Reviewerly/exHarmony huggingface dataset:
Electronic System Using Artificial Intelligence for Queue Management

The Covid-19 pandemic has brought changes in behaviour in public places.Indeed, the health and political authorities, in order to fight against the virus in a preventive manner, require the respect of barrier gestures: social distance, mask, vaccine, gel.Still in terms of public health, long waits in a place for a service have a negative impact on the health of fragile categories such as the disabled, pregnant women and the elderly.The technical devices used for queue management must now take into account the health context, identity, particularity and behaviour of people.This paper presents an electronic system developed with artificial intelligence for queue management in public facilities.This design personalises the user's ticket by automatically integrating the name, facial image, age and possible disability status.At the counters, a system of name calling, sound and screen display, allows users to follow the queue without having a ticket printed on thermal paper with a high carbon footprint.This solution also makes illiterate users autonomous in the queue, allowing them to maintain their dignity and to respect the safety distance between people.The device allows the establishment's manager, depending on the context, to activate positive discrimination of the disabled or the elderly, to control the Covid-19 mark or the health pass by QR Code.This queue manager performs biometric authentication by facial recognition before the user is registered in the queue register, which prevents fraud by people who do not want to respect the order of arrival of users.This work has led to the improvement of the technical management of queues by introducing more equity, inclusion, solidarity, health and ecology.
Analysis of Indicators for the Proper Development of Virtual Courses on the Moodle Platform

The contribution of this study is expressed in the framework of current trends in Higher Education, especially guaranteeing, structuring, and developing complete virtual courses on the web without the need to have profound knowledge of computer science. Virtual courses can be developed for the various existing teaching modalities and learning trends under the conditions and possibilities that allow it, electronic, blended, or mobile. However, it is necessary to know the aspects, elements, or indicators that comprise minimum indispensable requirements for these courses' homogenization and quality to establish criteria for their design, assembly, and optimal utility. Thus, this report offers specific quality indicators ensuring the proper structuring and functioning of undergraduate and postgraduate virtual courses on the Moodle platform used at the University of Havana. The indicators presented guarantee higher levels of teacher preparation for using ICT, particularly the Moodle platform, when designing and assembling the courses to be taught, the teaching activities, and determining the digital resources to develop them.
Fast Two-Step Blind Optical Aberration Correction

The optics of any camera degrades the sharpness of photographs, which is a key visual quality criterion. This degradation is characterized by the point-spread function (PSF), which depends on the wavelengths of light and is variable across the imaging field. In this paper, we propose a two-step scheme to correct optical aberrations in a single raw or JPEG image,i.e., without any prior information on the camera or lens. First, we estimate local Gaussian blur kernels for overlapping patches and sharpen them with a non-blind deblurring technique. Based on the measurements of the PSFs of dozens of lenses, these blur kernels are modeled as RGB Gaussians defined by seven parameters. Second, we remove the remaining lateral chromatic aberrations (not contemplated in the first step) with a convolutional neural network, trained to minimize the red/green and blue/green residual images. Experiments on both synthetic and real images show that the combination of these two stages yields a fast state-of-the-art blind optical aberration compensation technique that competes with commercial non-blind algorithms.
A coherent Ising machine based on a network of 100,000 degenerate optical parametric oscillator pulses

Coherent Ising machines (CIMs) are increasingly anticipated as a new computer that can solve combinatorial optimization problems. CIMs use degenerate optical parametric oscillator pulses as artificial spins. We constructed a 100,512-spin coherent Ising machine, the world's largest. Our CIM can find good solutions to maximum cut problems of 100,000-node graphs ~1000 times faster than simulated annealing. Furthermore, it provides both very good scores and a broad distribution when we operate it near the phase transition point.
MoDA: Map Style Transfer for Self-supervised Domain Adaptation of Embodied Agents

We propose a domain adaptation method, MoDA, which adapts a pretrained embodied agent to a new, noisy environment without ground-truth supervision. Map-based memory provides important contextual information for visual navigation, and exhibits unique spatial structure mainly composed of flat walls and rectangular obstacles. Our adaptation approach encourages the inherent regularities on the estimated maps to guide the agent to overcome the prevalent domain discrepancy in a novel environment. Specifically, we propose an efficient learning curriculum to handle the visual and dynamics corruptions in an online manner, self-supervised with pseudo clean maps generated by style transfer networks. Because the map-based representation provides spatial knowledge for the agent's policy, our formulation can deploy the pretrained policy networks from simulators in a new setting. We evaluate MoDA in various practical scenarios and show that our proposed method quickly enhances the agent's performance in downstream tasks including localization, mapping, exploration, and point-goal navigation.
Component Detection for Power Line Inspection Using a Graph-Based Relation Guiding Network

Detecting the components in aerial images is a crucial task in automatic visual inspection for power lines. Currently, deep learning models guided by external knowledge have achieved promising performances compared to directly applying the benchmark detectors. However, the component relationship, as human commonsense knowledge for object reasoning, is rarely investigated in this field. This study presents a graph-based relation guided network for power line component detection, which exploits correlations of regions, images, and categories. The visual relation module is employed to learn region-to-region relationship and enhance the visual features of each proposal that may contain components. Meanwhile, two guidance modules are proposed to capture image-to-region correlation and distinctively facilitate the category classification and position regression, which has not been considered in previous methods. Moreover, the category graphs built in these two modules are able to explore category-to-category dependencies that can further promote the network ability. Experimental results demonstrate that the proposed method can achieve more accurate and reasonable component detection compared to previous methods, which verifies the effectiveness of the proposed model incorporated with relation knowledge.
Comparison of Machine Learning Algorithms for Sepsis Detection

Sepsis is a very fatal disease, causing a lot of causalities all over the world, about 2, 70,000 die of Sepsis annually, thus early detection of Sepsis disease would be a remedy to prevent this disease and it would be a big relief to the family of sepsis patients. Different researchers have worked on sepsis disease detection and its prediction but still the need to have an improved model for Sepsis detection remains. We compared various machine learning algorithms for Sepsis detection and used the dataset publicly available for all the researchers at Physionet.org, the dataset contains many empty or Null values, we applied backward filling and forward filling techniques, and we calculated missing values of MAP using equation (1) which gives more precise results, we divided the 40,336 files of datasets A and B into 80% training set and 20% testing set. We applied the algorithms twice one time using vital signs and clinical values of patients and the second time using only vital signs of the patients; using vital signs only the training accuracy of KNN, Logistic Regression, Random Forest, MLP, and Decision Trees was 0.992, 0.999, 0.981, 0.981, and 0.981 respectively, while the testing accuracy of KNN, Logistic Regression, Random Forest, MLP, and Decision Trees was 0.987, 0.980, 0.983, 0.981, and 0.981 respectively, for Sepsis Label 0, the value of precision for KNN, Random Forest, Decision Trees, Logistic Regression, and MLP was 0.99, 0.98, 0.98, 0.98, and 0.98 respectively, while the value of recall for KNN, Random Forest, Decision Trees, Logistic Regression, and MLP was 1.00, 1.00, 1.00, 1.00, and 1.00 respectively; the comparison of all the above-mentioned algorithms showed that KNN leads over all the competitors regarding the accuracy, precision, and recall.
Global Optimization Method Based on the Survival of the Fittest Algorithm

One of the most important theoretical questions for evolutionary methods of global optimization is their convergence. The majority of evolutionary methods do not guarantee that the generated sequence of test points converges to a global extremum in any sense. The purpose of this paper is to construct and prove convergence of a new evolutionary global optimization algorithm. This algorithm is created on the base of the Survival of the Fittest algorithm using ideas of Differential Evolution. It is proved that the sequence of test points of this algorithm converges to the solution with probability one. The new method is compared with other evolutionary algorithms. It is shown that the method has higher efficiency for some classes of relevant multidimensional functions.
Comparing Zero-Shot Text Classification and Rule-Based Matching in Identifying Cyberbullying Behaviors on Social Media

The incidences of cyberbullying have skyrocketed due to the continuous expansion of social media users. Therefore, proactive efforts are necessary to address cyberbullying, including countermeasures for handling the various cyberbullying behaviors. Nevertheless, due to the large volume of social media texts being generated persistently, it is challenging to identify cyberbullying behaviors in a social text and not scalable in using the manual approach of human annotation. Most previous studies adopt the human annotation approach to determine whether a text is a cyberbully or non-cyberbully. Therefore, this paper aims to experiment with approaches that can improve the efficiency of recognizing the different cyberbullying behaviors through textual data using zero-shot classification and rule-based matching, and compare how they perform in classifying cyberbullying behaviors. This study uses techniques such as topic modelling, zero-shot text classification, and information extraction with rule-based matching to identify and classify the cyberbully behaviors underlying a cyberbully comment. The human annotation approach serves as the benchmark to compare the performance of both models in identifying cyberbullying behaviors. Our results show that zero-shot classification performed better accuracy in categorizing cyberbullying behaviors. Among the behaviors, the zero-shot model we generated presents a better accuracy rate in recognizing the flaming behavior, but it achieves a lower accuracy rate in identifying the other cyberbully behaviors.
Importance of Supra-Segmental Information and Self-Supervised Framework for Spoken Language Diarization Task

Spoken language diarization (LD) is a task of automatically extracting the monolingual segments present in a given code-switched utterance. Generally in the bilingual code-switched scenario, when a single speaker spokes both the languages, mostly the phoneme production of the secondary language is biased towards the primary, leading to acoustic similarity. It is also noticed that the turn duration of the primary language is significant over the secondary, leading to data imbalance. Due to the acoustic similarity and data imbalance, the performance of the available work is biased toward the primary language. The influence of acoustic similarity can be minimized by capturing the supra-segmental language specific information. Similarly, the influence of data imbalance can be suppressed, by prior capturing the language specific information through a pre-training framework. Therefore, this work proposes a wav2vec2 based self-supervised pre-training framework to capture the supra-segmental language specific information. The obtained results show that the proposed framework provides a relative improvement of $$33.4\%$$ in terms of Jaccard error rate (JER), over the available baseline deep-speech2 based approach. The improvement in JER suggests that the proposed approach can be able to resolve the performance bias issue to some extent.
The Gap Is Sensitive to Size of Preimages: Collapsing Property Doesn’t Go Beyond Quantum Collision-Resistance for Preimages Bounded Hash Functions

As an enhancement of quantum collision-resistance, the collapsing property of hash functions proposed by Unruh (EUROCRYPT 2016) emphasizes the hardness for distinguishing a superposition state of a hash value from a collapsed one. The collapsing property trivially implies the quantum collision-resistance. However, it remains to be unknown whether there is a reduction from the collapsing hash functions to the quantum collision-resistant hash functions. In this paper, we further study the relations between these two properties and derive two intriguing results as follows: Our results indicate that the gap between these two properties is sensitive to the size of preimages. As a corollary, our results also reveal the non-existence of polynomial bounded equivocal collision-resistant hash functions.
Human Action Recognition by Concatenation of Spatio-Temporal 3D SIFT and CoHOG Descriptors using Bag of Visual Words

In this paper, Spatio-Temporal Interest Points (STIPs) based technique is presented to recognize human actions using Bag of Visual Words (BoVW) representation. First, we extract densely sampled local 3-Dimensional Scale Invariant Feature Transform (3D SIFT) and global Co-occurrence Histograms of Oriented Gradients (CoHOG) feature descriptors from input video sequences. The discriminative features are selected by applying the Linear Discriminant Analysis (LDA) dimensionality reduction technique. The optimal features selected from both 3D SIFT and CoHOG features are concatenated to produce a single feature vector. To generate visual vocabulary we used k-means++ clustering. The prominent visual words are considered by the Term-Frequency Inverse-Document Frequency (TF.IDF) weighing scheme and are used to generate histograms. The Support Vector Machine (SVM) classifier is used for action classification. The proposed method is evaluated using two popular human action recognition datasets, such as the KTH dataset, and the Weizmann dataset. The experimental results obtained for our proposed method are compared with the state-of-the-art human action recognition techniques which demonstrate that the proposed method achieves the highest recognition accuracy 98.00% for KTH dataset and 98.7% for Weizmann dataset.
A Survey on IMU-and-Vision-based Human Pose Estimation for Rehabilitation

With the rapid development of human pose estimation, the human-pose-estimation method applied in rehabilitation therapy has attracted more and more attention in today's aging society. Among them, inertial measurement units (IMU) and computer vision are two significant methods for human pose estimation. We mainly pay attention to the application of these two methods in the rehabilitation therapy field. In this paper, we first summarize the research status of IMU and computer vision in the field of rehabilitation therapy. Subsequently, we find that combining the two methods can produce better results. Finally, the main problems of the current research and possible development directions are identified.
Named Entity Recognition for Audio De-Identification

Data anonymization is often a task carried out by humans. Automating it would reduce the cost and time required to complete this task. This paper presents a pipeline to automate the anonymization of audio data in French. We propose a pipeline, which takes audio files with their transcriptions and removes the named entities (NEs) present in the audio. Our pipeline is made up of a forced aligner, which aligns words in an audio transcript with speech and a model that performs named entity recognition (NER). Then, the audio segments that correspond to NEs are substituted with silence to anonymize audio. We compared forced aligners and NER models to find the best ones for our scenario. We evaluated our pipeline on a small hand-annotated dataset, achieving an F1 score of 0.769. This result shows that automating this task is feasible.
Using the Collision Attack for Breaking Cryptographic Modes

Presently the cipher system that is considered to be the industry gold standard is the Advanced Encryption Standard paired with the Cipher Block Chaining cryptographic mode (AES with CBC). The key space and randomization create such a large problem that even a Brute Force attack is thought to be mathematically infeasible. However, separate research by McGrew and Carlson has resulted in an attack known as either the Improbability or Collision attack that shows that the system can be broken with a reasonable amount of input data. In this paper, we present the attack, along with background mathematics, an example, and the experimental results reached by a team led by Carlson. Extensions to other modes are also presented, with the suggestion that the side channel attack renders modes futile. In fact, applying modes reveals so much information that employing them may even compromise the security of the transmitted message.
Grasping Cybersecurity Leadership as It Relates to Critical Infrastructure Protection

Leadership growth is a contemporaneous approach to learning. Developing healthcare leadership and decision-making concerning protecting U.S. critical infrastructure from a framework of sound cybersecurity protection approaches in the age of COVID-19 is the latest development for cybersecurity healthcare leaders. Examined is the creation of this framework from the leadership viewpoint to investigate arguments, practices, and how this information explains the assembly and effects of cybersecurity consciousness, skill, experience, behaviors, aims, agreement, and knowledge absorption. This examination concentrates on the development of the framework and focus group results as this framework is reasoned the suitable tool to expose how diverse backgrounds can be realized to blend new knowledge, the age of COVID-19, U.S. critical infrastructure, protection, variation, and diversity into enhanced knowledge, skill, and abilities. This data adds to the body of knowledge regarding cybersecurity leadership knowledge development for academics, practitioners, and learners.
Succinct Classical Verification of Quantum Computation

We construct a classically verifiable succinct interactive argument for quantum computation (BQP) with communication complexity and verifier runtime that are poly-logarithmic in the runtime of the BQP computation (and polynomial in the security parameter). Our protocol is secure assuming the post-quantum security of indistinguishability obfuscation (iO) and Learning with Errors (LWE). This is the first succinct argument for quantum computation in the plain model; prior work (Chia-Chung-Yamakawa, TCC ’20) requires both a long common reference string and non-black-box use of a hash function modeled as a random oracle. At a technical level, we revisit the framework for constructing classically verifiable quantum computation (Mahadev, FOCS ’18). We give a self-contained, modular proof of security for Mahadev’s protocol, which we believe is of independent interest. Our proof readily generalizes to a setting in which the verifier’s first message (which consists of many public keys) is compressed. Next, we formalize this notion of compressed public keys; we view the object as a generalization of constrained/programmable PRFs and instantiate it based on indistinguishability obfuscation. Finally, we compile the above protocol into a fully succinct argument using a (sufficiently composable) succinct argument of knowledge for NP. Using our framework, we achieve several additional results, including
REGROUP: A Robot-Centric Group Detection and Tracking System

To facilitate HRI's transition from dyadic to group interaction, new methods are needed for robots to sense and understand team behavior. We introduce the Robot-Centric Group Detection and Tracking System (REGROUP), a new method that enables robots to detect and track groups of people from an ego-centric perspective using a crowd-aware, tracking-by-detection approach. Our system employs a novel technique that leverages person re-identification deep learning features to address the group data association problem. REGROUP is robust to real-world vision challenges such as occlusion, camera egomotion, shadow, and varying lighting illuminations. Also, it runs in real-time on real-world data. We show that REGROUP outperformed three group detection methods by up to 40% in terms of precision and up to 18 % in terms of recall. Also, we show that REGROUP's group tracking method outperformed three state-of-the-art methods by up to 66% in terms of tracking accuracy and 20% in terms of tracking precision. We plan to publicly release our system to support HRI teaming research and development. We hope this work will enable the development of robots that can more effectively locate and perceive their teammates, particularly in uncertain, unstructured environments.
Video Demoiréing with Relation-Based Temporal Consistency

Moiré patterns, appearing as color distortions, severely degrade image and video qualities when filming a screen with digital cameras. Considering the increasing demands for capturing videos, we study how to remove such undesirable moiré patterns in videos, namely video demoiréing. To this end, we introduce the first hand-held video demoiréing dataset with a dedicated data collection pipeline to ensure spatial and temporal alignments of captured data. Further, a baseline video demoiréing model with implicit feature space alignment and selective feature aggregation is developed to leverage complementary information from nearby frames to improve frame-level video demoiréing. More importantly, we propose a relation-based temporal consistency loss to encourage the model to learn temporal consistency priors directly from ground-truth reference videos, which facilitates producing temporally consistent predictions and effectively maintains frame-level qualities. Extensive experiments manifest the superiority of our model. Code is available at ht tps:// daipengwa. github.io/VDmoire_ProjectPage/.