scientists, computer scientists and constraint programmers and analyzed their marks on paper (e.g., arrows), gestures (e.g., pointing) and the mappings to problem concepts (e.g., containers, sets). We provide foundations to guide future tool designs allowing people to effectively grasp, model and solve problems through visual representations.
Striving for performance of discrete optimisation via memetic agent-based systems in a hybrid CPU/GPU environment

Agent-based memetic computational systems have been proven to be an efficient concept for solving complex computational problems. In this paper, we propose and evaluate a new hybrid architecture for such systems that effectively utilise CPU and GPGPU processing power by delegating parallel part of computations to GPGPU. The paper illustrates how to design and implement particular parts of the systems and discusses required conditions for evolutionary parameters and memetic operators. The concept is illustrated with one of the complex discrete problems – LABS.
On password behaviours and attitudes in different populations

We explore the differences in password behaviours and attitudes of samples of university students, IT professionals, and the general population (non-student and non-IT professional). Currently, text-based password authentication is the most commonly deployed user authentication mechanism, despite numerous alternatives. Passwords are thus a popular research topic, where many password studies are done in universities where the majority of participants are students. Many studies also do not differentiate between IT professionals and others. Thus, we aim to gain insights about the effect of focusing on samples from university students and/or IT professionals. We conducted a 100-participant online user study involving three sessions over 8−10 days. The study tasks involved password creation and password recall. We also surveyed participant’s password-related behaviours and preferences for different authentication methods. Our results provide insights about the effect of using a large proportion of university students and/or IT professionals in password studies. Our findings should be of interest to the research community and also provide useful data to authentication system designers regarding user preferences and attitudes.
Statistical inference for geometric process with the Two-Parameter Lindley Distribution

The geometric process is a popular method for the modeling of arrival times with trend. In this study, the statistical inference problem for geometric process is considered when the distribution of first arrival time is two-parameter Lindley. The parameters a, λ and β are estimated using the maximum likelihood, modified least-squares, modified moments and modified L-moments methods. The estimation performances of the obtained estimators are compared by comprehensive simulations. The simulation results show that maximum likelihood estimators outperform the other estimators. Finally, on three real-life datasets, we present data analyses showing the modeling performance of geometric process against renewal process.