Reviewerly/exHarmony huggingface dataset:
Image restoration and contrast enhancement based on a nonlinear reaction-diffusion mathematical model and divide &amp; conquer technique

In this article, we present a new algorithm for digital image processing noised by mixed Gaussian-impulse noise. Our mathematical model is based on the divide-conquer technique coupled with a reaction-diffusion system. We first decompose our image into low and high-frequency components by convolving each with a predefined convolutional filter. Further, we use a simple scheme of different weights to integrate and collect these processed sub-images into a filtered image. Finally, we apply our Reaction-Diffusion system to increase the contrast in the image. A number of experimental results are described to illustrate the performance of our algorithm and show that it is very effective in eliminating mixed Gaussian-impulse noise, increasing the contrast of the image and preserving the edges.
Using additional training sensors to improve single-sensor complex activity recognition

We propose a method for single-sensor based activity recognition using multiple sensors during training time. The proposed method, based on learning a shared representation space, can be used to improve the accuracy and F-score of complex activity recognition with a single on-body accelerometer sensor by leveraging data from other sensors at training time. Results show improvements of 16% in accuracy and 20% in F-score.
Instance Independence of Single Layer Quantum Approximate Optimization Algorithm on Mixed-Spin Models at Infinite Size

This paper studies the application of the Quantum Approximate Optimization Algorithm (QAOA) to spin-glass models with random multi-body couplings in the limit of a large number of spins. We show that for such mixed-spin models the performance of depth <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>1</mml:mn></mml:math> QAOA is independent of the specific instance in the limit of infinite sized systems and we give an explicit formula for the expected performance. We also give explicit expressions for the higher moments of the expected energy, thereby proving that the expected performance of QAOA concentrates.
Remote User Testing

This paper describes a one-day INTERACT 2021 workshop on remote testing with users as participants. Remote user testing has been around since the mid-nineties, but the Covid-19 pandemic has boosted the interest in remote working in general and thus also remote testing. The workshop aim is to present and discuss the current state-of-the-art of remote testing methods and identify emerging trends. Subjects that will be discussed include, but are not restricted to: Remote testing methods and platforms; capturing of quantitative as well as qualitative data; moderated, unmoderated and remotely moderated tests; testing of physical products; and remote testing of participants with special needs.
Perceptually Optimized ABR Ladder Generation for Web Streaming

Fast track article for IS&amp;T International Symposium on Electronic Imaging 2021: Mobile Devices and Multimedia: Enabling Technologies, Algorithms, and Applications 2021 proceedings.
In-process measurement of a keyhole using a low-coherence interferometer with a high repetition rate

The shape of an instance hole (keyhole) created via a high-power laser was measured using a low-coherence interferometer with the following parameters: repetition rate, 10 MHz; center wavelength, 1550 nm; absolute spatial resolution, 10 µm; and measurement range, 5 mm. The keyhole was created on a 3-mm-thick stainless-steel plate using a high-power laser with 8-kW peak power and 1070-nm center wavelength. The cross-sectional area of the keyhole was measured to be 0.42 mm × 0.78 mm (width × depth) using the interferometer, and its side dimension was 0.46 mm × 0.78 mm (width × depth).
Cloud-Based Fuzzy Keyword Search Scheme Over Encrypted Documents

With the rapid growth and adoption of cloud computing, more sensitive information is centralized onto the cloud every day. For protecting this sensitive information, it must be encrypted before being outsourced. Current search schemes allow the user to query encrypted data using keywords, but these schemes do not guarantee the privacy of queries (i.e., when the user hits query more than once with the same keywords, the server can capture information about the data). This paper focuses on the secure storage and retrieval of ciphered data with preserving query privacy. The proposed scheme deploys the sparse vector space model to represent each query, which focuses on reducing the storage and representation overheads. And the proposed scheme adds a random number to each query vector. Hence, the cloud server cannot distinguish between queries with the same keywords, which ensures the privacy of the query. Experimental results show that the proposed scheme outperforms other relevant state-of-the-art schemes.
Chatbot Accessibility Guidance: A Review and Way Forward

Chatbots are an increasingly popular way to deliver and support online services. These services must be accessible for people with disabilities; however, it is not yet clear how to extend existing guidance on web content accessibility to chatbots. Researchers and developers from different sectors are producing recommendations by a variety of methods. This paper surveys existing chatbot accessibility guidance. We found seventeen different sources yielding 157 unique recommendations for creating an accessible chatbot experience, which we organized into five categories: content, user interface, integration with other web content, developer process and training, and testing. We identify and discuss trends, themes, and opportunities to refine these recommendations. We believe that there is a need to further refine and expand the recommendations presented in this paper to facilitate the creation of accessible chatbots. Through our analysis, we intend to guide future research, inform the development of materials for designing, developing, and evaluating chatbots; and promote a shared understanding of how chatbots can deliver a successful experience for all users.
Optimizing the Selection of Recommendation Carousels with Quantum Computing

It has been long known that quantum computing has the potential to revolutionize the way we find solutions of problems that are difficult to solve on classical computers. It was only recently that small but functional quantum computers have become available on the cloud, allowing to test their potential. In this paper we propose to leverage their capabilities to address an important task for recommender systems providers, the optimal selection of recommendation carousels. In many video-on-demand and music streaming services the user is provided with a homepage containing several recommendation lists, i.e., carousels, each built with a certain criteria (e.g., artist, mood, Action movies etc.). Choosing which set of carousels to display is a difficult problem because it needs to account for how the different recommendation lists interact, e.g., avoiding duplicate recommendations, and how they help the user explore the catalogue. We focus in particular on the adiabatic computing paradigm and use the D-Wave quantum annealer, which is able to solve NP-hard optimization problems, can be programmed by classical operations research tools and is freely available on the cloud. We propose a formulation of the carousel selection problem for black box recommenders, that can be solved effectively on a quantum annealer and has the advantage of being simple. We discuss its effectiveness, limitations and possible future directions of development.
Analysis of Functional Suitability and Usability in Sales Order Procedure to Determine Management Information System Quality

In the development of information systems, it is very important to pay attention to the quality of the system which can be reviewed through aspects of usability for its users and following the needs and specifications of its users. This study will analyze the quality of the sales order management information system based on two characteristics of the ISO 25010 quality standard system, namely functional suitability and usability. The functional suitability test was carried out by the BlackBox testing method to test three sub-characteristics of functional suitability that are functional completeness, functional correctness, and functional appropriateness. Usability tests use a use questionnaire which is divided into four criteria that are usefulness, ease of use, ease of learning, and satisfaction. In testing the suitability of functionality involving 5 respondents and 17 respondents in usability testing. The results of this study indicate that the sales order management system fulfills two characteristics of information system quality that are functional suitability and usability. Where all the features designed are by the successful features, and the usability characteristics are 85.45% which indicates that the system is very feasible to implement.
Defending against Universal Adversarial Patches by Clipping Feature Norms

Physical-world adversarial attacks based on universal adversarial patches have been proved to be able to mislead deep convolutional neural networks (CNNs), exposing the vulnerability of real-world visual classification systems based on CNNs. In this paper, we empirically reveal and mathematically explain that the universal adversarial patches usually lead to deep feature vectors with very large norms in popular CNNs. Inspired by this, we propose a simple yet effective defending approach using a new feature norm clipping (FNC) layer which is a differentiable module that can be flexibly inserted in different CNNs to adaptively suppress the generation of large norm deep feature vectors. FNC introduces no trainable parameter and only very low computational overhead. However, experiments on multiple datasets validate that it can effectively improve the robustness of different CNNs towards white-box universal patch attacks while maintaining a satisfactory recognition accuracy for clean samples.
Developmental Stage Classification of Embryos Using Two-Stream Neural Network with Linear-Chain Conditional Random Field

The developmental process of embryos follows a monotonic order. An embryo can progressively cleave from one cell to multiple cells and finally transform to morula and blastocyst. For time-lapse videos of embryos, most existing developmental stage classification methods conduct per-frame predictions using an image frame at each time step. However, classification using only images suffers from overlapping between cells and imbalance between stages. Temporal information can be valuable in addressing this problem by capturing movements between neighboring frames. In this work, we propose a two-stream model for developmental stage classification. Unlike previous methods, our two-stream model accepts both temporal and image information. We develop a linear-chain conditional random field (CRF) on top of neural network features extracted from the temporal and image streams to make use of both modalities. The linear-chain CRF formulation enables tractable training of global sequential models over multiple frames while also making it possible to inject monotonic development order constraints into the learning process explicitly. We demonstrate our algorithm on two time-lapse embryo video datasets: i) mouse and ii) human embryo datasets. Our method achieves 98.1% and 80.6% for mouse and human embryo stage classification, respectively. Our approach will enable more pro-found clinical and biological studies and suggests a new direction for developmental stage classification by utilizing temporal information.
Design and Implementation of Music Distance Education Learning System

With the rapid development of Internet technology, network and information technology, the traditional music education can establish a long-distance music education system, so that music education can change from on-site teaching to long-distance teaching, so as to break through the limitation of space and time, and give students and teachers more interaction space. In order to study the influence of music distance education system on education effect in detail, this paper analyzes the design principle and function classification of distance music teaching system in detail, designs the function, structure and application scope of the system according to the characteristics of music education, and verifies the feasibility of the system through system practice.
Design Space Cards

The potential space of game designs is astronomically large. This paper shows how game design theories can be translated into a simple, tangible card deck that can assist in the exploration of new game designs within a broader "design space." By translating elements of game design theory into a physical card deck, we enable users to randomly sample a design space in order to synthesize new game design variations for a new play platform ("Lumies"). In a series of iterative design and testing rounds with various user groups, the deck has been optimized to merge relevant game theory elements into a concise card deck with limited categories and clear descriptions. In a small, controlled experiment involving groups of design students, we compare the effects of brainstorming with the card deck or the "Directed Brainstorming" method. We show that the deck does not increase ideation speed but is preferred by participants. We further show that our target audience, children, were able to use the card deck to develop dozens of new game ideas. We conclude that design space cards are a promising way to help adults and children to generate new game ideas by making it easier to explore the game design space.
QBox: Partial Transfer Learning With Active Querying for Object Detection

Object detection requires plentiful data annotated with bounding boxes for model training. However, in many applications, it is difficult or even impossible to acquire a large set of labeled examples for the target task due to the privacy concern or lack of reliable annotators. On the other hand, due to the high-quality image search engines, such as Flickr and Google, it is relatively easy to obtain resource-rich unlabeled datasets, whose categories are a superset of those of target data. In this article, to improve the target model with cost-effective supervision from source data, we propose a partial transfer learning approach QBox to actively query labels for bounding boxes of source images. Specifically, we design two criteria, i.e., informativeness and transferability, to measure the potential utility of a bounding box for improving the target model. Based on these criteria, QBox actively queries the labels of the most useful boxes from the source domain and, thus, requires fewer training examples to save the labeling cost. Furthermore, the proposed query strategy allows annotators to simply labeling a specific region, instead of the whole image, and, thus, significantly reduces the labeling difficulty. Extensive experiments are performed on various partial transfer benchmarks and a real COVID-19 detection task. The results validate that QBox improves the detection accuracy with lower labeling cost compared to state-of-the-art query strategies for object detection.
The Impact of Governance on Economic Growth: An Empirical Assessment in Sub-Saharan Africa

In recent decades, issues of good governance and its impact on the well-being of populations have been at the heart of debates among researchers and international organizations. This article examines the impact of governance on economic growth in Sub-Saharan Africa. The panel data estimated shows that governance has a positive and significant impact on economic growth in Sub-Saharan Africa at the 1% threshold. An improvement of one unit of this composite governance indicator would lead to an increase in the economic growth of 19.43% on average. Effective governance and the rule of law are important determinants of economic growth. However, a country that improves these two indicators would encourage an increase in its economic growth. A better quality of institutions would reduce poverty through economic growth.
Emerging Technologies To Detect Fraud in Audit Testing: A Perception of Malaysian Big Four Auditors

Fraud surveys performed over the past decade remained at worrying levels in Malaysia. The Malaysian 1MDB scandal has attracted worldwide attention which severely damaged the local auditing profession, worsening the credibility crisis in the sector. The rise of emerging technologies is seen to bridge the expectation gap in strengthening stakeholder confidence in auditors by easing fraud detection. Unfortunately, a past study in Malaysia on task-technology fit between auditor’s knowledge and perceived importance on technology revealed a gap. A report by the Malaysian Institute of Accountants in 2019 highlighted that the lagging factor of emerging technology adoption in Malaysia is due to auditors’ non-realization of its usefulness in fraud detection. Furthermore, to render emerging technology as a useful medium for data extraction and analysis higher levels of scepticism is required. Besides, focus is placed on data quality as the accuracy of testing is heavily reliant on the input. Hence, this study intends to find whether there is a relationship between the perception of Malaysian Big Four external auditors and fraud detection in the audit testing phase. An online questionnaire based on a 5-point Likert scale was distributed and received responses from 108 external auditors where data was coded and input into the SPSS for further analysis. The results show that Malaysian Big Four external auditor’s perception of emerging technology affects the use for fraud detection in audit testing. As this study could not be generalized, further research should consider looking from the time constraints viewpoint or to study auditors from a similar firm for a greater understanding of their perception since firms have different degree of emerging technology adoption.
Chinese Character Inpainting with Contextual Semantic Constraints

Chinese character inpainting is a challenging task where large missing regions have to be filled with both visually and semantic realistic contents. Existing methods generally produce pseudo or ambiguous characters due to lack of semantic information. Given the key observation that Chinese characters contain visually glyph representation and intrinsic contextual semantics, we tackle the challenge of similar Chinese characters by modeling the underlying regularities among glyph and semantic information. We propose a semantics enhanced generative framework for Chinese character inpainting, where a global semantic supervising module (GSSM) is introduced to constrain contextual semantics. In particular, sentence embedding is used to guide the encoding of continuous contextual characters. The method can not only generate realistic Chinese character, but also explicitly utilize context as reference during network training to eliminate ambiguity. The proposed method is evaluated on both handwritten and printed Chinese characters with various masks. The experiments show that the method successfully predicts missing character information without any mask input, and achieves significant sentence-level results benefiting from global semantic supervising in a wide variety of scenes.
HoloAR: On-the-fly Optimization of 3D Holographic Processing for Augmented Reality

Hologram processing is the primary bottleneck and contributes to more than 50% of energy consumption in battery-operated augmented reality (AR) headsets. Thus, improving the computational efficiency of the holographic pipeline is critical. The objective of this paper is to maximize its energy efficiency without jeopardizing the hologram quality for AR applications. Towards this, we take the approach of analyzing the workloads to identify approximation opportunities. We show that, by considering various parameters like region of interest and depth of view, we can approximate the rendering of the virtual object to minimize the amount of computation without affecting the user experience. Furthermore, by optimizing the software design flow, we propose HoloAR, which intelligently renders the most important object in sight to the clearest detail, while approximating the computations for the others, thereby significantly reducing the amount of computation, saving energy, and gaining performance at the same time. We implement our design in an edge GPU platform to demonstrate the real-world applicability of our research. Our experimental results show that, compared to the baseline, HoloAR achieves, on average, 2.7 × speedup and 73% energy savings.
Legal regulation of a cryptocurrency used in Nigeria and Russia: a comparative study

Cryptocurrency becomes an integral part of modern financial markets all over the globe, meanwhile, in many states, there is no legal regulation of cryptocurrency. To elaborate legal framework legis...
Novel Cooperative Multi-Input Multilayer Perceptron Neural Network Performance Analysis with Application of Solar Irradiance Forecasting

To forecast solar irradiance with higher accuracy and generalization capability is challenging in the photovoltaic (PV) energy system. Meteorological parameters are highly influential in solar irradiance, leading to intermittent and randomicity. Forecasting using a single neural network model does not have sufficient generalization ability to achieve the optimal forecasting of solar irradiance. This paper proposes a novel cooperative multi-input multilayer perceptron neural network (CMMLPNN) to mitigate the issues related to generalization and meteorological effects. Authors develop a proposed forecasting neural network model based on the amalgamation of two inputs, three inputs, four inputs, five inputs, and six inputs associated multilayer perceptron neural network. In the proposed forecasting model (CMMLPNN), the authors overcome the variance based on the meteorological parameters. The amalgamation of five multi-input multilayer perceptron neural networks leads to better generalization ability. Some individual multilayer perceptron neural network-based forecasting models outperform in some situations, but cannot assure generalization ability and suffer from the meteorological weather condition. The proposed CMMLPNN (cooperative multi-input multilayer perceptron neural network) achieves better forecasting accuracy with the generalization ability. Therefore, the proposed forecasting model is superior to other neural network-based forecasting models and existing models.
Low-light image enhancement by diffusion pyramid with residuals

With the advancement of the camera-related technology in mobile devices, the vast amount of photos have been taken and shared in our daily life. However, many users still have unsatisfactory experiences with low-visible photos, which are frequently acquired under complicated real-world environments. In this paper, a novel yet simple method for low-light image enhancement has been proposed without any learning procedure. The key idea of the proposed method is to estimate properties of the scene illumination both in global and local manner by exploiting the diffusion pyramid with residuals. Specifically, the residual of each scale level in the diffusion pyramid is combined with the corresponding input. This restored result efficiently highlights local details across different scale spaces, thus it is helpful for preserving the boundary of illuminations. By conducting max-pooling with restored results from different levels of the diffusion pyramid, which are resized to the original resolution, the illumination component is accurately inferred from a given image. Compared to recent learning-based approaches, one important advantage of the proposed method is to effectively avoid the overfitting problem to the specific training dataset. Experimental results on various benchmark datasets demonstrate the efficiency and robustness of the proposed method for low-light image enhancement in real-world scenarios.