Reviewerly/exHarmony huggingface dataset:
Discriminating between standard Romanian and Moldavian tweets using filtered character ngrams.

We applied word unigram models, character ngram models, and CNNs to the task of distinguishing tweets of two related dialects of Romanian (standard Romanian and Moldavian) for the VarDial 2020 RDI shared task (Gaman et al. 2020). The main challenge of the task was to perform cross-genre text classification: specifically, the models must be trained using text from news articles, and be used to predict tweets. Our best model was a Naive Bayes model trained on character ngrams, with the most common ngrams filtered out. We also applied SVMs and CNNs, but while they yielded the best performance on an evaluation dataset of news article, their accuracy significantly dropped when they were used to predict tweets. Our best model reached an F1 score of 0.715 on the evaluation dataset of tweets, and 0.667 on the held-out test dataset. The model ended up in the third place in the shared task.
A Novel and Secure Hybrid iWD-MASK Algorithm for Enhanced Image Security

Background: Most of the work has been done using DES, AES, and MAES for key generation in the image encryption. These algorithms provide better results for text based encryption when compared with digital image encryption. These algorithms are less secure and efficient. A new hybrid approach has been introduced for digital image encryption i.e. Chaos-based approach. This approach is designed for image related data. Obtained results outperform the previous results of encryption. Objective: To get the original image after decryption at the receiver’s end. Methods: Matrix array symmetric key algorithm has been used for key generation in which same key is used for encryption and decryption process. Here, we have to fix the total number of iterations. There will be three choices of iterations 10, 12 and 14 for 128, 192 and 256 bits key lengths respectively. Once the key is generated, IWD algorithm is applied for encryption process and at the end of sender and same key has to be used for decryption purpose at receiver’s end. Otherwise, system will stop its functioning and will result into system error. Results &amp; Conclusion: The proposed hybrid concept of MASK with iWD algorithm is implemented on different set of images. Obtained results are satisfactorily while comparing the proposed approach with other existing approaches for the various parameters like precision, recall and Fmeasure. In future work, the proposed algorithm can be applied for the other types of cryptography as well, like text, video sharing, etc.
Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking

Graph neural networks (GNNs) have become a popular approach to integrating structural inductive biases into NLP models. However, there has been little work on interpreting them, and specifically on understanding which parts of the graphs (e.g. syntactic trees or co-reference structures) contribute to a prediction. In this work, we introduce a post-hoc method for interpreting the predictions of GNNs which identifies unnecessary edges. Given a trained GNN model, we learn a simple classifier that, for every edge in every layer, predicts if that edge can be dropped. We demonstrate that such a classifier can be trained in a fully differentiable fashion, employing stochastic gates and encouraging sparsity through the expected L0 norm. We use our technique as an attribution method to analyze GNN models for two tasks -- question answering and semantic role labeling -- providing insights into the information flow in these models. We show that we can drop a large proportion of edges without deteriorating the performance of the model, while we can analyse the remaining edges for interpreting model predictions.
The implementation of Total Quality Management (TQM) in the hotel industry

This study sets out to explore the Critical Success Factors (CSFs) necessary for Total Quality Management (TQM) implementation in hotels.It also aims to classify participating hotels into groups based on their TQM adoption by using cluster analysis.A quantitative survey method was applied.Data were collected from a sample of managers from four-and five-star hotels in Jordan, 170 questionnaires were distributed to managers and 104 usable questionnaires were returned.The findings revealed that TQM is existed and implemented in the hotel industry.The researcher then confirmed that four-and five-star hotels can be classified into two groups, namely, "high TQM adopters" and "low TQM adopters".
TripleTree: A Versatile Interpretable Representation of Black Box Agents and their Environments

In explainable artificial intelligence, there is increasing interest in understanding the behaviour of autonomous agents to build trust and validate performance. Modern agent architectures, such as those trained by deep reinforcement learning, are currently so lacking in interpretable structure as to effectively be black boxes, but insights may still be gained from an external, behaviourist perspective. Inspired by conceptual spaces theory, we suggest that a versatile first step towards general understanding is to discretise the state space into convex regions, jointly capturing similarities over the agent's action, value function and temporal dynamics within a dataset of observations. We create such a representation using a novel variant of the CART decision tree algorithm, and demonstrate how it facilitates practical understanding of black box agents through prediction, visualisation and rule-based explanation.
Estimating PQoS of Video Streaming on Wi-Fi Networks Using Machine Learning

Video on demand (VoD) services such as YouTube have generated considerable volumes of Internet traffic in homes and buildings in recent years. While Internet service providers deploy fiber and recent wireless technologies such as 802.11ax to support high bandwidth requirement, the best-effort nature of 802.11 networks and variable wireless medium conditions hinder users from experiencing maximum quality during video streaming. Hence, Internet service providers (ISPs) have an interest in monitoring the perceived quality of service (PQoS) in customer premises in order to avoid customer dissatisfaction and churn. Since existing approaches for estimating PQoS or quality of experience (QoE) requires external measurement of generic network performance parameters, this paper presents a novel approach to estimate the PQoS of video streaming using only 802.11 specific network performance parameters collected from wireless access points. This study produced datasets comprising 802.11n/ac/ax specific network performance parameters labelled with PQoS in the form of mean opinion scores (MOS) to train machine learning algorithms. As a result, we achieved as many as 93–99% classification accuracy in estimating PQoS by monitoring only 802.11 parameters on off-the-shelf Wi-Fi access points. Furthermore, the 802.11 parameters used in the machine learning model were analyzed to identify the cause of quality degradation detected on the Wi-Fi networks. Finally, ISPs can utilize the results of this study to provide predictable and measurable wireless quality by implementing non-intrusive monitoring of customers’ perceived quality. In addition, this approach reduces customers’ privacy concerns while reducing the operational cost of analytics for ISPs.
Super-Strict Implications

This paper introduces the logics of super-strict implications, where a super-strict implication is a strengthening of C.I. Lewis' strict implication that avoids not only the paradoxes of material implication but also those of strict implication. The semantics of super-strict implications is obtained by strengthening the (normal) relational semantics for strict implication. We consider all logics of super-strict implications that are based on relational frames for modal logics in the modal cube. it is shown that all logics of super-strict implications are connexive logics in that they validate Aristotle's Theses and (weak) Boethius's Theses. A proof-theoretic characterisation of logics of super-strict implications is given by means of G3-style labelled calculi, and it is proved that the structural rules of inference are admissible in these calculi. It is also shown that validity in the S5-based logic of super-strict implications is equivalent to validity in G. Priest's negation-as-cancellation-based logic. Hence, we also give a cut-free calculus for Priest's logic.
Evolution of Bad Smells in LabVIEW Graphical Models.

Bad smells often indicate potential problems in software, which may lead to long-term challenges and expensive maintenance efforts.Although bad smells often occur in source code, bad smells also exist in representations of design descriptions and models.We have observed that many users of graphical modeling environments (e.g., LabVIEW) are systems engineers who may not be aware of core software engineering techniques, such as refactoring of bad smells.Systems engineers often focus on implementation correctness and may be unaware of how their designs affect long-term maintenance properties that may increase design smells.There exists a large body of research focused on analysing bad smells embedded in the source code of textual languages, but there has been limited research on bad smells in systems models of graphical languages.In this paper, we present a semi-automated approach for extracting design smells across versions of LabVIEW graphical models through user-defined queries.We describe example queries that highlight the emergence of design smells that we discovered from posts in the LabVIEW user's forum.We then demonstrate the use of the example queries in understanding the evolution of seven bad smells we found in 81 LabVIEW models stored in 10 GitHub repositories.We analyze the evolution of these smells in order to understand the prevalence and introduction of bad smells, as well as the relationship between bad smells and the structural changes made to the models.Our results show that all of the models contain instances of at least one type of bad smell and the number of smells fluctuates as the size of a model increases.Furthermore, the majority of the structural changes across different versions of LabVIEW models involve the addition of new elements with a corresponding increase in the presence of design smells.This paper summarizes the need for better analysis of design smells in systems models and suggests an approach that may assist in improving the structure and quality of systems models developed in LabVIEW.
A Critical Comparison on Six Static Analysis Tools: Detection Agreement and Precision

Background. Developers use Automated Static Analysis Tools (ASATs) to control for potential quality issues in source code, including defects and technical debt. Tool vendors have devised quite a number of tools, which makes it harder for practitioners to select the most suitable one for their needs. To better support developers, researchers have been conducting several studies on ASATs to favor the understanding of their actual capabilities. Aims. Despite the work done so far, there is still a lack of knowledge regarding (1) which source quality problems can actually be detected by static analysis tool warnings, (2) what is their agreement, and (3) what is the precision of their recommendations. We aim at bridging this gap by proposing a large-scale comparison of six popular static analysis tools for Java projects: Better Code Hub, CheckStyle, Coverity Scan, Findbugs, PMD, and SonarQube. Method. We analyze 47 Java projects and derive a taxonomy of warnings raised by 6 state-of-the-practice ASATs. To assess their agreement, we compared them by manually analyzing - at line-level - whether they identify the same issues. Finally, we manually evaluate the precision of the tools. Results. The key results report a comprehensive taxonomy of ASATs warnings, show little to no agreement among the tools and a low degree of precision. Conclusions. We provide a taxonomy that can be useful to researchers, practitioners, and tool vendors to map the current capabilities of the tools. Furthermore, our study provides the first overview on the agreement among different tools as well as an extensive analysis of their precision.
Lossless Compression of Bilevel ROI Maps of Hyperspectral Images Using Optimization Algorithms

In this letter, we propose a novel scheme for the compression of the bilevel region of interest (ROI) maps of hyperspectral images. The scheme can achieve high compression ratios by novel ways of partitioning the intricate regions of the image into smaller blocks. We first analyzed the effect of a low-order linear predictive model on the original image. The analysis showed that linear prediction results in lower-entropy residual images compared to the original images, which in turn helps in improving the compression efficiency. We then use the optimization algorithm that finds the best combination of scan directions for the nonzero blocks. It is shown to offer increasingly better compression with additional iterations. The simulation results on various data sets show that our scheme outperforms the international standard for binary image compression [joint bi-level image expert (JBIG2)].
Video Quality Assessment Dataset for Smart Public Security Systems

Security and monitoring systems are more and more demanding in terms of quality, reliability and flexibility especially those dedicated to video surveillance. The quality of the acquired video signal strongly affects the performance of the high level tasks such as visual tracking, face detection and recognition. The design of a video quality assessment metric dedicated to this particular application requires a preliminary study on the common distortions encountered in video surveillance. To this end, we present in this paper a dataset dedicated to video quality assessment in the context of video surveillance. This database consists of a set of common distortions at different levels of annoyance. The subjective tests are performed using a classical pair comparison protocol with some new configurations. The subjective results obtained through the psycho-visual tests are analyzed and compared to some objective video quality assessment metrics. The preliminary results are encouraging and open a new framework for building smart video surveillance based security systems.
Correlated Participation Decision Making for Federated Edge Learning

Driven by the sheer amount of data generated at the network edge and improved computation capabilities of mobile devices, federated edge learning (FEL) emerges as a novel paradigm to achieve edge intelligence with a favorable property of protecting privacy for data generators, i.e., edge devices. However, limited computation and communication resources at the edge make it challenging to execute FEL cost-efficiently in practice. Faced with this challenge, lots of existing work focus on the optimization control during the learning process. However, these research take no precaution in terms of composing the FEL system given heterogeneous candidate devices, which can severely impact the implementation performance. To solve this issue, we define a participation game to capture the dependent but competitive relationships among edge devices with respect to making decisions on whether to participate in a round of FEL. Then we propose a correlated equilibrium based participation decision making strategy to achieve individual rationality and global profit maximization at the same time, which can maintain the efficiency and sustainability of FEL in the long term. Furthermore, we devise an improved method with polynomial computational cost to enhance the scalability of the game-theoretic solution. The performance of our proposed scheme is evaluated through extensive experimental results.
The relationship between math anxiety, student engagement in mathematics and responsibilities towards learning among middle school students

Learning responsibility is an important concept in students' school success because responsible learners perceive responsibility for learning as a tool for achieving school success and individual career goals.This study aimed to investigate if math anxiety and students' engagement in mathematics predict students' responsibilities toward learning above and beyond that accounted for by gender, grade level, loving math, loving math teacher, owning room, parents' involvement in students' studies using Hierarchical Regression Analysis.Data were collected from 568 middle school students.The results showed that students' social, emotional, and cognitive engagement in mathematics were found to be positively associated with students' responsibility towards learning whereas math anxiety was significant and negative predictor of the students' responsibility towards learning.The finding suggests that student's responsibility towards learning is associated with math-related activities and behaviors.Future studies may investigate a cause-effect relationship using same sample and other classes such as science, linguistic, history.
QuakeMigrate: a Modular, Open-Source Python Package for Automatic Earthquake Detection and Location

Earth and Space Science Open Archive PosterOpen AccessYou are viewing the latest version by default [v1]QuakeMigrate: a Modular, Open-Source Python Package for Automatic Earthquake Detection and LocationAuthorsTomWinderiDConorBaconiDJonathanSmithThomasHudsonTimGreenfieldRobertWhiteiDSee all authors Tom WinderiDCorresponding Author• Submitting AuthorUniversity of CambridgeiDhttps://orcid.org/0000-0001-7047-8673Retype the characters from the picture:Validateview email addressThe email was not providedcopy email addressConor BaconiDUniversity of CambridgeiDhttps://orcid.org/0000-0002-2558-8555Retype the characters from the picture:Validateview email addressThe email was not providedcopy email addressJonathan SmithCalifornia Institute of TechnologyRetype the characters from the picture:Validateview email addressThe email was not providedcopy email addressThomas HudsonUniversity of OxfordRetype the characters from the picture:Validateview email addressThe email was not providedcopy email addressTim GreenfieldUniversity of CambridgeRetype the characters from the picture:Validateview email addressThe email was not providedcopy email addressRobert WhiteiDUniversity of CambridgeiDhttps://orcid.org/0000-0002-2972-397XRetype the characters from the picture:Validateview email addressThe email was not providedcopy email address
Very Important Person Localization in Unconstrained Conditions: A New Benchmark

This paper presents a new high-quality dataset for Very Important Person Localization (VIPLoc), named Unconstrained-7k. Generally, current datasets: 1) are limited in scale; 2) built under simple and constrained conditions, where the number of disturbing non-VIPs is not large, the scene is relatively simple, and the face of VIP is always in frontal view and salient. To tackle these problems, the proposed Unconstrained-7k dataset is featured in two aspects. First, it contains over 7,000 annotated images, making it the largest VIPLoc dataset under unconstrained conditions to date. Second, our dataset is collected freely on the Internet, including multiple scenes, where images are in unconstrained conditions. VIPs in the new dataset are in different settings, e.g., large view variation, varying sizes, occluded, and complex scenes. Meanwhile, each image has more persons (&gt; 20), making the dataset more challenging. As a minor contribution, motivated by the observation that VIPs are highly related to not only neighbors but also iconic objects, this paper proposes a Joint Social Relation and Individual Interaction Graph Neural Networks (JSRII-GNN) for VIPLoc. Experiments show that the JSRII-GNN yields competitive accuracy on NCAA (National Collegiate Athletic Association), MS (Multi-scene), and Unconstrained-7k datasets. https://github.com/xiaowang1516/VIPLoc.
How many data clusters are in the Galaxy data set?

Abstract In model-based clustering, the Galaxy data set is often used as a benchmark data set to study the performance of different modeling approaches. Aitkin (Stat Model 1:287–304) compares maximum likelihood and Bayesian analyses of the Galaxy data set and expresses reservations about the Bayesian approach due to the fact that the prior assumptions imposed remain rather obscure while playing a major role in the results obtained and conclusions drawn. The aim of the paper is to address Aitkin’s concerns about the Bayesian approach by shedding light on how the specified priors influence the number of estimated clusters. We perform a sensitivity analysis of different prior specifications for the mixtures of finite mixture model, i.e., the mixture model where a prior on the number of components is included. We use an extensive set of different prior specifications in a full factorial design and assess their impact on the estimated number of clusters for the Galaxy data set. Results highlight the interaction effects of the prior specifications and provide insights into which prior specifications are recommended to obtain a sparse clustering solution. A simulation study with artificial data provides further empirical evidence to support the recommendations. A clear understanding of the impact of the prior specifications removes restraints preventing the use of Bayesian methods due to the complexity of selecting suitable priors. Also, the regularizing properties of the priors may be intentionally exploited to obtain a suitable clustering solution meeting prior expectations and needs of the application.
Sharper Generalization Bounds for Learning with Gradient-dominated Objective Functions

Stochastic optimization has become the workhorse behind many successful machine learning applications, which motivates a lot of theoretical analysis to understand its empirical behavior. As a comparison, there is far less work to study the generalization behavior especially in a non-convex learning setting. In this paper, we study the generalization behavior of stochastic optimization by leveraging the algorithmic stability for learning with β-gradient-dominated objective functions. We develop generalization bounds of the order O(1/(nβ)) plus the convergence rate of the optimization algorithm, where n is the sample size. Our stability analysis significantly improves the existing non-convex analysis by removing the bounded gradient assumption and implying better generalization bounds. We achieve this improvement by exploiting the smoothness of loss functions instead of the Lipschitz condition in Charles & Papailiopoulos (2018). We apply our general results to various stochastic optimization algorithms, which show clearly how the variance-reduction techniques improve not only training but also generalization. Furthermore, our discussion explains how interpolation helps generalization for highly expressive models.
Application of BP neural network on quantitative evaluation of curriculum reform in advanced education-A case study of signal and system

Abstract In this paper, BP neural network model is used to study and analyse the teaching reform of signal and system course. Referring to the structure of BP neural network, the quality evaluation index system of signal and system course is established. On this basis, the factors influencing students’ examination results are predicted, and the questionnaire is designed and BP neural network is used for prediction. The mean square deviation of the result is 0.7788. Through the correlation coefficient analysis of the data obtained, we can get the major factors that affect students’ final scores. In view of this, the paper puts forward the direction and specific measures of teaching reform of this course.