Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards a Hybrid Recommendation System for a Sound Library

Recommendation systems are widespread in music distribution and discovery services but far less common in music production software such as EarSketch, an online learning environment that engages learners in writing code to create music. The EarSketch interface contains a sound library that learners can access through a browser pane. The current implementation of the sound browser includes basic search and filtering functionality but no mechanism for sound discovery, such as a recommendation system. As a result, users have historically selected a small subsection of sounds in high frequencies, leading to lower compositional diversity. In this paper, we propose a recommendation system for the EarSketch sound browser which uses collaborative filtering and audio features to suggest sounds.
Comparative Evaluation of the Spectral and Spatial Consistency of Sentinel-2 and Landsat-8 OLI Data for Igneada Longos Forest

This study aims to test the spectral and spatial consistency of Sentinel-2 and Landsat-8 OLI data for the potential of monitoring longos forests for four seasons in Igneada, Turkey. Vegetation indices, including Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Normalized Difference Water Index (NDWI), were generated for the study area in addition to the five corresponding bands of Sentinel-2 and Landsat-8 OLI Images. Although the spectral consistency of the data was interpreted by cross-calibration analysis using the Pearson correlation coefficient, spatial consistency was evaluated by descriptive statistical analysis of investigated variables. In general, the highest correlation values were achieved for the images that were acquired in the spring season for almost all investigated variables. In the spring season, among the investigated variables, the Red band (B4), NDVI and EVI have the largest correlation coefficients of 0.94, 0.92 and 0.91, respectively. Regarding the spatial consistency, the mean and standard deviation values of all variables were consistent for all seasons except for the mean value of the NDVI for the fall season. As a result, if there is no atmospheric effect or data retrieval/acquisition error, either Landsat-8 or Sentinel-2 can be used as a combination or to provide the continuity data in longos monitoring applications. This study contributes to longos forest monitoring science in terms of remote sensing data analysis.
Accurate Tool Path Generation Method for Large-Scale Discrete Shapes

Three-axis ball end mills are used for the finishing of metal molds of complicated curved surfaces. Typically, a tool path of this shape machining is derived from the geometric calculations of a tool used, and a product model that is a computer aided design (CAD)-based polyhedron approximating the shape. The polyhedron is more complicated to approximate a shape with more curved surfaces, as it is highly time consuming. To solve this problem, methods to accelerate geometric calculations using a computer graphics drawing processing mechanism were proposed. However, these methods cannot guard against errors arising from the approximation of an inverse offset shape using a set of polygons. In the present study, we propose a method to generate tool paths accurately based on calculating the crossing points of the tool axis and defining the offset surface as a set of polygons, cylindrical surfaces, and spherical surfaces. With this method, it is expected that the height of an area, which was divided by fine polygons in previous methods, can be derived accurately, and a tool path can be generated with high precision.
Investigating Roundabout Properties and Bicycle Accident Occurrence at Swiss Roundabouts: A Logistic Regression Approach

The positive effects of active mobility on mental and physical health as well as on air quality are widely acknowledged. Increasing the share of active travel is therefore an aim in many countries. Providing bicycle-safe infrastructure is one way to promote cycling. Roundabouts are a common traffic infrastructure and are supposed to facilitate safe and smooth traffic flow. However, data on road traffic accidents indicate an over-proportional involvement of cyclists in accidents at roundabouts. In the present study, the influence of roundabout geometry and traffic flow on bicycle accident occurrence was investigated using a logistic regression approach on twelve parameters of N = 294 mostly small- and mini-sized single-lane roundabouts in the Canton of Berne, Switzerland. Average weekday motorized traffic was identified as a major factor in explaining bicycle accident occurrence at roundabouts. Further, the radius of the central island, the location of the roundabout (in town vs. out of town) and the number of legs were significantly related to bicycle accident occurrence. While these results are in general agreement with findings from similar studies, the findings regarding the central island’s radius and the number of legs underpin the need for roundabout type-specific studies: Some parameters may not prove relevant in intermediate- to large-sized roundabouts, but become critical in small or mini roundabouts, which are common in Switzerland and numerous in the present sample.
The current state of information and communication technology usage by small and medium Taiwanese construction companies

Information and Communication Technology (ICT) is helping the construction industry communicate better and has the potential to change the industry beyond recognition. This paper reports on the findings obtained from a questionnaire survey conducted between April 2008 and June 2008, with contributions received from 51 organisations representing small and medium Taiwanese construction companies. The results indicate that a reasonable ICT investment is considered to be one which is less than TWD 1 million (roughly less than 0.1% of the respondents’ annual turnover). It was also revealed that the three most useful ICT applications in improving the effectiveness of construction management in the Taiwanese construction industry are; Accounting, Computer-Aided Design and Drawing (CAD) and Financial Management.
Recognition of Parallelism Sentence Based on Recurrent Neural Network

Parallelism sentence is a group of sentence pairs with similar structure, same tone and similar semantics. Generally speaking, it consists of two or more sentences. Parallelism sentence have distinctive features such as compact structure, neat sentence patterns, expressiveness, etc. Parallelism sentence enhance language momentum and better express people thoughts and feelings. Widely used in various styles. The usage of the parallelism sentence can be used as a criterion for evaluating the article. Therefore, the identification of the paraphrase sentence is an important work in the text structure analysis. The current method is mainly based on feature extraction. Identifying from the grammar rules, structure and the degree of overlap. This type of method depends on the characteristics of artificial selection and does not take into account the content level of the sentence. Lack of understanding of the meaning of sentences. Therefore, the parallelism sentence recognition method based on recurrent neural network is proposed in this paper. The method does not require artificial design features and can avoid adverse effects on improper selection of features. At the same time, it can also consider the semantic meaning of sentences. The test results show that compared with similar researches, the accuracy of this method has greatly improved.
FAMOUS: Fake News Detection Model Based on Unified Key Sentence Information

Fake news detection causes a challenging problem due to the great influence of communication media over the public. In this paper, we shall present a new fake news detection model using unified key sentence information which can efficiently perform sentence matching between question and article by using key sentence retrieval based on bilateral multi perspective matching model. Our model makes use of one unified word vector for the key sentences of article by extracting them to the question from article and then merging the word vector for each key sentence. It can efficiently perform the sentence matching by executing matching operations between the contextual information obtained from the word vectors of question and key sentences through bidirectional long short term memory. Our model shows the competitive performance for fake news detection on the Korean article dataset over the previous result.
Slepian-Wolf Coding with Side-Information Having Insertion/Deletion Errors

This paper considers Slepian-Wolf (SW) coding in which side-information (SI) has insertion and deletion errors, presuming that the SI is given as a binary vector generated by temporal and/or spatial interpolation or extrapolation of related binary vectors. The coding could be regarded as a simplified model of SW coding in distributed video coding. From the generation process of SI vector, we define a hypothetical error model in the SI vector as an insertion/deletion/substitution error channel, and then show a coding scheme using nonbinary LDPC code. Decoding is based on an iterative multi-path decoding of forward-backward algorithm and FFT sum-product algorithm. Simulation results show the relations between insertion/deletion probability and decoded bit error rates.
An Optimal Tile-Based Approach for Viewport-Adaptive 360-Degree Video Streaming

In this paper, we propose a new adaptation approach for viewport-adaptive streaming of 360-degree videos over the Internet. The proposed approach is able to systematically decide versions of tiles according to user head movements and network conditions by taking into account not only viewport estimation errors, but also users head movements in each segment duration. The experimental results show that the proposed approach can effectively adapt 360-degree videos to both varying network conditions and user’s head movements. Compared to existing approaches, the proposed approach can improve the average viewport quality by up to 3.8 dB and reduce the standard deviation of the viewport quality by up to 1.1 dB. Also, the impacts of the segment duration and the buffer size are investigated. It is found that long segment durations and large buffer sizes have significant impacts on the performances of tile selection methods.
SAR Target Classification Method Based on Convolutional Neural Network

This paper constructs a characterization network that describes the characteristics of SAR target images. In the feature space extracted by the feature description network, class centers are established according to the training data. To recognize the test image samples, the characterization network transforms the test samples into the feature space of the class centers, and the similarity between the test sample and three classes are calculated. The proposed method does not rely on professional knowledge to describe the SAR target characteristics. It only utilizes data-driven method to obtain better recognition results, which can reduce the time consumption for modeling the recognition target. The proposed method achieves a classification accuracy of 99.65% on three classes of MSTAR dataset. And 100% classification performance was achieved on certain class.
Simulation of Stenosis in Blood Vessel by Lattice Boltzmann Method

The lattice Boltzmann method (LBM) for blood flow has been applied, the lattice Boltzmann model used in two dimensions and nine directions (D2Q9), solving by the Navier-Stokes (NS) equation with the Lattice Bhatnagar-Gross-Krook (LBGK) method and the equation of continuity with the Poiseuille's law to describe the behavior of the blood flow. In this work a LBM is used to simulate blood flow in case of stenosis in blood vessel and observe blood flow in condition of Raynolds number changes in blood vessel. The simulation results will show the velocity of blood flow and the high pressures emerge stenosis in blood vessel during to the obstacle become bigger in blood vessel with the time step in simulation.
MCAR: Multi-Path-Based Collusion Avoidance Routing for Wireless Ad-Hoc Networks

Collusion attack is a major concern in multi-path routing. A group of adversaries can collude together to pose a serious threat to multi-path-based routing system, especially in wireless ad-hoc networks. In this paper, a multi-path collusion avoidance routing (MCAR) scheme is proposed to avoid collusion, as well as other common attacks in wireless ad-hoc networks. By flooding RouteRequest packets into the network, MCAR is able to detect the potential adversaries and hence pick a secure and efficient path to route the messages. The XOR based encryption we adopted is proved to achieve perfect secrecy as long as adversaries do not have complete information regarding the message. The simulation results reveal that our scheme achieves significant improvements in delivery rate, only at a cost of negligible increasement of hop stretch.
DRDLC: Discovering Relevant Documents Using Latent Dirichlet Allocation and Cosine Similarity

In recent years, the availability of digital documents over web is increased drastically and there is a need for effective methods to retrieve and organize the digital documents. Since data is dispersed globally and is unorganized, it is a challenging task to develop an effective methods that can generate high quality features in these documents. It is necessary to reduce the gap between users search intention and the retrieved results known as semantic gap. In this paper, Discovering Relevant Documents using Latent Dirichlet Allocation and Cosine Similarity (DRDLC) is proposed. Word similarity is computed using CS Cosine Similarity present in search results documents. LDA is applied on extracted patterns and documents. Hashing is used to extract high relevant documents efficiently. Further, term synonyms are identified using word net and the documents are re-ranked. Experiments using the model Relevance Feature Discovery (RFD) on Reuters Corpus Volume-1 (RCV-1) show that the proposed DRDLC framework results in improved performance by providing more relevant documents to the user input query.
Spatial Semantic Images with Relationship Contents by Using Convolutional Neural Network and Support Vector Machine

In recently, semantic image is an active problem in the digital image processing field. A large number of new techniques and systems have researcher involved and attempted to improve the problems. The most of techniques is done by keyword searching model. Therefore, we propose a new approach to classify the relationships between object and action. The approach is composed of three main phases: (1) data preprocessing, (2) relationship between contents, and (3) measurement and evaluation. We train and test our model on a largescale image dataset of actions. The major information contents use the relationships between object and action. The results indicated that the proposed method offers significant performance improvements in semantic classification with a maximum success rate of 80.9%.
Explicit Model Predictive Control and L 1 – Navigation Strategies for Fixed – Wing UAV Path Tracking

A control strategy for fixed–wing Unmanned Aerial Vehicles is proposed and relies on the combination of linear model predictive control laws for the attitude dynamics of the system, along with an implementation of the L1–navigation logic that provides attitude reference commands to achieve precise path tracking. The employed predictive controllers ensure the performance characteristics of the critical attitude loops, while respecting the actuation limitations of the platform along with safety considerations encoded as state constraints. Being explicitly computed, these strategies are computationally lightweight and allow for seamless integration on the onboard avionics. Once the desired attitude response characteristics are achieved, tuning the cascaded nonlinear L1–navigation law becomes straightforward as lateral acceleration references can be precisely tracked. A wide set of experiments was conducted in order to evaluate the performance of the proposed strategies. As shown high quality tracking results are achieved.
OrumBot: Origami-Based Deformable Robot Inspired by an Umbrella Structure

Employing shape adjustment techn deployable structure is utilized in many cases to adaptability of a simple, stable wheel drive mechan mobile robot in different terrain. In this realm o mobile robot, changing the overall radius of its overcome high steps or narrow gaps is a popular roboticists. In this article, we offer OrumBot, a nove variable-diameter wheel inspired by the orlgami-bs structure approach. By solely engraving a pattern an umbrella shape, a variable-diameter wheel wa without employing much complex assembly proces prototypical umbrella inspired wheel's radius of gyl strated alteration more than double from 11 cm to weight was about only 46.20 g/wheel. The OrumE here is able to change the wheel's radius as pe environment, allowing it to pass over an obstacli ranging from 2 cm to 5 cm step when the origami-in is fully inflated with a principle linear actuation me mobility speed of variable diameter umbrella wheel drive robot proposed here can be varied as per the diameter from 4.92 ern/sec when fully compressed to 14.86 ern/sec when fully expanded.
Application of Convolutional Neural Network in Natural Language Processing

Convolutional neural network (Convolutionl Neural Network, CNN) is a multiple-layer neural network method to learn hierarchical characteristic of data. In recent years, CNN has developed rapidly in the design and calculation of natural language processing (NLP). This paper introduces the principles models and applications of CNN in natural language processing tasks and presents some personal insights into the use of CNN methods in NLP task processing.
On the Randomness Timing Analysis of Timing Side Channel Attack on Key Derivation Functions

A timing attack is an example of a side channel attack which relies on the leaking timing information from the implementation of a cryptosystem. The different type of cryptographic primitives used to construct the key derivation functions has resulted in timing variation which enables the adversary to perform timing attack on key derivation functions. Based on the paper published by Chuah and Koh, the software implementation of key derivation functions using hash functions, stream ciphers and block cipher as the cryptographic primitives are vulnerable to the timing attack. This kind of attack can be mitigated via masks the actual timing information using randomness timing solution. The randomness timing solution is based on the concept of "random for loop". This paper shows the outcome of experiments on implementing the randomness timing delay on key derivation functions based on three type of cryptographic primitives. The experiment's result indicated that the countermeasure propose is secure against timing side channel attack on key derivation functions proposals.
Evaluating Rule-based Programming and ReinforcementLearning for Personalising an Intelligent System

Many intelligent systems can be personalised by end-users to suit their specific needs. However, the interface for personalisation often trades off the degree of personalisation achievable with time, effort, and level of expertise required by the user. We explore two approaches to end-user personalisation: one asks the user to manually specify the system’s desired behaviour using an end-user programming language, while the other only asks the user to provide feedback on the system’s behaviour to train the system using reinforcement learning. To understand the advantages and disadvantages of each approach, we conducted a comparative user study. We report participant attitudes towards each and discuss the implications of choosing one over the other.
Diverse Visualization Techniques and Methods of Moving-Object-Trajectory Data: A Review

Trajectory big data have significant applications in many areas, such as traffic management, urban planning and military reconnaissance. Traditional visualization methods, which are represented by contour maps, shading maps and hypsometric maps, are mainly based on the spatiotemporal information of trajectories, which can macroscopically study the spatiotemporal conditions of the entire trajectory set and microscopically analyze the individual movement of each trajectory; such methods are widely used in screen display and flat mapping. With the improvement of trajectory data quality, these data can generally describe information in the spatial and temporal dimensions and involve many other attributes (e.g., speed, orientation, and elevation) with large data amounts and high dimensions. Additionally, these data have relatively complicated internal relationships and regularities, whose analysis could cause many troubles; the traditional approaches can no longer fully meet the requirements of visualizing trajectory data and mining hidden information. Therefore, diverse visualization methods that present the value of massive trajectory information are currently a hot research topic. This paper summarizes the research status of trajectory data-visualization techniques in recent years and extracts common contemporary trajectory data-visualization methods to comprehensively cognize and understand the fundamental characteristics and diverse achievements of trajectory-data visualization.
Age Estimation Based on the Maximum Joint Probability Classifier

Facial age classification has become relevant to an increasing amount of applications, particularly with the rise of social platforms and social media, facial age estimation becomes more essential. In this paper a new age classification method based on deep convolution neural network(DCNN) is proposed, the DCNN is used to extract facial age features, then the Maximum joint probability classifier based on collaborative representation (MJPCCR) is used to classify them. Three data sets are used to validate our approach. We also send the features extracted from the DCNN model to the SVM classifier, then compare the results with those obtained by our method. And we also send the age features output from the activations of the last layer into the MJPCCR and the SVM classifier separately, and compare their results. Experiments show that the performance of our method is superior to that of the previous methods.
Performance Analysis of Machine Learning Techniques in Intrusion Detection

This paper presents the performance analysis of machine learning techniques in intrusion detection. We analyze time to build (and to retrain) the models used by Intrusion Detection System. Machine Learning is a branch of computer science that allows computer to learn by themselves without programming sequence. These techniques can be applied to detect new threat that has never seen before. Due to the large volumes of security audit data as well as complex and dynamic properties of intrusion behaviors, optimizing the accuracy of IDS becomes an important open problem that is receiving attentions from the research community. However, the performance (time and space required) is usually ignored. Our study allows administrators work to make better decisions about how to select the proper hardware for intrusion detection in various environments. We proposed the models for estimating the time to build each model and the vector equation of the cut-off point is provided for determining the minimum number of CPU required for building Decision tree model and support vector machine model.