Reviewerly/exHarmony huggingface dataset:
Optimally Tuning Finite-Difference Estimators

We consider stochastic gradient estimation when only noisy function evaluations are available. Central finite-difference scheme is a common method in this setting, which involves generating samples under perturbed inputs. Though it is widely known how to select the perturbation size to achieve the optimal order of the error, exactly achieving the optimal first-order error, which we call asymptotic optimality, is considered much more challenging and not attempted in practice. In this paper, we provide evidence that designing asymptotically optimal estimator is practically possible. In particular, we propose a new two-stage scheme that first estimates the required parameter in the perturbation size, followed by running finite-difference based on the estimated parameter in the first stage. Both theory and numerical experiments demonstrate the optimality of the proposed estimator and the robustness over conventional finite-difference schemes based on ad hoc tuning.
Motion Analysis for Dragon Boat Athlete Using Deep Neural Networks

In the training of sports, video-based motion analysis is important to automatically capture the action of trainees and provide training suggestions. Focusing on the dragon boating which mainly involves periodic rowing actions, this paper proposes a motion analysis method by comparing the action patterns of the athletes in the video and the expert athletes. First, taking a dragon boating video as input, the key points of human body of the dragon boat athlete in every frame are extracted with the deep convolutional neural network HRNET. Second, the extracted key points of athlete’s body are constructed as a sequence to represent the motion of human body, and a mathematical analysis method is designed to obtain the related action parameters. Third, the parameters are compared with the standard actions of expert athletes to give the advice for action correction. Experimental results demonstrate that the proposed method can provide reliable suggestions for dragon boat athletes, which is robust to individual differences and non-uniform resolutions caused by different videos.
Overview on Hiding Data in Microsoft Office Documents

Steganography is the science of hiding information in various types of data. Some of the most popular formats for Microsoft Office electronic documents are docx, xlsx, and pptx. The paper analyzes the structure of documents according to the OOXML standard. Algorithms for embedding data in electronic documents such as docx, xlsx and pptx, using the functionality of Microsoft office, as well as corresponding to a zip archive, are considered. There are six types of data attachments: zip comments, restricted zip fields, file embedding, additional Xml fields, hidden text, and tracking text. The stability of the data algorithm and the main advantages and disadvantages are assessed.
Domain Knowledge Embedding Based Multimodal Intent Analysis in Artificial Intelligence Camera

Artificial Intelligence (AI) camera solutions like Google Lens and Bixby Vision are becoming popular. To retrieve more information about the detected objects in AI camera, services of the web based content providers are used. Choosing the right content provider is needed to ensure the information provided is as per the user’s intent. Smartphone’s user interface have limited real estate, hence information from specific and relevant content providers can be displayed. Sometimes specific content provider service is used for monetary reasons. To solve the above problems, proposed is a domain knowledge embedding based multimodal deep network model. The model consists of a CNN and Multi level LSTM combination for text channel, a VGG16 CNN model for image channel and a domain knowledge embedding channel. The output of this helps in getting appropriate information from content provider. Our model for Intent Classification achieves 91% classification accuracy for predicting 3 types of intents – Beauty products purchase interest, Generic information seeking and Movie information seeking intents.
KIAM publications in Crossref reports

The paper deals with the issues of placing the KIAM publications metadata in the Crossref database. All information contained in the metadata is analyzed by Crossref and displayed in various reports. The reports give the publisher an idea of the completeness and correctness of the bibliographic data presented. The completeness and correctness of metadata directly or indirectly affects the number of views and citations of a publication, and, accordingly, the ratings of scientific publications, authors and organizations.
Unobservable Monetary Policy Surprises and Exchange Rates

The paper applies Google’s Natural Language sentiment analysis to monetary policy statements and speeches of G3 central banks and examines the role of changes in the tone of statements on exchange rates after the great recession. The results show that one standard deviation increase in the sentiment of FOMC and ECB statements lead to depreciation of Dollar by 32 basis points and appreciation of Euro by 25 basis points respectively. Moreover, sentiment of monetary policy speeches is positively correlated with the sentiment of subsequent monetary policy statements for the three major central banks.
Design of emergency response assessment system using Feature-Driven Development approach

Abstract This research aims to analyze and design an emergency response assessment system for disaster that will aim for a voluntary team being able to conduct quick assessments in the affected area more effectively and quickly. Feature-Driven Development (FDD) approach is deemed appropriate for analysis and design and later will be developed an emergency response quick assessment system that will be used to collect data such as the impact of facilities and infrastructure, security situation, transportation access, food supply and so on. The aim of the assessment is to used the data will to find out whether an intervention is needed or not. After that, the data is processed and results in the provision of useful assistance on target and on benefits site following the conditions and regulations in Indonesia.
Captcha-Based Defense Mechanism to Prevent DoS Attacks

The denial of service (DoS) attack, in the current scenario, is more vulnerable to the banking system and online transactions. Conventional mechanism of DoS attacks consumes a lot of bandwidth, and there will always be performance degradation with respect to the traffic in any of the communication networks. As there is an advent over the network bandwidth, in the current era, DoS attacks have been moved from the network to servers and API. An idea has been proposed which is CAPTCHA-based defense, a purely system-based approach. In the normal case, the protection strategy for DDoS attacks can be achieved with the help of many session schedulers. The main advantage is to efficiently avoid the DoS attacks and increase the server speed as well as to avoid congestion and data loss. This is majorly concerned in a wired network to reduce the delays and to avoid congestion during attacks.
A Novel Dual Pointer Approach for Entity Mention Extraction

The named entity extraction task aims to extract entity mentions from the unstructured text, including names of people, places, institutions and so on. It plays an important role in many Natural language processing (NLP) tasks, such as knowledge bases construction, automatic question answering system and information extraction. Most of the existing entity extraction studies are based on the long text data, which are easier to annotate due to the sufficient contextual information. Extracting entities from short texts such as search queries, conversations is still a challenging task. This paper proposes a dual pointer approach for entity mention extraction, it extracts one entities by two position pointers of the input sentence. The end-to-end deep neural networks model based on the proposed approach can extract the entities by serially generating the dual pointers. The evaluation results on the Chinese public dataset show that the model achieves the state-of-the-art results over the baseline models.
Classification of micropollutants sources as components of road surface runoff pollution

Introduction. The current state and the development of road construction and infrastructure is the cause of increasing the environmental pollution, especially in close proximity to the roads.Problem Statement. Surface runoff from the roads is a significant volume of polluted water which most often enters water bodies and the surrounding area without treatment, which is contrary to environmental requirements. Not only accidental leaks of oil and chloride residues present in runoff, but also a significant volume of suspended solids, which are often settled and accumulated and are dangerous for the environment and public health. In order to predict measures or prevent such pollution and accumulation, it is necessary to determine the sources of suspended solids, qualitative characteristics, physical and chemical properties of pollutants.Purpose. The purpose of the work is to determine of suspended solids pollutants (micropollutants), their components, to develop a classification of sources and their formation.Materials and methods. Analytical, information retrieval methods were used for this study.Results. As a result of the work, classification, which presents potentially possible types of micropollutants which are the components of suspended solids found in road surface runoff, was developed.Conclusions. The developed classification of micropollutants sources is the initial stage to study such kind of pollution, migration in the environment, the impacts of accumulation and decomposition products, as well as to develop measures for reducing the formation and dissemination of micropollutants in the environment.Keywords: road pavement, micropollutants, composition, particles.
Analysis of Ink/Toner Savings of English and Thai Ecofonts for Sustainable Printing

The use of Ecofonts in printing can result in economic savings and lower environmental impact. However, most of the research on the use of Ecofonts focuses on Latin alphabets. Moreover, texts printed with Ecofonts can be perceived as being less legible than those printed with the original typefaces. This study (a) assesses toner use reductions in documents printed with English and Thai Ecofonts, and (b) studies the observers’ perception of texts printed either with Ecofonts or with original typefaces. To achieve this, black pixels were removed from 10 English and 13 Thai typefaces widely used in academia and other media. Visibility and legibility tests, as well as mass analyses tests, were then performed on texts printed with some such typefaces. Results from instrumental measurements and digital image analyses show that the use of Ecofonts reduces toner use of an inkjet printer by up to 28%. The study also proposes a new Ecofont typeface for the Thai language. Visual tests showed that the visual experience of text printed using this Thai Ecofont is satisfactory. Awareness of the benefits of using Ecofonts changes the users’ attitudes towards the printing quality of Ecofont. The removal of black pixels can lead to more sustainable printing, and this simple solution can be extended to other non-Latin languages as part of the global Green Information Technology efforts in South-East Asia.
Privacy-Preserving CCTV Analytics for Cyber-Physical Threat Intelligence

This paper describes the FUJITSU CCTV Analytics service (FCAS), a privacy-preserving CCTV surveillance module using AI based video analytics for cyber-physical threat intelligence. The design and architecture of a privacy preserving and thus GDPR friendly way to improve security by automatically analysing video feeds and sending events that can be interpreted as a threat to further analytics is illustrated. The system has been applied to several scenarios like data centre and at ATMs. The developments can also be applied to general public safety requests and may be utilized coping with the COVID-19 impacts. Finally, the solution shall be adapted to edge computing. First steps illustrate the capabilities of small form factor systems.
Employing Parallel Hardware Architectures to Diagnose Sickle Cell Anemia in Real-Time Basis

Among the solid components of the human blood, the type that has the largest number of them is well known as red blood cells (RBCs). These cells have flat-round shapes, where their centers are depressed like a doughnut missing its hole. When the cell shape is changed from circular to sickle, then this case is a blood disease named sickle cell anemia (SCA). Based on its number, the dangerous level is obtained. This paper employs parallel hardware architectures to detect the sickle cells and its dangerous level in a real-time basis. These parallel architectures include the field programmable gated array (FPGA) and the graphical processing unit (GPU). In addition, the central processing unit (CPU) as the common serial architecture is also employed for comparison basis in terms of time consuming and power consumption. The circular Hough Transform (CHT) method is employed for detecting the sickle cells. To determine the dangerous level, the number of sickle cells and the number of normal ones are counted. The detection, counting, and classification algorithms are all coded in the Verilog language (for the FPGA) and in the MATLAB software (for the GPU and CPU). The findings have achieved well-behaved performances and acceptable results are obtained.
Speech and Oral Work

The acquisition of good speech is a basic educational requirement. Practice in the spoken word provides a corner-stone in the foundation of a sound education. Reference to any good book on popular party games for children will suggest any number of others which can be modified in this way so that thoughtful speech is brought into play. There are a number of very useful suggestions for class games in the chapter on ‘Oral Work’ in Primary Teaching To-day. The chapter deals with the subject of story-telling and reading at some length because it is through this medium that one can provide all that is necessary; an interesting experience, examples of new vocabulary in a natural context and opportunities of practice in an interesting situation. Used to illustrate characters and scenes from the story, these periods are bound to give opportunities for informal discussion and conversation in which the new words will arise as a natural thing.
Hierarchical image peeling: A flexible scale-space filtering framework

The importance of hierarchical image organization has been witnessed by a wide spectrum of applications in computer vision and graphics. Different from image segmentation with the spatial whole-part consideration, this work designs a modern framework for disassembling an image into a family of derived signals from a scale-space perspective. Specifically, we first offer a formal definition of image disassembly. Then, by concerning desired properties, such as peeling hierarchy and structure preservation, we convert the original complex problem into a series of two-component separation sub-problems, significantly reducing the complexity. The proposed framework is flexible to both supervised and unsupervised settings. A compact recurrent network, namely hierarchical image peeling net, is customized to efficiently and effectively fulfill the task, which is about 3.5Mb in size, and can handle 1080p images in more than 60 fps per recurrence on a GTX 2080Ti GPU, making it attractive for practical use. Both theoretical findings and experimental results are provided to demonstrate the efficacy of the proposed framework, reveal its superiority over other state-of-the-art alternatives, and show its potential to various applicable scenarios. Our code is available at \url{https://github.com/ForawardStar/HIPe}.
Parallelization of Reinforcement Learning Algorithms for Video Games

This paper explores a new way of parallelization of Reinforcement Learning algorithms - simulation of environments on the GPU. We use the recently proposed framework called CUDA Learning Environment as a basis for our work. To prove the approach’s viability, we performed experimentation with two main class of Reinforcement Learning algorithms - value based (Deep-Q-Network) and policy based (Proximal Policy Optimization). Our results validate the approach of using GPU for environment emulation in Reinforcement Learning algorithms and give insight into convergence properties and performance of those algorithms.
Attributed Graph Matching via Seeds Guiding

Attributed graph is a typical class of graphs with a set of attributes on nodes, edges, even high‐order structure. Objects in the fields are naturally represented as attributed graphs with considerable noise and a large number of nodes, which make traditional graphs matching techniques confront with some new challenges, such as optimization difficulty, inability to match and so on. In order to solve these problems, we propose a new approach, called Attributed Graph Matching via Seeds Guiding (AGM‐SG) in this paper. Our approach introduces seed nodes to guide attributed graph matching, and considers explicitly the first‐order characteristics difference in the problem formulation. It is formulated as a quadratic optimization, and solved by Frank‐Wolfe algorithm with continuous relaxation. It only has O ( n 2 ) space complexity, and is suitable to apply to super‐large graphs under different types. We evaluate the proposed approach on the synthetic dataset and three different real datasets including Wikipedia dataset, Enron mail dataset, and Caenorhabditis elegans dataset. Compared with the existing graph matching algorithms, the proposed algorithm outperforms original SGM, RGM and AGMLG significantly, which has more than 5% matching accuracy improvement on all datasets. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
Program Equivalence Checking for the Facilitation of Quantum Offloading

Computational offloading involves the transfer of computational tasks to a separate device. We apply this concept to quantum computing, whereby particular algorithms (i.e. “quantum algorithms”) are automatically recognized and executed on a quantum computer. We propose a method that utilizes program equivalence checking to discern between code suited for execution on a conventional computer and a quantum computer. This process involves comparing a quantum algorithm's implementation with code written by a programmer, with semantic equivalence between the two implying that the programmer's code should be executed on a quantum computer instead of a conventional computer. Using a novel compiler optimization verification tool named CORK, we test for semantic equivalence between a portion of Shor's algorithm (the “prototype”) and various modified versions of this code (representing the arbitrary code written by a programmer). Some of the modified versions are intended to be semantically equivalent to the prototype while others semantically inequivalent. Our approach is able to correctly determine semantic equivalence or semantic inequivalence in a majority of cases.
Attentive Goal-based Dialogue System for ADHD Behavior Treatment

Atentive deficit and Hyperactivity disorder (ADHD) is one of the most common behavior disorders in children. The treatment methods of ADHD mainly include drug therapy, psychobehavioral therapy, family therapy, Electroencephalogram (EEG) biofeedback, etc. Family therapy requires parents to understand the behavior disorder and to improve the behavior of children with appropriate methods. Parents can get behavioral training advises by consulting therapists in medical institutions. However, it cannot assure the timeliness of guidance and requires therapists of some amount of repetitive work. Therefore, in this paper, we construct a data set for ADHD behavioral training, which is extracted from real-life dialogues between parents of ADHD children and therapists. Then, a dialogue system is developed for helping parents to deal with ADHD children. It incorporates self-attention mechanism in natural language understanding module to improve the accuracy of semantic analysis. It also adopts deep reinforcement learning in dialogue policy learning module, exploring the effective strategy. Experimental results show that the proposed dialogue system leads to significant improvement on task success rate and reduction in dialogue length comparing to rule-based training model.
A collaborative cloud service for big data analysis using trust calculation scheme

Cloud service collaboration has been chosen by businesses as an effective solution to the prime challenges in cloud computing services, especially regarding the data storage. This enables effective interaction among the users of multiple occupants of a cloud provider to work in association. During collaboration, the users might have to share, access and use files and resources belonging to themselves and other collaborating members. These resources may include the most sensitive data and computing tasks delivered by various users to cloud service properties. Cloud service providers must earn the trust from their users before enabling this. On the other hand, multiple secure transactions between users and service providers on their platform help the service providers to gain trust in business and to mark their business as reliable. Hence implementing trustworthy and collaborative cloud service is the fundamental demand along with ensuring increased speed and reduced overhead, especially in the case of big data. Evaluation of trust values of the `cloud computing schemes thus deserves critical attention and various trust models have been proposed each with its own features and limitations. Evaluation of the effectiveness of the trust models gains significance in this context which forms the basis motivation of this work. Here, we focus on the critical evaluation of the recently proposed fast and parallel trust computing model developed for big data analysis. The evaluation of the above said model on selected cloud collaborative service environments is carried out and the effectiveness of the model in each environment is analysed based on several parameters. We also use the model to estimate the trust value of selected cloud services under separate time bands and look forward for the significant remarks on time based evaluation.
Machine learning model development

Chapter 5 focuses on the development of neural network models. The common development process and software stacks in a GPU environment are first described. Next, to rapidly apply a neural network model, the common method for optimizing the code under a particular platform is described. In general, vectorization and SIMD speed up the operations, and the key to practical coding is to achieve an efficient memory access, and thus memory access optimization is also described. In addition, a common script language used in the development of neural network models, Python, and its virtual machine (VM) are introduced. Finally, CUDA, which is a common computing system using a GPU, is described.