Reviewerly/exHarmony huggingface dataset:
A Novel Approach to Video Compression using Region of Interest (ROI) Method on Video Surveillance Systems

With the increasing of criminal actions, people use various surveillance techniques to create a sense of security.One of the most widely used surveillance technique is installing CCTV cameras at various locations.On the surveillance systems, there are other supporting devices apart from CCTV cameras.One of such supporting devices is a hard disk to save the recorded data.The recording on CCTV has two modes: motion detection mode and continuous mode.The continuous mode will record continuously, which affects the amount of hard disk space used.Motion detection mode records one event only, not all recordings, saving hard disk space, however, it may miss some events.Based on these two modes, compression technology is required.The current compression technology applies the ROI method.A ROI (Region of Interest) is the part of the image that wants to filter to form some operations against it.ROI allows coding differently in certain areas of the digital image to have a higher quality than the surrounding area (background).This paper offers a novel approach to saving the foreground frame generated from the ROI method and compressing it.The novel approach will be applied to the AVI, MJPEG 2000, and MPEG-4 video formats.The decompression process is used to restore the original video data to measure the method's performance.To measure the proposed method's performance, it will compare the compression ratio and Peak Signal-to-Noise Ratio (PSNR) with the traditional method without implementing the ROI-based method.The PSNR value in this paper, that measures the quality of the compression result,s are above 40 dB.It indicates that the resulting video is similar to the original video.The ROI-based compression method can increase the compression ratio 5-7 times higher than the existing method for lossy AVI format video.While on MJPEG-2000 and MPEG-4 format video, it increases the compression ratio 7-15 times and 1-3 times, respectively.The PSNR value for the proposed method is above 40 dB, which indicates that the reconstructed video is similar to the original video, even though the pixel values have changed slightly.
Unsupervised Graph-Based Tibetan Multi-Document Summarization

Text summarization creates subset that represents the most important or relevant information in the original content, which effectively reduce information redundancy. Recently neural network method has achieved good results in the task of text summarization both in Chinese and English, but the research of text summarization in low-resource languages is still in the exploratory stage, especially in Tibetan. What’s more, there is no large-scale annotated corpus for text summarization. The lack of dataset severely limits the development of low-resource text summarization. In this case, unsupervised learning approaches are more appealing in low-resource languages as they do not require labeled data. In this paper, we propose an unsupervised graph-based Tibetan multi-document summarization method, which divides a large number of Tibetan news documents into topics and extracts the summarization of each topic. Summarization obtained by using traditional graph-based methods have high redundancy and the division of documents topics are not detailed enough. In terms of topic division, we adopt two level clustering methods converting original document into document-level and sentence-level graph, next we take both linguistic and deep representation into account and integrate external corpus into graph to obtain the sentence semantic clustering. Improve the shortcomings of the traditional K-Means clustering method and perform more detailed clustering of documents. Then model sentence clusters into graphs, finally remeasure sentence nodes based on the topic semantic information and the impact of topic features on sentences, higher topic relevance summary is extracted. In order to promote the development of Tibetan text summarization, and to meet the needs of relevant researchers for high-quality Tibetan text summarization datasets, this paper manually constructs a Tibetan summarization dataset and carries out relevant experiments. The experiment results show that our method can effectively improve the quality of summarization and our method is competitive to previous unsupervised methods.
Interpretable Multimodal Capsule Fusion

With the development of social networking platform, multimodal sentiment analysis has become increasingly prominent. Existing models focus on capturing intramodal and intermodal interactions to produce effective modality representations. However, they overlook the study of interpretability which reveals how modalities interact with each other and which modality contributes most to the final prediction. In this paper, we propose an interpretable model called Interpretable Multimodal Capsule Fusion (IMCF) which integrates routing mechanism of Capsule Network (CapsNet) and Long Short-Term Memory (LSTM) to produce refined modality representations and provide interpretation. By constructing features of different modalities into input sequence, we are able to obtain highly expressive representation of intermodal dynamics due to the strong ability of LSTM to produce representation of sequence. As routing mechanism is applied during modality fusion and prediction stages, the value of routing coefficient can reveal the contributions of different modalities or dynamics, which provides interpretation. Meanwhile, routing mechanism can iteratively adjust the information flows of different modalities, which makes the process of modality fusion more reasonable. The experimental results show that our model achieves competitive performance on two benchmark datasets with effective modality fusion by LSTM and interpretation provided by routing mechanism.
Lossless Compression Algorithm for Medical Images With High Precision Based on Discrete Wavelet Transform

In image distortion and low adaptive recognition after medical image compression, a high precision medical image lossless compression algorithm based on discrete wavelet transform is proposed. A 3D imaging model of multi-dimensional medical images is constructed, and adaptive information enhancement and image restoration processing are performed on the collected medical images. According to the results of high-dimensional segmentation and segmentation, discrete wavelet transform is used to achieve high-precision lossless compression of medical images. The results show that the medical image compression is better non-destructive and the image fidelity is higher, which improves the detection and adaptive recognition ability of medical images
Investment Value Analysis of Amazon

This paper analyzed the stock Amazon in different aspect to justify whether it is worth to invest or not. Some indexes and models such as PE ratio and SWOT analysis model were used to demonstrate the objective advantages and disadvantages of Amazon. These tools show the real condition of Amazon company. In view of its internal and external aspects, Amazon is worth to invest.
The Importance of Strengthening Legal Concepts in Overcoming Cybercrime During the Covid-19 Pandemic in Indonesia

This study aims to analyze the importance of strengthening legal concepts in overcoming cybercrime during the Covid-19 pandemic in Indonesia. The Covid-19 pandemic that hit Indonesia made some people lose many things in various aspects of their lives due to multiple types of cybercrimes that often occur in society, such as malware attacks, trojan activities, and information leaks. The Electronic Information and Transactions Law (ITE) Number 11 of 2008 and the revised version of the ITE Law Number 19 of 2016 have historically been the legal basis for regulating cyber security in Indonesia. However, this regulation does not include essential parts of cybersecurity such as information and network infrastructure and human resources with cybersecurity experience. Thus, it is necessary to know how important it is to strengthen legal concepts in overcoming cybercrime during the Covid-19 pandemic in Indonesia. This study uses a qualitative approach. The data sources are from various online news media and relevant research journals and analyzed using the NVivo12 Plus application. Based on the results of the analysis, this study found that the acceleration of the ratification of the Personal Data Protection Bill, the establishment of special regulations related to cybersecurity and cybercrime, the creation of a multi-sectoral cyber security management ecosystem, as well as increasing awareness and capacity of human resources in the cyber security sector are alternative policies that must be considered and realized to strengthen the concept of law in overcoming various cyber crimes during the Covid-19 pandemic in Indonesia.
Recognition and Counting of an Object Using Yolo and CNN

Object identification and classification are vital in automation domains. It has gained conscience in the recent days owing to the growing domain of image processing and automation based of the growth of multiple sectors implementing automations. Object detection, identification and counting mechanisms are gaining momentum. Random urgencies and the trending algorithms of machine learning and deep learning strategies have made the object detection domain even more vulnerable. This paper implements a strategy to detect, classify and count objects based on image classification machine learning algorithms using Yolo. The proposed work considered training data collection using the app dataset, pre-processing using the DWT and GLCM, feature extraction using supervised ML algorithm, prune the available data and classification based on the proposed prediction models using R-CNN algorithms. The proposed methodology provided better accuracy with less computational overheads than the existing methods.
Research and Application of Key Technologies of Multi-round Dialogue in Intelligent Customer Service

Natural language human–computer dialogue is a comprehensive problem in the field of artificial intelligence, covering important aspects such as speech processing, language processing, decision control, and knowledge reasoning. Among them, the research on key technologies of multi-round dialogue is a challenging task. This paper analyzes and studies the key technologies of multiple rounds of dialogue, analyzes and compares the commonly used models in the deep question answering platform under the traditional pan-layer network structure (CD-AN), and proposes improvements to the existing algorithms to improve the quality of interaction and calculations based on their existing problems. effectiveness.
ART for Art Revisited: Analysing Technology Adoption Through AR Taxonomy for Art and Cultural Heritage

Understanding how effective technology adoption is and how well opportunities created by advances in technology are utilised is vital for supporting adoption and development of technology. To this end, we propose an activity-based taxonomy method designed to produce technology adoption insights. The method is applied on adoption of Augmented Reality (AR) technology in the context of art and cultural heritage. Through this, we build an AR taxonomy for art and cultural heritage which we then used to classify 119 AR applications in this domain. The results of classification provide meaningful insight into technology adoption and how it changed compared to reports in the previous edition of this book. To name a few: (i) general lack of support for communication and personalisation activities persist; (ii) the quality of adoption remains below satisfying level, yet some improvements have been made within the past few years; (iii) despite limited immersion capacity, handheld AR systems persist to be the most commonly used systems; and (iv) irrespective of difficult and costly setups, a substantial proportion of classified systems represents spatial AR systems; yet, this ratio recently dropped due to the higher adoption of head-mounted display systems, which is largely limited to the research domain.
Research on CNN-Based Image Denoising Methods

As a fundamental task in digital image processing, an effective denoising method can restore an image disturbed by noise to its original characteristics of a clean image. In this paper, we introduce the current mainstream image denoising methods and use a deep learning model, convolutional neural network, to implement image denoising. In our experiments, our method can effectively achieve denoising of noisy images and can restore the original features of the images better.
Maintain Optimal Configurations for Large Configurable Systems Using Multi-Objective Optimization

To improve the maintenance and quality of software product lines, efficient configurations techniques have been proposed. Nevertheless, due to the complexity of derived and configured products in a product line, the configuration process of the software product line (SPL) becomes time-consuming and costly. Each product line consists of a various number of feature models that need to be tested. The different approaches have been presented by Search-based software engineering (SBSE) to resolve the software engineering issues into computational solutions using some metaheuristic approach. Hence, multiobjective evolutionary algorithms help to optimize the configuration process of SPL. In this paper, different multi-objective Evolutionary Algorithms like Non-Dominated Sorting Genetic algorithms II (NSGA-II) and NSGA-III and Indicator based Evolutionary Algorithm (IBEA) are applied to different feature models to generate optimal results for large configurable. The proposed approach is also used to generate the optimized test suites with the help of different multi-objective Evolutionary Algorithms (MOEAs).
Behavioral Optimization in a Robotic Serial Reaching Task Using Predictive Information

Prediction is a powerful approach to minimize errors and control problems in familiar environments and tasks. In human motor execution of sequential action, context effects can be observed, such as anticipation of or predictive movement toward target objects, where later subactions are affected by the execution of earlier subactions. In this article, we present a simulation framework for a serial reaching task using a 4-DoF robotic arm to examine the learning of context effects in simulated robotic reinforcement learning agents. As we demonstrate, giving robotic agents access to predictive information about a future target object’s identity results in motion optimization, where the identity of the next target modulates earlier subactions. Specifically, agents learn to anticipate and predict the location of the next target object, and move toward it before it appears, thus achieving higher rewards than agents that were not given predictive information.
Effects of Filled Pauses on Memory Recall in Human-Robot Interaction in Mandarin Chinese

In recent years, voice-AI systems have seen significant improvements in intelligibility and naturalness, but the human experience when talking to a machine is still remarkably different from the experience of talking to a fellow human. In this paper, we explore one dimension of such differences, i.e., the occurrence of disfluency in machine speech and how it may impact human listeners' processing and memory of linguistic information. We conducted a human-machine conversation task in Mandarin Chinese using a humanoid social robot (Furhat), with different types of machine speech (pre-recorded natural speech vs. synthesized speech, fluent vs. disfluent). During the task, the human interlocutor was tested in terms of how well they remembered the information presented by the robot. The results showed that disfluent speech (surrounded by "um"/"uh") did not benefit memory retention both in pre-recorded speech and in synthesized speech. We discuss the implications of current findings and possible directions of future work.
Image Encryption Algorithm Using 2-Order Bit Compass Coding and Chaotic Mapping

This paper proposes a novel image encryption algorithm based on an integer form of chaotic mapping and 2-order bit compass diffusion technique. In recent years, various chaotic maps have been widely used for image encryption. If the floating-point number generated by chaotic mapping is applied to image encryption algorithm, it will slow encryption and increase the difficulty of hardware implementation. An innovative pseudo-random integer sequence generator is proposed.In chaotic system, the result of one-iteration is used as the shift value of two binary sequences, and then XOR operation is performed to generate a new binary sequence. Multiple iterations can generate pseudo-random integer sequences. Here integer sequences have been used in scrambling of pixel positions. Meanwhile, this paper demonstrates that there is an inverse operation in the XOR operation of two binary sequences. A new pixel diffusion technique based on bit compass coding is proposed. The key vector of the algorithm comes from the original image and is hidden by image encryption. The efficiency of our proposed method in encrypting a large number of images is evaluated using security analysis and time complexity. The security is tested in entropy, 1 histogram differential attacks, gray value distribution(GDV), correlation coefficient, PSNR, key space, and sensitivity. Furthermore, the achieved results are compared to existing state-of-the-art methods. The comparison between the results of coefficient, entropy, PSNR, GDV and time complexity further proves the effectiveness of the algorithm.
Comparative Evaluation of EduClust and Its Transfer to a Virtual Reality Environment

Previous research successfully demonstrated the applicability and usefulness of EduClust, a web platform for understanding and learning clustering algorithms. This paper presents a quantitative evaluation of the educational framework, comparing the online teaching platform to traditional teaching material. Results of the conducted experiment in a real computer science course at the University of Konstanz demonstrate its merit concerning users’ confidence and perceived usefulness. Additionally, we dare a first try to exploit the potential social, perceptual, and motivational benefits of immersive media. We present VRClust, an initial prototype as the transfer of the web-based application to a virtual reality environment.
Ranking Task in RAS: A Comparative Study of Learning to Rank Algorithms and Interleaving Methods

Relational Aggregated Search (RAS) is a paradigm that refers to a set of techniques for retrieving and aggregating information nuggets based on their relationships. The performance of a relational aggregated search is dedicated to how to retrieve an information nugget, and, how to present this information to users to fulfill their information need. Recent RAS research has focused only on information retrieval strategies. As consequence, there is a critical need to research strategies for aggregating and ranking information nuggets. In this paper, we focus on the recent issue, namely answering the question of how information is shown to the users. In addition, what are the most effective approaches for the ranking task? We use the ranked results from RAS. These ranked results include different content types (images, videos, news, maps, blogs, groups, and books, etc.). Thus, the focal challenge in the vertical results aggregation task is how to rank these heterogeneous results to obtain pertinent information. Our experimental results of the application of learning to rank algorithms and interleaving methods are shown in this study. From the evaluation results, we can affirm that LambdaMART proves its performance compared with the other; LambdaRank, MART, ListNet, and RankNet. Finally, we apply the interleaving approaches to satisfy the user's need by providing appropriate, organized, and most relevant results. Our experimental study proves that VI-TDA performs better than TDA in all steps of our tests.
Dynamic Orthogonal Projection Constrained Discriminative Tracking

Due to the end-to-end feature learning with convolutional neural networks (CNNs), modern discriminative trackers improve the state of the art significantly. To achieve a strong discrimination, the learned features are usually high-dimensional, resulting in a massive number of parameters contained in the discriminative model and the increase of risk of over-fitting in the online tracking. In this letter, we try to alleviate the risk of over-fitting by means of the adaptive dimensionality reduction (DR) through CNNs. Specifically, an orthogonality constrained ridge regression model is proposed to reduce the dimensionality of features, and a dynamic sub-network (DOPNet) is designed to learn to perform DR. After trained with an orthogonality loss and a regression one, DOPNet generates a set of orthogonal bases (i. e., weights in FC layers) dynamically to reduce the feature dimensionality for a discriminative model in the online tracking. Based on the novel discriminative model and DOPNet, an effective and efficient tracker, DOPTracker, is developed. DOPTracker achieves the state-of-the-art results on four benchmarks, OTB-2015, VOT-2018, NfS, and GOT-10 k while running at 30 FPS.
Dcid: A Divide and Conquer Approach to Solving the Trade-Off Enhancement and Artifacts in Image Downscaling

Conventional research on image downscaling is conducted to improve the visual quality of the resultant downscaled image. However, there is a difficult problem to solve, which arises from the trade-off relationship between detail enhancement and artifacts such as aliasing or ringing that can occur during the image downscaling. To solve this problem, we propose a novel method that applies a divide-and-conquer approach for image downscaling (DCID). Specifically, the proposed DCID includes Weight-Net for dividing regions into enhancement first and artifact-free first regions and two generators that are optimized for divided regions to conquer the trade-off problem in the image downscaling task. The proposed method can generate a downscaled image without creating artifacts while preserving the perceptual quality of the input image. Our experimental results showed that the quality of the downscaled images generated by the proposed DCID was significantly better than that of benchmark methods in objective and subjective evaluations.
Prediction Machines, Insurance, and Protection: An Alternative Perspective on AI's Role in Production

Recent advances in AI represent improvements in prediction. We examine how decision-making and risk management strategies change when prediction improves. The adoption of AI may cause substitution away from risk management activities used when rules are applied (rules require always taking the same action), instead allowing for decision-making (choosing actions based on the predicted state). We provide a formal model evaluating the impact of AI and how risk management, stakes, and inter-related tasks affect AI adoption. The broad conclusion is that AI adoption can be stymied by existing processes designed to address uncertainty. In particular, many processes are designed to enable coordinated decision-making among different actors in an organization. AI can make coordination even more challenging. However, when the cost of changing such processes falls, then the returns from AI adoption increase.