Reviewerly/exHarmony huggingface dataset:
Optimal Camera Placement for 6 Degree-of-Freedom Immersive Video Streaming Without Accessing 3D Scenes

We consider a 6 Degree-of-Freedom (6DoF) immersive video streaming eco-system, which is composed of content creators, cloud service providers, and Head-Mounted Display (HMD) users. To avoid intellectual property leakage, content providers only offer rendering and query access, which significantly complicates the problem of placing source cameras to maximize the synthesized quality for multiple 6DoF HMD viewers. We address three key challenges: extremely large search space, nonlinear synthesized quality models, and computationally-intensive numerical algorithms. Three algorithm variants: C1G, C2G, and C2I, have been proposed. The first one employs a first-order coverage model, while the last two employ a second-order coverage model; moreover, the first two adopt a greedy method, while the last one adopts an Integer Programming (IP) solver. We implement a prototype system and carry out evaluations based on a real dataset consisting of 16 subjects collected by us. Our evaluation results reveal that, with 16 source cameras, our algorithm: (i) outperforms the baseline one by at least 2.37 dB in PSNR, 0.05 in SSIM, and 10.84 in VMAF and (ii) achieves an optimality gap as small as 1.00 dB in PSNR, 0.01 in SSIM, and 2.30 in VMAF.
MIAIS: A Multimedia Recipe Dataset with Ingredient Annotation at Each Instructional Step

In this paper, we introduce a multimedia recipe dataset with annotation of ingredients at every instructional step, named MIAIS (Multimedia recipe dataset with Ingredient Annotation at every Instructional Step). One unique feature of recipe data is that it is usually presented in a sequential and multimedia form. However, few publicly available recipe datasets contain multimedia text-image paired data for every cooking step. Our goal is to construct a recipe dataset that contains sufficient multimedia data and the annotations to them for every cooking step, which is important for many research topics, such as cooking flow graph generation, recipe text generation, and cooking action recognition. MIAIS contains 12,000 recipes; each recipe has 9.13 cooking instruction steps on average, each of which is a tuple of a text description and an image. The text descriptions and images are collected from the NII Cookpad Dataset and Cookpad Image Dataset, respectively. We have already released our annotation data and related information.
Multimodal Dish Pairing

Planning a food menu is an essential task in our daily lives. We need to plan a menu by considering various perspectives. To reduce the burden when planning a menu, this study first tackles a novel problem of multimodal dish pairing (MDP), i.e., retrieving suitable side dishes given a query main dish. The key challenge of MDP is to learn human subjectivity, i.e., one-to-many relationships of the main and side dishes. However, in general, web resources only include one-to-one manually created pairs of main and side dishes. To tackle this problem, this study assumes that if side dishes are similar to a manually created side dish, they are also acceptable for the query main dish. We then imitate a one-to-many relationship by computing the similarity of side dishes as side dish scores and assigning them to unknown main and side dish pairs. Based on this score, we train a neural network to learn the suitability of the side dishes through learning-to-rank techniques by fully leveraging the multimodal representations of the dishes. During the experiments, we created a dataset by crawling recipes from an online menu site and evaluated the proposed method based on five criteria: retrieval evaluation, overlapping ingredients, overlapping cooking methods, consistency of the dish styles, and human evaluations. Our experiment results show that the proposed method is superior to the baseline in terms of these five criteria. The results of the qualitative analysis further demonstrates that the proposed method can retrieve side dishes suitable for the main dish.
Engagement and Quality of Experience in Remote Business Meetings

Currently, most videoconferencing technologies do not keep employees sufficiently engaged during business meetings. Recent studies have shown how extended reality (XR) technologies can help in executing remote meetings in new and possibly better ways. One important factor for meetings in e.g. Virtual Reality (VR) is avatar realism, with the assumption that photorealistic representations of users increase the engagement during meetings compared to a model-based graphical representation. However, so far only limited studies have been conducted in a real-world setting with a social virtual reality communication system in which users are represented as photorealistic avatars. Therefore, in this paper, we present a pilot study using a social VR communication system that allows employees of an organisation to meet each other from different remote office locations in The Netherlands. The users are captured with a depth camera, after which the capture is rendered in the HMD's of the users. Furthermore, the research provides a novel way to subjectively investigate the engagement and quality of experience (QoE) in social VR in real-world settings and long-term use. Our correlation analysis shows that there are strong linear relationships between the quality of communication, embodiment, immersion, social presence, and meeting-engagement. Furthermore, there are strong linear relationships between usability, quality of interaction, and quality of experience.
THE EFFECT OF RIDDLE GAME ON STUDENTS’ VOCABULARY MASTERY

This research was aimed to find the effect of riddle game on students’ vocabulary mastery. This research was conducted by using experimental research design with two groups namely experimental and control. The population was all the students of grade VIII of SMP Al Washliyah 08 Medan in academic year 2019-2020 which consisted of two classes namely VIII2 and VIII4. The total number of the population was 60 students. Cluster random sampling was applied to decide the sample group. The experimental group was taught by applying Riddle Game whereas the control group was taught without specific strategy. The instrument for collecting the data was multiple choices test consisted of 20 items. After the data has been collected, the data then were analyzed by using t-test formula. Data analysis resulted that tobserved value was higher than ttable value (3.05 &gt; 2.00172) at level of significance 0.05 with degree of freedom (df) = 58. It meant Ha was accepted and Ho was rejected. Thus, the riddle game strategy significantly affected students’ vocabulary mastery.
Privacy-Preserving DNN Model Authorization against Model Theft and Feature Leakage

Today’s intelligent services are built on well-trained deep neural network (DNN) models, which usually require large private datasets along with a high cost for model training. It consequently makes the model providers cherish the pre-trained DNN models and only distribute them to authorized users. However, malicious users can steal these valuable models for abuse, illegal copy and redistribution. Attackers can also extract private features from even authorized models to leak partial training datasets. They both violate privacy. Existing techniques from secure community attempt to avoid parameter leakage during model authorization but yet cannot solve privacy issues sufficiently. In this paper, we propose a privacy-preserving model authorization approach, AgAuth, to resist the aforementioned privacy threats. We devise a novel scheme called Information-Agnostic Conversion (IAC) for forwarding procedure to eliminate residual features in model parameters. Based on it, we then propose Inference-on-Ciphertext (CiFer) mechanism for DNN reasoning, which includes three stages in each forwarding. The Encrypt phase first converts the proprietary model parameters to demonstrate uniform distribution. The Forward stage per-forms forwarding function without decryption at authorized side. Specifically, this stage just computes over ciphertext. The Decrypt phase finally recovers the information-agnostic outputs to informative output tensor for real-world services. In addition, we implement a prototype and conduct extensive experiments to evaluate its performance. The qualitative and quantitative results demonstrate that our solution AgAuth is privacy-preserving to defend against model theft and feature leakage, without accuracy loss or notable performance decrease.
Path Planning for Special Robotic Operations

The problem of robotic path planning has been the focus of countless investigations since the early works of the ’70s and, despite the large number of results available in literature, is still a topic that draws a great interest. In virtually all robotic applications it is required to somehow define a feasible and safe path, and such a problem can be cast and solved in many ways, given the several possible combination of robots—industrial robots, Autonomous Guided Vehicles (AGVs), Unmanned Aerial Vehicles (UAVs), underwater vehicles—and scenarios—a production line, a warehouse, an hazardous mountain—and therefore a large number of approaches and solutions have been, and are being, investigated. The aim of this chapter is to provide an overview of such widespread literature, first by briefly recalling some classic and general-purpose methods used in path planning, then by focusing on some application-specific problems, related to AGVs in industry, medical robotics and robotic welding. This choice is motivated by the prominent relevance of the path planning problem in these three applications. Then, a single application of great industrial interest, such as robotic spray painting, is analysed. Its specific features are described, and several techniques for task modelling and path planning are considered. A detailed comparison among these techniques is carried out, so as to highlight pros and cons of each one, and to provide a methodology to choose the most suitable one for the specific robotic spray painting application.
Empirical validation of ELM trained neural networks for financial modelling

Abstract The purpose of this work is to compare predictive performance of neural networks trained using the relatively novel technique of training single hidden layer feedforward neural networks (SFNN), called Extreme Learning Machine (ELM), with commonly used backpropagation-trained recurrent neural networks (RNN) as applied to the task of financial market prediction. Evaluated on a set of large capitalisation stocks on the Australian market, specifically the components of the ASX20, ELM-trained SFNNs showed superior performance over RNNs for individual stock price prediction. While this conclusion of efficacy holds generally, long short-term memory (LSTM) RNNs were found to outperform for a small subset of stocks. Subsequent analysis identified several areas of performance deviations which we highlight as potentially fruitful areas for further research and performance improvement.
A stochastic variance-reduced coordinate descent algorithm for learning sparse Bayesian network from discrete high-dimensional data

This paper addresses the problem of learning a sparse structure Bayesian network from high-dimensional discrete data. Compared to continuous Bayesian networks, learning a discrete Bayesian network is a challenging problem due to the large parameter space. Although many approaches have been developed for learning continuous Bayesian networks, few approaches have been proposed for the discrete ones. In this paper, we address learning Bayesian networks as an optimization problem and propose a score function which guarantees the learnt structure to be a sparse directed acyclic graph. Besides, we implement a block-wised stochastic coordinate descent algorithm to optimize the score function. Specifically, we use a variance reducing method in our optimization algorithm to make the algorithm work efficiently for high-dimensional data. The proposed approach is applied to synthetic data from well-known benchmark networks. The quality, scalability, and robustness of the constructed network are measured. Compared to some competitive approaches, the results reveal that our algorithm outperforms some of the well-known proposed methods.
Quantitative Approximation by Kantorovich-Shilkret Quasi-interpolation Neural Network Operators Revisited

In this chapter we exhibit multivariate basic approximation by a Kantorovich-Shilkret type quasi-interpolation neural network operators with respect to supremum norm. This is done with rates using the multivariate modulus of continuity. We approximate continuous and bounded functions on $$\mathbb {R}^{N}$$ , $$N\in \mathbb {N}$$ . When they are additionally uniformly continuous we derive pointwise and uniform convergences. We include also the related Complex approximation. Our activation functions are induced by the arctangent, algebraic, Gudermannian and generalized symmetrical sigmoid functions. It follows [8].
Corruption Trends and Graft Control Progress in Sierra Leone: Study of Causes and Remedies

Corruption continues to affect the vast majority of Sierra Leoneans and is regarded as the greatest culprit of poverty, inequality and social cohesion. This publication shed light on corruption trends, control measures, causes, development implications and remedies for combating corruption in Sierra Leone. The data for this article was sourced from secondary/desk review. The secondary data sources were mainly from published articles, government reports, Acts, policies, workshops and conference proceedings, newspaper commentary, Transparency International Annual Reports, Truth and Reconciliation Commission Reports, and NGOs among others. Key words like corruption in Sierra Leone, causes of corruption, negative effects of corruption and bribery, and control measures of corruption were used to search for relevant information. Specifically, the study revealed that corruption was more prevalent from 2003 to 2018 but declined from 2018 to 2021. Most corruption cases in Sierra Leone arise from the abuse of public offices for private gains. Furthermore, systemic analysis of literature showed that corruption arises as a result of low salary wages, political ambition and other sociological factors. From 2019 to 2021, Sierra Leone has made remarkable progress and is been rated below Africa’s corruption average. This might be attributed to the various reforms and the pre-conviction corrupt asset recovery strategy adopted and the massive public outreach and awareness-raising drive adopted by the commission. It is recommended that the Anti-Corruption Commission of Sierra Leone be adequately empowered to go after any individual engaged in corrupt practices regardless of social status, ethnicity, political connection and wealth. It further recommended that the Anti-Corruption Commissioner appointment should not be political but by integrity and sound track record of citizens.
An Empirical Analysis of Synthetic-Data-Based Anomaly Detection

Data is increasingly collected on practically every area of human life, e.g. from health care to financial or work aspects, and from many different sources. As the amount of data gathered grows, efforts to leverage it have intensified. Many organizations are interested to analyse or share the data they collect, as it may be used to provide critical services and support much-needed research. However, this often conflicts with data protection regulations. Thus sharing, analyzing and working with those sensitive data while preserving the privacy of the individuals represented by the data is needed. Synthetic data generation is one method increasingly used for achieving this goal. Using synthetic data would useful also for anomaly detection tasks, which often contains highly sensitive data. While synthetic data generation aims at capturing the most relevant statistical properties of a dataset to create a dataset with similar characteristics, it is less explored if this method is capable of capturing also the properties of anomalous data, which is generally a minority class with potentially very few samples, and can thus reproduce meaningful anomaly instances. In this paper, we perform an extensive study on several anomaly detection techniques (supervised, unsupervised and semi-supervised) on credit card fraud and medical (annthyroid) data, and evaluate the utility of corresponding, synthetically generated datasets, obtained by various different synthetisation methods. Moreover, for supervised methods, we have also investigated various sampling methods; sampling in average improves the results, and we show that this transfers also to detectors learned on synthetic data. Overall, our evaluation shows that models trained on synthetic data can achieve a performance that renders them a viable alternative to real data, sometimes even outperforming them. Based on the evaluation, we provide guidelines on which synthesizer method to use for which anomaly detection setting.
Adaptive Bilateral Filtering Detection Using Frequency Residuals for Digital Image Forensics

The credibility of digital images is crucial nowadays. Digital images bear ample information, and they even stand as evidence in a court of law. But editing an image is effortless due to easily usable image editing softwares. So image editing detection has become a hot topic for researchers recently. Over time, several antiforensic techniques like blurring and median filtering have emerged to mask image forgery. While several types of research are done to detect blurring and median filtering, this paper introduces the detection of another powerful antiforensic tool called adaptive bilateral filtering. The proposed method develops a new frequency domain residual feature by successive adaptive bilateral filtering of an image. The polarity of this feature can effectively detect whether an image is previously adaptive bilateral filtered or not. The experiments performed in this work show that the proposed detection method is more effective than commonly used median filtering detection performed in the frequency domain.
3D Dual-stream Convolutional Neural Networks with Simple Recurrent Unit Network: A New Framework for Action Recognition

Human action recognition is a supervised process of labeling an entire video image sequence with action labels in computer vision fields. Different from the recognition of static images, this process also needs to learn the contact information between video frames, such as the timing characteristics that reflect the changes of actions in the video. In existing deep learning methods, due to the size of the convolution kernel, the models use a small number of consecutive frames as input and trains to assign feature vectors to short sequences instead of the entire sequence. Therefore, even if the learned features contain time information, their evolution over time will be completely ignored. In this work, we propose a dual-stream deep fusion framework that can fully utilize the long-term information of a video. We preprocess the video into static frames and optical flow graphs and input them into a three-dimensional convolutional neural network to obtain the spatiotemporal feature stream with time series. Then, the spatiotemporal feature stream is input into a simple recurrent unit network to learn the long-term sequence features of the time dimension. Finally, SoftMax classifier is used for feature classification. We tested our model on the classic action recognition UCF -101 and HMDB-S1 data sets, and our model achieved better performance than existing methods.
Explainable symptom detection in telemetry of ISS with Random Forest and SpecTRM

Flight controllers of the JEM (Japanese Experiment Module), one element of the International Space Station (ISS), are continuously monitoring ISS status, and it is important for them to detect signs of anomaly of its equipment as early as possible. Automatic symptom detection, in this context, can help flight controllers to assess unusual telemetry trends. To assess the trends efficiently, it is essential to provide the reason of detections. In this paper, we propose a new systemic symptom detection method combining three methodologies: the Functional Resonance Analysis Method (FRAM), the Random Forest Regression (RF), and the Specification Tools and Requirement Methodology-Requirement Language (SpecTRM-RL). The method was verified with data of Low Temperature loop (LTL) of JEM; an actual failure event of pump inverter in LTL was selected as a case study. In this case study, a selected objective variable was successfully predicted based on explanatory variables in normal period, whereas the predicted values showed larger deviation from the actual measured values in off-nominal period. The information for explaining the cause of anomaly was eventually identified with the proposed methods and validated by engineering knowledge. These results show the effectiveness of the new methods as the explainable machine learning-based predictive failure detection. The proposed method can be applied to fields where a single mishap of a system could lead to catastrophic hazard or instantaneous loss of human life due to impossibility of physical access (e.g., deep space explorations and remote medicine).
survey on NLP based automatic extractive text summarization using spacy

Automatic abstraction of text is considered one of the most difficult problems because mathematically there is no real way to test a summary but one can distinguish between the appropriate summary. Also, abbreviations may be of a variety of abstractive forms in which new phrases and terms are used, in contrast Extractive form in which scoring sentences from enter textual content gets extracted as a summary sentence. The growing discovery of online facts has necessitated extensive research within the region of automated text within the Natural language processing (NLP). Over the course of the year, researchers worked on them in many domains. Therefore, the survey plans to look at the number of commonly used processes within single-line and multi-line summaries, providing unique emphasis on real-world approaches and clear strategies. Some well-known techniques that focus on specific information of the default summary are mentioned.
Blockchain-based identity dicovery between heterogenous identity management systems

Identity Management Systems (IdMS) have seemingly evolved in recent years, both in terms of modelling approach and in terms of used technology. The early centralized, later federated and user-centric Identity Management (IdM) was finally replaced by Self-Sovereign Identity (SSI). Solutions based on Distributed Ledger Technology (DLT) appeared, with prominent examples of uPort, Sovrin or ShoCard. In effect, users got more freedom in creation and management of their identities. IdM systems became more distributed, too. However, in the area of interoperability, dynamic and ad-hoc identity management there has been almost no significant progress. Quest for the best IdM system which will be used by all entities and organizations is deemed to fail. The environment of IdM systems is, and in the near future will still be, heterogenous. Therefore a person will have to manage her or his identities in multiple IdM systems. In this article authors argument that future-proof IdM systems should be able to interoperate with each other dynamically, i.e. be able to discover existence of different identities of a person across multiple IdM systems, dynamically build trust relations and be able to translate identity assertions and claims across various IdM domains. Finally, authors introduce identity relationship model and corresponding identity discovery algorithm, propose IdMS-agnostic identity discovery service design and its implementation with use of Ethereum and Smart Contracts.