Reviewerly/exHarmony huggingface dataset:
Sound Objects and Spatial Morphologies

One of Pierre Schaeffer’s achievements in his musical research was his proposal of the sound object as a basic unit of musical experience and his insistence on listening as a main focus of research. Out of this research grew a radical new music theory of sound-based composition. This article will draw on this extensive research to explore the spaces where this music is heard and present the claim that the space in which music is experienced is as much a part of the music as the timbral material itself. The key question here is the changes made to timbral material through acousmatic spatial listening and the subjective analysis affordance of the listeners’ placement and perspective. These consequences are studied from a phenomenological and psychoacoustic perspective and it is suggested that Schaeffer’s research on timbral and musical concepts can be extended to include spatial features.
Object-oriented Implementation of Chess Game in C++

In this paper, we implement the standard chess game using C++, a popular object-oriented programming language. Our program is developed and fully tested on Mac OS X system. It can be ran in terminal and allows two players to compete together. The object-oriented characteristics of C++ include namely, abstraction, inheritance, encapsulation and polymorphism are highly facilitates the development. At the end of the paper, we also points out some aspects that can be further improved.
Automatic reconstruction of overlapped cells in breast cancer FISH images

Abstract This paper presents a new image processing and analysis technique for the quality evaluation of cell nuclei to support medical diagnostics in breast cancer. The technique allows cell nuclei that are deformed or overlapped by biological material to be reconstructed. The paper proposes a sensitivity and similarity approach, enriching the PatchMatch correspondence algorithm in accurate cell reconstruction. Its application in reconstruction processes enables accelerated computations and an increased probability of obtaining appropriate segmentation results. The numerical results demonstrate that the developed system allows for automatic and effective cell nuclei reconstruction with an acceptable average area accuracy level above 85% compared with manual human results (assuming manual segmentation as a true value). The reconstruction system allows for the recovery of the proper shape of the analyzed distorted cells very rapidly and in a repeatable manner. An additional advantage of the procedure is that the nuclei area overlapped by artifacts or other cells can be determined. The experimental results prove the high utility of the method in final HER2 gene amplification assessment in breast cancer images.
79‐1: Framework for Evaluating Display Resolution and Size in the Context of Video Compression and Visual Acuity

We present a framework for assessing the interaction of display resolution, display size, and video compression in the context of visual acuity. We review retinal topology in the context of several kinds of acuity: reading (optotype) acuity; grating acuity; and hyperacuity. We show that display resolution and field‐of‐view combinations correspond to each level of visual acuity. We also show how quantization of spatial‐frequency coefficients in video compression relates to each level of visual acuity.
A pyramid machine learning model for polyp classification via CT colonography

In this article, we propose a pyramid multilayer machine learning method to combine classification and feature selection into the same model for polyp classification. This model provides a solution to pick the best attributes from three different texture features to form a new descriptor set with much better classification results. Generally, this method has several good properties including generalization, extendibility, and monotonicity. From its performance, the original metric image descriptor (MD) and the post-histogram-equalized metric image descriptor (PMD) form a descriptor pair as the preliminary unit of this pyramid framework. This model is driven by a feature merging performance unit run iteratively until the final results are obtained. After every feature merging step, a new attribute group is selected to construct a shorter but much stronger new descriptor. To reach this purpose, a forward selection method is adopted only to select attributes from every descriptor with positive gains for classification. Therefore, this feature merging performance provides a guarantee of the classification’s monotonicity in the practice. In our experiments, a simple scheme is designed to illustrate its construction and performance. Three image metrics are selected including intensity, gradient and curvature which are put into the gray-level co-occurrence matrix (CM) model to construct polyp descriptors. Random forest is chosen as the classifier and Gini coefficient is used to be the importance score. The AUC (area under the curve of receiver operating characteristics) scores are our evaluation measure. Experimental results showed that the pyramid learning model outperforms other methods over 4%-6% by AUC scores.
Computer-Aided Intervention for Reading Comprehension Disabilities

Our research work focuses on grouping of students based on error patterns in assessment outcomes for effective teaching of reading comprehension in early elementary education. The work can facilitate placement of students with similar reading disabilities in the same intervention group to optimize corrective actions. We collected ELA (English Language Arts) assessment data from two different schools in NY, USA, involving 365 students in total. To protect individual privacy of the participants, no background information that can possibly lead to their identification is collected for the study. To analyze underlying factors affecting reading comprehension without students' background information and to be able to evaluate the work, we transformed the problem to a K-nearest neighbor matching problem—an assessment should be matched to other assessments performed by the same student in the feature space. The framework allows exploration of various levels of reading skills as the features and a variety of matching mechanisms. In this paper, we present studies on low-level features using the computer-generated measures adopted by literacy experts for gauging the grade-level readability of a piece of writing, and high-level features using human-identified reading comprehension skills required for answering the assessment questions. For both studies, the matching criterion is the distance between two feature vectors. Overall, the low-level feature set performs better than the high-level set, and the difference is most noticeable for K between 15 and 30.
Multi-Attribute Probabilistic Linear Discriminant Analysis for 3D Facial Shapes

Component Analysis (CA) consists of a set of statistical techniques that decompose data to appropriate latent components that are relevant to the task-at-hand (e.g., clustering, segmentation, classification). During the past years, an explosion of research in probabilistic CA has been witnessed, with the introduction of several novel methods (e.g., Probabilistic Principal Component Analysis, Probabilistic Linear Discriminant Analysis (PLDA), Probabilistic Canonical Correlation Analysis). A particular subset of CA methods such as PLDA, inspired by the classical Linear Discriminant Analysis, incorporate the knowledge of data labeled in terms of an attribute in order to extract a suitable discriminative subspace. Nevertheless, while many modern datasets incorporate labels with regards to multiple attributes (e.g., age, ethnicity, weight), existing CA methods can exploit at most a single attribute (i.e., one set of labels) per model. That is, in case multiple attributes are available, one needs to train a separate model per attribute, in effect not exploiting knowledge of other attributes for the task-at-hand. In this light, we propose the first, to the best of our knowledge, Multi-Attribute Probabilistic LDA (MAPLDA), that is able to jointly handle data annotated with multiple attributes. We demonstrate the performance of the proposed method on the analysis of 3D facial shapes, a task with increasing value due to the rising popularity of consumer-grade 3D sensors, on problems such as ethnicity, age, and weight identification, as well as 3D facial shape generation.
POLAR

POLAR is a platform to create and test unique geometric display configurations combining light with kinetic expressions in an adaptable modular system. It enables the utilization of light and motion in spatial contexts to create integrated ambient displays within many different environments. The intention of the study is providing a framework to facilitate the exploration of physical motion combined with light for creating new dynamic spatial displays in public or semi-public spaces. The system consists of controllable movable light modules which can be arranged spatially in different configurations combined with a software authoring and playback workflow to create and test motion choreographies.
Design Patterns Formal Composition and Analysis

Informal description of design patterns was adopted to facilitate their understanding by software developers. However, these descriptions lead to ambiguities limiting their successful use in real applications. Recently, several formal methods have been employed to guarantee precise specification of design pattern elements without insisting on their behavior. The main goal of this article is to propose a rewriting logic-based framework for enhancing formal description and reasoning on design patterns composition. The proposed model is integrated and executed under Maude using K-Maude tool. It allows first to associate a meta model notation, according to MDA approach, to all design patterns elements and then, a Maude semantic basis for specifying both their structural and dynamic aspects, is given. In particular, it will be shown how the authors' combined modeling approach is exploited for defining a set of operations on patterns, and how it will be used to formal analyze patterns composition.
Modeling The Number of Flood Occurrence in Indonesia in 2015 Using Poisson Regression Model

Abstract The Poisson regression model is one of the regression models that can show the relationship between the Poisson distributed response variable with predictor variables through the regression parameter. In this paper, the number of flood occurrences in Indonesia in 2015 which is the count data using a Poisson regression model. To obtain the Poisson regression model of the number of flood occurrence are conducted using multicollinearity test on predictor variables, estimation of regression parameters, parameter test, and goodness of fit model test. The method used to estimate parameter values is the Maximum Likelihood Estimation method that is obtained from the iteration results using the Newton-Raphson iterative method. Based on the results of data processing, the Poisson regression model that is obtained for the number of flood occurrences shows that the average of the number of flood occurrences in Indonesia is affected by the number of tornado occurrences, the number of rainy days, and the sun exposure.
Single-Sentence Compression using XGBoost

Sentence compression is known as presenting a sentence in a fewer number of words compared to its original one without changing the meaning. Recent works on sentence compression formulates the problem as an integer linear programming problem (ILP) then solves it using an external ILP-solver which suffers from slow running time. In this article, the sentence compression task is formulated as a two-class classification problem and used a gradient boosting technique to solve the problem. Different models are created using two different datasets. The best model is taken for evaluation. The quality of compression is measured using two important quality measures, informativeness and compression rate. This article has achieved 70.2 percent in informativeness and 38.62 percent in compression rate.
Construction and Merging of ACM and ScienceDirect Ontologies

An Ontology is an absolute formal conceptualization of some realm of significance. Nowadays Ontologies play a vibrant part in Information Architecture, Biomedical Informatics, Electronic commerce, Software Engineering, Semantic Web, Knowledge management, Artificial Intelligence and etc. Huge number of Ontologies and extensive variety of Ontologies are available for every single domain. It creates very difficult to maintain and access all the existing Ontologies. Ontology merging is the solution to overcome this kind of problems. Ontology merging is a procedure of fetching two existing Ontologies as input and obtains a newly merged Ontology as output. The merged Ontology will have common concepts and relationships between two Ontologies. This paper presents how two Ontologies can be constructed and merged using Protege and Conexp tools with an example.
Numerical analysis of blood flow in intracranial artery stenosis affected by ischemic stroke using Finite Element method

Abstract Intracranial stenosis is a narrowing an artery inside the brain. This causes reduced blood flow to the cerebrum and damaged neurons in a short span of time due to reduced oxygenation. Intracranial stenosis can cause ischemic stroke due to a plaque occlusion. This research uses a mathematical model that expresses the velocity of arterial flow of an intracranial stenosis causing ischemic stroke that is expressed by the momentum equation and solved using the finite element method. The analysis of blood velocity is simulated using MATLAB and FLUENT software. This study looks at the effect of plaque thickness and initial speed of blood flow during an intracranial stenosis. The results shows that the larger the plaque thickness, the smaller the area reduction, so the greater the increases blood velocity and the higher the initial velocity of blood that given, the greater the increase of the initial velocity of blood in the artery Intracranial stenosis. According to the MATLAB simulation, blood velocity with 80% plaque thickness increases blood velocity to 1.8665 m/sec at rest and 1.8825 m/sec at exercises. When the initial velocity of blood was 0.2 m/sec, it increases to 0.5712 m/sec at rest and 0.70058 m/sec at exercises.
Data-Based Design of Inverse Dynamics Using Gaussian Process

Model-based controller design has been widely utilized for various purposes, and many methodologies have been proposed to identify accurate models of the target plants. In this paper, a different methodology to design dynamics model, particularly inverse dynamics model is proposed using Gaussian process. The design process and selection of training input pattern for inverse dynamics learning Gaussian process are analyzed in this paper. The simulation results reveal the potential and limitation of the proposed Gaussian process based inverse dynamics learning.
Adaptive steganography based on integer wavelet transform using block standard deviation

The capacity and security of confidential message on communication channels are two important challenges in steganography. The main objective of this study is introducing a new method based on integer wavelet transform to maintain the image quality and enhance the security of the confidential message. At first a three level integer wavelet transform is taken from the cover image. Then each level of the image is decomposed into 4 sub-bands and the middle frequencies which contain the horizontal and vertical details of the image in each level are split into non-overlapping 2×2 blocks. Next, the standard deviation of each block is calculated and based on the length of the confidential message the maximum value of the standard deviation Tmax is computed and selected for embedding. The confidential message is embedded in two changing Least Significant Bits (LSB) bits so that the standard deviation of each chosen block does not fall below Tmax. Additionally to increase the security, the confidential message is encrypted using the Advance Encryption Standard (AES) algorithm. The test results show that the proposed method has been considerably improved in terms of the Peak Signal to Noise Ratio (PSNR).
Architecture-aware Memory Access Scheduling for High-throughput Cascaded Classifiers

Cascaded classifier based object detectors are popular for many applications because of their high efficiency. Many researches have been devoted to developing the corresponding hardware accelerators. To reduce the circuit complexity while maintaining sufficient throughput, on-chip memories are commonly partitioned into several banks for parallel data access. However, since the coefficients of feature extraction are irregular, memory access conflict would frequently occur without proper scheduling. The proposed scheme explicitly schedules the access sequence as a post-processing for managing the coefficient memory. By formulating the desired sequence as a graph model, the classical graph coloring theory can then be adopted to solve the scheduling problem. In addition, the proposed graph model also considers the resource constraint on intermediate storage. Experimental results show that the throughput and area-efficiency of the target cascaded classifier can be greatly improved by adopting the proposed scheme as compared to the related work.
Abnormal Behavior Detection of ATM Surveillance Videos Based on Pseudo-3D Residual Network

As a major source of social big data, surveillance videos of self-service banks have been an important part of social security. Automated video analysis is required since manual screening of these video streams is costly and slow. Among current automated video analysis methods, automated 3D convolution networks are effective on video analysis but consume exceptional time and memory overheads. In this paper, we propose to apply Pseudo-3D Residual network (P3D ResNet) to replace 3*3*3 convolution, which can reduce the amount of calculation. To improve the accuracy of P3D ResNet on our videos, we further preprocess the videos by extracting human pose information with `openpose' tools. With the P3D ResNet model and `openpose' preprocessing tools, we build an abnormal behavior detection prototype for self-service banks surveillance videos. Due to the help of the pose information, the accuracy of behavior classification of video from banks is improved by at least 7%, which cannot be disclosed to the public. To ensure the audience can repeat our experiments, we further evaluated our prototyping system with self-made videos emulating bank settings to show that the accuracy of our system is improved by 11%.
Simultaneous Image Encryption and Compression Using Modified Huffman Tables

In this study, by modifying standard JPEG compression algorithm, simultaneous compression, encryption and simultaneous decompression, decryption procedure is inspected. Standard Huffman variable length coding tables are modified for encryption purposes. Simultaneous compression and encryption is achieved. Modified tables have not lost the variable length code properties.
Vulnerability analysis in critical infrastructures: A methodology

&lt;i&gt;Vulnerability assessment is a crucial aspect for the development of methodologies to define the levels of protection in critical infrastructures. Throughout this research, we discussed the concept of vulnerability and methodologies and processes for its assessment in critical infrastructures due to a terrorist threat. The research focused on the development of an analysis model, exploring a multi-criteria decision method, in order to limit the risks to the maximum extent possible. Through a qualitative research methodology, in which we applied an analysis model based on the Threat and Infrastructure dimensions and their respective factors, we verified that the vulnerability of a critical infrastructure consists in the probability of the success of an attack, conducted by a threat - properly identifi ed, characterised, analysed and categorised - against an infrastructure with certain characteristics, which value is defi ned by the user and aggressor’s point of view. The construction of an algorithmic model for vulnerability assessment, complemented by tools to support the calculations and records, allows, through a rational, scientific and algebraic process, a qualitative analysis of factors to be transformed into measurable and quantifi able values, whose algebraic operation integrates them into a final result that expresses, as a percentage, the degree of vulnerability of a critical infrastructure facing a terrorist threat.&lt;/i&gt;
A Novel Information Integration Algorithm for Speech Recognition System: Basing on Adaptive Clustering and Supervised State of Acoustic Feature

Abstract When utilizing the most likely state sequence (MLSS) criterion in Gauss mixture model-hidden Markov model (GMM-HMM) to acquire the best state series of observations, only the maximum likelihood state of speech frame is considered. Therefore, the influence of other states is neglected, which leads to the losing of some important information, and further reduces the recognition rate of the system. In this paper, we propose two new features, which are called state likelihood cluster feature (SLCF) and supervised state feature (SSF), to both reflect acoustic features and fuse state information. Combining SLCF and SSF with Mel frequency cepstrum coefficient (MFCC), Mel frequency cepstrum &amp; state likelihood cluster feature (MSLCF) and Mel frequency cepstrum &amp; supervised state feature (MSSF) are formed, respectively. By the proposed MSLCF and MSSF in Chinese speech recognition experiment, the relative error rate of the isolated word recognition system declines 6.10% and 9.66%, respectively, and the relative error rate of the continuous speech recognition system declines 2.53% and 11.05%, respectively.