Reviewerly/exHarmony huggingface dataset:
Lower Bounds on Lattice Sieving and Information Set Decoding

In two of the main areas of post-quantum cryptography, based on lattices and codes, nearest neighbor techniques have been used to speed up state-of-the-art cryptanalytic algorithms, and to obtain the lowest asymptotic cost estimates to date [May–Ozerov, Eurocrypt'15; Becker–Ducas–Gama–Laarhoven, SODA'16]. These upper bounds are useful for assessing the security of cryptosystems against known attacks, but to guarantee long-term security one would like to have closely matching lower bounds, showing that improvements on the algorithmic side will not drastically reduce the security in the future. As existing lower bounds from the nearest neighbor literature do not apply to the nearest neighbor problems appearing in this context, one might wonder whether further speedups to these cryptanalytic algorithms can still be found by only improving the nearest neighbor subroutines. We derive new lower bounds on the costs of solving the nearest neighbor search problems appearing in these cryptanalytic settings. For the Euclidean metric we show that for random data sets on the sphere, the locality-sensitive filtering approach of [Becker–Ducas–Gama–Laarhoven, SODA 2016] using spherical caps is optimal, and hence within a broad class of lattice sieving algorithms covering almost all approaches to date, their asymptotic time complexity of $$2^{0.292d + o(d)}$$ is optimal. Similar conditional optimality results apply to lattice sieving variants, such as the $$2^{0.265d + o(d)}$$ complexity for quantum sieving [Laarhoven, PhD thesis 2016] and previously derived complexity estimates for tuple sieving [Herold–Kirshanova–Laarhoven, PKC 2018]. For the Hamming metric we derive new lower bounds for nearest neighbor searching which almost match the best upper bounds from the literature [May–Ozerov, Eurocrypt 2015]. As a consequence we derive conditional lower bounds on decoding attacks, showing that also here one should search for improvements elsewhere to significantly undermine security estimates from the literature.
A Human Factor Approach to Threat Modeling

Cybersecurity has many challenges to address to ensure the protection of a system from an attacker. Consequently, strategies have been developed to address a system's weakness that an attacker may try to exploit. However, while these approaches may prevent an attacker from getting in from the outside, they do not consider the user's actions from the inside and how their behavior may inadvertently allow an attack to occur. This paper presents a human-centered approach to threat modeling titled STRIDE-HF, which extends the existing threat modeling framework STRIDE.
Hardness of case-based decisions

Stare decisis is a fundamental principle of case-based reasoning. Yet its application varies in complexity and depends, in particular, on whether relevant past decisions agree, or exist at all. The contribution of this paper is a formal treatment of types of the hardness of case-based decisions. The typology of hardness is defined in terms of the arguments for and against the issue to be decided, and their kind of validity (conclusive, presumptive, coherent, incoherent). We apply the typology of hardness to Berman and Hafner's research on the dynamics of case-based reasoning and show formally how the hardness of decisions varies with time.
Sage Advice? The Impacts of Explanations for Machine Learning Models on Human Decision-Making in Spam Detection

The impact of machine learning (ML) explanations and different attributes of explanations on human performance was investigated in a simulated spam detection task. Participants decided whether the metadata presented about an email indicated that it was spam or benign. The task was completed with the aid of a ML model. The ML model's prediction was displayed on every trial. The inclusion of an explanation and, if an explanation was presented, attributes of the explanation were manipulated within subjects: the number of model input features (3, 7) and visualization of feature importance values (graph, table), as was trial type (i.e., hit, false alarm). Overall model accuracy (50% vs 88%) was manipulated between subjects, and user trust in the model was measured as an individual difference metric. Results suggest that a user's trust in the model had the largest impact on the decision process. The users showed better performance with a more accurate model, but no differences in accuracy based on number of input features or visualization condition. Rather, users were more likely to detect false alarms made by the more accurate model; they were also more likely to comply with a model "miss" when more model explanation was provided. Finally, response times were longer in individuals reporting low model trust, especially when they did not comply with the model's prediction. Our findings suggest that the factors impacting the efficacy of ML explanations depends, minimally, on the task, the overall model accuracy, the likelihood of different model errors, and user trust.
A New Framework to Secure Cloud Based e-Learning Systems

Aissaoui, Karima Amane, Meryem Berrada, Mohammed Madani, Mohammed AmineCombining cloud computing with e-learning has led to a new form of systems called: cloud-based e-learning systems. Those systems take advantages and benefits of cloud computing, and combine them with e-learning systems. This combination offers some solutions to make e-learning systems more efficient and easier for use, and contribute to deal the best conditions of using distance learning systems. However, cloud-based e-learning systems present some challenges in two principal axis: security and storage. In this paper, we propose a new architecture that aims to resolve the problems of these systems, related to security and storage. It is based on a new security layer, responsible of controlling and storing all transactions, in order to generate a security key, and to give us the ability to use generated data to offer recommended systems in the future. Also, this architecture is proposed after a study that we conducted to cover many works done related with this field.
LibrARy – Enriching the Cultural Physical Spaces with Collaborative AR Content

In the last decade, technology has been rapidly evolving, making the humankind more dependent on digital devices than ever. We rely more and more on digitalization and we are excited to welcome emergent technologies like Augmented Reality (AR) into our everyday activities as they show great potential to enhance our standard of life. AR applications are becoming more popular every day and represent the future of human-computer interaction, by revolutionizing the way information is presented and transforming the surroundings into one broad user interface. This paper aims to present the concept of LibrARy – an AR system that intends to create innovative collaborative environments where multiple users can interact simultaneously with computer-generated content. Its main objective is to improve cooperation by enabling co-located users to access a shared space populated with 3D virtual objects and to enrich collaboration between them through augmented interactions. LibrARy will be developed as part of Lib2Life research project which addresses a topic of great importance – revitalizing cultural spaces in the context of recent technology advancements. In light of the research carried out for the Lib2Life project, a need for a more complex collaborative augmented environment has been identified. Therefore, we are now proposing LibrARy - an innovative component which has the potential to enhance the cooperation in multi-participant settings and provide unique interactive experiences. This paper will present in more detail the concept and the functionalities of LibrARy. It will then analyze the possible use case scenarios and lastly discuss further development steps.
A Deep Q-Learning based approach applied to the Snake game

In recent years, one of the highest challenges in the field of artificial intelligence has been the creation of systems capable of learning how to play classic games. This paper presents a Deep Q-Learning based approach for playing the Snake game. All the elements of the related Reinforcement Learning framework are defined. Numerical simulations for both the training and the testing phases are presented. A particular focus is given to the associated Neural Network hyperparameters tuning, which is a crucial step in the agent design process and for the achievement of a desired target level of performance.
Sample Selection for Efficient Image Annotation

Supervised object detection has been proven to be successful in many benchmark datasets achieving human-level performances. However, acquiring a large amount of labeled image samples for supervised detection training is tedious, time-consuming, and costly. In this paper, we propose an efficient image selection approach that samples the most informative images from the unlabeled dataset and utilizes human-machine collaboration in an iterative train-annotate loop. Image features are extracted by the CNN network followed by the similarity score calculation, Euclidean distance. Unlabeled images are then sampled into different approaches based on the similarity score. The proposed approach is straightforward, simple and sampling takes place prior to the network training. Experiments on datasets show that our method can reduce up to 80% of manual annotation workload, compared to full manual labeling setting, and performs better than random sampling.
A Metric for Machine Learning Vulnerability to Adversarial Examples

Recent studies in the field of Adversarial Machine Learning (AML) have primarily focused on techniques for poisoning and manipulating the Machine Learning (ML) systems for operations such as malware identification and image recognition. While the offensive perspective of such systems is increasingly well documented, the work approaching the problem from the defensive standpoint is sparse. In this paper, we define a metric for quantizing the vulnerability or susceptibility of a given ML model to adversarial manipulation using only properties inherent to the model under examination. This metric will be shown to have several useful properties related to known features of classifier-based ML systems and is intended as a tool to broadly compare the security of various competing ML models based on their maximum potential susceptibility to adversarial manipulation.
Webometric analysis of open access digital repository of agricultural sciences in Asia

Websites of an institution play an important role and act as a gateway of disseminating information. This study examines and analyzes the websites of 88 open accesses repositories (OARs) of agricultural sciences in Asia based on different criteria such as the number of web pages, link pages, web impact factor, and WISER indicator through Google search engine. It was found that the website of Science and Technology Development Center of the Ministry of Education of the People’s Republic of China occupied the first position with 723.98% SWIF presence amongst 88 digital agricultural repositories in Asia. But it again ranked 86th position with 221000 Web pages and 4090 in-link WebPages, and 0.018507 RWIF. The study aims at webometric evaluation of open-access digital repositories of agricultural science in Asia. Here, the impact and presence of websites of open-access digital repositories of agricultural sciences have been discussed and some suggestions have also been made to increase the presence of websites.
Emotion Mining: from Unimodal to Multimodal Approaches

Abstract In the last decade, Sentiment Analysis and Affective Computing have found applications in different domains. In particular, the interest of extracting emotions in healthcare is demonstrated by the various applications which encompass patient monitoring and adverse events prediction. Thanks to the availability of large datasets, most of which are extracted from social media platforms, several techniques for extracting emotion and opinion from different modalities have been proposed, using both unimodal and multimodal approaches. After introducing the basic concepts related to emotion theories, mainly borrowed from social sciences, the present work reviews three basic modalities used in emotion recognition, i.e. textual, audio and video, presenting for each of these i) some basic methodologies, ii) some among the widely used datasets for the training of supervised algorithms and iii) briefly discussing some deep Learning architectures. Furthermore, the paper outlines the challenges and existing resources to perform a multimodal emotion recognition which may improve performances by combining at least two unimodal approaches. architecture to perform multimodal emotion recognition.
Augmented and Virtual Reality Technologies in Education

The article examines the technologies of industrial revolution 4.0 - augmented reality and virtual reality. A detailed numerical analysis of known examples of the application of these technologies in the form of registered programs has been carried out. The possibilities of optimization of organizational and educational processes in higher technical education are analyzed. The methodology of using augmented reality in the organizational and educational process of a technical university is presented. A variant of augmented reality is proposed that allows you to “examine” a 3D model of an object by turning a paper marker. To implement it, the EV Toolbox augmented and virtual reality constructor is used.
CrossModalNet: exploiting quality preoperative images for multimodal image registration

A long-standing problem in image-guided radiotherapy is that inferior intraoperative images present a difficult problem for automatic registration algorithms. Particularly for digital radiography (DR) and digitally reconstructed radiograph (DRR), the blurred, low-contrast, and noisy DR makes the multimodal registration of DR-DRR challenging. Therefore, we propose a novel CNN-based method called CrossModalNet to exploit the quality preoperative modality (DRR) for handling the limitations of intraoperative images (DR), thereby improving the registration accuracy. The method consists of two parts: DR-DRR contour predictions and contour-based rigid registration. We have designed the CrossModal Attention Module and CrossModal Refine Module to fully exploit the multiscale crossmodal features and implement the crossmodal interactions during the feature encoding and decoding stages. Then, the predicted anatomical contours of DR-DRR are registered by the classic mutual information method. We collected 2486 patient scans to train CrossModalNet and 170 scans to test its performance. The results show that it outperforms the classic and state-of-the-art methods with 95th percentile Hausdorff distance of 5.82 pixels and registration accuracy of 81.2%. The code is available at https://github.com/lc82111/crossModalNet.
A Dirichlet Autoregressive Model for the Analysis of Microbiota Time-Series Data

Growing interest in understanding microbiota dynamics has motivated the development of different strategies to model microbiota time series data. However, all of them must tackle the fact that the available data are high-dimensional, posing strong statistical and computational challenges. In order to address this challenge, we propose a Dirichlet autoregressive model with time-varying parameters, which can be directly adapted to explain the effect of groups of taxa, thus reducing the number of parameters estimated by maximum likelihood. A strategy has been implemented which speeds up this estimation. The usefulness of the proposed model is illustrated by application to a case study.
Hypervolume under ROC manifold for discrete biomarkers with ties

Medical multi-category diagnostic problems may involve discrete biomarkers. Many traditional accuracy measures are based on the assumption that all biomarkers follow continuous distributions and consequently may underestimate the true discrimination ability of the discrete markers. In particular, we focus on Hypervolume Under ROC Manifold (HUM) in this paper and propose an extension of the familiar continuous version of HUM to incorporate discrete biomarkers with ties. Statistical estimation and inference procedures are proposed along with asymptotic properties. We carry out simulation studies to examine the performance of our proposed estimators for the new HUM measure. A real medical example is analysed to illustrate our methodology.
Sentiment Analysis Using AI: A Comparative Study Comparative Study of 5 Different Algorithms and Benchmarking Them with A Qualitative Analysis of Training time, Prediction time, and Accuracy

Sentiment analysis also called Opinion Mining has been one of the most ancient topics in Natural Language Processing (NLP). NLP which is a subfield of AI enables machines to read, understand, interpret and manipulate human languages. The netizens often express their sentiments through tweets, reviews and ratings. Therefore, it's becoming increasingly popular to analyse these sentiments for commercial applications such as market analysis, product reviews, customer services and many more. This analysis can either be lexicon-based or machine learning-based. Lexicon-based techniques use dictionaries of words, which is then used to calculate a score for the polarity. Machine learning-based classifiers use a dataset to train a model which is used to find the sentiment of a sentence or a paragraph. ML-based approaches are more robust compared to the lexicon-based approach. The main contribution of this paper is to demonstrate the performance of various Machine Learning and Deep Learning models in Sentiment Analysis. In this paper, various models viz. Multinomial Naive Bayes(MNB), Support Vector Machine(SVM), Hidden Markov Model(HMM), Long Short Term Memory(LSTM) and Bidirectional Encoder Representations from Transformers(BERT), are trained through supervised learning, and then they are benchmarked through a qualitative analysis of accuracy, effectiveness and speed to provide an extensive overview of the various algorithms used in the field. The performance comparison of the various models presents an in-depth understanding of the different techniques within the framework of a comprehensive evaluation and discussion.
A Speculative Execution Framework for Big Data Processing Systems

Due to the extreme growth in information and data, there is a need for automatic solutions for processing the increased volume of structured and unstructured data. Recently, the big data processing systems such as Hadoop, Spark, Flink, and other platforms provide decision making and process automation in a reasonable time. However, there are performance degradations in parallel systems due to the occurrence of anomalous tasks. This occurred when a machine takes an extra-long time to finish execution of a task, which decreases the system throughput. To overcome this effect, a speculative execution framework is introduced, which allows late tasks to run in alternative nodes. This paper proposes a framework for speculative execution in big data processing system which is composed of three stages. The first stage handles the anomalous tasks, while the second stage provides a selection policy for backup nodes, and eventually, the third stage assesses the speculative execution efficiency. The proposed framework provides a blueprint for evolving real-world computing systems, and gives a comprehensive perception, which can be applied to characterize and design robust and reliable speculative execution mechanisms for big data systems.
A Fusion Approach for Paper Submission Recommendation System

Building a recommendation system to support researchers in the conference or journal selection process to submit their work has been one interesting topic during the last few years. The current state-of-the-art approaches usually use the available information of one paper submission: the title, the abstract, and the list of keywords. In this paper, we propose a new approach for the paper submission recommendation problem by constructing a suitable architecture to extract the useful information from the "Aims and Scopes" of journals and combine the textual features computed from the title, the abstract, and the list of keywords of the paper submission. We collect one dataset with 414512 papers related to computer science to measure the performance of our proposed method. The experimental results show that a fusion approach of these features can significantly bypass the state-of-the-art techniques and contribute to the relevant research topic.
A Survey of Machine Learning Approach to Software Cost Estimation

Software companies are growing fast every day due to the high demand for software, while software development cost increasing at the same time. In order to overcome this concern, we require an efficient technique for more accurately estimating software costs to manage and control the costs and further make the software more reliable and competitive. Software development by default is a challenging process that may face deep and essential problems especially when we trying to create accurate and reliable software cost estimates. These issues are strengthening due to the high level of difficulty, and complexity of the software process. The goal of this study is to address the difficulties of estimating the software development cost using conventional approaches. Further identifying the necessary steps for computable entities which affect the software cost and presenting the research works that utilize them with machine learning approaches to build a reliable estimation method. The various proposed software cost estimation methods with distinct designs assessed and collected outcomes reported.
The Perception of Avatars in Virtual Reality During Professional Meetings

Social Virtual Reality (VR) offers virtual social spaces, where people can meet, collaborate and socialize via head-mounted displays. In VR applications, users can create their own avatars that can control to walk around and interact directly with other users. Recently, social VR gained importance for professional meetings, in order to allow socializing and collaborating during times of travel restrictions. Unlike VR in gaming for the purpose of amusement, where avatars can be fantasy figures, avatars in professional meetings usually look more serious and more like the person behind the avatar. Based on semi-structured interviews in two different scenarios – a conference and a company business meeting – we report about the role and perception of avatars in professional meetings. Our interview results reveal that avatars were perceived differently in both cases. In the conference scenario, avatars were seen as both enablers and obstacles for interaction with other users during the social event. In the company scenario, interviewees did not express any feelings of being restricted in their collaboration with others by the visual appearance of the avatar that represents them.