Miracle-dz/UniFAR-FTUs huggingface dataset:
Algorithms to estimation of size and shape tomato using Artificial Vision Techniques

– This paper describes the development of estimation algorithms for the size and shape of tomato fruits, implemented on a portable electronic system used in greenhouses, with the purpose to classify and collect of tomatoes milano and chonto type. The algorithms were implemented in a Raspberry-Pi embedded card through the use of free software libraries (Python, OpenCV, Pillow and Scikit-Image) and using a Pi-Camera for real-time processing, taking into account the parameters of classification defined in national (NTC1103-1) and international (USDA) standards. Were applied classification algorithms based on Canny to estimate the size of the tomato fruit, as well as techniques based on estimation of the eccentricity and statistical moments descriptors for the measurement of the shape, which were evaluated with a performance superior to 90 %. Classification techniques of tomato shape and size using artificial vision overcame subjective techniques of visual and tactile type classification carried out by farmers and specialized technicians.
Efficient Compressed Sensing Based Non-Sample Spaced Sparse Channel Estimation in OFDM System

This paper aims to develop an efficient compressed sensing (CS) based channel estimation method for non-sample spaced sparse channels in orthogonal frequency division multiplexing (OFDM) systems, which can effectively balance the channel estimation performance, spectral efficiency and computational complexity. To realize this goal, a novel delay tracking and residual norm minimization (DT-RNM) method is proposed. In this method, the idea of reference delay grids (RDG) inspired by the effective local delay tracking (DT) is formulated, which is proven to be much more efficient than current algorithms in estimating non-sample spaced sparse channels. Both theoretical derivation and simulation results show the effectiveness of the proposed method.
Screen Content Compression Based on Enhanced Soft Context Formation

The compression of screen content has attracted the interest of researchers in the last years as the market for transferring data from computer displays is growing. It has already been shown that especially those methods can effectively compress screen content which are able to predict the probability distribution of next pixel values. This prediction is typically based on a kind of learning process. The predictor learns the relationship between probable pixel colours and surrounding texture. Recently, an effective method called ‘soft context formation’ (SCF) had been proposed which achieves much lower bitrates for images with less than 8 000 colours than other state-of-the-art compression schemes. This paper presents an enhanced version of SCF. The average lossless compression performance has increased by about 5% in application to images with less than 8 000 colours and about 10% for images with up to 90 000 colours. In comparison to FLIF, FP8v3, and HEVC (HM − 16.20 + SCM − 8.8), it achieves savings of about 33%, 4%, and 11% on average. The improvements compared to the original version result from various modifications. The largest contribution is achieved by the local estimation of the probability distribution for unpredictable colours in stage II of the compression scheme.
Hierarchical Affordance Discovery using Intrinsic Motivation

To be capable of life-long learning in a real-life environment, robots have to tackle multiple challenges. Being able to relate physical properties they may observe in their environment to possible interactions they may have is one of them. This skill, named affordance learning, is strongly related to embodiment and is mastered through each person's development: each individual learns affordances differently through their own interactions with their surroundings. Current methods for affordance learning usually use either fixed actions to learn these affordances or focus on static setups involving a robotic arm to be operated. In this article, we propose an algorithm using intrinsic motivation to guide the learning of affordances for a mobile robot. This algorithm is capable to autonomously discover, learn and adapt interrelated affordances without pre-programmed actions. Once learned, these affordances may be used by the algorithm to plan sequences of actions in order to perform tasks of various difficulties. We then present one experiment and analyse our system before comparing it with other approaches from reinforcement learning and affordance learning.
A Network Delay Factor Model Based on the Hidden Markov Model and Latent Dirichlet Allocation

Network systems are widely used in today’s digital communication systems and applications. Network delay is a very important factor affecting the efficiency and reliability of the system, and researchers often use a model to predict and understand the causes and patterns of delay. For our model, we propose beginning with the Hidden Markov Model (HMM) of the transmission network, which is used to analyze the network’s operational situation and to predict the approximate state of the system into the future using the HMM prediction algorithm. Then, employing Latent Dirichlet Allocation (LDA), we put forward the Delay Factor Model (DFM) value for the delay. In the DFM, we need to map the delay’s interval to an integer, designated as DII (Delay Interval Integer), and the factors returned define the hidden states of the HMM. From the view of the DFM, DII is generated from a factor and the previous DII randomly. We use the Gibbs Sampling approach to obtain an estimation of the DFM’s parameters. By defining the HMM and DFM, we can forecast future delay with high accuracy, and the result can be shown to follow the peaks and troughs of the real operation of the network system’s delay patterns.
Bidirectional Prediction-Based Underwater Data Collection Protocol for End-Edge-Cloud Orchestrated System

The proliferation of advanced underwater technology and the emergence of various cloud services promote the horizon of cloud-based underwater acoustic sensor network (UASN). Sending end data to cloud for analysis is becoming a prominent trend, driving cloud computing as an indispensable computing paradigm. However, UASN bears tremendous burdens with respect to data collection from end to cloud, such as large transmission power consumption and high delay, which makes it difficult to meet the delay-sensitive and context-aware service requirements by using cloud computing alone. To this end, a two-level bidirectional data prediction model for end-edge-cloud orchestration is proposed in this article. The mobility and computing ability of edge elements are exploited to analyze and collect data. Edge elements predict the future data based on historical information and trend to decrease acoustic communication. Moreover, a data collection protocol with mobile edge elements is designed. With this protocol, computing paradigms are shifted from centralized cloud to distributed edge, and the differentiated capability of heterogeneous devices is exploited. After extensive experiments, the results show that the data collection cost is dramatically decreased while the bandwidth utilization is increased, which is critical for underwater acoustic communication. The proposed method and protocol strike a good balance between data accuracy and energy consumption for the new end-edge-cloud orchestrated system.
Translating Arabic as low resource language using distribution representation and neural machine translation models

Rapid growth in social media platforms makes the communication between users easier. According to that, the communication increased the importance of translating human languages. Machine translation technology has been widely used for translating several languages using different approaches such as rule based, statistical machine translation and more recently neural machine translation. The quality of machine translation depends on the availability of parallel datasets. Languages that lack sufficient datasets have posed many challenges related to their processing and analysis. These languages are referred to as low resource languages. In this research, we mainly focused on low resource languages, particularly Arabic and its dialects. Dialectal Arabic can be treated as non-standard text that is used in Arab social media and need to be translated to their standard forms. In this context, the importance and the focus of machine translation have been increased recently. Unlike English and other languages, translation of Arabic and its dialects have not been thoroughly investigated, where existing attempts were mostly developed based on statistic and rule-based approaches, while neural network approaches have hardly been considered. Therefore, a distribution representation model (embedding model) has been proposed to translate dialectal Arabic to Modern Standard Arabic. As Arabic is a rich morphology language that has different forms of the same words the proposed model can help to capture more linguistic features such as semantic and syntax features without any rules. Another benefit of the proposed model is that it has the capability
Investigations into the role of the ErbB4 receptor in cardiomyocyte hypertrophy and adult cardiac function

ii Declaration by author . iv Publications during candidature . v Publications included in this thesis . v Contributions by others to the thesis . vi Statement of parts of the thesis submitted to qualify for the award of another degree . vi Acknowledgements . vii
A Compact 0.3-V Class AB Bulk-Driven OTA

In this article, a new solution for an ultralow-voltage (ULV) ultralow-power (ULP) operational transconductance amplifier (OTA) is presented. Thanks to the combination of a low-voltage bulk-driven nontailed differential stage with the multipath Miller zero compensation technique, a simple class AB power-efficient ULV structure has been obtained, which can operate from supply voltages less than the threshold voltages of the employed MOS transistors, while offering rail-to-rail input common-mode range at the same time. The proposed OTA was fabricated using the 180-nm CMOS process from Taiwan Semiconductor Manufacturing Company (TSMC) and can operate from $V_{\mathbf {DD}}$ ranging from 0.3 to 0.5 V. The 0.3-V version dissipates only 12.6 nW of power while showing a 64.7-dB voltage gain at 1-Hz, 2.96-kHz gain-bandwidth product, and a 4.15-V/ms average slew-rate at 30-pF load capacitance. The measured results agree well with simulations.
Multi-Modal Deep Analysis for Multimedia

With the rapid development of Internet and multimedia services in the past decade, a huge amount of user-generated and service provider-generated multimedia data become available. These data are heterogeneous and multi-modal in nature, imposing great challenges for processing and analyzing them. Multi-modal data consist of a mixture of various types of data from different modalities such as texts, images, videos, audios etc. In this article, we present a deep and comprehensive overview for multi-modal analysis in multimedia. We introduce two scientific research problems, data-driven correlational representation and knowledge-guided fusion for multimedia analysis. To address the two scientific problems, we investigate them from the following aspects: 1) multi-modal correlational representation: multi-modal fusion of data across different modalities, and 2) multi-modal data and knowledge fusion: multi-modal fusion of data with domain knowledge. More specifically, on data-driven correlational representation, we highlight three important categories of methods, such as multi-modal deep representation, multi-modal transfer learning, and multi-modal hashing. On knowledge-guided fusion, we discuss the approaches for fusing knowledge with data and four exemplar applications that require various kinds of domain knowledge, including multi-modal visual question answering, multi-modal video summarization, multi-modal visual pattern mining and multi-modal recommendation. Finally, we bring forward our insights and future research directions.
Simulating Replication and B-Trees

The synthesis of forward-error correction has explored superblocks, and current trends sug- gest that the development of randomized algorithms will soon emerge. After years of in- tuitive research into SMPs, we verify the emulation of telephony. In order to overcome this question, we confirm that extreme pro- gramming and replication [1, 2] are rarely incompatible [1, 2].
Modeling Incomplete Knowledge of Semantic Web Using Bayesian Networks

ABSTRACT Interoperable ontologies already exist in the biomedical field, enabling scientists to communicate with minimum ambiguity. Unfortunately, ontology languages, in the semantic web, such as OWL and RDF(S), are based on crisp logic and thus they cannot handle uncertain knowledge about an application field, which is unsuitable for the medical domain. In this paper, we focus on modeling incomplete knowledge in the classical OWL ontologies, using Bayesian networks, all keeping the semantic of the first ontology, and applying algorithms dedicated to learn parameters of Bayesian networks in order to generate the Bayesian networks. We use EM algorithm for learning conditional probability tables of different nodes of Bayesian network automatically, contrary to different tools of Bayesian networks where probabilities are inserted manually. To validate our work, we have applied our model on the diagnosis of liver cancer using classical ontology containing incomplete instances, in order to handle medical uncertain knowledge, for predicting a liver cancer.
The Effect of Brand Experience on Customers’ Engagement Behavior within the Context of Online Brand Communities: The Impact on Intention to Forward Online Company-generated Content

The purpose of this study is to assess the antecedent role of brand experience (BE) in the intention to forward online company-generated content (CGC) within an online Islamic banking sector. The present study analyzed 387 valid responses collected through an online survey conducted among a number of online Islamic bank customers in Palestine. The results of this study revealed that BE has a stronger influence on customers’ intention to forward online company-generated contents. This research pioneers the empirical research in Palestinian Islamic banking systems exploring the instrumental role of BE on customers’ engagement behaviors, as well as the intention to forward online CGC. In addition, this research aims to fill the existing gap in the under-researched area of the online branding of Islamic banking services.
Optimized Verifiable Fine-Grained Keyword Search in Dynamic Multi-Owner Settings

Ciphertext-Policy Attribute-Based Keyword Search (CP-ABKS) schemes support both fine-grained access control and keyword-based ciphertext retrieval, which make these schemes attractive for resource-constrained users (i.e., mobile or wearable devices, sensor nodes, etc.) to store, share and search encrypted data in the public cloud. However, ciphertext length and decryption overhead in the existing CP-ABKS schemes grow with the complexity of access policies or the number of data users’ attributes. Moreover, such schemes generally do not consider the practical multi-owner setting (e.g., each file needs to be signed by multiple data owners before being uploaded to the cloud server) or prevent malicious cloud servers from returning incorrect search results. To overcome these limitations, in this paper we first design an optimized Verifiable Fine-grained Keyword Search scheme in the static Multi-owner setting (termed as basic VFKSM), which achieves short ciphertext length, fast ciphertext transformation, accelerated search process, and authentic search result verification. Then, we extend the basic VFKSM to support multi-keyword search and multi-owner update (also called as extended VFKSM). Finally, we prove that the basic (or extended) VFKSM resists the Chosen-Keyword Attack (CKA) and external Keyword-Guessing Attack (KGA). We also evaluate the performance of these schemes using various public datasets.
Comparison of Telephone Recordings and Professional Microphone Recordings for Early Detection of Parkinson's Disease, Using Mel-Frequency Cepstral Coefficients with Gaussian Mixture Models

Vocal impairments are among the earliest symptoms in Parkinson’s Disease (PD). We adapted a method classically used in speech and speaker recognition, based on Mel-Frequency Cepstral Coefficients (MFCC) extraction and Gaussian Mixture Model (GMM) to detect recently diagnosed and pharmacologically treated PD patients. We classified early PD subjects from controls with an accuracy of 83%, using recordings obtained with a professional microphone. More interestingly, we were able to classify PD from controls with an accuracy of 75 % based on telephone recordings. As far as we know, this is the first time that audio recordings from telephone network have been used for early PD detection. This is a promising result for a potential future telediagnosis of Parkinson's disease.
Novel molecular mechanisms of neuronal and vascular protection in experimental glaucoma

Paranodal axoglial junctions are essential for the segregation of myelinated axons into distinct domains and efficient conduction of action potentials. Here, we show that netrin-1 and deleted in colorectal cancer (DCC) are enriched at the paranode in CNS myelin. We then address whether netrin-1 signaling influences paranodal adhesion between oligodendrocytes and axons. In the absence of netrin-1 or DCC function, oligodendroglial paranodes initially develop and mature normally but later become disorganized. Lack of DCC or netrin-1 resulted in detachment of paranodal loops from the axonal surface and the disappearance of transverse bands. Furthermore, the domain organization of myelin is compromised in the absence of netrin-1 signaling: K channels inappropriately invade the paranodal region, and the normally restricted paranodal distribution of Caspr expands longitudinally along the axon. Our findings identify an essential role for netrin-1 and DCC regulating the maintenance of axoglial junctions.
Movements and Balance Control of a Wheel-Leg Robot Based on Uncertainty and Disturbance Estimation Method

In order to control the posture, balance and movements of a wheel-leg robot(WLR) precisely, some problems have to be solved. The major problems involve the strongly coupling of movement states, the composite uncertainty with modeling errors, the time-varying internal and the external disturbances. In this paper, a force controller based on the uncertainty and disturbance estimation(UDE) method is designed. The feedforward compensation based on dynamic model allows for compliant and more precise motion control. The UDE can estimate and compensate the composite uncertainty without any priori information effectively. The posture controller is designed based on the virtual model control. The whole controller which includes the posture controller and the movements balance controller is proposed and applied for WLR that built in Webots. The simulation results of trajectory control, balance control and robust test are presented to demonstrate the effectiveness of the whole controller.
Big Data Processing Platform on Intelligent Transportation Systems

Nowadays, with the incredible demographic explosion that we have witnessed in the last few decades, management of transport is of paramount importance. The reason for this is that we have to face the management of problems relating to traffic detection, traffic jams created by urban public transport, data on motorway tolls, meteorological data and traffic safety, etc. These types of traffic data are numerous and enormous. Traditional tools are now unable to solve these problems. With the rapid development of Big Data technologies, the new way of thinking about intelligent transport has become an obligation; as a result, new architectures are mainly needed to work with big data. In order to overcome this problem, it is essential to create a Big Data modeling approach for ITS, which pays particular attention to the creation of multiple layers. Among these we find Management and Processing layer which in its turn contains three levels: processing, analyzing and storing. In this paper, we are interested in the processing level, which attracts the attention of researchers. In fact, we will propose a Big Data processing design applied to Intelligent Transportation Systems. We will adopt a data modeling approach that treats both the transmission and the processing data.
The effects of employer branding and career anchor on intention to leave

This research aims to investigate the effects of employer branding and career anchor on the intention to leave. The object of this research is the companies operating in the technology, finance and pharmaceutical sectors while the subject is 229 employees. Three variables from the research data were gathered through instruments in the form of valid and reliable questionnaires. Statistical Analysis of the research data used hierarchical regression analysis with the significance in accordance with the output of SPSS 20.0. The review of the findings obtained from the data demonstrated a statistically significant correlation between the employer brand and career anchor; employer brand and the intention to leave. Findings indicated that employer branding has a significant effect on career anchor and intention to leave; career anchor also has a significant effect on the intention to leave. However, the mediator effect of career anchor did not appear in the relationship between employer branding and intention to leave.
Rakugo speech synthesis using segment-to-segment neural transduction and style tokens — toward speech synthesis for entertaining audiences

We have been working on constructing rakugo speech synthesis as a challenging example of speech synthesis that entertains audiences. Rakugo is a traditional Japanese form of verbal entertainment that is similar to one-person stand-up comedy. In rakugo, a performer himself/herself plays multiple characters, and conversations by them make the story progress. We tried to build a rakugo synthesizer with state-of-the-art encoder-decoder models with attention such as Tacotron 2. However, it did not work well because the expressions of rakugo speech are far more diverse than those of read speech. We therefore use segment-to-segment neural transduction (SSNT) in place of a combination of attention and decoder. Furthermore, we experimented with global style tokens (GST) and manually-labeled context features to enrich the speaking styles of synthesized rakugo speech. The results show that SSNT greatly helps align the encoder and decoder time steps and that GST help reproduce characteristics better.
The Regional Tax Policy: A Review in Greece

The continuing ineffectiveness of the tax reforms in Greek economy raises concerns about the need for a meaningful restructuring of the structure of fiscal administration. Tax restructuring that enhances regional, local or European taxation by weakening government taxation can be a key factor in reforming the tax system. The degree of tax decentralization of the tax policy applied is likely to improve the fiscal order and management and, on the other hand, it can be strengthened tax revenues by enhancing tax compliance. The main purpose of this paper is to emphasize that the decentralization of taxation to a more socially acceptable regional fiscal policy can lead to an acceptable tax reform in the case of the Greek economy. A reform of the revenue management at local, regional, central and European level with a more socio-centric fiscal expenditure mix is more desirable.