Reviewerly/exHarmony huggingface dataset:
CNN Based Facial Expression Recognition System Using Deep Learning Approach

In this fast-growing world of 21st century Machine Learning (ML) is one of the most powerful tools for solving a variety of real-life problems. Machine Learning helps both normal as well as differently-abled people. Facial Expression Recognition (FER) system can be widely used in different variety of research areas, such as diagnosis of mental disease and human physiological interaction detection. Machine Learning can automate processes by learning from different datasets. ML is commonly used for tasks like prediction, detection, regression and classification. The core idea of ML is to learn from previous and produce results accordingly. This result may be the output of new input or predictions based on past actions. With this, Deep Learning a subset of ML made possible to learn human facial expression from an image dataset and identify the facial expression performed in real life. The advancement in facial recognition systems helps in the purpose of diagnosis and also market feedback. But, due to various complexities and noise factors in facial expression images, there is still a need for improvement in this field. With different technological advancements in hardware and software, Facial Expression Recognition systems have been developed to help and support real-world applications.
Local path planning based on improved Dynamic window approach

An improved dynamic window method is designed as a local path planning method. Aiming at the problem of unreasonable path selection in traditional dynamic window approach when it is close to the target point and dense obstacles, an improved dynamic window approach is proposed. A new objective function is introduced in the algorithm to improve the speed selection when the robot approaches the target point. The number of obstacles is introduced into the velocity evaluation function to improve the problem that dynamic window approach is easy to bypass dense obstacles. Simulation experiments in different environments show the effectiveness of the improved algorithm.
Impulse Noise Removal Algorithm Using Modified Adaptive Distance-Related Weighted Mean Filter

Halder, Amiya Choudhuri, RudrajitReduction of impulse noise is a common preprocessing step for many image processing and computer vision applications. Salt-and-pepper noise is a common type of impulse noise which is obtained during image acquisition, storage, and transmission. In such a scenario, the image histogram has a high peak at both ends, leading to a corrupt image caused by the noisy pixels having maximum or minimum intensity values. In the proposed method, an adaptive distance-related weighted mean filter has been implemented to remove impulse noise from a corrupt image. In this technique, each noisy pixel is restored by the distance-related weighted mean of the first occurring non-noisy pixel in every immediate neighboring direction and thus it improves the quality of the image. The proposed technique is tested on standard sample images and it produces better quantitative results than the other existing state-of-the-art algorithms.
Robust Audio Patch Attacks Using Physical Sample Simulation and Adversarial Patch Noise Generation

Deep neural network (DNNs) based Automatic Speech Recognition (ASR) systems are known vulnerable to adversarial attacks that are maliciously implemented by adding small but powerful distortions to the original audio input. However, most existing methods that generate audio adversarial examples targeting ASR models cannot achieve successful robust attacks against defense methods. This paper proposes a novel framework for robust audio patch attacks using Physical Sample Simulation (PSS) and Adversarial Patch Noise Generation (APNG). First, the proposed PSS simulated real-audio with selected room impulse response for training the adversarial patches. Second, the proposed APNG generates the imperceptible audio adversarial patch examples using the voice activity detector to hide the adversarial patch noise into the non-silent locations of the input audio. Furthermore, the design Sounds Pressure Level-based adaptive noise minimization algorithm helps us further reduce the perturbation during the attack. The experimental results show that our proposed method can achieve the highest attack success rates and SNRs in various cases, comparing with other state-of-the-art attacks.
Privacy-preserving Federated Learning for Resisting Reconstruction Attacks

With the development of big data, artificial intelligence has widely walked to aspects of life, bringing life scientific and technological convenience, but also hidden dangers, the most important of which is the issue of data privacy. For privacy issues, researchers focus on data protection, while ignoring reconstruction attacks caused by the leakage of parameters such as gradients in machine learning. In fact, the attacker can indeed recover the original training data through model gradient parameters. In order to address the privacy problem, we design a privacy-preserving federated learning based on homomorphic encryption, which can resist reconstruction attacks. We analyze the correctness of the designed system theoretically and prove its accuracy experimentally with public datasets. The experimental results show that our algorithm performs a good effect while ensuring high accuracy and considerable computation time.
A free market in extreme speech: Scientific racism and bloodsports on YouTube

Abstract Around 2018, YouTube became heavily criticized for its radicalizing function by allowing far-right actors to produce hateful videos that were in turn amplified through algorithmic recommendations. Against this ‘algorithmic radicalization’ hypothesis, Munger and Phillips (2019, A supply and demand framework for YouTube politics. Preprint. https://osf.io/73jys/download; Munger and Phillips, 2020, Right-wing YouTube: a supply and demand perspective. The International Journal of Press/Politics, 21(2). doi: 10.1177/1940161220964767.)) argued that far-right radical content on YouTube fed into audience demand, suggesting researchers adopt a ‘supply and demand’ framework. Navigating this debate, our article deploys novel methods for examining radicalization in the language of far-right pundits and their audiences within YouTube’s so-called ‘Alternative Influence Network’ (Lewis, 2018, Alternative Influence. Data &amp; Society Research Institute. https://datasociety.net/library/alternative-influence/ (accessed 9 December 2020).). To that end, we operationalize the concept ‘extreme speech’—developed to account for ‘the inherent ambiguity of speech contexts’ online (Pohjonen and Udupa, 2017, Extreme speech online: an anthropological critique of hate speech debates. International Journal of Communication, 11: 1173–91)—to an analysis of a right-wing ‘Bloodsports’ debate subculture that thrived on the platform at the time. Highlighting the topic of ‘race realism’, we develop a novel mixed-methods approach: repurposing the far-right website Metapedia as a corpus to detect unique terms related to the issue. We use this corpus to analyze the transcripts and comments from an archive of 950 right-wing channels, collected from 2008 until 2018. In line with Munger and Phillips’ framework, our empirical study identifies a market for extreme speech on the platform, which came into public view in 2017.
Barriers to Shift-Left Security

Background: Automated unit and integration tests allow software development teams to continuously evaluate their application's behavior and ensure requirements are satisfied. Interest in explicitly testing security at the unit and integration levels has risen as more teams begin to shift security left in their workflows, but there is little insight into any potential pain points developers may experience as they learn to adapt their existing skills to write these tests. Aims: Identify security unit and integration testing pain points that could negatively impact efforts to shift security (testing) left to this level. Method: An mixed-method empirical study was conducted on 525 Stack Overflow and Security Stack Exchange posts related to security unit and integration testing. Latent Dirichlet Allocation (LDA) was applied to identify commonly discussed topics, pain points were learned through qualitative analysis, and links were analyzed to study commonly-shared resources. Results: Nine topics representing security controls, components, and scenarios were identified; Authentication was the most commonly tested control. Developers experienced seven pain points unique to security unit and integration testing, which were all influenced by the complexity of security control designs and implementations. Most linked resources were other Q&A posts, but repositories and documentation for security tools and libraries were also common. Conclusions: Developers may experience several unique pain points when writing tests at this level involving security controls. Additional resources are needed to guide developers through these challenges, which should also influence the creation of strategies and tools to help shift security testing to this level. To accelerate this, actionable recommendations for practitioners and future research directions based on these findings are highlighted.
A wp Characterization of Jump Statements

In this paper we present a formal characterization of goto statements in terms of Dijkstra’s weakest precondition model. We show that the goto semantics so defined captures the nature of goto statements and even applies when jumping into program constructs like alternation and repetition. With this formal definition of goto, we can characterize break and continue statements in a loop, and prove a new form of the traditional loop invariance theorem, which works with break and continue statements and has a special instance for an invariance theorem about goto statements. We, in turn, take this invariance theorem for goto statements as a basis and generalize it, although not as straightforward as hoped, to treat the multiple sequentially composed labels, which can cover essentially all scenarios.
Authentication of Digital Media Using Reversible Watermarking

Digital watermarking is a technique to hide and transmit the data in such a manner that attackers cannot perceive it. The watermarks can be used for authentication, and the creator/owner of the digital data can claim the rights, in case of any dispute. The reversible digital watermarking ensures the reusability of the cover media. In this research paper, we are suggesting a more robust method of watermarking using the combination of LWT-SVD. The digest of the message is generated using MD5 algorithm, and a quantum representation is used as the trap door. The results are measured for the existing quantum technique, LSB quantum watermarking, and the proposed algorithm. We have compared the results for MSE, SSIM, and PSNR in the analysis section. The improvements can be seen in the results persistently.
Academic Project Information Management System

Academic projects are an important part of any Engineering undergraduate course. It gives students a chance to illustrate all they have learned. This paper describes an automated system for the conduct of final year projects process. Currently many colleges manage final year project related data in an offline manner that is spreadsheet entries of all the groups, manual group formation and assignment of supervisor, maintaining hard copy of the documents submitted by the students. If the number of groups is more then there is a high possibility of mistakes to occur while updating. So the main idea of the system is to reduce human intervention. The paper presents how a web based automated system will rectify all the issues and errors occurring while maintaining it in an offline manner. The obj ective of this study is to propose a system for managing groups, automated guide allocation, document sharing, smoothen the process of communication between guides and students, maintain a log of all the activities and supervise student's project progress.
An NMT-Based Approach to Translate Natural Language Questions to SPARQL Queries

SPARQL is a powerful query language which has already been widely used in various natural language question answering (QA) systems. As the advances of deep neural networks, Neural Machine Translation (NMT) models are employed to directly translate natural language questions into SPARQL queries in recent years. In this paper, we propose an NMT-based approach with Transformer model to generate SPARQL queries. Transformer model is chosen due to its relatively high efficiency and effectiveness. We design a method to encode a SPARQL query into a simple sequence with only RDF triples reserved. The main purpose of this step is to shorten the sequences and reduce the complexity of the target language. Moreover, we employ entity type tags to help modify the translation result due to the vocabulary mismatch problem. The proposed approach is evaluated with an open-domain question answering dataset (QALD-7) on BLEU score and accuracy which result in an outstanding 80.83% on BLEU score and 78.07% on accuracy.
A Novel Bangla Font Recognition Approach Using Deep Learning

Font detection is an essential pre-processing step for printed character recognition. In this era of computerization and automation, computer composed documents such as official documents, bank checks, loan applications, visiting cards, invitation cards, educational materials are used everywhere. Beyond just editing and processing documents, converting documents from one format to another, such as an invitation card, billboards is another major application area where a designer has to recognize the font details from the images. There is a lot of re-search on automatic font detection published for high-resource languages such as English. Still, not much has been reported for a low resource language such as Bangla. Bangla has a complex structure because of the use of diacritics, compound characters and graphemes. Furthermore, because of the popularity of digital, online publications, there has been a recent surge of fonts in Bangla. Font detection can also help analysts to detect changes in font choices based on sociopolitical divides: for example, consider that fonts common in Bangladesh may not be as popular among Bangla publications in India. In this paper, we present a convolutional neural network (CNN) approach for detecting Bangla fonts, using a space adjustment method dependent on a stacked convolutional auto-encoder (SCAE). As part of the work, we built a large corpus of printed documents consisting of 12,187 images in 7 different Bangla fonts, forming a total of 77,728 samples by augmentations to train and validate our model. Our proposed model achieves 98.73% average font recognition accuracy in the validation set.
File Tracking and Security System Using BLE and Blockchain

In this era of data security crisis where the world around us works primarily with data, from the giant MNCs, Hospitals, Courts, to small businesses generate billions of terabytes of data every day. With this much data getting produced every day, it raises a massive problem of storing and keeping it safe. Handling this amount of data is the biggest challenge, and if a single piece of information is lost, that can cause a massive loss to the organization. For storing data, we can do it either electronically or physically. The probability of a file getting lost is more with hard copies. Still, most companies, government organizations, and others use old-traditional file management methods, making tracking the file between different sections difficult. Tracking and managing files are essential for every business, which also permits you to assess the overall business growth, which is amongst the most significant resources for future growth. The primary goal of this paper is to develop a more effective and practical approach to tracking files utilizing BLE (Bluetooth Low Energy) beacons and locator nodes for physical copies of information. It provides us with the file's real-time location and decreases the risk of essential files misplaced. And for the protection of electronic files, we propose a more reliable solution with cryptography and decentralization using blockchain.
Encryption, File Splitting and File compression Techniques for Data Security in virtualized environment

Nowadays cloud computing has become the crucial part of IT and most important thing is information security in cloud environment. Range of users can access the facilities and use cloud according to their feasibility. Cloud computing is utilized as safe storage of information but still data security is the biggest concern, for example, secrecy, data accessibility, data integrity is considerable factor for cloud storage. Cloud service providers provide the facility to clients that they can store the data on cloud remotely and access whenever required. Due to this facility, it gets necessary to shield or cover information from unapproved access, hackers or any sort of alteration and malevolent conduct. It is inexpensive approach to store the valuable information and doesn't require any hardware and software to hold the data. it gives excellent work experience but main measure is just security. In this work security strategies have been proposed for cloud data protection, capable to overpower the shortcomings of conventional data protection algorithms and enhancing security using steganography algorithm, encryption decryption techniques, compression and file splitting technique. These techniques are utilized for effective results in data protection, Client can easily access our developed desktop application and share the information in an effective and secured way.
An Automatic Analysis and Evaluation System Used for Teaching Quality in MOOC Environment

To solve the problem of automatically analyzing and evaluating the teaching content and effect of teachers in massive open online courses (MOOC) environment, an automatic teaching evaluation system is proposed in this paper to evaluate the sentiment of teacher and content of “online classes”. Firstly, the multimodal sentiment analysis model based on voice and text is built, which can determine the degree of “positive” and “negative” sentiments of teachers. Then, the textbook and Baidu Encyclopedia are used as two kinds of syllabus. The “3D matching degree decision model” is built to compare the differences between the teaching content and the syllabus, then the matching degree of teaching content is given. According to the results of the sentiment analysis and matching with syllabus, the teaching quality can be effectively judged. Finally, experiments in are conducted in MOOC environment. The results of the automatic analysis and evaluation system used for teaching quality perform well.
Verifiable image revision from chameleon hashes

Abstract In a digital society, the rapid development of computer science and the Internet has greatly facilitated image applications. However, one of the public network also brings risks to both image tampering and privacy exposure. Image authentication is the most important approaches to verify image integrity and authenticity. However, it has been challenging for image authentication to address both issues of tampering detection and privacy protection. One aspect, image authentication requires image contents not be changed to detect tampering. The other, privacy protection needs to remove sensitive information from images, and as a result, the contents should be changed. In this paper, we propose a practical image authentication scheme constructed from chameleon hashes combined with ordinary digital signatures to make tradeoff between tampering detection and privacy protection. Our scheme allows legitimate users to modify contents of authenticated images with a privacy-aware purpose (for example, cover some sensitive areas with mosaics) according to specific rules and verify the authenticity without interaction with the original authenticator. The security of our scheme is guaranteed by the security of the underlying cryptographic primitives. Experiment results show that our scheme is efficient and practical. We believe that our work will facilitate image applications where both authentication and privacy protection are desirable.
A User Centered News Recommendation System

Spending an uncontrolled quantity and quality of time on digital information sites is affecting our well-being and can lead to serious problems in the long term. In this paper, we present a sequential recommendation framework that uses deep reinforcement learning to capture the users' short and long-term interests, with a proposed use case of blending social news with recommended micro-learning informative news items that can help users derive useful outcomes out of their presence online.
A New super-resolution restoration method with Generated Adversarial Network for underground video images in coal mines

Abstract The computer can be used in Super-resolution reconstruction (SR) to process low-resolution images to obtain high-resolution images. Aiming at solving problems of complex underground video image acquisition environment, uneven brightness, blurred images etc, this paper adopts the idea of deep learning to perform super-resolution restoration of underground video images in coal mines, and proposes a generational confrontation network to super-resolution underground video images in coal mines. The experiment proves that Generated Adversarial Network (GAN), while being compare with Super-resolution Deep Convolutional Neural Network (SRCNN), Efficient Sub-Pixel Convolutional Neural Network (ESPCN), Deeply Recursive Convolutional Network (DRCN) the effect of GAN method is better, because it can better realize the super-resolution restoration of underground video images in coal mines and provide preliminary support for the subsequent and further application research of underground images in coal mines.
Virtual Spider for Real-Time Finding Things Close to Pedestrians

The active growth of technology gives a big push to the way we collect the spatial data either by surveying which means collect the data by surveyors and then use it to create highly precise maps, they calculate the precise position of points, distances, and angles through geometry. In this paper, we consider the collection of timely spatial data by a method based on spiders’ behavior. The data can be collected by remote sensing which uses satellites orbiting the Earth to capture information of the surface and atmosphere. One of the major challenges in collecting spatial data is to get it as fast as possible and to be able to scale contingent on the amount and the size of the data. In this matter, we see that messages brokers like Kafka can be very useful, thus it gives the ability to provide a real-time architecture for a real-time streaming data, it is scalable, durable, and a fault-tolerant published-subscribe messaging system. The other major challenge in collecting spatial data is to be able to manipulate this data with a smoother and faster way. Yet geography is a natural data domain for graphs and graph databases. Geometries and topologies could simply be drawn on graph databases like Neo4j. With their ability of expressing geo data intuitive way, graph databases could be used from calculating routes between locations in an abstract network such as a road or rail network, airspace network, or logistical network to spatial operations such as find all points of interest in a bounded area.