Reviewerly/exHarmony huggingface dataset:
Mapping Sound

Each playthrough of Ed Key and David Kanaga's Proteus (2013) presents players with a new, randomly generated island to explore. This unstructured exploration is accompanied by a procedurally generated ambient soundtrack that incorporates both harmonic textures and melodic motives, and abstract musical representations of environmental sounds. In the absence of clearly defined goals—except to progress through four distinct “seasons” of the game—the player's relationship to the soundtrack becomes a core gameplay element, and a playthrough of Proteus becomes, among other things, a kind of improvised performance art. Viewed from this perspective, Proteus's combination of free exploration and chance strongly evokes ideas from mid-twentieth-century musical modernism, including the graphic scores of Cardew and Cage and the “mobile form” works of Stockhausen and Ligeti. Proteus further complicates analysis by concealing the mechanisms that produce particular musical fragments and by eliding the roles of listener and player/performer. This article examines the tensions inherent in the complementary actions of playing/performing Proteus and listening to/analyzing it, and argues that the game challenges the distinctions between creator, performer, and observer by vividly embodying the most deeply ingrained metaphors of music analysis.
Devise Sparse Compression Schedulers to Enhance FastText Methods

In natural language processing(NLP), the general way to understand the meaning of a word is via word embedding. The word embedding training model can convert words into multidimensional vectors and make the words that do not know “meaning” into vectors with “meaning”. Famous word embedding training models, include models such as FastText, Word2Vec, and GloVe. They can train words into vectors and then they are used for further semantic classifications. In this paper, we work on the efficient support for the FastText. FastText is an open source library created by Facebook(FAIR) lab that allows users to learn word embedding and text classification. We focus on the word representation application in FastText, in which general matrix-Vector multiplication(GEMV) is one of the most computationally intensive operations. In this paper, we adjust the software architecture of FastText, and pre-process the pre-trained model offline. In addition, we introduce a new accelerating method with sparse matrix compression in Halide, which improves performance by compressing the matrix. Our support with Halide sparse compression schedulers include hybrid compression schemes and re-ordering methods to improve the performance.
Safe and Optimal Path Planning for Autonomous UAV Using a Decomposition-Coordination Method

In this paper, we propose a new Decomposition-Coordination Method (DCM) to solve the nonlinear problem of optimal path planning, for autonomous Unmanned Aerial Vehicle (UAV) in a dynamic environment. The main objective of this work is to enable safe autonomous navigation to the UAV. Our algorithm of decomposition-coordination computes initially an optimal path leading to the desired position, and according to the information supplied by a deciding unit, the UAV can predict the potential collisions, and avoid them by computing new collision-free paths if needed, allowing more reactivity to the UAV. This approach consists of first, choosing a suitable mathematical model that depicts the dynamics of the UAV, to which we associate the objective functions to end up with a multi-objective optimization problem. We proceed then to the resolution of the nonlinear system, by subdividing it into a set of smaller interconnected subsystems. The DCM is then used to achieve a local treatment of the non-linearity and reduce the processing time, where each subsystem is split between two levels for parallel processing, and the coordination is ensured after the resolution of the system using the method of Lagrange multipliers. We study as well the convergence and the stability of the algorithm, then we present the results of our simulation on Matlab to corroborate the potential of this theoretical method.
Evaluation of ELINT system effectiveness based on Grey Relational Optimization Algorithm

Abstract Aiming at the problems and shortcomings of the current electronic intelligence reconnaissance system effectiveness evaluation methods, an ELINT system effectiveness evaluation algorithm based on grey correlation optimization algorithm is proposed. The algorithm firstly combines the principle of ELINT system to build its evaluation index system. Then, based on the subjective and objective attributes of the index, the algorithm combines the G1 sequence subjective weighting method with the CRITIC objective weighting method to obtain the combined weight of each index. Finally, the improved gray correlation optimization algorithm model is used to analyze the correlation of the index value of the system under test, and the system performance is determined by comparing each distance with the optimal system. Theoretical analysis and simulation results show that the method effectively combines qualitative and quantitative analysis and is feasible in the performance evaluation of ELINT system.
Fast Hartley Transform based Elliptic Curve Cryptography for resource constrained devices

The growing security and vulnerability threats in apps, software, etc., affect smart gadgets. But, it is computationally not feasible to run the advanced security algorithms in resource-constrained devices (smart gadgets). For example, the encryption and the decryption time of security algorithms in smart gadgets are higher than the workstation/servers. Therefore, there is a need to optimize the time complexity of efficient security algorithms without compromising its security. Elliptic Curve Cryptography (ECC) is computationally less complex among the existing security algorithms due to its less key size. In this paper, to reduce the complexity involved in the scalar multiplication operation of ECC, Fast Hartley transform (FHT) based Hybrid ECC is proposed. From the experimental results, it is evident that using FHT in ECC has an improvement (4% to 8%) over FFT based ECC in terms of encryption and decryption time.
CrowbarLimbs: A Fatigue-Reducing VR Typing System

We present ”CrowbarLimbs”, a new method with two deformable extending virtual limbs for text entry in virtual reality (VR) which relies on a crowbar-like metaphor. Text entry is the basis of many applications but remains challenging in VR environments, where some body parts of a user may quickly get fatigued by using previous selection-based methods [Speicher et al. 2018], such as the ray metaphor [Lee and Kim 2017] and ”DrumStick” [Doronichev 2016]. By adding two deformable virtual limbs and placing the virtual keyboard at a user-preferred location, our method can help users to place their hands in a comfortable posture, thus reducing the physical fatigue of different body parts, such as arms and shoulders. To the best knowledge of ours, previous text entry papers have not yet discussed which metaphor is more suitable for reducing physical fatigue. We thus introduce “CrowbarLimbs” to allow less fatigue, good system usability, and comparable text entry speed/accuracy to previous methods.
Spatio-Temporal Attentive Network for Session-Based Recommendation

Session-based recommendation aims to predict the user's next click behavior based on the existing anonymous session information. Existing methods either only utilize temporal information of the session to make recommendations or only capture complex item transitions from spatial perspective to recommend, they are insufficient to obtain rich item representations. Besides, user's real purpose of the session is also not emphasized. In this paper, we propose a novel session-based recommendation method, named Spatio-Temporal Attentive Session-based Recommendation, STASR for brevity. Specifically, we design a hybrid framework based on Graph Neural Network (GNN) and Gated Recurrent Unit (GRU) to obtain richer item representations from spatio-temporal perspective. During the process of constructing corresponding session graph in GNN, an individual-level skipping strategy, which considers the randomness of user's behaviors, is proposed to enrich item representations. Then we utilize attention mechanism to capture the user's real purpose involved user's initial will and main intention. Extensive experimental results on three real-world benchmark datasets show that STASR consistently outperforms state-of-the-art methods on a variety of common evaluation metrics.
3D scan process optimisation study for rapid virtualization.

Introduction – Many of the problems facing 3d scanning as a digitisation method around the human form are caused by the time it takes to scan the entity. This can be solved by using multiple cameras organised in a way to scan the extremity simultaneously from multiple directions. This paper is the exploration around the minimum number of cameras needed to obtain a usable model. Methodology – Using a 5-stage experimental process for 17 subjects and batch processing each stage, determined the most efficient workflow. Results – Excluding the exploration subject, it was found that the use of 4 cameras simultaneously was 5.5 times faster, including processing time then it was to use a single camera. Conclusion – using multiple cameras makes the process 5.5 times faster, as well as batch processing, and having a standardised method to enable the use of algorithmic file processing.
A preliminary study on using acceptance tests for representing business requirements of smart contracts

In this work, we address the following question: "Which acceptance testing techniques are being used for representing business requirements of smart contracts?" To answer this question we performed a systematic mapping to determine whether acceptance tests for smart contracts and blockchain are being utilized to represent requirements and which acceptance testing techniques are being applied. This systematic mapping shows that, in fact, acceptance testing techniques are still not commonly used to represent the business requirements of smart contracts. We only found two studies using acceptance testing techniques with smart contracts. Moreover, only one of them uses an acceptance testing technique, the BDD (Behavior-Driven Development) notation, to represent the business requirements. In order to confirm that it is possible to represent business requirements of smart contracts, we developed an example that uses the BDD notation to represent requirements of a smart contract deployed in the Hyperledger blockchain. Preliminary results show that it is possible to use acceptance tests to represent business requirements.
The sparse group lasso for high-dimensional integrative linear discriminant analysis with application to alzheimer's disease prediction

In the big data era, high-dimensional data from multi-type data sources are ubiquitous and pose great challenge for statistical analysis. In this paper, we focus on two-class classification problem for this kind of data. Motivated by the least squares formulation of linear discriminant analysis, we propose an integrative discrimination analysis method by virtue of sparse group Lasso penalty. Thorough numerical studies are conducted and the results show that the proposed method compares favourably with other popular sparse discriminant proposals in terms of both variable selection accuracy and classification error. A real-data example is also given to illustrate its advantages.
Mobile-Based Hospital Recommendation According To Patient Needs Using SAW Method (Case Study: Banda Aceh)

To get the appropriate treatment, there are still obstacles in determining which hospital should be chosen, because it often does not suit the needs/conditions of the patient. Also, more time is needed to determine the hospital. So, a tool is needed to choose a hospital by considering the patient's needs and speeding up the process of determining the destination hospital. Several criteria are taken into consideration in choosing a hospital, namely: facilities, insurance, fees, availability of specialist doctors and distance. Simple Additive Weighting is used because it can make recommendations from several criteria and has a faster processing time than other methods. This system is built on a mobile basis because most people already have a smartphone, in addition to getting information on the value of the distance from the user's location to the hospital. Based on the results of testing, the average time needed by the system to produce hospital recommendations is 28.99 seconds. Based on the comparison of recommendations from experts and systems for 51 test cases, the similarity level was 88.24%. With these results, the system managed to provide hospital recommendations based on patient needs.
Watermark Image Reconstruction Based on Deep Learning

To solve the problem of low watermark reading rate caused by external factors when trademark images identify watermark information, this paper proposes a watermark image reconstruction model based on super resolution and deep learning. The model consists of two parts: feature extraction and image reconstruction. Seven convolution layers with 3*3 convolution kernels and skip connections form the feature extraction part. Several 1*1 convolution layers form the image reconstruction part. The watermark reading rate is evaluated by the number of watermark blocks read and the average watermark score. Experimental results show that the algorithm has better performance for watermark image reconstruction.
A Computational Personality Traits Analysis Based on Facial Geometric Features

This paper investigates the association between personality traits and facial attributes. A facial image-based computational personality traits classification is conducted to verify the connection between facial attributes and personality traits. Firstly, a database consisting of the facial images and corresponding personality traits score are collected, and facial attributes are defined based on automatic detected facial landmarks. Secondly, the classifiers are trained based on the extracted facial attributes to perform personality traits classification. The proposed method is then conducted in one-factor and two-factor personality traits classification. The one-factor classification results shows that personality traits conscientiousness and neuroticism are significantly associated with extracted facial attributes. Furthermore, the two-factor classification results reveal that the personality traits with higher heritability have more concrete association with internal facial features.
Invertible Bidirectional Metalogical Translation Between Prolog and RuleML for Knowledge Representation and Querying

The paper presents BiMetaTrans(Prolog, RuleML), an invertible bidirectional metalogical translator across subsets of ISO Prolog and RuleML/XML 1.02 on the level of Negation-as-failure Horn logic with Equality. BiMetaTrans, itself written in Prolog, introduces a tighter integration between RuleML and Prolog, which enables the reuse of, e.g., RuleML Knowledge Bases (KBs) and query engines. A Prolog/’$V’ encoding is defined as the BiMetaTrans translation source-and-target counterpart to RuleML/XML. This metalogical encoding, along with the introduction of the split translation pattern, allows BiMetaTrans to build upon the abstraction of Definite Clause Grammars (DCGs), supporting invertible bi-translation. The BiMetaTrans DCG is explored and an invertibility proof is outlined. BiMetaTrans is exemplified for knowledge representation and querying applied to an Air Traffic Control KB.
Predicting Users’ Revisitation Behaviour Based on Web Access Contextual Clusters

Most modern browsers record all previously visited web pages for future revisitation. However, not all users utilize such feature. One of the reasons is that the records are displayed at once as a single list, which may overwhelm the users. This paper proposes a predictive model to decide whether a web page will be revisited in the future based on a particular visit. The model can be used to filter web records so that only web pages that may be re-visited are presented. According to our evaluation, the model is considerably effective. It can generate 53.195% accuracy when measured with 10-fold cross-validation and 95% meaningful topic identification. Further, attributes rooted from the same website’ access frequency are the most salient ones for prediction. In addition, contextual similarities based on k-means clustering and cosine similarity (which are used for defining some attributes) are considerably effective.
Stochastic Filtration for Moving Intensity of Poisson Flow with Random Jumps at Random Moments

A wide range of events in Queuing networks, stock exchanges, and automatic systems can be described by a Poisson flow model with changing intensity that undergoes random jumps at a priori unknown moments. The direct solution of the identification problem in such conditions leads to algorithms with exponential complexity. An asymptotically optimal ML filtering algorithm for observed Poisson flow with polynomial computational complexity is proposed. It is shown that the optimal estimates of the moments of jumps must coincide with one of the moments of the observed events.
Kernel correlation filters based on feature fusion for visual tracking

Abstract This paper presents a CF-based method for robust visual tracking by multiple features fusion. HOG features and high-level CNN features are used to enhance tracking performance. First, different features are extracted from the target. Secondly, the extracted features are employed to learn the corresponding discriminant models, which are used to generate response maps respectively. Finally, an accurate response map can be obtained by fusing multiple response maps. In addition, we propose a model update strategy to reduce model drift under occlusion. The OTB benchmark is used to test the proposed method. Experimental results show that the proposed method can significantly improve tracking performance in complex situations especially in occlusion and fast motion. In addition, compared with several state-of-the-art methods, the proposed method shows competitive accuracy rate and success rate.
A Fault Diagnosis Method for Valve Train of Diesel Engine Considering Incomplete Feature Set

Abnormal valve clearance is a common fault of diesel engine, and early warning of abnormal valve clearance plays an important role in the condition based maintenance of diesel engine. Although information fusion technology can improve the accuracy of fault diagnosis, it cannot guarantee that the fused features can perfectly represent the required key information. For the incomplete feature set, a method combining multi-domain feature and improved support vector machine is proposed. Firstly, the extraction of multi-domain feature is carried out to deeply explore the state information of valve train contained in the original vibration signal. The statistical characteristics and waveform characteristics are extracted from time domain vibration signals, and the frequency domain feature similar to time-domain feature is extracted after the Fourier transform of the vibration signal. What's more, according to the working principle of diesel engine, the energy characteristics in angular frequency domain are extracted. Then, an improved support vector machine method based on multi-domain feature is proposed to further reduce the diagnostic errors caused by incomplete feature set. Finally, the proposed method is compared with other traditional methods about the fault diagnosis of valve train of diesel engine. The results show that the proposed method is applicable to the fault diagnosis of valve train of diesel engine with good accuracy, and the generalization ability of diagnostic model is greatly improved.
A Simplified Method to Identify the Sarcastic Elements of Bahasa Indonesia in Youtube Comments

Comments on Youtube media provide feedback to the content owner of how their video influence audience. However, some comments may have intention to convey readers not to the original goals of the video content. Moreover, the sarcasm statements may also provide misleading information that possibly put the conversation into a hatred comments. This paper presents a simplified method to identify sarcasm within comments in a youtube video channel, which enable the channel or video owner to identify and remove or eliminate some comments that may arise hatred between the audience. To identify sarcasm comments, we use Naïve Bayes to find out which statements may have sarcasm elements. Our approach, specifically, designed to detect and explicate the elements of sarcastic words or statements in Bahasa Indonesia. To demonstrate our approach, an Indonesian political Youtube Channel comments for the range of 7 months prior to the 2019 Indonesian Presidential election has been collected and analysed. Our result shows that Naïve Bayes identified 79% of sarcasm within the comments, with accuracy 0,802, precision 1.0 and recall 0,651.
Smart System Sustainability and Smart System Management

Recently the concepts of smart systems, smart education, smart engineering, smart medicine, smart city, smart universities, smart technology and smart communication were introduced in scientific methodology and practice for a modern society. All of these entities are the components of a new system—smart society system. These components must be equipped with corresponding connections that ensure their reliability, sustainability and security, or, in other words, to have an appropriate smart infrastructure. It turns out that we approach the concept of smart infrastructure from the standpoint of tensor estimation. So, the main questions to consider here are the following ones: verbal tensor estimates in the Smart Systems Theory, methods to increase system reliability, tensor estimation of system \( Q \)—quality, the algorithm of tensor estimation of system \( Q \)—quality, formalization the approaches and the concept of system sustainability, the algorithm of tensor estimation of system sustainability, sustainability of a quasi-fractal system (a system modeled by a quasi-fractal algebraic system), system sustainability violation: violation of the system’s closeness property; malfunction of one of the system’s factors, violation of feedback in the system; factor-flexible quasi-fractal system, substitution of system’s functions; system’s compensational possibilities, compensational functions of a factor-flexible quasi-fractal system.
Design and analysis of intelligent retrieval system for drilling data and completion data based on cloud platform

Abstract In the context of the accelerating cloud platform construction in the oil industry, this paper solves the problem of difficulty in drilling and completion data retrieval by classifying and sorting drilling data and completion data to construct an intelligent retrieval model for drilling and completion data. Based on the cloud platform environment, this paper studies the method of constructing an retrieval system for drilling data and completion data through the microservice architecture. This system provides a unified retrieve page for structured data and unstructured data of drilling and completion operations. By using the ElasticSearch full-text search engine to build a retrieval model, it provides users with fast, efficient, and convenient comprehensive query and retrieval services, and lays a foundation for the intelligent construction of drilling and completion platforms.