Reviewerly/exHarmony huggingface dataset:
Innovations in COVID times: which lessons to learn for the cultural industry?

The COVID-19 pandemic, and the lockdown of populations and businesses that followed, disrupted the operations of many industries. It also paved the way for a large number of innovations and for an overall technological acceleration, both from companies and from consumers. More than other sectors, culture was largely affected by the crisis through the closing of theatres and of borders and had no other solution than adaptation and innovation in order to survive. This paper discusses a number of such innovations, both in terms of technology and of business models for the cultural industry (i.e. performing arts). Consumers’ evaluation of such innovations is assessed through a quantitative survey involving streaming experiences with theatre programs and broadcasted concerts.
Performance evaluation and design of B‐128 modified Blowfish algorithm

Abstract The field of information security has many uses in the modern day and beyond. Encryption is a method used to secure information from unauthorized access. Since symmetric key algorithms can decrypt data much more quickly than asymmetric key algorithms, the former are more popular. Blowfish is an unpatented, freely useable, compact, quick, and efficient symmetric key encryption technique. Additionally, this method has a high level of security. The size of its blocks (64 bits) is limiting its use, though. The paper aims to propose a modified version of the Blowfish algorithm that performs high‐speed encryption with high throughput and supports 128‐bit block size, enhancing its applicability in various areas. The algorithm can be an alternative to the AES algorithm with limited power consumption. The proposed algorithm is compared with the original Blowfish algorithm based on execution speed, throughput, and the avalanche effect. The algorithm's performance is also evaluated on images based on diffusion properties, image histogram, entropy, and correlation coefficient.
Economic Sectors and Globalization Channels to Gender Economic Inclusion in Sub-Saharan Africa

This study complements the extant literature by assessing economic sector and globalization channels for gender economic inclusion. The study is focused on 35 countries in sub-Saharan Africa for the period 1995-2019 and the empirical evidence is based on fixed effects regressions. The following findings are established. First, economic and political globalization positively affect female employment in agriculture and the positive effect of economic globalization is driven by the trade globalization dynamic while social globalization negatively affects female employment in agriculture and the negative effect of social globalization is driven by cultural and informational globalization dynamics. Second, aggregate globalization and sub-components (i.e. economic globalization, social globalization and political globalization) negatively affect gender employment in the industry and the negative effect is driven by the financial globalization sub-component of economic globalization and by the informational and cultural components of social globalization. Third, aggregate globalization and sub-components positively affect gender employment in the service sector and the corresponding positive effect is driven by the trade globalization sub-component of economic globalization and by all sub-components (i.e. interpersonal, informational and cultural dimensions) of social globalization. In the terms of policy implications, policy makers should focus on promoting dimensions of globalization that are established to positively influence female employment as well as put in place measures that are designed to reverse the negative incidence of globalization dynamics that have been established to affect female employment. Moreover, policy makers should also be aware of the fact that when formulating the corresponding policies, the effect of globalization is contingent on globalization dynamics as well as on various economic sectors.
Improvisation in Opinion Mining Using Negation Detection and Negation Handling Techniques: A Survey

Negation handling is one of the challenges of natural language processing. Nowadays, negation detection and negation handling have significance in numerous fields like sentiment analysis, chat-bots, etc. In this digital era, a huge amount of data is generated from e-commerce websites, social media, etc. In order to perceive the sentiments from this data, an accurate classification of data is required, but in the opinion mining, stopwords are removed during preprocessing step and most of the negative words are removed from the text. Negation words are mostly responsible for flipping the polarity of sentences and if removed in preprocessing may cause incorrect classification of sentences or documents and result in inaccurate predictions. Negation handling is used in many domains and among all medical domains is a crucial domain. If it is not used, in the medical domain may produce wrong predictions about the disease of the patient and may prove to be fatal. Before the era of machine learning and deep learning, mainly rule based and lexicon approaches were used and these approaches were domain dependent. With the advent of machine learning and deep learning, more emphasis has been exercised on negation handling. Negations are mainly of two types implicit and explicit, and these negations can be handled using various approaches such as rule based, lexicon based, machine learning, deep learning, and hybrid. This article presents an overview of various negation detection and negation handling techniques existing in literature to get an insight into negation detection and negation handling.
Type-II Fuzzy Kernel-Based Multi-layer Extreme Learning Machine

Kernel-based multi-layer extreme learning machine (KMLELM) is developed by integrating ML-ELM and a kernel function (RBF). The model is aimed at partially solving the issues of H-ELM and ML-ELM such as eliminating the number of hidden nodes in every layer, achieving optimal model generalization, reducing the reconstruction error using invertible matrix, and decreasing the execution time by compacting the memory storage of all the hidden layers. The KMLELM can deal with small datasets effectively, but it becomes slow while dealing with large datasets. The KMLELM has also limitation of handling uncertainty which arises from overlapping regions between class boundaries. To overcome all these issues, a new type-II fuzzy KMLELM for representation learning and classification is developed by integrating type-II fuzzy set theory into KMLELM. The type-II fuzzy set is used for handling uncertainty. Further, applications of representation learning and classification to the proposed model are provided.
Deep Model Pruning By Parallel Pooling Attention Block

Deep neural network models have achieved great success in various fields, including computer vision and recommendation systems. However, deep models are usually large and computationally time-consuming. Reducing the size of deep models and speeding up the learning process without a sharp drop in accuracy becomes a promising goal, both in research and practice. The channel pruning is one of the most effective methods, which can not only compress deep model size, but also directly speed up inference. In this paper, we propose a novel channel attention block called parallel pooling attention block (PPAB) that is designed on top of the squeeze and excitation (SE) blocks. There are two optimization improvements of PPAB. First, parallel max-pooling branch is added on top of SE blocks. Second, we avoid dimensionality reduction during the excitation phase. Both of these optimizations improve the channel importance measurement capabilities of PPAB. Experiment results show that PPAB outperforms general SE blocks on the channel attention objective. The proposed pruning method could be applied efficiently both in computer vision and recommendation systems.
3D PET/CT Tumor Co-Segmentation Based on Background Subtraction Hybrid Active Contour Model

Accurate tumor segmentation in medical images plays an important role in clinical diagnosis and disease analysis. However, medical images usually have great complexity, such as low contrast of computed tomography (CT) or low spatial resolution of positron emission tomography (PET). In the actual radiotherapy plan, multimodal imaging technology, such as PET/CT, is often used. PET images provide basic metabolic information and CT images provide anatomical details. In this paper, we propose a 3D PET/CT tumor co-segmentation framework based on active contour model. First, a new edge stop function (ESF) based on PET image and CT image is defined, which combines the grayscale standard deviation information of the image and is more effective for blurry medical image edges. Second, we propose a background subtraction model to solve the problem of uneven grayscale level in medical images. Apart from that, the calculation format adopts the level set algorithm based on the additive operator splitting (AOS) format. The solution is unconditionally stable and eliminates the dependence on time step size. Experimental results on a dataset of 50 pairs of PET/CT images of non-small cell lung cancer patients show that the proposed method has a good performance for tumor segmentation.
Steady state coherence and entanglement in a dissipative two-qubit system and its application for decision making

Quantum decision theory was recently established based on the mathematics of Hilbert spaces to formalize the key concepts of quantum mechanics such as uncertainty and superposition that particularly manifest in cognitive processes. We aim to address how a two-qubit system interacting with a global squeezed vacuum field reservoir can be exploited as a black box model for quantum decision-making. To do so, we encode the input of the black box into the density matrix of the two-qubit system. The global reservoir drives the system towards its steady state via the decoherence process. The outcome of the black box can be obtained by performing measurement on the stationary state of the two-qubit system. We reveal that the squeezed reservoir can be used to control the coherence and entanglement of the black box’s output. Interestingly, it is shown that the steady state entanglement and coherence between two qubits, which have the same value, can be obtained by measuring the output state of the black box. Besides, we examine the potential application of the considered model for entanglement decision-making.
Enhanced Model for Automatic Tamil Text Summarization

Automatic Text Summarization (ATS) is a process of extracting few text portions from the given document. Such Text can also contain sentence that express the overall meaning of the document. Most of the research work in this field is carried out for Languages like English and Chinese with high accuracy. In this proposed work, hybrid model is used that combines Keyword-based score, sentiment score and Text Ranking-based score to perform Automatic text summarization on Tamil Language. To evaluate the performance of the proposed model, dataset that has been formulated from the scratch by using two reputed Tamil newspapers is considered. The Dataset have been categorized into four classes as Sports, Cinema, Astrology and Politics. The hybrid model increases the accuracy of Tamil text summarization when compared with previous research work. By using the proposed model average accuracy of around 0.81 as Recall score, 0.61 as Precision score and 0.67 as F score is achieved.
Medical Image Fusion Solid Works Supporting Multiple Techniques with Feature-Level Transforms

The use of group classifiers on similar features has resulted in computationally expensive and tedious, making them unsuitable for online applications. The clustering techniques are evaluated for both inter-subject and intra-subject variations, and it is discovered that intra-subject order execution is superior. Another approach to dealing with object recognition from medical images is considered. This is known as similarity matching, and it is a computationally simple process as compared to costly and complex characterization computations. The major challenges that occur in the image processing are high noise levels, limited image resolution, and geometric deformations throughout the image.
Reimagining Telecom Industry Using Blockchain Technology

Blockchain technology was first introduced at the start of the last decade and since then the technology has been exploited in numerous industries – The Telecom industry being no such exception. This study takes into account the recent innovations in the fabrication of the bridge between the newly adapted blockchain industry worldwide with the ever-developed and matured telecommunication industry which has led to excellent opportunities for us, who are extensively targeting the uniqueness of blockchains which is their capability to store and allow retrieval of data that is trusted to be immutable where the process of doing such is much faster, reliable, and adheres to the current security needs after data leakage and data fraudulency has been much into practice recently. The research methodology used for this study is the qualitative approach, where is used to perform content analysis on the literature available to extract the implications of the merging of these two industries at a scale above and beyond the continent boundaries. The results indicate that blockchain’s smart contract feature can resist fraud, and eliminate human error and lack of transparency.
Completing and Predicting Internet Traffic Matrices Using Adversarial Autoencoders and Hidden Markov Models

Internet traffic matrices are used nowadays for a variety of network management operations, from planning to repairing. Despite years of research on the topic, obtaining a global view of traffic is still challenging and error-prone. Due to flaws in the measurement systems and possible failure in data collection tools, missing values are unavoidable. It is thus helpful for many network operators to recover the missing data from the partial direct measurements. While some existing matrix completion methods allowed this reconstruction, they do not fully consider network traffic behavior and hidden traffic characteristics, showing the inability to adapt to multiple scenarios. Others instead make assumptions about the matrix structure that may be invalid or impractical, curtailing the applicability. In this paper, we propose Hide & Seek, a novel matrix completion and prediction algorithm based on a combination of generative autoencoders and Hidden Markov Models. After an extensive experimental evaluation based on both real-world datasets and on a testbed, we demonstrated how our algorithm can accurately reconstruct missing values while also predicting their short-term evolution.
Virtual steering with gesture control using 3D hand modeling

Hand gesture consciousness has surprisingly improved research in computer vision. Hand pose estimation identifies wrist and hand key points to precisely apprehend the postures of folks given via way of video currently. So, the above step is integral perquisite to perform a couple of tasks of computer vision which embody human laptop computer interaction, sign languages, video surveillance, human motion recognition and hand gesture analysis. The advancing depth sensors are going to inspire exclusive hand gesture techniques and applications, in which these are constrained in 2D with traditional model cameras. If we check the lookup for 3D hand gesture focus in distinct factors then, it would be static hand gesture recognition, hand trajectory recognition, nonstop hand gesture recognition, 3D hand modeling. During the exploration of hand gesture cognizance approaches, meanwhile the related purposes and ordinary systems additionally summarized for practioners. From introduction by way of classifying it as a single hand or more than one hands estimation based totally on range of people needed to be tracked, then slowly the processes used in hand gesture estimation are defined earlier than listing functions and flaws going through pose estimation.
Improving English-Assamese Neural Machine Translation Using Transliteration-Based Approach

Natural language translation is a well-defined task of linguistic technology that minimizes communication gap among people of diverse linguistic backgrounds. Although neural machine translation attains remarkable translational performance, it requires adequate amount of train data, which is a challenging task for low-resource language pair translation. Also, neural machine translation handles rare word problems, i.e., low-frequency words translation at the subword level, but it shows weakness for highly inflected language translation. In this work, we have explored neural machine translation on low-resource English-Assamese language pair with a proposed transliteration approach in the data preprocessing step. In the transliteration approach, the source language is transliterated into target language script that leverages a smaller subword vocabulary for the source-target languages. Moreover, the pre-trained embeddings on the monolingual data of transliterated source and target languages are used in the training process. With our approach, the neural machine translation significantly improves translational performance for English-to-Assamese and Assamese-to-English translation and obtain state-of-the-art results.
Non-Complex Statistics-Driven Algorithm for Enhancement of Grayscale and Color Images

Low-contrast images are viewed with obscured details and are unfavorable to the observer. Hence, it is a necessity to process such an effect efficiently to get images with lucid details as the need for clear images become a global demand. Therefore, a statistics-based algorithm of simple complexity is introduced in this research to process color and grayscale images with low contrast. The proposed algorithm consists of five stages, where the first and second stages include the use of two different statistical s-curve transformations, the third stage combines the outputs of the aforesaid stage, the fourth stage improves the brightness, and the fifth stage reallocates the pixels to the natural interval. The proposed algorithm is compared with six modern algorithms, and the outputs are evaluated using two no-reference methods. The obtained results show that the proposed algorithm performed the best, providing the highest image evaluation readings and it was the fastest among the comparison methods.
Coherent manipulation of a tunable hybrid qubit via microwave control

Hybrid qubits enable the hybridization of charge and spin degrees of freedom, which provides a way to realize both a relatively long coherence time and rapid qubit manipulation. Here, we use microwave driving to demonstrate the coherent operation of a tunable hybrid qubit, including X -rotation, Z -rotation, and rotation around an arbitrary axis in the X – Y panel of the Bloch sphere. Moreover, the coherence properties of the qubit and its tunability are studied. The measured coherence time of the X -rotation reaches ∼14.3 ns. While for the Z -rotation, the maximum decoherence time is ∼5.8 ns due to the larger sensitivity to noise. By employing the Hahn echo sequence to mitigate the influence of the low-frequency noise, we have improved the qubit coherence time from ∼5.8 ns to ∼15.0 ns. Our results contribute to a further understanding of the hybrid qubit and a step towards achieving high-fidelity qubit gates in the hybrid qubit.
From Yellow Peril to Model Minority: Asian stereotypes in social media during the COVID-19 pandemic

Heightened racial tensions during the COVID-19 pandemic contributed to the increase and rapid propagation of online hate speech towards Asians. In this work, we study the relationship between the racist narratives and conspiracy theories that emerged related to COVID-19 and historical stereotypes underpinning Asian hate and counter-hate speech on Twitter, in particular the Yellow Peril and model minority tropes. We find that the pandemic catalyzed a broad increase in discourse engaging with racist stereotypes extending beyond COVID-19 specifically. We also find that racist narratives and conspiracy theories which emerged during the pandemic and gained widespread attention were rooted in deeply-embedded Asian stereotypes. In alignment with theories of idea habitat and processing fluency, our work suggests that historical stereotypes provided an environment vulnerable to the racist narratives and conspiracy theories which emerged during the pandemic. Our work offers insight for ongoing and future anti-racist efforts.
LightEQ: On-Device Earthquake Detection with Embedded Machine Learning

The detection of earthquakes in seismological time series is central to observational seismology. Generally, seismic sensors passively record data and transmit it to the cloud or edge for integration, storage, and processing. However, transmitting raw data through the network is not an option for sensors deployed in harsh environments like underwater, underground, or in rural areas with limited connectivity. This paper introduces an efficient data processing pipeline and a set of lightweight deep-learning models for seismic event detection deployable on tiny devices such as microcontrollers. We conduct an extensive hyperparameter search and devise three lightweight models. We evaluate our models using the Stanford Earthquake Dataset and compare them with a basic STA/LTA detection algorithm and the state-of-the-art machine learning models, i.e., CRED, EQtransformer, and LCANet. For example, our smallest model consumes 193 kB of RAM and has an F1 score of 0.99 with just 29k parameters. Compared to CRED, which has an F1 score of 0.98 and 293k parameters, we reduce the number of parameters by a factor of 10. Deployed on Cortex M4 microcontrollers, the smallest version of LightEQ-NN has an inference time of 932 ms for 1 minute of raw data, an energy consumption of 5.86 mJ, and a flash requirement of 593 kB. Our results show that resource-efficient, on-device machine learning for seismological time series data is feasible and enables new approaches to seismic monitoring and early warning applications.
Web Table Formatting Affects Readability on Mobile Devices

Reading large tables on small mobile screens presents serious usability challenges that can be addressed, in part, by better table formatting. However, there are few evidenced-based guidelines for formatting mobile tables to improve readability. For this work, we first conducted a survey to investigate how people interact with tables on mobile devices and conducted a study with designers to identify which design considerations are most critical. Based on these findings, we designed and conducted three large scale studies with remote crowdworker participants. Across the studies, we analyze over 14,000 trials from 590 participants who each viewed and answered questions about 28 diverse tables rendered in different formats. We find that smaller cell padding and frozen headers lead to faster task completion, and that while zebra striping and row borders do not speed up tasks, they are still subjectively preferred by participants.