Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comparative Study of Indian Food Image Classification Using K-Nearest-Neighbour and Support-Vector-Machines

Food being the vital part of everyone’s lives, food detection and recognition becomes an interesting and challenging problem in computer vision and image processing. In this paper we mainly propose an automatic food detection system that detects and recognises varieties of Indian food. This paper uses a combined colour and shape features. The K-Nearest-Neighbour (KNN) and Support-Vector -Machine (SVM) classification models are used to classify the features. A comparative study on the performance of both the classification models is performed. The experimental result shows the higher efficiency of SVM classifier over KNN classifier.
Learning quality, aesthetics, and facial attributes for image annotation

Every day, a large number of digital images are produced by users of social networks, smartphone users, photography professionals, etc. This caused a problem in the management, organization, indexing, and recovery of digital images. In order to ease this problem, several methods have been introduced in the literature to catalog images automatically. These methods are designed to associate images with one or more keywords belonging to a predefined dictionary or to associate images with visual attributes such as, for example, quality, aesthetics, sentiment, memorability, interestingness, and complexity, etc. This thesis investigates the use of deep convolutional neural network for automatic estimation of image quality and image aesthetics. In the last few years, several methods for automatic image quality assessment have been proposed. Most of them have been designed to deal with synthetically distorted images, which by definition do not truly model distortions afflicting real-world images. In this thesis a method for the automatic quality assessment of authentically distorted images is investigated. It shows better performances than state-of-the-art methods both on synthetically and authentically distorted images datasets. Differently from the image quality, which characterizes the perceived quality of the image signal, aesthetics depicts perceived beauty. As first step, the problem of aesthetic quality assessment of real-life general content images has been investigated. The proposed solution outperformed state-of-the-art methods on the largest publicly available dataset. Given that one of the most popular visual contents is the face (e.g. on social networks for photo sharing), aesthetics assessment is, therefore, further investigated on the specific case of portrait images. To this end, in this thesis an algorithm involving the combination of the previously investigated visual attributes (i.e. quality and aesthetics of general content images) and the facial attributes (i.e. smiling, hair style, makeup) description is proposed. Facial attributes description is achieved thanks to two proposed methods. The first algorithm is a robust smile detector (it represents an important visual feature for portrait aesthetics), the second is a multiple-task model designed in order to simultaneously estimate soft biometrics and attributes such as hair colors and styles, types of beards. While the first algorithm outperforms state-of-the-art methods (also respect to highly distorted images), the multi-task model demonstrates comparable performance. Experimental results for the portrait image aesthetic assessment thanks to the use of the proposed algorithm show promising performance on three standard datasets.
Morphological Amoeba-based Patches for Exemplar-Based Inpainting

Inpainting is the process of replacing areas in an image with a perceptually plausible substitution. A common technique is to iteratively match and fill small patches at the edge of the target region making use of similar patches from the same image. Nearly all inpainting algorithms based on this approach use a single patch size for the entire image. Yet, it seems clear that differently sized structures within the same image – for example a leaf versus a car tire – may require different patch sizes in order to achieve reasonable inpainting results. Likewise, a fixed patch size will give different results for the same image when the image resolution is doubled. A reasonable patch should therefore take into account the overall image size as well as the size and shape of the structures at the patch location. The aim of our paper is to study the effect of adaptively altering size and shape of the patch. We show that this technique leads to a better quality of the inpainting result compared to a fixed patch size.
A Continuous Space Generative Model

Generative models are a class of machine learning models capable of producing digital images with plausibly realistic properties. They are useful in such applications as visualizing designs, rendering game scenes, and improving images at higher magnifications. Unfortunately, existing generative models generate only images with a discrete predetermined resolution. This paper presents the Continuous Space Generative Model (CSGM), a novel generative model capable of generating images as a continuous function, rather than as a discrete set of pixel values. Like generative adversarial networks, CSGM trains by alternating between generative and discriminative steps. But unlike generative adversarial networks, CSGM uses only one model for both steps, such that learning can transfer between both operations. Also, the continuous images that CSGM generates may be sampled at arbitrary resolutions, opening the way for new possibilities with generative models. This paper presents results obtained by training on the MNIST dataset of handwritten digits to validate the method, and it elaborates on the potential applications for continuous generative models.
Irregularity Detection in Output Power of Distributed Energy Resources Using PMU Data Analytics in Smart Grids

The output power of distributed energy resources (DERs) may experience irregular fluctuations due to variations of renewable sources, which need to be monitored in order to reliably control the grid. This paper proposes a novel approach for centralized detection of such irregularities based on the time-series analysis of the data reported by phasor measurement units (PMUs). In this approach, a network controller constructs datasets of time-aligned real/reactive powers for different zones. The datasets are transformed into sequences of short-time local outlier probability (ST-LOP) that are analyzed to identify the DER events. The network controller estimates features such as the average duration and the similarity degree that is a measure of spatio-temporal correlation between the DER events. As a use case, event-triggered control of solar photovoltaic (PV) systems with energy storage devices is investigated. The simulation results for the IEEE 123-bus network corroborate the effectiveness of the developed analytics for detection and mitigation of ramp-rate solar power fluctuations. Smart microgrids and active distribution networks can employ the developed analytics to improve a range of diagnostic and control functionalities.
DDoS protection with stateful software‐defined networking

Distributed denial of service (DDoS) attacks represent one of the most critical security challenges facing network operators. Software‐defined networking (SDN) permits fast reactions to such threats by dynamically enforcing simple forwarding/blocking rules as countermeasures. However, the centralization of the control plane requires that the SDN controller, besides network management operations, should also collect information to identify and mitigate the security menaces. A major drawback of this approach is that it may overload the controller and the control channel. On the other hand, stateful SDN represents a new concept, developed to improve reactivity and offload the controller by delegating local treatments to the switches. In this article, we embrace this paradigm to protect end‐hosts from DDoS attacks. We propose StateSec, a novel approach based on in‐switch processing capabilities to detect and mitigate flooding threats. StateSec monitors packets matching configurable traffic features without resorting to the controller. By feeding an entropy‐based detection algorithm with such monitoring features, it detects and mitigates several threats such as (D)DoS with high accuracy. We implemented StateSec in an SDN platform comparing it with state‐of‐the‐art approaches. We show that StateSec is far more efficient: It achieves very accurate detection levels, reducing at the same time the control plane overhead. We have also evaluated the memory footprint of StateSec for a possible use in production. Finally, we deployed StateSec over a real network to tune its parameters and assess its suitability to real‐world deployments.
ICM based ANFIS MPPT controller for grid connected photovoltaic system

In this paper, grid-connected photovoltaic (PV) system is presented. PV system consists of a photovoltaic module, a boost converter, and voltage source inverter. ANFIS based ICM (Incremental Conductance Method) MPPT (Maximum Power Point Tracking) controller is utilized to produce gate signal for DC-DC boost converter. This controller is used for optimizing the total performance of the Photovoltaic system in turn the errors were reduced in Voltage Source Inverter (VSI). The grid-connected PV system performance is evaluated and har-monics occurred in the system are decreased. The proposed methodology is implemented in MATLAB/Simulink.
Two statistical treatments of spanish vocabulary: composite indices of frequency and dispersion and principal component analysis applied to ordinal frequencies

In this study, first, the characteristics of the collected materials to carry out the comparative analysis will be explained. Next, the statistical method based on the combination of two important indexes, frequency and dispersion, will be described, for which it is proposed to convert gross frequencies into ordinals. In the next section, principal component analysis will be performed to find the important factors within the multitude of current parameters. Finally conclusions will be drawn on the two methods and the application to lexical studies will be considered in general.
Performance Improvement Using CNN for Sentiment Analysis

The approach using Deep Learning method provides great results in various field implementations, especially in the field of Sentiment Analysis. One of Deep Learning methods is CNN which has the ability to provide great accuracy in some previous research. However, there are some parts of the training process which can be improved to upgrade the accuracy level and the training time. In this paper, we try to improve the accuracy and processing time of sentiment analysis using CNN model. By tuning the filter size, frameworks, and pre-training, the results show that the use of smaller filter size and pre-training word2vec provide greater accuracy than some previous studies.
The Climax Expression Analysis Based on the Shot-list Data of Movies

Recently studies about audio-visual immersion are being carried out due to development of digital video but studies analysing quantitatively the content or climax of video has not carried out. The paper is analysed quantitatively by using shot size, camera angle, camera direction and camera position, objective & subjective which is general component of video expression. We can see the climax effects in the video destroying the principles because there is a rule when they use the component. This thesis analyses shot-list based on video expression in existing movies therefore it can analyse quantitatively several method commonly used in order to make the climax. These suggesting method that can find the part of climax based on analysis of shot-list can be used when you search the specific part of video like long movie or when you search the genre of the movie or when you index the genre. Also it can be very effective in various information services companies offering climax movie which is searched. Keyword : quantitative, component, video expression, climax video a) 덕성여자대학교 정보미디어대학, 디지털미디어학과(Duksung University, College of Information and Media, School of Digital Media) ‡Corresponding Author : 임양미(Yangmi Lim) E-mail: yosimi@duksung.ac. kr Tel: +82-2-901-8350 ORCID: http://orcid.org/0000-0002-3725-0025 ・Manuscript received September 5, 2016; Revised November 1, 2016; Accepted November 1, 2016. Copyright C 2016 Korean Institute of Broadcast and Media Engineers. All rights reserved. “This is an Open-Access article distributed under the terms of the Creative Commons BY-NC-ND (http://creativecommons.org/licenses/by-nc-nd/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited and not altered. ” 일반논문 (Regular Paper) 방송공학회논문지 제21권 제6호, 2016년 11월 (JBE Vol. 21, No. 6, November 2016) http://dx.doi. org/10.5909/JBE.2016. 21.6. 965 ISSN 2287-9137 (Online) ISSN 1226-7953 (Print) 966 방송공학회논문지 제21권 제6호, 2016년 11월 (JBE Vol. 21, No. 6, November 2016)
A Scalable Distriubuted Ledger for Internet of Things based on Edge Computing

—Internet of Things (IoT) is becoming necessities of people’s daily life and establishing itself as an essential part of future Internet. One of the challenges for using IoT is the security of data collected by trillions of IoT devices and used by millions of services. Distributed ledger technology (DLT) provides a distributed security method which can benefit IoT. Yet challenges are put forward when integrating DLT with IoT, such as scalability and heterogeneous capability of IoT devices. In this paper, we propose a mechanism for integrating DLT in IoT by using edge computing technology, taking the scalability and heterogeneous capability of IoT devices into consideration. IoT devices are clustered dynamically into groups based on various proximity context information. A cluster head is used to bridge the IoT devices with the blockchain network where smart contract is deployed. Through this way, the security of the IoT is improved and the scalability and latency are solved. We elaborate our mechanism and discuss issues that should be considered and implemented when using the proposed mechanism.
TECHNICAL AND SCALE EFFICIENCY IN VEGETABLE CROPS PRODUCTION UNDER FADAMA IN NORTH CENTRAL NIGERIA

A study was conducted in Kaduna state located in the north central zone of Nigeria during dry season of 2007/2008. Technical and scale efficiency in vegetable production was estimated using data envelopment analysis for a sample of 192 Fadama farmers. The technical efficiency in study area ranged from 39 to 100 percent with a mean value of 93 percent. Scale efficiency on the other hand ranged from 2 to 100 percent with a mean of 82 percent. The low average scale efficiency score implies that scale inefficiency was the major source of inefficiency in vegetable production under Fadama. Further, average pure technical efficiency score for vegetables in mixtures (93.6 percent) relative to that of sole vegetable enterprises (91.5 percent) implies that Fadama farmers can be more efficient if they grow vegetables in mixtures. To eliminate scale inefficiency, policies that could encourage land consolidation in Fadama communities should be formulated.
Enhanced Startup Diagnostics of LCL Filter for an Active Front-End Converter

The reliability of grid-connected inverters can be improved by algorithms capable of diagnosing faults in LCL filters. A fault diagnostic method during inverter startup is proposed. The proposed method can accurately generate and monitor information on the peak value and the location of the peak frequency component of the step response of a damped LCL filter. To identify faults, the proposed method compares the evaluated response with the response of a healthy higher-order damped LCL filter. The frequency components in the filter voltage response are first analytically obtained in closed form, which yields the expected trends for the filter faults. In the converter controller, the frequency components in the filter voltage response are computed using an appropriately designed fast Fourier transform and compared with healthy LCL response parameters using a finite state machine, which is used to sequence the proposed startup diagnostics. The performance of the proposed method is validated by comparing analytical results with the simulation and experimental results for a three-phase grid-connected inverter with a damped LCL filter.
Cyberattack Detection for Cyber Physical Systems Security – A Preliminary Study

Cyber-physical systems (CPS) security has become an increasingly important research topic in recent years. Geared towards more advanced cyberattack detection techniques as part of strategies for enhancing the security of CPS, in this paper we propose a machine learning based cyber-attack detection scheme. The proposed scheme is a physical-domain technique; specifically, it assumes the physical measurements of the system carry sufficient information for capturing the system behavior, thus can be used for differentiating normal operation and attacks. CPS are complex in nature and the number of physical measurements available for CPS is often overwhelmingly high. Thus, accurately modeling CPS’ dynamic behavior, more importantly, distinguishing normal and adversary activities based on the large number of physical measurements, can be challenging. To address the challenge, we have focused our research effort on feature engineering, that is, to intelligently derive a set of salient signatures or features from the noisy measurements. We make sure the derived features are more compact and, more importantly, have more discriminant power than the original physical measurements, thus enabling us to achieve more accurate and robust detection performance. To demonstrate the effectiveness of the proposed scheme, in our experimental study we consider gas turbines of combined cycle power plants as the cyber-physical system. Using the data from the high-fidelity simulation we show that our proposed cyberattack detection scheme is able to achieve high detection performance.
DineTogether: A Social-Aware Group Sequential Recommender System

Group recommendations become important in many practical scenarios, e.g., couples would like to share movies together, families dine together in a restaurant, a group of friends plan to spend vacation in some points of interest. Although some previous research eﬀorts have been done in this area, most of them only consider a small portion of contextual factors but ignore the time series features of the user historical behavior and next recommended location/event to do, that makes the system performance not as satisfactory as expected. Here, we propose a novel group sequential recommender system, called “DineTogether”, for a group of people dinning together. It is able to capture comprehensive contextual, social factors when make recommendations. We design a computational model, called “Social-and-Time-Aware (STA)” model, and a novel algorithm, Generalized Random Walk with Restart (GRWR). Experiment results show that our approach outperforms the state-of-the-art group recommendation approaches.
Voltage clamp simulations of cardiac excitation: FPGA implementation

This paper presents the simulation study of voltage clamp technique that enables to analyse current-voltage (I-V) characteristics of ion currents based on Luo-Rudy Phase-I (LR-I) model by using a Field Programmable Gate Array (FPGA). Here, the I-V relationship presents the characterization of each ion channel by a relation between membrane voltage, Vm and resulting channel current. In addition, the voltage clamp technique also allows the detection of single channel currents in biological membranes and is known to be applicable in identifying variety of electrophysiological problems in the cellular level. As computational simulations devote a vast amount of time to run due to the increasing complexity of cardiac models, a real-time hardware implementation using FPGA could be the solution as it provides high configurability and performance, and able to executes in parallel mode operation for high-performance real-time systems. For shorter time development while retaining high confidence results, FPGA-based rapid prototyping through HDL Coder from MATLAB software has been used to construct the algorithm for the simulation system. Basically, the HDL Coder is capable to convert the designed MATLAB Simulink blocks into hardware description language (HDL) for the FPGA implementation. As a result, the MATLAB Simulink successfully simulates the voltage clamp of the LR-I excitation model and identifies the I-V characteristics of the ionic currents through Xilinx Virtex-6 XC6VLX240T development board.
Linguistic consequences of event segmentation in visual narratives: implications for prominence

ABSTRACT This experiment uses comic-type visual narratives to investigate effects of event segmentation on the representation of causal events (with an agent and a patient) in discourse. Prior work leaves open the question of whether the prominence of entities and events in mental models of discourse can be dissociated. By presenting participants with one-panel vs. two-panel segmentations of the same event, we tested whether differences in event segmentation boost the prominence of the patient, the consequence event, or both, as reflected in people’s choices about what is mentioned next. The results show participants are more likely to mention consequences in two-panel than one-panel conditions, indicating that panel segmentation influences the cognitive prominence of event-level representations. However, we find no clear evidence for segmentation influencing the cognitive prominence of entities. This suggests language users separately track expectations about who is mentioned next and expectations about what kind of event is described next.
BAHASA DAN SASTRA BALI DALAM KONTEKS BAHASA DAN SASTRA JAWA KUNA

Balinese is the mother tongue, which is the first language obtained from her mother or the language used since birth to native Balinese. The connection of Old Javanese language with Balinese Language cannot be taken away in a cultural study that exists today. Various cases that can be found through the alkururat between Balinese residents and the spread of Javanese people who came to Bali. This can be attributed to a number of archipelago histories, including the sources of the collapse of the Majapahit kingdom and the culture brought to Bali, and can be attributed to the ancient Javanese language having a connection with Balinese. Based on a glimpse of the background, there are a number of problems that will be examined by formulating the following: (1) how is the distribution of Old Javanese language in Bali ? , (2) how is the variety of Old Javanese languages absorbed by Balinese language ? , and (3) how the development of Old Javanese Language in the case of Balinese literature? Related to the study of Balinese language and literature in the context of Old Javanese language and literature, the following can be briefly presented as the results of the study as follows. (1) the spread of Old Javanese language to Balinese language includes acculturation of culture brought about by the collapse of the Majapahit kingdom, so that all Old Javanese literary literature is brought to Bali, (2) the type of absorption words used in Balinese include intralinguistic factors and extralinguistic factors, (3) the development of Old Javanese in Balinese literature is an acculturation from Old Javanese to Balinese, and vice versa, which can be seen in literary works in the form of Gancaran, kakawin, and Palawakya.