Reviewerly/exHarmony huggingface dataset:
Learnable Quantization Loss Function Based on Expectation

Low-bit quantization is an effective way to save storage and computing resources. However, the accuracy of the low-bit quantization model is low. The loss of the quantization model’s precision is the major cause. Creating a quantization loss function is the most direct and effective method to optimize the quantization error. However, Because of the underivable quantization function, the optimized quantization loss function is difficult to be designed. To solve these problems, we propose a learnable quantization loss function base on expectation for minimizing quantization error. Extensive experiments on image classification show that our method outperforms the state-of-the-art techniques for all bit widths. The accuracy of our method is much higher than that of the existing methods. Significantly, the accuracy of ResNet18 with 1-bit weight and 1-bit activation is 5.18% higher than that of the current state-of-the-art binary network on the ImageNet classification task.
The hidden dimension of open science: “Peopleware”

In discussing open science, one forgets that its key concept is collaboration, which may be either accelerated or hampered by digital technologies. Collaboration in personal interactions is hard; how much harder, then, is it to collaborate across temporal, geographical, or cultural barriers? Open science can be seen as a worldwide case study on peopleware-a major source of costs, but a huge asset.
SOME CONSIDERATIONS ON THE ATTRIBUTION OF THE ‘NEW APULEIUS’

Abstract The ‘New Apuleius’ is a set of Latin summaries of Plato's works first published in 2016 by Justin Stover, who attributed it to Apuleius. The present article attempts to assess two key aspects of Stover's argument, viz. his reconstruction of the manuscript transmission of the new text and his use of computer-assisted stylometric techniques. The authors suggest that both strands of his argument are inconclusive. First, it is argued that the transposition of gatherings in the archetype of the Apuleian philosophica as envisaged by Stover is highly unrealistic. Second, replications of Stover's stylometric experiments show that their results are highly dependent on the particular algorithm settings and on the composition of the corpus. It is further shown that Stover's choice of highly specialized stylometric techniques is suboptimal, because popular generalist methods for statistical data analysis are demonstrably more successful in correctly identifying authors of Latin text fragments and do not support the case for Apuleius’ authorship of the new text. The authors conclude that there are no solid grounds to conclude that the ‘New Apuleius’ was indeed written by Apuleius.
Review of Wallis (2021) statistics in corpus linguistics research: A new approach

This review critiques Sean Wallis's Statistics in Corpus Linguistics Research: A New Approach. Wallis introduces research design and statistical methods in corpus linguistics, proposing the choice-based approach which underlines the application of the Wilson interval. This article will review the main contents of this book and evaluate the proposed new approach.
Forecasting of adaptive network’s dynamics

We investigate the possibility of forecasting the dynamics of the adaptive network, which topology changes in time, by using Reservoir Computing (RC). As an input signal, we use a signal averaged over 100 Kuramoto oscillators. Such macroscopic signal has a similar nature with EEG signal which is a macroscopic signal of a group of neurons that connections adapt in time. We find the optimal values of RC’s parameters for achieving maximal correlation between the output and the target signal.
Leveraging Smartphones for Distributed Global Navigation Satellite System Post-Processing

Smartphones play an important role in today's society. Although their performance increases rapidly, we are witnesses of their small average daily usage. In this paper, we focus on executing post-processing tasks required for the growing amount of daily generated GNSS data. The research hypothesis is to check whether we can take an advantage to use smartphones while they are idle for such kind of post-processing. We have realized a prototype model and measured the performance as a proof of concept use-case to provide evidence if such an implementation is effective.
Ranking of documents of topical corpus according to their mutual relevance in the problem of estimating of affinity of a text to the sense standard

Abstract The offered paper is devoted to the problem of oneness and integrity of image for the semantic pattern (i.e., sense standard) revealed phrase by phrase for some text within a topical collection. One phrase corresponds here to an extended natural-language sentence. The basis of estimating affinity to the standard is the classifying of words of each phrase in a text according to the TF-IDF value relative to some text corpus. Texts to the corpus are pre-selected by an expert. The essence of the problem: for each phrase, its maximal affinity to the sense standard is achieved concerning the individual corpus document, and, consequently, it is necessary to estimate the mutual relevance of such documents concerning different phrases of the analyzed text. Based on distances between vectors of TF-IDF for words of a separate phrase obtained relative to different corpus documents, the significance estimation for each such document is entered into consideration to choose a pair of mutual relevant.
Development of an Evaluation System for Outdoor Advertising Using Smartphone Eye Tracking

In cities, outdoor advertisements are a major spatial component that attracts attention, and the effectiveness of outdoor advertisements has been measured using users' gaze. There are several eye-tracking devices, but they are not easy to use because they require special and expensive devices. In this study, we developed an eye tracking tool using a smartphone camera to evaluate outdoor advertisements. The developed system is capable of recording video of gazing points in a real urban space. This system has a wide range of applications, including new advertising contents, landscape evaluation, and driving support.
A Novel Threshold Secret Sharing Scheme for CP-ABE

Secret sharing is an integral part of CP-ABE implementation. Currently, Linear Secret Sharing Scheme (LSSS) and Lagrange interpolation are used in existing CP-ABE implementation. Implementation is complex and computational cost is high for generating the access tree structure using these schemes. In this paper, a novel construction of CP-ABE has been developed which implements counting based secret share generation and reconstruction method to generate access tree structure and reconstruct secret shares. The proposed development reduces the cost of access structure generation and reconstruction of secret and improves security by generating the large number of secret shares for small-sized secret. Hence, the new construction enhances the efficiency of encryption and decryption operations of CP-ABE.
An Improved Multi-objective Visual Tracking Algorithm

An improved real-time multi-target tracking algorithm based on the combination of improved YOLOV3-Tiny and deep-sort is proposed. The target tracking environment and target are simple, so it is easier to use the self-built data set to fit and test the vehicle it belongs to. In order to prevent the phenomenon of over-fitting, the affine transformation algorithm is used to expand and transform the data set of heavy trucks. After adding the self-built data set, it has a better effect on the original data set test. By replacing the main network of Yolov3-Tiny with a more concise neural network structure and using the effective combination of Kalman filter and Hungarian algorithm, the whole structure is more portable and fast, easier to deploy to small computers even mobile terminals. Compared with Faster RCNN, MobilenetV2, YOLOV3-Tiny and other algorithms on self-built data sets and Pcal VOC data sets, the proposed algorithm has higher real-time performance and accuracy, and robustness. After the neural network is improved, the accuracy of the proposed algorithm is improved by more than 2% compared with the original algorithm. And the computing speed is faster, and the network is more portable.
A PointNet-Based Multi-platform Multi-target Data Association Algorithm

In this paper, a data association problem is investigated in which a multi-platform detecting multi-target scenario is considered. A PointNet-based data association algorithm for matching measurements and targets is proposed which can deal with the measurement noises, false alarms, missed detections due to environmental interference and limitations on sensor resolution, as well as unknown target motions. The constantly changing number of targets and measurements are also taken into account. First, the data preprocessing method is provided. Then, a PointNet-based data association algorithm is designed by which the data association probabilities between the real targets and the measurements are given. Then, the effectiveness and the accuracy of the proposed algorithm are proved by the test experiments.
Is the Unequal COVID-19 Burden in Canada Due to Unequal Levels of Citizens’ Discipline Across Provinces?

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
An Empirical Statistical Analysis of COVID-19 Curve Through Newspaper Mining

Verma, Shriya Garg, Sonam Chamoli, Tanishq Gupta, AnkitIn India, thousands of suspected cases have been tested positive of corona virus resulting in more than 10 M confirmed corona virus cases. The newspaper played a vital role in supplementing abundant and accurate information through corona-related articles. This research paper presents a detailed analysis toward the rising coverage of COVID-19 and its related terms aiming at newspapers like "THE HINDU" using the base technology text mining. The PDFs of the newspaper are extracted through automation and converted to text files free from any ambiguous characters. The graphs are plotted for data visualization depicting the percentage coverage of COVID-19-related words and lines from March 2020 to July 2020 in the newspaper. The objective is that the given idea can be employed on various such topics and provide us refined information that can be easily used or represented graphically.
Challenges for the computer vision community

Abstract Computer vision (CV) is rapidly advancing as a tool to make conservation science more efficient, for example, by accelerating the annotation of images from camera traps and aerial surveys. However, before CV can become a widely used approach, several core technology challenges need to be addressed by the CV community. Taking into consideration several case studies in CV where tremendous progress has been made since the emergence of deep learning, this chapter will introduce core concepts in CV, survey several areas where CV is already contributing to conservation, and outline key challenges for the CV community that will facilitate the adoption of CV in mainstream conservation practice.
Coordination of Multiple Wheeled Mobile Robots with Convoy and Delta Formations

This work develops a control strategy based on a mechatronic approach to coordinate a group of three differential drive robots which are able to navigating through a reference trajectory maintaining a formation (convoy or delta) and avoiding obstacles either by modifying the trajectory or the formation. To overcome this problem, the leader-follower formation is considered. For the location of the robots, the internal and external parameters of the system are used to obtain an initial pose (combination of the position and orientation of the robot) for each robot by implementing the Iterative Closest Point (ICP) algorithm. Then, for the navigation of the robots, a proportional controller is used to follow the defined trajectories, as well as the implementation of the Vector Field Histogram (VHF) method for the obstacle avoidance. Finally, for the formation and joint movement, a proportional controller capable of carrying out and maintaining both the convoy formation and the delta formation is implemented. Finally, the performance of the proposed technique is verified with three TurtieBot3 Burger robots under different trajectories and obstacles.
Obstacle Avoidance of Multiple Manipulators Based on 3D Artificial Potential Field Method

This paper proposes a multiple manipulators obstacle avoidance algorithm based on the 3D artificial potential field method. The distance model of the manipulators is established to detect the possible collision of the manipulators. The 3D artificial potential field method is used to deal with the obstacle avoidance problem in the operation of multiple manipulators. Consequently, a smooth collision-free trajectory can be obtained, which is dynamically adjusted according to the parameters of potential field. The experimental results with the UR manipulator illustrate that the developed system is efficient and fast in detecting collisions as well as planning paths.
Image Correction for Improving Visual Acuity Using Zernike-Based Vision Simulation

This paper proposes a novel image correction method to improve visions of individuals with refractive errors such as myopia, hyperopia, and astigmatism. Refractive errors can be corrected by eyeglasses, contact lenses, or surgery, but there are problems such as maintenance or cost. Our method corrects images so that the corrected images are perceived similarly to the original images without eyeglasses. Our method consists of two models: image correction model and blur simulation model. The image correction model is a convolutional neural network model designed for super-resolution tasks, and the blur simulation model simulates the perceived blur of a human eye. Perceived blur can be modeled by Zernike polynomials-based point spread function (PSF), and blurred images are computed by convolving the images with PSF. Our model works as follows. First, images are fed to the image correction model to produce corrected images. Then, these corrected images are passed to the blur simulation model. The image correction model is trained so that blurred corrected images become identical to the original images. After training is completed, only the first step is required to correct images. Our approach can correct any type of image using the same model without significant contrast loss.
Trust in Banking Management System using Firebase in Python using AI

The Banking Management System (BMS) is windows based Trusted GUI application which can be used to store the data of bank account holders into a trusted real time database in Firebase which could be later used to fetch the details of account by any system using internet. Multiple users can have access at the same time. The main objective of Banking Management System is to allow the administrator/client or the user to edit and access the details of the bank account online. It also allows multiple users/clients to have access from different systems at the same time using internet. Hence, the same data will access by the banks as well as the account holders. It fetches the data from real time database of firebase very quick within seconds. The Scope of BMS is to help the banks to allow their account holders or users to gain access onto their particular account from sitting at home itself using internet. Making their data accessible to them online. This helps the users to make their data safe in database and also leads to built trust among the banks and their customers.
Design and Validity Test of The Disaster Mitigation Information System Using EUCS Method

Natural disasters always cause loss of lives and properties in all regions of Indonesia. So far, coordination and disaster mitigation have not been integrated properly between disaster agencies and stakeholders so that negative impacts cannot be minimized. This research proposes an automated disaster mitigation system to increase the effectiveness of emergency response and aid after a natural disaster strikes, which the validity of the system's performance will be tested using the End-User Computing Satisfaction (EUCS) method. The disaster mitigation information system receives input of disaster event data which disaster information is then distributed them to agencies and stakeholders, and finally stakeholders can provide a response of aid through the system so that the synchronization and integration of the aid is well organized. The result of the validity test using the End-User Computing Satisfaction method shows a test score of 3.4 out of 4 for a total satisfaction score, thus this result indicates that the built disaster mitigation information system is in the category of satisfied and acceptable system by users.
Current Interest Enhanced Graph Neural Networks for Session-based Recommendation

Session-based recommendation, aimed at predicting the unknown part of the anonymous interactive session, is formed via the known interaction information between users and items. Existing studies are limited to mine the session characteristics between items to detect the interest of users for the moment through treating session sequences as graphs. Additionally, as the interactive time sequence of items can reveal the recent changes in the user’s interest behavior, the seriality of the session will lose after being modeled as a graph structure. This paper proposes a Current Interest Enhanced Graph Neural Network(CIE-GNN) model for the session-based recommendation. By design, the session sequences are represented by directed graphs, in an attempt to harness the graph neural network’s power to capture rich item transitions in sessions. Then we take the last click in the original sequence as the query employed in the multi-head mechanism to adaptively acquire the user’s current interest expression more accurately. At the same time, position embedding is used to combine conversation’s sequential nature, to leverage both sequential perceptron and graphic perceptron models. Numerous researches concerning two genuine datasets exhibit that this method is predominant to other state-of-the-art methods reliably.
An Approach Towards Privacy and Security in Pervasive Healthcare System

This digital era is facing an exponential proliferation of the number of advanced hardware- and sophisticated software-based solutions which are capable of interacting with the users, collecting, accessing, and analyzing the range of data about us, in nearly every sensitive area of our lives,. These types of the data and the traced information interpreted out of it are frequently too private and confidential to be accessed by unauthorized and unwanted hands, thus making users worry about the privacy of their data that are continuously being collected through these digital platforms. In recent years, the availability and use of the advanced Electronic Healthcare Record (EHR) systems have increased rapidly. During the treatment process, more health datasets have been developed in healthcare sectors such as hospitals and other laboratories. For the sake of improving the quality of healthcare services, these documents depending on their specifications when exchanged can be used by many people. As the outcome, serious security and privacy issues are experienced, which indeed is a matter of high concern and must be monitored and reduced in order to make the effective use of EHR. Numerous studies and research have been performed earlier to improve the security and privacy requirements in the e-healthcare sectors. In this paper, a systematic analysis is carried out by observing and analyzing prior existing models and approaches that have been suggested for e-healthcare systems’ protection and privacy. To protect and promote the privacy of e-healthcare sectors, an intelligence-based security and privacy model is also proposed. This approach focuses on different issues of protection and privacy faced by the e-healthcare community and also frames certain mechanisms to protect it. In order to maintain and enhance security and privacy in the pervasive healthcare systems, our approach along with a collection of access control mechanisms and various encryption techniques may also be considered in the future research direction.
PosEmotion - Combining Real-Time 2D Body Pose Estimation and Facial Emotion Recognition to Analyze Human Behavior

Facial emotion recognition and pose estimation have been topics of great research interest in the past, especially in the last two decades. While several methods have achieved commendable results in both domains, most of them have focused on the fidelity and accuracy of predictions. Moreover, we believe that combining facial emotion recognition with body pose/posture estimation provides a more comprehensive understanding of the actions and behavior of a person. We propose an end-to-end pipeline that combines both i) pose estimation and ii) facial emotion recognition to analyze human behaviour. We achieve real-time performance due to optimal network size and reduced number of parameters compared to existing techniques, with a minimal trade-off in accuracy. We train the networks separately and combine them at inference time to form an end-to-end pipeline. Our technique has a low inference time, achieves real-time performance without compromising in accuracy, which can be beneficial for real-world applications such as surveillance, remote supervision, and proctoring systems.
Extractors: Low Entropy Requirements Colliding with Non-malleability

Two-source extractors are deterministic functions that, given two independent weak sources of randomness, output a (close to) uniformly random string of bits. Cheraghchi and Guruswami (TCC 2015) introduced two-source non-malleable extractors that combine the properties of randomness extraction with tamper resilience. Two-source non-malleable extractors have since then attracted a lot of attention, and have very quickly become fundamental objects in cryptosystems involving communication channels that cannot be fully trusted. Various applications of two-source non-malleable extractors include in particular non-malleable codes, non-malleable commitments, non-malleable secret sharing, network extraction, and privacy amplification with tamperable memory. The best known constructions of two-source non-malleable extractors are due to Chattopadhyay, Goyal, and Li (STOC 2016), Li (STOC 2017), and Li (CCC 2019). All of these constructions require both sources to have min-entropy at least 0.99n, where n is the bit-length of each source. In this work, we introduce collision-resistant randomness extractors. This allows us to design a compiler that, given a two-source non-malleable extractor, and a collision-resistant extractor, outputs a two-source non-malleable extractor that inherits the non-malleability property from the non-malleable extractor, and the entropy requirement from the collision-resistant extractor. Nested application of this compiler leads to a dramatic improvement of the state-of-the-art mentioned above. We obtain a construction of a two-source non-malleable extractor where one source is required to have min-entropy greater than 0.8n, and the other source is required to have only $$\text {polylog} (n)$$ min-entropy. Moreover, the other parameters of our construction, i.e., the output length, and the error remain comparable to prior constructions.