Reviewerly/exHarmony huggingface dataset:
Benchmark of software developed in different component models

Over last few decades a component based software engineering became a very popular paradigm for software development. Such a software is developed from independent components by their composition into one entity. In this paper we deal with different component models and their benchmarks. We cover the process of creating three programs of the same functionality but created using different component models and programming languages. Then we proceed with benchmarks of those programs and we present comparison of results.
Quality assessment model for smartphone camera photo based on inception network with residual module and batch normalization

The popularity of smartphones has made it increasingly com¬mon to take photos with smartphones. For those who design and develop cameras, as well as those who use cameras, it is advantageous to have a way to assess the image quality of a smartphone camera. On account of the distortion of pictures taken by smartphones is different from that of traditional pic¬tures, traditional methods of image quality assessment (IQA) cannot be directly applied to pictures taken by smartphones. In this paper, we submit four models for quality assessment of photos taken by smartphones. We use a pre-trained saliency prediction model SalGAN to preprocess data, and extract dif¬ferent features of the image for different indicators such as exposure, noise, texture, color. Then we input them to the modified Inception network with residual module and batch normalization for training. Our models outperform traditional no-reference IQA methods on the training set. The average SROCC reaches 0.45, 0.36, 0.33, 0.36 for exposure, color, noise, texture respectively.
Customer Profiling Using Demographic Analysis by Video Face Detection and Recognition

Biometric technique uses biological or behavioural characteristics to recognize individuals. A number of biometric features have been developed and studied, including fingerprint, voice, face, palm-print, iris, gait, and many other features. Other than identity, biometric data has the potential to reveal various other information about a person. Among these, analysis of demographics (age, sex and ethnicity) has become a popular subject of interest because of its endless applications and enormous potential. One plausible application is to create a detailed customer profile that can be used by the business intelligence model to benefit on several grounds. The two attributes of the demographics namely, age and gender provide us with a great deal of understanding about the customer visiting the outlet. The advantages which can be tapped by having a database of the age and gender of the customer are enormous.
INVESTIGATING THE IMPACT OF SCALE IN DESIGN SESSIONS SUPPORTED BY A SPATIAL AUGMENTED REALITY (SAR) TOOL

Abstract Spatial Augmented Reality (SAR) differs from other forms of AR by allowing the projection of digital images onto a model. This allows the AR to be more tangible and for interaction to be more realistic. The scale of the model plays a role in the realism but may be constrained by technical factors. This study attempts to understand the influence scale has on a design session by analysing the concept generation process, the ease of designing and the design behaviour. Understanding how these factors are influenced by the model scale betters the understanding of how SAR can influence design.
A Combined Weighting for the Feature-Based Method on Topological Parameters in Semantic Taxonomy Using Social Media

Abstract The textual analysis has become most important task due to the rapid increase of the number of texts that have been continuously generated in several forms such as posts and chats in social media, emails, articles, and news. The management of these texts requires efficient and effective methods, which can handle the linguistic issues that come from the complexity of natural languages. In recent years, the exploitation of semantic features from the lexical sources has been widely investigated by researchers to deal with the issues of “synonymy and ambiguity” in the tasks involved in the Social Media like document clustering. The main challenges of exploiting the lexical knowledge sources such as 1 WordNet 3.1 in these tasks are how to integrate the various types of semantic relations for capturing additional semantic evidence, and how to settle the high dimensionality of current semantic representing approaches. In this paper, the proposed weighting of features for a new semantic feature-based method as which combined four things as which is “Synonymy, Hypernym, non-taxonomy, and Glosses”. Therefore, this research proposes a new knowledge-based semantic representation approach for text mining, which can handle the linguistic issues as well as the high dimensionality issue. Thus, the proposed approach consists of two main components: a feature-based method for incorporating the relations in the lexical sources, and a topic-based reduction method to overcome the high dimensionality issue. The proposed method approach will evaluated using WordNet 3.1 in the text clustering and text classification.
Intra-day Forecast of Ground Horizontal Irradiance Using Long Short-term Memory Network (LSTM)

Accurate forecast of global horizontal irradiance (GHI) is one of the key issues for power grid managements with large penetration of solar energy. A challenge for solar forecasting is to forecast the solar irradiance with a lead time of 1–8 hours, here termed as intra-day forecast. This study investigated an algorithm using a long short-term memory (LSTM) model to predict the GHI in 1–8 hours. The LSTM model has been applied before for inter-day (> 24 hours) solar forecast but never for the intra-day forecast. Four years (2010–2013) of observations by the National Renewable Energy Laboratory (NREL) at Golden, Colorado were used to train the model. Observations in 2014 at the same site were used to test the model performance. According to the results, for a 1–4 hour lead time, the LSTM-based model can make predictions of GHIs with root-mean-square-errors (RMSE) ranging from 77 to 143 W m−2, and normalized RMSEs around 18.4–33.0 %. With five-minute inputs, the forecast skill of LSTM with respect to smart persistence model is 0.34–0.42, better than random forest forecast (0.27) and the numerical weather forecast (−0.40) made by the Weather Research and Forecasting (WRF) model. The performance levels off beyond 4-hour lead time. The model performs better in fall and winter than in spring and summer, and better under clear-sky conditions than under cloudy conditions. Using adjacent information from the reanalysis as extra inputs can further improve the forecast performance.
Implementation of Multimedia System-on-Chip in Field Programmable Gate Array Device

The consumer, hand-held and other electronic devices are typically very rich in multi-media capabilities today. Thanks to under-the-hood System-on-Chip devices, which are making this possible. With exponentially shrinking silicon manufacturing process geometries, it is possible to pack more and more functionality into tiny System-on-Chip devices. However, typical development cycles of such System-on-Chip devices are too long and the market opportunity window is relatively short. Moreover, apart from huge development costs, System-on-Chip devices carry the risk of re-spins. Further, re-spin not only causes additional costs but also delays the product launch. Hence, for time-to-market critical products, it may be worthwhile to consider alternate options, wherever feasible. One of the alternate low-risk options is using a Field Programmable Gate Array device in realizing intended System-on-Chip functionality. This especially makes sense, when the expected product volumes are not very high and per-device cost and power dissipation are not prohibitively high. Hence, it is a trade-off between product volumes, development cycle time, development cost, per-device cost and device power dissipation. When the product volumes are very high, developing System-on-Chip device using custom silicon is an obvious choice. That will result in the best per-device cost and lowest power dissipation. In addition, wherever product volumes are low-to-medium, realizing System-on-Chip in Field Programmable Gate Array device is a choice. That is expected to result in a very short development cycle, low development cost and low risk, even at the expense of higher per-device cost and power dissipation. This paper highlights implementation aspects and challenges of realizing a Multimedia System-on-Chip in a Field Programmable Gate Array Device.
Multi-feature learning for low-light image enhancement

Low-light images are not suitable for direct use in computer vision tasks due to the low visibility of the images. The existing low-light image enhancement methods usually produce colour distortion and noise amplification. This paper proposes a low-light image enhancement method based on multi-feature learning. Our method is different from most methods that decompose the image into two parts: an illumination image and a reflection image. In our learning model, these features are designed based on the pixel level, which makes the model concise and ensures colour fidelity. Our network learns three categories of image features: global features, local features, and texture features. A loss function part based on SSIM is used to ensure that multiple features are extracted effectively. Furthermore, a loss function part based on Sobel is designed to suppress noise and protect the image details. Subjective and objective experimental results demonstrate the effectiveness of our approach for low-light image enhancement.
The Effect of Translation on Text Coherence: A Quantitative Study

Investigating the coherence of translated texts is an important issue in multilingual studies. In this paper, we aim to study text coherence in human translated texts and its relation to the text properties by a quantitative approach. For this purpose, we assigned a word importance value to each word-type of a text and construct the text 'importance time series' from the original and translated texts. Then, we calculated text global coherence by applying Detrended Fluctuation Analysis (DFA) to these time series. By means of this procedure, we were able to compare the coherence of the original and translated texts. Our results show that a translation does not always decrease text coherence, as many people may suppose; there are many cases where text coherence is increased by translation. We also studied the relation of text coherence and the text properties such as text size or vocabulary size; we observed no relevance. Our findings suggest that the coherence of a text depends on the translator's abilities rather than the state of being original or translated.
Spatiotemporal Density-Based Clustering for Dynamic Spectrum Sensing

Dynamic spectrum access is one promising model for managing spectrum congestion and ensuring primary users, such as essential radar systems, unimpeded access to spectral resources. However, this requires the secondary user to identify the temporal and spectral resources consumed by primary users. Thus, in a congested radar environment, the secondary user must be capable of resolving multiple emitter waveforms in the presence of channel noise, waveform ambiguities, and pulse-on-pulse artifacts. We propose a new kernel density estimator-based clustering technique which uses the time of arrival of radar pulses in addition to other features, such as angle of arrival, center frequency, and pulse width, to identify patterns in radar pulse trains with a wide range of possible pulse repetition frequencies, which is a weakness of many density-based clustering techniques, in the presence of measurement error and outliers.
ASSESSING THE FUTURE: METHODS AND CRITERIA

Abstract As time-to-market is getting shorter, customer needs have to be identified as early as possible in product development. Correctly applied, corporate foresight can give a glimpse into the future to anticipate such needs and thus gain a competitive advantage. A support tool to choose the appropriate method of foresight is not available yet. Thus, a literature study on foresight methods in industry is performed and a novel decision support tool is proposed which avoids high entrepreneurial risks. Based on the findings, potentials for future work are identified for different types of methods.
Artificial intelligence and the rights to assembly and association

The rights to assembly and association are fundamental rights guaranteed in the Universal Declaration of Human Rights. They are essential to the establishment and functioning of a democracy and ensure that individuals and groups can peacefully come together to pursue their common goals. These, and other human rights, are being challenged by the development and widespread deployment of artificial intelligence systems on the internet. Indeed, the development of artificial intelligence has been a cause for concern among human rights activists, scholars and practitioners.While much existing literature has examined how AI will impact privacy and freedom of expression, its impact on the rights to assembly and association has been neglected. To develop a more well-rounded body of literature about AI and human rights, this paper will examine the impacts of artificial intelligence on the rights to assembly and association. It will discuss AI’s impact on two key areas: content display, whereby AI determines the content we see, and content moderation, where AI determines which content exists. The paper concludes with policy recommendations and the hope that these recommendations will serve as a starting point for a discussion on protecting these important rights in the age of artificial intelligence.
Meta-Requirement Mapping Model

Abstract The challenges faced by software vendor in competing for tenders in todays are massive and are desperately in need of intervention by the kind people of the research community. With very small resources available at their disposal, it is a daunting task them in the pursue to gain new software development projects each time new tenders are coming out. The objective of this publication is to propose an initial model that utilizes meta-requirement as a medium to validate the User Requirement Specification document that is included in the RFT. Looking forward, an algorithm will be developed for realizing the associations that was define in the propose model.
Improvement of the Training Dataset for Supervised Multiclass Classification

The classification of objects based on corresponding classes is an important task in official statistics. In the previous study, the overlapping classifier that assigns classes to an object based on the reliability score was proposed. The proposed reliability score has been defined considering both the uncertainty from data and the uncertainty from the latent classification structure in data and generalized using the idea of the T-norm in statistical metric space. This paper proposes a new procedure for the improvement of the training dataset based on a pattern of reliability scores to get a better classification accuracy. The numerical example shows the proposed procedure gives a better result as compared to the result of our previous study.
Surface Consistent Light Field Extrapolation Over Stratified Disparity And Spatial Granularities

The light field captures both the spatial and angular configurations of the scene, which facilitates a wide range of imaging possibilities. In this work, we propose an LF view extrapolation algorithm which renders high quality novel LF views far outside the range of given angular baselines. A stratified synthesis strategy is adopted which projects the scene content based on stratified disparity layers and across varying scales of spatial granularities. Such a stratified methodology proves to help preserve scene structures over large angular shifts, and provide informative clues for inferring the contents of occluded regions. A generative-adversarial network model is further adopted for parallax correction and occlusion completion conditioned on surface consistent feature. Experiments show that our proposed model can provide more reliable novel view extrapolation quality at large baseline extension ratios compared with state-of-the-art LF synthesis algorithms.
ServiceGroup: A Human-Machine Cooperation Solution for Group Chat Customer Service

With the rapid growth of B2B (Business-to-Business), how to efficiently respond to various customer questions is becoming an important issue. In this scenario, customer questions always involve many aspects of the products, so there are usually multiple customer service agents to response respectively. To improve efficiency, we propose a human-machine cooperation solution called ServiceGroup, where relevant agents and customers are invited into the same group, and the system can provide a series of intelligent functions, including question notification, question recommendation and knowledge extraction. With the assistance of our developed ServiceGroup, the response rate within 15 minutes is improved twice. Until now, our ServiceGroup has already supported thousands of enterprises by means of millions of groups in instant messaging softwares.
CNN-Based Inter Prediction Refinement for AVS3

The third generation of audio video coding standard, also known as AVS3, is the latest audio video coding standard developed by China AVS working group. With the newly adopted coding tools, AVS3 has achieved 20% coding gain compared to HEVC. To further improve the coding performance of AVS3, a CNN-based inter prediction refinement algorithm is proposed in this paper. A fully convolutional neural network structure is adopted to enhance CUs with different shapes. In order to ensure the network robust for various CU shapes, a progressive training scheme is applied. Experimental results demonstrate that the proposed method achieves on average 1.88% and 1.17% BD-rate gain under LD and RA configuration following the common test condition of AVS3.
A Smart Graduation Thesis Templates Parsing and Generation Model

Aiming at the problems of complicated formats in graduation thesis writing, a smart graduation thesis format parsing and generation model is proposed. First, a model is built based on the features of the thesis templates analyzed and extracted. Then templates are generated automatically according to user preferences. Finally, content of the thesis is incorporated into the specified template. Experiment shows that it can automatically generate format information according to the templates without human intervention, which could save a lot of time and reduce format errors.
Learning of monotone functions with single error correction

Abstract Learning of monotone functions is a well-known problem. Results obtained by V. K. Korobkov and G. Hansel imply that the complexity φ M ( n ) of learning of monotone Boolean functions equals <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mtable rowspacing="4pt" columnspacing="1em"> <m:mtr> <m:mtd> <m:mstyle displaystyle="true"> <m:msubsup> <m:mi>C</m:mi> <m:mi>n</m:mi> <m:mrow> <m:mo fence="false" stretchy="false">⌊</m:mo> <m:mi>n</m:mi> <m:mrow> <m:mo>/</m:mo> </m:mrow> <m:mn>2</m:mn> <m:mo fence="false" stretchy="false">⌋</m:mo> </m:mrow> </m:msubsup> </m:mstyle> </m:mtd> </m:mtr> </m:mtable> </m:math> $\begin{array}{} \displaystyle C_n^{\lfloor n/2\rfloor} \end{array}$ + <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mtable rowspacing="4pt" columnspacing="1em"> <m:mtr> <m:mtd> <m:mstyle displaystyle="true"> <m:msubsup> <m:mi>C</m:mi> <m:mi>n</m:mi> <m:mrow> <m:mo fence="false" stretchy="false">⌊</m:mo> <m:mi>n</m:mi> <m:mrow> <m:mo>/</m:mo> </m:mrow> <m:mn>2</m:mn> <m:mo fence="false" stretchy="false">⌋</m:mo> <m:mo>+</m:mo> <m:mn>1</m:mn> </m:mrow> </m:msubsup> </m:mstyle> </m:mtd> </m:mtr> </m:mtable> </m:math> $\begin{array}{} \displaystyle C_n^{\lfloor n/2\rfloor+1} \end{array}$ ( φ M ( n ) denotes the least number of queries on the value of an unknown monotone function on a given input sufficient to identify an arbitrary n -ary monotone function). In our paper we consider learning of monotone functions in the case when the teacher is allowed to return an incorrect response to at most one query on the value of an unknown function so that it is still possible to correctly identify the function. We show that learning complexity in case of the possibility of a single error is equal to the complexity in the situation when all responses are correct.
Chapter 19. Orality on the searchable web

As Culpeper and Kytö (2010) discuss, one challenge of historical linguistics is the extent to which written texts represent the linguistic characteristics of speech. Synchronic linguists face similar challenges, leading to the practice of using a web corpus to represent the spectrum of oral–literate registers. However, there has been little research that tests the validity of this practice. The present chapter begins by summarizing the patterns of register variation on the searchable web documented in Biber and Egbert (2018). While that study documents the importance of oral–literate linguistic dimensions, it does not investigate whether involved web registers represent the linguistic characteristics of spoken registers. We explore that research question here, comparing the multi-dimensional profiles of online registers and spoken conversation.
Compensation Methods for Inhomogeneous Illumination in Whole Slide Imaging System

Image quality and scanning time are critical in digital pathology. However, the tile-effect issue typically occurs in some cases. The root cause could be the not acceptable sample quality or the applied scanner system. There are several different algorithm solutions to compensate image heterogeneity. We implemented and validated four different approach for compensating output images created with an area scanner system.
Fast MSER

Maximally Stable Extremal Regions (MSER) algorithms are based on the component tree and are used to detect invariant regions. OpenCV MSER, the most popular MSER implementation, uses a linked list to associate pixels with ERs. The data-structure of an ER contains the attributes of a head and a tail linked node, which makes OpenCV MSER hard to be performed in parallel using existing parallel component tree strategies. Besides, pixel extraction (i.e. extracting the pixels in MSERs) in OpenCV MSER is very slow. In this paper, we propose two novel MSER algorithms, called Fast MSER V1 and V2. They first divide an image into several spatial partitions, then construct sub-trees and doubly linked lists (for V1) or a labelled image (for V2) on the partitions in parallel. A novel sub-tree merging algorithm is used in V1 to merge the sub-trees into the final tree, and the doubly linked lists are also merged in the process. While V2 merges the sub-trees using an existing merging algorithm. Finally, MSERs are recognized, the pixels in them are extracted through two novel pixel extraction methods taking advantage of the fact that a lot of pixels in parent and child MSERs are duplicated. Both V1 and V2 outperform three open source MSER algorithms (28 and 26 times faster than OpenCV MSER), and reduce the memory of the pixels in MSERs by 78%.
Towards the Instant Tile-Switching for Dash-Based Omnidirectional Video Streaming: Random Access Reference Frame

In this work, we propose an instant tile-switching mechanism for the tile-based omnidirectional DASH video streaming, which solves the problem that the client cannot switch to the high quality tiles immediately as the viewport changes. More specifically, we present the random access reference frame (RARF) on the server for each P frame as the random access point. When users change the viewport, the corresponding P frame at the viewport change moment will be replaced by RARF to transferred to the client for an immediately quality switching. By using the proposed instant tile-switching mechanism, the client can acquire the high quality video in the new viewport at any time and provide users a much better quality of experience.