Reviewerly/exHarmony huggingface dataset:
Prying into Private Spaces Using Mobile Device Motion Sensors

Human made structures are designed in a predictable manner, conforming to the expectations of those who use them. These underlying patterns lend themselves to repetition in the way people get between different locations. We investigated the feasibility of an attacker using motion sensor data against their target, with the objective of predicting where they move and what activities they engaged in. In this work, we show that the gyroscope and accelerometer can be used to drive a privacy attack that stealthily maps out a user's private space with high accuracy. In particular, we show that a mobile app with access to this data can leverage it to analyze a user's step execution dynamics, turn operations, and general body movement activities and then methodically combine this information to map out paths and landmarks in protected spaces, such as houses. Using a dataset of 26 users who executed a number of activities and a combination of classification, regression, and distance matching techniques, we show this privacy attack to generate maps whose Normalized Hausdorff Distance from the ground-truth is as low as 0.1159.
Accurate 3D Measurement of Highly Specular Surface using Laser and Stereo Reconstruction

In the production line, Inspection and Quality Control are essential to maintain the quality of the products. Same as the manufacturing process, the process of quality checking should also be automatic [1]. In addition to automation in quality checking, the process needs to be simple, fast, accurate and cost-effective. "Machine vision is the technology and methods used to provide imaging-based automatic inspection and analysis [2]." However, Reflective Surface of the object puts some limitations on traditional methods of machine vision. This research paper focuses on the development of a simple and accurate 3D measuring system for the inspection of reflective surface objects. The idea is to combine the concepts of stereo vision and sheet of light techniques. The main advantage of this method is that it overcomes the limitations of traditional methods and works robustly in ambient lighting condition. In this paper, we described a number of experiments carried out on different shapes and sizes of reflective surface objects using the proposed method. The qualitative and quantitative reconstructed 3D profiles of different shapes of reflective surface objects are shown. In the end, the results are compared with the ground truth three-dimensional model of the acquited objects for accuracy evaluation.
GPU-Based Decompression for the 842 Algorithm

In an age of ever-growing data volumes, lossless data compression is unarguably one of the most relevant techniques to handle vast data sets. To facilitate high throughput compression, modern IBM POWER CPUs provide hardware acceleration for the proprietary 842 compression algorithm. The 842 algorithm is optimized for main memory compression, and a software-based implementation of the algorithm is available as a part of the Linux kernel. Even though GPU-equipped computers are vital for many of todays data intensive applications, GPUs have thus far been unable to interoperate with 842-compressed data due to the lack of GPU-based decompressors. The main contribution of this paper is to fill this gap by providing optimized implementations for 842 decompression in both CUDA and OpenCL. We demonstrate that GPU-based decompression provides 4.5-9.5x speed-up on integrated GPUs and 30-34x speed-up on dedicated GPUs when compared to software-based decompression on CPUs for various test systems. Test systems range from energy-efficient SoC-based hardware with integrated GPUs to high end workstations and servers with dedicated GPUs.
ViSeR: A Visual Search Engine for e-Retail

Web search engines play a significant role in many applications in our everyday lives. Among them, online shopping is one major technological advancement which has made our life easier and comfortable. Currently, most e-commerce websites support either text-based or voice-based search. The problem with text and voice-based approaches is that they need an appropriate item name or description for the search results to be accurate. Also, with the huge variety of items available online, it is not easy to find the desired object in the top results. Lately, some top e-commerce websites started supporting visual search, where the user can submit an image of an item they'd like to find. However, this domain is still in its infancy. In this work, we propose ViSeR, a visual search engine architecture using deep learning and machine learnging techniques, with a proof-of-concept implementation focusing on the fashion eretail industry. ViSeR first classifies the query image to the right category using image classification. Then all the images in that category are ranked based on their similarity and the top images are retrieved as recommendations. We present in detail the experimental results with different deep and machine learning algorithms and provide additional details with regards to deploying this model to achieve high accuracy and low latency (in terms of training and recommendation time).
"SmartPointer"

The concept and design guidelines for an universal gesture-based remote control with simple intuitive operating are presented. The hand-held buttonless "SmartPointer" emits "structured" infrared (IR) light with a spatial pattern projected by a diffractive optical element (DOE). A cost-effective array of photodiodes on or near the devices to be remotely controlled (such as light, blinds, windows, heating/air conditioning or TV/radio) records the light intensities while a gesture is carried out. Based on the cross-correlation of the spatio-temporal intensity changes at all pairs of photodiodes, the trajectory of motion of the pattern is reconstructed and the gesture is recognized (classified). During tests with uninstructed persons, horizontal, vertical, circular and targeting gestures were most frequently used. Extensive simulations addressing design parameters of the 2D projection pattern and the receiver array were carried out to identify suitable parameter sets for highly reliable gesture reconstruction and recognition of the recorded gestures. As a result, DOE with various pseudo-random beam distributions can be applied.
Performance Evaluation of Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF)

The importance of character recognition cannot be overemphasized. It finds applications in many automated systems. In most cases, these applications require high precision (e.g. automatic grading system, document digitization, license plate recognition systems, e.t.c) as well as low resource overhead. However, these are conflicting requirements, because the more the precision required, the more computation needed hence the more increase in resource overhead. In the research, two classification algorithms in Artificial Neural Networks (ANN): Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) were applied to hand-written digit recognition and their performance is investigated. The duo was compared in terms of resources required for training and accuracy. It is found that MLP-NN is much faster to train (5.5min) compared to RBF (50.0min). However, during testing, it is found that both have an accuracy of ≈ 95%.
Medical Image Registration Using Landmark Registration Technique and Fusion

The process of image registration and fusion is used to obtain a resultant image with better characteristics. The concept of image registration and fusion can be used in various domains such as remote sensing, satellite imaging, medical imaging, etc. But, as the smallest of errors can have great consequences in medical images, applying these concepts in medical imaging will make a great difference. Aligning images spatially into one single framework is attained through image registration. There are various methods or techniques to implement image registration. Landmark registration, a semi-automatic registration method is used where images are spatially aligned by selecting control points from both the images and applying geometric transformation that helps in translation, rotation, scaling and shearing. During fusion process intermixing of traits from source images is performed using Discrete Wavelet Transform and Bicubic interpolation. The resultant image helps the doctors for better clinical diagnosis.
Protecting the Intellectual Properties of Digital Watermark Using Deep Neural Network

Recently in the vast advancement of Artificial Intelligence, Machine learning and Deep Neural Network (DNN) driven us to the robust applications. Such as Image processing, speech recognition, and natural language processing, DNN Algorithms has succeeded in many drawbacks; especially the trained DNN models have made easy to the researchers to produces state-of-art results. However, sharing these trained models are always a challenging task, i.e. security, and protection. We performed extensive experiments to present some analysis of watermark in DNN. We proposed a DNN model for Digital watermarking which investigate the intellectual property of Deep Neural Network, Embedding watermarks, and owner verification. This model can generate the watermarks to deal with possible attacks (fine tuning and train to embed). This approach is tested on the standard dataset. Hence this model is robust to above counter-watermark attacks. Our model accurately and instantly verifies the ownership of all the remotely expanded deep learning models without affecting the model accuracy for standard information data.
Developing a Mobile Augmented Reality Application for Enhancing Early Literacy Skills

Mobile applications allow connecting the world around us with digital information. Augmented Reality (AR) applications are commonly used in museums, education and for research. AR applications can attract users with contextual and locally specific information. We present a concept, design and development of augmented reality (AR) application “Tilsimli arifler” in which we create literate environment for motivating of children in practicing reading and writing. In order to enhance the kids reading ability we propose to develop a learning mobile application that allows using modern mobile technologies, in particular, using the technology of augmented reality, to study the alphabet and learn how to make words. In this case, a prerequisite was the use of special (drawn) cards and a camera of a mobile device. When scanning a card with a camera on a user’s smartphone, a three-dimensional image of the object deposited on the card should appear and which can be rotated in different directions (“spinning”) in real time.
Gaussian process‐based Bayesian non‐linear filtering for online target tracking

This study deals with the problem of online target tracking based on Gaussian process regression and deterministic sampling. A general mathematical formulation of the non-linear system identification problem is constructed based on the Gaussian process model, in which the computational efficiency is improved with a sparsification method, and the regular Gaussian process regression algorithm is extended to the online case where the learned surrogate model is able to be updated recursively. After that, the problem of non-linear transformation with stochastic uncertainty is considered, in which the mean and covariance of a random variable is transformed based on a Gaussian process model. Then the proposed non-linear transformation method is applied to solve the Bayesian non-linear filtering problem with unknown dynamics and measurement models. In the end, extensive Monte-Carlo simulations are conducted to verify the performance of the Gaussian process-based non-linear filtering method in a reentry target tracking scenario.
Website Identity Notification: Testing the Simplest Thing That Could Possibly Work

Users are used to authenticating themselves to websites, but not for websites to authenticate to them. One readily available mechanism that may help users make safer online decisions lies in website certificates that contain website identity information. Fraudulent websites are now short-lived and present valid certificates without any identity information. Our goal was to create and test the effectiveness of simpler certificate interfaces, made to help users differentiate between identity-verified websites and those without such verification, and thus, potentially fraudulent. We conducted a study with a certificate interface prototype with simple identity notification types. Our findings suggest that presenting identity information to users can help them differentiate between real and potentially fraudulent websites. Some users were suspicious of the notifications and incorrectly felt that they could make decisions based on website appearance, so building user background knowledge is essential.
Development of an Intelligent Dialogue Agent for Older Adults : Evaluation of Functions to Control Spontaneous Talk and Coordinate Speech Content

This paper aims to develop an agent, the Intelligent Dialogue Agent (IDA), which is able to engage in appropriate dialogue with a variety of older adults depending on their situation. Incorporating the IDA into preventive care frameworks is expected to encourage older adults to habitually engage in preventive care behaviors (e.g., exercise and cognitive training) due to their familiarity with the frameworks. We implemented a function into the IDA to spontaneously talk to older adults. Reinforcement learning based on the policy gradient method was also employed to develop a function to coordinate the IDA's speech content. In preparation for application to older adults, we carried out experiments with students to evaluate their impression of the IDA's spontaneous talking function using a prototype IDA with a smart speaker and also conducted the experiments using a text-based chat-bot with a speech-contents-coordinating function to discuss basic characteristics and performance of the function.
Blockchain-Based Transparent and Secure Decentralized Algorithm

Blockchain is a distributed system that is utilized to give transparency to the system. It is ready to change pretty much every feature of our lives from advanced exchanges to even decentralization of the water. It is a system, which is utilized in a decent and legitimate way to change the essence of the world and the strategy for managing the world's concerns. It's primary weapon is that it is a decentralized system as it does not rely upon any central authority. In blockchain Technology the problem of centralization in beautifully handled by algorithms that is very simple yet they work like magic. Blockchain can possibly, effectively rethink how business, administration, and social work function. In any case, even after numerous years after its presentation, many people are still doubtful about its utilization. In this paper, we will demonstrate how a blockchain innovation can help in building a voting application that is decentralized and that cannot be hacked by anyone nor it can be shut down.
Where Are They Now?

In home displays (IHD's) of energy use offers potential to engage households with energy use and improve energy literacy. Yet explorations of use over longer time periods are lacking. Based on qualitative in-home interviews with 19 participants, this research provides accounts from owners of IHD's up to 10 years after their initial deployment. Findings indicate the near complete abandonment of the devices and participants' reflections exemplify the short-lived value provided by simple cent-a-meter IHD's. The paper reflects on IHD's as a component of broadscale energy literacy programs, closing with suggestions for how designers might re-frame energy engagement as a service.
Semantics of Data Mining Services in Cloud Computing

The recent incorporation of new Data Mining and Machine Learning services within Cloud Computing providers is empowering users with extremely comprehensive data analysis tools including all the advantages of this type of environment. Providers of Cloud Computing services for Data Mining publish the descriptions and definitions in many formats and often not compatible with other providers. From a functional point of view, having the possibility to describe complete Data Mining services is fundamental to maintain the usability and especially the portability of these services independently of the software/hardware support or even the differences between cloud platforms. The main objective of this article is to design a Data Mining service definition which allows to compose with a single and simple definition a complete service, in such way a data mining workflow can be ported and deployed in different providers or even in a Market Place of this type of ready-to-consume services. This article presents a semantic scheme for the definition and description of complete Data Mining services considering both the management of the service by the provider (price, authentication, Service Level Agreement, ...) and the definition of the Data Mining workflow as a service. It represents a solid contribution for paving the way to the standardization and industrialization of Data Mining services. To asses the validity of the scheme a list of services from Data Mining providers have been described and an example of a full service for a Random Forest algorithm has been defined as a service. In addition, a practical scenario has been developed, creating a deployment platform for Data Mining services to give functional support to the scheme, illustrating the practical benefits of the proposal for the end user.
CocoQa: Question Answering for Coding Conventions Over Knowledge Graphs

Coding convention plays an important role in guaranteeing software quality. However, coding conventions are usually informally presented and inconvenient for programmers to use. In this paper, we present CocoQa, a system that answers programmer's questions about coding conventions. CocoQa answers questions by querying a knowledge graph for coding conventions. It employs 1) a subgraph matching algorithm that parses the question into a SPARQL query, and 2) a machine comprehension algorithm that uses an end-to-end neural network to detect answers from searched paragraphs. We have implemented CocoQa, and evaluated it on a coding convention QA dataset. The results show that CocoQa can answer questions about coding conventions precisely. In particular, CocoQa can achieve a precision of 82.92% and a recall of 91.10%. Repository: https://github.com/14dtj/CocoQa/ Video: https://youtu.be/VQaXi1WydAU.
Coherent Rendering of Virtual Smile Previews with Fast Neural Style Transfer

Coherent rendering in augmented reality deals with synthesizing virtual content that seamlessly blends in with the real content. Unfortunately, capturing or modeling every real aspect in the virtual rendering process is often unfeasible or too expensive. We present a post-processing method that improves the look of rendered overlays in a dental virtual try-on application. We combine the original frame and the default rendered frame in an autoencoder neural network in order to obtain a more natural output, inspired by artistic style transfer research. Specifically, we apply the original frame as style on the rendered frame as content, repeating the process with each new pair of frames. Our method requires only a single forward pass, our shallow architecture ensures fast execution, and our internal feedback loop inherently enforces temporal consistency.
Block Matching in Noise Suppression Filter for Video Coding

This paper considers specific block matching methods which are used inside Nose Suppression Filter (NSF) in-loop filter for video coding. Different approaches of the block matching procedure, which are combined in NSF are described. In general block matching part of NSF comprises of a specific search region and a statistical search method which are applied to a certain area of a picture. As a result total complexity of block matching part inside NSF significantly decreases while coding efficiency of NSF algorithm is kept. In addition theoretical complexity analysis of the block matching part inside NSF is provided.
Fraudulent user detection on rating networks based on expanded balance theory and GCNs

Rating platforms provide users with useful information on products or other users. However, fake ratings are sometimes generated by fraudulent users. In this paper, we tackle the task of fraudulent user detection on rating platforms. We propose an end-to-end framework based on Graph Convolutional Networks (GCNs) and expanded balance theory, which properly incorporates both the signs and directions of edges. Experimental results on four real-world datasets show that the proposed framework performs better, or even best, in most settings. In particular, this framework shows remarkable stability in inductive settings, which is associated with the detection of new fraudulent users on rating platforms. Furthermore, using expanded balance theory, we provide new insight into the behavior of users in rating networks, that fraudulent users form a faction to deal with the negative ratings from other users. The owner of a rating platform can detect fraudulent users earlier and constantly provide users with more credible information by using the proposed framework.
Proposed Parallel Algorithms to Encryption Image Based on Hybrid Enhancement RC5 and RSA

In recent years, image encryption algorithms have expanded for the defense of information from attackers to provide privacy to recipients. RC5 is a well-known block cipher which has some disadvantages such as weak against differential attacks and can be used for image encryption. RSA is an asymmetric key algorithm to provide privacy to carry information across the network which has some disadvantages such as require a greater amount of calculation that leads to the increased time. In this paper, the proposed system based on the use of the parallel environment in two proposed enhancement algorithms (ERC5 and ERSA) to get fast as possible with efficient encryption and decryption for the images. Enhancement RC5 algorithm (ERC5) that use for encrypts image by using four different cipher keys and four cores, instead of using a single key and one core. Enhancement RSA algorithm (ERSA) that use for encrypts keys ERC5 by using four prime numbers, two public keys, two private keys, and four cores, rather than using two prime numbers, one public key, one private key, and one core. According to the results of security analysis, the speed of the proposed system based on parallel environment is greater than the original (RC5, RSA) algorithms based a sequential environment, Increased the time to knowledge the private keys and effectively determine it, which makes the proposed system stronger against brute force attacks, the differential and statistical attacks.
Flexible calibration method for visual measurement using an improved target with vanishing constraints

In this paper, an improved calibration method based on vanishing constraints is proposed for calculating the extrinsic parameters of cameras. First, we come up with a improved target based on the conventional target with two groups of orthogonal parallel lines. The novel target is composed of two groups of parallel lines with a certain angle range from 80° to 90°, which can reduce the difficulty of target production and the manufacturing cost. Furthermore, in the optimization process, we design a new function with a more robust penalty factor instead of using the experienced values to get the extrinsic parameters for the binocular vision sensors. Finally, on account of using the improved target and the novel optimiazation function, the proposed method is more flexible and robust compared with Zhang's method.