Reviewerly/exHarmony huggingface dataset:
Convolutional Neural Network Optimization Using Modified NSGA-II

Convolutional Neural Networks (CNN) are becoming deeper and deeper. It is challenging to deploy the networks directly to embedded devices because they may have different computational capacities. When deploying CNNs, the trade-off between the two objectives: accuracy and inference speed, should be considered. NSGA-II (Non-dominated Sorting Genetic Algorithm II) algorithm is a multi-objective optimization algorithm with good performance. The network architecture has a significant influence on the accuracy and inference time. In this paper, we proposed a convolutional neural network optimization method using a modified NSGA-II algorithm to optimize the network architecture. The NSGA-II algorithm is employed to generate the Pareto front set for a specific convolutional neural network, which can be utilized as a guideline for the deployment of the network in embedded devices. The modified NSGA-II algorithm can help speed up the training process. The experimental results show that the modified NSGA-II algorithm can achieve similar results as the original NSGA-II algorithm with respect to our specific task and saves 46.20% of the original training time.
Automated identification of insight seeking behaviours, strategies and rules: a preliminary study

In this paper, we demonstrate how insight seeking strategies and rules can be captured from analyst interactions with a question-answer system, as they perform an investigation. We present our analysis of an interactive investigation exercise undertaken by 14 experienced intelligence analysts. We propose that our approach to model the abstract higher order cognition involved in insight seeking provides a means to design intelligent systems that can reward and optimise potential lines of inquiry, ultimately creating the environment from which insights can be derived.
Strengthening Disaster Literacy as An Effort to Reduce The Risk of Disaster in D.I. Yogyakarta Society

Abstract This research aims to find out how to optimize GLS in building awareness of disaster literacy, the role of families in GLS to encourage the disaster literacy movement, and opportunities to strengthen disaster literacy in the digital era. The research subjects are the society and schools in D.I. Yogyakarta area. The research method used is literature study, data collection techniques are carried out by reviewing several books, literature, and other documents that are appropriate to disaster literacy studies. The results showed that optimizing the school literacy movement directed towards disaster literacy awareness can increase public understanding, disaster preparedness and can reduce the risk of disasters that will cause material and non-material losses. The strategy and pattern of implementing the school literacy movement includes three stages, namely: habituation, development, and learning activities. The role of the family will contribute to disaster literacy that can provide experience and knowledge of the society according to the conditions of the residence. Technological advances are also able to contribute to the society as literacy materials and provide information quickly and accurately related to natural disasters.
Incremental Update of Knowledge Graph Embedding by Rotating on Hyperplanes

Knowledge graph embedding (KGE) plays an important role in downstream tasks, such as question answering, recommendation system, and entity recognition. Most existing KGE methods focus on modeling static knowledge graphs. However, many knowledge graphs are incremental in reality. Existing KGE methods are time-consuming to update the embedding space incrementally, and have difficulty in keeping the timeliness of the knowledge graph embedding. To address this problem, we propose a novel knowledge graph embedding method by rotating on hyperplane (RotatH), which supports updating the embedding space incrementally and ensures the timeliness and accuracy of knowledge graph embedding. Specifically, our proposed method first employs relation-specific hyperplanes to update the incremental entities into the trained vector space efficiently. Meanwhile, by combining hyperplane and rotation, our method can deal with complex relations, such as many-to-many and symmetry relations, and has high performance in both incremental and static environments. Moreover, our method introduces a mean-based method to constraint the density of incremental entities. We conduct extensive link prediction experiments on two real-world incremental datasets and two benchmark datasets. The experimental results show that our model incrementally updates embedding space efficiently and outperforms static models on benchmarks.
Target Assignment of Heterogeneous Multi-UAVs Based on Pigeon-Inspired Optimization

Aiming at the problem of heterogeneous multi-UAVs cooperative target assignment, this paper studies the mechanism and strategy of multi-aircraft cooperative target assignment, and comprehensively considers the UAV’s range constraints, load constraints, and task execution timing constraints, and proposes a pigeon group optimization algorithm solution to the task assignment problem. Propose a new encoding and decoding method. Remove the constraint information of the UAV during encoding, reduce the generation of invalid solutions, and select the optimal execution solution for each target during decoding; use the PBX (Position-based Crossover) operator to discretize the pigeon-inspired algorithm to improve the algorithm efficiency and accuracy. The simulation results show that the discrete pigeon-inspired algorithm can effectively solve the problem of heterogeneous UAV target assignment under multiple constraints. Compared with genetic algorithms, the task completion is higher and the consumption cost is reduced by about half.
Spontaneous Utilization: A Classic Grounded Theory of Utilizing Ambient Displays in Professional, Large-Scale Agile Software Development Environments

This research addresses an interdisciplinary problem area concerning the long-term deployment of socially embedded technology in authentic environments. It concentrates on the case of ambient displays, where long-term research in the real world is still scant and evidentially requires methodological development. This study seeks to advance on this situation at both findings and methodological levels. To this end, we introduce our Ambient Surfaces solution that was deployed in the agile software development department of a company for circa 5 years. Classic grounded theory was chosen to methodologically guide the evaluation, while the theoretical contribution of this work is a substantive theory exemplified by its core category of Spontaneous utilization. The theory reveals insights on how ambient displays are utilized by practitioners in professional and large-scale agile environments. We found, among others, that staff members used the Ambient Surfaces largely not on purpose, that our solution evolved toward having a strong emphasis on progress tracking information, and that inter-team awareness as well as intra-team communication were encouraged.
Highly precise measurement of depth for μLED based on single camera

Highly precise measurement of depth in micro meter is very important in many fields. Many methods measuring depth for spherical objects have been proposed. However, few of them can measure the depth for plane objects, such as μLED, which has advantages in brightness, power consumption and response speed. The depth measurement is one of the key technology for the transfer printing of μLED. In this paper, we proposed a method measuring depth for μLED array using single camera. The proposed method was calibrated by a precision stage. The uncertainties, such as camera distortion, camera tilt, curve fitting and surface roughness of μLED were estimated. The results showed that the relative standard uncertainty in depth position was about 2.2%. This showed that the proposed system was very stable and repeatable.
Development of a Board Game Using Mixed Reality to Support Communication

Ice break has been attracting attention as a tool for enhancing communications during the first encounter. Previous research showed that collaborative tabletop games are effective for ice break. However, few research discuss the factors of these games that influence this enhancement. In this research, we developed a tabletop game where a piece on a board is manipulated by inclining the board with four levers to navigate the piece to follow the predefined path. Our hypothesis is that communication may be enhanced by counter-intuitive behavior of the piece, such as climbing up the slope that is against the laws of physics on the earth. Though what the players manipulate is the real levers, what they see is virtual board and piece rendered through MR device (HoloLens2). In this research we have implemented two behaviors of the piece, one is normal gravity behavior where the peace goes down the slope while the other is anti-gravity behavior where the peace climbs up the slope. To evaluate the effect of the counter-intuitive behavior on the enhancement of the communication, we have conducted experiment where the pair of players played the game under two different conditions. The results of the five-point Likert scale questionnaire demonstrated that the counter-intuitive behavior had positive effect on the freshness and interestingness of the game while it had no effect on the difficulty of the cooperation. However, it was confirmed that the verbal communication between the players increased 6.7% in average which partially support our hypothesis.
Depth Estimation of Martian Surface using ISRO’s MARS Color Camera Images

Depth estimation from satellite images is a fundamental task when studying the terrain of a cetestiat body. Existing solutions for depth estimation often use datasets that include either images of an indoor scene or images related to (self-driving). This paper presents a Pix2pix type of neural network for computing the depth of the Martian surface using the images captured by IRSRO’s Mars Orbiter. In this paper, we have implemented Pix2Pix (Generator Discriminator) architecture using two different toss functions-LSGAN and Vanilla toss functions and then compared their results to select a better toss function for computing accurate depth maps. We also created our own dataset using images provided by ISRO that contains 343 Training Images and 37 testing images.
Automatic Notes Generation from Lecture Videos

Videos are an unavoidable part in academia. In day-to-day lives of either students or professors, we notice that we tend to refer to many online resources, majority of which are videos, as part of our learning process. This paper proposes a method to generate notes and summarized PowerPoint Presentations for non-blackboard style (i.e. videos without usage of images, diagrams or graphical media) lecture videos. The different phases involved are audio extraction from the uploaded video, speech recognition, punctuating the transcript, applying Natural Language Processing (NLP) techniques on the generated transcripts, generating Portable Document Format (PDF) from the text and creating PowerPoint Presentation by extracting important points from the text using K-means clustering and LexRank extractive text summarization.
Adaptive multi-task learning for cross domain and modal person re-identification

Person re-identification (re-ID) aims at matching a person-of-interest across various non-overlap cameras with distinguished visual appearance variances. Pre-existing research methods mainly employ deep neural models to train large-scale person re-ID datasets, achieving good performance. However, these methods are primarily deployed only on visual data, which can be easily influenced by the environment variances (e.g., viewpoints, poses, and illuminations). In this paper, we propose an adaptive multi-task learning (MTL) scheme for cross domain and modal person re-ID. It can effectively utilize the visual and language information from multiple datasets for improving learning performance. Comprehensive experiments are also conducted on the widely-used person re-ID datasets, i.e., Market-1501 and DukeMTMC-reID, validating the effectiveness of the proposed method. It can model the domain difference and the relationship between the vision and language modalities and achieve state-of-the-art performance. The source code of our proposed method will be available at https://github.com/emdata-ailab/Multitask_Learning_ReID.
Blockchain for IoT Security and Privacy

The Internet of Things (IoT) is increasing day by day as time is succeeding continuously. A rapid improvement is seen in the research and industrial area, yet it meets all promises of security that could be used to mislead it in a wrong way. Security as well as protection approaches are not able to be applied for IoT purpose, mostly because of not having any central area of command and that's the most important rule of most of the devices. Blockchain which supports the secret computer codes i.e., Bitcoin can be used to give security and protection to different distributed systems that is used to serve comparison between things used in IoT. In many cases Blockchain are computer based and expensive too, yet they include high ability of transmission to hold something and to also delay putting something which is not reasonable for any IoT devices. This paper proposes a basic structure of blockchain technology that can be used to improve security and privacy to manage IoT devices and different networks. IoT which is considered as identifying someone's identity is done through servers by leading the privacy and security concern. There are chances of less reliable data, false verification and spoofing in data sharing. IoT security and privacy concerns are mainly challenged because of its massive distribution of networks in all sectors
Factoring the Modulus of Type N = p2q by Finding Small Solutions of the Equation er − (Ns + t) = αp2 + βq2

The modulus of type N=p2q is often used in many variants of factoring-based cryptosystems due to its ability to fasten the decryption process. Faster decryption is suitable for securing small devices in the Internet of Things (IoT) environment or securing fast-forwarding encryption services used in mobile applications. Taking this into account, the security analysis of such modulus is indeed paramount. This paper presents two cryptanalyses that use new enabling conditions to factor the modulus N=p2q of the factoring-based cryptosystem. The first cryptanalysis considers a single user with a public key pair (e,N) related via an arbitrary relation to equation er−(Ns+t)=αp2+βq2, where r,s,t are unknown parameters. The second cryptanalysis considers two distinct cases in the situation of k-users (i.e., multiple users) for k≥2, given the instances of (Ni,ei) where i=1,…,k. By using the lattice basis reduction algorithm for solving simultaneous Diophantine approximation, the k-instances of (Ni,ei) can be successfully factored in polynomial time.
Named Entity Recognition for Entity Linking: What Works and What's Next.

Entity Linking (EL) systems have achieved impressive results on standard benchmarks mainly thanks to the contextualized representations provided by recent pretrained language models. However, such systems still require massive amounts of data – millions of labeled examples – to perform at their best, with training times that often exceed several days, especially when limited computational resources are available. In this paper, we look at how Named Entity Recognition (NER) can be exploited to narrow the gap between EL systems trained on high and low amounts of labeled data. More specifically, we show how and to what extent an EL system can benefit from NER to enhance its entity representations, improve candidate selection, select more effective negative samples and enforce hard and soft constraints on its output entities. We release our software – code and model checkpoints – at https://github.com/Babelscape/ner4el.
Mode connectivity in the loss landscape of parameterized quantum circuits

Variational training of parameterized quantum circuits (PQCs) underpins many workflows employed on near-term noisy intermediate scale quantum (NISQ) devices. It is a hybrid quantum-classical approach that minimizes an associated cost function in order to train a parameterized ansatz. In this paper we adapt the qualitative loss landscape characterization for neural networks introduced in Goodfellow et al. (2014); Li et al. (2017) and tests for connectivity used in Draxler et al. (2018) to study the loss landscape features in PQC training. We present results for PQCs trained on a simple regression task, using the bilayer circuit ansatz, which consists of alternating layers of parameterized rotation gates and entangling gates. Multiple circuits are trained with 3 different batch gradient optimizers: stochastic gradient descent, the quantum natural gradient, and Adam. We identify large features in the landscape that can lead to faster convergence in training workflows.
A Study on the History and Future Direction of Fishery Science in Korea

The first educational institution of fishery science in Korea was the Simplified Fishery School established in 1910, where the subjects of the education were mostly for fishery technologies. In 1941 the Busan Fisheries High School has been established as a higher fisheries educational institution, and the formal fisheries education was initiated for the first time for teaching major subjects of fishery science. As secondary educational institutions, fisheries high schools were established to teach elementary level of fishery science starting in 1950s, however, those institutions have been partially replaced by higher edutional institutions since 1960s. Off-school fisheries educational institutions were also established since 1965, for example, the Korea Fishing Technology Training Center has trained fishermen and general public for basic fisheries education. Recently, the United Nations has made efforts to maintain sustainable marine food resources, such as setting the Sustainable Development Goal(SDG) 14, which is a new paradigm for the conservation of marine ecosystems and the sustainable use of fisheries resources. However, the effort to comply to this new paradigm is not sufficient in Korea. In this study future direction to the education of fishery science was suggested to comply with the new global paradigm. First, the education goal should be adjusted to the new paradigm of the ecosystem-based fisheries approach to management, considering not only fishery in itself, but also it’s environment of the ecosystem. Second, the multi-disciplinary integrated education is necessary to acquire advanced widened knowledge of the ecosystem-based fisheries approach. Third, the contents of education such as curriculums for the new education scope, should be developed using Knowledge-Information Matrix approach. Finally, the advanced fishery science education system should be established based upon the new global paradigm, recognizing three suggestions above.
Hierarchical Graph Neural Nets can Capture Long-Range Interactions

Graph neural networks (GNNs) based on message passing between neighboring nodes are known to be insufficient for capturing long-range interactions in graphs. In this paper we study hierarchical message passing models that leverage a multi-resolution representation of a given graph. This facilitates learning of features that span large receptive fields without loss of local information, an aspect not studied in preceding work on hierarchical GNNs. We introduce Hierarchical Graph Net (HGNet), which for any two connected nodes guarantees existence of message-passing paths of at most logarithmic length w.r.t. the input graph size. Yet, under mild assumptions, its internal hierarchy maintains asymptotic size equivalent to that of the input graph. We observe that our HGNet outperforms conventional stacking of GCN layers particularly in molecular property prediction benchmarks. Finally, we propose two benchmarking tasks designed to elucidate capability of GNNs to leverage long-range interactions in graphs.
Robust Control for Autonomous Surveillance with Unmanned Aerial Manipulator

Unmanned aerial manipulator (UAM) that integrates an unmanned aerial vehicle (UAV) and an aerial manipulator extends the capability of the underlying UAV to a wide range of potential applications, such as flexible inspection, interactive manipulation, and scientific sampling. Typical challenges faced by UAM surveillance include maintaining a surveillance distance, eliminating disturbances caused by model uncertainties and the external environment, and adjusting the camera’s orientations. Focused on the development of autonomous control for UAM during a process of flexible inspection, this paper presents a novel control scheme for autonomous inspection that is required to cope with model uncertainties and external disturbances. First, surveillance waypoints are obtained with respect to inspective objects and the radius of geometric relationship. In particular, the desired linear velocities are obtained through consideration of the angular velocity of the surveillance and the objective inspective radius. A robust controller is designed to ensure the tracking performance of the given UAM. Second, a disturbance observer is adopted to eliminate the model uncertainties and external disturbances. Finally, according to inspection requirements, the orientation of the onboard manipulator is adjusted so that the end effector (camera) of orientation is towards the object sustainably. The stability analysis of the proposed controller is provided. Experimental simulations demonstrate that the proposed approach enables the UAM to accurately track prescribed waypoints.
Identification and prioritization of security challenges of big data on cloud computing based on SLR: A fuzzy‐TOPSIS analysis approach

Abstract Nowadays, data is increasing exponentially, although cloud computing is an eminent approach for the organization, processing, and availability of data for organizational growth over the internet. Besides, a lot of advantages of cloud computing, yet it is suffered from security challenges, which affect big data while using cloud services on the internet. For this purpose, we conducted a detailed systematic literature review (SLR) study, to identify and capture the security challenges of big data on the cloud computing platform. Our research findings determine and develop a taxonomy, based on the prioritization of the security challenges of big data on cloud computing. We identified a total of 15 critical security challenges using the proposed SLR, with the frequency of each challenge &gt;25%, and are further validated by the industrial specialists using a questionnaire survey study. The identified security challenges are data secrecy issue, geographical data location issue, unauthorized data access issue, lack of control, lack of data management, network‐level issues, data integrity issue, data recovery issue, lack of trust, data sharing issue, data availability, asset issues, legal amenabilities, lack of quality issues, and lack of consistency. The security challenges identified and captured through the SLR study are categorized into four levels, namely, steadiness, management, control, and eminence. Conclusively, we applied the fuzzy‐TOPSIS approach to prioritize and identify the significance of each identified security challenge for the big data usage on cloud computing. Based on our proposed approach, the “data secrecy issue” has been identified as the most prominent security challenge with the captured value of “0.765.” The fuzzy TOPSIS is an effective and innovative research approach in the field of computer science. It has been applied positively to other research areas to address and identify the fuzziness and uncertainty of multiple decision‐making glitches. The findings of the research paper will assist the software vendor organization when using the cloud platform for big data security. Also, using the proposed approach, software vendors can prioritize and analyze the uncertainty and ambiguousness among these security challenges.