Miracle-dz/UniFAR-FTUs huggingface dataset:
Real-time oil palm FFB ripeness grading system based on ANN, KNN and SVM classifiers

The high accuracy performance, cost and processing time are commonly the key factors for evaluate and validate the systems of agricultural crop quality inspections. External and internal grading system based on image and signal processing and analysis were implemented based on general steps as acquisition, pre-processing, segmentation, feature measurement, and classification for fruit maturity grading system. The classification step using supervised machine learning classifiers was commonly based on the method namely; two-thirds for training and the other third for testing. In the other hand, the purpose of the supervised machine learning is to construct predicted model for the training data of the distribution of the different class labels as known features which can be used for the further testing data as unknown features. In this paper, real-time oil palm FFB ripeness grading system based on ANN, KNN and SVM Classifiers was demonstrated. System training and testing were implemented in order to appropriately classify the ripeness of oil palm FFB based on the external feature of the fruit. System evaluation has been carried out based on the system performance, processing time and the system cost in order to enhance the system methodology for classification proficiency. The results show that the real-time oil palm FFB ripeness grading system has achieved the highest accuracy 93 % and the fastest image processing speed 0.40 (s) by using BGLAM texture feature based on ANN classifier compared to the other feature extraction techniques and machine learning classifiers.
InferPortOIE: A Portuguese Open Information Extraction system with inferences

Abstract Nowadays, there is an increasing amount of digital data. In the case of the Web, daily, a vast collection of data is generated, whose contents are heterogeneous. A significant portion of this data is available in a natural language format. Open Information Extraction (Open IE) enables the extraction of facts from large quantities of texts written in natural language. In this work, we propose an Open IE method to extract facts from texts written in Portuguese. We developed two new rules that generalize the inference by transitivity and by symmetry. Consequently, this approach increases the number of implicit facts in a sentence. Our novel symmetric inference approach is based on a list of symmetric features. Our results confirmed that our method outstands close works both in precision and number of valid extractions. Considering the number of minimal facts, our approach is equivalent to the most relevant methods in the literature.
Spectral Clustering in Heterogeneous Information Networks

A heterogeneous information network (HIN) is one whose objects are of different types and links between objects could model different object relations. We study how spectral clustering can be effectively applied to HINs. In particular, we focus on how meta-path relations are used to construct an effective similarity matrix based on which spectral clustering is done. We formulate the similarity matrix construction as an optimization problem and propose the SClump algorithm for solving the problem. We conduct extensive experiments comparing SClump with other state-of-the-art clustering algorithms on HINs. Our results show that SClump outperforms the competitors over a range of datasets w.r. t. different clustering quality measures.
Condition detection of overhaul platform based on image processing

EMU(Electric Multiple Units) maintenance platform is one of the key equipments of the motor Train base and EMU application, provides convenient for EMU daily maintenance and inspection, maintenance and repair. In the process design of the equipment, hydraulic crossover and other methods are adopted to meet the needs of the maintenance personnel on different types of EMUs. Therefore, the tipping boards are set up on the actual platform and protection network to ensure the safety of the workers' reaching the top. The EMUs usually use a four line storehouse, and the number of tipping plates is hundreds of them. Most of them are reversing / receiving by cylinder driving, and a large number of flip plates are distributed in the workshop. Besides, the state of the retractable boards and the faults have important influence on the operation. Therefore, it is very important to monitor the state of the flip plate. At present, it is more dependent on the monitoring and controlling system of the mechanical stroke switch in China. Due to the state of the hardware and the stability of the system, the method has the risk of omission and false alarm in some cases. Meanwhile, the switch signal can not directly display the real-time state of the tipping board. Therefore, the present invention provides a high-definition network camera image processing board state detection system based on double plate state real-time monitoring and detection, enhance the safety of EMU maintenance platform. The real-time image acquisition, calculating the specified texture information area gray co-occurrence matrix, generating feature vector descriptor, using support vector machine (SVM) classification method for feature vector classification, training classifier, and use the classifier to recognize the flap, and locate the flap position. And SVM is a novel method of machine learning evolving from statistics. SVM presents many own advantages in solving machine learning problems such as small samples, nonlinearity and high dimension. In this paper, the image texture SVM classification method construct feature vectors through the extraction of image gray level co-occurrence matrix texture information.The metioned texture imformation include gray-level co-occurrence matrix energy, contrast and entropy. And the gray level co-occurrence matrix reflects the image direction, adjacent interval and the change in value of integrated information.The results show that the image texture SVM classification method can effectively combine the method of template matching for the identification of condition detection of overhaul platform.
PENGARUH SYNTHETIC MINORITY OVERSAMPLING TECHNIQUE(SMOTE), REPRESENTASI FITUR, DAN ALGORITMA KLASIFIKASI PADA SENTIMENT ANALYSIS

Komentar-komentar pada layanan sewa hotel online seperti Traveloka merupakan sumber daya sangat penting yang bisa digunakan bagi pihak penyedia layanan tersebut termasuk pengelola hotel terkait untuk melakukan kontrol kualitas pada layanan sewa hotel mereka, yang berakhir pada meningkatnya kepuasan pelanggan. Sentiment Analysis (SA) merupakan tool untuk melakukan analisis terhadap komentar-komentar tersebut. Permasalahanpermasalahan yang muncul pada sentiment analysis adalah tidak seimbangnya data komentar (imbalanced datasets) dalam hal jumlah dari masing-masing kelas, kemudian algoritma klasifikasi serta representasi fitur yang akan digunakan. Penelitian ini akan mencoba melihat bagaimana SMOTE (Synthetic Minority Oversampling Technique) dalam usaha menyeimbangkan jumlah data dari masing-masing kelas, penggunaan algoritma klasifikasi Naive Bayes, Logistic Regression, dan Support Vector Machine, dan penggunaan representasi fitur term presence, term occurrence, dan TF-IDF dalam pengaruhnya terhadap hasil kinerja sentiment analysis. Penggunaan SMOTE terbilang cukup efektif dalam memperbaiki kinerja model pada kasus klasifikasi dengan data tidak seimbang, yang dibuktikan dengan peningkatan kinerja rata-rata model sebesar kurang lebih 12%. Representasi fitur term occurrence menghasilkan nilai g-mean score rata-rata sebesar 81,68%, kemudian term presence sebesar 79,89%, dan terakhir TF-IDF sebesar 79,31%. Sedangkan untuk algoritma klasifikasi, Logistic Regression menghasilkan nilai g-mean score rata-rata sebesar 81,65%, kemudian Support Vector Machine sebesar 81,55%, dan terakhir Naive Bayes sebesar 77,68%.
Residential Load Scheduling With Renewable Generation in the Smart Grid: A Reinforcement Learning Approach

The significance and need of demand response (DR) programs is realized by the utility as a means to reduce the additional production cost imposed by the accelerating energy demand. With the development in smart information and communication systems, the price-based DR programs can be effectively utilized for controlling the loads of smart residential buildings. Nowadays, the use of stochastic renewable energy sources like photovoltaic (PV) by a small domestic consumer is increasing. In this paper, a generalized model for the residential load scheduling or load commitment problem (LCP) in the presence of renewable sources for any type of tariff is presented. Reinforcement learning (RL) is an efficient tool that has been used to solve the decision making problem under uncertainty. An RL-based approach to solve the LCP is also proposed. The novelty of this paper lies in the introduction of a comprehensive model with implementable solution considering consumer comfort, stochastic renewable power, and tariff. Simulation experiments are conducted to test the efficacy and scalability of the proposed algorithm. The performance of the algorithm is investigated by considering a domestic consumer with schedulable and nonschedulable appliances along with a PV source. Guidelines are given for choosing the parameters of the load.
An Improved Logistic Function for Mapping Raw Scores of Perceptual Evaluation of Speech Quality (PESQ)

Voice service being the major offering of telecommunication networks, its level of Quality of Service (QoS) largely determines the performance of these networks. This work evaluated the state-of-the-art Perceptual Evaluation of Speech Quality (PESQ) objective model for perceptual estimation of the quality of transmitted speech signals. Perceptual estimation of the quality of speech is predominantly done by subjective techniques and the results presented as Mean Opinion Scores (MOS), which has a scale from 1 for poor quality to 5 for excellent quality. Despite constraints of the subjective approach to perceptual speech quality estimation, its scores serves as the basis for correlating quality scores from objective techniques for speech quality estimation. Original or reference speeches were recorded using professional studio equipment and software, and guided by provisions of ITU-T P.830. The speeches were transmitted over three mobile wireless networks. A speech database consisting of 64 original (32 male and 32 female) and 192 transmitted speeches was developed. Reference speeches and their corresponding transmitted (network-degraded) speeches were tested on the PESQ model to estimate their quality scores. The raw PESQ quality scores are within the scale range of -0.5 and 4.5. They were mapped to the MOS scale for linear comparison of the scales. Study of PESQ model showed several shortcomings, some of which have been improved upon by previous researchers. Evaluating PESQ mapping function (in ITU-T Rec P.862.1) showed the need for better coverage of the MOS scale. Analysis of solution for the logistic growth function was done and parameters were optimised which resulted in the development of a new robust logistic mapping function. The raw PESQ quality scores were mapped using the developed mapping function as well as two known standard mapping functions, namely: ITU-T P.862. 1 and Morfitt and Cotanis mapping functions. The mapped scores known as PESQ MOS-listening quality objective (PESQ MOS-LQO) obtained with the three functions were tested using ANOVA at a significant figure of . The developed logistic mapping function offered a quality score coverage of 98.6% of the MOS scale. This was evaluated against the two known standard mapping functions and the developed function offered improvement of 11.8 and 4.9% over and above their 86.8 and 93.7% coverage of the MOS scale respectively. At the significance level of , an F-value of 60.6042, a critical-F of 3.04, and a p-value of 4.61721E-21 were obtained. With p < 0.05, the Null Hypothesis was rejected, and the critical-F value being less than the F-statistic value confirmed the rejection. Therefore, the data distribution of at least one of the functions has a different mean and belongs to a separate population of performance.
Prostate cancer detection from multi-institution multiparametric MRIs using deep convolutional neural networks

Abstract. Multiparametric magnetic resonance imaging (mpMRI) of the prostate aids in early diagnosis of prostate cancer, but is difficult to interpret and subject to interreader variability. Our objective is to generate probability maps, overlaid on original mpMRI images to help radiologists identify where a cancer is suspected as a computer-aided diagnostic (CAD). We optimized the holistically nested edge detection (HED) deep convolutional neural network. Our dataset contains T2, apparent diffusion coefficient, and high b-value images from 186 patients across six institutions worldwide: 92 with an endorectal coil (ERC) and 94 without. Ground-truth was based on tumor segmentations manually drawn by expert radiologists based on histologic evidence of cancer. The training set consisted of 120 patients and the validation set and test set included 19 and 47, respectively. Slice-level probability maps are evaluated at the lesion level of analysis. The best model: HED using 5 × 5 convolutional kernels, batch normalization, and optimized using Adam. This CAD performed significantly better (p < 0.001) in the peripheral zone (AUC = 0.94 ± 0.01) than the transition zone. It outperforms a previous CAD from our group in a head-to-head comparison on the same ERC-only test cases (AUC = 0.97 ± 0.01; p < 0.001). Our CAD establishes a state-of-the-art performance for predicting prostate cancer lesions on mpMRIs.
Optimising low-cost GNSS positioning for road constrained vehicles using a Bayesian filter

Co-operative Intelligent Transportation Systems (C-ITS) are a growing industry that have significant implications for the safety and efficiency of on-road vehicle use. Accurate and reliable vehicle positioning is a critical component in C-ITS in order for the component vehicles and infrastructure to interact effectively and information-share for collectively enhanced services. Such services include vehicle navigation, collision-avoidance systems, driver assistance and emergency notifications (for example, mobilised if an ambulance needs to pass through the street occupied by the positioned vehicle). A range of existing and emerging technologies have been developed and tailored to improve the accuracy and continuity of positioning for C-ITS, which include Global Navigation Satellite Systems (GNSS), digital road maps, inertial sensor technologies, dedicated short-range communication (DSRC) and other technologies. However many of these technologies are expensive and in many cases prohibitive for large scale rollout and general public use. Therefore it is important to develop and optimise low-cost vehicle positioning methods to complement more expensive technologies in order to assist with the large scale feasibility of C-ITS, as well as meet individual-scale immediate user needs at viable cost. The subject of this PhD research is optimising low-cost vehicle positioning; that is maximising the accuracy and continuity of position estimates generated from exclusively low-cost data sources. The focus of this research is improving mathematical methods for data fusion (the statistical combination of diverse data inputs for estimation), and a new Bayesian approach is presented for real-time vehicle positioning from the data fusion of low-cost GNSS data and freely available digital road map data. The design of this new method, the Gaussian Cluster Approximate Filter (GCAF), is driven in part by a detailed study of common shortcomings and the causes of failure-modes of existing methods in a range of common urban vehicular situations, in order to be robust to these positioning challenges. Through robust testing it is demonstrated that effective combination of these two low-cost data sources using statistically robust mathematical methods provides substantial improvement to existing approaches, and lessens the need for more expensive sensors in many common vehicular environments. The method is designed to be easily modified to allow fusion with other data sources as future applications may require.
Study on Phishing Attacks

Now a day there is a lot of data security issues. Hackers are now very much expert in using their knowledge for hack into someone else’s system and grab the information. Phishing is one such type of methodologies which are used to acquire the information. Phishing is a cyber crime in which emails, telephone, text messages, personally identifiable information, banking details, credit card details, password is been targeted. Phishing is mainly a form of online identify theft. Social Engineering is being used by the phisher to steal victim’s personal data and the account details. This research paper gives a fair idea of phishing attack, the types of phishing attack through which the attacks are performed, detection and prevention towards it.
Controlling the Drift of Semantic Indexing Systems

: Document classiﬁcation is often meant to serve as semantic indexing to help readers ﬁnding documents related to a given topic. However, the quality of indexing often deteriorates with time: some categories are misused or forgotten by indexers, others become obsolete or too general to be useful. This paper proposes measures to assess the quality of an indexing system and an algorithm that guides indexers in restructuring their indexes. Focus is put on the reader’s rather than on the annotator’s point of view (Does the classiﬁcation really help accessing information? vs. Is a category adequate with the content of the document? ). The whole approach is illustrated on a corpus of 20 blogs which posts are associated with categories. We show that indexers have difﬁculties to adapt the blogs indexing systems when the number of posts increases and we show that our approach can signiﬁcantly improve the quality of these indexing systems, by simulating blog restructuring.
Journal of Digital Forensics, Journal of Digital Forensics, Security and Law Security and Law

A common task in digital forensics investigations is to identify known contraband images. This is typically achieved by calculating a cryptographic digest, using hashing algorithms such as SHA256, for each image on a given medium, and comparing individual digests with a database of known contraband. However, the large capacities of modern storage media and time pressures placed on forensics examiners necessitates the development of more eﬃcient processing methods. This work describes a technique for ﬁngerprinting JPEGs with optimised Huﬀman tables which requires only the image header to be present on the media. Such ﬁngerprints are shown to be robust across large datasets, with demonstrably faster processing times.
Fast Univariate Time Series Prediction of Solar Power for Real-Time Control of Energy Storage System

In this paper, super-short-term prediction of solar power generation for applications in dynamic control of energy system has been investigated. In order to follow and satisfy the dynamics of the controller, the deployed prediction method should have a fast response time. To this end, this paper proposes fast prediction methods to provide the control system with one step ahead of solar power generation. The proposed methods are based on univariate time series prediction. That is, instead of using external data such as the weather forecast as the input of prediction algorithms, they solely rely on past values of solar power data, hence lowering the volume and acquisition time of input data. In addition, the selected algorithms are able to generate the forecast output in less than a second. The proposed methods in this paper are grounded on four well-known prediction algorithms including Autoregressive Integrated Moving Average (ARIMA), K-Nearest Neighbors (kNN), Support Vector Regression (SVR), and Random Forest (RF). The speed and accuracy of the proposed algorithms have been compared based on two different error measures, Mean Absolute Error (MAE) and Symmetric Mean Absolute Percentage Error (SMAPE). Real world data collected from the PV installation at the University of California, Riverside (UCR) are used for prediction purposes. The results show that kNN and RF have better predicting performance with respect to SMAPE and MAE criteria.
Building and evaluating privacy-preserving data processing systems

Large-scale data processing prompts a number of important challenges, including guaranteeing that collected or published data is not misused, preventing disclosure of sensitive information, and deploying privacy protection frameworks that support usable and scalable services. In this dissertation, we study and build systems geared for privacy-friendly data processing, enabling computational scenarios and applications where potentially sensitive data can be used to extract useful knowledge, and which would otherwise be impossible without such strong privacy guarantees. For instance, we show how to privately and efficiently aggregate data from many sources and large streams, and how to use the aggregates to extract useful statistics and train simple machine learning models. We also present a novel technique for privately releasing generative machine learning models and entire high-dimensional datasets produced by these models. Finally, we demonstrate that the data used by participants in training generative and collaborative learning models may be vulnerable to inference attacks and discuss possible mitigation strategies.
On the Employment of Unpunctured Turbo-Trellis Coded Modulation Channel Coding to Improve the Performance of WiMax System

The wireless communications evolved considerably in the last few years. This fact encouraged the deployment of several systems or cellular networks in multi technological environment. Worldwide Interoperability for Microwave Access (WiMax) is an emerging standard for next generation wireless communication system with high data rate and broacoverage. In this paper, we present the simulation of a digital communication system based on the specifications of IEEE 802.16d standard (WiMax) in order to evaluate the performance of this system. WiMax standard allows a good transmission, considering relatively high SNRs using RS-CC channel encoder. Considering the UTTCM encoder with iterative decoding, the aim is to decrease the SNR (reduce the transmission power) and thus improve the performance of the transmission system. This work focuses on the performance comparison of the standard WiMax system and WiMax system using UTTCM as channel coding. The simulation results have shown that, with lower decoding complexity, the UTTCM-based WiMax system achieved better performance.