Reviewerly/exHarmony huggingface dataset:
Adversarial Refinement Network for Human Motion Prediction

Human motion prediction aims to predict future 3D skeletal sequences by giving a limited human motion as inputs. Two popular methods, recurrent neural networks and feed-forward deep networks, are able to predict rough motion trend, but motion details such as limb movement may be lost. To predict more accurate future human motion, we propose an Adversarial Refinement Network (ARNet) following a simple yet effective coarse-to-fine mechanism with novel adversarial error augmentation. Specifically, we take both the historical motion sequences and coarse prediction as input of our cascaded refinement network to predict refined human motion and strengthen the refinement network with adversarial error augmentation. During training, we deliberately introduce the error distribution by learning through the adversarial mechanism among different subjects. In testing, our cascaded refinement network alleviates the prediction error from the coarse predictor resulting in a finer prediction robustly. This adversarial error augmentation provides rich error cases as input to our refinement network, leading to better generalization performance on the testing dataset. We conduct extensive experiments on three standard benchmark datasets and show that our proposed ARNet outperforms other state-of-the-art methods, especially on challenging aperiodic actions in both short-term and long-term predictions.
A Proposed Learning Content for Teaching Handheld Augmented Reality in a Classroom Setting

Augmented reality is an interactive experience wherein digital objects are placed on the physical environment. Augmented reality (AR) applications have gained popularity especially when smartphones gained capable camera sensors, gyroscope, and other sensors. However, there has been a lack of framework or recommendations on how to formally teach developing mobile AR applications in a classroom setting. In this paper, we present a learning content, called ARVRDEV Courseware, that aims to teach undergraduate students how to develop augmented reality applications deployed on handheld devices. We carefully designed the lessons of the course, consistent with AR techniques from literature, and designed hands-on activities not typically presented in publicly available materials. A pilot run of the course was offered at De La Salle University, where a total of 76 undergraduate students took the course as an elective during their 4th year. We analyzed the effectiveness of the hands-on activities based on the assessment results of the students and discussed the outstanding projects developed by the students, which are observed to be inspired by the hands-on activities designed for the course.
Towards a new general IMC-based high-order minimum-energy perfect control design paradigm

The advanced investigation in the field of energy-based inverse model control (IMC) methodology is presented in this manuscript. Following the recent papers, it should be concluded, that the IMC-oriented minimum-energy perfect control design can only be established through heuristic methods. However, in the novel authors' work it has been proved for the first time, that such issue can be considered in an analytical manner. Nevertheless, the obtained results have only been valid for the second-order state-space systems. Therefore, the proposed in the manuscript motivation instance, confirming the possibility of extending such paradigm to higher-order plants, will clearly contribute to the introduction of the unified minimum-energy perfect control theory.
Nonparametric filtering for stochastic nonlinear oscillations

This paper proposes a method of nonparametric filtering for stochastic nonlinear oscillations with particular interest in their derivative estimation. Based on a second-order ordinary differential equation, a stochastic oscillation is modeled by a two-variate stochastic differential equation without specifying the function form of the drift function, where the first variable is assumed to be observable but not the other. Given the discrete time series with observation error, the proposed method enables us to estimate the values of the drift function and its derivatives including those of the unobservable variable. According to the results of numerical experiments to compare estimation accuracy with a parametric method, the proposed method shows better performance in the estimation of nonlinear models.
MineFOL: a Game for Learning First Order Logic

The role of games in computing science has been given some consideration in the literature. We aim to design games that can be used for teaching logic. Specifically, we build on the Minefield game used in the Stanford Intro to Logic course, and present a version, which we call MineFOL, that incorporates time constraints. We also move beyond the Stanford examples in that we provide a formalisation of the game, with a view to eventually generating game instances automatically as well as from user input, and show how MineFOL can be used as a learning tool. We also developed an online platform supporting learners in at least three ways. First, the learners can practice reasoning in First Order Logic (FOL) and proving strategies such as resolution through various MineFOL games. Second, the learners can practice formalisation in FOL, by allowing learners to build their own games. Third, the learners become aware of the need of interleaving various technologies to solve a logicbased task. In this line, MineFOL is formalised in such way to allow augmenting reasoning in FOL with search strategies.
Context-free grammars to detect straight segments and a novel polygonal approximation method

A new method for the polygonal approximation is presented. The method is based on the search for break points through a context-free grammar, that accepts digital straight segments with loss of information, as well as the decrease in the error committed employing the comparison of a tolerable error. We present an application of our method to different sets of objects widely used, as well as a comparison of our results with the best results reported in the literature, proving that our method achieves better values of error criteria. Besides, a new way to find polygonal approximations, with context-free grammars to recognize digital straight segments without loss of pixels, it is also addressed.
Object Removal and Inpainting from Image using Combined GANs

As recent research on deep learning methods has been actively conducted, a number of deep learning methods have been proposed. In this paper, we propose a method of removing the desired object from an image using generative adversarial networks(GANs) structure. We composed the network in which two GANs are fused. The first GAN erases the target object from the input image, and the second GAN generates an image that fills the empty space with the background. Through this network, we can erase the desired object from the input image and get an image with the erased part filled with the background without any object detection method. We show that the removal of people and vehicles from images of roads using the CityScapes Dataset.
NORD: A python framework for Neural Architecture Search

Neural Architecture Search (NAS) is a research field investigating the generation and optimization of neural network architectures for specific tasks. As manually designing the architectures is quite laborious and challenging to execute without adequate experience, NAS enables discovering novel, state-of-the-art architectures. Nonetheless, successfully implementing NAS processes also requires extensive experience with both neural networks and optimization processes. Neural Operations Research and Development (NORD) decouples implementing and designing the networks, enabling the application of existing methods on novel datasets and fairly comparing results. Thus, it aims to make NAS more accessible to researchers, as well as industry practitioners.
Cloud Computing Resources Utilization and Cost Optimization for Processing Cloud Assets

In the field of Cloud Content Platform, where a user can store varieties of cloud assets (e.g. PDF documents) and can access them on any other platform such as Web, Desktop or Mobile. A creative content application may create different types of assets and store them on cloud content platform. A corresponding micro-service or cloud-worker should be present to handle asset processing request. Multiple cloud-workers should be present to handle different kind of asset processing requests. Most of the times, cloud computing resources are under utilized and cost spent on them for handling such requests is not optimized. Also, for a large and complex asset, the overall response time may increase due to high asset processing time. In this paper, we present novel method and system to predict cloud computing resources to serve input asset processing request. The method and system are designed in such a way to maximize resource utilization, minimize cost spent and minimize processing time by such computing resources. The method includes training of supervised learning based predictive model with historic data which includes asset processing requests, asset properties and their corresponding cloud computing resource utilization. The method also includes greedy approach to identify appropriate number of cloud computing resources required to process input asset processing job requests.
Spatio-temporal Clustering for Grouping in Online Handwriting Document Layout Analysis with GRU-RNN

One of the document analyzer's essential functions is dividing input documents into stroke groups that belong to the particular type of handwriting. In this paper, we analyze existing approaches for clustering input strokes of the handwritten document and present a new one that establishes a novel state of the art result. It is the first approach where a grouping of input strokes is considered as a clustering problem and standard metrics for it are provided. This research could be used as a baseline for further ones in the clustering of handwritten documents.
Hierarchical Multimodal Adaptive Fusion (HMAF) Network for Prediction of RGB-D Saliency

Visual saliency prediction for RGB-D images is more challenging than that for their RGB counterparts. Additionally, very few investigations have been undertaken concerning RGB-D-saliency prediction. The proposed study presents a method based on a hierarchical multimodal adaptive fusion (HMAF) network to facilitate end-to-end prediction of RGB-D saliency. In the proposed method, hierarchical (multilevel) multimodal features are first extracted from an RGB image and depth map using a VGG-16-based two-stream network. Subsequently, the most significant hierarchical features of the said RGB image and depth map are predicted using three two-input attention modules. Furthermore, adaptive fusion of saliencies concerning the above-mentioned fused saliency features of different levels (hierarchical fusion saliency features) can be accomplished using a three-input attention module to facilitate high-accuracy RGB-D visual saliency prediction. Comparisons based on the application of the proposed HMAF-based approach against those of other state-of-the-art techniques on two challenging RGB-D datasets demonstrate that the proposed method outperforms other competing approaches consistently by a considerable margin.
WBCs detection depending based on a binary conversion of the color component in a Ycbcr color space

Abstract Detection of white blood cells (WBCs) automatically is an important issue that has many applications in the field of medical imaging, in this research, we tend to detect white blood cells depending on the Ycbcr color space. The proposed method has been used for the binary conversion of color compounds cbcr depending on the certain threshold limits. In the experimental results from microscopy images of blood samples, the proposed algorithm was compared with several other algorithms for detection by using a quality scale that compares manual cell count with automatic detection of algorithms where the proposed algorithm obtained a high distinction accuracy reached to 100% compared to other methods.
E-Services Integration Framework Based on SOA

A group of governmental agencies is connected over a virtual private network (VPN), and uses variant computing systems to perform the daily tasks, they need to exchange formal data electronically, at the same time, and they cannot replace their legacy systems. In this research, I will study this case and propose a suitable solution based on service-oriented architecture (SOA), which has proven highly efficiency within the integration of distributed systems. The research has developed e-services to extract data from the legacy systems as per each agency policy, designed and developed a framework, which will conduct data exchanging between e-services, monitor e-services consuming, notify providers by the recent activities and apply the security policy. In addition, the research has tested the performance of the solution over variable loads, scanned the security gaps and proposed a load balancing method to maintain the stability of the system on subscribers increase in number.
Automatic generation of software components of the Praxeme methodology from ReLEL

Praxeme is an enterprise methodology that allows you to master the complexity of a system through the principle of separating the concerns of the enterprise into a homogeneous whole called `aspects'. The use of eLEL (elaborate Lexicon Extended Language) is interesting to specify the intentional aspect of Praxeme since all the information that makes up the logical services of this methodology is described in eLEL. Consequently, the automatic localization of the logical service from eLEL at the time of logical modeling becomes obvious. However, the resulting logical services cannot be used as code skeletons for an object-oriented application. We thought that ReLEL was a terminology database that contained all the specific terms used by the company. Therefore our strategy in this article focuses on the automatic generation of software component of the Praxeme methodology from the intentional aspect represented by ReLEL. To achieve this, we have proposed rules allowing to automatically translate according to the model-to-text approach of the MDA framework the logical components obtained from ReLEL into software components while taking into account the technical aspect of the Praxeme methodology. However to validate our approach, we measured the coupling and cohesion for the UML class diagram out of Java class generated from the approach proposed in this paper using the slicing technique. The result indicates a weak coupling and a strong cohesion allowing to deduce that the approach proposed in this paper makes it possible to produce a more reliable and efficient system.
From Do You See What I See? to Do You Control What I See? Mediated Vision, From a Distance, for Eyewear Users

We discuss engineering aspects for shifting from “do you see what I see?” applications that stream the user’s field of view to remote viewers toward “do you control what I see?” features in which remote viewers are given the opportunity and tool to control the primary user’s field of view. To this end, we present two applications for (1) smartglasses with embedded video camera for live video streaming and (2) the HoloLens HMD that presents users with mediated versions of the visual world controlled by remote viewers.
An evaluation of voice conversion with neural network spectral mapping models and WaveNet vocoder

This paper presents an evaluation of parallel voice conversion (VC) with neural network (NN)-based statistical models for spectral mapping and waveform generation. The NN-based architectures for spectral mapping include deep NN (DNN), deep mixture density network (DMDN), and recurrent NN (RNN) models. WaveNet (WN) vocoder is employed as a high-quality NN-based waveform generation. In VC, though, owing to the oversmoothed characteristics of estimated speech parameters, quality degradation still occurs. To address this problem, we utilize post-conversion for the converted features based on direct waveform modifferential and global variance postfilter. To preserve the consistency with the post-conversion, we further propose a spectrum differential loss for the spectral modeling. The experimental results demonstrate that: (1) the RNN-based spectral modeling achieves higher accuracy with a faster convergence rate and better generalization compared to the DNN-/DMDN-based models; (2) the RNN-based spectral modeling is also capable of producing less oversmoothed spectral trajectory; (3) the use of proposed spectrum differential loss improves the performance in the same-gender conversions; and (4) the proposed post-conversion on converted features for the WN vocoder in VC yields the best performance in both naturalness and speaker similarity compared to the conventional use of WN vocoder.
A Survey on Data Leakage Detection and Prevention

Nowadays, many organizations do the business form one level to another level. They do the business through the different agents and organizations. With the increment of the business level the storage and transmission of the data via one place to other also increased so in the middle may be any user can leak the data. Our main focus is to detect the guilt agent who has leak the Sensitive data. Information leakage can be referred to as the unofficial move of arranging data from a server area to the outside world. In those papers ,it describes the appropriation model for data leakage counteractive action and chooses a document allotment plan with minimal cover between the sets of records of the clients ,as a result ,it can detect leaked sources with high probability.Sensitive information in organizations may be company internal policies, money related data, individual charge card information, and organization-related data, this type of sensitive data can be a leak by a malicious user. Information leakage leads to a major issue for various organizations .In some Data Leakage Detection model used the ‘fake objects’ which are store in the server database. The fake objects help to identify the user who has leaked the file. Every user has a probability to leak the file which probability is called the guilt probability. Those users who have the probability to leak the file is known as the guilt probability. Many models of detection of the data leakage focus on the fake object which is including with the database to find out the leaker.
Development of the algorithm for graphematic analysis and isolating of semantically significant constructions in poorly structured text

Abstract The article presents the results of analyzing the process of detecting patterns in semi-structured and unstructured texts. Text preprocessing is aimed at removing terms that do not carry useful information, bringing the words of the text to a single representation and forming a set of indices of a text information resource - statistical, linguistic, structural. An algorithm for graphematic analysis has been developed as the first stage of automatic processing of texts in natural language. The algorithm makes it possible to single out semantically significant natural language constructions in a semi-structured text using graphematic descriptors, to detect and replace abbreviations and abbreviations.
Hidden Markov Model-Based Human Action and Load Classification With Three-Dimensional Accelerometer Measurements

This paper proposes a classifier that simultaneously classifies the lifting action and its load weight based on the kinematic quantities of body motion. The proposed classifier is synthesized using the hidden Markov model framework. It is configured to classify an action as lowering, holding, or lifting, and a load weight as 0 kg or 20 kg. We conducted a lifting experiment with 30 subjects and collected the acceleration measurements of five body parts from the subjects when they performed the designated sequence of lifting actions. We performed a five-fold cross validation to evaluate the accuracy and the classification latency of the proposed classifier. The average accuracy of the action classification was determined to be 99.81%, and the average latency for the correct classification was 106.4 ms(±67.9). Further, the average accuracy of the load classification was determined to be 78.70%(±7.55) and the average latency was 88.1 ms(±24.1). Additionally, we proposed a “resetter” of the observed sequence for fast action classification. It was designed for a real world observed sequence that includes transient changes of actions. The result of the video frame analysis demonstrated that the proposed classifier with the resetter classifies the action and the load with an acceptable latency, in which the observed sequence includes the observations of different actions.
Weakly-Supervised Reconstruction of 3D Objects with Large Shape Variation from Single In-the-Wild Images

Existing unsupervised 3D object reconstruction methods can not work well if the shape of objects varies substantially across images or if the images have distracting background. This paper proposes a novel learning framework for reconstructing 3D objects with large shape variation from single in-the-wild images. Considering that shape variation leads to appearance change of objects at various scales, we propose a fusion module to form combined multi-scale image features for 3D reconstruction. To deal with the ambiguity caused by shape variation, we propose side-output mask constraint to supervise the feature extraction, and adaptive edge constraint and initial shape constraint to supervise the shape reconstruction. Moreover, we propose background manipulation to augment the training images such that the obtained model is robust to background distraction. Extensive experiments have been done for both non-rigid objects (birds) and rigid objects (planes and vehicles), and the results prove the superiority of the proposed method.
A Novel HOSFS Algorithm for Online Streaming Feature Selection

In recent days, Data stream mining is important for many of the real time and IOT based applications. Online feature selection is the one big topic of data stream mining which attracted researchers with intensive interest. This technique reduces the dimensionality of the streaming features by excluding inappropriate and redundant features. The researchers have proposed many online feature selection algorithm for streaming features like Grafting, Alpha-investing, OSFS, OGFS and SAOLA. Based on above studies the exiting algorithm has limitation over prediction accuracy and the large number of selected features. To overcome the limitations of above mentioned approaches, we propose an online feature selection algorithm for streaming features called Heuristic Online Streaming Feature Selection (HOSFS) which has advantages on choosing features from streaming features and omits the irrelevant and redundant features in real-time by using self-adaption sliding window protocol, and Heuristic function. The HOSFS algorithm assigns heuristic value to the features using the trained heuristic function and selects features with higher heuristic value where other features are considered as irrelevant features. This proposed technique results reduced number of strongly related features and obtains greater prediction accuracy with optimal features. HOSFS algorithm efficiency was tested with three different Health care datasets using MOA tools. Through the experimental outcomes, HOSFS has greater prediction accuracy and reduced number of selected features than alpha - investing, OSFS, and SAOLA.