Reviewerly/exHarmony huggingface dataset:
Fringe Projected Profilometry on a Mobile Phone

Until now, various kinds of methods for 3D modelling have been proposed and some have come into use. However, current available approaches generally require a precise and huge set-up, which greatly limits the using scenario. To get rid of the limit of equipment and enable convenient measurement, we propose a method to reconstruct a 3D model using fringe projected profilometry carried out on a mobile phone, taking advantage of the now high developed computation ability of smart phone. A fringe pattern can be projected by a mini-projector and the deformed fringe pattern is captured by the camera of a mobile phone. Through phase retrieval and calibration, the point cloud can be calculated and displayed on the mobile phone. The interface of the devices and the algorithms of the system are researched and implemented. Experiment results have proved the effectiveness of the system and the system can thus bring 3D modelling to a much larger application scenario.
PENDIDIKAN BERBASIS TEKNOLOGI (PERMASALAHAN DAN TANTANGAN)

Educational technology is designed to help solve educational problems, so as to be able to provide benefits in an effort to improve the quality of learning. Various forms of learning experiences, both those that can be achieved in the classroom and outside the classroom and learning messages, can be packaged by taking into account the principles and principles of educational technology. With the use of learning technology it is expected that educational messages can be packaged more systemically-systematically in both physical and virtual packaging, which is no longer limited by the dimensions of space or time, so that students can be accepted well, easily, and extensively, and create pleasant education. flexible in the dimensions of time, space, and developing the potential of students individually. In addition to providing benefits, technology in the field of education also presents several obstacles that have a positive impact on education.
Supervised Learning of the Global Risk Network Activation from Media Event Reports

The World Economic Forum (WEF) publishes annual reports on global risks which have the high impact on the world's economy. Currently, many researchers analyze the modeling and evolution of risks. However, few studies focus on validation of the global risk networks published by the WEF. In this paper, we first create a risk knowledge graph from the annotated risk events crawled from the Wikipedia. Then, we compare the relational dependencies of risks in the WEF and Wikipedia networks, and find that they share over 50% of their edges. Moreover, the edges unique to each network signify the different perspectives of the experts and the public on global risks. To reduce the cost of manual annotation of events triggering risk activation, we build an auto-detection tool which filters out over 80% media reported events unrelated to the global risks. In the process of filtering, our tool also continuously learns keywords relevant to global risks from the event sentences. Using locations of events extracted from the risk knowledge graph, we find characteristics of geographical distributions of the categories of global risks.
Research on Real-time Obstacle Avoidance of Unmanned Boats in Complex Water Environment

In order to improve the real-time obstacle avoidance ability of unmanned boats in unknown waters and even complex waters, this paper proposes an improved A* real-time obstacle avoidance algorithm for the traditional A* algorithm to maintain a large number of open list and close list. First, the binary value of the estimated value of the nodes in the open list is sorted, and the node with the smallest estimated cost value is placed at the top of the binary heap for quick access. Secondly, the maintenance of the close list is improved to the mark expansion node. Finally, the Bezier curve is used to smooth the path. The experimental results show that compared with the traditional algorithm, the improved algorithm has faster planning speed and smoother planning path in complex water environment, and has a wider application background.
Analyzing DFA Attack on AES-192

It has been detected that the existing Differential Fault Analysis needs to modify the Advanced Encryption Standard for easy key retrieval process. The attack of DFA on the AES-128 requires a massive amount of faulty ciphertext pairs. Key retrieval using DFA attack on AES-128 requires minimal of 250 pairs of faulty cipher-text. This can be reduced to 14 to 16 pairs of faulty cipher-text samples in AES-192 bits and AES-256 bits. This was done by injecting faults randomly into the 11th cycle key which is saved in the Static RAM-Random Access Memory. Researches are made on methods for finding the initial key and that has only been suggested in AES-128. This paper puts forward a system which, intends to induce DFA attack on AES-192 and AES-256 algorithms with less number of faulty cipher-texts.
Enhanced Data Security Architecture in Enterprise Networks

Encryption and storing important information is one of the risky and most challenging tasks. It is the need of the hour in today's fast growing technological transformations that the world is undergoing. A simple Enterprise network is the communication backbone of any organization. It mostly provides better information storage and efficient retrieval, which helps the organization to function smoothly, without having to think twice about their crucial data's security aspects. The information technology paradigm, cloud computing is used to help the organization to focus on its core business. In cloud computing is dealing with many services. That service is used for provide Platform service with infrastructure and software service. This paper, promotes the idea of combining various security and encryption algorithms to connect different enterprise networks using cloud computing, security layer concepts and giving no room for hackers to intrude into the confidential system of data.
Biometric Voting System

In India, the voting system plays a major role during elections. Election Commission of India uses electronic voting machines for the election. According to the survey, in the 2014 general election, only 66.4% votes got registered. At the same time, voting systems have encountered with serious challenges in delivering voting confirmation messages and lack of awareness among people to precisely use their fundamental right "Right to Vote". For avoiding misconceptions on the election time, many advanced techniques are being proposed earlier, by efficiently using various methods. In this paper, we proposed a solution for solving various issues in the present voting system which effectively uses biometric identification as a major concept. The biometric identification will provide better trustworthy results in the election process.
Repeated Leader Follower Game for Managing Cloud Networks With Limited Resources

Virtual resource arbitration, adjustment, and migration are techniques used by infrastructure providers (InPs) to manage limited resources and maximize revenue by serving as many virtual network operators (VNOs) as possible. However, these techniques tend to violate service level agreements (SLAs) and require InPs to compensate VNOs, resulting in reduced benefits. As reported in the literature, no more than 20% of leased cloud resources are actually used. Therefore, InPs and VNOs can both benefit if some of these resources can be returned to the InPs. In this paper, we model a process for negotiating resource returns using a repeated leader follower game. InP (the leader) offers VNOs (the followers) compensation for returning resources with the objective of creating a pool of resources that can be allocated to other VNOs. In this manner, we can maximize the utility of InP and VNO. In particular, an InP increases utility by increasing its revenue, and VNOs increase their utility because their workload can be completed at a lower price owing to received compensations. We numerically evaluate these utilities using the three strategies that we propose and make comparisons against two conventional strategies. Our three strategies focus on revenue maximization of the InP, preferences of VNOs, and the previous negotiation histories. We observe that, after serving new requests using our first and third proposed strategy, the utility of InP increases by 5% and 13%, respectively, compared with using conventional strategies.
Recognition of Characters in a Securely Transmitted Medical Image

Advances in the field of medicine has led to increase in Health Information Exchange (HIE). The main idea of these HIEs is to help the patients' get multiple opinions from different physicians around the world. Medical records carry sensitive information such as patient's diagnosis reports where storage and transmission of data cannot be compromised. Therefore, the medical reports generated can be encrypted in a medical image and can be transmitted securely using the Adaptive LSB with randomized encoding technique, such that the size of the image is unaltered thereby minimizes the chance of an intruder to detect an embedded medical report. On decryption, the medical report that contains a printed text can be recognized using an OCR. Also the text regions embedded in these medical images can contain parameters that are sensitive for diagnosis. These text regions are extracted and are recognized to further help in analysis. This paper combines the techniques of encryption, image steganography and document understanding to securely transmitted the data and recognize the characters to understand the medical report.
Fast Multi-scale fHOG Feature Extraction Using Histogram Downsampling

Object detection is crucial for autonomous robotic systems to interact with the world around them but, in robots with low computational resources, deep learning is difficult to take advantage of. We develop incremental improvements to related work on feature approximation and describe an adaptive fHOG feature pyramid construction scheme based on histogram downsampling, together with a SVM classifier. Varying the pyramid level to which the scheme is applied gives control over the trade-off between precision or speed. We evaluate the proposed scheme on a modern computer and on a NAO humanoid robot in the context of the RoboCup competition, i.e., robot and soccer ball detection, in which we obtain significant increase (1.57x and 1.68x on PC and robot respectively) in pyramid construction speed relative to our baseline (the dlib library) without any loss in detection performance. The scheme can be adapted to increase speed while trading off precision until it reaches the conditions of a state-of-the-art power law feature scaling method.
Dynamic Ordered Sets with Approximate Queries, Approximate Heaps and Soft Heaps

We consider word RAM data structures for maintaining ordered sets of integers whose select and rank operations are allowed to return approximate results, i.e., ranks, or items whose rank, differ by less than Delta from the exact answer, where Delta=Delta(n) is an error parameter. Related to approximate select and rank is approximate (one-dimensional) nearest-neighbor. A special case of approximate select queries are approximate min queries. Data structures that support approximate min operations are known as approximate heaps (priority queues). Related to approximate heaps are soft heaps, which are approximate heaps with a different notion of approximation.We prove the optimality of all the data structures presented, either through matching cell-probe lower bounds, or through equivalences to well studied static problems. For approximate select, rank, and nearest-neighbor operations we get matching cell-probe lower bounds. We prove an equivalence between approximate min operations, i.e., approximate heaps, and the static partitioning problem. Finally, we prove an equivalence between soft heaps and the classical sorting problem, on a smaller number of items.Our results have many interesting and unexpected consequences. It turns out that approximation greatly speeds up some of these operations, while others are almost unaffected. In particular, while select and rank have identical operation times, both in comparison-based and word RAM implementations, an interesting separation emerges between the approximate versions of these operations in the word RAM model. Approximate select is much faster than approximate rank. It also turns out that approximate min is exponentially faster than the more general approximate select. Next, we show that implementing soft heaps is harder than implementing approximate heaps. The relation between them corresponds to the relation between sorting and partitioning.Finally, as an interesting byproduct, we observe that a combination of known techniques yields a deterministic word RAM algorithm for (exactly) sorting n items in O(n log log_w n) time, where w is the word length. Even for the easier problem of finding duplicates, the best previous deterministic bound was O(min{n log log n,n log_w n}). Our new unifying bound is an improvement when w is sufficiently large compared with n.
Biometric Passport Security Using Encrypted Biometric Data Encoded in the HCC2D Code

In this paper, proposed a biometric encryption in the HCC2D (High capacity 2D barcode) code for national security. The proposed biometric encryption technique is using the AES (Advanced Encryption Standard) algorithm and encoded into the HCC2D code. This technique will enhance the safety and security for a biometric passport from unauthorized access without knowing the passport holder. The proposed method will provide more security for the border crossing and illegal immigrants.
Cloud Virtualization with Data Security: Challenges and Opportunities

In recent years, Cloud Computing is emerging as a torrid research area for both academicians and industrialists. It provides effective ways to handle and store the data in advanced system processing applications. Furthermore, it also leverages a radical change in the way the users access and use the available resources. Despite the hype, it also has the challenge of slow data transition from present physical storage to the cloud based platform. This is mainly due to the security challenges associated with the Cloud Computing applications. Hence, data protection has become very critical and always requires an efficient and effective security protocol into the existence. So, the security and reliability of the cloud platform would definitely attract more researchers to this platform. This article discusses an overview of Cloud paradigm and the different virtualization techniques adopted to overcome the security issues associated with the cloud computing platform.
Natural Language Interface for Relational Database

This paper is a brief of Natural Language Programming queries to RDBMS structured queries. We propose an approach for building a questioning system through that a user can provide a queries in natural language. This system will interpret the input sentence and process them into SQL queries. The generated queries are run on particular database to gather the expected information in the form of output. Hence it will dramatically simplify the process of handling with large data and making data available for everyone.
Hybrid HSW Based Zero Watermarking for Tampering Detection of Text Contents

In this paper, the Hybrid Structural component and word length (HSW) based zero watermarking technique is proposed in which the content of text document is not altered for watermark embedding. It involves two steps namely watermark embedding and extraction. From the data owner the plain text is obtained and the watermark is generated. It is based on utilizing the characteristics of text. The text and the watermark are registered to the certifying authority and it is further used for pattern matching which detects the tampering in text document. The proposed embedding algorithm HSW is used for embedding the text. After watermarking, the tampering is applied to the content. The extraction algorithm for HSW is utilized for getting the applied watermark from the text content. For each sub pattern, the corresponding watermarking pattern is extracted from the certified authority. The extracted patterns are compared with the generated pattern. The pattern matching procedure is taken into account for detecting the tampered content
Bluetooth Enabled Electronic Gloves for Hand Gesture Recognition

Human beings have a natural ability to see, listen and interact with their outside surroundings. Unluckily there are some people among us, they are differently talented and do not have the capability to utilize their senses to the greatest extent possible. There are around 300 million hearing and speech impaired people in the world. They depend on other means of communication such as communication by hand gesture called as sign language. This produces a major barrier for people in the deaf and mute communities when they attempt to interact with others, especially in their educational, societal and professional environments. To have communication between speech impaired people and normal people, gesture communication is challenging one. To diminish this communication gap between them, hand gloves are designed which can be easily worn and operated by the hearing and speech impaired person. The flex sensors, accelerometer sensor and contact sensors are fitted in hand gloves, which can measure the bend and movement of hand gesture of the user. These sign signals are converted into a digital signal using ADC. The microcontroller is used to recognize the hand gestures. After recognition of gesture, these signals are sent to the android phone via Bluetooth module. The received text signal at an Android phone is converted into speech signal using Talking display android app. The converted text to speech output is presented with the help of speaker system so that it will be helpful to normal people to understand sign performed by the dumb person. The LCD module is also used to display recognized gesture at microcontroller.
Distributed Communicating Neural Network Architecture for Smart Environments

The deployment of millions of embedded sensors plagued by resource constraints in sophisticated, complex and dynamic IoT smart environments continues to inspire the need to build novel architectures and models for automated, efficient inference and communication in distributed smart settings. In such settings, practical challenges related to energy efficiency, computational power and reliability, tedious design implementation, effective communication, optimal sampling and accurate event classification, prediction and detection exist. Sensors operating in smart environments must be capable of overcoming such challenges and enable scalable monitoring of dynamic phenomena while conducting real-time operations. The development of Machine Learning (ML) continues to motivate a new wave of innovative solutions that intermarry embedded sensors, IoT, and ML to enable various applications in smart environments. We propose a distributed communicating architecture based on Recurrent Neural Networks (RNNs) that can be instantiated on smart devices observing unique data and performing automated distributed inference via hidden-state communication. Our model uses a data-driven approach to collectively solve various distributed objectives, as evidenced by a series of systematic analyses we present. Although demonstrated on a small setup (2/3) nodes, this work sets out a new direction for automatically learning to communicate to solve tasks in distributed settings.
Using Topic Specific Features for Argument Stance Recognition

Argument detection and its representation through ontologies are important parts of today’s attempt in automated recognition and processing of useful information in the vast amount of constantly produced data. However, due to the highly complex nature of an argument and its characteristics, its automated recognition is hard to implement. Given this overall challenge, as part of the objectives of the RecomRatio project, we are interested in the traceable, automated stance detection of arguments, to enable the construction of explainable pro/con argument ontologies. In our research, we design and evaluate an explainable machine learning based classifier, trained on two publicly available data sets. The evaluation results proved that explainable argument stance recognition is possible with up to .96 F1 when working within the same set of topics and .6 F1 when working with entirely different topics. This informed our hypothesis, that there are two sets of features in argument stance recognition: General features and topic specific features.
Representative discovery of structure cues for coronary heart disease recognition based on quality assessment

Image recognition is an indispensable technique in computer vision, such as face recognition, vehicle recognition. It can be also widely used in medical research. Coronary heart disease recognition is still a big challenge due to artifacts or noise. In addition, recognizing coronary heart disease manually is a huge workload. So in this paper, we exploit representative structure cues for coronary heart disease recognition. More specifically, we first leverage Iterative Reconstruction (IR) algorithm to reduce the radiation dose during CT angiography acquisition. For structure cues discovery, we propose grid-based image segmentation algorithm, where each grid is represented by an according image quality score. High probability grid of disease cues corresponds to high quality score. Then, each training image is represented using quality score matrix. Afterward, we incorporate low-level features (grayscale and texture) with quality score matrix. Finally, image matching algorithm is proposed for structure cues discovery from test images. Experimental results demonstrate the effectiveness of our proposed method.
Player Perception Augmentation for Beginners Using Visual and Haptic Feedback in Ball Game

We developed Sports Support System that augments the perception of beginner players and supports situation awareness to motivate beginners in multiplayer sports through visual and haptic feedback. Our system provides positional relationship of the opponents using visual feedback. In addition, the position of the opponent beyond the field of view was provided using haptic feedback. An experiment of pass interception as used to compare the visual and haptic feedback. The results confirmed the effectiveness and characteristics of these feedback processes in a multiplayer ball game.
Adversarial Video Captioning

In recent years, developments in the field of computer vision have allowed deep learning-based techniques to surpass human-level performance. However, these advances have also culminated in the advent of adversarial machine learning techniques, capable of launching targeted image captioning attacks that easily fool deep learning models. Although attacks in the image domain are well studied, little work has been done in the video domain. In this paper, we show it is possible to extend prior attacks in the image domain to the video captioning task, without heavily affecting the video's playback quality. We demonstrate our attack against a state-of-the-art video captioning model, by extending a prior image captioning attack known as Show and Fool. To the best of our knowledge, this is the first successful method for targeted attacks against a video captioning model, which is able to inject 'subliminal' perturbations into the video stream, and force the model to output a chosen caption with up to 0.981 cosine similarity, achieving near-perfect similarity to chosen target captions.