Reviewerly/exHarmony huggingface dataset:
Research on Debris Removal Robot

This study developed a debris removal robot for the Fukushima Daiichi Nuclear Power Plant. The creative robot contest for decommissioning the nuclear reactors of the Fukushima Daiichi Nuclear Power Station is an annual competition based on problem-based learning. It aims to stimulate students’ interest in decommissioning nuclear reactors while simultaneously imparting problem-solving skills. The development of our debris removal robot started with this contest. The proposed robot can travel in a pipe, has a 3 m telescopic arm, and is equipped with a debris removal device known as a spiral catcher. This report provides an overview of this robot. We expect that complex robot development will be achieved by putting forth several ideas from various fields and fostering advanced problem-solving skills by encouraging students to address difficult tasks. Therefore, we successfully fostered the social implementation skills of students and developed a debris removal robot.
Implementation of Simplified Data Encryption Standard on FPGA using VHDL

In recent years, dramatic changed has been made in communication sector. Due to enormous development in communication devices, globally internet-connected network largely used in all human activities. The security of information has been becoming a major concern for all users and clients, whom depend on network system. The cryptography has played significant role to combat these challenges and improve confidentiality, integrity, and authentication of data communication in the network. The Data Encryption Standard (DES) is one of most familiar type of cryptography and widely used in the modern network system, which has been adopted in encryption and decryption a digital information for several decades. The DES is replaced by a number of new cryptographical methods, which based on DES, like AES and 3DES. In the same time some hardware tools have gained a lot of attention and become interested for researchers and academics to design and implement their model proposals with these hardware-based tools. Therefore, this paper, shows the design of a Simplified Data Encryption Standard (S-DES) by using VHDL language. The design is synthesized, compiled and implemented on the FPGA Altera board, which, consists Quartus II software environment, and Altera Cyclone IV 4CX150FPGA device. The S-DES has been successfully implemented with few numbers of logic elements.
Real-Time Automatic Translation Algorithm for Chinese Subtitles in Media Playback Using Knowledge Base

Currently, speech technology allows for simultaneous subtitling of live television programs using speech recognition and the respeaking approach. Although many previous studies on the quality of live subtitling utilizing voice recognition have been proposed, little attention has been paid to the quantitative elements of subtitles. Due to the high performance of neural machine translation (NMT), it has become the standard machine translation method. A data-driven translation approach requires high-quality, large-scale training data and powerful computing resources to achieve good performance. However, data-driven translation will face challenges when translating languages with limited resources. This paper’s research work focuses on how to integrate linguistic knowledge into the NMT model to improve the translation performance and quality of the NMT system. A method of integrating semantic concept information in the NMT system is proposed to address the problem of out-of-set words and low-frequency terms in the NMT system. This research also provides an NMT-centered read modeling and decoding approach integrating an external knowledge base. The experimental results show that the proposed strategy can effectively increase the MT system’s translation performance.
SPCABS: Signature-Policy Comparable Attribute-Based Signatures

Attribute-based signature is an attractive cryptographic primitive and finds broad applications in many fields. Existing attribute-based signature schemes deal with attributes in the way of “with” or “without,” and there is no attribute-based signature scheme that supports comparing attributes. Using the 0-encoding and 1-encoding, we propose an access structure algorithm and an attribute expansion algorithm, enabling the attribute-based signature scheme to effectively deal with the comparative attributes. Then, we propose a signature-policy comparable attribute-based signature scheme using the proposed expansion algorithms. The proposed scheme is existentially unforgeable under the computational Diffie–Hellman exponent (CDHE) assumption and achieves privacy in the sense of information theory. Theoretical analysis and simulation experiments show that our method is practical and has significant advantages in storage and computation overhead compared with the trivial way. Comparable attribute-based signature dramatically expands the application scenarios of attribute-based signature.
An Improved Quality Evaluation Method for Foreign Trade English Using GA-RBF Neural Network

The teaching evaluation of foreign trade English has been in a quite difficult position because some universities have foreign trade courses, but there is no corresponding foreign trade practice base, so the evaluation of this course is currently relatively simple. This research provides an improved BP neural network evaluation method to address the issues of single teaching evaluation and poor accuracy rate of English as foreign trade in English courses. First, an improved genetic algorithm is utilized to obtain the weight factor of the neural network, which is the data input of the neural network. Second, the middle layer of the network is optimized, so that the output efficiency can be further improved. Finally, the improved and optimized neural network is simulated. The experimental simulation shows that the method proposed in this paper has an energy-efficient and objective evaluation of the quality of foreign trade English teaching with certain accuracy.
<i>K</i>-CDFs: A Nonparametric Clustering Algorithm via Cumulative Distribution Function

We propose a novel partitioning clustering procedure based on the cumulative distribution function (CDF), called K-CDFs. For univariate data, the K-CDFs represent the cluster centers by empirical CDFs and assign each observation to the closest center measured by the Crame´r-von Mises distance. The procedure is nonparametric and does not require assumptions on cluster distributions imposed by mixture models. A projection technique is used to generalize the K-CDFs for univariate data to an arbitrary dimension. The proposed procedure has several appealing properties. It is robust to heavy-tailed data, is not sensitive to the data dimensions, does not require moment conditions on data and can effectively detect linearly nonseparable clusters. To implement the K-CDFs, we propose two kinds of algorithms: a greedy algorithm as the classical Lloyd’s algorithm and a spectral relaxation algorithm. We illustrate the finite sample performance of the proposed algorithms through simulation experiments and empirical analyses of several real datasets. Supplementary files for this article are available online.
Mobile Interface Design for Job Competency-based Blind Resume

Objectives The purpose of this study is to design a mobile interface for creating blind resumes based on job competency.&#x0D; Methods The research methods carried out to realize the research purpose were literature study, case study, rapid prototyping, and expert evaluation. Literature study and case study collected and analyzed related literature and cases related to the research topic, and used it to devise the design principle. In addition, the development model for interface design utilizes rapid prototyping that can be modified and developed immediately upon receiving the end user's request. Expert evaluation was carried out to find defects in the initial interface and to develop them in a completed form by correcting them.&#x0D; Results The mobile interface of the job competency-based blind resume is largely composed of three areas. First, as a general area, there is a section where a user can sign up and browse necessary information. Second, it is a part where the user's job competency can be left professionally, and it is composed of an area to analyze one's job competency, an area to describe a desired job, an area to explore job-related experiences, and a self-diagnosis test area. Third, it includes the area where such information can be used to receive consulting from experts in related fields.&#x0D; Conclusions The job competency blind resume should be composed of contents that can exclude individual specifications such as academic background, history, appearance, family relationships, etc. It should be composed of areas that can receive counseling and consulting from experts in order to develop.&#x0D;
Explorations of Interactive Research Artifacts in Use

New technologies and digitization have the potential to vastly alter our knowledge infrastructures. Specifically, this work focuses on the effects of interactive technologies on research practices, referred to as “interactive research artifacts.” Current research investigates the communicative affordances of such technologies, but minimal work critically examines the creative ways scholars are engaging with these artifacts. Through in-depth interviews with 14 scholars, and Design Studies literature, this work arrives at an understanding of interactive research artifacts as knowledge creation tools, rather than simply communicative tools. As such, to design for a future where interactive research artifacts become widely used scholarly tools, a comprehensive understanding of the ways in which these artifacts are used as means of knowledge creation is critical.
Fashion and Sustainable Practices: Fashionable Companion Robots

This abstract outlines the research trajectory during my doctoral study, including completed work and future plans. My primary research focus is fashion and sustainable practices in the context of companion robots. After exploring people’s spontaneous behaviors of dressing up their robots, I want to further extend the connotation of robot fashion and use fashion and sustainability as frameworks to explore how people treat their companion robots.
Abstract 6329: Using low-resolution, low-cost histopathology images to predict esophageal squamous cell carcinoma via deep learning

Abstract Like many cancers, esophageal squamous cell carcinoma, has a substantially better survival rate if detected in early stages, making early detection a critical element in successful treatment. On the other hand, deep learning tools that employ high-resolution whole slide images and therefore substantially greater compute power and are not easily accessible in low-resource settings. In this study we train and test deep learning models using low-resolution and low-cost digital histopathology images acquired using a 5-megapixel Leica ICC50 camera mounted on a Leica DM500 binocular microscope, at two different magnification levels, Under IRB approvals, we acquired a total of 64 Hematoxylin and Eosin (H&amp;E) stained tissue slides of biopsies taken from patient presenting at Rehman Medical Institute from 2016 to 2020 that were anonymized. A total of 2370 images were captured at two different magnification levels, i.e. 10x and 40x, and were labelled with four classes by at least one expert pathologist (677 well differentiated, 1066 moderately differentiated, 152 poor differentiated and 475 normal) to develop the ESCC image dataset. Preprocessing was performed to clean the dataset, generate patches and resize the images. Deep learning models were trained on the ESCC image dataset for feature extractions. This was followed by machine learning classifier for the diagnosis of ESCC. Deep learning models that were used in this study are Resnet50, VGG16, VGG19, and Inception_Resnet_V2 with Logistic Regression (LR) used as classifier. An ensemble model which stacks up the Inception and Resnet. This model preformed with the highest accuracy at 96% with Precision, Recall, and f1 score values at 96%, 96%, and 96%, respectively. This study reports 1) a local ESCC image repository for research purposes, 2) an ensemble ML/DL model that predicts ESCC with 96% accuracy and 3) also a computer-aided diagnosis (CAD) system for ESCC with a user-interface which will assist histopathologists in diagnosing ESCC with higher speed, efficiency and accuracy. Citation Format: Mansoor Alam, Ibrar Amin, Ayesha Sajjad, Hina Zamir, Saad Zaheer, Maria T. Khattak, Iqbal Muhammad, Faisal F. Khan. Using low-resolution, low-cost histopathology images to predict esophageal squamous cell carcinoma via deep learning [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6329.
Colored Edge Detection Using Thresholding Techniques

Background: In this research, a novel algorithm is formulated through the combination of gradient and adaptive thresholding. A set of 5 X 5 convolution kernels were generated to determine the gradients in the four main directions of the image Objectives: The researcher converted the gaussian equation into a normalized kernel, which was convolved with the gradients to suppress the impact of noise Methods: The edges derived were partitioned into a set of 5 x 5 matrices. A weighted variance was calculated for each local window in the image. The pixel that generated the minimum variance was used for the segmentation process in each local window. The researcher then trimmed multiple pixel width edges into singles by developing a set of 5 X 5 Structuring Elements (SE). These elements were placed over the image to remove boundary pixels. In order to produce colored edges, the algorithm was executed over all the channels and the results were concatenated to produce the skeletal colored edges. Results: From the evaluations conducted, the proposed algorithm exhibited better performance than most of the recent algorithms with respect to Human Perception Clarity and time complexity in both noisy and nonuniform illuminated images. Conclusion: The reason for this performance is that it is able to extract edges moving in the various directions of images. It also ensures that identified edges are single pixel width instead of multiple.
Exploring the Narrative Construction of Situated Co-Speculation through Fiction Collages

In this paper, we present our exploration of using and interpreting a generative tool, fiction collages, which integrates different perspectives in a materialized form and constructs narrative design ideas through a co-speculation process. We conducted a workshop with a research group of multidisciplinary experts who were ideating techno-social interventions for Yunlin, an aging county in Taiwan. The purpose of collaging is to provide a malleable but robust metaphor that stays open to interpretation. Four types of visual materials, e.g., app icons, everyday objects, scenes photos, and local publications, are served as content for narrative construction. The results illustrate how the generated collages facilitate situated co-speculation through both solo- and co-narration among experts. The juxtaposed collages and elicited stories as a whole construct the design fiction for near-future Yulin. To conclude, the study contributes to the community with a narrative way of using collages for co-speculation that aims for mutual informing.
A Study on the Relationship Between Manufacturing–R&amp;D Integration and the Customer Satisfaction: Focusing on the Role of a Cross-Functional Team and Manufacturing Flexibility

Given that the small and medium-sized enterprises (SMEs) are working in an increasingly competitive business environment with limited resources, manufacturing flexibility can be a vital source of a competitive advantage. Thus, having strong attention to manufacturing flexibility as a good means for improving the competitiveness of SMEs in the Korean manufacturing industry, this study investigates 1) the relationship between cross-functional integration (CFI), manufacturing flexibility, and customer satisfaction and 2) the role of a cross-functional team (CFT) made up of employees from different functional departments. From a large-scale sample of 504 SME data, this study empirically confirms that a higher level of integration between manufacturing and R&amp;D tends to improve manufacturing flexibility, which ultimately increases customer satisfaction. Moreover, even when manufacturing-R&amp;D integration is insufficient, forming a CFT secures a high level of manufacturing flexibility, which implies that a CFT officializes the need for the concentration and orchestration of a firm's diverse competence and helps to cultivate capabilities for enhancing the organizational performance.&#x0D;
Exploring Bodily Heirlooms: Material Ways to Prolong Closeness Beyond Death

The body plays an important role in building and maintaining emotional relationships. When a loved one dies, it is inevitable that their body will decease, meaning that physical closeness can no longer be experienced. In this paper, we engage with material ways to extend experiences of physical and emotional nearness beyond death. We explore the design space of 'bodily heirlooms': crafted, precious, interactive objects designed to pass on bodily impressions beyond lifespan. We review cultural practices that intend to bequeath the body, and related work that addresses death, bereavement and digital heirlooms. After that, we describe the design and making process of three bodily heirlooms, reflections in and on action, and reactions of visitors who inspected the heirlooms during an exhibition. We conclude by discussing notions of heirlooms' preciousness, the emotional bond between heirlooms and their maker and how the process can be understood as a feminist practice.
Blockchain Technology in Digitizing Bancassurance

Bancassurance was initiated as a supplementary medium for dispensing insurance-related products and services. The proposal was to make certain auxiliary profits for banks exclusive of the necessity to inculcate extra funds in a period when restrictions were under force and credit off take was unresponsive. Insurance firms thus had the time to amplify the market penetration for them. On the other hand, online and cybernetic intrusion in the banking sector and the inception of Bancassurance were nearly associated. The chapter will emphasize the need for a customer-centric approach and recognize the tactic desired for giving a boost to the Bancassurance business model through innovations and intensification of the digital infrastructure of banks. The recommendation is that rather than solely relying on digital, Bancassurance will benefit from the flawless amalgamation of omni channels (digital and physical) to contact customers.
Movement interaction with a loudspeaker: an index of possibilities

Creating sound by manipulating the location of sound-producing objects using gestures is an interesting interaction paradigm. To understand it better, we analyzed videos of users interacting with ‘Random Access Lattice’. In this sound installation, users move a loudspeaker to explore sound laid out in space using a time to space mapping. We performed an inductive analysis of user movement in relation to the visitor intention and the sonic outcome. We identified several body, hand, and grip gestures which were performed with different movement qualities to manipulate the loudspeaker at variable speeds, orientations, and body areas. These were used to search and trace the sonic material in a goal-oriented fashion but also to interact creatively with sound by looping and modulating sounds. We provide a visual index of our findings which can be used when designing gesture interactions with sound.
Secure Algorithm for IoT Devices Authentication

Internet of Things (IoT) security is a major concern owing to the sensitive data that flows in these networks. The fifth generation (5G) network provides high bandwidth, endearing it as an ideal underlying network for IoT communication. In addition, 5G can facilitate seamless integration of 2G, 3G, 4G, and WiFi to realize faster services, high capacity, and very short latencies. Although 5G features such as high bandwidth and seamless integration are ideal for IoT implementations, the underlying network is vulnerable to attacks such as eavesdropping, de-synchronization, sink hole, denial of service (DoS) and replay attacks, among others. To address these challenges, a number of protocols based on techniques such as elliptic curve cryptography (ECC), trusted authority, quantum cryptography, public keys, private keys, pseudonymous certificates, group handover authentication, multi-signature, and aggregate message authentication code (AMAC) technology have been proposed. Unfortunately, these protocols either have high computation and communication costs or do not provide robust security required for IoT devices communication. This renders them inefficient and susceptible to attacks such as impersonation, privacy and location sniffing, eavesdropping, session key disclosure attacks, modification, and insider attacks. Consequently, there is need for an efficient and secure key agreement and session authentication protocol for IoT deployments. In this paper, an efficient and secure handover protocol for IoT devices is proposed. The simulation results showed that this protocol exhibited lower computation and turnaround time, high stability, and moderate communications costs. It was also demonstrated to be robust against masquerading, packet replay, eavesdropping, free riding attacks, privacy and location sniffing.
Kronecker Decomposition for Knowledge Graph Embeddings

Knowledge graph embedding research has mainly focused on learning continuous representations of entities and relations tailored towards the link prediction problem. Recent results indicate an ever increasing predictive ability of current approaches on benchmark datasets. However, this effectiveness often comes with the cost of over-parameterization and increased computationally complexity. The former induces extensive hyperparameter optimization to mitigate malicious overfitting. The latter magnifies the importance of winning the hardware lottery. Here, we investigate a remedy for the first problem. We propose a technique based on Kronecker decomposition to reduce the number of parameters in a knowledge graph embedding model, while retaining its expressiveness. Through Kronecker decomposition, large embedding matrices are split into smaller embedding matrices during the training process. Hence, embeddings of knowledge graphs are not plainly retrieved but reconstructed on the fly. The decomposition ensures that elementwise interactions between three embedding vectors are extended with interactions within each embedding vector. This implicitly reduces redundancy in embedding vectors and encourages feature reuse. To quantify the impact of applying Kronecker decomposition on embedding matrices, we conduct a series of experiments on benchmark datasets. Our experiments suggest that applying Kronecker decomposition on embedding matrices leads to an improved parameter efficiency on all benchmark datasets. Moreover, empirical evidence suggests that reconstructed embeddings entail robustness against noise in the input knowledge graph. To foster reproducible research, we provide an open-source implementation of our approach, including training and evaluation scripts as well as pre-trained models.1