Reviewerly/exHarmony huggingface dataset:
A Blockchain-Based Proxy Oriented Cloud Storage Public Audit Scheme for Low-Performance Terminal Devices

Due to the advantages of cloud computing, more and more people tend to store their data to cloud server (CS). Cloud storage audit scheme can help data owner (DO) to confirm the integrity of their stored data, but the existing schemes still have some limitations. Therefore, in this paper, we propose a blockchain-based proxy oriented cloud storage public audit scheme (BBPO-PA) for low performance terminal devices. Firstly, we employ a proxy that is authorized by DO. The proxy can process and upload ciphertext files for DO. Secondly, we introduce blockchain into our scheme and utilize smart contracts instead of centralized trusted third party audit (TPA) to improve the reliability and stability of audit results. Subsequently, the security analysis shows that our scheme has achieved the designed security goals. Finally, the performance evaluation demonstrates that our scheme has significant advantages over other schemes, in particular significantly reducing the resource overheads of DO.
A hefty dose of lemons: the importance of rituals for audiences and performers at the online Edinburgh Festival Fringe 2020

When the pandemic began to affect the performance world, both festival artists and producers started to adopt creative approaches to moving their work online. In the study presented here, we focus on the 2020 Edinburgh Festival Fringe, which offered a unique opportunity to understand how performers coped with the enforced switch to digital. Underpinning the Fringe Festival ethos is the attitude of experimentation, and we propose that there is much to learn from the response of performers and producers to this unprecedented situation. As one interviewee put it; ‘we got given a hefty dose of lemons, and the point of all of this was, just go and make lemonade and see what happens’ (Yvette). In this article, we focus on the challenge of managing the audience experience in the digital space, particularly before and after a performance. We note that familiar rituals play a key role for physical audiences and we position this idea within the Trajectories Framework, identifying coherent journeys through a user experience (Benford and Giannachi 2011. Performing Mixed Reality. The MIT Press. ISBN:978-0-262-01576-9), in order to frame it with digital audiences in mind. We provide recommendations regarding aspects for performers and producers to address as online and digital becomes an increasingly accepted part of the festival landscape.
Random number generator via hexagonal boron nitride heterostructure

Abstract The nonlinearity is the heart of cryptographic methods which are based on the generation of pseudo-random numbers. In this study, the metal-insulator transition in hexagonal boron nitride (hBN) was investigated as a true random number generator. We have focused on the environmental-dependent behavior of hBN lattices, such as lattice size, impurity types, and applied external voltage on conductivity. The study was carried out based on random matrix theory, with spectral analysis of energy level spectrum and fractal dimension. The results indicate that by increasing the value of the electrical field, the hBN lattice behavior changes from Poisson (regular) to Wigner distribution (chaotic). The study of current-voltage variation has shown the periodic windows of quasi ohmic and the negative differential resistance. The produced electrical current ability as a random number has been examined through generating a dynamical S-box.
Application of NLP and supervised machine learning in business evaluation and data analysis

Natural language processing and text classification are becoming more and more popular in various fields. This paper mainly introduces the application of natural language processing in business analysis or business risk assessment in machine learning and text classification based on TF-IDF and w2v model. Then, the model is introduced to optimize the accuracy of the model by using different performances obtained by different classifiers (such as KNN, VSM, random forest and logistic regression). At the same time, other specific solutions for model optimization are given, such as model sampling method and modification of vector window size.
A new approach applying Sentiment Analysis in Greek Language

In recent years, observing the rapid development of web applications, we are leading to the result of a huge volume of web texts that has come from forums, social media and blogs. These applications are a means of communication, commentary, criticism, presentation of proposals but also of political and religious beliefs. All these texts, small or large, capture the feelings, desires, intentions, but also the perceptions on various issues of the users, with the result that this data is valuable. In this article we present an approach for the analysis of text data in the Greek language in order to extract important information about the author's emotion. This approach classifies user-generated comments into the appropriate polarity category using basic classifications methods.
Adversarial Watermark Based Image Privacy Protection Algorithm

In the era of rapid growth of visual information, deep hash learning is becoming more and more popular in the image retrieval community, which greatly improves search efficiency. However, privacy risks become prominent when online images are retrieved at scale and used as a rich repository of personal information. An attacker could extract a private image by querying for similar images of any available model from the target category. Existing privacy protection methods hide key information through image processing, which is at the expense of visual effects and the practicability of images. In this paper, we propose an image privacy protection method, which is based on adversarial attack and digital watermark, to provide image practicability and postmortem copyright security while maintains visual perception. We firstly convert the way of adding digital watermarking used as adversarial perturbation into an optimization problem to determine the best adding region and transparency. Then we add adversarial noise in watermark in order to improve the attacking effect meanwhile to make it more transparent to keep its visual imperceptibility. Extensive experiments demonstrate that MI-PriAWM can successfully make a given watermark with small perturbations to mislead hash retrieval models. The perceptibility of these adversarial watermarks in various settings is also considered.
An Experimental Study of Neural Approaches to Multi-Hop Inference in Question Answering

Question answering aims at computing the answer to a question given a context with facts. Many proposals focus on questions whose answer is explicit in the context; lately, there has been an increasing interest in questions whose answer is not explicit and requires multi-hop inference to be computed. Our analysis of the literature reveals that there is a seminal proposal with increasingly complex follow-ups. Unfortunately, they were presented without an extensive study of their hyper-parameters, the experimental studies focused exclusively on English, and no statistical analysis to sustain the conclusions was ever performed. In this paper, we report on our experience devising a very simple neural approach to address the problem, on our extensive grid search over the space of hyper-parameters, on the results attained with English, Spanish, Hindi, and Portuguese, and sustain our conclusions with statistically sound analyses. Our findings prove that it is possible to beat many of the proposals in the literature with a very simple approach that was likely overlooked due to the difficulty to perform an extensive grid search, that the language does not have a statistically significant impact on the results, and that the empirical differences found among some existing proposals are not statistically significant.
Unmanned Aerial Multi-Object Dynamic Frame Detection and Skipping Using Deep Learning on the Internet of Drones

The Internet of Drones (IoD) has a revolutionary impact on monitoring and preserving the environment. Traffic regulations face enormous challenges due to rapid growth in the number of vehicles. In IoD, multiple-aerial-drone video sensing infrastructure can increase detected objects. However, the main difficulty lies in picture quality due to lighting conditions, the angle of view, and the physical structure of vehicles. This research mainly focuses on the development and deployment of a deep-learning-based system to analyze traffic congestion. The model uses multiple drone video feeds and vehicle information to detect, classify, and count a transport vehicle in a live traffic feed. The model is trained with a deep learning approach to first align the video frame and then detect the object in a top-down aerial drone video. The dynamic skipping method helps process a long video feed and accurately compares the video frame to the viewer, and then, using the standard vehicle query (i.e., make, model and year of manufacture), detect traffic scenarios in real time. The proposed model has many applications requiring a particular area to monitor real-time data analysis and drone routine tasks.
Deadline-Aware Transmission Control for Real-Time Video Streaming

The deadline requirements of real-time applications rapidly increase in recent years (e.g., cloud gaming, cloud VR, online conferencing). Due to diverse network conditions, meeting deadline requirements for these applications has become one of the research hotspots. However, the current schemes focus on providing high bitrate instead of meeting deadline requirements. In this paper, we propose D3T, a flexible deadline-aware transmission mechanism that aims to improve user quality of experience (QoE) for real-time video streaming. To fulfill the diverse deadline requirements over fluctuating network conditions, D3T uses a deadline-aware scheduler to select the high priority frame before the deadline. To reduce congestion and retransmission delay, we leverage a deep reinforcement learning algorithm to make decisions of sending rate and FEC (forward error correction) redundancy ratio based on observed network status and frame information. We evaluate D3T via trace-driven simulator spanning diverse network environments, video contents and QoE metrics. D3T significantly improves the frame completion rate by reducing the bandwidth waste before the deadline. In the considered scenarios, D3T outperforms previously approaches with the improvements in average QoE of 57%.
Modeling and Representation of Software Framework Cost Estimation Using Convolution Neural Networks

Software cost estimation is the important activity in the development of projects. It aids project managers and software developers in resource planning and management. The cost of constructing a software design was predicted using the convolution neural network (CNN) technique and compared to the conceptual design stage. The goal of cost estimates is to properly analyze necessary assets and timeframes for software enhancement projects, and it covers a wide range of topics, including calculation of the dimensions of software designs to be created, assessment of the work necessary, and assessment of the project's cost. The accuracy of development cost projection has an influence on the total project life cycle. To adapt the framework with its parameters to estimate Software Development (SD) effort, a multilayer feed forward neural network (FFNN) is proposed. The back propagation (BP) learning approach is utilized for training the network by repeatedly processing training samples by comparing the model prediction with the original effort. The COCOMO framework employs a multi layer FFNN. The COCOMO dataset is updated to test and develop the system. The purpose of this work is to provide a quantitative measure that can be used in both the present and suggested models.
A Study on the Combination of Image Preprocessing Method Based on Texture Feature and Segmentation Algorithm for Breast Ultrasound Images

Breast cancer is the most common cancer in women. Obtaining the tumor part of breast ultrasound image is of great significance for medical assistance. Breast ultrasound images have the characteristics of variable tumor morphology, more shadows, and blurred borders. Therefore, image preprocessing is usually required before segmentation. However, the traditional image preprocessing method is difficult to effectively distinguish the tumor area and tissue shadow in the ultrasound image, which affects the segmentation result of the tumor part. Therefore, this paper proposes an ultrasound image preprocessing method based on texture features. First, extract the different texture feature images of the breast ultrasound image, and then concatenate the original image and two different texture feature images together to form a new 3-channel RGB image. In the experiment, combining different preprocessing methods and different segmentation methods, the segmentation results of breast ultrasound images are evaluated. Compared with traditional preprocessing methods, the preprocessing methods proposed in this paper have improved in all segmentation evaluation indexes. The experimental results show that the Intersection-over-Union (IoU) and Dice-Similarity-Coefficient (DSC) increased to 0.6022 and 0.7554 respectively.
Face Recognition Based on Fusion of Multiple Masks Local Feature Sets Using Wavelet Transform

Although face recognition confronts several challenges, it has garnered considerable attention over the last two decades. A new proposal of local directional pattern (LDP) operator for face recognition has been introduced to overcome different challenges confronting face recognition systems. This new proposal is known as Multi-Mask Local Directional Pattern (MMLDP), which integrates the advantages of LDP operator depending on Robinson and Kirsch masks into one feature vector to give more discrimination information. Firstly, the LDP operator applies various masks to obtain the face image feature, and after that, methods such as Left-Right fusion, Right-Left fusion, Down-Up fusion, and Up-Down fusion are used to fuse the features. Finally, by employing a support vector machine (SVM), the classification of the feature ensues. The conducted experiments utilized the Yale database, and outcomes show that fusion of the features for various masks based on LDP improved performance with the best classification accuracy of 94.2857% compared to LDP and other conventional approaches for face recognition.
Semantic-Aware Noise Driven Portrait Synthesis and Manipulation

Semantic portrait synthesis has drawn consistent attention and has made significant progress, yet achieving style diversity and semantic controllability simultaneously is still a challenge. Existing methods either 1) directly take a semantic label map as input, ignoring various possibilities of semantic styles, or 2) sample global noise as input, ignoring controllability of local semantics. To fill this gap, we propose semantic-aware noise, a simple but effective input that tackles both issues and shows improved results over baselines. Semantic-aware noise introduces semantic information into noise, and each semantic is sampled from the noise separately, combining the semantic controllability and the noise sampling diversity. To further expand and manipulate real images, we propose a novel ternary network structure, allowing simultaneous diverse semantic image synthesis and real image manipulation in a unified framework. Extensive experiments demonstrate that the proposed method achieves quantitatively superior and perceptually pleasing results compared to state-of-the-art methods. We also analyze the performance of our method with respect to different noise structures and real-life applications in diverse synthesis, interactive manipulation, and extreme pose scenarios.
A map of the Urals emotional perception (based on modern regional poetry)

Abstract The study of emotional categories in the literary texts of contemporary regional authors and creating a map of the Urals emotional perception based on the data obtained with the Digital Humanities methods is believed to be of great value for solving an important scientific and socio-cultural problems of revealing local specifics and regional identity that faced the Russian society at the turn of the 20th and 21st centuries. For several decades, the modern Ural literature has been a striking socio-cultural phenomenon, numbering more than a hundred writers from different cities (Perm, Yekaterinburg, Chelyabinsk, etc.). One of the key features of the modern Ural poetry is the reflection of regional identity in literary texts. The poems of the Ural writers are full of local toponyms, images of the Urals’ industrial cities and unique nature, as well as of local myths. In this article, a wide range of emotional categories (such as surprise, fear, anger, disgust, joy, contempt, sadness, love, etc.) in modern poetry is investigated based on the emotional models by the American psychologists Robert Plutchik (Emotion: Theory, Research, and Experience, Vol. 1: Theories of Emotion. New York: Academic), Carroll Izard (Izard, C. E., 2012, The Psychology of Emotions. New York: Plenum), and Paul Ekman (Ekman, P., 2007, Emotions Revealed: Recognizing Faces and Feelings to Improve Communication and Emotional Life. New York: Holt Paperbacks). The purpose of the research is to discover what emotions prevail in modern poetic texts dedicated to the Ural region by analysing how literary works absorb and critically rethink the space of the Urals. A comprehensive research methodology is proposed that combines a qualitative study of the literary material and automated quantitative-digital analysis of corpus data with the subsequent visualization of the results.
Hate Speech Detection in Clubhouse

With the rise of voice chat rooms, a gigantic resource of data can be exposed to the research community for natural language processing tasks. Moderators in voice chat rooms actively monitor the discussions and remove the participants with offensive language. However, it makes the hate speech detection even more difficult since some participants try to find creative ways to articulate hate speech. This makes the hate speech detection challenging in new social media like Clubhouse. To the best of our knowledge, all the hate speech datasets have been collected from text resources like Twitter. In this paper, we take the first step to collect a significant dataset from Clubhouse as the rising star in social media industry. We analyze the collected instances from statistical point of view using the Google Perspective Scores. Our experiments show that, the Perspective Scores can outperform Bag of Words and Word2Vec as high level text features.
Ethereum-based decentralized car rental system

Abstract Blockchain is a pioneering technology that allows the creation of innovative information exchange ecosystems thanks to unique properties, such as immutability, distribution and transparency. By combining this technology with the emergent internet of things (IoT), many innovative business models can be created. The main objective of this work is to present the design and an initial implementation of a decentralized rental system that takes advantage of smart contracts developed on a public blockchain, combined with the potential of the IoT and its intimate relationship with cyber-physical systems. The logic to be implemented in the blockchain is applied in this paper by using the Ethereum programming language, so that the developed application covers the entire car rental process offered in traditional web applications, but adding more autonomy, functionalities and ease of use for both lessees and lessors. Following Ethereum application development guidelines, all business logic is located in the smart contracts implemented in the network, where they can be used to control the car rental system. While this is a work in progress, the results obtained in the first proof of concept have been very promising.
Do student teachers see what learners see? – Avoiding instructional dissonance when designing worksheets

Background: The judicious use of worksheets ought to contribute to the establishment of literacy, with a special significance for multilingual classrooms where neither teachers nor learners are mother tongue speakers of the instructional language. Disparity between the pedagogical intention of the worksheet and learners’ interpretation of the message often creates instructional dissonance.Aim and setting: The aim of this nested study was to establish the quality, and (mis)use of worksheets as implemented by student teachers during their work-integrated learning stint in selected urban South African primary schools.Design and methodology: Using a self-designed grid, a qualitative document analysis underpinned by visual ethnography was conducted on 45 worksheets. These texts were prepared by the student teachers for literacy, numeracy and life skills lessons offered to 6- to 9-year-olds. Criteria used for the simple analysis included appropriateness for the age group, visual complexity, accuracy of language use, cultural compatibility, layout, clarity of instructions and alignment with expected learning outcomes.Findings: Findings suggested the hasty conceptualisation and creation, or inappropriate choice of worksheets used as learning support material. Apart from linguistic barriers because of poorly formulated tasks, the worksheets were generally culturally insensitive, and contained grammatical inaccuracies compounded by technical and design shortcomings. These lacunae defeated the pedagogical purpose of most worksheets and generated instructional dissonance.Conclusion and implications for teacher education: The careful crafting and implementation of worksheets coupled with sound content knowledge of language and literacy principles would alleviate learner bafflement and enhance the learning opportunity. We take the position that well-designed worksheets should serve a focused purpose and link directly to literacy and learning of the instructional language.
A Lossless Compression Algorithm Based on High Frequency Intensity Removal for Grayscale Images

In the current world where data is one of the most important entities in our life, we cannot afford to save everything in its full size. Compression is a technique to represent the data in fewer bits as compared to the original data. Compression has been in practice since the early days of the information age and researchers continue to build better algorithms to increase its efficiency. But, the problem with widely used compression techniques is that they are designed to work on all types of data and generalize the algorithm. Having a specialized compression algorithm for a particular type of data will present more compression efficiency by using the intrinsic properties of that data. Therefore, we propose a fully lossless compression algorithm made for grayscale images that applies various pre-processing steps and exploits pixel properties of the images to produce a high compression ratio. The algorithm runs in multiple rounds to remove the pixel intensity that occurs the most number of times in the image and stores its position for the decompression. After the rounds are completed, we combine all files into one compressed file using Lempel–Ziv–Markov chaining algorithm. The decompression process is the reverse of the compression process. We test the algorithm over 100s of images and present the results generated by our algorithm. We compare the results for standard test images with existing algorithms to demonstrate the high efficiency of our algorithm. In addition, we suggest the future scope of our work as well.