Reviewerly/exHarmony huggingface dataset:
A World Camera for Recording the Game Tactics in Martial Arts using Bamboo Swords

The proposed system was designed for tankendo, kendo, or Japanese fencing using bamboo swords that also use a mask. First, a lightweight, miniaturized world-camera that is 3mm square, or smaller than the width of the vertical bar in the mask, is attached to the center of the vertical bar. This keeps the world-camera out of the field of vision of the competitor and protects the camera from damage. Second, a lengthened signal cable is connected to the control computer and compact power supply attached to the competitor's waist. This minimizes any discomfort a competitor might feel when using the system.
Electromyogram-based algorithm using bagged trees for biometric person authentication and motion recognition

With the recent rising interest in health and convenience, bio-signal measurement, and sensor technology are developing rapidly. Using bio-signals, one can calculate motion and other variables, can authenticate individuals using fingerprints, and can control objects using gestures. These biosignals are convenient because they can be measured easily, anytime, anywhere. However, there are cases of misuse of biosignal based technology. To solve this problem, we develop an electromyogram (EMG)-based algorithm that can be used for biometric authentication. The proposed algorithm uses two channels to acquire EMG data, and hand motion recognition and authentication are carried out through signal processing. As a result of dividing 50 data sets into artificial neural networks and applying the ensemble technique, the authentication success rate achieved by the proposed algorithm was 83.8%.
Unsupervised dimensionality reduction for very large datasets: Are we going to the right direction?

Given a set of millions or even billions of complex objects for descriptive data mining, how to effectively reduce the data dimensionality? It must be performed in an unsupervised way. Unsupervised dimensionality reduction is essential for analytical tasks like clustering and outlier detection because it helps to overcome the drawbacks of the “curse of high dimensionality”. The state-of-the-art approach is to preserve the data variance by means of well-known techniques, such as PCA, KPCA, SVD, and other techniques based on those that have been mentioned, such as PUFS. But, is it always the best strategy to follow? This paper presents an exploratory study performed to compare two distinct approaches: (a) the standard variance preservation, and; (b) one alternative, Fractal-based solution that is rarely used, for which we propose one fast and scalable Spark-based algorithm using a novel feature partitioning approach that allows it to tackle data of high dimensionality. Both strategies were evaluated by inserting into 11 real-world datasets, with up to 123.5 million elements and 518 attributes, at most 500 additional attributes formed by correlations of many kinds, such as linear, quadratic, logarithmic and exponential, and verifying their abilities to remove this redundancy. The results indicate that, at least for large datasets of dimensionality with up to ∼1,000 attributes, our proposed Fractal-based algorithm is the best option. It accurately and efficiently removed the redundant attributes in nearly all cases, as opposed to the standard variance-preservation strategy that presented considerably worse results, even when applying the KPCA approach that is made for non-linear correlations.
Lightweight Convolutional Neural Network for Image Processing Method for Gaze Estimation and Eye Movement Event Detection

Advancements in technology have recently made it possible to obtain various types of biometric information from humans, enabling studies on estimation of human conditions in medicine, automobile safety, marketing, and other areas. These studies have particularly pointed to eye movement as an effective indicator of human conditions, and research on its applications is actively being pursued. The devices now widely used for measuring eye movements are based on the video-oculography (VOG) method, wherein the direction of gaze is estimated by processing eye images obtained through a camera. Applying convolutional neural networks (ConvNet) to the processing of eye images has been shown to enable accurate and robust gaze estimation. Conventional image processing, however, is premised on execution using a personal computer, making it difficult to carry out real-time gaze estimation using ConvNet, which involves the use of a large number of parameters, in a small arithmetic unit. Also, detecting eye movement events, such as blinking and saccadic movements, from the inferred gaze direction sequence for particular purposes requires the use of a separate algorithm. We therefore propose a new eye image processing method that batch-processes gaze estimation and event detection from end to end using an independently designed lightweight ConvNet. This paper discusses the structure of the proposed lightweight ConvNet, the methods for learning and evaluation used, and the proposed method's ability to simultaneously detect gaze direction and event occurrence using a smaller memory and at lower computational complexity than conventional methods.
A novel construction for quantum stabilizer codes based on binary formalism

In this research, we propose a novel construction of quantum stabilizer code based on a binary formalism. First, from any binary vector of even length, we generate the parity-check matrix of the quantum code from a set composed of elements from this vector and its relations by shifts via subtraction and addition. We prove that the proposed matrices satisfy the condition constraint for the construction of quantum codes. Finally, we consider some constraint vectors which give us quantum stabilizer codes with various dimensions and a large minimum distance with code length from six to twelve digits.
A VR gun controller with Recoil Adjustability

This paper provides the design and implementation of a VR gun controller with haptic feedback for the HTC Vive with recoil adjustability. The primary focus of this design is to provide realistic haptic feedback for games utilizing projectile launching weapons to improve gaming immersion on a level further than simply audio and visual.
VA Algorithm for Elderly's Falling Detection with 2D-Pose-Estimation

This paper aims to reduce the losses in emergency cases of elderly falling in residential living environments. We design a falling detection system that can determine the human pose-estimation using the TensorFlow APIs to identify the falling of seniors. The proposed specific VA algorithm that considers time, velocity and acceleration factors of human movement, the falling detection system can better analyze the falling and obtain more accurate pose-estimation. To examine the proposed system, the experiments were conducted to testify basic specifications of fallings upon real data traces of human motion records. Results show the acceleration of human movement can relatively affect the classification of actions. the proposed approach achieves an accuracy of 88% on the test data on falling detection.
Comparison-limited Vector Quantization

A variation of the classic vector quantization problem is considered, in which the analog-to-digital (A2D) conversion is not constrained by the cardinality of the output but rather by the number of comparators available for quantization. More specifically, we consider the scenario in which a vector quantizer of dimension d is comprised of k comparators, each receiving a linear combination of the inputs and producing zero/one when this signal is above/below a threshold. Given a distribution of the inputs and a distortion criterion, the values of the linear combination and threshold are to be configured so as to minimize the distortion between the quantizer input and its reconstruction. This vector quantizer architecture naturally arises in many A2D conversion scenarios in which the quantizer's cost and energy consumption are severely restricted. For this novel vector quantizer architecture, we propose an algorithm to determine the optimal configuration and provide the first performance evaluation for the case of uniform and Gaussian iid sources.
D-Loc Apps

The purpose of this research is to develop a disaster location detector application, specifically about flood that happens in the area of Jakarta, Indonesia. According to social media data updated by the users of twitter, search inquiry is done by its users with the keyword 'seputaran banjir' (in Bahasa Indonesia) which is combined with the Location Based Service function of the determined word search. The method in creating this application is Extreme Programming that uses repetitive and incremental development process approach. By using twitter status data about Jakarta's area, this application has been successfully developed and the result can be shown in the form of twitter status updates from its users that contain words about flood. The search results of flood location validation are coordinates, which are done by checking the availability of the geospatial information from each update of the data. The average accuracy of the search result is 83.7% out of all the location search results.
RecurSIA-RRT: Recursive Translatable Point-Set Pattern Discovery with Removal of Redundant Translators

We introduce two algorithms, RecurSIA and RRT, designed to increase the compression factor achievable using point-set cover algorithms based on the SIA and SIATEC pattern discovery algorithms. SIA computes the maximal translatable patterns (MTPs) in a point set, while SIATEC computes the translational equivalence class (TEC) of every MTP in a point set, where the TEC of an MTP is the set of translationally invariant occurrences of that MTP in the point set. In its output, SIATEC encodes each MTP TEC as a pair, $$\langle P,{V}\rangle $$ , where $$P$$ is the first occurrence of the MTP and $${V}$$ is the set of non-zero vectors that map $$P$$ onto its other occurrences. RecurSIA recursively applies a TEC cover algorithm to the pattern $$P$$ , in each TEC, $$\langle P,{V}\rangle $$ , that it discovers. RRT attempts to remove translators from $${V}$$ in each TEC without reducing the total set of points covered by the TEC. When evaluated with COSIATEC, SIATECCompress and Forth’s algorithm on the JKU Patterns Development Database, using RecurSIA with or without RRT increased compression factor and recall but reduced precision. Using RRT alone increased compression factor and reduced recall and precision, but had a smaller effect than RecurSIA.
Identification and application of a Carlin-type gold mineral system for exploration targeting in the Takab geothermal basin, NW Iran

In this research, a mineral system approach applied in a GIS environment to predict favorable gold potential areas in a district to deposit scale at the north Takab geothermal basin, NW Iran. In a context of mineral system, initially the world class Zarshuran gold deposit studied and modeled to identify important conceptual controls on gold mineralization. Conceptual controls such as sources of heat, metals and mineralizing hydrothermal fluids, active fluid flow pathways, lithological and geochemical accumulation environments were identified in a deposit scale and converted to mappable spatial controls or targeting elements for district-scale gold exploration. Intermediate magma and metamorphosed ophiolites respectively considered as the main heat and gold sources. Major particular faults and their intersections with the axis of anticlines, strongly brecciated dacite lithic tuff and its contact with the favorable host lithologies presented as the most important indicators of active fluid flow pathways. The intersection of the main faults and anticline boundaries were determined as an important factor in fluid accumulation and gold mineralization. Silicified carbonate and black shale considered as the favorable chemical scrubber or reactive host. Dome-like structure such as Iman Khan anticline, formed through uplifting, was introduced as an important physical throttle. Finally, by introducing a mineral system model for the Zarshuran Carlin-type gold deposit, these critical processes converted to mappable gold exploration features by using the district-scale geological map and remotely- sensed hydrothermal alterations of the area in a GIS environment. Based on the proposed gold mineral system, the identified exploration features ranked by appropriate weighting for targeting gold mineralization. A final gold potential map of the area was created by using weighted index overlay data integration modeling to locate high gold potential areas for future exploration in the surrounding areas of the Zarshuran deposit.
Revisiting (R)CCA Security and Replay Protection

This paper takes a fresh approach to systematically characterizing, comparing, and understanding CCA-type security definitions for public-key encryption (PKE), a topic with a long history. The justification for a concrete security definition X is relative to a benchmark application (e.g. confidential communication): Does the use of a PKE scheme satisfying X imply the security of the application? Because unnecessarily strong definitions may lead to unnecessarily inefficient schemes or unnecessarily strong computational assumptions, security definitions should be as weak as possible, i.e. as close as possible to (but above) the benchmark. Understanding the hierarchy of security definitions, partially ordered by the implication (i.e. at least as strong) relation, is hence important, as is placing the relevant applications as benchmark levels within the hierarchy.
Evolution Analysis of Large Graphs with Gradoop

The temporal analysis of evolving graphs is an important requirement in many domains. We are therefore extending the distributed graph analysis framework Gradoop and its graph data model to support temporal graph analysis. This paper contains an overview of our work in progress and an example use case from the financial domain demonstrating the flexibility of the temporal graph model and its operators.
Rules for Orthographic Word Parsing of the Philippines' Cebuano-Visayan Language Using Context-Free Grammars

Syllabication is essential in the preprocessing stage of speech systems. In the context of the Philippines' Cebuano-Visayan language's syllabication rules, the existing rules do not include hyphenated words although the hyphen defines the syllable boundary in a word. Hence, this study created grammar rules for hyphenated words which include sequences of a hyphen between vowel-consonant, consonant-consonant, vowel-vowel, and consonant-vowel. The test was done for the enhanced grammar rules for Cebuano-Visayan syllabication with 1,465 representative hyphenated and non-hyphenated words of varying lengths. The result further implies that the syllabication analysis for hyphenated words showed that hyphens improve the naturalness and intelligibility in the utterance of the words, thereby enhancing the understanding and comprehension of the Cebuano-Visayan discourse.
Geometry of the Hough Transforms with Applications to Synthetic Data

In the framework of the Hough transform technique to detect curves in images, we provide a bound for the number of Hough transforms to be considered for a successful optimization of the accumulator function in the recognition algorithm. Such a bound is consequence of geometrical arguments. We also show the robustness of the results when applied to synthetic datasets strongly perturbed by noise. An algebraic approach, discussed in the appendix, leads to a better bound of theoretical interest in the exact case.
CNN Based Optimal Intra Prediction Mode Estimation in Video Coding

The amount of video data is so large that efficient video compression is required for the storage and transmission. Intra prediction is one of important components in video compression. In this paper, we examine various Convolutional Neural Network (CNN) structures to estimate optimal intra prediction mode as Most Probable Modes (MPMs). Moreover, we investigate several combinations of the MPMs obtained by the CNN and MPMs derived from High Efficiency Video Coding Test Model (HM). From these experimental results, we find that using 6 MPMs from both CNN and HM with moderate number of channels or kernel size is preferred.
Method Combining Machine Vision and Machine Learning for Reed Positioning in Automatic Aerophone Manufacturing

The free reed aerophone, such as accordion, harmonica and melodica, is one of the most popular categories of music equipment in the world. The key operation of the free reed aerophone manufacturing process is to weld multiple reeds onto the reed frame precisely and quickly. In this paper, we propose a method combining machine vision and machine learning algorithms to assist the mechanical device to estimate adjusting displacement and to determine the correctness of the reed positioning operation. Images of reeds on frames are captured and processed, and then some novel features are defined and extracted. Classification models and regression models such as artificial neural network (ANN), support vector machine (SVM), decision tree (DT), k-nearest neighbor (KNN) and linear regression (LR) are applied and trained to estimate if the reed position is correct and to measure the adjusting displacement if necessary. It is found that the Back propagation neural network (BPNN) presents 100% accuracy for the correctness estimation and ±0.025mm measuring precision.
Inshore ship detection based on multi-aspect information in high-resolution SAR images

A novel algorithm for inshore ship detection based on multi-aspect information in high-resolution Synthetic Aperture Radar (SAR) images is proposed. Based on the internal and external characteristics of inshore ship and harbor regions, multi-aspect information, including coastline information, context information, scattering mechanism, shape contour and deep feature information, are considered respectively to detect inshore ship targets. The algorithm is verified to be robust and efficient to exact the Region-of-Interest (ROI) of inshore ship, and achieve a good performance with detection rate 94.24%. Experiments demonstrate good performance with detection rate 94.24%. The results show that the method is simple and robust, which can effectively determine the Region-of-Interest (ROI) of inshore ship.
Image Steganography Using an Eight-Element Neighborhood Gaussian Markov Random Field Model

Currently, the design of cost functions which measure the embedding distortion becomes the main task left in steganography with the emergence of Syndrome-Trellis Codes. Whether heuristically designed, e.g., Hill, WOW, or statistical model-based, e.g., HUGO, MiPOD, the embedding distortion is almost a sum of each element's distortion. This paper proposes a new non-additive cost function designed by incorporating an eight-element neighborhood Gaussian Markov Random Field Model (8-GMRF). This proposed scheme, which could be viewed as an extension to MiPOD, derives change probabilities from minimizing the total KL divergence with a given payload and then implements adaptive steganography. Experimental results demonstrate that the proposed 8-GMRF performs superior or comparable to some of the state-of-the-art schemes in resisting steganalysis detectors.
A Novel Lossless Data Hiding Scheme in Homomorphically Encrypted Images

In this paper, we have presented a novel lossless data hiding scheme in a homomorphically encrypted image. After applying the homomorphic encryption function to the original image by the content owner, the data hider can embed the additional data in a lossless manner, without revealing the original content of the image. Meanwhile, the data hider chooses a threshold value and generates a location map accordingly. This location map is considered as an auxiliary information. The receiver can recover the additional data using the shared auxiliary information in a separable manner. And the original image is recovered losslessly, without any post-processing, by the homomorphic decryption function. In our proposed scheme, no distortion is introduced in the data hiding phase, as a result the directly decrypted image is the same as the original image. The experimental results show that, the scheme has better performance than some of the state-of-the-art methods with high embedding rate and distortionless directly decrypted image.
Image Super-Resolution and Deblurring Using Generative Adversarial Network

Image super-resolution and deblurring are two highly ill-posed problems that are usually dealt separately. However, real-world images are often low-resolution and have complex blurring. This paper focuses on ordinary natural scene images and reconstructs clear high-resolution images directly from blurred low-resolution inputs. Firstly, we propose a model based on generative adversarial network to jointly process image super-resolution and non-uniform motion deblurring. Secondly, we decouple this joint problem into feature extraction module, super-resolution reconstruction module and deblurring module. The modules promote each other and reconstruct clearer high-resolution images. Finally, we use bilinear interpolation followed by a convolutional layer to achieve upsampling instead of using the common deconvolution layer, which effectively suppresses checkerboard artifacts. The experimental results show that the proposed method is efficient and can perform better than the existing advanced algorithms in both quantitative and qualitative performance.
Hyperspectral Image Compression and Super-Resolution Using Tensor Decomposition Learning

As the field of remote sensing for Earth Observation is rapidly evolving, there is an increasing demand for developing suitable methods to store and transmit the massive amounts of the generated data. At the same time, as multiple sensors acquire observations with different dimensions, super-resolution methods come into play to unify the framework for upcoming statistical inference tasks. In this paper, we employ a tensor-based structuring of multi-spectral image data and we propose a low-rank tensor completion scheme for efficient image-content compression and recovery. To address the problem of low-resolution imagery, we further provide a robust algorithmic scheme for super-resolving satellite images, followed by a state-of-the-art convolutional neural network architecture serving the classification task of the employed images. Experimental analysis on real-world observations demonstrates the detrimental effects of image compression on classification, an issued successfully addressed by the proposed recovery and super-resolution schemes.
Mixed Word Representation and Minimal Bi-GRU Model for Sentiment Analysis

In the mission of natural language processing, sentiment analysis is a formidable challenge due to the complexity of deep network architecture and the lack of standard sentiment word representation. In this paper, we proposed a new learning method of the word representation for the comprehensive information of texts and a minimal Bi-GRU (bidirectional gate recurrent unit) model for the task of sentiment classification. First, for capturing sentiment information of words, the supervised three-layer network is used for construct sentiment word representation. We propose the mixed word representation to denote the classification characteristics, which combines the word embedding of neural probabilistic language model with the proposed the sentiment word representation. Next, we propose bidirectional GRU network including forward and backward propagation to consider the semantic relations before and after sentences, meanwhile, to simple the architecture, we apply minimal GRU network. Then, we combine minimal Bi-GRU model with the mixed word representation taking a full account of semantic and sentiment information to classify the sentiment data set as Movie Reviews and IMDB data set. Experimental results demonstrate that the simplicity of the model and superiority of the performance.