Reviewerly/exHarmony huggingface dataset:
A Rich Stereoscopic 3D High Dynamic Range Image &amp; Video Database of Natural Scenes

The consumer market of High Dynamic Range (HDR) displays and cameras is blooming rapidly with the advent of 3D video and display technologies. Specialised agencies like Moving Picture Experts Group and International Telecommunication Union are demanding the standardization of latest display advancements. Lack of sufficient experimental data is a major bottleneck for the development of preliminary research efforts in 3D HDR video technology. We propose to make publicly available to the research community, a diversified database of Stereoscopic 3D HDR images and videos, captured within the beautiful campus of Indian Institute of Technology, Madras, which is blessed with rich flora and fauna, and is home to several rare wildlife species. Further, we have described the procedure of capturing, aligning, calibrating and post-processing of 3D images and videos. We have discussed research opportunities and challenges, and the potential use cases of HDR stereo 3D applications and depth-from-HDR aspects.
API Integration of National Complaint Handling System in Indonesia: A State of The Art Review

In the new global economy, public service has become a central matter for achieving sustainability. The way in which public service provider handling either feedbacks or complains become a major key success factor to consider the successfulness of its service. The Government of Indonesia has established SP4N-LAPOR! (National Complaint Handling System) since 2013 to become the first national centralized complaint-handling channel. One major issue in centralized national complaint handling system is Unintegrated System; Duplication with existing local institution application or system. The specific objective of this study was to identify the state of the art of API Integration of SP4N-LAPOR! The researcher has utilized historical analysis to extract the pattern from API Integration. The project used a convenience sample of three months API integration consist of the total complaints generated from parent vs client apps. The findings reported here shed new light on pattern identified as such: 1) Twitter API, 2) Parent domination, 3) Client domination, 4) Balanced proportion and 5) Embed HTML. One of the more significant findings to emerge from this study is that the definition of integration stage: a) partial integration 1, b) partial integration 2, and c) full integration.
NVSRN: A Neural Variational Scaling Reasoning Network for Initiative Response Generation

Open-domain multi-turn dialogue systems are booming in human-machine interactions, which encourage to chat actively and freely in an intelligent natural way. Previous generative conversational models usually employ a single and deterministic encoder-decoder framework to model the semantic consistency between the context and corresponding response. However, they neglect the various dialog patterns (we denote the regularity of topic shifting as dialog pattern) in the conversations, leading to uninformative, non-initiative yet plausible responses. Although the existing variational methods have improved the response diversity to some extent by introducing a global variability into the generative process, they fail to simulate the transfer between topics with directional information due to the weak interpretability of the Gaussian-distributed latent variables. In this paper, we propose a novel Neural Variational Scaling Reasoning Network (NVSRN) for initiative response generation. To this end, our approach has two core ingredients: neural dialog pattern reasoner (reasoner) and topic scaling mechanism. Specifically, inspired by the advantage of von Mises-Fisher (vMF) distribution modeling the directional data (e.g., the topic transfer state), we employ it as the latent space of the reasoner to explore the regularity of topic shifting, which is then used to reason the topic of response. Based on this, a topic scaling mechanism is designed to control the transfer degree of topic in the response generator. The experimental results on two large dialog datasets demonstrate that the proposed model outperforms state-of-the-art baselines. The human evaluation shows the proposed model can produce more informative and initiative responses actively.
Memory optimization of CNN Heterogeneous Multi-Core Architecture

Convolutional neural networks(CNNs) computing complexity raises challenges to the power and cost of CNN accelerators. The power consumption and delay of the data access between on-chip and off-chip memories have been a bottleneck of the on-chip system for CNN acceleration. We propose a static scheduling framework based on our heterogeneous programmable multi-core ASIP (Application Specific Instruction-set Processor) for inference of deep learning. This scheduling framework minimizes power consumption and promotes performance under the hardware resource limitation, including datapath computing capability, access bandwidth limitation, and on-chip memory size. We also discussed how to achieve theoretical maximum performance for a set of fixed hardware constraints. Comparing to the scheduling on DNA(deep neural architecture) under the same AlexNet platform, our scheduling achieves 11.8% more average resource utilization. By reusing and broadcasting local data, the average DRAM access of our design is reduced down to 41% of the access of DNA. The estimated power consumption of this design is 35% lower than the power of DNA, mostly because of the power reduction from DDR data accesses. Meanwhile, through the ping-pang memory and the careful design of data partition, the time of data access between DRAM and SRAM is reduced 77% by access hidden.
A High Performance FPGA-Based Accelerator Design for End-to-End Speaker Recognition System

Speaker recognition technique is significant for identification applications. X-vectors, a robust text-independent speaker recognition system, spends plenty of time on extracting voiceprint features due to massive neural network computation and scoring with all the people registered in the database to find the best match person. In this paper, an FPGA-based high-performance accelerator for this end-to-end speaker recognition system is proposed, which contains three parts: Mel Frequency Cepstral Coefficients (MFCC), time delay neural network (TDNN) and probabilistic linear discriminant analysis (PLDA) classifier. A quantitative analysis is presented to balance the bit width and the recognition accuracy. In addition, an optimization strategy to make a trade-off between the system parallelism and the FPGA resource utilization is introduced. As a comparison, the proposed accelerator running on Xilinx XCVU9P FPGA of UltraScale+ VCU118 board can achieve a peak performance of 1.067 TOP/s and 1.30×105 voice frames per second (vFPS) with 200MHz, which can obtain 1296× speedup compared with X-vectors software implementation running on a 2.5GHz Intel Xeon E5-2620 processor and 6.42× energy efficiency than Nvidia TITAN Xp GPU solution.
Novel Fuzzy Preview Control With Granular Computing for Underactuated Systems

This chapter provides an elaborate discussion on the use of fuzzy preview control designed with the help of look up tables and granular computing for the studies on under actuated systems which are dynamic systems which have lesser degrees of freedom than the actuating input signals. It is an important design consideration for most systems to be categorized under this section which makes them the essential component for analysis. The chapter provides an in depth analysis taking the case studies of various control system test beds such as the family of the inverted pendulum systems which make the prototype models for validation before applying the developed logic to control of high end complex dynamic systems in terms of stability, robustness, speed of operation and more importantly to see the fuzzy structure that controls these systems in terms of lookup tables.
The Measurement of Software Size based on Generation Model using COSMIC FSM

The software size measurement plays essential roles in developing a software project to estimate costs, efforts and other resources. It provides the necessary input to estimate the effort at the early stage of the software development process. Thus, COSMIC FSM (Common Software Measurement International Consortium Functional Size Measurement) is one of the well-known methods of FSM which is suitable to estimate the size of software project at the early stage of development process. The industries use not only the standardized methods but also the well-defined modeling notations to measure the functional size of software. Some research has focused on the specific design notations to facilitate the design of the system. Therefore, this paper proposes a new model which is named as a generation model that can be accepted from various types of modeling notations and then these types converted into common general model for calculating the functional size of COSMIC concept. The generation model can help many researchers and estimators to understand the concepts of complex diagram notations easily. Then, the mapping rules define between the COSMIC FSM and generation model to measure the size of software. Finally, the function size results of the system are calculated by using COSMIC method.
A Hybrid Approach for Video Steganography by Stretching the Secret Data

In this Internet era, the digital communication is unavoidable, and its contribution to the development is significant. Due to the enormous data communication, there is always a question about the security of the data. In this paper, a hybrid reversible data hiding method is proposed to transmit the data in secure manner through video files. Secret data like one-time password (OTP) can be embedded into one of the frames in the video. This achieves more security than the regular ways. The proposed method has compared with mean square error (MSE) and peak signal-noise ratio (PSNR).
Survey of Big Data Warehousing Techniques

There is a growing need in the industry toward the development of new and sophisticated tools for storing the exponentially growing volume, velocity and variety of data, which is collectively referred to as big data. There has been a paradigm shift from traditional data warehousing techniques to inclusion of NoSQL technology in order to fulfill the requirements of big data. While Hadoop has powerful features, which is not a replacement to Data Warehouse, rather it is a complement. Data Warehouse is already good at processing structured data so when used in conjunction with Hadoop, it becomes a winning combination. Hadoop can be considered as one of the back ends of Data Warehouse for handling unstructured data. Hence there is research on enhancing existing Data Warehouse with new features that have been successful at handling big data, and most popular one among them is MapReduce. We discuss the different tools and techniques used for improving Data Warehouse by adding these features and discuss the limitations associated with them.
Electronic aircraft maintenance record signature technology based on SM2 cryptographic algorithm

The paperlessness of maintenance records is an inevitable trend in the development of civil aviation informationization. This paper proposes a method for the use of reliable electronic signatures for electronic records in maintenance paperless, and uses domestic cryptographic algorithms to propose electronic signatures for maintenance records. And for the specific work scenario of maintenance, the special signature idea of structured data and layout archive synchronous double signature is proposed. The electronic maintenance record signature method based on SM2 algorithm proposed in this paper is applied in the construction of an airline's electronic signature system.
Adapting Virtual Training Systems for Industrial Procedures to the Needs of Older People

Integration of older employees into the workforce is critical for a successful manufacturing industry due to demographic change. However, technological developments to create more flexible manufacturing environments are leading to increasingly complex machinery that has to be operated by an aging workforce. Virtual training systems prepare operators for the interaction with industrial machines. Current virtual training systems do not address the perceptive and cognitive abilities of older users to enable a satisfying and efficient training. This article develops adaptations of the visualization and the interaction techniques of a virtual training system to address the abilities of older operators. The results of a between-subjects study indicate that the adaptations improve the subjective perception of the training system and decrease the training time. The paper concludes that adaptive training systems can support the participation of diverse user groups in the manufacturing industry by providing more effective and satisfying training.
Intelligent System for Recommending Study Level in English Language Course Using CBR Method

In the admission process, an English Course uses a level placement test. The implementation of the test encountered some problems such as slow determination of student learning levels based on the results of paper based test that are still conventional. The purpose of this research provides the recommendations for an intelligent knowledge-based system in recommending student learning levels using the Case-Based Reasoning (CBR) method. CBR is one of the method that uses the Artificial Intelligence approach and focuses on solving problems based on knowledge from the previous cases, by calculating numerical local similarity and global similarity using the nearest neighbor algorithm as the basic for the technical development of this intelligent system. The result of the study was tested for the data accuracy with the confusion matrix method by the result 100% for the accuracy. For evaluating the system systematically was using the User Acceptance Test (UAT) method with the results of the evaluation is 88% of the system meets user needs and expectations.
Realization of Game Tree Search Algorithms on FPGA: A Comparative Study

This paper deals with realization of search algorithms used in the game solvers on the FPGA. Three algorithms namely Minimax, Alpha-Beta Pruning, and NegaScout are realized and compared amongst each other, and also with their software realization for the sake of completion. Results show that the FPGA based implementations are exceptionally faster than their software counterparts, with the NegaScout algorithm outperforming the conventionally used Alpha-Beta Pruning, and the Minimax algorithm, both in software and hardware. The NegaScout algorithm is 1.3 times faster than the Alpha-Beta Pruning algorithm and 2.6 times faster than the Minimax algorithm on hardware, while incurring a nominal cost in terms of FPGA resource utilization.
Event Sequence Interpretation of Structural Geomodels: A Knowledge-Based Approach for Extracting Tectonic Sequences

The tasks of obtaining past event occurrences and their temporal order information are important parts of the cognition of the external world. We call this kind of tasks Event Sequence Interpretations (ESI). In this work, we focus in the ESI in structural geomodels and propose a knowledge-based approach for extracting tectonic sequences, which is crucial for the cognition of structural geomodels. As a cognitive task, tectonic sequence interpretation has not been highly automated due to the need to use a large amount of expert knowledge for recognition and reasoning. Meanwhile, artificial ESI may introduce cognitive biases that ultimately lead to subjective uncertainty in the results, which affects the credibility of the interpretations and increases risks in oil and gas production. One potential solution is making personal knowledge better available for computers so that computers can also do ESI. Therefore, we proposed a meta-model for formally representing expert knowledge. The instance of the knowledge representation (KR) meta-model is called an Event Pattern (EP), which describes the associations between event occurrences and geometric features in the models. Moreover, we proposed a new pattern matching model called Joint Prototype Model (JPM) to find evidences of event occurrences from the raw geological data. The temporal relations of the events can be extracted according to the spatial topology of the geological objects. Our approach can also be extended from structural geomodels to other spatial geometric models. We show the effectiveness of the approach by an application to a real structural geomodel dataset.
Frame-Wise CNN-Based View Synthesis for Light Field Camera Arrays

The paper proposes a novel frame-wise view synthesis method based on convolutional neural networks (CNNs) for wide-baseline light field (LF) camera arrays. A novel neural network architecture that follows a multi-resolution processing paradigm is employed to synthesize an entire view. A novel loss function formulation based on the structural similarity index (SSIM) is proposed. A wide-baseline LF image dataset is generated and employed to train the proposed deep model. The proposed method synthesizes each subaperture image (SAI) from a LF image based on corresponding SAIs from two reference LF images. Experimental results show that the proposed method yields promising results with an average PSNR and SSIM of 34.71 dB and 0.9673 respectively for wide baselines.
Making Augmented Reality Learning Media In Conformation of Alkane and Cycloalkane Concepts

The purpose of this study are describe the results display of each stage of making augmented reality learning media on the conformation concepts of alkanes and cycloalkanes, analyze the results of the feasibility test of augmented reality learning media on the conformation concepts of alkanes and cycloalkanes. This research uses Research and Development (R & D) method which produces products in the form of AR learning media on the conformation concepts of alkanes and cycloalkanes. The results display of AR learning media is visual characteristic, that is a medium that visualizes the 3D form of each alkane and cycloalkane conformation by realizing a vistual form into a real form. The feasibility test phase and limited trial aim to get assessment, suggestions, and improvements to aspects of learning, material substance aspects, visual communication aspects, and software engineering aspects. The results of the overall feasibility test obtained showed high feasibility with a count of 0.73–0.87. While the results of the limited trials obtained showed high feasibility with a percentage of 75–87.5% feasibility. These results indicate that AR learning media on the alkane and cycloalkane conformation concepts are suitable for use as learning aids
Encoding Adaptability of Software Engineering Tools as Algorithm Configuration Problem: A Case Study

Nowadays software is often highly configurable, and the required adaptation is a complex and tedious task when performed manually. Moreover, hand-crafted configurations are often far from optimal. In this paper, we study the software configuration problem in the context of the model comparison tool SiDiff, which needs to be carefully adapted to domain-specific modeling languages used in model-driven engineering. To tackle the configuration challenge, we propose to draw from the field of automated algorithm configuration, a research area which has studied the optimization of parameterizable algorithms for many years and which has gained particular momentum through its applications to hyper-parameter tuning in machine learning. Specifically, we report on ongoing work encoding the adaptability of SiDiff as an algorithm configuration problem which is amenable to a sequential model-based optimization tool known as SMAC. While empirical evaluation results are left for future work, the main goal of this paper is to foster active discussions at the workshop and to collect early feedback on our ongoing research.
Autonomous cars: A driving force for change in motor liability and insurance

In this article, I review the legal and regulatory obstacles to the introduction of autonomous vehicles.I provide an overview of the key legislation which is relevant to the introduction of autonomous vehicles in England and Wales.I discuss the motor liability and insurance implications of the introduction of autonomous cars and the legal framework for the testing of autonomous vehicles on public roads.I conclude that there is likely to be significant volume of emerging legislation that car manufacturers and suppliers will be required to navigate as they launch increasingly autonomous driving systems.It is also likely that we will see an increase in the volume and complexity of litigation involving parties such as vehicle manufacturers, software companies, suppliers and mapping agencies.
On the Robust Splitting Criterion of Random Forest

Splitting criteria have played an important role in the construction of decision trees, and various trees have been developed based on different criteria. This work presents a unified framework on various splitting criteria from the perspective of loss functions, and most classical splitting criteria can be viewed essentially as the optimizations of loss functions in this framework. We further introduce a new splitting criterion, named pairwise gain, which is motivated from a lower bound on the mutual coupling of pairwise loss. Theoretically, we prove that this new criterion is robust to symmetric and asymmetric label noises simultaneously. Based on this new criterion, we develop another variant of random forests, and extensive experiments are provided to verify its robustness.
Graph-based Semi-Supervised Classification for Online Customer Reviews Using Consensus Clustering

The purpose of this research is to present a graph-based semi-supervised learning (GBSSL) method with high classification accuracy for handling a large number of customer reviews. Following recent developments in information and communication technology, it has become essential for companies to employ efficient methods for analyzing customer reviews for improving their products and services. In analyzing these reviews, it is necessary that they are classified based on the review content. However there are only a few labelled customer reviews that can be used for the purpose of classification. Semi-supervised learning is effective in such case. In this research, we introduce GBSSL and show how it improves classification accuracy with the use of unsupervised clustering. The proposed learning method defines the result of consensus clustering based on the similarity between nodes. This research classifies customer reviews for a beauty salon using the proposed method and compares its accuracy with that of other machine learning methods to demonstrate the former's effectiveness.
A 2020 research commentary on the trust repair life cycle for “How to use apology and compensation to repair competence- versus integrity-based trust violations in e-commerce.”

Trust is the stepping-stone for successful e-commerce relationships while trust violations in e-commerce are common phenomena. Recovering consumer trust as quickly and effectively as possible is a top priority for e-commerce companies. However, what the underlying mechanism for trust violation repair is remains unclear so far. In order to address this gap, more studies should be conducted for trust repair in the e-commerce context. According to the prior literature, approaches for repairing trust in the short term have begun to be addressed, whereas the long-term approaches have received little attention. Accordingly, a comprehensive model that combines both short-term and long-term repairing approaches is necessary and should be explored.