Reviewerly/exHarmony huggingface dataset:
A Quest of Self-Explainability: When Causal Diagrams meet Autonomous Urban Traffic Manoeuvres

While autonomous systems are increasingly capturing the markets, they also become more and more complex. Thus, the (self-) explainability of these complex and adaptive systems becomes evermore important. We introduce explainability to our previous work on formally proving properties of autonomous urban traffic manoeuvres. We build causal diagrams by connecting actions of a crossing protocol with their reasons and derive explanation paths from these diagrams. We strive to bring our formal methods approach together with requirements engineering approaches, by suggesting to use run-time requirements engineering to update our causal diagrams at run-time.
Aggregating Viewpoints for Effective View-Based 3D Model Retrieval

The bag-of-features (BoF) model is the standard platform for image retrieval systems and successfully extended to systems for exploring 3D models through their projected views. However, we need a large number of views for each 3D model to achieve shape retrieval systems with high accuracy, which results in increased data storage and long computation time for shape comparison. This paper presents an approach for reducing projected images in such image-based shape retrieval by aggregating views of each 3D model. Our approach begins by discovering a proper metric for evaluating dissimilarity between 3D models by referring to their high-dimensional feature vectors obtained from the BoF model. We then introduce a variant of the k-means clustering method to identify the representative views of each 3D model, given the number of such essential views. Finally, we adjust the degree of such view aggregation by assessing the number of plane symmetries for each 3D model. We test our approach with a dataset containing 200 3D models and we learn that we can reduce the number of views to less than 10% while limiting the degradation of accuracy to approximately 5%.
Adaptive Anomaly Detection Network for Unseen Scene Without Fine-Tuning

Anomaly detection in video is a challenging task with great application value. Most existing approaches formulate anomaly detection as a reconstruction/prediction problem established on the encoder-decoder structure. However, they suffer from the poor generalization performance when the model is directly applied to an unseen scene. To solve this problem, in this paper, we propose an Adaptive Anomaly Detection Network (AADNet) to realize few-shot scene-adaptive anomaly detection. Our core idea is to learn an adaptive model, which can identify abnormal events without fine-tuning when transferred to a new scene. To this end, in AADNet, a Segments Similarity Measurement (SSM) module is utilized to calculate the cosine distance of different input video segments, based on which the normal segments will be gathered. Meanwhile, to further exploit the information of normal events, we design a novel Relational Scene Awareness (RSA) module to capture the pixel-to-pixel relationship between different segments. By combining the SSM module with RSA, the proposed AADNet becomes much more generative. Extensive experiments on four datasets demonstrate our method can adapt to a new scene effectively without fine-tuning and achieve the state-of-the-art performance.
Determining Subjective Bias in Text through Linguistically Informed Transformer based Multi-Task Network

The predominance of biased articles and its consumption by the readers is becoming a considerable issue. Researchers across domains have made efforts to mitigate biases in language. However, due to the subjective nature of the problem, it is not trivial to detect bias embedded in a text. In this paper, we propose a deep linguistically informed multi-task transformer-based model to automatically detect bias in written text. The model is fine-tuned with a domain-specific corpus and further trained for learning the objectives. We evaluate the performance of the proposed model with respect to baseline systems across multiple datasets. We observed that augmenting linguistic features along with contextual embedding improves the performance of the neural network model to automatically detect bias in text.
Multi-objective Few-shot Learning for Fair Classification

In this paper, we propose a general framework for mitigating the disparities of the predicted classes with respect to secondary attributes within the data (e.g., race, gender etc.). Our proposed method involves learning a multi-objective function that in addition to learning the primary objective of predicting the primary class labels from the data, also employs a clustering-based heuristic to minimize the disparities of the class label distribution with respect to the cluster memberships, with the assumption that each cluster should ideally map to a distinct combination of attribute values. Experiments demonstrate effective mitigation of cognitive biases on a benchmark dataset without the use of annotations of secondary attribute values (the zero-shot case) or with the use of a small number of attribute value annotations (the few-shot case).
Lightweight Multi-Scale Context Aggregation Deraining Network With Artifact-Attenuating Pooling and Activation Functions

Single image deraining is a fundamental pre-processing step in many computer vision applications for improving the visual effect and system performance of the downstream high-level tasks in adverse weather conditions.This study proposes a novel multi-scale context aggregation network, to effectively solve the single image deraining problem.Specifically, we exploit a lightweight residual structure subnet as the baseline architecture to extract fine and detailed texture context at the original scale and further incorporate a multi-scale progressive aggregation module (MPAM) to learn the complementary high-level context for enhancing the modeling capability of the overall deraining network.The MPAM, designed as a plug-and-play module to be utilized in the arbitrary network, is composed of multi-scale convolution blocks to learn a wide variety of contexts in multiple receptive fields, and then carries out progressive context aggregation between adjacent scales with residual connections, which is expected to concurrently disentangle the multi-scale structures of scene contents and multiple rain layers in the rainy images, and models more representative contexts for reconstructing the clean image.To reduce the learnable parameters in the MPAM, we further explore a context hallucinate block for replacing the multi-scale convolution block, and propose a lightweight MPAM.Moreover, for being specially adaptive to deal with the input rainy images with a lot of unwanted components (rain layers), we delve into the artifact-attenuating pooling and activation functions via taking into consideration of the surrounding spatial context instead of pixel-wise operation and propose the spatial context-aware pooling (SCAP) and activation (SCAA) for incorporating with our deraining network to boost performance.Extensive experiments on the benchmark datasets demonstrate that our proposed method performs favorably against state-of-the-art deraining approaches.
Guardian

Modern processors can offer hardware primitives that allow a process to run in isolation. These primitives implement a trusted execution environment (TEE) in which a program can run such that the integrity and confidentiality of its execution are guaranteed. Intel's Software Guard eXtensions (SGX) is an example of such primitives and its isolated processes are called enclaves. These guarantees, however, can be easily thwarted if the enclave has not been properly designed. Its interface with the untrusted software stack is a perhaps the largest attack surface that adversaries can exploit; unintended interactions with untrusted code can expose the enclave to memory corruption attacks, for instance.
Creative Projects as a Link Between Theory and Practice

The Russian national innovation system is based on the creativity of the economy, the value of which is expressed in new ideas, not traditional resources. Innovative ideas change the economy as a whole, transforming the approach to production, trade, and the provision of services, while preserving the local identity of the territory in the era of globalization. Educational institutions that train intellectual and creative personnel for the new economy play a special role in the creative economy. The article describes the experience of organizing interdisciplinary projects according to the instructions of employers by students of different study areas and different levels of training for the development of professional competencies and the development of creative thinking. The conclusion is made about the success of the proposed tool for obtaining practice-oriented knowledge. The results of the study can be used as a justification for the implementation of the principles of interdisciplinary project-based learning in higher education institutions.
HHP-Net: A light Heteroscedastic neural network for Head Pose estimation with uncertainty

In this paper we introduce a novel method to estimate the head pose of people in single images starting from a small set of head keypoints. To this purpose, we propose a regression model that exploits keypoints computed automatically by 2D pose estimation algorithms and outputs the head pose represented by yaw, pitch, and roll. Our model is simple to implement and more efficient with respect to the state of the art &#x2013;faster in inference and smaller in terms of memory occupancy &#x2013;with comparable accuracy.Our method also provides a measure of the heteroscedastic uncertainties associated with the three angles, through an appropriately designed loss function; we show there is a correlation between error and uncertainty values, thus this extra source of information may be used in subsequent computational steps. As an example application, we address social interaction analysis in images: we propose an algorithm for a quantitative estimation of the level of interaction between people, starting from their head poses and reasoning on their mutual positions.
Workshop: Challenges for XR in Digital Entertainment

Extended Reality as a consolidated game platform was always a dream for both final consumers and game producers. If for one side this technology had enchanted and called the attention due its possibilities, for other side many challenges and difficulties had delayed its proliferation and massification. This workshop intends to rise and discuss aspects and considerations related to these challenges and solutions. We try to bring the most relevant research topics and try to guess how XR games should look in the near future. We divide the challenges into 7 topics: Cybersickness, User Experience, Displays, Rendering, Movements, Body Tracking and External World Information.
About Chemical Modifications of Finite Dimensional QED Models

Suggestion of modifications of finite-dimensional quantum-electrodynamic (QED) models are proposed for interpreting chemical reactions in terms of artificial atoms and molecules on quantum dots placed in optical cavities. Moving both photons and atoms is possible between the cavities. Super dark states of diatomic systems are described, in which the motion of atoms between cavities is impossible due to quantum interference. Chemical processes with two level atoms and three level atoms with lambda spectrum are schematically modeled by solving the single quantum master equation with the Lindblad operators of photon leakage from the cavity and influx into it; association and dissociation reactions then differ only in the initial states. An example is given of the optical interpretation of the transition of an electron from atom to atom in terms of the multilevel Tavis-Cummings-Hubbard model with an estimate of the accuracy. Polyatomic chemical reactions are too complex for accurate modeling. Our method of rough interpretation helps to obtain their long-term results, for example, the form of stationary states of reagents, such as dark and super dark states.
A Lightweight One-Stage 3D Object Detector Based on LiDAR and Camera Sensors

Currently, many kinds of LiDAR-camera-based 3D object detectors have been developed with two individual heavy branches, one is used to extract features from LiDAR data, and the other one is utilized to extract features from camera data. In contrast, this paper proposes an early-fusion method to exploit both LiDAR and camera data for fast 3D object detection using only one backbone, achieving a good balance between accuracy and efficiency. Massive experiments evaluated on the KITTI benchmark suite show that the proposed approach outperforms state-of-the-art LiDAR-camera-based methods on the three classes. Additionally, the proposed model runs at 23 frames per second (FPS), which is almost 2× faster than state-of-the-art fusion methods for LiDAR and camera.
Query Reformulation for Descriptive Queries of Jargon Words Using a Knowledge Graph based on a Dictionary

Query reformulation (QR) is a key factor in overcoming the problems faced by the lexical chasm in information retrieval (IR) systems. In particular, when searching for jargon, people tend to use descriptive queries, such as "a medical examination of the colon" rather than "colonoscopy," or they often use them interchangeably. Thus, transforming users' descriptive queries into appropriate jargon queries helps to retrieve more relevant documents. In this paper, we propose a new graph-based QR system that uses a dictionary, where the model does not require human-labeled data. Given a descriptive query, our system predicts the corresponding jargon word over a graph consisting of pairs of a headword and its description in the dictionary. First, we train a graph neural network to represent the relational properties between words and to infer a jargon word using compositional information of the descriptive query's words. Moreover, we propose a graph search model that finds the target node in real time using the relevance scores of neighborhood nodes. By adding this fast graph search model to the front of the proposed system, we reduce the reformulating time significantly. Experimental results on two datasets show that the proposed method can effectively reformulate descriptive queries to corresponding jargon words as well as improve retrieval performance under several search frameworks.
Automatic Graph Learning with Evolutionary Algorithms: An Experimental Study

In recent years, automated machine learning (AutoML) has received widespread attention from academia and industry owing to its ability to significantly reduce the threshold and labor cost of machine learning. It has demonstrated its powerful functions in hyperparameter optimization, model selection, neural network search, and feature engineering. Most AutoML frameworks are not specifically designed to process graph data. That is, in most AutoML tools, only traditional neural networks are integrated without using a graph neural network (GNN). Although traditional neural networks have achieved great success, GNNs have more advantages in processing non-Euclidean data (e.g., graph data) and have gained popularity in recent years. However, to the best of our knowledge, there is currently only one open-source AutoML framework for graph learning, i.e., AutoGL. For the AutoGL framework, traditional AutoML optimization algorithms such as grid search, random search, and Bayesian optimization are used to optimize the hyperparameters. Because each type of traditional optimization algorithm has its own advantages and disadvantages, more options are required. This study analyzes the performance of different evolutionary algorithms (EAs) on AutoGL through experiments. The experimental results show that EAs could be an effective alternative to the hyperparameter optimization of GNN.
Development and testing of an explorative BPM acceptance model: Insights from the COVID-19 pandemic

When emerging technologies transform an organization's way of working, explorative business process management (BPM) becomes a new challenge. Although digital innovations can boost process efficacy and business productivity, employees do not necessarily accept the implied work changes. We therefore looked at the increased digitalization efforts during the COVID-19 lockdowns, during which employees were forced to drastically rethink work by heavily depending on technology for communication and almost all business tasks. This global setting allowed us to scrutinize disruptive work changes and how employees can cope with disruptive work adaptations. We also looked into the explorative skillset needed to adapt to these changes. To theorize about an explorative BPM acceptance model, eleven hypotheses were supported based on a solid theoretical foundation. We followed a quantitative research design using partial least squares for structural equation modeling (PLS-SEM) at the university administration settings in two regions, including purposive sampling. Data analysis covered both a measurement model assessment and structural model assessment. Our findings reveal that employees' perceived work modalities, feeling creative and feeling flexible are more promising features than perceived influence and attitude related to explorative work and skill development. We also offer novel insights into explorative business process management (BPM) skills, and which skills are more productive in uncertain or dynamic working conditions. This research is a learning path for managers struggling with flexible or competitive business environments, and more specifically to facilitate employee willingness.
Syllabic quantity patterns as rhythmic features for Latin authorship attribution

Abstract It is well known that, within the Latin production of written text, peculiar metric schemes were followed not only in poetic compositions, but also in many prose works. Such metric patterns were based on so‐called syllabic quantity , that is, on the length of the involved syllables, and there is substantial evidence suggesting that certain authors had a preference for certain metric patterns over others. In this research we investigate the possibility to employ syllabic quantity as a base for deriving rhythmic features for the task of computational authorship attribution of Latin prose texts. We test the impact of these features on the authorship attribution task when combined with other topic‐agnostic features. Our experiments, carried out on three different datasets using support vector machines (SVMs) show that rhythmic features based on syllabic quantity are beneficial in discriminating among Latin prose authors.
White Blood Cells Subtypes Classification Using Fast Traditional Convolutional Neural Network

In the medical field, detection and classification of white blood cells are essential. This analysis can be constructive when several diseases and infections are detected and treated. The purpose of this study is to develop an image classifier capable of automatically classifying the image of blood cells into four white blood cell subtypes: the cell neutrophil, eosinophil, lymphocyte, and Monocyte. A Fast Traditional Convolutional Neural Network (FTCNN) model has been designed to create an image classifier, which ensures high efficiency and accuracy when working with a large number of images. The proposed model has been trained on about 12,515 subtyped blood microscopic images and provided 98.23% accuracy and 84.64% accuracy, respectively, during training and testing. To help the medical department combat WBC-related diseases, this blood cell classification model can be used as a base for establishing various diagnostic systems.
Topic-Oriented Text Features Can Match Visual Deep Models of Video Memorability

Not every visual media production is equally retained in memory. Recent studies have shown that the elements of an image, as well as their mutual semantic dependencies, provide a strong clue as to whether a video clip will be recalled on a second viewing or not. We believe that short textual descriptions encapsulate most of these relationships among the elements of a video, and thus they represent a rich yet concise source of information to tackle the problem of media memorability prediction. In this paper, we deepen the study of short captions as a means to convey in natural language the visual semantics of a video. We propose to use vector embeddings from a pretrained SBERT topic detection model with no adaptation as input features to a linear regression model, showing that, from such a representation, simpler algorithms can outperform deep visual models. Our results suggest that text descriptions expressed in natural language might be effective in embodying the visual semantics required to model video memorability.
Machine Learning With Health Care: A perspective

Abstract ML “machine learning” is an ever-expanding research field with plenty of possibilities for study and implementation. Mr. James Collin stated at MIT that ML is the technology defining this decade, even though it has had a meagre effect on healthcare. Several fresh businesses in the ML industry are applying themselves earnestly on healthcare. Even google has jumped in to the race and it has designed a ML application for identification of cancer tumour on mammograms. To identify skin cancer, Stanford uses a Deep Learning algorithm. Around one trillion GB of data is getting generated per year by the USA health system. Various academic experts and scientists have worked out various characteristics and several factors of risk involved in of chronic illness. Additional data stands for more learning for machine, but for higher precision, these many features need a huge quantity samples. So if machines can harvest clinically greater risk oriented feature it would definitely be better. Precision is improved when the data in the form of exploratory data analysis and feature engineering is pre-processed. The multi-class classification might be capable of evaluating a patient’s different disease risk levels. In health care, correct identification of the percentage of diseased individuals “sensitivity” is a primary concern rather than correct identification of the percentage of healthy individuals “ specificity”. Our paper introduces one of ML’s super challenger and emergent application i.e. Healthcare. The careful and sympathetic relation with care providers will always be necessary for the patients. ML cannot remove this, but will become instrumental for health professionals in improving and strengthening on-going care. Our paper discusses the several established models of ML along with its applications in healthcare system. We also bring up directions for imparting more efficiency to ML model. In addition to this we have also discussed ML’s case study for Brest cancer.
Ontology-Based Scenario Modeling for Cyber Security Exercise

The growing demand for cyber security professionals with practical knowledge is boosting the development and conduct of cyber security exercises around the world. Scenarios stand a central position of the exercise, which sets the stage for later action by providing contextual information that the participants will need during the exercise. To manage the increasing numbers of scenario creation in the different contexts, we propose an ontology to model scenarios for cyber security exercises. This ontology identifies aspects of scenario modeling relevant to cyber security that can be used as a means to achieve a defined taxonomy of knowledge items and a standard vocabulary for cyber scenarios. With the semantic framework based on RDF/OWL, this ontology provides a common structure at a semantic level that allows scenarios to be shared and reused across applications and community boundaries. In this paper, we present the design, implementation, and evaluation of the proposed ontology.