Reviewerly/exHarmony huggingface dataset:
PoPL: Proof-of-Presence and Locality, or how to secure financial transactions on your smartphone.

The security of financial apps on smartphones is threatened by a class of advanced and persistent malware that can bypass all existing security measures. Strong cryptography and trusted on-chip hardware modules are powerless against sophisticated attacks that supplant device owners through device input record/replay functionality, effectively hijacking their credentials, privileges, and actions. In this paper, we introduce Proof-of-Presence and Locality (PoPL), a new security measure that tackles this threat by leveraging sensors to prove the physical presence of device owners and therefore discriminate between malware-initiated transaction requests and legitimate ones. Moreover, PoPL neither imposes the expense of additional hardware nor compromises app usability. In order to demonstrate PoPL’s practicality, we developed PoPLar, a challenge puzzle implementation of the PoPL concept that ensures usability even on limited screen sizes by the use of a dendrogram. We have made it available as an open-source library ready to be integrated with minimal effort with existing apps. We demonstrate PoPLar’s effectiveness and ease of integration through case studies involving apps from the three top cryptocurrency exchanges and an open-source crypto wallet.
A Machine Learning Approach for Screening Individual’s Job Profile Using Convolutional Neural Network

Pursuing human things through machines is the current hype in the information technology domain, especially in Artificial Intelligence (AI). As a part of AI, machine learning has grabbed the vogue by spreading its branches to various dimensions and degrees. Deep learning is one of the latest areas, being applied in the massive processing-related fields. In the area of NLP, various tools and technologies have been groomed and faded for their comparative pros and cons. Candidate’s job profiles can be screened by analyzing their CV/Resume for selection through the most intellectual and advanced process according to the HR requirement. Each profile is read one by one and marked whether selected for the preliminary stage or not. In this work, we aim to automate this screening process through a depth analysis of NLP to revolutionize the entire recruitment process by making employer tasks easier than ever and we are capable of ranking the CVs of the individuals by matching the total fields in the CVs with the required fields. Moreover, our classification model has provided an accuracy of 74% in terms of precision, recall and f1-score for the BDJOBS site that outperforms other existing methods.
Standardized Health data and Research Exchange (SHaRE): promoting a learning health system

Aggregate de-identified data from electronic health records (EHRs) provide a valuable resource for research. The Standardized Health data and Research Exchange (SHaRE) is a diverse group of US healthcare organizations contributing to the Cerner Health Facts (HF) and Cerner Real-World Data (CRWD) initiatives. The 51 facilities at the 7 founding organizations have provided data about more than 4.8 million patients with 63 million encounters to HF and 7.4 million patients and 119 million encounters to CRWD. SHaRE organizations unmask their organization IDs and provide 3-digit zip code (zip3) data to support epidemiology and disparity research. SHaRE enables communication between members, facilitating data validation and collaboration as we demonstrate by comparing imputed EHR module usage to actual usage. Unlike other data sharing initiatives, no additional technology installation is required. SHaRE establishes a foundation for members to engage in discussions that bridge data science research and patient care, promoting the learning health system.
A Semi-supervised Bilingual Lexicon Induction Method for Distant Language Pairs Based on Bidirectional Adversarial Model

Bilingual lexicon induction (BLI) can transfer knowledgefrom well- to under- resourced language, and has been widelyapplied to various NLP tasks. Recent work on BLI is projection-based that learns a mapping to connect source and target embedding spaces, with the isomorphism assumption. Unfortunately, the isomorphism assumption doesn't hold gener-ally, especially in typologically distant language pairs. Moreover, without supervised signals guiding, the training will further com-plicates BLI, making the performance of unsupervised methods unsatisfactory. To broke the restrict of isomorphism, we propose a semi-supervised method for distant BLI tasks, named A Semi-supervised Bilingual Lexicon Induction method in Latent Space based on Bidirectional Adversarial Model. First, two latent spaces are learned by two autoencoders for source and target domain independently to weaken the constraint of isomorphism in the embedding spaces. Then we add a few pairs of dictionary to learn the initial mapping to connect the Latent Space. Last, based on initial mapping, Cycle-Consistency is combined with Distance constraint constraint to maintain the geometry structure of both embedding spaces stable in the learning of bi-direction mapping based on adversarial model. By conducting extensive experiments, our method gets state-of-the-art results on most language pairs, especially with significant improvements on distant language pairs.
Analysis of the Frequency of Letters of the Uzbek Language Based on the Cyrillic Alphabet

The current state of cryptography has deep historical scientific roots. And these results had a strong impact on the development of cryptography tools and methods. These methods, of course, are directly related to the development of cryptanalysis methods. The first method of cryptanalysis is the frequency method. The first traces of frequency analysis of the text can be found in the works of scientists of the East since the X century. For example, scientists like Al-Kindi have made a great contribution to cryptanalysis. This paper presents a frequency analysis of the text in the Uzbek language based on the Cyrillic alphabet. At the same time, it should be noted that at the moment the Uzbek language is based on the Latin alphabet. And now further changes in the alphabet of the Uzbek language are being considered. Determining the frequencies of the letters of the Latin alphabet of the Uzbek language is accompanied by difficulties associated with the fact that some letters are combinations of letters from the original alphabet, for example, the letter ‘sh’. Here the letters ‘s’ and ‘h’ belong to this alphabet. And this, in turn, leads to problems when defining bigrams. Based on this, the texts in the Uzbek language are analyzed here using the Cyrillic alphabet, where each letter corresponds to one character. Texts from official web resources are used as content. Based on this, in this article, we present the results on determining the letter frequencies of the Uzbek language, with a further comparison with other languages.
VR for Diversity: Amelia’s Dream

This demo is the first outcome of the research project VR for Diversity. The theoretical backgrounds for the project are shortly discussed and the concept for Amelia’s Dream is presented. Amelia’s Dream is a VR experience that is filmed using volumetric video capture technology, in which a young woman shares some of her dreams and concerns, relating to issues of gender equality. Focusing on how parasocial and physical interaction may impact the persuasive effects of VR, the research plan shortly elaborates on how the installation will be used for experimental studies into the possibilities of VR as a perspective shifter.
EFEKTIVITAS BELAJAR DARI RUMAH DI TENGAH PANDEMI COVID-19

The aim of this study was to describe students' responses to the effectiveness of learning from home, since world pandemic of Covid-19. The research method used was descriptive quality research methods. The research subjects were 24 students of class VB, the homeroom teacher of class VB, and 6 parents of class students. The data collection used were questionnaires, interviews, and documentation. The results of this study is : First, the student's response to the effectiveness of learning from home during the Covid-19 pandemic percentage score of 72.35% so it is included in the Good category. Second, the factors that become obstacles are health, interest, school, and physical environmental. Third, the efforts of teachers and parents are collaborating intensively; giving directions to children how behavior is used as examples; preparing study schedules; giving rewards; being friends and discussion partners; condition teaching and learning to use the online system; assignments are given in the form of worksheet; instill ways to maintain interpersonal relationships; provide ways of overcoming emotional behavior; provide effective learning instructions; provide opportunities for independent learning; do not turn on television when children learn; and discuss the rules of learning at home.
Detecting Absence of Bone Wall in Jugular Bulb by Image Transformation Surrogate Tasks

Anomaly detection in medical images is important in computer-aided diagnosis. It is a challenging task due to limited anomaly data, sample imbalance, and local differences between the normal and abnormal patterns. Abnormal manifestations in medical images have a definite clinical definition and descriptions, which can be introduced to improve the accuracy of detection rate. In this paper, we propose an anomaly detection method via image transformation surrogate tasks and apply it to detect the absence of bone wall in jugular bulb of temporal bone CT images. First, we design a pair of contrastive surrogate tasks, including an abnormal region completion and a normal background erasure, to decouple the similarity of the normal and abnormal examples. Then, image synthesis strategies for the surrogate tasks are designed, which alleviates the problem of limited abnormal data. Further, an abnormal scoring module is proposed, which includes MSE, SSIM, and local error intensity, to fuse the results of the surrogate tasks. We verify the effectiveness of our proposed method on the jugular bulb data set and experimental results show that the accuracy of our method is 0.995 and the AUC (Area Under the Curve) is 0.994.
Convolutional Neural Networks in the Ovarian Cancer Detection

The mainKowalski, Piotr A. goalBłoniarz, Jakub ofChmura, Łukasz the work is to create an artificial neural network capable of recognizing tumor cell groups in histopathological slides. Here, the Convolution Neural Network was used in the course of research on the detection of areas that include ovarian cancer cells. Theoretical and practical investigations included the issues of both preparing data for learning and selecting the appropriate architecture of the artificial neural network. The obtained results show that neural networks can be used as a competitive method for designating areas in which pathological cells of cancer origin are present. An additional goal of the work is to obtain superior results without losing the quality of the input image as one of the major limitations to this is the memory and computing capabilities of the computer on which the tests are carried out.
Embedding Color Watermark by Adjusting DCT using RGB Gray Scale Watermarking

Digital Watermarking with PSO and Fuzzy Logic is an attempt to find suitable location using PSO and Fuzzy Logic by looking at the surrounding pixels and adaptively adjusting the pixel intensity values to encode the watermark bits. The result obtained using this technique indicates that following adaptive insertion on the pixels after finding location by PSO is more effective to obtain better fidelity and robustness. The inverse tradeoff between robustness and fidelity is also demising. The method proposed includes color watermarking as an extension of gray scale watermarking by dividing color image into RGB gray scale and then uniting them together.
Mobile Blockchain-Empowered Federated Learning: Current Situation And Further Prospect

The recent simultaneous research expansion of machine learning (ML) and mobile computing has given birth to the concept of Federated Learning (FL). FL downscales ML&#x2019;s enormous computation power requirement by delegating parts of learning tasks to smaller devices using the devices&#x2019; own dataset. Results of these bits then proceed to be aggregated to produce a global model. Blockchain, a (semi-)decentralized distributed ledger, enhances FL in reliability, security, correctness, and availability. Nevertheless, a plain blockchain-based FL (BFL) is not always ideal in mobile settings: mobile devices have limited resources to process blockchain routines and training. Plain BFL also relies on wireless connection which is often unstable. In addition, the heterogeneous nature of these devices cannot guarantee optimal model quality. Thus, this survey covers issues in mobile BFL and recent works which give effort to solving the problems and identifies further research potentials in this field. At the end, this work offers a hypothetical prototype of an ideal mobile-based BFL (MBFL).
Secure server-aided attribute-based signature with perfect anonymity for cloud-assisted systems

Attribute-based signature (ABS) is an attractive cryptographic primitive, but it is unsuitable for resource-constrained scenarios because of its high computational cost. Server-Aided Attribute-Based Signature (SAABS) was introduced to overcome this shortcoming by using cloud computing technology. In this paper, we perform cryptanalysis on Xiong et al. (2020) and Cui et al. (2018). We show that Xiong et al.'s scheme is incorrect and forgeable. Neither of these two schemes achieves anonymity. In both schemes, an adversary can collude with the server to replace the attribute set and make the verifier accept an invalid signature. Our attacks imply that the previous definitions of unforgeability and anonymity are not conforming to the actual cloud-assisted scenarios. Then, we present more accurate security models for SAABS. We define a stronger definition for unforgeability, a stronger notion called perfect anonymity, and a new notion called server-aided verification security. Finally, we propose a new idea to prevent the server from attacking anonymity and an improved scheme to fix our attacks. Our scheme achieves all the security properties, supports expressive access structure, reduces the computational overhead to a low level, and is especially suitable for cloud-assisted systems.
Hyperspectral Image Restoration via Subspace-Based Nonlocal Low-Rank Tensor Approximation

In this letter, we present a subspace-based nonlocal low-rank tensor approximation framework (SNLRTA) for hyperspectral image (HSI) restoration. The proposed method consists of a subspace learning method to achieve an accurate subspace characterization of HSI and a nonlocal low-rank tensor approximation to take spatial nonlocal self-similarity into consideration. Specifically, the HSI first exploits residual statistics on median filtered image to estimate a robust subspace. Laplacian scale mixture (LSM) modeling is then investigated to model tensor coefficients from overlapping cubes in low-rank subspace. Both the hidden scale parameters and the sparse coefficients therein are adaptively shrink, characterizing the sparsity of similar patches. Meanwhile, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{1}$ </tex-math></inline-formula> data fidelity facilitates the implicit detection of outliers after median filtering. Substantiated by extensive experimental results, the proposed method outperforms several state-of-the-art approaches on mixed noise removal, qualitatively and quantitatively.
Residual Connection based TPA-LSTM Networks for Cluster Node CPU Load Prediction

Accurate prediction for node CPU load is crucial for resource allocation in cluster. In this paper, we proposed a novel deep learning model named R-TPA-LSTM for the cluster node CPU load prediction. The proposed model is composed of two components, non-linear and linear component. The non-linear component contains residual LSTM-Conv module and attention module. Residual LSTM-Conv module includes two LSTM layers with residual connection and convolutional neural network for the sake of choosing the most informative timestep in the historical window while attention module captures the relationship among different features. The goal of the linear component, which is an AR module, is to catch the drastic changes in the data. The experimental results on a real-world dataset, show that the proposed model achieves better prediction performance for CPU load than conventional models.
Unsupervised spatial data mining for the development of future scenarios: a Covid-19 application

In the context of Futures Studies, the scenario development process permits to make assumptions on what the futures can be in order to support better today decisions. In the initial stages of the scenario building (Framing and Scanning phases), the process requires much time and efforts to scanning data and information (reading of documents, literature review and consultation of experts) to understand more about the object of the foresight study. The daily use of social networks causes an exponential increase of data and for this reason here we deal with the problem of speeding up and optimizing the Scanning phase by applying a new combined method based on the analysis of tweets with the use of unsupervised classification models, text-mining and spatial data mining techniques. For the purpose of having a qualitative overview, we applied the bag-of-words model and a Sentiment Analysis with the Afinn and Vader algorithms. Then, in order to extrapolate the influence factors, and the relevant key factors (Kayser and Blind, 2017; 2020) the Latent Dirichlet Allocation (LDA) was used (Tong and Zhang, 2016). Furthermore, to acquire also spatial information we used spatial data mining technique to extract georeferenced data from which it was possible to analyse and obtain a geographic analysis of the data. To showcase our method, we provide an example using Covid-19 tweets (Uhl and Schiebel, 2017), upon which 5 topics and 6 key factors have been extracted. In the last instance, for each influence factor, a cartogram was created through the relative frequencies in order to have a spatial distribution of the users discussing each particular topic. The results fully answer the research objectives and the model used could be a new approach that can offer benefits in the scenario developments process.
Analysis of Edit Operations for Post-editing Systems

Abstract Post-editing has become an important part not only of translation research but also in the global translation industry. While computer-aided translation tools, such as translation memories, are considered to be part of a translator's work, lately, machine translation (MT) systems have also been accepted by human translators. However, many human translators are still adopting the changes brought by translation technologies to the translation industry. This paper introduces a novel approach for seeking suitable pairs of n -grams when recommending n -grams (corresponding n -grams between MT and post-edited MT) based on the type of text (manual or administrative) and MT system used for machine translation. A tool that recommends and speeds up the correction of MT was developed to help the post-editors with their work. It is based on the analysis of words with the same lemmas and analysis of n -gram recommendations. These recommendations are extracted from sequence patterns of the mismatched words (MisMatch) between MT output and post-edited MT output. The paper aims to show the usage of morphological analysis for recommending the post-edit operations. It describes the usage of mismatched words in the n -gram recommendations for the post-edited MT output. The contribution consists of the methodology for seeking suitable pairs of words, n -grams and additionally the importance of taking into account metadata (the type of the text and/or style and MT system) when recommending post-edited operations.
BeagleRover: An Open-Source 3D-Printable Robotic Platform for Engineering Education and Research

Recent years have witnessed the rapid development of robotic platforms specialized in engineering education. And more robotic courses are being designed and taught in the curricula of different school levels and college programs. Among a number of the educational mobile robotic platforms, however, one can rarely find a small-sized, 4-wheel-driven mobile robot which can be considered as a chassis design for extraterrestrial or off-road rovers. In addition, it could difficult for the learners to understand the circuits configuration and electrical actuation of embedded systems from an off-the-shelf robotic platform. In this paper, we propose a low-cost open-source robotic platform named BeagleRover. BeagleRover is a 4-wheel-driven mobile robot of the size 236 x 223 x 85mm (L x W x H). It includes 4 DC servo motors and 4 DC gear motors to over-actuate its motion in order to realize more precise path-tracking on uneven terrains. Most of its components are 3D-printable using hobbyist printers or can be procured at relatively low costs from hobbyist websites. The controller board which BeagleRover uses is BeagleBone® Blue by default but can be replaced by some other SoC computers due to BeagleRover's multiple sets of mounting holes. In addition, We utilized BeagleRovers during the MAE 40 and MAE 144 classes at UC San Diego to help instruct electrical circuit fundamentals and classical control theories, respectively. Next, code-based and Simulink-based software approaches are discussed for users to choose and start with for their own projects. Last but not least, we explore the feasibility of using BeagleRover as a research platform for indoor navigation and SLAM and object detection studies.
Comparative Analysis of Machine Learning Algorithms for Histopathological Images of Oral Cancer

Artificial Intelligence (AI) has gained enormous prominence for recent years and is becoming a core technology in various fields. The use of AI is becoming increasingly prevalent in medical diagnosis. Many algorithms have been developed to assist the physicians and doctors in their clinical screening work. In this study, a comparative analysis of six widely used machine learning algorithms [Support Vector Machine (SVM), Random Forest, Naïve Bayes, K-Nearest Neighbor (KNN), Neural network, Decision Tree] has been done to find the most suitable model for the classification of oral cancer. The effectiveness of the ML methods are compared and evaluated in terms of accuracy, precision and recall using histopathological images of oral cancer. The experimental results shows that, Neural Network outperformed the other models by giving accuracy of 90.4%. Thus, this model can be used as a tool for the screening of binary classification of oral histopathological images.
Research on Path Planning and Control of Driverless Logistics Train

Driverless logistics train is an important application of driverless vehicle in the field of cargo transportation. In this paper, firstly, the kinematics modeling of the driverless logistics train platform is carried out, and its kinematics characteristics are analyzed. Then, based on the driverless logistics train platform, a path planning algorithm based on quintic polynomial and a path tracking control strategy based on feedforward and feedback are proposed. The control method of linear quadratic regulator (LQR) is adopted in the feedback control, and its goal is to reduce the lateral error and direction deviation between the vehicle and the target path. Finally, vehicle experiments are carried out based on the driverless logistics train, and the results verify the effectiveness and accuracy of the proposed method. In addition, in the experiment on the campus road, considering the inaccurate positioning and drift of GPS in the shade of trees, the positioning method of Simultaneous Localization and Mapping (SLAM) is used, which can solve the above problems effectively.