Reviewerly/exHarmony huggingface dataset:
A Robustified Posterior for Bayesian Inference on a Large Number of Parallel Effects

Many modern experiments, such as microarray gene expression and genome-wide association studies, present the problem of estimating a large number of parallel effects. Bayesian inference is a popular approach for analyzing such data by modeling the large number of unknown parameters as random effects from a common prior distribution. However, misspecification of the prior distribution can lead to erroneous estimates of the random effects, especially for the largest and most interesting effects. This article has two aims. First, we propose a robustified posterior distribution for a parametric Bayesian hierarchical model that can substantially reduce the impact of a misspecified prior. Second, we conduct a systematic comparison of the standard parametric posterior, the proposed robustified parametric posterior, and nonparametric Bayesian posterior which uses a Dirichlet process mixture prior. The proposed robustified posterior when combined with a flexible parametric prior can be a superior alternative to nonparametric Bayesian methods.
Promoting Reusability and Extensibility in the Engineering of Domain-Specific Conversational Systems

Conversational systems are computer programs that interact with users using natural language. Considering the complexity and interaction of the different components involved in building intelligent conversational systems that can perform diverse tasks, a promising approach to facilitate their development is by using multiagent systems (MAS). This paper reviews the main concepts and history of conversational systems, and introduces an architecture based on MAS. This architecture was designed to support the development of conversational systems in the domain chosen by the developer while also providing a reusable built-in dialogue control. We present a practical application in the healthcare domain. We observed that it can help developers to create conversational systems in different domains while providing a reusable and centralized dialogue control. We also present derived lessons learned that can be helpful to steer future research on engineering domain-specific conversational systems.
Dynamic Projection Mapping Using Kinect-Based Skeleton Tracking

This paper presents a novel dynamic projection mapping which tracks a skeleton of a user and projects a sequence of images on the body of the user based on robust camera-project calibration without depending on accurate camera calibration using RGB-D image of the Kinect. As the first step of the proposed method, the camera-projector calibration using RGB-D images of the Kinect is performed without the printed calibration board. In the second step, the planes for the torso and palms are defined in 3D world coordinates based on the 2D skeleton information and RGB-D image. In the last step, the projection screen coordinates for the planes of the torso and palms are computed based on the homography between the camera image and the screen image for the projection with considering depth values of the planes of torso and palms. We proved the accuracy and convenience of the proposed method through the projection mapping experiments on a user in various poses.
No-Reference Stereoscopic Image Quality Algorithm Based on Features on DCT Domain

Existing image quality assessment methods can not extract stereoscopic features effectively. In this paper, a no-reference quality assessment method is proposed for stereoscopic images based on DCT domain features. The main steps of the proposed methods areas follows: (1)Synthesize the left and right images to two “cyclopean”maps respectively according to the binocular fusion characteristics and the binocular rivalry characteristics. (2)Extract the statistical features in DCT domain by using normalized DCT coefficient of the distorted image pairs and the obtained two “cyclopean”maps. (3)Use support vector regression(SVR) to predict the objective scores of stereoscopic images by establishing a map between the stereoscopic image feature vector and the difference mean opinion score(DMOS). The proposed method is evaluated by images from the LIVE Phase I and the LIVE Phase II database. Experimental results show that compared with other methods, the proposed method is consistent with subjective perception, and hence outperforms the existing methods.
A Support System for Designing Camera Blocking of Battle Scenes in Robot Anime

Japanese robot animation has a long history and many masterpieces have been produced. Robots in the anime have important roles for showing deep storytelling and unique drama. A “battle scene” which visualize a combat against one or more opponents is one of the most important factors of modern robot anime. In this paper, we propose a support system for designing a camera blocking of a battle scene in the robot anime. We have extracted 2531 shots in battle scenes in 10 existing robot animation works and classified them by 17 items such as robot's action, camera movement and shot size. The users can browse classified scenes and their detailed information on camera work and camera blocking stored in our library system, and use desired one as a reference when they draw their original battle scene. Experimental results show that our system is useful for the user without production experience, and facilitates drawing a camera blocking of the battle scene.
Soundscape

The design of New Musical Interfaces (NMIs) has been informed by principles and methods core to Human-computer Interaction. Largely, the field of NMIs remains focused on usability as it evaluates and designs interfaces. Both fields, however, recognize but still wrestle with the need to adopt a more humanistic approach to both design and evaluation. In this project a design process informed by participatory design principles was followed. Through the discussions and evaluations within that process, the details of musicians' needs and values emerged indicating a need to step beyond usability. Soundscape, the resulting NMI, has value as a physical embodiment of the range of needs and values of those musicians in its design as an interface to support creativity. Further, it is hoped that through demonstration to the NMI design community critical discourse around humanistic design considerations will be supported.
Piecewise multi-regression

We have coined a new "Multi-regression" technique by the name "Piecewise Multi-regression". This is a kind of a non-linear extension to "Segmented Regression" or else known as "Broken-Stick Regression". This is a new method in regression in which the independent variables are partitioned into intervals and a separate non-linear function segment is fit into each interval independently for each dependent variable. In order to demonstrate the power of the new technique, we have chosen the domain of "Housing" in the Kingdom of Saudi Arabia - in particular housing in Riyadh, the capital. In this paper we focus on Parcels of Land, Villas, and buildings index/prices selling transactions and renting per month/year. We compute "Housing Index" for selling transactions per month/year, and for rental per month/year. In this paper we use a test bed experimental environment utilizing the KNIME tool to demonstrate the power of the "Piecewise Multi-Regression". We use Neural Network Regression, Bayesian Non-Linear Regression, Further Boosted Decision Tree Regression, Non-Native Non-Linear Regression, and Advanced Decision Forest Regression as possible regression algorithms for various segments (districts) to better fit the best piecewise prediction model. We compare "Global Prediction" model with both "Ensemble" and "Piecewise" Multi-regressions. We have found that the new "Piece-Wise Multi-Regression" is superior to "Ensemble of Multi-Regression" for prediction index and prices.
Fusion Learning Model for Embedded Face Safe Detection and Facial Gesture Analysis

This paper proposes a cloud-assisted semi-automated image annotation method, and a deep learning model of cascaded fusion Embedded Face Safe Detection and facial pose analysis. It uses only the same type of network with different complexity to complete facial area detection and facial gesture analysis. At the same time, thanks to the lightweight design of the network, the algorithm has extremely fast running speed. It can basically meet the real-time processing requirements of specific video, and has practical application value.
Distributed SGD Generalizes Well Under Asynchrony

The performance of fully synchronized distributed systems has faced a bottleneck due to the big data trend, under which asynchronous distributed systems are becoming a major popularity due to their powerful scalability. In this paper, we study the generalization performance of stochastic gradient descent (SGD) on a distributed asynchronous system. The system consists of multiple worker machines that compute stochastic gradients which are further sent to and aggregated on a common parameter server to update the variables, and the communication in the system suffers from possible delays. Under the algorithm stability framework, we prove that distributed asynchronous SGD generalizes well given enough data samples in the training optimization. In particular, our results suggest to reduce the learning rate as we allow more asynchrony in the distributed system. Such adaptive learning rate strategy improves the stability of the distributed algorithm and reduces the corresponding generalization error. Then, we confirm our theoretical findings via numerical experiments.
Design method for an aspheric Schwarzschild objective with a low obscuration and an infinite conjugate distance

This paper proposes a design method for an aspheric Schwarzschild objective with a low obscuration and infinite conjugate distance. The method takes the obscuration ratio as a priority condition, establishes the relationship between magnification and image distance of the infinite reflector objective, and solves the expressions of aspheric curvature radius and aspheric coefficient. It overcomes the constraint of concentric sphere in the conventional design method and effectively reduces the obscuration ratio. For different structural parameters of the Schwarzschild objective, the model can be used to obtain an initial structure whose imaging quality is close to the diffraction limit.
Genetic algorithm based estimation of non–functional properties for GPGPU programs

Non-functional properties, like execution time or memory access information, of programs running on graphics processing unit (GPUs) can raise safety and security concerns. For example, understanding the execution time is critical for embedded and real-time applications. To this end, worst-case execution time (WCET) is an important metric to check the real-time constraints imposed on embedded applications. For complex execution platforms, such as GPUs, analysis of WCET imposes great challenges due to the complex characteristics of GPU architecture as well as GPU program semantics. GPUs also have specific memory access behavior. Observing such memory access behavior may reveal sensitive information (e.g. a secret key). This, in turn, may be exploited to launch a side-channel attack on the underlying program. In this paper, we propose GDivAn, a measurement-based analysis framework for investigating the non-functional aspects of GPU programs, specifically, their execution time and side-channel leakage capacity. GDivAn is built upon a novel instantiation of genetic algorithm (GA). Moreover, GDivAn improves the effectiveness of GA using symbolic execution, when possible. Our evaluation with several open-source GPU kernels, including GPU kernels from the OpenSSL and MRTC benchmark suite, reveals the effectiveness of GDivAn both in terms of finding WCET and side-channel leakage.
A Novel Permutation Based Encryption Using Tree Traversal Approach

Background: In 21st century one of the emerging issues is to secure the information stored and communicated in digital form. There is no assurance that the data we have sent may be hacked by any hacker and the data we have sent may reach correctly to the receiver or not. Thus, confidentiality, integrity, and authentication services play major role in Internet communication. Encryption is the process of encoding messages in such a way that only authorized parties can read and understand after successful decryption. Several data security techniques have been emerged in recent years, but still there is a need to develop new and different techniques to protect the digital information from attackers. This paper provides a new idea for data encryption and decryption using the notion of binary tree traversal to secure digital data. Objective: To develop a new data encryption and decryption method using the notion of binary tree traversal to secure data. Methods: The proposed method uses both transposition and substitution techniques for converting plaintext into ciphertext. The notion binary tree in-order traversal is adapted as transposition and Caesar cipher technique for substitution. Results: From the result, it observed that the repeating letters in the plaintext are replaced with different cipher letters. Hence, it is infeasible to predict the plaintext message easily using letter frequency analysis. From the experimental result, it is concluded that the proposed method provides different ciphertext for the same plaintext message when the number of round varies. The time taken by the proposed method for encryption is very less. Conclusion: A simple encryption method using binary tree in-order traversal and Caesar cipher is developed. Encrypting data using binary tree traversals is a different way while compared with other traditional encryption methods. The proposed method is fast, secure and can be used to encrypt short messages in real time applications.
CoLR: Classification-Oriented Local Representation for Image Recognition

Naïve sparse representation has stability problem due to its unsupervised nature, which is not preferred for classification tasks. For this problem, this paper presents a novel representation learning method named classification-oriented local representation (CoLR) for image recognition. The core idea of CoLR is to find the most relevant training classes and samples with test sample by taking the merits of class-wise sparseness weighting, sample locality, and label prior. The proposed representation strategy can not only promote a classification-oriented representation, but also boost a locality adaptive representation within the selected training classes. The CoLR model is efficiently solved by Augmented Lagrange Multiplier (ALM) scheme based on a variable splitting strategy. Then, the performance of the proposed model is evaluated on benchmark face datasets and deep object features. Specifically, the deep features of the object dataset are obtained by a well-trained convolutional neural network (CNN) with five convolutional layers and three fully connected layers on the challenging ImageNet. Extensive experiments verify the superiority of CoLR in comparison with some state-of-the-art models.
Action Scheduling Optimization using Cartesian Genetic Programming

Action scheduling optimization is a problem that involves chronologically organizing a set of actions, jobs or commands in order to accomplish a pre-established goal. This kind of problem can be found in a number of areas, such as production planning, delivery logistic organization, robot movement planning and behavior programming for intelligent agents in games. Despite being a recurrent problem, selecting the appropriate time and order to execute each task is not trivial, and typically involves highly complex techniques. The main objective of this work is to provide a simple alternative to tackle the action scheduling problem, by using Cartesian Genetic Programming as an approach. The proposed solution involves the application of two simple main steps: defining the set of available actions and specifying an objective function to be optimized. Then, by the means of the evolutionary algorithm, an automatically generated schedule will be revealed as the most fitting to the goal. The effectiveness of this methodology was tested by performing an action schedule optimization on two different problems involving virtual agents walking in a simulated environment. In both cases, results showed that, throughout the evolutionary process, the simulated agents naturally chose the most efficient sequential and parallel combination of actions to reach greater distances. The use of evolutionary adaptive metaheuristics such as Cartesian Genetic Programming allows the identification of the best possible schedule of actions to solve a problem.
Redesigning Current Banknotes with Blockchain Infrastructure: A Model Proposal

The aim of the model proposed is to introduce a new hybrid system that can be used with less effort and requiring very few modifications in the existing structures. The model combines the advantages of both digital currency and paper-based banknote system. It uses QR Coded banknotes, digital wallets and blockchain technology to ensure, confirm and enhance the ownership of the banknotes. This model is intended to be used at the macro level by governmental policies since the money system can only be regulated by them.
Mining Requirements Arguments from user Forums

In order to sustain, software systems have to evolve in favor of its main target users. Due to the pervasive adoption of online user forums and social media, collecting users feedbacks and comments become possible. However, such crowd generated data are often fragmented, with various viewpoints mentioned during a series of message exchange. The aim of the thesis is to propose an argumentation-based CrowdRE approach, which represents such group conversations as a user argumentation model with the original conversation structure reserved. Based on the argumentation model, we are able to identify new features proposed by the crowd-users or issues encountered, and their supporting and attacking arguments using argumentation theory. To accomplish this research, we adopted an abstract argumentation, bipolar argumentation framework, and coalition-based meta argumentation framework. In addition, to provided automated support to our proposed approach, algorithms will be developed for bipolar argumentation, coalition-based meta argumentation, and end-users voting mechanism. Finally, this thesis employees different machine learning algorithms to automatically classify crowd-users comments into rationale elements and identify conflict-free features or claims based on their supporting and attacking arguments. Initial results show that the proposed approach can identify features, issues and their supporting and attacking arguments with acceptable performance.
The OpenScience Slovenia metadata dataset

The OpenScience Slovenia metadata dataset contains metadata entries for Slovenian public domain academic documents which include undergraduate and postgraduate theses, research and professional articles, along with other academic document types. The data within the dataset was collected as a part of the establishment of the Slovenian Open-Access Infrastructure which defined a unified document collection process and cataloguing for universities in Slovenia within the infrastructure repositories. The data was collected from several already established but separate library systems in Slovenia and merged into a single metadata scheme using metadata deduplication and merging techniques. It consists of text and numerical fields, representing attributes that describe documents. These attributes include document titles, keywords, abstracts, typologies, authors, issue years and other identifiers such as URL and UDC. The potential of this dataset lies especially in text mining and text classification tasks and can also be used in development or benchmarking of content-based recommender systems on real-world data.
Design of Image Generation System for DCGAN Based Picture Book Text

When a picture book is photographed with a smart device, the text is analyzed for meaning and associated images are created. Image creation is the first step in learning DCGAN using class lists and images. In this study, DCGAN was trained with 11 classes and images of 1688 bears, which were collected by ImageNet for design. The second step is to shoot the image and text of the picture book on a smart device, and convert the text part of the shot image into a system readable character. We use the morpheme analyzer to classify nouns and verbs in text, and Discriminator learn to recognize the classified parts of speech as latent vectors of images. The third step is to create an associated image in the text. In the picture book, take the text of the part without the image and extract nouns and verbs. The extracted parts of speech and the learned latent vector are used as Generator parameters to generate images associated with the text.
Learning Non-Stationary Dynamic Bayesian Network Structure from Data Stream

Dynamic Bayesian network (DBN) is a useful model for identifying conditional dependencies in time-series streaming data. Non-stationary Dynamic Bayesian network (nsDBN) is a special DBN in which the conditional dependence structure of the underlying data-generation process is permitted to change over time. The current nsDBN structure learning methods can handle abrupt concept drift in streaming data well, but not gradual concept drift. In this paper, we propose a nsDBN learning method that can handle gradual concept drift well. This method first learns the initial DBN structure using local search and global optimization algorithms and then updates the transition networks by continuously searching the structure space with one edge change for each step. The Bayesian-Dirichlet equivalent (BDe) metric is extended for efficient calculation and local searching sub-tasks are executed in parallel from historical local optimized structures. The experimental evaluations show that this method had better precision than other popular methods when learning nsDBN from stream data with gradual concept drift.
Convolutional Grid Long Short-Term Memory Recurrent Neural Network for Automatic Speech Recognition

The Grid Long Short-Term Memory (Grid-LSTM), which is consisted of three steps, i.e., two-dimensional grid splitting, local feature projection, and grid sequence modeling, has been widely used in Automatic Speech Recognition (ASR) tasks, since it has a strong time-frequency modeling ability. However, the network suffers from a serious problem that heavy computing time is always required. It can be found that the reason for this problem is in the last step, two cross-working LSTMs are employed to model time-frequency features in the grid via an analysis of its process. Thus, we try to speed up the Grid-LSTM by using a smaller grid and propose two enhanced Grid-LSTM models, i.e., Convolutional Grid-LSTM (ConvGrid-LSTM) and Multichannel ConvGrid-LSTM (MCConvGrid-LSTM) to reduce the grid size from the two dimensions of the Grid-LSTM respectively. In the frequency axis, we try to do this by using a large frequency stride and further to prevent performance loss by embedding a CNN in the Grid-LSTM. Moreover, in the time axis, we model several adjacent frames by the multichannel processing ability of CNN. Our method achieves $$54\%$$ relative reduction of training time and $$19\%$$ relative reduction of Word Error Rate (WER) for a character level End-to-End ASR task.