Miracle-dz/UniFAR-FTUs huggingface dataset:
A SIEM Architecture for Advanced Anomaly Detection

Dramatic increases in the number of cyber security attacks and breaches toward businesses and organizations have been experienced in recent years. The negative impacts of these breaches not only cause the stealing and compromising of sensitive information, malfunctioning of network devices, disruption of everyday operations, financial damage to the attacked business or organization itself, but also may navigate to peer businesses/organizations in the same industry. Therefore, prevention and early detection of these attacks play a significant role in the continuity of operations in IT-dependent organizations. At the same time detection of various types of attacks has become extremely difficult as attacks get more sophisticated, distributed and enabled by Artificial Intelligence (AI). Detection and handling of these attacks require sophisticated intrusion detection systems which run on powerful hardware and are administered by highly experienced security staff. Yet, these resources are costly to employ, especially for small and medium-sized enterprises (SMEs). To address these issues, we developed an architecture -within the GLACIER projectthat can be realized as an in-house operated Security Information Event Management (SIEM) system for SMEs. It is affordable for SMEs as it is solely based on free and open-source components and thus does not require any licensing fees. Moreover, it is a Self-Contained System (SCS) and does not require too much management effort. It requires short configuration and learning phases after which it can be self-contained as long as the monitored infrastructure is stable (apart from a reaction to the generated alerts which may be outsourced to a service provider in SMEs, if necessary). Another main benefit of this system is to supply data to advanced detection algorithms, such as multidimensional analysis algorithms, in addition to traditional SIEMspecific tasks like data collection, normalization, enrichment, and storage. It supports the application of novel methods to detect security-related anomalies. The most distinct feature of this system that differentiates it from similar solutions in the market is its user feedback capability. Detected anomalies are displayed in a Graphical User Interface (GUI) to the security staff who are allowed to give feedback for anomalies. Subsequently, this feedback is utilized to fine-tune the anomaly detection algorithm. In addition, this GUI also provides access to network actors for quick incident responses. The system in general is suitable for both Information Technology (IT) and Operational Technology (OT) environments, while the detection algorithm must be specifically trained for each of these environments individually. TYPE OF PAPER AND
Interpolation des fonctions de deux variables suivant le principe de la flexion des plaques minces

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On a novel probability distribution for zero-laden compositional data

Summary Recent advances in probabilistic machine learning have given rise to a new family of distributions on the simplex. This distribution, termed the continuous categorical , bears similarities with the Dirichlet in that it deﬁnes an exponential family with a particularly simple closed-form density. However, unlike the Dirichlet (or any log-ratio based method), the continuous categorical log-likelihood function is well deﬁned even in the presence of zero-valued components, making this distribution a valid likelihood model for zero-laden compositional data without requiring imputation of the zeros. In this abstract, we review the key properties of our novel distribution and we present an application where it can be used for dimensionality reduction of compositional data. We also highlight some underexplored connections between the ﬁelds of machine learning and compositional data analysis, to which our novel distribution is closely related.
Technoeconomic analysis of perovskite photovoltaic manufacturing for powering telecommunications Towers

In recent years, metal halide perovskite solar cells have gained traction as a potential competitor to the mature silicon-based solar cells in terms of both cost and performance. Being a young technology, however, means little is known about its true market value. In order to understand this, it is important to both get an accurate estimate of manufacturing cost and explore potential applications. In this thesis, we develop a Monte Carlo cost analysis method and apply it towards a realistic perovskite module manufacturing sequence today. We determine a nominal cost estimate of $101.7/m, which for a 15% efficient module comes out to $0.68/W. Compared to silicon, which hovers around $0.40/W, this is rather competitive, since most of the installed system cost comes from non-module components such as installation labor and racking. With the lightweight and flexible form factor of perovskite solar modules, new applications become possible. One such application is the installation of perovskite photovoltaics (PV) vertically on telecommunications towers. Since such towers cannot support the weight of conventional silicon PV, this is a potential market for perovskite PV that silicon cannot satisfy. Using HOMER microgrid simulation software, we determine that it is financially feasible to install vertical PV in countries with high diesel prices and low grid reliability, such as India. Thesis Supervisor: Vladimir Bulović Title: Professor of Electrical Engineering and Computer Science
Cost Function Based Prediction Markets Aggregate Risk-Averse Experts’ Beliefs as Opinion Pools

Prediction markets are a type of market which are used to aggregate probabilistic beliefs of agents over the outcome of an event. At a high-level, in a prediction market, agents trade securities whose payoffs are tied to the outcome of some future event. Empirical evidence shows that the probability of an event occurring is proportional to the price of the security associated with that security. In this thesis, we focus on a certain type of prediction market called the cost function based prediction market , in which agents purchase or sell shares of a security according to a cost function, which states how much it costs to buy or sell a share of that security. We show that cost function based prediction markets aggregate risk-averse experts’ beliefs about the fair number of net shares that should be bought or sold in the market in the same way as a more traditional method of belief aggregation, called an opinion pool . We also present an equivalence between cost function based prediction markets and another kind of prediction market called market scoring rules , and through this correspondence, we present a new characterization of a class of opinion pools called quasi-arithmetic opinion pools .
Synchronous video-based supervision and feedback in nursing education – a scoping review

The purpose of this review was to identify studies and experiences reporting with triadic synchronous video-based supervision (TSVBS) in nursing education. It is important for nursing students to be supervised by preceptors and academic teachers during their practicum. There are some challenges to performing in-person supervision involving those three parties, like long travel distances and the restrictions caused by the Covid-19 pandemic. TSVBS may be a solution. There is, however, limited knowledge about TSVBS in nurse education. We performed a scoping review focusing on TSVBS to provide an overview of the existing publications on the approach. Only six studies were found describing the use of TSVBS, all of them relating to nurse education. No studies evaluating the effect of TSVBS were found. Instead, the studies only compared the cost-effectiveness of TSVBS with in-person supervision. The supervisory relationship between the three could also not be separated from a more general teacher-student relationship. our description of the as-is situation in the field of triadic synchronous video-based supervision (TSVBS) in higher education.
ELSR: Efficient Line Segment Reconstruction with Planes and Points Guidance

Three-dimensional (3D) line segments are helpful for scene reconstruction. Most of the existing 3D-line-segment reconstruction algorithms deal with two views or dozens of small-size images; while in practice there are usually hundreds or thousands of large-size images. In this paper, we propose an efficient line segment reconstruction method called ELSR11Available at https://skyearth.org/publication/project/ELSR. ELSR exploits scene planes that are commonly seen in city scenes and sparse 3D points that can be acquired easily from the structure-from-motion (SfM) approach. For two views, ELSR efficiently finds the local scene plane to guide the line matching and exploits sparse 3D points to accelerate and constrain the matching. To reconstruct a 3D line segment with multiple views, ELSR utilizes an efficient abstraction approach that selects representative 3D lines based on their spatial consistence. Our experiments demonstrated that ELSR had a higher accuracy and efficiency than the existing methods. Moreover, our results showed that ELSR could reconstruct 3D lines efficiently for large and complex scenes that contain thousands of large-size images.
AsNER - Annotated Dataset and Baseline for Assamese Named Entity recognition

We present the AsNER, a named entity annotation dataset for low resource Assamese language with a baseline Assamese NER model. The dataset contains about 99k tokens comprised of text from the speech of the Prime Minister of India and Assamese play. It also contains person names, location names and addresses. The proposed NER dataset is likely to be a significant resource for deep neural based Assamese language processing. We benchmark the dataset by training NER models and evaluating using state-of-the-art architectures for supervised named entity recognition (NER) such as Fasttext, BERT, XLM-R, FLAIR, MuRIL etc. We implement several baseline approaches with state-of-the-art sequence tagging Bi-LSTM-CRF architecture. The highest F1-score among all baselines achieves an accuracy of 80.69% when using MuRIL as a word embedding method. The annotated dataset and the top performing model are made publicly available.
The Impact of Corporate Governance on Auditors’ Independence of Some Selected Quoted Companies in Nigeria

This study examines the impact of corporate governance on auditors’ independence of some selected multi-national companies in Nigeria. The objectives of the study are to establish the type of relationship that exists between the audit committee as a tool of corporate governance and auditors’ independence, to ascertain the impact of non-executive director on auditors’ independence, to ascertain the impact of executive director on auditors’ independence, to examine the effect of board size and audit committee on auditors’ independence. This study employed ex-post facto research design using panel data analyses of financial information extracted from Financial Statements for years 2011-2015 of 18 companies listed on the floor of Nigeria Stock Exchange. To achieve the study objective, stratified and purposive random sampling techniques were adopted and all the eleven (11) sectors present on the Nigeria Stock Exchange were represented. The study used the static panel ordinary least square (OLS) to model the relationship between corporate governance and auditors’ independence among listed multi-national firms on the Nigerian Security Exchange (NSE). The recommends that more powers should be given to the external auditor in order to free him from influence or intimidation by the client and the auditor should avoid being in the audit of the client for longer than three (3) years.
Utility Tokens as a Commitment to Competition∗

We show that utility tokens can limit the rent-seeking activities of two-sided platforms with market power while preserving efficiency gains due to network effects. We model platforms where buyers and sellers can meet to exchange services. Tokens serve as the sole medium of exchange on the platform and can be traded in a secondary market. Tokenizing a platform allows a firm to give up monopolistic rents associated with the control of the platform and to make a credible commitment to long-run competitive prices. Crowd-funding through token sales or the threat of entrants can incentivize developers to tokenize platforms.
IgboBERT Models: Building and Training Transformer Models for the Igbo Language

This work presents a standard Igbo named entity recognition (IgboNER) dataset as well as the results from training and fine-tuning state-of-the-art transformer IgboNER models. We discuss the process of our dataset creation - data collection and annotation and quality checking. We also present experimental processes involved in building an IgboBERT language model from scratch as well as fine-tuning it along with other non-Igbo pre-trained models for the downstream IgboNER task. Our results show that, although the IgboNER task benefited hugely from fine-tuning large transformer model, fine-tuning a transformer model built from scratch with comparatively little Igbo text data seems to yield quite decent results for the IgboNER task. This work will contribute immensely to IgboNLP in particular as well as the wider African and low-resource NLP efforts Keywords: Igbo, named entity recognition, BERT models, under-resourced, dataset
Advances in the Treatment of Acute ITP

Copyright © 2022 by the author(s). Published by Bilingual Publishing Co. This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License. (https://creativecommons.org/licenses/by-nc/4.0/). *Corresponding Author: Jing Yang, School of Nursing, Hainan Medical University, Haikou, Hainan, 570100, China; Email: onlymyang@163.com Advances in the Treatment of Acute ITP
Dialogue Collection for Recording the Process of Building Common Ground in a Collaborative Task

To develop a dialogue system that can build common ground with users, the process of building common ground through dialogue needs to be clarified. However, the studies on the process of building common ground have not been well conducted; much work has focused on finding the relationship between a dialogue in which users perform a collaborative task and its task performance represented by the final result of the task. In this study, to clarify the process of building common ground, we propose a data collection method for automatically recording the process of building common ground through a dialogue by using the intermediate result of a task. We collected 984 dialogues, and as a result of investigating the process of building common ground, we found that the process can be classified into several typical patterns and that conveying each worker’s understanding through affirmation of a counterpart’s utterances especially contributes to building common ground. In addition, toward dialogue systems that can build common ground, we conducted an automatic estimation of the degree of built common ground and found that its degree can be estimated quite accurately.
Mitigating Dataset Artifacts in Natural Language Inference Through Automatic Contextual Data Augmentation and Learning Optimization

In recent years, natural language inference has been an emerging research area. In this paper, we present a novel data augmentation technique and combine it with a unique learning procedure for that task. Our so-called automatic contextual data augmentation (acda) method manages to be fully automatic, non-trivially contextual, and computationally efficient at the same time. When compared to established data augmentation methods, it is substantially more computationally efficient and requires no manual annotation by a human expert as they usually do. In order to increase its efficiency, we combine acda with two learning optimization techniques: contrastive learning and a hybrid loss function. The former maximizes the benefit of the supervisory signal generated by acda, while the latter incentivises the model to learn the nuances of the decision boundary. Our combined approach is shown experimentally to provide an effective way for mitigating spurious data correlations within a dataset, called dataset artifacts, and as a result improves performance. Specifically, our experiments verify that acda-boosted pre-trained language models that employ our learning optimization techniques, consistently outperform the respective fine-tuned baseline pre-trained language models across both benchmark datasets and adversarial examples.
Evaluating Usability of Academic Web Portals for Social-Academic Learning

The importance of an institution’s web portal for alternative learning outside physical classrooms or lecture halls has been spotlighted by the impact of the coronavirus pandemic. In this paper, the usability of a social-academic platform has been evaluated as one of the vital services that foster collaborative learning and increase the usage of academic web portals. The evaluation adopted a standard post-system usability assessment tool – System Usability Scale, to examine the platform’s usability and validate the construct of perceived social learning by random evaluators. The outcome of the post-system evaluation noted empirically that, the system is perceived with a 70.9% learnability and 83.9% usability scale score with excellent usability in the 90th-95th percentile range. Consequently, the study shows that the social academic web portal platform in such a pandemic era is adaptable, usable, and can be beneficial in fostering social academic interactions between lecturers and students in learning institutions, and therefore increase the web portal’s usage.
Backwards Reachability for Probabilistic Timed Automata: A Replication Report

Backwards reachability is an eﬃcient zone-based approach for model checking probabilistic timed automata w.r. t. PTCTL properties. Current implementations, however, are restricted to maximum probabilities of reachability properties. In this paper, we report on our new implementation of backwards reachability as part of the Modest Toolset . Its support for minimum and maximum probabilities of until formulas makes it the most general implementation available today. We compare its behaviour to the experimental results reported in the original papers presenting the backwards reachability technique.
A General Framework for Detecting Metaphorical Collocations

This paper aims at identifying a specific set of collocations known under the term metaphorical collocations. In this type of collocations, a semantic shift has taken place in one of the components. Since the appropriate gold standard needs to be compiled prior to any serious endeavour to extract metaphorical collocations automatically, this paper first presents the steps taken to compile it, and then establishes appropriate evaluation framework. The process of compiling the gold standard is illustrated on one of the most frequent Croatian nouns, which resulted in the preliminary relation significance set. With the aim to investigate the possibility of facilitating the process, frequency, logDice, relation, and pretrained word embeddings are used as features in the classification task conducted on the logDice-based word sketch relation lists. Preliminary results are presented.
Manuscript Template Full titles ： Lateral Hypothalamus Calcium/calmodulin-dependent protein kinase Ⅱ α Neurons Encode Novelty-Seeking Signals to Promote Predatory Eating Short titles ： Lateral Hypothalamus CaMKIIα + Neurons Encode Hunting

Predatory hunting is an innate appetite-driven and evolutionarily conserved behavior essential for animal survival, integrating sequential behaviors including searching, pursuit, attack, retrieval, and ultimately consumption. Nevertheless, neural circuits underlying hunting behavior with different features remain largely unexplored. Here, we deciphered a novel function of lateral hypothalamus (LH) Calcium/calmodulin-dependent protein kinase Ⅱ α (CaMKIIα + ) neurons in hunting behavior and uncovered upstream/downstream circuit basis. LH CaMKIIα + neurons bi-directionally modulate novelty-seeking behavior, predatory attack and eating in hunting behavior. LH CaMKIIα + neurons integrate hunting-related novelty-seeking information from the medial preoptic area (MPOA) and project to the ventral periaqueductal gray (vPAG) to promote predatory eating. Our results demonstrate that LH CaMKIIα + neurons are the key hub that integrate MPOA-conveyed novelty-seeking signals and encode predatory eating in hunting behavior, which enriched the neuronal substrate of hunting behavior. optically stimulating
Annotation of Valence Unfolding in Spoken Personal Narratives

Personal Narrative (PN) is the recollection of individuals’ life experiences, events, and thoughts along with the associated emotions in the form of a story. Compared to other genres such as social media texts or microblogs, where people write about experienced events or products, the spoken PNs are complex to analyze and understand. They are usually long and unstructured, involving multiple and related events, characters as well as thoughts and emotions associated with events, objects, and persons. In spoken PNs, emotions are conveyed by changing the speech signal characteristics as well as the lexical content of the narrative. In this work, we annotate a corpus of spoken personal narratives, with the emotion valence using discrete values. The PNs are segmented into speech segments, and the annotators annotate them in the discourse context, with values on a 5-point bipolar scale ranging from -2 to +2 (0 for neutral). In this way, we capture the unfolding of the PNs events and changes in the emotional state of the narrator. We perform an in-depth analysis of the inter-annotator agreement, the relation between the label distribution w.r. t. the stimulus (positive/negative) used for the elicitation of the narrative, and compare the segment-level annotations to a baseline continuous annotation. We find that the neutral score plays an important role in the agreement. We observe that it is easy to differentiate the positive from the negative valence while the confusion with the neutral label is high. Keywords: Personal Narratives, Emotion Annotation, Segment Level Annotation