Reviewerly/exHarmony huggingface dataset:
Citizen Science Fiction: The Potential of Situated Speculative Prototyping for Public Engagement on Emerging Technologies

Abstract In response to calls for a research and innovation system that is more open to public scrutiny, we have seen a growth of formal and informal public engagement activities in the past decades. Nevertheless, critiques of several persistent routines in public engagement continue to resurface, in particular the focus on expert knowledge, cognitive exchange, risk discourse, and understandings of public opinion as being static. In an attempt to break out of these routines, we experimented with an innovative engagement format that employs situated speculative prototyping to support citizens in contextualizing and discussing developments regarding—in this case—nanotechnology. This format invites participants to imagine and critically reflect on technological futures through collaborative prototyping and story-writing. In this paper, we outline five reconstructed contextualization patterns in which participants engaged during the format’s exercises and use these to assess the value of the format in the current engagement landscape. We show that situated speculative prototyping has potential in the realm of informal public engagement initiatives, taking an explorative approach, but also warn of ‘the designer fallacy’ as a prominent pitfall of prototyping that could reproduce techno-scientific framings and obstruct critical reflection on technological directions and implications if not treated with caution.
Motion Detection with Segmentation of Optical Flows for Infrared Images

This paper presents a combined process of calculating optical flows between two consecutive images in a video sequence and segmenting the resulting optical flows to separate the background from the objects of interest, and to identify the relevant motion of the objects of interest captured in the field of view of the video camera. The technique is applied to infrared images for military and emergency situations where the lack of lighting or smoke in the air can block the line of sight between the observing camera and the objects of interest. Efficient implementable algorithms are developed, and numerical results are presented as proof of concept, with additional discussion for applications in night vision and search & rescue missions.
Hitching Medical IoT Devices to Blockchain for Personal Health Information Management

Ubiquitous availability of Internet connectivity IoT enabled medical devices and advances in electronic health records have given rise to massive amounts of personal health data. However, being sensitive in nature, there are stringent legal requirements related to the sharing and processing of personal health data. There is a growing trend of using Personal Information Management System (PIMS) to gather, store, manage, and provide secure and privacy-aware access to personal data. Recently, blockchain technology has emerged as a lucrative option for personal health information management. In this chapter, we propose a layered architecture for a blockchain-based PIMS for managing data originating from medical IoT devices. The proposed architecture has four layers as follows: (1) A blockchain layer that provides functionality to implement and host blockchain database; (2) An IoT device layer that consists of electronic data devices such as health watch, weighing scale, and blood pressure measurement sensors; (3) An application layer that collects data from the health devices and displays consolidated data view to the user; and (4) An adapter layer that has a responsibility of interfacing an application layer with the blockchain layer. The proposed architecture is extensible to accommodate additional electronic devices and services and it is also language and platform agnostic. We are currently building a Proof of Concept (PoC) prototype for validating proposed architecture using Hyperledger Fabric platform.
Optimizing Adaptive Neuro Fuzzy Inference System (ANFIS) parameters using Cuckoo Search (Case study of world crude oil price estimation)

Abstract There are some methods that have found for estimating data and one of them is Adaptive Neuro Fuzzy Inference System (ANFIS). In estimation using ANFIS, there are some initial parameters such as premise parameters (nonlinear) and consequent parameters (linear) which should be fixed to be trained forward and backward by gradient descent. In this research with case study of world crude oil price estimation, initial ANFIS parameters will be optimized by Cuckoo Search method. Cuckoo Search uses reproduction strategy i.e. laying their eggs in the other bird’s nest. When the eggs are hatched, their chicks are fed by other birds. In Cuckoo Search method, initial ANFIS parameters is represented as bird nest position. Based on simulation, Cuckoo Search method can optimize initial ANFIS parameters giving the best estimation both of training data and testing data in world crude oil price estimation.
ORStereo: Occlusion-Aware Recurrent Stereo Matching for 4K-Resolution Images

Stereo reconstruction models trained on small images do not generalize well to high-resolution data. Training a model on high-resolution image size faces difficulties of data availability and is often infeasible due to limited computing resources. In this work, we present the Occlusion-aware Recurrent binocular Stereo matching (ORStereo), which deals with these issues by only training on available low disparity range stereo images. ORStereo generalizes to unseen high-resolution images with large disparity ranges by formulating the task as residual updates and refinements of an initial prediction. ORStereo is trained on images with disparity ranges limited to 256 pixels, yet it can operate 4K-resolution input with over 1000 disparities using limited GPU memory. We test the model’s capability on both synthetic and real-world high-resolution images. Experimental results demonstrate that ORStereo achieves comparable performance on 4K-resolution images compared to state-of-the-art methods trained on large disparity ranges. Compared to the baseline methods that are only trained on low-resolution images, our method has 60% or less error on 4K-resolution images.
IGIP Prototype Curriculum, Teachers’ Professional Development and Distance Education in Russia During COVID-19 Pandemic

IGIP Prototype Curriculum pays special attention to the development of the ability of engineering university teachers to use effectively technical aids, information and communication technologies (ICT). In Russia, there are various additional professional programs for the same purpose. The outbreak of COVID-19 pandemic caused closures of engineering universities of many countries including Russia for the spring semester 2020. Since the end of March 2020 in Moscow Automobile and Road Construction State Technical University (MADI), all the teaching has been taking place remotely/online. The situation of this large-scale natural experiment gives the opportunity to analyze its results and to try to find answers to numerous questions. The paper is devoted to the survey of the attitude of MADI foreign language teachers and students towards distance learning technologies. The survey identifies the distance technologies used, the opinion of the respondents of their efficiency, the difficulties the respondents faced during remote educational process, the respondents' opinion of the prospects of distance technologies in foreign language training, the differences and similarities of the attitudes of foreign language teachers and students.
Echo state network based symbol detection in chaotic baseband wireless communication

The Chaotic Baseband Wireless Communication System (CBWCS) is expected to eliminate the Inter-Symbol Interference (ISI) caused by multipath propagation by using the optimal decoding threshold that is the sum of the ISI caused by past decoded bits and the ISI caused by future transmitting bits. However, the current technique is only capable of removing partial effects of the ISI, because only past decoded bits are available for the sub-optimal decoding threshold calculation. The unavailability of the future information needed for the optimal decoding threshold is an obstacle to further improve the Bit Error Rate (BER) performance. In contrast to the previous method using Echo State Network (ESN) to predict one future bit, the proposed method in this paper predicts the optimal decoding threshold directly using ESN. The proposed ESN-based threshold prediction method simplifies the symbol decoding operation by avoiding the iterative prediction of the output waveform points using ESN and accumulated error caused by the iterative operation. With this approach, the calculation complexity is reduced compared to the previous ESN-based approach. The proposed method achieves better BER performance compared to the previous method. The reason for this superior result is twofold. First, the proposed ESN is capable of using more future symbols information conveyed by the ESN input to obtain more accurate threshold rather than the previous method in which only one future symbol was available. Second, the proposed method here does not need to estimate the channel information using Least Squared (LS) method, which avoids the extra error caused by inaccurate channel information estimation. Simulation results and experiment based on a wireless open-access research platform under a practical wireless channel show the effectiveness and superiority of the proposed method.
Lightweight Deep Extraction Networks for Single Image De-raining

In bad weather, artifacts such as rain streaks degrade the image quality. In addition, artifacts in the damaged image obstruct human vision and adversely affect the accuracy of object detection. Hence, single image rain removal is an important issue to improve image quality. However, state-of-the-art methods have limitation that require a lot of training data. This paper proposes a lightweight Deep Extraction Network (DEN), which performs well on image de-raining even with a small training dataset. Particularly, we design a novel Light Residual Block (LRB), which is connected in five cascading layers for extracting a deep local feature. Furthermore, DEN deploys a residual learning for training only artifacts. The experimental results on synthetic and real-world rainy image demonstrate the effectiveness in terms of visual and quantitative performance.
Collaborative Learning: From Face-to-face to the Virtual Classroom in the COVID-19 Era

This work discusses in-person to virtual teaching adaptation through an applied, collaborative learning strategy among engineering students. It details the face-to-face didactic sequence and how it adapted to online sessions. During implementation, the teacher gathered observations. Students answered a questionnaire on collaborative work, its arrangements, and the role of teacher as facilitator in the collaborative learning environment at the end of the session. The findings indicate a positive assessment of collaborative online learning and reveal the challenges that arose in adapting to this teacher-student environment.
A tutorial of boomerang attack on SMALLPRESENT-[4]

Abstract In 2010, Leander proposed a new version of the block cipher PRESENT with smaller block size, namely SMALLPRESENT-[n]. This algorithm is designed to deepen understanding of how the running time of the algorithm and the vulnerability of attack during the round increase with the number of rounds and s-boxes not determined on the algorithm. The boomerang attack is a variation of the differential attack introduced by Wagner in 1999. This attack uses two differential characteristics that are mutually independent in short rounds with high probability. This paper intends to present a lucid explanation of the boomerang attack. We illustrate the practical application of the boomerang attacks on two rounds of the SMALLPRESENT-[4], that includes the steps to find differential characteristic until key recovery, and explain the concept of attack for novice cryptanalysis. Based on experiments conducted, we found a boomerang differential characteristic on two rounds of SMALLPRESENT-[4] with a probability 2 -10 and 4-bit subkey in the first round of the algorithm.
SNAP: Shaping neural architectures progressively via information density criterion

Excellent neural network architecture is built on the specific target task and device. As the target task or device is different, the neural architecture we need will be different, too. Rather than redesigning or searching a brand new one, adjusting the existing architecture automatically is an alternative yet efficient way. To this end, we propose a method to Shape the existing Neural Architectures Progressively (SNAP) to adapt the target task and device better. Inspired by the streamline of water drop shaped by air resistance, we define an information density criterion (play the role of resistance) to drive the network architecture reducing the size of the part with the lowest information density. Iteratively, a more adaptive architecture will be obtained progressively in a greedy way. Theoretically, we prove that the greedy strategy is reasonable and can shape a better architecture. Because of the small adjustment of architecture each time, new architecture can inherit the parameters in old architecture to avoid retraining it from scratch. So the proposed method is very efficient in no need of high computation cost. Experimental results show that proposed method can effectively improve the given network by adjusting its architecture. And it can generate different architectures for different tasks and devices to adapt them well. Compared with search-based auto-generated neural architectures, our approach can achieve comparable or even better performance in no need of tremendous computation resources.
Impact of Augmented Reality on learning Fused Deposition Modeling based 3D printing Augmented Reality for skill development

The aim of this study is to evaluate the impact of various modes of Augmented Reality (AR) in learning the skills associated with a 3D printing technology called Fused Deposition Modeling (FDM). Three versions of an AR application named ED-AR (Educational-AR) are created using the software: Unity 3D engine and Vuforia engine. Each version contains different levels of augmentation like meret extual instructions, demonstration videos and interactive 3D (three dimensional) model simulations. Batches of students from different engineering disciplines are exposed to self-learning using the different versions of the developed application. The results show that the version with interactive simulations helped the students to learn the 3D printing skills effectively at a faster rate and also permitted good retention of the learned procedure.
Survey on Recommendation Systems

In recent decade's recommendation systems (RSs) plays an essential role in many applications as World Wide Web. Also recommendation system is one of the most important research area in machine learning. Recommendation system functions as a helper to find the interest of users by making relevant suggestions to users. The RSs mainly use four filtering methods to provide personalized recommendations to users, the most popular ones are: Collaborative filtering (CF), Content-based filtering, Demographic filtering and hybrid filtering. Data mining is one of the important analysis techniques used in RSs to predict user interest in information, products and services among the vast amount of available items. The data mining techniques that are most commonly used in RSs are: classification, clustering and association rule discovery. This paper performs a survey on recommendation systems, techniques, challenges and issues and lists some research papers solve these obstacles, also data mining methods used in recommender systems.
A Privacy-Preserving Framework for Surveillance Systems

The ability to visually track people present in the scene is essential for any surveillance system. However, the widespread deployment and increased advancement of video surveillance systems have raised awareness of privacy to the public, i.e., human identity in the videos. The existing indoor surveillance systems allow people to be watched remotely and recorded continuously but do not prevent any party from viewing activities and collecting personal visual information of people in the videos. Because of this problem, we propose a privacy-preserving framework to provide each user (e.g., parents) with a personalized video where the user see only selected target subjects (e.g., child, teacher, and intruder) while other faces are dynamically masked. The primary services in our framework consist of a video streaming service and a personalized service. The video streaming service is responsible for detecting, segmenting, recognizing, and masking face images of the human subjects in the video. Notably, it classifies human subjects into insider and outsider classes and then applies the de-identification (i.e., masking) to those in the insider class, including the target subjects. Subsequently, the personalized service receives the visual information (i.e., masked and unmasked faces) from the streaming service and processes it at the user's mobile device. The output is then a personalized video for each user. For security reasons, we require the surveillance videos stored in the cloud in an encrypted form. To ensure an individual remains anonymous in a group, we propose a dynamic masking approach to mask the human subjects in the video. Our framework can deliver both reliable visual privacy protection and video utility. For instance, users can have confidence that their target subjects are anonymized in other views. To utilize the personalized video, users can use analytics software installed on their mobile devices to analyze the activities of their target subjects.
Design and Implementation of Distributed Video Live-Streaming System Based on SRS

Since the COVID-19 pandemic, video live-streaming technologies that ensure learning continuity have evolved rapidly. With the epidemic under control in China, a growing number of colleges classroom teaching will be online live simultaneously. Traditional offline teaching will gradually transform to the combination of online and offline mode. With the demand for the normality high-definition online live-streaming, the traditional live broadcast system faces the problems of limited scalability, poor fault tolerant ability and poor user experience. In this paper, a distributed online live-streaming system cross multiple campuses which based on SRS is designed and realized. Through dynamic distributed push, upstream and downstream traffic separation, and multi-level load balancing mechanism, the system can satisfy the need of the automatic and unattended normal video live-streaming teaching of large-scale classrooms. In this loosely coupled system, all components are distributed on each campus and communicate via message queues and HTPP API. The system has been applied to Xi'an Jiaotong University daily online and offline teaching for 700 classrooms spread over three campuses.
Heisenberg-Limited Quantum Metrology Using Collective Dephasing

The goal of quantum metrology is the precise estimation of parameters using quantum properties such as entanglement. This estimation usually consists of three steps: state preparation, time evolution during which information of the parameters is encoded in the state, and readout of the state. Decoherence during the time evolution typically degrades the performance of quantum metrology and is considered to be one of the major obstacles to realizing entanglement-enhanced sensing. We show, however, that under suitable conditions, this decoherence can be exploited to improve the sensitivity. Assume that we have two axes, and our aim is to estimate the relative angle between them. Our results reveal that the use of Markovian collective dephasing to estimate the relative angle between the two directions affords Heisenberg-limited sensitivity. Moreover, our protocol based on Markovian collective dephasing is robust against environmental noise: it is possible to achieve the Heisenberg limit by applying the collective dephasing even under the effect of independent dephasing. Our counterintuitive proposal with the decoherence leads to alternative applications in quantum metrology.
Automatic Gesture Recognizer using Motion Tracking Device and Support Vector Machine

The proposed model describes a system consisting of hardware + software recognizing the character gestures made in free space and interpret the corresponding result. Hardware devices used are NodeMCU, ESP8266 and MPU9250. MPU9250 acts as a sensor and NodeMCU acts as a controller in the system. Thus, helps in acquiring data of each alphabet, digit, and special character gestures. Data generated by hardware will be in numeric form and this numeric data is further used for analysis. The classification carried out by algorithms namely are KNN (K-Nearest Neighbours), Decision-Tree, SVM (Support Vector Machine). The performance measures are calculated using accuracy, precision, recall, and F1-Score. The comparative analysis shows that the SVM algorithm with linear Kernel provides better results with an accuracy of 91.66%, precision 92.68%, recall 92.49%, and F1-Score 92.11%.
Data Structures for Ordered Short Character-Sequences

A lexicon, or dictionary of key-value pairs, is a general abstraction that is widely used in diverse areas of computer science, notably compilers and database systems. The primary operations of interest on such lexicons are membership testing and extraction of a value associated with a key appearing in the lexicon. This paper focuses on the special case of ordered lexicons with keys that are short sequences of characters. An important motivating application is the representation of the large and growing lexicon of emoji in the Unicode standard. It presents space-efficient data structures for some specialized but practically significant cases. In particular, the methods take advantage of contiguous sequences of keys in the lexicon to yield a very highly compressed representation while maintaining efficiency in lookup operations.
Methodology for refining subject terms and supporting subject indexing with taxonomy: A case study of the APO digital repository

In digital repositories, it is crucial to refine existing subject terms and exploit a taxonomy with subject terms, in order to promote information retrieval tasks such as indexing, cataloging and searching of digital documents. In this paper, we address how to refine an existing set of subject terms, often containing irrelevant ones or creating noise, that are used to index digital documents. Further, we present how to automatically induce a subject term taxonomy to capture and utilise the semantic relations among subject terms. Most related works have little studied these problems, focusing mostly on creating subject terms or building a taxonomy of key terms from text documents. We propose a methodology2 for refining an existing set of subject terms in a digital repository by identifying their semantics, as well as inducing a taxonomy with subject terms by analysing their mutual usages, maximising their semantic relatedness. Then, we present a case study using the (Analysis & Policy Observatory) APO digital repository to analyse the proposed methodology and demonstrate its applicability. Further, to validate the generalisability of the proposed taxonomy inducing method, we evaluate it using a gold-standard taxonomy in life sciences, Medical Subject Headings (MeSH), in comparison with the state–of-the-art taxonomy inducing method, TaxoFinder. Our evaluation shows that our methodology has a high potential for refining an existing set of subject terms and capturing their semantic relationships by inducing a subject term taxonomy.