Reviewerly/exHarmony huggingface dataset:
A Secure IoT System Using Quantum Cryptography with Block Cipher

ABSTRACTIn our days Iot technology used in a wide aspect of life and became an important topic for researchers in the scope of the technologies. Although all these benefits it needs more security and stays have security intimidation, such as data breaking or channel attacks via eavesdroppers and viruses, especially when the people used IoT for long distance for example for city or country, etc. or even using IoT to transfer secret information about things in big offices, for this reasons needed to use a method which increases IoT security and protection our special data. In this paper, we will use the internet of things IoT with quantum key distribution (QKD) and block cipher RC6 algorithm, where QKD is the science and art of using the quantum mechanical effect to perform cryptographic tasks and generates a secret key. Also, we need to overcome the loss of information which occurs because the information transition effected by noise or outside operators when using quantum cryptography, so the optimal solution is using the quantum bit error rate (QBER) to produce a more safe way for quantum communication among things in IoT techniques. QBER is done by using servers to correct error after sending a key for decryption method by another server to decrypt information using RC6 block cipher algorithm, during creating a secret key we need to calculate quantum correct probability and compare the result with threshold suggested and agreed by the servers.
Online Lectures Using Zoom Application For Undergraduate Students During Covid-19 Pandemic Period

This research aimed to obtain information about online learning using the Zoom application for the PGSD students of FKIP of Widya Gama Mahakam University of Samarinda during the Covid-19 pandemic. This research used a qualitative descriptive approach. This research subjects were PGSD students who actively participated in online lectures and selected nine sources, namely four students of class 2018 and five students of class 2019. The selection of these sources used a purposive sampling method. Data collection techniques used were observation, interviews via Zoom meeting and WhatsApp, also documentation. The activities in analyzing the data obtained are data reduction, data presentation, drawing-conclusions, and verification. This research used triangulation techniques to test the validity of the data. It can be concluded from the results of this research that lecturers and students as users of the Zoom application need to understand the steps of using the Zoom application to make it easier to take online lectures. The Zoom application is quite effective as a medium for online lectures because it has effectiveness in terms of place, time, services, and facilities, even though there are also obstacles in its use.
Large Scale Face Recognition In the Wild: Technical Challenges and Research Directions

Face recognition (FR) is the most effective and preferable biometric technique for both verification and identification of humans as compared to iris, voice, retina eye scanning, fingerprint, gait, hand, and ear geometry. FR is the most highlighted and fast-growing research area in computer vision for the past couple of years. It is observed in the literature that most techniques don't perform well because of unconstrained environments like occlusion, noise, position, angle of view, lighting, illumination, ageing, bad picture quality, low resolution, blurriness, and in many cases uncertainty in data. In this paper, a critical review on prior mentioned issues and their proposed solutions to resolve these issues are analyzed and presented through the state of the art techniques which has been proposed in the literature. Apart from this, it has been observed that various new techniques are applied in the form of loss function revamping, adding regularization, using transfer/reinforcement learning, and some new proposed architectures in the literature.
Uncertain Service Skyline Queries Based on Cloud Model in Mobile Application

The QoS based on mobile user ratings shows the fuzziness and randomness, and the number of services with the same function is larger, which makes service selection based on QoS hard. To solve above issue, this paper firstly proposes to use cloud model to describe QoS based on mobile user ratings. Then to reduce the search range for optimal service, this paper puts forward uncertain service Skyline calculation method based on cloud model and designs four kinds of uncertain service Skyline query algorithms (BNL_CM, D&C_CM, NN_CM and BBS_CM). Examples and experiments show that: 1) the cloud model has better performance in the description of QoS than the mean, interval number, fuzzy numbers, and random number; 2) Uncertain service Skyline can effectively reduce the service search range; 3) BBS_CM has better performance compared to BNL_CM, D&C_CM and NN_CM.
SLFS: Semi-supervised Light-field Foreground-background Segmentation

Efficient segmentation is a fundamental problem in computer vision and image processing. Achieving accurate segmentation for 4D light field images is a challenging task due to the huge amount of data involved and the intrinsic redundancy in this type of images. While automatic image segmentation is usually challenging, and because regions of interest are different for different users or tasks, this paper proposes an improved semi-supervised segmentation approach for 4D light field images based on an efficient graph structure and user's scribbles. The recent view-consistent 4D light field superpixels algorithm proposed by Khan et al. is used as an automatic pre-processing step to ensure spatio-angular consistency and to represent the image graph efficiently. Then, segmentation is achieved via graph-cut optimization. Experimental results for synthetic and real light field images indicate that the proposed approach can extract objects consistently across views, and thus it can be used in applications such as augmented reality applications or object-based coding with few user interactions.
Co-creating Design Opportunities for Social Technology in the Context of Dementia

Maintaining social participation in late life is an important component of general well-being. For people with dementia, participating in social interactions may become increasingly challenging with the progression of the condition. Social technologies can potentially support and empower them in this setting, yet there is a need to get a comprehensive understanding of how their social participation is hindered and facilitated through technology. This paper presents the outcomes of four co-creation sessions conducted together with four couples. We explored their experienced social challenges and identified opportunities for design to improve their social participation. These focus on compensating for internal and external barriers through design, enhancing agency through the social and physical environment, and learning from previous experiences with existing social technologies. Furthermore, we provide recommendations for future research in the context of social technologies for people with dementia. We argue that design-research should focus on embracing the importance of a broader social context, sharing meaningful activities, and incorporating social contact into the everyday routines. The significance of these outcomes indicates the relevance of well-designed novel social technologies to enrich people’s with dementia experience in the social context.
CogNLP-Sheffield at CMCL 2021 Shared Task: Blending Cognitively Inspired Features with Transformer-based Language Models for Predicting Eye Tracking Patterns

The CogNLP-Sheffield submissions to the CMCL 2021 Shared Task examine the value of a variety of cognitively and linguistically inspired features for predicting eye tracking patterns, as both standalone model inputs and as supplements to contextual word embeddings (XLNet).Surprisingly, the smaller pretrained model (XLNet-base) outperforms the larger (XLNet-large), and despite evidence that multi-word expressions (MWEs) provide cognitive processing advantages, MWE features provide little benefit to either model.
AI Based Facial Expression Recognition for Autism Children

In Automatic facial expression recognition is a challenging problem in computer vision, and has gained significant importance in the applications of human-computer interactions. The vital component of any successful expression recognition system is an effective facial representation from face images. In this project, we have derived an appearance-based feature descriptor, the Local Directional Pattern variance (LDPv), which characterizes both the texture and contrast information of facial components. The feature dimension is then reduced by extracting the most discriminative elements of the representation with Principal Component Analysis (PCA). The recognition performance based on our LDPv descriptor has been evaluated using Cohn-Kanade expression database with a CNN classifier.This project has been verified using Matlab simulation.
Emoji Prediction from Sentence

Emojis are small pictures typically used in text messages via social media. The synthesis of the visual and textual quality of the same message creates a new way of conversation. Despite being commonly used in social media, from the prespective of natural language processing, the underlying semantics of emojis have gained limited attention. We explore the relationship between words and emojis in this project, researching the challenging task of predicting the emojis when conveyed by textual twitter posts. We experimented variant of word embedding techniques, and train several models such as SVC, LinearSVC, Random Forest Classifier and Decision Tree Classifier.
Efficient Open-Set Adversarial Attacks on Deep Face Recognition

Different from close-set classification task, deep face recognition models are often used in open-set scenarios, where the models need to handle arbitrary faces. Open-set adversarial attacks can identify the vulnerability of deep face recognition models. Compared to time-consuming iterative gradient-based methods, generator-based methods can produce adversarial examples with only one forward pass, which greatly improves attack efficiency. However, existing generator-based attack methods need to train an individual model for each target identity and can only generate a fixed perturbation pattern regardless of different attack intensity constraints, which is impractical and sub-optimal for open-set adversarial attacks. In this paper, we propose an efficient generator-based Single Model ARbitrary Target (SMART) approach for open-set adversarial attacks against deep face recognition models. Given an arbitrary source-target face image pair, SMART first generates an additive perturbation and then adds it to the source image to obtain the final adversarial face image. After the training with various source-target pairs randomly sampled on large scale face images, SMART could effectively learn inherent perturbation patterns for arbitrary source-target face images pairs. Besides, we also propose a novel Constraint-aware Adversarial Decoder (CAD) module, which makes SMART the first generator-based method that could produce adaptive adversarial patterns according to different constraints on attack intensity. Extensive experimental results in various settings demonstrate the effectiveness of the proposed method.
ON AUTOMATICALLY EXTRACTING THE STATISTICAL INFORMATION OF PORES IN HETEROGENEOUS MATERIALS FROM THE SEM MORPHOLOGY

Heterogeneous materials, whose macroscopic properties largely depend on the distribution, shape, size, and number of internal pores, as well as inhomogeneities and defects, have been widely used in engineering applications. SEM imaging is an effective measure to quantitatively identify the microscopic characteristics of these materials by extracting and analyzing the relevant micro-composition. In this paper, a novel strategy is developed to automatically extract the statistical microscopic information about porous media containing pores of various complex shapes and sizes from SEM images by using the Bwlabel function to detect the connected components and fitting the relevant outlines using a proposed scheme. It is noteworthy that our method can be applied to analyze the pore constitution and extract the statistical information of porous materials with a maximum porosity of 0.3 and improve the integrity and generality of information extraction. The materials' micro-information obtained by this method provides a good support for better simulating the actual performance of materials. Two typical examples are presented to demonstrate our developed strategy.
Text File Compression Using Hybrid Run Length Encoding (Rle) Algorithm With Even Rodeh Code (Erc) And Variable Length Binary Encoding (Vlbe) To Save Storage Space

Abstract The increase of data usage causes problems in data storage, indirectly making the need for data storage also to increase. One alternative solution that can be done is to compress the file so that the file becomes smaller in size so it saves storage space. The algorithm used in this research is the Run Length Encoding algorithm, the Even Rodeh Code algorithm, and the Variable Length Binary Encoding algorithm which are the types of lossless compression. The algorithm will calculate its performance based on Compression Ratio, Ratio of Compression, Redundancy, Compression Time, and Decompression Time. The file that will be used in the data compression process is the file extension *.txt. This study used homogeneous strings (strings that have the same character) and heterogeneous strings (strings that have different characters) in testing the algorithm. In the compression process with a homogeneous string, the combination of the Run Length Encoding algorithm with the Variable Length Binary Encoding algorithm is better than the combination of the Run Length Encoding algorithm and the Even Rodeh Code algorithm with a Compression Ratio of 18.84% and a decompression time of 0.01295 ms. While the compression process on heterogeneous strings from the combination of the Run Length Encoding algorithm with the Even Rodeh Code algorithm is better than the combination of the Run Length Encoding algorithm with Variable Length Binary Encoding algorithms with Compression Ratio of an average of 52.45% and fewer decompression times of 4.93002 ms
Development and Evaluation of a Mixed Reality Assistance System in the Context of Manual Assembly

Manufacturing environments face the challenge of ever-increasing complexity in their products due to the continuously increasing degree of individuality. Together with the demand for meeting high quality standards, this leads to the requirement to assist workers during a manufacturing process. Cognitive assistance systems which provide work-related information during their work tasks have been proven to be a successful aid for these challenges. Whenever a new emerging technology is applied for worker assistance, the technology should be tested to evaluate its positive or negative impact in a specified assembly process. This work aims to test the impact of an emerging technology, the Microsoft HoloLens 2 Head Mounted Display (HMD), in a manufacturing environment. Therefore, a prototypical Mixed Reality (MR) application is introduced which provides assembly-related information to support the worker during the work process and to ensure a zero failure culture. To evaluate the impact of the presented application a comparative study is conducted, comparing existing text-based instructions on touchscreens to the newly-integrated interactive 3D assembly instructions. Although an efficiency can not be shown in the presented study, a significant increase in quality of the assembled product was gained, leading the way into a zero failure culture.
An Efficient Algorithm for Real-Time Vehicle Detection Using Deep Neural Networks

Vehicle detection is one of the major tasks in the field of Computer vision and Intelligent video surveillance systems. In this paper, we present a novel approach to detect vehicles and classify them. The proposed system is based on the YOLOv3 object detection algorithm. The backbone network we used for Feature Extraction is pretrained DenseNet121. The YOLOv3 network is further tweaked on the detection layer by pacing an extra prediction scale to form the desired architecture of our research. We also incorporated Distance IoU loss and DIoU-NMS in the network which further boosts the accuracy of the network. This network has four scales of prediction layers, with the fusion of these layers, networks predictive function increases. The proposed network is tested by modifying various parameters to find the optimal results.  Our network is proven to be effective in the detection of vehicles during night time, long-distance vehicles, and occluded vehicles. Our experiments on PASCAL VOC 2007, 2012 and COCO datasets achieves desired results in the improved network. We also created a dataset based on night-time vehicle images from Traffic CCTV footages to maximize detection accuracy during extreme weather conditions, especially at night. The Improved YOLOv3-Net model with input size 608 × 608 accomplishes high mAP of 82.8 % which is a promising result in detecting real time videos.
Bayesian Probability and Tanimoto Based Recurrent Neural Network for Question Answering System

Question Answering (QA) has become one of the most significant information retrieval applications. Despite that, most of the question answering system focused to increase the user experience in finding the relevant result. Due to the continuous increase of web content, retrieving the relevant result faces a challenging issue in the Question Answering System (QAS). Thus, an effective Question Classification (QC), and retrieval approach named Bayesian probability and Tanimoto-based Recurrent Neural Network (RNN) are proposed in this research to differentiate the types of questions more efficiently. This research presented an analysis of different types of questions with respect to the grammatical structures. Various patterns are identified from the questions and the RNN classifier is used to classify the questions. The results obtained by the proposed Bayesian probability and Tanimoto-based RNN showed that the syntactic categories related to the domain-specific types of proper nouns, numeral numbers, and the common nouns enable the RNN classifier to reveal better result for different types of questions. However, the proposed approach obtained better performance in terms of precision, recall, and F-measure with the values of 90.14, 86.301, and 90.936 using dataset-2.
Phasic Policy Gradient

We introduce Phasic Policy Gradient (PPG), a reinforcement learning framework which modifies traditional on-policy actor-critic methods by separating policy and value function training into distinct phases. In prior methods, one must choose between using a shared network or separate networks to represent the policy and value function. Using separate networks avoids interference between objectives, while using a shared network allows useful features to be shared. PPG is able to achieve the best of both worlds by splitting optimization into two phases, one that advances training and one that distills features. PPG also enables the value function to be more aggressively optimized with a higher level of sample reuse. Compared to PPO, we find that PPG significantly improves sample efficiency on the challenging Procgen Benchmark.
Inductive general grammar

General-linguistic datasets that have become available in recent years promise to enable new progress toward a theory of general grammar. A barrier to success is the incompatibility between the inductive, externalist approach that is natural for exploiting the datasets and the deductive, mentalist philosophy that is currently dominant within linguistics. I argue that the externalist philosophy is viable and that there are reasons to consider it preferable. I argue that the mainstream approach is in some cases unnecessarily concerned with psychological reality, and in other cases too quick to reject required subtheories on the grounds that they belong to “language processing” rather than linguistics, with the result that current grammars give systematically inaccurate answers to questions of the linguistic status of sentences. I suggest that the inductive development of general grammar is already being carried out (though not in those terms) in the field of natural language processing, and that linguistic participation in the effort would be of benefit to both fields.
Using augmented reality devices for remote maintenance and repair of industrial equipment as new challenges in the COVID-19 pandemic

The COVID-19 pandemic has unexpectedly transformed the access and the organization of repair services for industrial equipment. Operation or maintenance and a breakdown of industrial equipment require interaction with the specialists of the equipment manufacturer. The personal presence of a specialist repairman at an industrial facility leads to high financial costs, which consist of losses from prolonged downtime of equipment and production in general and transportation costs. A way out in such situations can be remote work of enterprise engineers and an expert of an equipment supplier, organized using a remote assistant's hardware and software complex. The article compares AR systems by numerous parameters in the following groups: software functionality, technical features for software operation, data security, compatibility requirements. This analysis will allow potential users of such systems to determine the optimal implementation approach in production based on the required parameters. The correct choice of such systems will ensure that employees can work safely and efficiently in production, reduce the risk of incorrect work and reduce production downtime.
Mitaka: Faster, Simpler, Parallelizable and Maskable Hash-and-Sign Signatures on NTRU Lattices

This talk introduces the Mitaka signature scheme: a new hash-and-sign signature scheme over NTRU lattices which can be seen as a variant of NIST finalist Falcon. It achieves comparable efficiency but is considerably simpler and easier to parallelize and protect against side-channels, thus offering significant advantages from an implementation standpoint.
Implementation of Cloudflare Hosting for Speeds and Protection on The Website

Abstract. Information is one of the important factor in the success of a company. Informationcan be distributed through official websites that are intentionally made for marketing purposes.The website created must meet standards in terms of speed and safety. CloudFlare is one of theCDN (Content Delivery Network) web performance and security company. CloudFlare is thefirst defense against hacker attacks, DDOS Attack and protection from other threats.CloudFlareprotects and speeds up websites. The website is registered and is already on the cloudflareserver. All access to the website through CloudFlare first. CloudFlare automatically optimizesthe delivery of webpage data so it becomes faster with excellent performance. CloudFlareblocks threats and limits bots. CloudFlare improves quality speeds by up to 95% and reducesspam and threats significantly. Smart system caching on cloudflare saves bandwidth, whichmeans saving money and reducing the burden on the server. CloudFlare lowers bandwidth byan average of 60%. CloudFlare cuts open time for 50% website page, which means higherspeed. Keywords: Implementation, Cloudflare, Speeds, Protection, Website.