Reviewerly/exHarmony huggingface dataset:
Attribute-Based Fully Homomorphic Encryption Scheme from Lattices with Short Ciphertext

Attribute-based encryption (ABE) is a good choice for one-to-many communication and fine-grained access control of the encryption data in a cloud environment. Fully homomorphic encryption (FHE) allows cloud servers to make valid operations on encrypted data without decrypting. Attribute-based fully homomorphic encryption (ABFHE) from lattices not only combines the bilateral advantages/facilities of ABE and FHE but also can resist quantum attacks. However, in the most previous ABFHE schemes, the growth of ciphertext size usually depends on the total number of system’s attributes which leads to high communication overhead and long running time of encryption and decryption. In this paper, based on the LWE problem on lattices, we propose an attribute-based fully homomorphic scheme with short ciphertext. More specifically, by classifying the system’s attributes and using the special structure matrix in MP12, we remove the dependency of ciphertext size on system’s attributes <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>ℓ</mi> </math> and the ciphertext size is no longer increased with the total number of system’s attributes. In addition, by introducing the function <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <msup> <mrow> <mi mathvariant="bold">G</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </math> in the homomorphic operations, we completely rerandomize the error term in the new ciphertext and have a very tight and simple error analysis using sub-Gaussianity. Besides, performance analysis shows that when <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mi>ℓ</mi> <mo>=</mo> <mn>2</mn> </math> and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mi>n</mi> <mo>=</mo> <mn>284</mn> </math> according to the parameter suggestion given by Micciancio and Dai et al., the size of ciphertext in our scheme is reduced by at least 73.3%, not to mention <math xmlns="http://www.w3.org/1998/Math/MathML" id="M5"> <mi>ℓ</mi> <mo>&gt;</mo> <mn>2</mn> </math> . The larger the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M6"> <mi>ℓ</mi> </math> , the more observable of our scheme. The short ciphertext in our construction can not only reduce the communication overhead but also reduce the running time of encryption and decryption. Finally, our scheme is proved to be secure in the standard model.
A Robust Face Recognition Method for Expression and Pose Variant Images

Face Recognition (FR) has been vastly studied in the past few decades and researchers have been able to achieve success in this field, mostly in controlled environments. However, the FR algorithm faces huge drop in its accuracy due to the variation in pose and expression in face images. This paper proposes a novel FR method to overcome the issues of pose and expression. The proposed FR algorithm initially locates faces using dual shot face detection algorithm. Later, an intelligent combination of Linear Discriminant Analysis (LDA) and Adaptive Boosting is used to extract features from detected and normalized face images. Finally, classic nearest center classifier is applied to achieve classification. Detailed experiments are conducted on JAFFE and KDEF databases. Moreover, a thorough analysis and comparison of the effect of pose and expression variation on the accuracy of FR is presented. The experimental results show that the proposed algorithm achieves better recognition accuracy than the recent FR methods.
Research on a novel construction of probabilistic visual cryptography scheme (k,n,0,1,1)−PVCS for threshold access structures

In this paper, we propose new algorithms to construct a probabilistic visual cryptography scheme (k,n,0,1,1)−PVCS for threshold access structures based on the deterministic visual cryptography schemes (DVCS) and classify the probabilistic schemes into online and offline ones according to the reference mode of distribution column vectors. First in consideration of the relationship between DVCS and PVCS we propose a new method for expanded DVCS based on a matrix with binary vectors of Vnk as column vectors so as to construct PVCS without pixel expansion. Then, using the extended DVCS we suggest PVCS construction method which does not require storing of the distribution column vectors and delineate the relationship between our online PVCS and visual cryptograms of random grids (VCRG). We also propose a new infinite tree in order to generate binary vectors of Vnk.
Video image scaling technology based on adaptive interpolation algorithm and TTS FPGA implementation

With the rapid development of digital multimedia terminals, the scaling of video images has solved the needs of high-end displays for low-resolution signals and high-definition signals displayed by low-end displays. The main purpose of this article is to convert the input video image signal into a video image signal with the required resolution through video image scaling. This article mainly studies various image scaling algorithms, analyzes their advantages and disadvantages, and adopts a top-down design method to implement FPGA video scaling design and verify the correctness of the results. This article introduces various traditional image interpolation algorithms. On the basis of comparing the subjective and objective quality of the image after the summation of various algorithms, the fuzzy interpolation algorithm is used to zoom the video image. In FPGA design, horizontal scaling and vertical scaling are handled separately, which reduces the computational complexity of image upgrades and facilitates control logic and specific implementation. The experimental results of this paper show that the maximum working frequency that the designed image scaling unit can reach is 153.12 MHz, which can process 1080p video signals in real time. Video image scaling technology has broad market prospects.
Recognition of Devanagari Scene Text Using Autoencoder CNN

Scene text recognition is a well-rooted research domain covering a diverse application area. Recognition of scene text is challenging due to the complex nature of scene images. Various structural characteristics of the script also influence the recognition process. Text and background segmentation is a mandatory step in the scene text recognition process. A text recognition system produces the most accurate results if the structural and contextual information is preserved by the segmentation technique. Therefore, an attempt is made here to develop a robust foreground/background segmentation(separation) technique that produces the highest recognition results. A ground-truth dataset containing Devanagari scene text images is prepared for the experimentation. An encoder-decoder convolutional neural network model is used for text/background segmentation. The model is trained with Devanagari scene text images for pixel-wise classification of text and background. The segmented text is then recognized using an existing OCR engine (Tesseract). The word and character level recognition rates are computed and compared with other existing segmentation techniques to establish the effectiveness of the proposed technique.
An Analysis on the Need for Mobile Learning-Based Interactive Learning Media in Vocational High School

Abstract Technology development encourages many changes, one of which occurs in education realm resulting in the concept of learning media. The presence of online learning media will be effective and efficient, because learning can be done not only in face-to-face (physical) meeting between teachers and students, but also whenever and wherever the students are. However, in fact, most teachers found some constraints with managing the archiving learning and students often complained in the archiving learning; for that reason, a mobile learning-based interactive multimedia should developed. This research employed a descriptive qualitative approach, analyzing the result of interview with the teachers of Archiving subject in Vocational High Schools throughout ex-Surakarta residency. The students expect that the problem in the lesson can be minimized by the teachers by providing a mobile learning-based interactive multimedia learning media. The use of mobile learning interactive multimedia is expected to facilitate the learning process anytime and anywhere and to attract the students’ learning interest in archiving subject.
Run-Time Dynamic Resource Adjustment for Mitigating Skew in MapReduce

MapReduce is a widely used programming model for large-scale data processing.However, it still suffers from the skew problem, which refers to the case in which load is imbalanced among tasks.This problem can cause a small number of tasks to consume much more time than other tasks, thereby prolonging the total job completion time.Existing solutions to this problem commonly predict the loads of tasks and then rebalance the load among them.However, solutions of this kind often incur high performance overhead due to the load prediction and rebalancing.Moreover, existing solutions target the partitioning skew for reduce tasks, but cannot mitigate the computational skew for map tasks.Accordingly, in this paper, we present DynamicAdjust, a run-time dynamic resource adjustment technique for mitigating skew.Rather than rebalancing the load among tasks, DynamicAdjust monitors the runtime execution of tasks and dynamically increases resources for those tasks that require more computation.In so doing, DynamicAdjust can not only eliminate the overhead incurred by load prediction and rebalancing, but also culls both the partitioning skew and the computational skew.Experiments are conducted based on a 21-node real cluster using real-world datasets.The results show that DynamicAdjust can mitigate the negative impact of the skew and shorten the job completion time by up to 40.85%.
Calculation of a confidence interval of semantic distance estimates obtained using a large diachronic corpus

Abstract Several methods for detection changes in words semantics and appearance of new word meanings have been suggested. These methods use different techniques of estimating semantic distance between words. They are based both on neural network vector models and on simpler vector representations that use frequencies of n-grams including the studied words. This paper proposes a method for calculation the confidence interval of the semantic distance estimations obtained based on the frequency data of n-grams extracted from the large diachronic corpus. This task is complicated because the question about the law of distribution of frequency fluctuations of words and n-grams, despite a number of studies, remains open. The confidence intervals are calculated by statistic modeling using random permutations of n-gram frequencies. To test the proposed method, estimation of semantic distance between two Russian synonyms is used as an example.
A Fault Diagnosis Method based on Improved Synthetic Minority Oversampling Technique and SVM for Unbalanced Data

Abstract Equipment usually breaks down suddenly and irregularly, so most of the data sets obtained for fault diagnosis have unbalanced characteristics, and the amount of data varies greatly from different fault types. In this paper, three problems in the application of synthetic minority oversampling technique (SMOTE) are studied, and the improved SMOTE algorithm combined with support vector machine (SVM) is proposed. The validity of the model is verified by CWRU bearing data compared with SVM and SMOTE+SVM methods, and the result of fault diagnosis is satisfactory.
Deep Face Swapping via Cross-Identity Adversarial Training

Generative Adversarial Networks (GANs) have shown promising improvements in face synthesis and image manipulation. However, it remains difficult to swap the faces in videos with a specific target. The most well-known face swapping method, Deepfakes, focuses on reconstructing the face image with auto-encoder while paying less attention to the identity gap between the source and target faces, which causes the swapped face looks like both the source face and the target face. In this work, we propose to incorporate cross-identity adversarial training mechanism for highly photo-realistic face swapping. Specifically, we introduce corresponding discriminator to faithfully try to distinguish the swapped faces, reconstructed faces and real faces in the training process. In addition, attention mechanism is applied to make our network robust to variation of illumination. Comprehensive experiments are conducted to demonstrate the superiority of our method over baseline models in quantitative and qualitative fashion.
A Case Study of Investigation of Crime Management System Using ICT

Keeping the recent history of Anti Narcotic Force with accused records has been a growing concern for the agency. Nature of Addiction (NA) has witnessed a number of cases which resulted in death just because of mismanagement of records files. The author concludes the result of the software with a greater potential for new features. The author, therefore, considered also the possibility to add a secure way to authenticate the user. This chapter applies the idea of emancipation to apprehend the usage of management system amongst crime investigation in city and rural regions related to ICT and era; the author examines the Karachi Provincial Office for the Nature of Addiction (NA) and centers the product on keeping records of the accused and their employees' information through Excel sheets and paper files, which are managed by the mill to handle and keep each of the records by ordering.
A Data Integrity Verification Scheme of Deduplication for Cloud Ciphertexts

Cloud data deduplication can save cloud storage resources and network communication bandwidth, and improve cloud storage efficiency. A series of deduplication technologies are proposed to achieve cloud data deduplication. However, the traditional deduplication scheme mainly implements data deduplication without providing data integrity verification, so it cannot be verified whether the CSP correctly stores user data. This paper proposes a data integrity verification scheme of deduplication for cloud ciphertexts, including cloud ciphertext deduplication and cloud data integrity verification. In the data integrity verification, the proxy re-signature method is adopted. In addition, our scheme realizes user revocation, ensures the privacy of cloud data, and satisfies unforgeability of tags. Performance analysis results show that it has higher efficiency than similar schemes.
Anomaly detection in Context-aware Feature Models

Feature Models are a mechanism to organize the configuration space and facilitate the construction of software variants by describing configuration options using features, i.e., a name representing a functionality. The development of Feature Models is an error prone activity and detecting their anomalies is a challenging and important task needed to promote their usage.
The Deep Radial Basis Function Data Descriptor (D-RBFDD) Network: A One-Class Neural Network for Anomaly Detection

Anomaly detection is a challenging problem in machine learning, and is made even more so when dealing with instances that are captured in low-level, raw data representations without a well-known and well-behaved set of engineered features. Images or data streams from sensors are good examples of such low-level, raw data representations. The Radial Basis Function Data Descriptor (RBFDD) network is an effective solution for anomaly detection, however, it is a shallow model that does not deal effectively with raw data representations. This paper investigates approaches to improving the RBFDD network to transform it into a deep one-class classifier suitable for anomaly detection problems with low-level, raw data representations. We show that approaches based on simple transfer learning are not effective and our results suggest that this is because the latent representations learned by generic classification models are not suitable for anomaly detection. Instead, we show that an approach that adds multiple convolutional layers before the Radial Basis Function (RBF) layer of an RBFDD network&#x2014;to form a Deep Radial Basis Function Data Descriptor (D-RBFDD) network&#x2014;is very effective. This is shown in a set of evaluation experiments using multiple anomaly detection scenarios created from publicly available image classification datasets, and a real-world anomaly detection dataset in which different types of arrhythmia are detected in electrocardiogram (ECG) data. Our experiments show that the D-RBFDD network out-performs state-of-the-art anomaly detection methods including the Deep Support Vector Data Descriptor (Deep SVDD), One-Class Support Vector Machine (OCSVM), and Isolation Forest on the image datasets, and produces competitive results on the ECG dataset.
Holistic animation and functional solution for digital manikins in context of virtual assembly simulations

Assembly simulations in virtual reality were used increasingly in recent years. Even though a multitude of solutions exists in the commercial market, research is focusing on improving soft factors like usability and financial accessibility. Therefore, this paper proposes a holistic solution for animation and integration of functionality of a digital manikin in Unity. The solution is using Unity's integrated inverse kinematics system and physics engine. The result is a fully animated human manikin based on the tracking data of a head-mounted display and its two controllers, which is extrapolating motion data for untracked body parts. Furthermore, the solution enables the user to manipulate objects and explore the virtual environment. The solution is validated in comparison to established motion capture methods using joint error analysis.
Security Types for Synchronous Data Flow Systems

Synchronous reactive data flow is a paradigm that provides a high-level abstract programming model for embedded and cyber-physical systems, including the locally synchronous components of IoT systems. Security in such systems is severely compromised due to low-level programming, ill-defined interfaces and inattention to security classification of data. By incorporating a Denning-style lattice-based secure information flow framework into a synchronous reactive data flow language, we provide a framework in which correct-and-secure-by-construction implementations for such systems can be specified and derived. In particular, we propose an extension of the Lustre programming framework with a security type system. We prove the soundness of our type system with respect to the co-inductive operational semantics of Lustre by showing that well-typed programs exhibit non-interference.
Lexicon Development for COVID-19-related Concepts Using Open-source Word Embedding Sources: An Intrinsic and Extrinsic Evaluation

Scientists are developing new computational methods and prediction models to better clinically understand COVID-19 prevalence, treatment efficacy, and patient outcomes. These efforts could be improved by leveraging documented COVID-19-related symptoms, findings, and disorders from clinical text sources in an electronic health record. Word embeddings can identify terms related to these clinical concepts from both the biomedical and nonbiomedical domains, and are being shared with the open-source community at large. However, it's unclear how useful openly available word embeddings are for developing lexicons for COVID-19-related concepts.Given an initial lexicon of COVID-19-related terms, this study aims to characterize the returned terms by similarity across various open-source word embeddings and determine common semantic and syntactic patterns between the COVID-19 queried terms and returned terms specific to the word embedding source.We compared seven openly available word embedding sources. Using a series of COVID-19-related terms for associated symptoms, findings, and disorders, we conducted an interannotator agreement study to determine how accurately the most similar returned terms could be classified according to semantic types by three annotators. We conducted a qualitative study of COVID-19 queried terms and their returned terms to detect informative patterns for constructing lexicons. We demonstrated the utility of applying such learned synonyms to discharge summaries by reporting the proportion of patients identified by concept among three patient cohorts: pneumonia (n=6410), acute respiratory distress syndrome (n=8647), and COVID-19 (n=2397).We observed high pairwise interannotator agreement (Cohen kappa) for symptoms (0.86-0.99), findings (0.93-0.99), and disorders (0.93-0.99). Word embedding sources generated based on characters tend to return more synonyms (mean count of 7.2 synonyms) compared to token-based embedding sources (mean counts range from 2.0 to 3.4). Word embedding sources queried using a qualifier term (eg, dry cough or muscle pain) more often returned qualifiers of the similar semantic type (eg, "dry" returns consistency qualifiers like "wet" and "runny") compared to a single term (eg, cough or pain) queries. A higher proportion of patients had documented fever (0.61-0.84), cough (0.41-0.55), shortness of breath (0.40-0.59), and hypoxia (0.51-0.56) retrieved than other clinical features. Terms for dry cough returned a higher proportion of patients with COVID-19 (0.07) than the pneumonia (0.05) and acute respiratory distress syndrome (0.03) populations.Word embeddings are valuable technology for learning related terms, including synonyms. When leveraging openly available word embedding sources, choices made for the construction of the word embeddings can significantly influence the words learned.
Using Multi-Agent Microservices for a Better Dynamic Composition of Semantic Web services

The development of new technologies and recent paradigms should be explored to support interoperability in the medium and long term. This paper examines the integration of micro-services and multi-agent systems (MAS), implementing a new paradigm called Multi-Agent Microservices (MAMS) for the dynamic composition of Semantic Web services. The objective is to improve certain agility in the development of the system and to facilitate the deployment of Multi-Agent Systems (MAS) within microservices. This approach uses a "dynamic" composition and must consider the optimization of the QoS (Quality of Service), the satisfaction of the user's global constraints, and the preservation of the semantic coherence of the data flow exchanged between the different composite services. To validate the proposed approach, we give an example of applied to tourism.
A Path Planning and Tracking Control for Autonomous Vehicle With Obstacle Avoidance

This paper presents a path planning and tracking framework to implement obstacle avoidance for autonomous car. The safe driving area model, which is made up of a serious of nodes, designed by using the longitudinal and lateral motion relationship of the vehicle, is proposed to describe the location of automobile with sideslip constraint in driving and position of the obstacles on the road, and Q-learning algorithm is used to learn the optimal strategy on each node and the optimal path is obtained under the predefined rules. In order to execute precise path tracking control, linear output regulation method is introduced to design the tracking controller based on both the kinematic and dynamic vehicle models. CarSim simulations are conducted with different scenarios and the effectiveness of the proposed framework is demonstrated.