Reviewerly/exHarmony huggingface dataset:
PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation

Anomaly detection methods require high-quality features. In recent years, the anomaly detection community has attempted to obtain better features using advances in deep self-supervised feature learning. Surprisingly, a very promising direction, using pretrained deep features, has been mostly overlooked. In this paper, we first empirically establish the perhaps expected, but unreported result, that combining pretrained features with simple anomaly detection and segmentation methods convincingly outperforms, much more complex, state-of-the-art methods. In order to obtain further performance gains in anomaly detection, we adapt pretrained features to the target distribution. Although transfer learning methods are well established in multi-class classification problems, the one-class classification (OCC) setting is not as well explored. It turns out that naive adaptation methods, which typically work well in supervised learning, often result in catastrophic collapse (feature deterioration) and reduce performance in OCC settings. A popular OCC method, DeepSVDD, advocates using specialized architectures, but this limits the adaptation performance gain. We propose two methods for combating collapse: i) a variant of early stopping that dynamically learns the stopping iteration ii) elastic regularization inspired by continual learning. Our method, PANDA, outperforms the state-of-the-art in the OCC, outlier exposure and anomaly segmentation settings by large margins.
Image Synthesis as a Pretext for Unsupervised Histopathological Diagnosis

Anomaly detection in visual data refers to the problem of differentiating abnormal appearances from normal cases. Supervised approaches have been successfully applied to different domains, but require abundance of labeled data. Due to the nature of how anomalies occur and their underlying generating processes, it is hard to characterize and label them. Recent advances in deep generative based models have sparked interest towards applying such methods for unsupervised anomaly detection and have shown promising results in medical and industrial inspection domains. In this work we evaluate a crucial part of the unsupervised visual anomaly detection pipeline, that is needed for normal appearance modelling, as well as the ability to reconstruct closest looking normal and tumor samples. We adapt and evaluate different high-resolution state-of-the-art generative models from the face synthesis domain and demonstrate their superiority over currently used approaches on a challenging domain of digital pathology. Multifold improvement in image synthesis is demonstrated in terms of the quality and resolution of the generated images, validated also against the supervised model.
Ontology-based metamorphic testing for chatbots

Abstract Modern-day demands for services often require an availability on a 24/7 basis as well as online accessibility around the globe. For this sake, personalized virtual assistants, called chatbots, are implemented. Such systems offer services, goods or information in natural language. These natural language processing (NLP) programs respond to the user in real time and offer an intuitive and simple interface to interact with. Advantages like these make them increasingly popular. Therefore, ensuring correct functionality of chatbots is of increasing importance. However, since different implementations and user behaviour result in unpredictable results, the chatbot’s input and output data are difficult to predict and classify as well. Under such circumstances, test cases can be inferred from the domain of possible inputs of a system under test (SUT). Ontologies are concepts used in AI to provide formal representations of knowledge for a specific domain. Such ontological models contain structured information that is used for test generation. On the other hand, testing of chatbots represents a challenge because of the absence of a test oracle. In this paper, both challenges are addressed by conceptualizing ontologies for input generation and output processing in form of a metamorphic testing approach. In this scenario, both concepts are applied for automated testing of chatbots. The approach is demonstrated on a real system from the tourism domain, thereby discussing the obtained results.
Face Mask Detection System using Mobilenetv2

In this pandemic, it is getting more and more difficult to keep a track of people who are wearing masks regularly or not. It cannot solely depend on human efforts to take care of this task and therefore there is a need to develop software that can automatically detect whether any given person is wearing a mask or not. Face Detection has evolved as a really popular problem in image processing and computer vision. Many new algorithms are being devised using convolutional architectures to form the algorithm as accurately as possible. These convolutional architectures have made it possible to extract even the pixel details. Training is performed through Fully Convolutional Neural Networks to semantically segment out the faces present in that image. Feature detection and feature extraction techniques help us identify whether a person is wearing a mask or not. The face mask detector will use a dataset of morphed masked images. Therefore, the created model will be accurate and it will also be computationally efficient and easily deployable in embedded systems since the MobileNetV2 architecture will be incorporated (Raspberry Pi, Google Coral, etc.). This framework can also be used in real-time applications that, due to the outbreak of Covid-19, require face-mask detection for safety purposes. This project can be merged with embedded application systems at airports, train stations, workplaces, schools, and public places to ensure compliance with the guidelines for public safety. The above topic is very prominent in recent times as the identification process will not only help us classify individuals but also will reduce the workforce required to do the same exponentially.
The Performance of Siamese Neural Network for Face Recognition using Different Activation Functions

The state-of-the-art method for image identification tends to achieve high performance. However, it requires a vast set of datasets making it infeasible to train machine learning models with sufficient samples while having newly-found samples. On the other hand, one-shot learning can be trained with limited samples. In addition to that, one-shot learning can also be trained with only one sample per person. One of the renowned methods is by using Siamese Neural Network. A Siamese network operates by having two identical networks with different images and learn the absolute difference between the two feature vectors while calculating the similarity score between the two images. However, the best activation function for this task is somehow unknown. Therefore, this paper attempts to evaluate the performance of Siamese Neural Network for face recognition using different activation functions. From the results, the most suitable activation function with the most stable performance is sigmoid, with an average accuracy of 92% for N-way Siamese Neural Network.
Real-time Hand-Gesture Recognition based on Deep Neural Network

Hand gestures are a kind of nonverbal communication in which visible bodily actions are used to communicate important messages. Recently, hand gesture recognition has received significant attention from the research community for various applications, including advanced driver assistance systems, prosthetic, and robotic control. Therefore, accurate and fast classification of hand gesture is required. In this research, we created a deep neural network as the first step to develop a real-time camera-only hand gesture recognition system without electroencephalogram (EEG) signals. We present the system software architecture in a fair amount of details. The proposed system was able to recognize hand signs with an accuracy of 97.31%.
Compressing Neural Networks With Inter Prediction and Linear Transformation

Because of resource-constrained environments, network compression has become an essential part of deep neural networks research. In this paper, we found a mutual relationship between kernel weights termed as Inter-Layer Kernel Correlation (ILKC). The kernel weights between two different convolution layers share a substantial similarity in shapes and values. Based on this relationship, we propose a new compression method, Inter-Layer Kernel Prediction (ILKP), which represents convolutional kernels with fewer bits through similarity between kernel weights in convolutional neural networks. Furthermore, to effectively adapt the inter prediction scheme from video coding technology, we integrate a linear transformation into the prediction scheme, which significantly enhances compression efficiency. The proposed method achieved 93.77% top-1 accuracy with 4.1× compression ratio compared to the ResNet110 baseline model on CIFAR10. It means that 0.04% top-1 accuracy improvement was achieved by using less memory footprint. Moreover, incorporating quantization, the proposed method achieved a 13× compression ratio with little performance degradation compared to the ResNets baseline model trained on CIFAR10 and CIFAR100.
Nonverbal Communication and Emojis Usage in Arabic Tweets: A Cross-Cultural Study

People spend most of their time communicating their thoughts, ideas, attitudes, and emotions on social media platforms like Twitter, however, an important mode of communication as the nonverbal component which requires visual and audible cues is not allowed due to the nature of these text-based platforms.The aim of this research is to discover the alternative ways Arabs use across different dialects to compensate for the absence of the nonverbal component.To be able to discover that the researchers collected a corpus of tweets written in the Arabic language by using python through the Twitter application programming interface (API).The results can be summed up as follows: emojis helped Arabs to communicate their facial expressions and the top used emoji across the different dialects was Face with Tears of Joy, it was also apparent that the top used emojis reflected the universal emotions, regarding the usage of hand gestures, Egyptian dialect came in the first place and Emirati dialect in the second place.Prosodic features such as the tone and loudness of the voice are expressed by the mean of character repetition, Punctuation usage across the Arabic dialects was limited, and Lebanese seemed to use them the most, Arabs tend to replace punctuation marks with emojis, finally, Arabs used vocal expressions like Interjections to communicate their affective state.
A Comparative Study on Schema-Guided Dialogue State Tracking

Frame-based state representation is widely used in modern task-oriented dialog systems to model user intentions and slot values.However, a fixed design of domain ontology makes it difficult to extend to new services and APIs.Recent work proposed to use natural language descriptions to define the domain ontology instead of tag names for each intent or slot, thus offering a dynamic set of schema.In this paper, we conduct in-depth comparative studies to understand the use of natural language description for schema in dialog state tracking.Our discussion mainly covers three aspects: encoder architectures, impact of supplementary training, and effective schema description styles.We introduce a set of newly designed bench-marking descriptions and reveal the model robustness on both homogeneous and heterogeneous description styles in training and evaluation.
Psychological factors of ICT competence formation in part-time students of the specialty “Preschool education”

The transience of time and the use of information and communication technology (ICT) have led to the urgent necessity – the formation of the educational process subjects’ ICT competence. In today’s educational sector in Ukraine and foreign countries, the studied phenomenon has acquired special significance. The goal: a) theoretical analysis of various aspects of the individual’s ICT competence formation, clarification of the essential content of the concept and offer its working definition; b) figuring out psychological factors of influence and their constructs on the ICT competence formation; determination of criteria, indexes and levels of the studied phenomenon formation; c) empirical study of the influence of psychological factors on the ICT competence formation in applicants for educational services of higher education institutions (part-time students of 2 Ukrainian universities). Conceptually psychological factors which have an influence on the ICT competence formation were characterized. The main criteria of ICT competence were offered, their indexes were defined and the description of levels of formation was given. According to the results of the empirical study the dependence of the influence of various psychological factors on the ICT competence formation in accordance with the pedagogical experience of 187 respondents was found out.
Student’s strategies in online autonomous English language learning

This study investigates the integration of online technology by English language learners, especially those done in autonomous learning. To be more specific, this study aims to discover how systematic is students’ strategies in online autonomous language learning and the supports they expect from the educational institutions and teachers/lecturers in terms of conducting effective online autonomous learning. This study mainly employs survey design which is supported by interview. The population of the study was the undergraduate students of English Language Education Department in a private university in Malang, East Java, Indonesia. The findings suggest that learners were less systematic in their strategy use when conducting autonomous language learning. They showed lack of long term goal setting and reported difficulty in evaluation, and mostly resort to short term planning, resource finding and execution of learning. For the reasons, avoidance of classroom-like feeling and not knowing how to evaluate was repeatedly noted. Further, they also reported high expectation of teachers and institutions’ support and assistance in conducting autonomous language learning. The current study and some previous studies in the area have noted that learners tend to consider goal setting and evaluation as teachers’ areas, influenced by the fixed curriculum imposed in schools and university. As such, some flexibility in curriculum enactment is proposed and stronger intervention by teachers and institutions to support learners’ autonomy are proposed.&#x0D;
DeepGG: A Deep Graph Generator

Learning distributions of graphs can be used for automatic drug discovery, molecular design, complex network analysis, and much more. We present an improved framework for learning generative models of graphs based on the idea of deep state machines. To learn state transition decisions we use a set of graph and node embedding techniques as memory of the state machine.
E-learning Platform and Pedagogical Monitoring in the Context of Digital Transformation

Upholding the quality of education in the absence of face-to-face contact requires an environmental approach and the creation of the unified online space and eLearning platform. The article makes the case for the relevance of creating automated tools for assessing the existing competencies of a listener necessary to form an individual learning route. The authors have developed methods for providing an automated pedagogical monitoring, described the principles of work of the automated diagnostic systems, and also gave the examples of its implementation. The suggested method combining key words and Template matching enables to optimize the eLearning platform. The article considers the possibilities of the system of automated pedagogical monitoring application when using the case technologies and students’ project work remotely in an electronic environment. In particular, the implementation of an expert assessment method using image theory and machine learning is presented. The article argues that electronic didactics makes it possible to expand the functionalities of pedagogical monitoring in conditions of digitalization, while maintaining the principles of traditional pedagogy. Taking into account the fact that there is a huge variety of different foreign and domestic aggregators of online courses it is necessary to develop a technology providing a selection of on-demand courses to reduce the time expenditure.
Defining, evaluating, preparing for and responding to a cyber Pearl Harbor

Despite not having a clear meaning, public perception and awareness makes the term cyber Pearl Harbor an important part of the public discourse. This paper considers what the term has meant and proposes its decomposition based on three different aspects of the historical Pearl Harbor attack, allowing the lessons from Pearl Harbor to be applied to threats and subjects that may not align with all aspects of the 1941 attack. Using these three definitions, prior attacks and current threats are assessed and preparation for and response to cyber Pearl Harbor events is discussed.
Comparative Analysis of Classical Test Theory and Item Response Theory using Chemistry Test Data

Assessment of learning involves deciding whether or not the content and objectives of education are down pat by administering quality tests. This study assesses the standard of Chemistry action take a look at and compares the item statistics generated mistreatment CTT and IRT strategies. A descriptive survey was adopted involving a sample of N=530 students. The specialised XCALIBRE 4 and ITEMAN 4 softwares were used to conduct the item analysis. Results indicate that, both the two methods commonly identified 13(32.5%) items as “problematic” and 27(67.5%) were “good”. Similarly, a significantly higher correlation exists between item statistics derived from the CTT and IRT models, [(r=-0.985,) and (r=0.801) p&lt;0.05] for item difficulty and discrimination respectively; the study concludes that the Chemistry Achievement test used do not pass through the processes of standardisation. Secondly, CTT and IRT frameworks appeared to be effective and reliable in assessing test items as the two frameworks provide similar and comparable results. The study recommends that the teacher made Chemistry tests used in measuring students’ achievement should be made to pass through all the processes of standardisation. Meanwhile, CTT and IRT approaches of item analysis ought to be integrated within the aspects of item development and analysis because of their superiority within the investigation of reliability and minimising measurement errors
Classification method of IPv6 traffic based on convolutional neural network

Abstract This paper proposes a method of IPv6 traffic classification based on convolutional neural network. It aims to solve the problems of low accuracy, high dependence on traffic feature selection, inefficiency in IPv6 traffic classification technology based on machine learning, and the lack of research on IPv6 traffic classification methods. The method transforms IPv6 traffic into two-dimensional matrix as input of convolution neural network. Then, it constructs a traffic classification model of feature autonomous learning. The model consist of one input layer, two convolutional pooling layers, one full connection layer and one output layer. The experimental results show that the designed model has a good performance in IPv6 traffic classification.
Modern education technologies: 21<sup>st</sup>century trends and challenges

Since modern technologies produce new global education trends, education prospects redesign, and new opportunities and threats assessments are needed. The new educational technology development phase was researched as an answer to modern world challenges. The strategic priorities of education in the EU are analyzed. New technologies, practices, and related needs: financing, digital integration, accessibility, informational security were studied and generalized. Higher education key trends and their transformation in the context of the COVID-19 crisis new opportunities and threats were researched. The analysis of the seven years of NMC Horizon and other research emerging educational technologies adoption forecasts provided in the research shows significant discrepancies related to extreme uncertainty. Five educational trend groups from the NMC Horizon report 2020 were reviewed. The author defined the remote education tools and their importance on the example of the university providing online/mixed learning. The anonymous polls among lecturers and students performed in the spring of 2020 (just after the COVID-19 quarantine announcement) show the most highly demanded tools are Google Meet, email, specific learning systems such as Moodle. The barriers to fully-fledged remote education are technical problems and occasionally poor Internet connectivity. The participants are quite ready to learn and to use digital tools for education. However, the absence of live communication increases workload during online learning, and other factors arise as the factors lowering student activity. During severe transformation, the main tasks of education are accessibility and diversity.
PatchGuard++: Efficient Provable Attack Detection against Adversarial Patches

An adversarial patch can arbitrarily manipulate image pixels within a restricted region to induce model misclassification. The threat of this localized attack has gained significant attention because the adversary can mount a physically-realizable attack by attaching patches to the victim object. Recent provably robust defenses generally follow the PatchGuard framework by using CNNs with small receptive fields and secure feature aggregation for robust model predictions. In this paper, we extend PatchGuard to PatchGuard++ for provably detecting the adversarial patch attack to boost both provable robust accuracy and clean accuracy. In PatchGuard++, we first use a CNN with small receptive fields for feature extraction so that the number of features corrupted by the adversarial patch is bounded. Next, we apply masks in the feature space and evaluate predictions on all possible masked feature maps. Finally, we extract a pattern from all masked predictions to catch the adversarial patch attack. We evaluate PatchGuard++ on ImageNette (a 10-class subset of ImageNet), ImageNet, and CIFAR-10 and demonstrate that PatchGuard++ significantly improves the provable robustness and clean performance.
Automated curriculum learning for embodied agents a neuroevolutionary approach

We demonstrate how the evolutionary training of embodied agents can be extended with a curriculum learning algorithm that automatically selects the environmental conditions in which the evolving agents are evaluated. The environmental conditions are selected to adjust the level of difficulty to the ability level of the current evolving agents, and to challenge the weaknesses of the evolving agents. The method does not require domain knowledge and does not introduce additional hyperparameters. The results collected on two benchmark problems, that require to solve a task in significantly varying environmental conditions, demonstrate that the method proposed outperforms conventional learning methods and generates solutions which are robust to variations and able to cope with different environmental conditions.