Reviewerly/exHarmony huggingface dataset:
The Different Shades of Infinite Session Types

Abstract Many type systems include infinite types. In session type systems, infinite types are important because they specify communication protocols that are unbounded in time. Usually infinite session types are introduced as simple finite-state expressions "Equation missing"<!-- image only, no MathML or LaTex --> or by non-parametric equational definitions "Equation missing"<!-- image only, no MathML or LaTex -->. Alternatively, some systems of label- or value-dependent session types go beyond simple recursive types. However, leaving dependent types aside, there is a much richer world of infinite session types, ranging through various forms of parametric equational definitions, to arbitrary infinite types in a coinductively defined space. We study infinite session types across a spectrum of shades of grey on the way to the bright light of general infinite types. We identify four points on the spectrum, characterised by different styles of equational definitions, and show that they form a strict hierarchy by establishing bidirectional correspondences with classes of automata: finite-state, 1-counter, pushdown and 2-counter. This allows us to establish decidability and undecidability results for type formation, type equivalence and duality in each class of types. We also consider previous work on context-free session types (and extend it to higher-order) and nested session types, and locate them on our spectrum of infinite types.
MsfNet: a novel small object detection based on multi-scale feature fusion

This paper proposes a small object detection algorithm based on multi-scale feature fusion. By learning shallow features at the shallow level and deep features at the deep level, the proposed multi-scale feature learning scheme focuses on the fusion of concrete features and abstract features. It constructs object detector (MsfNet) based on multi-scale deep feature learning network and considers the relationship between a single object and local environment. Combining global information with local information, the feature pyramid is constructed by fusing different depth feature layers in the network. In addition, this paper also proposes a new feature extraction network (CourNet), through the way of feature visualization compared with the mainstream backbone network, the network can better express the small object feature information. The proposed algorithm is evaluated on MS COCO dataset and achieves the leading performance. This study shows that the combination of global information and local information is helpful to detect the expression of small objects in different illumination. MsfNet uses CourNet as the backbone network, which has high efficiency and a good balance between accuracy and speed.
ROCKY: Rotation Countermeasure for the Protection of Keys and Other Sensitive Data

Fault attacks exploit the possibility to inject a fault in an electronic device, during the execution of a cryptographic algorithm, leading the device to erroneous operation(s). An adversary can exploit these errors and extract valuable information, making the development of countermeasures against fault attacks necessary. In this work, we present a novel countermeasure that applies to cryptographic primitives that make use of a permutation with almost shift-invariant round functions. Our countermeasure offers protection against fault attacks that rely on the injection of a fault in multiple executions of the same algorithm. In order to demonstrate the hardware overhead of the proposed countermeasure, we implement an FPGA-oriented protected version of Xoodyak, an authenticated encryption lightweight scheme in the NIST lightweight cryptography standardization competition.
Contextual Text Analytics Framework for Citizen Report Classification: A Case Study Using the Indonesian Language

Citizen science has emerged in many countries to contribute to the prompt resolution of individual field problems and has been shifted toward Information System (IS) research. In the domain of IS, a citizen report mechanism has been introduced in many local governments to understand regional problems based on the public participation. The rising of social media enforces many organizations including the local governments to utilize any information from the citizens including texts. Text mining has been utilized in various types of analyses such as sentiment analysis. However, it shows many challenges when it comes to the local context. The local context of words could cause various conflation errors that highly affect the learning task such as classification methods. This study aims to propose a context-based text processing and feed the proposed approach into a machine learning framework to classify the data of citizen reports-. The context-based text preprocessing utilized statistical- and semantic-based measurements to extract the local context and elaborate domain expertise to verify the misinterpretation for further text processing such as feature extractions. Subsequently, the n-gram language models together with the Term Frequency and Inverse Document Frequency schemes were performed to build the features. The result showed that the context-based text preprocessing improved the classification performance in majority classifiers in about 3% with the combinations of n-gram features.
A Survey of Learning-Based Methods for Cold-Start, Social Recommendation, and Data Sparsity in E-commerce Recommendation Systems

With the continuous development of the economy and technology, people more and more rely on online shopping, especially during the pandemic of COVID19. On the other hand, sellers display many products, so customers need to make a great effort to find suitable products to meet their needs. To reduce the efforts of customers, researchers have developed many recommendation systems for online products. In this paper, to help further study recommendation systems in e-commerce, we survey the learning-based methods for solving the cold-start problem in a recommendation, social recommendation, and data sparsity. In particular, we compare these methods’ pros and cons and point out the directions for further study.
Sociocultural Trend Reports as an Intelligence Tool of Strategic Cultural Management

This work aims to explore the nature of sociocultural trend-related reports, considering their objectives and contents. These strategic objects are intelligence tools that guide social and business actors. After understanding their nature, we explore an adaptable structure that can guide the development of future trend-related reports from an academic and/or a business perspective. To inform our proposal, we articulated the results of a literary review and of a content analysis performed on a sample of reports that illustrate the contents and structures of these works. The resulting outline has an adaptable scheme that can work for different types of reports according to specific intelligence needs, informing decisions and contextualizing major sociocultural patterns and changes.
EfficientNetv2 Model for Breast Cancer Histopathological Image Classification

The incidence of breast cancer in women is 24.2% worldwide, ranking first among female cancers. Combination of modern medical image processing technology and deep learning algorithm can be effectively applied to remote diagnosis, instant file access and simplified procedure consultation. In this paper, using Tensorflow Keras backend, and the basic architecture of convolutional neural network based, we compared the accuracy of breast cancer prediction under different pre-trained convolutional neural network models including Efficientnetv2, Resnet_v2, Inception_v3, Mobilenet_v2 and Inception_Resnet_v2 with 9,109 microscopic images of breast tumor tissue from P&D Laboratory in Parana, Brazil. As a whole, Efficientnetv2-b0 to b2 performs as almost well as Mobilenet_ v2 and Inception_Resnet_ v2. Efficientnetv2-b1 did the best in our research with the accuracy score as 0.978. Inception _ v3 was far behind other models with the accuracy score as only 0.739.
Harnessing Virtual Reality Nature to Promote Well-Being

Abstract With virtual reality (VR) industries and research focusing on gaming, training and, somewhat, in healthcare, there seems to be a lack of VR as simpler form of relaxing leisure, despite its potential. When emotions are targeted, there appears to be a focus on visceral ones like jump scares, panic, high adrenaline, fears and anything related to bad feelings and instinctive reactions. This study explores and proves that VR can effectively relax the user. We conducted an experiment where the users were exposed to VR-based natural sceneries, and we measured its effect on their positive feelings and relaxation levels. We found evidence, with a high degree of significance, of an increase in calmness and a reduction of arousal and energy. This effect is present in short sessions. We also explored the possibility of VR sunsets affecting our subjects’ circadian rhythm.
Topic Modeling on Podcast Short-Text Metadata

Podcasts have emerged as a massively consumed online content, notably due to wider accessibility of production means and scaled distribution through large streaming platforms. Categorization systems and information access technologies typically use topics as the primary way to organize or navigate podcast collections. However, annotating podcasts with topics is still quite problematic because the assigned editorial genres are broad, heterogeneous or misleading, or because of data challenges (e.g. short metadata text, noisy transcripts). Here, we assess the feasibility to discover relevant topics from podcast metadata, titles and descriptions, using topic modeling techniques for short text. We also propose a new strategy to leverage named entities (NEs), often present in podcast metadata, in a Non-negative Matrix Factorization (NMF) topic modeling framework. Our experiments on two existing datasets from Spotify and iTunes and Deezer, a new dataset from an online service providing a catalog of podcasts, show that our proposed document representation, NEiCE, leads to improved topic coherence over the baselines. We release the code for experimental reproducibility of the results ( https://github.com/deezer/podcast-topic-modeling ).
Health Chatbots in Africa: Scoping Review

This scoping review explores and summarizes the existing literature on the use of chatbots to support and promote health in Africa.The primary aim was to learn where, and under what circumstances, chatbots have been used effectively for health in Africa; how chatbots have been developed to the best effect; and how they have been evaluated by looking at literature published between 2017 and 2022. A secondary aim was to identify potential lessons and best practices for others chatbots. The review also aimed to highlight directions for future research on the use of chatbots for health in Africa.Using the 2005 Arksey and O'Malley framework, we used a Boolean search to broadly search literature published between January 2017 and July 2022. Literature between June 2021 and July 2022 was identified using Google Scholar, EBSCO information services-which includes the African HealthLine, PubMed, MEDLINE, PsycInfo, Cochrane, Embase, Scopus, and Web of Science databases-and other internet sources (including gray literature). The inclusion criteria were literature about health chatbots in Africa published in journals, conference papers, opinion, or white papers.In all, 212 records were screened, and 12 articles met the inclusion criteria. Results were analyzed according to the themes they covered. The themes identified included the purpose of the chatbot as either providing an educational or information-sharing service or providing a counselling service. Accessibility as a result of either technical restrictions or language restrictions was also noted. Other themes that were identified included the need for the consideration of trust, privacy and ethics, and evaluation.The findings demonstrate that current data are insufficient to show whether chatbots are effectively supporting health in the region. However, the review does reveal insights into popular chatbots and the need to make them accessible through language considerations, platform choice, and user trust, as well as the importance of robust evaluation frameworks to assess their impact. The review also provides recommendations on the direction of future research.
Examining Penetration Tester Behavior in the Collegiate Penetration Testing Competition

Penetration testing is a key practice toward engineering secure software. Malicious actors have many tactics at their disposal, and software engineers need to know what tactics attackers will prioritize in the first few hours of an attack. Projects like MITRE ATT&amp;CK™ provide knowledge, but how do people actually deploy this knowledge in real situations? A penetration testing competition provides a realistic, controlled environment with which to measure and compare the efficacy of attackers. In this work, we examine the details of vulnerability discovery and attacker behavior with the goal of improving existing vulnerability assessment processes using data from the 2019 Collegiate Penetration Testing Competition (CPTC). We constructed 98 timelines of vulnerability discovery and exploits for 37 unique vulnerabilities discovered by 10 teams of penetration testers. We grouped related vulnerabilities together by mapping to Common Weakness Enumerations and MITRE ATT&amp;CK™. We found that (1) vulnerabilities related to improper resource control (e.g., session fixation) are discovered faster and more often, as well as exploited faster, than vulnerabilities related to improper access control (e.g., weak password requirements), (2) there is a clear process followed by penetration testers of discovery/collection to lateral movement/pre-attack. Our methodology facilitates quicker analysis of vulnerabilities in future CPTC events.
Internationalization in Russian universities during the pandemic of COVID‐19: Lessons for succeeding in the new reality

Abstract The pandemic of COVID‐19 became a resilience test for internationalization in Russia and emphasized many systemic trends, both promising and concerning. The turbulence and instability during the pandemic initiated the growth of internationalization activities at the institutional level in two directions: first, it increased attention to the quality of work with current international students, including student support, adaptation issues, student engagement, and second, due to the risks in student enrolment, it brought universities to rethink their internationalization instruments and strategies for work with international applicants and future students. Indeed, the ongoing changes in global education call for reconsideration of the priorities and methods of internationalization: new forms of internationalization become widespread practices, new instruments are necessary for student recruitment, and new push and pull factors come into play. While internationalization of education in Russia during the pandemic was addressed at the national level to a degree, the universities carried out the key activities and had to come up with solutions “in the field”. The paper aims to provide a comprehensive analysis of Russia's transition through the pandemic and tackle the changes at the university level, based on a comparison of data of the survey of international departments of Russian universities conducted in 2020 and the survey of 2021, supported by semi‐structured interviews. The research offers insights on the capacity for change and resilience in Russian universities within the internationalization domain and can help to develop data‐driven approaches for improvement of organizational capacity of universities.
Content-Aware Detection of Temporal Metadata Manipulation

Most pictures shared online are accompanied by temporal metadata (i.e., the day and time they were taken), which makes it possible to associate an image content with real-world events. Maliciously manipulating this metadata can convey a distorted version of reality. In this work, we present the emerging problem of detecting timestamp manipulation. We propose an end-to-end approach to verify whether the purported time of capture of an outdoor image is consistent with its content and geographic location. We consider manipulations done in the hour and/or month of capture of a photograph. The central idea is the use of supervised consistency verification, in which we predict the probability that the image content, capture time, and geographical location are consistent. We also include a pair of auxiliary tasks, which can be used to explain the network decision. Our approach improves upon previous work on a large benchmark dataset, increasing the classification accuracy from 59.0% to 81.1%. We perform an ablation study that highlights the importance of various components of the method, showing what types of tampering are detectable using our approach. Finally, we demonstrate how the proposed method can be employed to estimate a possible time-of-capture in scenarios in which the timestamp is missing from the metadata.
Breast Cancer Prediction using Different Machine Learning Algorithms

Due to Breast Cancer, many women die all across the globe. Breast Cancer has two stages Benign and Malignant. Several factors are responsible for causing Breast Cancer. Machine Learning is used to find out the optimal parameters from all the parameters and to predict the stage of cancer based on these parameters. Different machine learning algorithms are utilized to identify highly important features, and the cancer stage is forecasted based on these features. To determine the best model for Breast Cancer prediction, the SVM, RF, and KNN are implemented. A variety of factors are used to evaluate the models. With Accuracy=98.25, Precision=98.26, Recall=98.25, and F1-Score=98.26, it has been determined that SVM is the best model for Breast Cancer prediction.
News Classification System using Machine Learning Approach

As more and more sources publish a large amount of news on the Internet every day, it is necessary to categorize news articles so that users can effectively obtain information. The amount of information from print and digital media is huge, which leads to intelligent analysis of large amounts of data, all of which are large amounts of unclassified data, stored together will not give results, so we can make some decisions, so we need categorize messages and use different algorithms to assign multiple tags based on their category. Text message classification using machine learning is the process of automatically classifying message information according to predefined tags, which are based on their content and whose accuracy comes from the training message records. The research paper analyzed many algorithms to build the system, such as Naive Bayes, SVM and neural networks for classification based on machine learning algorithms. Articles are classified according to business, entertainment, politics, sports, technology and other fields. In this way, with the help of the news classification system, users can find news articles they are interested in, which saves time and eliminates news overhead.
Template-Aware Transformer for Person Reidentification

Person reidentification (ReID) is a challenging computer vision task for identifying or verifying one or more persons when the faces are not available. In ReID, the indistinguishable background usually affects the model's perception of the foreground, which reduces the performance of ReID. Generally, the background of the same camera is similar, whereas that of different cameras is quite different. Based on this finding, we propose a template-aware transformer (TAT) method which can learn intersample indistinguishable features by introducing a learnable template for the transformer structure to cut down the model's attention to regions of the image with low discrimination, including backgrounds and occlusions. In the multiheaded attention module of the encoder, this template directs template-aware attention to indistinguishable features of the image and gradually increases the attention to distinguishable features as the encoder block deepens. We also increase the number of templates using side information considering the characteristics of ReID tasks to adapt the model to backgrounds that vary significantly with different camera IDs. Finally, we demonstrate the validity of our theories using various public data sets and achieve competitive results via a quantitative evaluation.
Improving entanglement of even entangled coherent states via superposition of number-conserving operations

A scheme for improving entanglement is proposed by repeatedly performing number-conserving generalized superposition of products (GSP) operation, i.e., saa†+ta†am with s2+t2=1, on each modes of even entangled coherent state (EECS). Both Einstein–Podolsky–Rosen (EPR) correlation and concurrence are used as the measures for describing entanglement. It is found that both single-mode and two-mode-symmetric GSP operations can be used for the entanglement improvement. In particular, for the optimal EPR correlation, the GSP-EECS with two-mode-symmetric first-order operations presents a better performance for the entanglement improvement than the cases of high-order GSP operations. Compared to several special non-Gaussian operations, including photon subtraction-then-addition (PSTA) (s=0) and photon-addition-then-subtraction (PATS) (s=1), it is shown that the entanglement of GSP-EECS is always superior to those of both PSTA-EECS and PATS-EECS at a small amplitude level. When the GSP-EECS is used as the entangled resource for teleportation, our optimal teleportation fidelity presents the best performance.
Software Acceleration of the Deformable Shape Tracking Application

Shape tracking is based on landmark detection and alignment. Open-source code and pre-trained models are available for an implementation that is based on an ensemble of regression trees. The C++ Deformable Shape Tracking (DEST) implementation of face alignment that is using Eigen template library for algebraic operations is employed in this work. The overhead of the C++ Eigen library calls is measured and selected computational intensive operations are ported from Eigen implementation to custom C code achieving a remarkable acceleration in the shape tracking application. An important achievement of this work is the fact that the restructured code can be directly implemented with reconfigurable hardware for further speed improvement. Driver drowsiness and distraction detection applications are exploiting shape tracking by measuring landmark distances in order to detect eye blinking, yawning, etc. Fast video processing and accuracy is mandatory in these safety critical applications. The modified software implementation of the original DEST face alignment method presented in this paper, is almost 250 times faster due to the custom implementation of computational intensive vector/matrix operations and rotations. Eigen library is still used in non-time critical parts of the code for compact description and higher readability. Flattening of nested routines and inline implementation is also used to eliminate excessive argument copies and data type checking and conversions.
Robustness of Different Loss Functions and Their Impact on Network's Learning

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