Miracle-dz/UniFAR-FTUs huggingface dataset:
Sport entrepreneurial ecosystems and knowledge spillovers

ABSTRACT This study focuses on sport entrepreneurial ecosystems by providing an analysis of how city amenities, culture and internationalisation affect development. Whilst there has been increased interest by policy planners about the impact of entrepreneurship on a region, few studies have specifically examined the sport context. This study explains how entrepreneurship develops from a variety of factors uniquely related to knowledge spillovers resulting from sport including emotional attachment, cultural conditions and societal attitudes. A qualitative research approach is used to analyse stakeholder’s perceptions of knowledge spillovers on entrepreneurial ecosystems. This will enable more sport policies to harness the entrepreneurial spirit of communities.
CD3T: Cross-Project Dependency Defect Detection Tool

Nowadays, every software project usually has a large number of third-party components depending on the repository, some of which have some unsafe code. Due to complex references and dependencies, code defects and vulnerabilities in upstream dependent libraries will inevitably affect downstream software. In this paper, we design a cross-project dependency defect detection system based on Java, called CD3T. The entire implementation process of CD3T uses Apache Maven as a project dependent package management tool and uses IntelliJ IDEA as an integrated basic environment for the development of coding, compilation, and packaging. The system uses a full-text search engine and H2 Database that supports the engine that formats and stores vulnerability data, and the Apache Velocity template engine drives report generation. Finally, it obtains and formats the data that stores the U.S. national common vulnerability database, file dependency analysis, file dependency vulnerability checking, and check result output.
Model Prediksi Interaksi Senyawa dan Protein untuk Drug Repositioning menggunakan Deep Semi-Supervised Learning

Drug repositioning adalah penggunaan senyawa obat yang sudah lolos uji sebelumnya untuk mengatasi penyakit baru selain penyakit awal obat tersebut ditujukan. Drug repositioning dapat dilakukan dengan memprediksi interaksi senyawa obat dengan protein penyakit yang bereaksi positif. Salah satu tantangan dalam prediksi interaksi senyawa dan protein adalah masalah ketidakseimbangan data. Deep semi-supervised learning dapat menjadi alternatif untuk menangani model prediksi dengan data yang tidak seimbang. Proses pre-training berbasis unsupervised learning pada deep semi-supervised learning dapat merepresentasikan input dari unlabeled data (data mayoritas) dengan baik dan mengoptimasi inisialisasi bobot pada classifier . Penelitian ini mengimplementasikan Deep Belief Network (DBN) sebagai pre-training dan Deep Neural Network (DNN) sebagai classifier . Data yang digunakan pada penelitian ini adalah dataset ion channel, GPCR, dan nuclear receptor yang bersumber dari pangkalan data KEGG BRITE, BRENDA, SuperTarget, dan DrugBank. Hasil penelitian ini menunjukkan pada dataset tersebut, pre-training berupa ekstraksi fitur memberikan efek optimasi dilihat dari peningkatan performa model DNN pada akurasi (3-4.5%), AUC (4.5%), precision (5.9-6%), dan F-measure (3.8%). Abstract Drug repositioning is the reuse of an existing drug to treat a new disease other than its original medical indication. Drug repositioning can be done by predicting the interaction of drug compounds with disease proteins that react positively. One of the challenges in predicting the interaction of compounds and proteins is imbalanced data. Deep semi-supervised learning can be an alternative to handle prediction models with imbalanced data. The unsupervised learning based pre-training process in deep semi-supervised learning can represent input from unlabeled data (majority data) properly and optimize initialization of weights on the classifier. This study implements the Deep Belief Network (DBN) as a pre-training with Deep Neural Network (DNN) as a classifier. The data used in this study are ion channel, GPCR, and nuclear receptor dataset sourced from KEGG BRITE, BRENDA, SuperTarget, and DrugBank databases. The results of this study indicate that pre-training as feature extraction had an optimization effect. This can be seen from DNN performance improvement in accuracy (3-4.5%), AUC (4.5%), precision (5.9-6%), and F-measure (3.8%).
Asynchronous Spiking Neurons, the Natural Key to Exploit Temporal Sparsity

Inference of Deep Neural Networks for stream signal (Video/Audio) processing in edge devices is still challenging. Unlike the most state of the art inference engines which are efficient for static signals, our brain is optimized for real-time dynamic signal processing. We believe one important feature of the brain (asynchronous state-full processing) is the key to its excellence in this domain. In this work, we show how asynchronous processing with state-full neurons allows exploitation of the existing sparsity in natural signals. This paper explains three different types of sparsity and proposes an inference algorithm which exploits all types of sparsities in the execution of already trained networks. Our experiments in three different applications (Handwritten digit recognition, Autonomous Steering and Hand-Gesture recognition) show that this model of inference reduces the number of required operations for sparse input data by a factor of one to two orders of magnitudes. Additionally, due to fully asynchronous processing this type of inference can be run on fully distributed and scalable neuromorphic hardware platforms.
Research of open-circuit fault simulation and diagnosis for wind power converter

In this paper, the converter of a 2MW direct-driven permanent-magnet wind turbine is researched for exactly locating the open-circuit fault of wind power converter. The structure and fault characteristics of the converter is analysed, and open-circuit fault location of the converter is implemented by defining a fault feature variable based on grid-side current signal. Based on Matlab/Simulink platform, the fault simulation model of wind power converter is built, the waveforms changes of grid-side current and fault feature variables are analysed under different fault conditions. The simulation results show the feasibility and effectiveness of the model. It provides a fast-position diagnosis method for the open-circuit fault of wind power converter, and has the certain application value for project.
Hierarchical Sound Event Classification

Task 5 of the Detection and Classiﬁcation of Acoustic Scenes and Events (DCASE) 2019 challenge is “urban sound tagging”. Given a set of known sound categories and sub-categories, the goal is to build a multi-label audio classiﬁcation model to predict whether each sound category is present or absent in an audio recording. We developed a model composed of a preprocessing layer that converts audio to a log-mel spectrogram, a VGG-inspired Convolutional Neural Network (CNN) that generates an embedding for the spectrogram, a pre-trained VGGish network that generates a separate audio embedding, and ﬁnally a series of fully-connected layers that converts these two embeddings (concatenated) into a multi-label classiﬁcation. This model directly outputs both ﬁne and coarse labels; it treats the task as a 37-way multi-label classiﬁcation problem. One version of this network did better at the coarse labels CNN+VGGish1 ); another did better with ﬁne labels on Micro AUPRC CNN+VGGish2 ). A separate family of CNN models was also trained to take into account the hierarchical nature of the labels ( Hierarchical1 , Hierarchical2 , and Hierarchical3 ). The hierarchical models perform better on Micro AUPRC with ﬁne-level classiﬁcation.
UKARA: A Fast and Simple Automatic Short Answer Scoring System for Bahasa Indonesia

This paper presents UKARA, a fast and simple automatic short-answer scoring system for Bahasa Indonesia. Automatic short-answer scoring holds an important role in speeding up automatic assessment process. Although this area has been widely explored, only very limited number of previous work have studied Bahasa Indonesia. One of the major challenges in this field is the different type of questions which require different assessments. We are addressing this problem by implementing a combination of Natural Language Processing (NLP) and supervised machine learning techniques. Our system works by training a classifier model on human-labeled data. Using three different types of Programme for International Student Assessment (PISA) student responses, our system successfully produced the F1-score above 97% and 70% on dichotomous and polytomous scoring types respectively. Moreover, UKARA provides a user-friendly interface which is simple and easy to use. UKARA offers a flexibility for human grader to do re-scoring and re-training the model until the optimal performance is obtained.
Research on Fast Image Style Transformation Based on Residual Network

Image style migration technology refers to the conversion of an image into a similar image to a famous painting style through learning (using a convolutional neural network). The A Neural Algorithm of Artistic Style (NAAS) proposed by Gatys uses the VGG network to design a loss network, and the style migration image is obtained through repeated iterations, and the calculation speed is slow. Li Feifei introduced the residual network on the basis of Gatys, and accelerated the calculation by using the fast connection feature of the residual element. This method has a great improvement in the generation speed but still has room for improvement. This paper proposes improvements for the following two aspects: firstly, the classical residual element structure is adjusted, and the standard convolution is converted into point convolution and deep convolution, which reduces the calculation amount while ensuring the convolution effect; secondly, the loss network is simplified. The fourth and fifth layers in the model are highly consistent in structure, and the style restoration of the two layers is basically the same as the content reconstruction effect. Therefore, the fifth layer is deleted, the redundant parameters are removed, and the parameter amount is reduced. At the same time, the effect of style restoration and content reconstruction is guaranteed.
Toward compression-aware prefetching

The speed gap between CPU and memory is impairing performance. Cache compression and hardware prefetching are two techniques that could confront this bottleneck by decreasing last level cache (LLC) misses. This paper explores opportunities for compression-aware prefetching, which creates a synergy between compressed cache and prefetching. The idea is to prefetch contiguous blocks, which can be compressed and compacted together, with the requested block on a miss access. Prefetched blocks that share storage with existing blocks do not need to evict a valid existing entry; therefore, it avoids cache pollution. Based on our expe-rimental evaluations, compression-aware prefetching has the potential to reduce the number of cache misses, by prefetching the potential blocks. We show that compression-aware prefetching could avoid up to 28% of misses by prefetching compressible and co-allocatable blocks in advance.
IntelliHome: An internet of things‐based system for electrical energy saving in smart home environment

Despite there has been an increasing energy price due to factors such as supply, demand, government regulation, among others, users do not like to spend their time to analyze their power consumption and establish actions to save money. Hence, there is a need for smart solutions that help users to save energy at home in an easy way. The smart home concept is attracting the attention of both academia and industry to address this need. Nowadays, high volumes of data are available in the smart home context, facilitated by the growth of internet of things (IoT)‐based devices and advanced sensing infrastructure. Therefore, it is necessary to automatically extract useful knowledge from this information to cost‐effective use of energy at home. In this sense, this work presents IntelliHome, a smart‐home system that aims to reduce electrical energy consumption at home. To this end, IntelliHome uses big data analytics technologies and Machine Learning and statistical techniques to provide users with a meaningful perspective of their electricity consumption habits aiming to actively involve them in the energy‐saving process through real‐time information and energy‐saving recommendations. This work also discusses a case study and an evaluation aligned with the objectives of this work. The obtained results verify the effectiveness of the proposed system regarding electrical energy saving.
A method to estimate horse speed at canter from IMU data with Machine Learning

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. A method to estimate horse speed at canter from IMU data with Machine Learning Amandine Schmutz, Laurence Chèze, Julien Jacques, Pauline Martin
CUDA-Parttree: A Multiple Sequence Alignment Parallel Strategy in GPU

In this paper, we propose and evaluate CUDA-Parttree, a parallel strategy that executes the first phase of the MAFFT Parttree Multiple Sequence Alignment tool (distance matrix calculation with 6mers) on GPU. When compared to Parttree, CUDA-Parttree obtained a speedup of 6.10x on the distance matrix calculation for the Cyclodex gly tran (50, 280 sequences) set, reducing the execution time from 33.94s to 5.57s. Including data conversion and movement to/from the GPU, the speedup was 2.59x. With the sequence set Syn 100000 (100, 000 sequences), a speedup of 4.46x was attained, reducing execution time from 209.54s to 47.00s.
An Inherent Property-Based Rumor Dissemination Model in Online Social Networks

Rumor propagation is an important issue in online social networks (OSNs), while modelling rumor propagation is critical and challenging. Existing studies are restrictive for unrealistic assumptions. We find that the inherent property of a rumor has determinant impact on propagation dynamics, and rumor propagates across different clustered communities with variable transmissibility. In this letter, we formulate the problem using information theory and establish a rigorous two layer epidemic model. As a result, the proposed model reflects the studied subject better than the previous models. Extensive experiments have been conducted through real-world twitter dataset, the results confirm our theoretical findings, and demonstrate that the proposed model outperforms the existing ones.
Characterizing infrastructure of DDoS attacks based onDDoSDB fingerprints

Distributed Denial of Service (DDoS) attacks are a big problem in the current digital landscape. Many research is conducted on various sub parts of DDoS. However, little is known about the infrastructure behind the attacks. It can be of interest to know how attacker choose their infrastructure. It is possible that they choose their attacking nodes very specific based on some characteristics. This paper aims to characterize the infrastructure of a DDoS attack to gain more insights in the infrastructures and how attackers choose their attacking nodes. The paper will focus on seven different attack types and will analyze their infrastructure. We will show that DNS recursion is still enabled on a lot of DNS resolvers, that the non-RFC-compliant implementation of Chargen in Windows is widely misused and that small ISPs are the most common in DDoS attacks attacking nodes.
Amplitude independent versus amplitude dependent muscle activity detection algorithms: a comparative study

The amplitude dependent muscle activity detection algorithms of the surface electromyography (sEMG) signals are very sensitive to the changes in the background noise levels and the performance of these amplitude-based methods is highly deteriorated when the Signal to Noise ratio (SNR) of the sEMG signal is low. sEMG signals of deep and small muscles as well as sEMG signals recorded from patients that have neuromuscular diseases may not meet this SNR requirement which motivates the need for amplitude independent algorithms that can detect weak muscle activities. Moreover, the sEMG signal amplitude is not constant during the recording time due to the variation in the characteristics of the electrode-skin interface and due to the changes in the ground reference level. Therefore, the performance of the muscle activity detection algorithms should not be affected by the involuntary amplitude variations of the sEMG signal in order to achieve reliable control of robotic devices intended for disabled people.To accentuate the importance of the amplitude independent muscle activity detection methods over the amplitude dependent detection methods, a comparative study has been conducted in this paper between the performance of an amplitude independent muscle activity detection algorithm (FLA-MSE algorithm) and three amplitude dependent algorithms with respect to the detection capability of weak muscle activities and with respect to the immunity against false alarms. The results have showed that the performance of the amplitude independent algorithm outperformed the performance of the amplitude dependent algorithms for detecting weak muscle activities and for robustness against false alarms.
Measuring user similarity using check-ins from LBSN: a mobile recommendation approach for e-commerce and security services

ABSTRACT Social friendship and geographical position information often reflects individuals’ personal preferences and other types of knowledge that can be used to extract their similarity for recommendation systems. This paper finds that users are more likely to move around some specific centres or check in at some hotspots; a few individuals check in frequently, whereas most locations are rarely visited. Based on these findings, we propose a multi-centre clustering algorithm to capture users’ mobile patterns and develop a user similarity measurement method. Complexity analysis shows the method’s efficiency in handling large datasets and experimental results demonstrate its good applicability.
Graph Database Schema for Multimodal Transportation in Semarang

Background: Semarang has broad area that cannot be covered entirely by single transportation mode. To reach a specific location, people often use more than one public transportation mode. Apart from Bus Rapid Transit, another exist namely angkot or city transportation. Multimodal traveler information is then required to help passenger searching for a route. Several studies of multimodal traveler information system has been conducted, however the data model for multimodal transportation did not conceived in detail.Objective: Proposes a database of multimodal transportation design using graph data model by taking Semarang as a case study.Method: We create our model in oriented entity-relationship diagram (O-ERD) and map this O-ERD to the graph database schema.Result: We develop our data model in graph database schema and we implement the model using Neo4J graph database for validation purpose. Our model consist of three graph node label namely Shelter, Angkot Stopper, and Closer Place. To validate our model, we execute a search query using the Cypher query to look for location with closer place to it.Conclusion: Our data model was successfully developed and implemented. Searching transportation route in the implementation of our model has been conducted using cypher query. It can successfully display all possible paths and routes. Our query can distinguish between one mode of transportation with another.Keywords: Graph database, Multimodal transportation, Neo4j, Cypher
A Stationary SVBRDF Material Modeling Method Based on Discrete Microsurface

Microfacet theory is commonly used to build reflectance models for surfaces. While traditional microfacet‐based models assume that the distribution of a surface's microstructure is continuous, recent studies indicate that some surfaces with tiny, discrete and stochastic facets exhibit glittering visual effects, while some surfaces with structured features exhibit anisotropic specular reflection. Accordingly, this paper proposes an efficient and stationary method of surface material modeling to process both glittery and non‐glittery surfaces in a consistent way. Our method comprises two steps: in the preprocessing step, we take a fixed‐size sample normal map as input, then organize 4D microfacet trees in position and normal space for arbitrary‐sized surfaces; we also cluster microfacets into 4D K‐lobes via the adaptive k‐means method. In the rendering step, moreover, surface normals can be efficiently evaluated using pre‐clustered microfacets. Our method is able to efficiently render any structured, discrete and continuous micro‐surfaces using a precisely reconstructed surface NDF. Our method is both faster and uses less memory compared to the state‐of‐the‐art glittery surface modeling works.
Automatic Classification of Infant- and Adult-directed Speech from Acoustic Speech Signals

Einari Vaaras: Automatic Classification of Infantand Adult-directed Speech from Acoustic Speech Signals Bachelor’s Thesis, 31 pages, 2 appendix pages Tampere University Bachelor’s Degree Programme in Electrical Engineering May 2019 Major: Signal Processing and Machine Learning Examiner: Okko Räsänen, D. Sc. (Tech.) Paralinguistic speech processing (PSP) is a field of audio processing where the focus of the analysis is beyond the literal message. One potential task in the area of PSP is the classification of samples into infant-directed speech (IDS) and adult-directed speech (ADS). In the present study, a system which classifies samples into IDS/ADS as accurately as possible was examined by experimenting with different classifiers used in other fields of PSP, and by testing different sets of manually defined features. The classification results showed that the best set of features was a set which included all speech-relevant features extracted in this study except spectrogram. Additionally, support vector machines (SVMs) performed the best of the individual classifiers used in the study, while an ensemble classifier outperformed all individual classifiers. These results were in line with the previous IDS/ADS classification studies in the field.
MILES: a Java tool to extract node-specific enriched subgraphs in biomolecular networks

The growing availability of biomolecular networks has led to a need for analysis methods that are able to extract biologically meaningful information from these complex data structures. Here we present MILES (MIning Labeled Enriched Subgraphs), a Java-based subgraph mining tool for discovering motifs that are associated to a given set of nodes of interest, such as a list of genes or proteins, in biomolecular networks. It provides a unique extension to the widely used enrichment analysis methodologies by integrating network structure and functional annotations in order to discern novel biological subgraphs which are enriched in the targets of interest. The tool can handle various types of input data, including (un)directed, (un)connected and multi-label networks, and is thus compatible with most types of biomolecular networks. MILES is available as a platform-independent Java application at https://github.com/pmoris/miles-subgraph-miner alongside a user manual, example datasets and the source code. Supplementary data are available at Bioinformatics online.