Reviewerly/exHarmony huggingface dataset:
Recognition of Assembly Instructions Based on Geometric Feature and Text Recognition

Recent advances in machine learning methods have increased the performances of object detection and recognition systems. Accordingly, automatic understanding of assembly instructions in manuals in the form of electronic or paper materials has also become an issue in the research community. This task is quite challenging because it requires the automatic optical character recognition (OCR) and also the understanding of various mechanical parts and diverse assembly illustrations that are sometimes difficult to understand even for humans. Although deep networks are showing high performance in many computer vision tasks, it is still difficult to perform this task by an end-to-end deep neural network due to the lack of training data, and also because of diversity and ambiguity of illustrative instructions. Hence, in this paper, we propose to tackle this problem by using both conventional non-learning approaches and deep neural networks, considering the current state-of-the-arts. Precisely, we first extract components having strict geometric structures, such as characters and illustrations, by conventional non-learning algorithms, and then apply deep neural networks to recognize the extracted components. The main targets considered in this paper are the types and the numbers of connectors, and behavioral indicators such as circles, rectangles, and arrows for each cut in do-it-yourself (DIY) furniture assembly manuals. For these limited targets, we train a deep neural network to recognize them with high precision. Experiments show that our method works robustly in various types of furniture assembly instructions.
From design to difference

This workshop aims to identify and generate critical pathways to more sustainable impact, that maximizes the value of IDC research in children's everyday lives. Whilst the importance of collaboration between research and practice has been well established, and notably embedded into IDC research, the most effective strategies to maximize subsequent sustainable impact are yet to be fully explored and realized. To achieve this, we need a clearer understanding of the audiences that will benefit from IDC research, and how best to reach them beyond current forms of knowledge exchange, infrastructuring, and industry links, to not only ensure sustainability, but also to foster the growth of impact. This workshop will bring together IDC community members together with practitioners and industry to identify and develop ways to sustainably maximize the impact of their work, to make a difference in children's lives, as we look ahead to design for the future.
Feasibility of Well Water as a Source of Clean Water in the Coastal Coastal Settlement Area

Well, water in coastal areas has been under heavy pressure due to various things such as increasing community needs, arid conditions (reducing water debit), contamination by various pollutants from domestic community activities, and seawater intrusion. The aim is to find out how far the well water in the maritime coastal settlement is worthy of being a source of clean water for the community. The method used is through in situ and laboratories to find out the water content in settlements, in addition to doing observations to determine the conditions around the well. The result is that some well water contents exceed the clean water standard values from physical, chemical, and microbiology tests, also found that the distance has less effect on the well water salinity levels.
4 <sup> <i>th</i> </sup> KidRec workshop - "what does good look like?"

Data about people are constantly collected and stored to help understand patterns which can then be leveraged by information retrieval systems (IRS), such as search engines or recommender systems, to identify and rank resources that respond to diverse users' needs. As a significant group of technology users, children's data are also collected for IRS. In the 4th edition of our proposed workshop, we seek to continue to build professional community connected to children's IRS and expand on the framework identified in the 3rd KidRec that outlines how to evaluate good IRS. This time, we are particularly interested in exploring how design, research, and practice perspectives can cohesively define policy in this area.
Exploring children's everyday journeys with user-generated AR

This paper describes the design of a tool to allow children to create their own Augmented Reality (AR) content as part of creative engagement with their local environment. We are exploring AR Maps (physical maps with augmented digital content) as a way of documenting children's experiences of their local area and encouraging their understanding and appreciation of objects and sites in their communities. We have piloted the approach with children in a local primary school, with positive feedback. However, children were not able to create the AR content themselves, and their physical artwork had to be scanned by researchers. In this paper we give an overview of the design context and describe the design and implementation of a tool to allow children to create their own 3D models for the AR environment by digitising their own artwork.
An end-to-end graph convolutional kernel support vector machine

Abstract A novel kernel-based support vector machine (SVM) for graph classification is proposed. The SVM feature space mapping consists of a sequence of graph convolutional layers, which generates a vector space representation for each vertex, followed by a pooling layer which generates a reproducing kernel Hilbert space (RKHS) representation for the graph. The use of a RKHS offers the ability to implicitly operate in this space using a kernel function without the computational complexity of explicitly mapping into it. The proposed model is trained in a supervised end-to-end manner whereby the convolutional layers, the kernel function and SVM parameters are jointly optimized with respect to a regularized classification loss. This approach is distinct from existing kernel-based graph classification models which instead either use feature engineering or unsupervised learning to define the kernel function. Experimental results demonstrate that the proposed model outperforms existing deep learning baseline models on a number of datasets.
Design and Development Executive Information System Application with Drilldown and What-If Analysis features

Abstract Indonesia has a big potential in the agriculture sector, paddy is the leading commodity in the food crop sub-sector. To achieve food self-sufficiency, the availability of paddy commodities information is needed to support stakeholders in making decisions. The problem presented in this paper is how to develop executive information system software that provides summarized information related to production, land area and productivity of paddy. To answer the question above, the following steps are needed: a. design and b. develop the Design process from this paper using the iconix process and result from the build process is an application Executive Information System (EIS). The application developed uses the drill-down graphics and what-if analysis facility to display land area, production, and productivity, the information displayed provides valuable insights into the industry and helps in decision making.
Banknote Identification Methodology for Visually Impaired People

Around the world, there are many people with disabilities; it is estimated that 39 million people are blind and 246 million have limited vision, giving a total of 285 million visually impaired people. The use of information and communication technologies can help disabled people to achieve greater independence, quality of life and inclusion in social activities by increasing, maintaining or improving their functional capacities. In this context, this paper presents an automatic methodology for identifying banknotes that can be widely used by people with visual impairment. For this, we evaluated a set of four point-of-interest detectors, two descriptors, seven ways of generating the image signature, and six classification methodologies, which can be used as a basis for the development of applications for the identification of banknotes. Experiments performed on US Dollar (USD), Euro (EUR) and Brazilian Real Banknotes (BRL) obtained rates of accuracy of 99.78%, 99.12%, and 96.92%, respectively.
Design of Children Monitoring Application for Outdoor Activities Using a Smart Watch

Abstract The background of this research is the increasing of criminalization in children, one of which is kidnapping of children, Meanwhile many parents have to work so they don’t have enough time to be always close to their children. The purpose of this research is to develop applications that can help a parents to monitor the movements of their children’s outdoor activities. The application uses a smart watch to get the child’s GPS position and at the same time the device sends the last position of the child to the firebase database server. Parents can monitor the child’s movements through the android smart phone device in realtime. The application design uses several UML (unified modeling Language) diagrams namely use case diagrams, activity diagrams and squence diagrams. This application requires data objects including parent, child, family and child’s position (latitude and longitude). The main use cases in this application are sending position, case view, child location on map and view path of moving each child. The application can run only if there is an internet connection and the child uses smart time.
An Empirical Evaluation of Sharpening Filters Applied to Magnified Images

Blur and other undesirable visual artifacts are often produced from simple interpolation methods when applied to magnification from low-resolution into higher resolution images. This work evaluates the effect of existing sharpening filters when applied to images obtained from interpolation in order to determine whether image quality can be improved. Additionally, a novel sharpening filter is proposed which uses information from the interpolation process to prevent the occurrence of blur. The novel filter is shown to achieve better results when compared to other sharpening filters from the literature.
Towards a Multimedia Ontology for Tunisian Archaeology Field

Archaeologists are based on different documents (e.g., excavation rapports, archives, etc.) in order to achieve their works. In fact, many experts, coming from different fields, collect the data saved on these documents; however, these data are neither related nor specialized. Therefore, for the process of structuring the knowledge implemented by the archaeology field, ontology seems to be the best solution. On the other hand, this knowledge can be presented in several forms (e.g., text, image, video, audio, etc). Indeed, the classical ontology is unable to support such multimedia content so that multimedia ontology has been appeared to overcome this issue. Actually, our ongoing research is directed to create multimedia ontology of archaeology domain which enables to describe all the Tunisian archaeological sites. We benefit from the existence of several sources that describe this domain. The development of ontology will be realized in 6 steps among of which we describe the principal ones in this paper. We will be used the MOWL as a knowledge representation language for representing the multimedia ontologies used in our experiments.
Heuristic Search in LegalTech: Dynamic Allocation of Legal Cases to Legal Staff

We investigate an allocation problem inspired by the process of assigning legal cases (matters) to staff in law firms. Addressing this problem is important as it can prevent issues around unbalanced workloads and over-recruitment, thus decreasing costs. This initial study on the topic frames the problem as a combinatorial dynamic single-objective problem (minimising tardiness) with constraints modelling staff-client relationships, staff capacities, and earliest start dates of matters. The paper motivates the allocation problem and puts it in context with the literature. Further contributions include: (i) a formal problem definition, (ii) the proposal and validation of a feature-rich problem generator to create realistic test cases, (iii) an initial analysis of the performance of selected heuristics (a greedy approach, a nature-inspired approach, and random search) on different test instances, and finally (iv) a discussion on directions for future research.
Spatiotemporal Bundle Adjustment for Dynamic 3D Human Reconstruction in the Wild

Bundle adjustment jointly optimizes camera intrinsics and extrinsics and 3D point triangulation to reconstruct a static scene. The triangulation constraint, however, is invalid for moving points captured in multiple unsynchronized videos and bundle adjustment is not designed to estimate the temporal alignment between cameras. We present a spatiotemporal bundle adjustment framework that jointly optimizes four coupled sub-problems: estimating camera intrinsics and extrinsics, triangulating static 3D points, as well as sub-frame temporal alignment between cameras and computing 3D trajectories of dynamic points. Key to our joint optimization is the careful integration of physics-based motion priors within the reconstruction pipeline, validated on a large motion capture corpus of human subjects. We devise an incremental reconstruction and alignment algorithm to strictly enforce the motion prior during the spatiotemporal bundle adjustment. This algorithm is further made more efficient by a divide and conquer scheme while still maintaining high accuracy. We apply this algorithm to reconstruct 3D motion trajectories of human bodies in dynamic events captured by multiple uncalibrated and unsynchronized video cameras in the wild. To make the reconstruction visually more interpretable, we fit a statistical 3D human body model to the asynchronous video streams. Compared to the baseline, the fitting significantly benefits from the proposed spatiotemporal bundle adjustment procedure. Because the videos are aligned with sub-frame precision, we reconstruct 3D motion at much higher temporal resolution than the input videos. Website: http://www.cs.cmu.edu/~ILIM/projects/IM/STBA.
Efficient Electricity Management System for Optimal Peak/Off-Peak Hour Pricing

With the advent of new technologies and an alarming increase in the world&rsquo;s population, there has been a rapid increase in energy consumption. Consequently, this has resulted in a surge in developing sources that generate electricity and concurrently escalating global warming levels. Owing to their contributions in vast applications, dependence on renewable energy is a reliable option. However, it is known that a complete and efficient utilization of the incoming solar radiation is not feasible, taking into account the various losses associated. Our proposal addresses concerns resulting in the efficient utilization of solar energy based on optimal cost analysis by the mathematical procedure. This methodology when used along with a battery-based photovoltaic (PV) system effectively reduces the amount of electricity imported from the grid. The implementation of this method scales down the monthly electricity consumption by 67.1%. Our findings were established considering South Korea&rsquo;s residential electricity tariff system. Our system works based on a principle where the batteries are charged with solar PV during off-peak hours and discharged during peak hours. The state of charge of the battery could be monitored using a web server. In situations, wherein the load demand cannot be sustained by the batteries, grid power can be utilized during peak hours. The sequence of these events can be implemented by a series of algorithms. Our proposed system also helps in achieving the goal-7 of the sustainable development goals (SDG) prescribed by the United Nations (UN), which is to boost the consumption of renewable energy which ultimately results in monetary savings to a large extent.
How the Apriori Algorithm Can Help to Find Semantic Duplicates in Ontology

Ontology-based data integration attempts to overcome the semantic heterogeneity problem in data integration. Semantic heterogeneity refers to an ambiguous interpretation of terms that describes the meaning of data in heterogeneous resources. However, the presence of semantic duplicates such as similar attributes in the integrated ontologies can lead to incomplete query results. This paper proposes to use the Apriori algorithm from market basket analysis to find similar attributes in an ontology.
Area and power efficient hard multiple generator for radix-8 modulo 2 − 1 multiplier

In this paper, we introduce an area and power efficient algorithm to design a hard multiple generator for radix-8 modulo 2n − 1 multiplier, which is based on parallel prefix computation of carry propagate. Only odd carry is used to generate hard multiple bits. The proposed architecture uses ⌈log2n⌉-2 prefix level with n2 prefix operators. The Post-synthesis result of proposed architecture shows 27.91%–36.89%, 9.64%–22.45% and 0.02%–88.62% of saving in area, power and PDP, respectively while post-layout result shows 27.66%–36.88%, 14.45%–33.53% and 11.40%–81.11% of saving in area, power and PDP, respectively.
Application Research of Virtual Reality and Augmented Reality

With the continuous development of information science and technology, human beings have been able to use computers to build diversified mass information space to fully express the objective world. Virtual reality technology on the basis of information modeling of the objective world, by means of human-computer interaction mechanism of multi-channel, real-time to provide realistic sensory information, help mankind to break and physical limitations of time and space, to perceive and research all kinds of complex events in the occurrence and development process of equivalent to the real environment, and human cognition of the world and to provide a new method and means. According to tend to the characteristics of different virtual reality, the virtual reality technology can be divided into the desktop virtual reality technology, the use of personal computers or desktop workstations are simulated, the virtual world of the computer screen as a participant observation window, this kind of technology in the aspect of immersive is poorer, but the cost is low, wide applicability, is the main entry point in the field of virtual reality technology into various applications. Immersive virtual reality technology: using the projection or head-mounted device build complete virtual reality environment, enables the user to fully immersed among them, the high cost of such technologies, application of pertinence, a typical system has the CAVE system based on the university of Illinois and the low stereoscopic display HMD. The augmented reality: the virtual environment of the graphic overlay on real objects, allowing the user to watch reality environment at the same time the object and the virtual environment. A more typical application is the penetration helmet-mounted display. The cost of such technologies is high, and the key technologies need to be further improved [1–2].
Improved KASUMI block cipher for GSM-based mobile networks

KASUMI is an eight-round Feistel network that produces 64-bit output from 64-bit input by using 128-bit key. Three functions FL, FO and FI are involved in each round and each function has its respective keys KL, KO and KI. In this work, modifications in the existing KASUMI are proposed to reduce the encryption time. In proposed KASUMI, the single FL function is divided into two functions such as FLleft and FLright. The FLleft function is constructed with two Rijndael’s 32-bit S-boxes. The FLright function is constructed with four stages where third and fourth stages are performing XOR and shift operations before final concatenation of two 16-bit data of FL function. The simulation results are provided in terms of encryption time taken versus number of iteration and encryption speed versus number of iteration to show that the proposed KASUMI block cipher design outperforms the conventional KASUMI block cipher.
Uncertainty with deep learning: a practical view on out of distribution detection

Discriminative neural networks eventually fail when confronted with data that was not part of the distribution of the training data samples. Moreover, out of distribution (OOD) data points are frequently ill-predicted, which can lead to misleading results and interpretation, especially in real-world applications of such decision systems. Recently, several methods had been developed to tackle this problem. These studies often rely on quality parameters, deduced by theoretical considerations, rather than real world challenges. In this work, we explain practical considerations and test five methods for OOD detection by comparing the achievable result quality and computational cost of obtaining these results.
A Lightweight Chinese Character Recognition Model for Elementary Level Hanzi Learning Application

The Chinese language is widely spoken and written by a quarter of the earth's population. Its usage is recently increased due to the rise of China as a new world power in trade and economy. This attracts new learners of Chinese and Chinese is often taught as early as elementary school in countries such as Indonesia, which regard Chinese as a new foreign trade and social language. However, without proper and continuous exercise, mastering Chinese, especially the written, is a big challenge. Previous studies has proposed and affirmed the use of information technology as a learning aid to study Chinese. They show positive results, but has left out the writing exercise section. This research proposes a modest Optical Character Recognition (OCR) model applicable to aid learning of writing Chinese characters, also known as Hanzi, for elementary education level. The goal aimed is not just the functionality, but due to its modesty, it should be able to be applied to a broader condition; in wider range of devices and by wider level of programmers. Experiment results shown that for the defined environment, the model give an acceptable accuracy of 95% in recognising handwritten Chinese characters. However, if it is planned to be applied using a more complex set of characters and writing styles, the statistical features used should be replaced and improved.
Decentralized Cryptocurrency Security and Financial Implications

The worldwide growth in electronic commerce has led to the emergence of digital currencies, which have been gaining public and research attention as an alternative method of payment. In recent years, Bitcoin has become a global currency with a multibillion USD market value. This is a result of the many attractive features of the currency including its distributed algorithms, cryptography, and incentive-driven performance. This chapter offers an in-depth investigation of the Bitcoin network along with its security and financial implications. All the components of the network are described along with details of the cryptographic foundations of the system. The chapter also describes the security and privacy implications of Bitcoin and proposes methods to minimize their effect. Finally, the chapter discusses the regulatory and financial implications of the Bitcoin network to show how countries around the world are reacting to the widespread use of cryptocurrencies.