Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative assessment of Western Podolia meadow steppes (Ukraine) based on the synphytoindication method

Abstract The aim of our study is to establish the determinant ecological factors that have the greatest differential impact on the distribution of meadow steppe plant communities of Western Podolia (Ukraine) on the base of a synphytoindication analysis. There were 8 study sites within the study area in the L’viv and Ivano-Frankivsk regions. To determine the coenotic affinity of the studied species, 48 relevés were analyzed. Numerical classification based on the Modified TWINSPAN algorithm divided the dataset into five clusters, which have been identified as the associations Pastinaco sativae-Arrhenatheretum elatioris, Geranio sanguinei-Trifolietum alpestris, Inuletum ensifoliae, Jurineo calcareae-Stipetum capillatae and Lembotropio nigricans-Potentilletum arenariae. The results of gradient analyses indicate that the distribution of meadow steppe communities in the Podolia Upland closely correlates with a number of climatic (thermoregime and continentality of climate) and especially edaphic (soil humidity, soil acidity and carbonate content) ecological factors.
Prevalence of KIR2DL2/DS2 and KIR2DL3 and Presence of B19V in Patients with Thyroid Disorders

Abstract The functions of human natural killer cells are controlled by diverse families of antigen receptors. Prominent among these are the killer cell immunoglobulin-like receptors (KIR), controlled by a family of genes clustered in one of the most variable regions of the human genome — on chromosome 19q13.4. This study aimed to investigate the possible interplay between KIR allotype, B19 infection, and thyroid disorders. Thyroid gland tissue of 30 patients with autoimmune thyroid gland diseases (AITD), 30 patients with non-autoimmune thyroid gland diseases (non-AITD) and 30 deceased subjects whose histories did not show any of autoimmune or thyroid diseases (control group) were enrolled in the study. The presence of B19V, KIR2DL2/DS2, and KIR2DL3 was detected using PCRs (nPCR, PCR). The results showed that 28% of samples of thyroid tissue from patients with AITD and 67% with non-AITD were positive for the presence of B19V, in contrast only 5% control tissue samples harbored B19V DNA. B19V-positive AITD patients had higher frequency of KIR2DL2/DS2 homozygosity and lower frequency of homozygous KIR2DL3 genotype compared to B19V negative cases (33% vs 21% and 17% vs 46%, respectively). Although our data showed that B19V positive patients with AITD had a higher frequency of homozygosity for KIR2DL2/DS2, further studies with larger groups of patients are necessary to confirm the relationship between KIR, B19V and susceptibility to thyroid disease.
Expressing uncertainty in neural networks for production systems

Abstract The application of machine learning, especially of trained neural networks, requires a high level of trust in their results. A key to this trust is the network’s ability to assess the uncertainty of the computed results. This is a prerequisite for the use of such networks in closed-control loops and in automation systems. This paper describes approaches for enabling neural networks to automatically learn the uncertainties of their results.
A New Benchmark for NLP in Social Sciences: Evaluating the Usefulness of Pre-trained Language Models for Classifying Open-ended Survey Responses

In order to evaluate transfer learning models for Natural Language Processing on a common ground, numerous general domain (sets of) benchmark data sets have been established throughout the last couple of years. Primarily, the proposed tasks are classification (binary, multi-class), regression or language generation. However, no benchmark data set for (extreme) multi-label classification relying on full-text inputs has been proposed in the area of social science survey research to this date. This constitutes an important gap, as a common data set for algorithm development in this field could lead to more reproducible, sustainable research. Thus, we provide a transparent and fully reproducible preparation of the 2008 American National Election Study (ANES) data set, which can be used for benchmark comparisons of different NLP models on the task of multi-label classification. In contrast to other data sets, our data set comprises full-text inputs instead of bag-of-words representations or similar. Furthermore, we provide baseline performances of simple logistic regression models as well as performance values for recently established transfer learning architectures, namely BERT (Devlin et al., 2018), RoBERTa (Liu et al., 2019) and XLNet (Yang et al., 2019).
HyperPass: Secure Password Input Platform

Confidential information, like passwords and credit card numbers, travel from the user’s local machine to a remote server via secure communication protocols. Whereas remote servers are serviced by security specialists, local machines are more vulnerable to memory attacks and keyloggers. To counter these attacks we propose a secure password input platform, called HyperPass, which is based on a thin hypervisor. The thin hypervisor acts as an isolated and secure environment for entering and encrypting user’s confidential information. HyperPass uses the keyboard’s scroll lock LED as a security indicator. Our evaluation shows that the performance overhead of HyperPass is insignificant (≈ 2.79%).
Identifying Depression Clues using Emotions and AI

According to the World Health Organization (WHO), close to 300 million people of all ages suffer from depression. Also, for WHO, depression is the leading reason for disability worldwide and is a major contributor to the global burden of disease. Different than the mood fluctuation raised by the common life’s activities, depression can be a serious health problem, particularly when it is a long-term and mid/high intensity. Luckily, despite depression is a silent disease, people when suffering leaves some clues. Due to the massive use of social media, these clues can be collected through the texts posted on social media, such as Twitter, Facebook, Instagram, and later, analysed to identify if the writing style matches with a depressive pattern. This paper presents an approach that can be applied by Machine Learning models to help psychologists to identify depressive clues in texts. The model examines profiles on Twitter based on clues provided by users in their posts. Combining Sentiment Analysis, Machine Learning and Natural Language Processing techniques, we achieved a precision of 98% by Machine Learning models when identifying Twitter profiles that post potential
On using Theorem Proving for Cognitive Agent-oriented Programming

: Demonstrating reliability of cognitive multi-agent systems is of key importance. There has been an extensive amount of work on logics for verifying cognitive agents but it has remained mostly theoretical. Cognitive agent-oriented programming languages provide the tools for compact representation of complex decision making mechanisms, which offers an opportunity for applying a theorem proving approach. We base our work on the belief that theorem proving can add to the currently available approaches for providing assurance for cognitive multi-agent systems. However, a practical approach using theorem proving is missing. We explore the use of proof assistants to make verifying cognitive multi-agent systems more practical.
SciModeler: A Metamodel and Graph Database for Consolidating Scientific Knowledge by Linking Empirical Data with Theoretical Constructs

: An important purpose of science is building and advancing general theories from empirical data. This process is complicated by the immense volume of empirical data and scientiﬁc theories in some ﬁelds. Particularly, the systematic linking of empirical data with theoretical constructs is currently lacking. Within this article, we propose a prototypical solution (i.e., a metamodel and graph database) for consolidating scientiﬁc knowledge by linking theoretical constructs with empirical data. We conducted a case study within the ﬁeld of health behavior change where the system is used to record three scientiﬁc theories and three empirical studies as well as their mutual links. Finally, we demonstrate how the system can be queried to accumulate knowledge.
An Empirical Study on Machine Learning Models for Potato Leaf Disease Classification using RGB Images

In this work, an empirical study is conducted on classification models built using RGB images of potato leaves. A series of experiments are done by training convolutional neural network (CNN) and support vector machine (SVM) using images captured in laboratory and field conditions and processed samples of images captured in field. A salient region based segmentation algorithm is devised to generate processed version of the images captured in field which performed well with respect to manually segmented ground truth of the dataset. Severe inconsistencies are observed in experimental results, particularly when train and test samples of models are similar images but captured under different environmental conditions. Following the analysis of obtained results, we come up with a set of clear directions to create an image dataset, which can lead to a reliable classification accuracy.
Improving Classification of Malware Families using Learning a Distance Metric

: The objective of malware family classiﬁcation is to assign a tested sample to the correct malware family. This paper concerns the application of selected state-of-the-art distance metric learning techniques to malware families classiﬁcation. The goal of distance metric learning algorithms is to ﬁnd the most appropriate distance metric parameters concerning some optimization criteria. The distance metric learning algorithms considered in our research learn from metadata, mostly contained in the headers of executable ﬁles in the PE ﬁle format. Several experiments have been conducted on the dataset with 14,000 samples consisting of six prevalent malware families and benign ﬁles. The experimental results showed that the average precision and recall of the k -Nearest Neighbors algorithm using the distance learned on training data were improved signiﬁcantly comparing when the non-learned distance was used. The k -Nearest Neighbors classiﬁer using the Mahalanobis distance metric learned by the Metric Learning for Kernel Regression method achieved average precision and recall, both of 97.04% compared to Random Forest with a 96.44% of average precision and 96.41% of average recall, which achieved the best classiﬁcation results among the state-of-the-art ML algorithms considered in our experiments.
Incremental Learning for Real-time Partitioning for FPGA Applications

The co-design approach consists in defining all the sub-tasks of an application to be integrated and distributed on software or hardware targets. The introduction of conventional cognitive reasoning can solve several problems such as real-time hardware/software classification for FPGA-based applications. However, this requires the availability of large databases, which may conflict with real-time applications. The proposed method is based on the Incremental Kernel SVM (InKSVM) model. InKSVM learns incrementally, as new data becomes available over time, in order to efficiently process large, dynamic data and reduce computation time. As a result, it relaxes the assumption of complete data availability and provides fully autonomous performance. Hence, in this paper, an incremental learning algorithm for hardware/software partitioning is presented. Starting from a real database collected from our FPGA experiments, the proposed approach uses InKSVM to perform the task classification in hardware and software. The proposal has been evaluated in terms of classification efficiency. The performance of the proposed approach was also compared to reference works in the literature. The results of the evaluation consist in empirical evidence of the superiority of the InKSVM over state-of-theart progressive learning approaches in terms of model accuracy and complexity.
Drivable Area Extraction based on Shadow Corrected Images

Drivable area detection is a complex task that needs to operate efficiently in any environmental condition to ensure wide adoption of autonomous vehicles. In the case of low cost camera-based drivable area detection, the spatial information is required to be uniform as much as possible to ensure the robustness and reliability of the results of any algorithm in most weather and illumination conditions. The general change in illumination and shadow intensities present a significant challenge and can cause major accidents if not considered. Moreover, drivable area detection in unstructured environments is more complex due to the absence of vital spatial information such as road markings and lanes. In this paper, a shadow reduction approach combining Computer Vision (CV) Image Processing (IM) with Deep Learning (DL) is used on a low cost monocular camera based system for reliable and uniform shadow removal. In addition, a validation test is applied with a DL model to validate the approach. This system is developed for the Self-driving Car (SDC) lab at the German University in Cairo (GUC) and is to be used in the shell eco-marathon autonomous competition 2021.
New Maximum Similarity Method for Object Identification in Photon Counting Imaging

The paper discusses a new approach to recognition / identification of the test objects according to their intensity shape in the images registered by photon counting detectors. The main problem analyzed within the framework of the proposed approach is related to the identification decision (inference ) based on a registered set of discrete photocounts (~ photons) regarding the similarity of the shape of the object's intensity in the image to the shape of previously observed objects (precedents). It is shown that when the intensity shape is approximated by a mixture of Gaussian components within the framework of this approach, a recurrent identification algorithm can be synthesized, similar to the well-known K-means clustering algorithm in the machine (statistical) learning.
Novel Radar-based Gesture Recognition System using Optimized CNN-LSTM Deep Neural Network for Low-power Microcomputer Platform

The goal of the embedded hand gesture recognition based on a radar sensor is to improve a human-machine interface, while taking into consideration privacy issues of camera sensors. In addition, the system has to be deployable on a low-power microcomputer for the applicability in broadly defined IoT and smart home solutions. Currently available gesture sensing solutions are ineffective in terms of low-power consumption what prevents them from the deployment on the low-power microcomputers. Recent advances exhibit a potential of deep learning models for a gesture classification whereas they are still limited to high-performance hardware. Embedded microcomputers are constrained in terms of memory, CPU clock speed and GPU performance. These limitations imply a topology design problem that is addressed in this work. Moreover, this research project proposes an alternative signal processing approach – using the continuous wavelet transform, which enables us to see the distribution of frequencies formed by every gesture. The newly proposed neural network topology performs equally well compared to the state of the art neural networks, however it needs only 54.6% of memory and it needs 20% of time to perform inference relative to the state of the art models. This dedicated neural network architecture allows for the deployment on resource constrained microcomputers, thus enabling a human-machine interface implementation on the embedded devices. Our system achieved an overall accuracy of 95.05% on earlier unseen data.
Is Your Chatbot Perplexing?: Confident Personalized Conversational Agent for Consistent Chit-Chat Dialogue

Chatbots are being researched and employed not only in academic settings but also in many fields as an application. Ultimately, conversational agents attempt to produce human-like responses along with dialogues. To achieve this goal, we built a novel framework that processes complex data consisting of personalities and utterances and fine-tuned a large-scale self-attention-based language model. We propose a consistent personalized conversational agent(CPC-Agent) for the framework. Our model was designed to utilize the complex knowledge of a dataset to achieve accuracy and consistency. Together with a distractor mechanism, we could generate confident responses. We compared our model to state-of-the-art models using automated metrics. Our model scored 3.29 in perplexity, 17.59 in F1 score, and 79.5 in Hits@1. In particular, the perplexity result was almost four times smaller than that of the current state-of-the-art model that scored 16.42. In addition, we conducted a human evaluation of each model to determine its response quality because the automatic evaluation metrics in dialogue tasks are still considered insufficient. Our model achieved the best rates from the voters, which indicated that our model is adequate for practical use.
Automated Black Box Detection of HTTP GET Request-based Access Control Vulnerabilities in Web Applications

Automated and reproducible security testing of web applications is getting more and more important, driven by short software development cycles and constraints with respect to time and budget. Some types of vulnerabilities can already be detected reasonably well by automated security scanners, e.g., SQL injection or cross-site scripting vulnerabilities. However, other types of vulnerabilities are much harder to uncover in an automated way. This includes access control vulnerabilities, which are highly relevant in practice as they can grant unauthorized users access to security-critical data or functions in web applications. In this paper, a practical solution to automatically detect access control vulnerabilities in the context of HTTP GET requests is presented. The solution is based on previously proposed ideas, which are extended with novel approaches to enable completely automated access control testing with minimal configuration effort that enables frequent and reproducible testing. An evaluation using four web applications based on different technologies demonstrates the general applicability of the solution and that it can automatically uncover most access control vulnerabilities while keeping the number of false positives relatively low.
The Determinants of Social Media Engagement for Fashion Industry in Oman: A Descriptive Analysis

The use of social media has completely remodelled the way people interact, communicate, and engage. Social media platforms play an essential role in reshaping the relationship between customers and companies. Present companies establish their accounts in social media to reach and engage with their customers, listen and take their opinion, enhance the purchase decision, and increase the revenue. The main goal of this study is to determine the factors that affect customer engagement. From 296 Instagram business accounts with 530,366 posts published, the dataset was scraped and used to understand what impacts customer engagement. Different descriptive analysis techniques were adopted to answer the questions of the study. Among the key finding of this study, customer engagement is positively affected by the number of comments and shares. The number of likes of published posts is not influenced. Moreover, video posts attract more customer interaction than other types of posts. Uncovered the property of three kinds of customer engagement (low, moderately,
Malware Classification using Long Short-term Memory Models

Signature and anomaly based techniques are the quintessential approaches to malware detection. However, these techniques have become increasingly ineffective as malware has become more sophisticated and complex. Researchers have therefore turned to deep learning to construct better performing model. In this paper, we create four different long-short term memory (LSTM) based models and train each to classify malware samples from 20 families. Our features consist of opcodes extracted from malware executables. We employ techniques used in natural language processing (NLP), including word embedding and bidirection LSTMs (biLSTM), and we also use convolutional neural networks (CNN). We find that a model consisting of word embedding, biLSTMs, and CNN layers performs best in our malware classification experiments.
TrajNet: An Efficient and Effective Neural Network for Vehicle Trajectory Classification

Vehicle trajectory classification plays an important role in intelligent transportation systems because it can be utilized in traffic flow estimation at an intersection and anomaly detection such as traffic accidents and violations of traffic regulations. In this paper, we propose a new neural network architecture for vehicle trajectory classification by modifying the PointNet architecture, which was proposed for point cloud classification and semantic segmentation. The modifications are derived based on analyzing the differences between the properties of vehicle trajectory and point cloud. We call the modified network TrajNet. It is demonstrated from experiments using three public datasets that TrajNet can classify vehicle trajectories faster and more slightly accurate than the conventional networks used in the previous studies.
Feature Extraction and Neural Network-based Analysis on Time-correlated LiDAR Histograms

Time correlated single photon counting (TCSPC) is used to obtain the time-of-flight (TOF) information generated by single-photon avalanche diodes. With restricted measurements per histogram and the presence of high background light, it is challenging to obtain the TOF information in the statistical histogram. In order to improve the robustness under these conditions, the concept of machine learning is applied to the statistical histogram. Using the multi-peak extraction method, introduced by us, followed by the neural-network-based multi-peak analysis, the analysis and resources can be focused on a small amount of critical information in the histogram. Multiple possible TOF positions are evaluated and the correlated soft-decisions are assigned. The proposed method has higher robustness in allocating the coarse position (± 5 %) of TOF in harsh conditions than the case using classical digital processing. Thus, it can be applied to improve the system robustness, especially in the case of high background light.
Unsupervised Word Sense Disambiguation based on Word Embedding and Collocation

This paper proposes a novel unsupervised word sense disambiguation (WSD) method. It utilizes two useful features for WSD. One is contextual information of a target word. The similarity between words in a context and a sense of a target word is measured based on the pre-trained word embedding, then the most similar sense to the context is chosen. Furthermore, we introduce a procedure not to use irrelevant words in a context in a calculation of the similarity. The other is a collocation, which is an idiomatic phrase including a target word. High-precision rules to determine a sense by a collocation is automatically acquired from a raw corpus. Finally, the above two methods are integrated into our final WSD system. Results of the experiments using Senseval-3 English lexical sample task showed that our proposed method could improve the precision by 4.7 point against the baseline.