Reviewerly/exHarmony huggingface dataset:
Collection of Eye Tracking Data when a Human Operates a Mobile Robot based on Robot's Viewpoint

Our laboratory aim to enable robots to avoid obstacles as smoothly as humans, using only image information. It is necessary to define what information to focus on and what information to use. To get a hint for making such definition, we investigated human gaze points on the image obtained from robot's viewpoint. For this purpose, we developed an experimental system incorporated with an eye tracking system, in which a human rides on an automatic electric wheelchair considered as a mobile robot, inside which he gazes at the image obtained by a stereo camera. He controls the velocity and direction of the robot based on the image information. The obtained eye tracking data are shown and what the human gazed at are discussed.
Redundancy in cost functions for Byzantine fault-tolerant federated learning

Federated learning has gained significant attention in recent years owing to the development of hardware and rapid growth in data collection. However, its ability to incorporate a large number of participating agents with various data sources makes federated learning susceptible to adversarial agents. This paper summarizes our recent results on server-based Byzantine fault-tolerant distributed optimization with applicability to resilience in federated learning. Specifically, we characterize redundancies in agents' cost functions that are necessary and sufficient for provable Byzantine resilience in distributed optimization. We discuss the implications of these results in the context of federated learning.
Design and Implementation of Distance Music Education Platform Based on Cloud Computing

The rapid development of distance education has played an important role in promoting the informatization process of education, promoting the popularization of higher education, learning education system and social services for all, and establishing a lifelong education system. Due to the rapid development of computer networks, distance network education has attracted widespread attention in the field of education with its rich teaching resources and flexible teaching methods. Due to China's huge land area, large number of participants, lack of hardware and software resources and other realistic constraints, the development of distance education is not perfect. In practice, the distance education system will inevitably be updated and improved according to the specific application situation. This study analyzes the characteristics of today's distance music education platform, and puts forward the design method of distance music education platform based on cloud computing technology in view of the shortcomings of distance education such as vague standards, poor ability of sharing resources and poor interactivity.
An Information Theoretic Approach to assist in Identifying Counterfeit Consumer Goods

In an increasingly connected world where products are just a click away, there is a growing need for systems that seek to equip consumers with the necessary tools to identify misrepresented products. Sub-standard ingredients used in the production of sanitary towels can pose a serious health risk to the consumer. Informal retailers or Spaza-shops have been accused of selling counterfeit food products to unsuspecting consumers. In this paper, we propose a system that can be used by consumers to scan a quick response (QR) code printed on the product. Built into an android application, is a system that applies the RSA public key encryption algorithm to secure the data prior to encoding into the QR code. The proposed system is also responsible for updating location data of previous scans on a dedicated cloud database. Upon completion of a field test, having collected months of consumer data, counterfeit prediction can be improved. In addition, a timely warning can be sent to a customer and relevant authorities if a unique product batch number is scanned outside of an expected area.
Music Recommendation Algorithm Based on Knowledge graph Propagation User Preference

Now a days, researchers have applied auxiliary information to music recommendation algorithms in order to solve the inevitable problems of data sparsity and cold start in recommendation systems, and obtain more potential information through data mining to improve the accuracy of recommendation. This paper describes a model SYT_RippleNet, which combines knowledge graph with deep learning, The knowledge graph is used to explore the potential connection between users and projects and to find the potential interests of users. Then, it promotes the propagation of user preferences on the entity set of the knowledge graph, which is realized through the triple attention mechanism during the preference propagation. Finally, the user's preference distribution for candidate items formed by the user's historical click information is used to predict the final click prediction rate. The music data set Last.FM is applied to SYT_RippleNet model, and good recommendation prediction results are achieved. In addition, the improved loss function is used in the model and optimized by Adam optimizer. Finally, the tanh function is added to predict the click probability to improve the recommendation performance. Compared with the current mainstream recommendation methods, SYT_RippleNet recommendation algorithm has a very good performance in AUC and ACC evaluation indicators, and has a substantial improvement in music recommendation.
Research Skills Session 2: Selecting keywords

Keywords (commonly called search terms), are the words that researchers enter into the database search box. Keywords search, help researchers to find relevant papers to their research interest. The researcher need to select appropriate alternative keywords to reach the maximum number of published documents. There are some tools for choosing the best set of keywords for a research. Dr. Nader has collected over 700 Research Tools that enable researchers to follow the correct path in research for producing high-quality research outputs with more accuracy and efficiency. The workshop concentrates on how to choose the right keywords for your research.
Adaptive Route Optimization for Mobile Robot Navigation using Evolutionary Algorithm

As technologies are advancing, demand for an intelligent mobile robot also increases. In autonomous robot design, the main problem faced by researchers is the path planning of mobile robot. Various kind of path planning algorithm was introduced in the past, but no algorithm has absolute superior towards the others algorithm. Classical methods like artificial potential field, grid search, and visual method have been easily overtaken by artificial intelligence due to its adaptability and ability to learn from the past mistakes or experience. For example, Ant Colony Optimization (ACO) is an optimization algorithm based on swarm intelligence which is widely used to solve path planning problem. However, the performance of ACO is highly dependent on the selection of its parameters. In this paper, the proposed adaptive ACO introduced two different ants, namely abnormal ant and random ant into the normal ACO to increase its global search ability and reduce the high convergence rate of ACO. Conventional ACO and adaptive ACO are compared in this paper and the results showed that adaptive ACO has better performance than conventional ACO in path planning.
Educational Content Delivery in Mixed Online/Offline University Campuses

This paper works with recent issues related to educational content delivery which either have become acute or newly arisen due to the COVID-19 pandemic. While several tools including Zoom, Meet, Teams, WebEx, and others are common in online classrooms today, this paper considers other models including those in which traffic is offloaded to network edge. Via trace-based modeling based on real datasets describing network performance in AWS EC2 services, this paper identifies that at least two distinct cloud-based models of content delivery can be implemented even in large realtime classrooms.
Automating integration under emergent constraints for embedded systems

Abstract As embedded applications are subject to non-functional requirements (latency, safety, reliability, etc.) they require special care when it comes to providing assurances. Traditionally, these systems are quite static in their software and hardware composition. However, there is an increasing interest in enabling adaptivity and autonomy in embedded systems that cannot be satisfied with preprogrammed adaptations any more. Instead, it requires automated software composition in conjunction with model-based analyses that must adhere to requirements and constraints from various viewpoints. A major challenge in this matter is that embedded systems are subject to emergent constraints which are affected by inter-dependent properties resulting from the software composition and platform configuration. As these properties typically require an in-depth evaluation by complex analyses, a holistic formulation of parameters and their constraints is not applicable. We present a compositional framework for model-based integration of component-based embedded systems. The framework provides a structured approach to perform operations on a cross-layer model for model enrichment, synthesis and analysis. It thereby provides the overarching mechanisms to combine existing models, analyses and reasoning. Furthermore, it automates integration decisions and enables an iterative exploration of feasible system compositions. We demonstrate the applicability of this framework on a case study of a stereo-vision robot that uses a component-based operating system.
Geologic Structure, Mineralogy, and Geochemistry of the Nerunda Gold Ore Field (Northern Transbaikalia)

Abstract —We consider the geologic structure of the Nerunda gold ore field located in the Nerunda–Mama ore district in northern Transbaikalia. Gold–quartz low-sulfide formation and ore-bearing carbonate-terrigenous strata and intrusive complexes are briefly described. An ore complex of beresite–listvenite metasomatites hosting carbonate–quartz veins and vein–veinlet zones is characterized. Two stages of ore formation have been recognized. Anomalous geochemical associations and the composition of ore mineralization typical of these stages have been established. Mineralogical and geochemical studies of gold-bearing metasomatites of the Nerunda ore field were carried out. The known geochemical and mineralogical search criteria used for the assessment of the erosion zone level of gold deposits were applied to the geologic conditions of the Nerunda ore field and the Nerunda–Mama gold ore district as a whole. The emphasis was made on the express assessment of the erosion zone level at the early stage of prospecting. We draw a conclusion about the gold potential of the poorly studied ore objects at depth and give guidelines for the following geological prospecting.
Using the Solution Space Constraint to Pick the Best Velocity Automatically

Picking the best velocity from the velocity spectrum is one of the keys to process seismic data. Aiming at the problems of lower efficiency of manual picking and poor precision of general automatic picking, a solution space constraint method to pick the best velocity automatically was proposed. Firstly, according to the signal similarity coefficient criterion, the original velocity solution space P is constrained to obtain the space P’; Secondly, using the signal in-phase criterion perform the peak match based on kd-Tree’s nearest neighbor search, the space P’ is changed into the space P” by the matching results; Finally, in accordance with the objective function, the automatic picking of the optimal velocity is achieved by the improved particle swarm model in constraint space P”. Experimental results show that the calculation speed of this algorithm is faster, and the error between the automatic picking result and the real reflected signal value is smaller, which meets the needs of actual engineering.
Emotional Speech Cloning using GANs

Speech cloning is one of the most sought-after applications of deep learning. While there have been great strides in the field, many have been data-inefficient and have not yielded good results. Furthermore, most synthesized speech is monotonous. The challenge in trying to recreate a given individual's emotions with very limited data is another challenge by itself. In this paper, an alternate approach to speech cloning is proposed by exploring the possibility of treating synthesized voice and synthesized emotion as two separate entities and combining the outputs sequentially. The first part of the proposed network contains a neural voice synthesizer to generate non-emotional speech using as little data as possible. The output of this network is then combined with an array of different speaker emotions and passed to a modified version of CycleGAN network called EmoGAN with the aim being to seamlessly add in different emotions as required in the context of different sentences. The EmoGAN has been trained on two emotions from the Toronto Emotional Speech Set (TESS) database: sadness and anger. This is evident by the convergence plots of generator and discriminator and listening to the synthesized speech and evaluating the subjective quality.
Choreographic Encounters in XR: Reflections on Dancing in/Dancing with the Digital

This article presents findings from first-person accounts of shifting choreographic practice into mixed-reality environments. Dancing in/Dancing with the Digital, a transdisciplinary practice-based project exploring embodiment and movement in XR. In the research and design phases of the project, all authors kept reflective practice journals, which form the data for this auto-ethnographic and phenomenologically-driven reflexive practice analysis. We further support these perspectives by user-testing with professional dancers and the engineering students who developed the XR configurations. Through a kaleidoscope of perspectives, including dance, psychology, somatic practices, digital technologies, and design approaches, we explore embodied awareness and practice in existing and novel XR design. Themes discussed include elements of disorientation in digital environments and touch as a grounding point, the prospect of multimodal creative stimuli, complications of representation in digital spaces, and the digital-as-site for choreography.
Automatic Coloring Method for Ethnic Costume Sketch Based on Pix2Pix Network

Ethnic minority costume culture is an indispensable part of ethnic minority culture and an important content of ethnic minority culture protection and inheritance. It plays a very important role in Chinese traditional culture. The coloring of minority costume sketches has many practical application environments. It is a research topic with scientific significance and application prospects. On the basis of coloring the sketches of ethnic minority costumes on the GAN network, this paper proposes a coloring model of ethnic clothing sketches based on the Pix2Pix network, which can automatically colorize ethnic clothing sketches. The network is implemented based on the CGAN network. Among them, the ResNet is used as the network Generator. In order to achieve the constraints on the target image generation process and further ensure the coloring effect of the generated image, we use the ethnic minority costume sketch as a “label” input in the Generator, and the L1 loss is used as the loss function. The network is trained on the data set constructed in this paper. In order to verify the effectiveness of the network, we compared it with a variety of coloring methods. The results show that the peak signal-to-noise ratio reaches 24.061 and the structural similarity reaches 0.820, which further verifies that the coloring method proposed in this paper has good coloring performance.
Summarization and Prioritization of Amazon Reviews based on multi-level credibility attributes

It is common for most people to check Amazon reviews of competing products before a purchase decision. However, the ratings and reviews could be from a customer who has not actually purchased the product on Amazon. There is also a chance that the reviews are doctored by paid reviewers either to enhance a particular product's appeal or lower that of a competitor's product. So, the ratings and reviews do not always reflect the reality. We provide a mechanism to eliminate un-authenticated reviews prioritizing them based on their credibility score and summarize both positive and negative keywords. In this paper, we present a methodology to eliminate un-authenticated reviews based on three levels of pruning. The final rating of the review is adjusted using helpfulness ratio of the review, how old the review is and the helpfulness and experience of the reviewer. We also summarize the positive and negative keywords using ‘term frequency-inverse document frequency’ and ‘Long Short-Term Memory networks’. The reviews are prioritized the based on their credibility score. We were able to summarize and prioritize the reviews for easy analysis by the user. We also did an empirical validation of our proposed solution and found that the overall helpfulness factors improved.
Transferability of Structure Parameters and Features in Convolutional Neural Network Layers

Many convolutional neural networks (CNNs) trained on images exhibit a phenomenon in common: the features extracted by a CNN's lower-level layers tend to be general, while higher-level layers extract more specific features that are limited to datasets and tasks. Based on this network characteristic, many scholars have demonstrated the transferability of convolutional network layers through experiments. In this study, we explored the transferability of each layer of the CNNs from two perspectives: network structure parameters and output features. First, the similarities of a network's structure parameters and output features were quantified using common distance measures (e.g., Euclidean distance). Then we conducted a transfer experiment to compare and analyze the relationship between the similarity and transferability of each layer of the network. We concluded that the more similar the structure parameters and output features of each layer of the CNN, the better the transfer effect.
Informal Communication in a Formal Context

In recent years, there has been a growing interest in integrating mobile technologies into the formal system in the field of formal education and, in particular, higher education. Furthermore, due to the circumstances caused by the pandemic panorama that extended to most countries in the world in 2020, various sectors, in which education is included, were forced to reinvent and transform themselves in order to adapt to emergency remote education. What the authors propose with this chapter is to make an analysis of the state of the art on the theme of informal communication in the educational context, and particularly in higher education, with the use of the available technology, namely mobile media. Through a literature review already initiated in previous studies, it is intended to know the national and international panorama about the use of mobile applications in the context of higher education and in what way the devices traditionally conceived for informal communication are being used and adapted to a formal context.
Human Praying Structure Classifıcation with Transfer Learning

Action recognition is one of the most important fields in computer vision. Hence, there is an open question of the high accuracy of complex background of human activities. A deep learning approach has recently been used to increase recognition validity with different application areas such as video surveillance, entertainment, autonomous driving vehicles, and human–machine interactions, etc. The aim of this research is to recognize human religious actions that differ in different activities. In our study, we have created our dataset from religious praying videos collected from YouTube, which has been classified into four different classes in terms of religion. We have applied a deep convolutional neural network using the Resnet-50 model for identifying human activity recognition (HAR) and we have got 98.79% accuracy. This research will help to cover more human action recognition tasks of daily activities.
Flood Disaster Impact Analysis On Infrastructure In Mataram

research-article Share on Flood Disaster Impact Analysis On Infrastructure In Mataram Authors: Shaifany Fatriana Kadir Brawijaya University, Indonesia Brawijaya University, IndonesiaView Profile , Fatwa Ramdani Brawijaya University, Indonesia Brawijaya University, IndonesiaView Profile , Agung Setia Budi Universitas Brawijaya, Indonesia Universitas Brawijaya, IndonesiaView Profile Authors Info & Claims SIET '21: 6th International Conference on Sustainable Information Engineering and Technology 2021September 2021 Pages 203–208https://doi.org/10.1145/3479645.3479684Published:03 November 2021Publication History 0citation51DownloadsMetricsTotal Citations0Total Downloads51Last 12 Months43Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
An Era of Recommendation Technologies in IoT: Categorisation by techniques, Challenges and Future Scope

The evolution of the Internet of Things (IoT) accelerates the augmentation of data present on the Internet and possibilities for connections to the more dynamic and heterogeneous devices to the Internet. Recommendation technologies have proven their capabilities of digging the personalised information by proactive filtering in many application domains and can also be a backbone platform in IoT for identifying personalised things, services and relevant artefacts by prevailing over information overload problems. This paper is a comprehensive literature review that categorises IoT recommender systems by exploring the literature’s different IoT based recommendation techniques. We conclude the paper by discussing the challenges and future scope for IoT based recommendations techniques to advancing and widening the frontiers of this research area.
Emotional Nuances: Critical Reflections on Emoji Methods

The present article critically reflects on the use of visual method and specifically emojis when interviewing young students diagnosed with autism. Based on disability studies and visual method, the following questions are discussed: What are the opportunities and limitations of using emojis in the individual interview situation? Which questions are possible to ask – and which are not? The aim of the article is to critically discuss the use of emojis in the individual interview situation. Four challenging phases in which use of emojis is multi-dimensional are illustrated. The phases are: Changed positions in the interview situation. Which questions are possible to ask? Which answers are possible to give? Use of emojis to evaluate the experience of being interviewed. The article highlights how emojis enable the expression of emotional nuances, while also giving rise to some difficulties in asking follow-up questions on a “deeper level”.