Reviewerly/exHarmony huggingface dataset:
The Power of Collaborative Synergy

Cross-disciplinary collaboration is proving to be a vital tool for leading creative and technological fields. The need for varied expertise is rising, especially as more complex and ambitious projects are being created. We are moving towards a future in creative fields that is repurposing older ideas and merging them with new insight through multidisciplinary input. This paper covers the production and premiere of US, a theatre production created in collaboration between the departments of Visualization, Dance Science, and Liberal Arts at Texas A&M University. The performance emphasizes the importance of collaborative input between the performers and audience members in order to achieve an immersive and effective experience. The paper uses US as an example to emphasize the positive outcomes resulting from the synergy of cross disciplinary collaboration and the effectiveness of audience-performer collaboration. These two objectives become a necessity when tackling a topic like immigration, which has been discussed time and time again for many generations.
Crypto Art, Aesthetic Techno-Hybridism and Artificial Intelligence

This essay addresses the effects of the advancement of artificial intelligence in the field of artistic creation, as well as the algorithmic-statistical analysis of images in the history of art and in digital culture. It seeks strategies for the appropriation of these technologies as means for creation in the arts and ways of apprehending all the change that operates from these technologies. Currently, artificial intelligence enhances the research of images in the visual arts and presents new tools and modes of production, both in creation and in research in art history. Its presence is ubiquitous in theoretical-critical thinking in the field of digital culture and art. The notion of techno-hybridism seeks to update concepts of hybridism initially proposed by Bruno Latour. Artificial intelligence would be this link for updating the concept initially proposed by Latour. Originally the hybrid for Latorian thought (hole in the ozone layer), if on the one hand it is an effect of human action, on the other hand it moves away and gains a singular existence affecting the sociocultural field and nature negatively, at least in our day. Thus, artificial intelligence here is analyzed as a techno-hybrid actant - it stems from the human and optimizes our productive-cognitive functions. In this context, crypto art emerges as an element that, on the one hand, imposes a radical change in the system and in artistic production, causes a crack in the current system allowing greater freedom for the market allowing instant access for artists - an ultra-contemporary way linked to the production methods of the past. In another sense, it resumes ways of thinking and producing art that in terms of advances achieved in the 20th century was anachronistic and leave something to be desired such as the number of copies, possession, authorship and ownership of works and the repositioning of the human in the center of creation - eliminating creations assisted by or made by artificial intelligence. Thus, a critical reflection on these themes and a creative-methodological appropriation for the field of aesthetics and the theory of digital analytical culture in the arts is necessary.
Motorcycle Image Evaluation by Contrast Stretching Method

The Contrast stretching method is used to obtain and increase image quality. This method usually applied with software platform or hardware platform. In this study, motorcycle image was evaluated using contrast stretching method on hardware platform. Results showed that motorcycle image with high threshold value has high quality compared to the lower threshold value. The pixel of image is tabulated uniformly in the high quality image. Meanwhile in low quality image, the pixel scattered in random position and not inconsistently distributed.
Low/Zero Fault Diagnosis of Porcelain Insulator Based on HSV Color Space Image Processing Technology

In this paper, the typical disk suspension porcelain insulators in transmission and distribution lines were taken as the research object, the infrared image differences between low/zero insulators and normal insulators were analyzed, and the low/zero fault diagnosis of porcelain insulator was carried out by HSV color space image recognition technology constructively. Firstly, the prediction model of porcelain insulator deterioration was constructed by using YOLOv3 algorithm, and the smallest rectangular area of insulator was extracted from the infrared image. Then the insulator was segmented from the rectangular region by using K-means algorithm to avoid background interference. On this basis, the normalized values of HSV color space features, including the maximum and minimum values of H component, the average value of S component and the maximum value of V component, were drawn according to the long axis direction by using the rotating rectangle to correct the insulator image. Combined with the correlation between HSV components and temperature rise, the detection criterion of low/zero fault of porcelain insulator is deduced, the operation state evaluation of insulator is accomplished.
Centralized information fusion with limited multi-view for multi-object tracking

In practical radar detection applications, due to the limitation of the beam width of the pattern, limited field of view (FOV) lacks the overall perception ability of the area of interest (AOI). Especially, when unknown and time-varying targets appear in AOI, it can easily lead to missing even wrong tracking of key objects. In view of the above problems, the radar network is adopted to fuse the observation data of limited multi-view to obtain the global field of view information, and then realize the trajectories estimation of multi-object in the fusion center. Based on FInite Set STatistics (FISST) framework, mapping the newborn and death process of multiple targets within FOVs as multi- Bernoulli process, the posteriori density of multi objects is propagated recursively followed Bayesian criterion in time. The simulation results of multi-object trajectories estimation with four kinds of multi-Bernoulli (MB) filters are given under three scenarios, which illustrates that the number of interest objects and the accuracy of trajectories estimation are improved, along with the increase of the number of local observation fields of view. Furthermore, the tracking performance of labeled multi-Bernoulli (LMB) filter is superior to that of unlabeled filter.
Relative Design and Investigation of Fixed/Single Axis and Dual Axis Tracking System for Diverse Weather Conditions

The generation of solar power is enormous, copious, priceless and green non-conventional energy resource. Owing to all these aforesaid merits, the entire globe is nowadays trying to explore the utmost practicably and most viably augmented approach of extracting the energy and solar tracking arrangement in different and more advanced way is the consequence of this expedition. The current work instigates an ephemeral overview of the various kinds of solar tracking arrangements with a special focus on single axis, dual axis solar tracking systems. The main motive of this paper is to contrast a relative comparison and study of three distinct kinds of stellar tracking methods that is static, single and dual axis in order to conclude their appropriateness, aptness and price-efficacy for dissimilar solicitations. The entire incident stellar energy on the solar PV array of similar dimensions for the three diverse methods have been evaluated over a certain period of time, first devoid of tracking system and then by using the entire set up with the help of tracking system. The results after the experimentation show that significant differences between the two outputs so obtained have been observed which thereby strongly advocate the use of solar tracking systems.
Differential Query Semantic Analysis

We present a novel strategy for analyzing E-Com search logs called Differential Query Semantic Analysis (DQSA) to discover explicit interpretable knowledge from search logs in the form of a semantic lexicon that makes context-specific mapping from a query segment (word or phrase) to the preferred attribute values of a product. Evaluation on a set of size-related query segments and attribute values shows that DQSA can effectively discover meaningful mappings of size-related query segments to their preferred specific attributes and attributes values in the context of a product type. DQSA has many uses including improvement of E-Com search accuracy by bridging the vocabulary gap, comparative analysis of search intent, and alleviation of the problem of tail queries and products.
Gold Price Prediction Using an Evolutionary Extreme Learning Machine

Prediction of gold price has always been most fascinated due to its nonlinearity and dynamic time series behavior, which is constrained by so many influencing factors such as economic data, monetary policy, supply and demand, inflation, and currency movements. Immemorial gold is always having the highest degree of monetary rewards and has been termed as oldest precious metal used in global currency. After understanding the hidden pattern behind the prediction of various things, which needs very fast decisions to make the computational cost of the market, researchers have proposed many statistical and machine learning models for gold price prediction. In this study, an evolutionary extreme learning machine (ELM) is designed for future gold price prediction, where two evolutionary estimation paradigms are suggested such as particle swarm optimization (PSO) and differential evolution (DE) during the training stage to optimize the weights of the network. The performance of the prediction model is measured through mean square error (MSE) and evaluated on GOLD/USD collected with six-year period of time. Through this study, a better prediction model can be designed, which will help the gold investor in taking decision for the best time of investing money in the gold market.
A Cooperative Evolution Framework Based on Fish Migration Optimization

The Fish Migration Optimization (FMO) method was inspired by the fish swim and migration behaviors and has been proofed to be a brilliant algorithm for solving numerical optimization problems. However, FMO still has the problems of premature convergence, search stagnation and easy to fall into local optimum. In this paper, a cooperative evolution framework based on fish migration optimization (CEFMO) is proposed. By dividing the whole swarm into several subsets and introducing an evaluation function, at the end of each iteration, all the individuals are evaluated and when the evaluation result meets the conditions, the cooperative evolution is triggered. In order to verify the performance of the proposed algorithm, CEFMO was compared with serval iconic swarm intelligence algorithms, such as Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA) and Black Hole (BH) algorithm under CEC-2013 benchmark function and the result shows that the CEFMO is slightly better than the compared algorithms.
TEGRAS: An Efficient Tegra Embedded GPU-Based RSA Acceleration Server

Industrial Internet of Things (IIoT) has strict requirements on the performance and security of devices. Public-key cryptography, as a kind of computing resource-consuming algorithm, is widely used in the digital signature, key exchange, and so on. The embedded graphics processing units (GPUs) are now rapidly achieving extraordinary computing power, such as NVIDIA Tegra K1/X1/X2/Xavier, which are also treated as edge computing devices. They are widely used in IIoT environments, such as intelligent manufacturing, smart cities, and vehicle-mounted systems. The performance advantages endow embedded GPUs with the possibility of accelerating cryptography that also requires high-density computing. This article implements an efficient Tegra-based embedded GPU RSA acceleration server-oriented IIoT, named TEGRAS. Various optimization methods are employed to promote efficiency, including multithreaded Montgomery multiplication and Chinese Remainder Theorem implementation on the resource-constricted embedded GPUs. With about 40–50 W of power consumption, TEGRAS can deliver 34 kops/s of RSA2048 signature generation and 1007 kops/s of RSA signature verification, which outperforms implementations in the desktop GPUs and embedded CPUs in the perspective of performance-to-power ratio. To evaluate TEGRAS in real-world scenarios, we additionally build a network stack to deliver digital signature services, which can provide more than 34 and 978 kops of signature generation and signature verification, respectively. In a word, based on the embedded GPU, we provide a high-throughput, low-latency, and ready-to-use RSA accelerator-oriented IIoT.
Reverse Bibliography

A reverse bibliography, i.e. a list of referring publications – is a dynamically compiled list of works published after the publication in question and contained a reference to it. Leading publishers of scientific journals, as a rule, post lists of referring publications on the web pages of their articles. In Russia, the placement of lists of citing publications has not yet become the norm, but it is gradually becoming widespread. The reverse bibliography is extremely interesting to the reader since it allows you to find out in which direction the research presented in the original edition is developing. A traditional bibliography, i.e. an ordinary bibliographic list of references, can successfully exist both in a printed publication and online. In contrast, the list of referring publications is essentially an exclusively online, dynamic structure formed “on the fly” on the screen of an online reader. The list of referring publications always brings the reader a fresh current state of accumulated bibliographic data. Publications appearing on the Internet and indexed in bibliographic databases containing a link to the scientific work in question in their bibliography are reflected here almost immediately. The implementation of the list of referring publications in a number of characteristic projects is considered. The terminology used is discussed.
DPRL: Labeling Relation Based on Distant Supervision and POS Rule

The lack of high-quality labeled data is one of the main challenges to a lot of relation extraction tasks. Distant supervision introduces many noises, while labeling data manually is costly. In this paper, we design a novel labeling rule called the POS rule. POS rule improves the precision of relation extraction by considering the position, ontology, and semantic of relations. We propose a POS rule mining and optimization framework to generate high-quality labeled data. Experiments on real datasets show that the proposed discovery algorithms can find high-quality POS rules which accurately label training data for relation extraction.
Simplification of Internet Services in the Knowledge Society

For several years, observable are processes of simplifying IT systems and collectivization of network services. An example is the simplification of Linux operating system, which in first versions from clumsy, based on the command line and virtually devoid of the graphical elements evolved towards an open and user friendly operating system. The same applies to the posting of data on the Internet – work in cloud computing, building web pages. In addition, it is difficult not to agree with the fact that information is now one of the key consumer goods, and dynamic scientific and technological development in the twenty-first century civilization fosters referred to as a knowledge society. These considerations seem to indicate an increase simplification of communication systems and collective web services with the idea of the knowledge society. Undoubtedly, this issue deserves deeper consideration, and this article is a contribution to reflect on the issue of dissemination and simplify the network in the perspective of the knowledge society.
Software and Hardware Integrated Accelerators for Hadoop Appliance

With the development of the Internet, the data center for next-generation must be capable of data processing at PB/s level, ensuring a high throughput of the storage and network. This paper displays the design of the hardware accelerator for tuning the throughput of the storage and network. First, we illustrate the importance of a lossless data compression algorithm for data-intense applications based on the analysis of the characteristics of the Hadoop distributed system. Second, we effectively implement Deflate compression algorithm on the FPGA. The experimental results show that the compression speed of the Deflate compression algorithm hardware accelerator can reach 2.44 Gb/S, and the compression ratio is 2.08 on the Calgary standard evaluation set. Finally, we propose a hardware/software integration architecture to combine Deflate compression algorithm hardware accelerator and Hadoop distributed system. This architecture also incorporated the unique design of semaphore and shared-memory mechanism. The overall design can double the performance of a single mapping process and retain good scalability.
Research on Hardware Implementation Technology of Dark Channel Priori Dehazing Algorithm Based on PYNQ

This paper performs a complete study on the dark channel priori dehazing algorithm and proposes an effective method of mapping the dark channel acquisition, minimum filter, mean filter, matrix calculation and atmospheric light calculation involved in the algorithm on FPGA. The dark channel priori dehazing algorithm is implemented as a hardware accelerator on the Xilinx PYNQ platform and the experimental results show that the proposed FPGA accelerator achieves 7.285 speedup compared to performing the algorithm on PC (CPU: Ï7-6700H, frequency:2.6GHz, memory: 4GB DDR3). Its frame rate reaches 18.8 FPS.
Sampling methods for improved benthic habitat maps based on reducing integrated Bernoulli variance

Oceanographic sampling to improve our knowledge about benthic habitats is common nowadays. Underwater vehicles are used to explore the seafloor to enable refined habitat maps, but considering the limited available resources during the sampling efforts, one must plan the deployments carefully. This question of effective sampling designs for underwater vehicles motivates our work. We aim to improve the sampling by using statistical models for the habitat features and to define a sampling criterion based on expected information gain. The criterion we use here is the expected integrated Bernoulli variance which we relate to the variance of presence-absence of corals in our example. Under modeling assumptions relying on a latent Gaussian model and logistic models for presence-absence data, we show partly closed form solutions for this criterion. This enables effective calculations of several possible designs, which is required in adaptive operations as is sometimes useful on for instance autonomous underwater vehicles. We illustrate the criterion in synthethic examples and in a retrospective analysis of ground truth dataset from the Trondheim fjord in Norway.
Acquiring identity and expression information from monocular face image

Abstract In this paper, a flexible deep learning‐based framework is proposed that can extract expression and identity information from monocular images and can combine the extracted identity and expression from different images to generate new face models. In this framework, two encoders are used to extract expression and identity information, and three decoders are used to visualize the information by generating face models containing only expression, only identity, and fused expression and identity. By aligning the corresponding vertices of the parts with the same semantic on the face, an error evaluation method between face models with different topologies is proposed, which can more intuitively reflect the error distribution. The experimental results show that the proposed framework has higher accuracy than face component extraction by blendshape. The framework can be used for the facial expression generation of virtual humans, which is helpful for emotion transmission and language supplementation.
Ear Talk Project: Participatory co-composition on YouTube and the Web

In this article, we present Ear Talk – a co-composition and live performance project that enables remote music collaboration through technologically mediated systems. The Ear Talk project currently exists in two distinct implementations, one that repurposes YouTube’s live-streaming technology, and one that utilises a stand-alone website. Although Ear Talk was conceived prior to the 2020 COVID-19 pandemic, the necessity for remote collaboration became more apparent during the lockdown, when a vast majority of live events and music concerts were cancelled. The Ear Talk project enables a socially distanced form of online musical collaboration and offers a platform through which to respond to such a crisis, and has grown to be adopted and presented by many different performing groups across the world. In addition to describing the technical implementations of these two systems, we discuss issues that arise from our participatory practice: from musical quality concerns in regard to social aesthetics and artistic ingenuity, to accessibility concerns when designing technologically mediated collaborative systems. Ear Talk embraces continuous musical loops as well as highly asynchronous (i.e., perpetual) collaborative paradigms among remote participants, which raises a conceptual inquiry as to which part of its sonic and social experience constitutes music in the end. Finally, we evaluate performer–audience relationships (i.e., hierarchical versus horizontal interactions) and the efficacy of the Ear Talk systems at enabling socially engaged co-composition.
Using Neural Networks to Detect Emotions in Documents

It is seminal to make non-verbal cues comprehensible to computers for the advancement of human–computer interaction. There is an abundance of published research in the domain of sentiment analysis as opposed to emotion detection in textual forms. In this research, our objective is to cull out the emotions latent through lexical analysis using predictive models. To this end, we worked on detecting seven prominent emotions. We determined the best approach by performing a comparative analysis. The research findings show that long short-term memory (LSTM)-based model delivered the best returns.