Reviewerly/exHarmony huggingface dataset:
A multilabel approach to morphosyntactic probing

We propose using a multilabel probing task to assess the morphosyntactic representations of multilingual word embeddings.This tweak on canonical probing makes it easy to explore morphosyntactic representations, both holistically and at the level of individual features (e.g., gender, number, case), and leads more naturally to the study of how language models handle co-occurring features (e.g., agreement phenomena).We demonstrate this task with multilingual BERT (Devlin et al., 2018), training probes for seven typologically diverse languages: Afrikaans, Croatian, Finnish, Hebrew, Korean, Spanish, and Turkish.Through this simple but robust paradigm, we verify that multilingual BERT renders many morphosyntactic features simultaneously extractable.We further evaluate the probes on six held-out languages: Arabic, Chinese, Marathi, Slovenian, Tagalog, and Yorùbá.This zero-shot style of probing has the added benefit of revealing which cross-linguistic properties a language model recognizes as being shared by multiple languages.
Image Splicing Detection Scheme Using Surf and Mean-LBP Based Morphological Operations

&#x0D; Tampering with images and changing them without giving any evidence has become very popular because of the presence of an enormous degree of intense altering device. The image forensics technique has grown popular for deciding whether a picture has been changed with a copy-move, splicing, or different forgery techniques. This paper present’s a novel passive image splicing forgery detection technique which includes three steps. Firstly, extracting an interest region (ROI) by using SURF descriptor (Speed up Robust Transform) Points is concerned based on the morphological procedure. Secondly, the Extraction of Mean-LBP (Mean Local Binary Pattern) Highlights on ROI is carried out. Lastly, a Classification of Mean- LBP characteristics is done with the SVM classifier. This novel method has shown the best outcome as SVM is used for classification. As well as it has shown a higher accuracy on the standard three datasets CASIA TIDE V1.0, CASIA TIDE V2.0, and Columbia University, revealed that current method has achieved higher accuracy of 97.9%, 98.2%, and 98.9% respectively. Finally, in terms of accuracy, the proposed SFD scheme outperformed the best recent works in this area.&#x0D;
A Complex Dynamic Systems Perspective to Researching Language Classroom Dynamics

Complexity Theory, now referred to in second language development as Complex Dynamic Systems Theory (CDST), introduces a holistic, ecological, and relational systems account into research on language classrooms. It is a post-structuralist, non-reductionist approach that accords agency to language learners and considers learning to be a dynamic nonlinear process holistic, ecological, and relational systems account.
Strengthening Quality of Chaotic Bit Sequences

We discuss chaos and its quality as measured through the 0-1 test for chaos. When the 0-1 test indicates deteriorating quality of chaos, because of the finite precision representations of real numbers in digital implementations, then the process may eventually lead to a periodic sequence. A simple method for improving the quality of a chaotic signal is to mix the signal with another signal by using the XOR operation. In this paper, such mixing of weak chaotic signals is considered, yielding new signals with improved quality (with K values from the 0-1 test close to 1). In some sense, such a mixing of signals could be considered as a two-layer prevention strategy to maintain chaos. That fact may be important in those applications when the hardware resources are limited. The 0-1 test is used to show the improved chaotic behavior in the case when a continuous signal (for example, from the Chua, Rössler or Lorenz system) intermingles with a discrete signal (for example, from the logistic, Tinkerbell or Henon map). The analysis is presented for chaotic bit sequences. Our approach can further lead to hardware applications, and possibly, to improvements in the design of chaotic bit generators. Several illustrative examples are included.
Analysis and illustration of the practical impact of Artificial Intelligence and Intelligent Personal Assistants on business processes in small- and medium-sized service enterprises

Artificial intelligence (AI) and intelligent personal assistants (IPAs) are becoming more and more important. This is no longer limited to private use but also becoming increasingly important in everyday business life. The identification of optimization potentials through the use of AI and IPAs is therefore relevant from both a theoretical and a practical point of view. This paper, therefore, identifies concrete use cases for relevant processes in small- and medium sized enterprises (SME) in the service industry and enhances them with AI and IPA capabilities. Based on a prototype, the use cases were presented to 10 experts who were interviewed regarding their usefulness and influencing factors. Subsequently, the results of the interviews were categorized and validated again by a quantitative survey within the expert panel. As a result, the use cases were evaluated with regard to the specific influencing factors and the potential for optimization was determined. The use cases were evaluated based on this data. It was shown that IPA features in particular are perceived as useful. On average, AI and IPA features have a cost savings potential of over 31%. This shows the importance of these features and the need to consider them when modeling modern business processes.
Two-Person Mutual Action Recognition Using Joint Dynamics and Coordinate Transformation

Skeleton-based action recognition has attracted lots of attention in computer vision.Human mutual interaction recognition relies on extracting discriminative features for better understanding details.In this work, we propose two vectors to encode joint dynamics and spatial interaction information.The proposed model shows remarkable performance at handling sequential data.Experimental results demonstrate that our model outperforms state-of-the-art approaches with much less overheads.
SECURING DIGITAL ECONOMIES: DETECTION OF PHISHING ATTACKS USING MACHINE LEARNING APPROACHES

Due to the wide use of web resources such as online banking, social networking, education, entertainment, downloading software, and a rise in the economy&apos;s digitalisation, the prevalence of online fraud is rising. Phishing is mostly a launch attack on the internet. Phishing is a criminal attack that obtains credential data such as bank information, username, password and credit card information, etc which can be used to damage a single victim or the entire organisation. Criminals always use disasters as an opportunity to take advantage of recessions, hurricanes, and difficult times. The latest example is the Covid-19 pandemic. The phishing attacks are growing rapidly in 2020, cyber attackers launched Covid-19 themed phishing attacks against healthcare facilities, the unemployed and workers. A lot of anti-phishing solutions are available heuristic detection, virtual similarity detection, black and whitelisting, and machine learning. This paper compares various research for each machine learning technique in terms of detecting phishing attacks and discusses the benefits and drawbacks of each methodology. In addition, this paper presents a detailed list of existing phishing attack threats as well as potential research directions in this sphere.
Interorganizational Information Sharing: Collaboration during Cybersecurity Threats

Information sharing is examined to determine if the willingness to share information relates to the willingness to collaborate in interorganizational actions used to address cybersecurity threats. This study employs ANOVA by placing 85 organizational representatives that participated in a cyber terrorism training exercise into two categories: higher versus lower willingness to share information. The dependent measures in this study are proxies for collaboration measured by willingness to seek assistance from other organizations and willingness to offer assistance to other organizations. There is a significant statistical difference between the two categorizations of organizational representatives. The category that had a lower tendency to share information also had a lower willingness to collaborate with other organizations. This research shows that sharing information is critical when collaborative interorganizational action is required to face cybersecurity threats. Managers of organizations that respond to cybersecurity threats should promote the tendency to share information with other organizations that have the resources to aid in addressing the threat. Organizations involved in cybersecurity threats that are willing to share information are more willing to participate in integrative interorganizational efforts. This study includes a sample of actual decision makers of organizations that would handle cybersecurity attacks in their community. In this study, we demonstrate the role of information sharing for enhancing
Distance Closures: Unifying Search- and Lookup-based Shortest Path Speedup Techniques

Previous chapter Next chapter Full AccessProceedings 2022 Proceedings of the Symposium on Algorithm Engineering and Experiments (ALENEX)Distance Closures: Unifying Search- and Lookup-based Shortest Path Speedup TechniquesDaniel Bahrdt, Stefan Funke, Sokol Makolli, and Claudius ProisslDaniel Bahrdt, Stefan Funke, Sokol Makolli, and Claudius Proisslpp.1 - 12Chapter DOI:https://doi.org/10.1137/1.9781611977042.1PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract Most popular speed-up techniques for shortest path queries in road networks are based either on pruned graph search or clever lookup schemes and allow for answering of shortest path distance queries within continent-sized road networks in less than a milli-(search-based) or even microsecond (lookup-based) compared to several seconds of a normal Dijkstra run. While both paradigms previously have been considered mostly separately, we present a framework that unifies these seemingly different views on shortest path computations. Apart from the conceptual novelty, this allows for new and (practically very attractive) space-time tradeoffs for shortest-path computation. To our knowledge we are also the first to report on computational results for the largest connected component of the Open-StreetMap planet road network with more than half a billion nodes. Previous chapter Next chapter RelatedDetails Published:2022eISBN:978-1-61197-704-2 https://doi.org/10.1137/1.9781611977042Book Series Name:ProceedingsBook Code:PRAL22Book Pages:iii-220
Offline Signature Recognition u sing Pretrained Convolution Neural Network Model

Offline Signature recognition plays an important role in Forensic issues. In this paper, we explore Signature Identification and Verification using features extracted from pretrained Convolution Neural Network model (Alex Net). All the experiments are performed on signatures from three dataset (SigComp2011) (Dutch, Chinese), SigWiComp2013 (Japanese) and SigWIcomp2015 (Italian). The result shows that features extracted from pretrained Deep Convolution neural network and SVM as classifier show better results than that of Decision Tree. The accuracy of more than 96% for Japanese, Italian, Dutch and Chinese Signatures is obtained with Deep Convolution neural network and SVM as classifier.
Non-functional Requirements Classification using Artificial Neural Networks

Requirements classification is a task commonly made by the human. This fact makes the process error-prone and expensive in a matter of time and effort. This study aims to classify non-functional requirements using a Shallow Artificial Neural Network to support the requirements classification while analyzing its architectural features. We used an existing preprocessed dataset to compare the results. The non-functional requirements classified were: availability, failure tolerance, maintainability, performance, scalability, security, and usability. A set of experiments were performed to adjust and analyze the features of the Artificial Neural Network: number of neurons, activation functions, additional hidden layer, and learning rate. The obtained neural network, which resulted in a simpler architecture, outperforms the results reported in the literature for that particular dataset.
The Mediating Effects of Key Audit Matters on the Relationship Between Audit Quality and Earnings Management: Evidence from Jordan

The relationship between audit quality and earnings management has not been tested with consideration of key audit matters as a mediating variable. This study examined whether audit quality (AQ) decreases earnings management (EM) in shareholding corporations through improving key audit matters (KAMs) in Jordan’s emerging environment. A regression analysis was carried out on a sample that included financial reports and auditor reports of 105 industrial and service shareholdings companies listed on the Amman Stock Exchange (ASE) from 2017 to 2019. The study found a negative relationship between audit quality and earnings management. The results showed that audit quality increases key audit matters, which, in turn, decreases earnings management. Also, the study confirmed the mediating effect of KAMs between audit quality and earnings management. The study confirms the importance of key audit matters to provide more relevant and useful information for the users of financial reports and provides important indications to the regulatory authorities and standards bodies that key audit matters should be given more attention regarding the way that they are presented and disclosed.
Novel Pruning of Dendritic Neuron Models for Improved System Implementation and Performance

Pruning is widely used for neural network model compression. It removes redundant links from a weight tensor to lead to smaller and more efficient neural networks for system implementation. A compressed neural network can enable faster run and reduced computational cost in network training. In this paper, a novel pruning method is proposed for a dendritic neuron model (DNM). It calculates the significance of each DNM dendrite. The calculated significance is expressed numerically and a dendrite whose significance is lower than a pre-set threshold is removed. Experimental results verify that it obtains superior performance over the existing one in terms of both accuracy and computational efficiency.
Manifold-adaptive dimension estimation revisited

Data dimensionality informs us about data complexity and sets limit on the structure of successful signal processing pipelines. In this work we revisit and improve the manifold adaptive Farahmand-Szepesvári-Audibert (FSA) dimension estimator, making it one of the best nearest neighbor-based dimension estimators available. We compute the probability density function of local FSA estimates, if the local manifold density is uniform. Based on the probability density function, we propose to use the median of local estimates as a basic global measure of intrinsic dimensionality, and we demonstrate the advantages of this asymptotically unbiased estimator over the previously proposed statistics: the mode and the mean. Additionally, from the probability density function, we derive the maximum likelihood formula for global intrinsic dimensionality, if i.i.d. holds. We tackle edge and finite-sample effects with an exponential correction formula, calibrated on hypercube datasets. We compare the performance of the corrected median-FSA estimator with kNN estimators: maximum likelihood (Levina-Bickel), the 2NN and two implementations of DANCo (R and MATLAB). We show that corrected median-FSA estimator beats the maximum likelihood estimator and it is on equal footing with DANCo for standard synthetic benchmarks according to mean percentage error and error rate metrics. With the median-FSA algorithm, we reveal diverse changes in the neural dynamics while resting state and during epileptic seizures. We identify brain areas with lower-dimensional dynamics that are possible causal sources and candidates for being seizure onset zones.
Optimal Accuracy Zone Identification in Object Detection Technique – A Learning Rate Methodology

In the recent past, Deep Learning models [1] are predominantly being used in Object Detection algorithms due to their accurate Image Recognition capability. These models extract features from the input images and videos [2] for identification of objects present in them. Various applications of these models include Image Processing, Video analysis, Speech Recognition, Biomedical Image Analysis, Biometric Recognition, Iris Recognition, National Security applications, Cyber Security, Natural Language Processing [3], Weather Forecasting applications, Renewable Energy Generation Scheduling etc. These models utilize the concept of Convolutional Neural Network (CNN) [3], which constitutes several layers of artificial neurons. The accuracy of Deep Learning models [1] depends on various parameters such as ‘Learning-rate’, ‘Training batch size’, ‘Validation batch size’, ‘Activation Function’, ‘Drop-out rate’ etc. These parameters are known as Hyper-Parameters. Object detection accuracy depends on selection of Hyper-parameters and these in-turn decides the optimum accuracy. Hence, finding the best values for these parameters is a challenging task. Fine-Tuning is a process used for selection of a suitable Hyper-Parameter value for improvement of object detection accuracy. Selection of an inappropriate Hyper-Parameter value, leads to Over-Fitting or Under-Fitting of data. Over-Fitting is a case, when training data is larger than the required, which results in learning noise and inaccurate object detection. Under-fitting is a case, when the model is unable to capture the trend of the data and which leads to more erroneous results in testing or training data. In this paper, a balance between Over-fitting and Under-fitting is achieved by varying the ‘Learning rate’ of various Deep Learning models. Four Deep Learning Models such as VGG16, VGG19, InceptionV3 and Xception are considered in this paper for analysis purpose. The best zone of Learning-rate for each model, in respect of maximum Object Detection accuracy, is analyzed. In this paper a dataset of 70 object classes is taken and the prediction accuracy is analyzed by changing the ‘Learning-rate’ and keeping the rest of the Hyper-Parameters constant. This paper mainly concentrates on the impact of ‘Learning-rate’ on accuracy and identifies an optimum accuracy zone in Object Detection.
The Effectiveness of Using Big Book Media on Early Reading Skills in 5-6 Years Old

Purpose – This study aims to determine the effectiveness of using Big Book media on early reading skills in children aged 5-6 years old.Design/methods/approach – This study used a quantitative approach which was carried out in an experimental method with a pre-post-test design. The sampling technique uses a simple random technique with 30 children. Data collection technique is by means of tests. The analysis technique for testing the hypothesis uses the difference test (t-test).Findings – The results showed that children in the experimental group who used Big Book media had a higher average score when compared to children in the control group who used magazine media. Based on the t-test calculation, findings indicate that there was a significant difference between the two groups. Thus, it can be concluded that the Big Book media is effective on early reading skills in children aged 5-6 years old.Research implications/limitations – This study was limited to one location, Mutiara Al-Madani Kindergarten, Jambi Indonesia, and with a small sample size, so the generalizability of the findings was also limited. It is necessary for future researchers to conduct studies with a wider scope and a more diverse population.Practical implications – The use of Big Book media can be an alternative learning media that can attract children's interests and can actively involve children in gaining rich literacy experiences in improving early reading skills.Originality/value – This study contributes to understanding of teacher's reference material in using learning media for early reading skills in children aged 5-6 years old.&#x0D; Paper type Research paper
Quantum Computing for Dealing with Inaccurate Knowledge Related to the Certainty Factors Model

In this paper, we illustrate that inaccurate knowledge can be efficiently implemented in a quantum environment. For this purpose, we analyse the correlation between certainty factors and quantum probability. We first explore the certainty factors approach for inexact reasoning from a classical point of view. Next, we introduce some basic aspects of quantum computing, and we pay special attention to quantum rule-based systems. In this context, a specific use case was built: an inferential network for testing the behaviour of the certainty factors approach in a quantum environment. After the design and execution of the experiments, the corresponding analysis of the obtained results was performed in three different scenarios: (1) inaccuracy in declarative knowledge, or imprecision, (2) inaccuracy in procedural knowledge, or uncertainty, and (3) inaccuracy in both declarative and procedural knowledge. This paper, as stated in the conclusions, is intended to pave the way for future quantum implementations of well-established methods for handling inaccurate knowledge.
Value-aware Approximate Attention

Following the success of dot-product attention in Transformers, numerous approximations have been recently proposed to address its quadratic complexity with respect to the input length.However, all approximations thus far have ignored the contribution of the value vectors to the quality of approximation.In this work, we argue that research efforts should be directed towards approximating the true output of the attention sub-layer, which includes the value vectors.We propose a valueaware objective, and show theoretically and empirically that an optimal approximation of a value-aware objective substantially outperforms an optimal approximation that ignores values, in the context of language modeling.Moreover, we show that the choice of kernel function for computing attention similarity can substantially affect the quality of sparse approximations, where kernel functions that are less skewed are more affected by the value vectors.