Miracle-dz/UniFAR-FTUs huggingface dataset:
Low‐Temperature Sintering and Mechanical Property Evaluation of Nanocrystalline 8 mol% Yttria Fully Stabilized Zirconia

Fully stabilized zirconia containing 8 mol% yttria (8Y-FSZ) in nanocrystalline form has been synthesized by the coprecipitation method. The formation of an easily filterable hydroxide is facilitated by the addition of ammonium sulfate and polyethylene glycol during precipitation. The precipitate is then calcined to produce nanocrystalline powder. Using this powder, it has been possible to obtain a sintering density of more than 95% at a temperature as low as 1150°C by following a conventional sintering schedule. Adoption of a two-stage sintering schedule, in which heat treatment of the powder compact has been carried out initially to a high temperature, followed by a long holding at a lower temperature, resulted in further lowering of the sintering temperature. Hardness and toughness values have been found to be dependent on the microstructure in low-temperature-sintered samples.
Effect of amoxycillin/clavulanic acid (Augmentin 625) on antioxidant indices and markers of renal and hepatic damage in rats

Amoxycillin/clavulanic acid (Augmentin 625®) is an antibiotic which is being advocated by WHO as a drug for treatment of infection. Its efficacy and potential to inhibit microorganisms had led to their increased use. There is the possibility that the drug may have potential toxic effects on humans following consumption. The present investigation was therefore undertaken to study the status of antioxidant defense system, oxidative stress and some biochemical indices following Augmentin 625® treatment in rats. 20 rats were divided into two groups: Group 1 (control) received no drug, while group 2 received Augmentin 625 (21.83 mg/kg body weight) twice for seven days. The result indicated that the drug induced marked renal and liver failure characterized by a significant increase (p < 0.05) in plasma creatinine, urea, and bilirubin, by 76, 45, and 100% respectively. Similarly, the drug also significantly increased total cholesterol, HDL cholesterol, LDL cholesterol and Triglycerides by 38.1, 83.5, 96 and 26% respectively compared to control. Furthermore, the administration of the drug significantly increased plasma AST and ALT by 42.6 and 44.4% respectively compared with the control. The drug also caused a significant decrease in hepatic Glutathione (GSH) and Vitamin C by 51 and 44% respectively. Similarly hepatic SOD, Catalase and GST activities were significantly decreased by 56, 33 and 53% respectively. Overall, the result of the present investigation shows that acute dose of amoxycillin/clavulanic acid (Augmentin 625®) altered enzymatic and non-enzymatic antioxidant defense system and induced oxidative stress in rats. Key words: Amoxycillin/clavulanic acid, respiratory tract infection, antimicrobial resistance, antioxidant indices, oxidative stress, reactive oxygen species.
Augmented Spanish-Persian Neural Machine Translation

Neural Machine Translation (NMT) performs training of a neural network employing an encoder-decoder architecture. However, the quality of the neural-based translations predominantly depends on the availability of a large amount of bilingual training dataset. In this paper, we explore the performance of translations predicted by attention-based NMT systems for Spanish to Persian low-resource language pairs. We analyze the errors of NMT systems that occur in the Persian language and provide an in-depth comparison of the performance of the system based on variations in sentence length and size of the training dataset. We evaluate our translation results using BLEU and human evaluation measures based on the adequacy, fluency, and overall
CdS based novel photo-impedance light sensor

A novel photo-impedance (hybrid photocapacitive-photoresistive) sensor for visible light employs gated cadmium sulfide photoresistance connecting two fixed capacitances. The changes of the photoresistance and of the metal–insulator (dielectric)-semiconductor (MIS) under illumination affect the overall device capacitance. The MIS and photoresistor frequency dispersion allows for increasing the dynamic range by measuring the capacitance response at lower frequencies. By making this device a part of the tank circuit of a radio frequency oscillator, the information on illumination intensity can be relayed as the signal frequency and detected remotely.
Low Energy ECG Features Extraction for Atrial Fibrillation Detection in Wearable Sensors

The Internet of Health Things plays a key role in the transformation of health care systems as it enables wearable health monitoring systems to ensure continuous and non-invasive tracking of vital body parameters. To successfully detect the cardiac problem of Atrial Fibrillation (AF) wearable sensors are required to continuously sense and transmit ECG signals. The traditional approach of ECG streaming over energyconsuming wireless links can overwhelm the limited energy resources of wearable sensors. This paper proposes a low-energy features’ extraction method that combines the RR interval and P wave features for higher AF detection accuracy. In the proposed scheme, instead of streaming raw ECG signals , local AF features extraction is executed on the sensors. Results have shown that combining time-domain features with wavelet extracted features, achieved a sensitivity of 98.59% and a specificity of 97.61%. In addition, compared to ECG streaming, on-sensor AF detection achieved a 92% gain in energy savings.
Masked Hard Coverage Mechanism on Pointer-generator Network for Natural Language Generation

Natural Language Generation (NLG) task is to generate natural language utterances from structured data. Seq2seq-based systems show great potentiality and have been widely explored for NLG. While they achieve good generation performance, over-generation and under-generation issues still arise in the generated results. We propose maintaining a masked hard coverage mechanism in the pointer-generator network, a seq2seqbased architecture that trains a switch policy to produce output sequences by partially copying from input structured data. The proposed mechanism can be regarded as the inner controlling module to keep track of the copying history and force the network to generate sentences accurately covering all information provided in structured data. Experimental results show that our coverage mechanism alleviates the over-generation and under-generation issues and achieves decent performance on the E2E NLG dataset.
Towards Formal Security Verification of Over-the-Air Update Protocol: Requirements, Survey and UpKit Case Study

The fast growing number of connected devices through the Internet of Things requires the capability of performing secure and efficient over-the-air updates in order to manage the deployment of innovative features and to correct security issues. Pushing an updated image in a device requires a complex protocol exposed to security threats which could be exploited to block, spy or even take control of the updated device. Hence, such update protocols need to be carefully designed and verified. In the scope of this paper, we review some representative update protocols and related threats based on MQTT, TUF (Uptane) and the blockchain. We then show how the adequate management of those threats can be verified using a formal modelling and verification approach using the Tamarin tooling. Our work is applied to the concrete case of the UpKit protocol which exhibits an interesting design.
Interpretability in Word Sense Disambiguation using Tsetlin Machine

: Word Sense Disambiguation (WSD) is a longstanding unresolved task in Natural Language Processing. The challenge lies in the fact that words with the same spelling can have completely different senses, sometimes depending on subtle characteristics of the context. A weakness of the state-of-the-art supervised models, however, is that it can be difﬁcult to interpret them, making it harder to check if they capture senses accurately or not. In this paper, we introduce a novel Tsetlin Machine (TM) based supervised model that distinguishes word senses by means of conjunctive clauses. The clauses are formulated based on contextual cues, represented in propositional logic. Our experiments on CoarseWSD-balanced dataset indicate that the learned word senses can be relatively effortlessly interpreted by analyzing the converged model of the TM. Additionally, the classiﬁcation accuracy is higher than that of FastText-Base and similar to that of FastText-CommonCrawl.
Twin-GAN for Neural Machine Translation

In recent years, Neural Machine Translation (NMT) has achieved great success, but we can not ignore two important problems. One is the exposure bias caused by the different strategies between training and inference, and the other is that the NMT model generates the best candidate word for the current step yet a bad element of the whole sentence. The popular methods to solve these two problems are Schedule Sampling and Generative Adversarial Networks (GANs) respectively, and both achieved some success. In this paper, we proposed a more precise approach called “similarity selection” combining a new GAN structure called twinGAN to solve the above two problems. There are two generators and two discriminators in the twin-GAN. One generator uses the “similarity selection” and the other one uses the same way as inference (simulate the inference process). One discriminator guides generators at the sentence level, and the other discriminator forces these two generators to have similar distributions. Moreover, we performed a lot of experiments on the IWSLT 2014 German→English (De→En) and the WMT’17 Chinese→English (Zh→En) and the result shows that we improved the performance compared to some other strong baseline models which based on recurrent
A Novel Deep Learning Power Quality Disturbance Classification Method using Autoencoders

Automatic identification and classification of power quality disturbances (PQDs) is crucial for maintaining efficiency and safety of electrical systems and equipment condition. In recent years emerging deep learning techniques have shown potential in performing classification of PQDs. This paper proposes two novel deep learning models, called CNN(AE)-LSTM and CNN-LSTM(AE) that automatically distinguish between normal power system behaviour and three types of PQDs: voltage sags, voltage swells and interruptions. The CNN-LSTM(AE) model achieved the highest average classification accuracy with a 65:35 train-test split. The Adam optimiser and a learning rate of 0.001 were used for ten epochs with a batch size of 64. Both models are trained using real world data and outperform models found in literature. This work demonstrates the potential of deep learning in classifying PQDs and hence paves the way to effective implementation of AIbased automated quality monitoring to identify disturbances and reduce failures in real world power systems.
Attesting Digital Discrimination Using Norms

More and more decisions are delegated to Machine Learning (ML) and automatic decision systems recently. Despite initial misconceptions considering these systems unbiased and fair, recent cases such as racist algorithms being used to inform parole decisions in the US, low-income neighborhood's targeted with high-interest loans and low credit scores, and women being undervalued by online marketing, fueled public distrust in machine learning. This poses a significant challenge to the adoption of ML by companies or public sector organisations, despite ML having the potential to lead to significant reductions in cost and more efficient decisions, and is motivating research in the area of algorithmic fairness and fair ML. Much of that research is aimed at providing detailed statistics, metrics and algorithms which are difficult to interpret and use by someone without technical skills. This paper tries to bridge the gap between lay users and fairness metrics by using simpler notions and concepts to represent and reason about digital discrimination. In particular, we use norms as an abstraction to communicate situations that may lead to algorithms committing discrimination. In particular, we formalise non-discrimination norms in the context of ML systems and propose an algorithm to attest whether ML systems violate these norms.
Multi-class Motor Imagery EEG Classification using Convolution Neural Network

Electroencephalogram (EEG) signals based on Motor Imagery (MI) are a widely used form of input in Brain Computer Interface (BCI). Although there are several ways to classify data, a question remains as to which method to use in EEG signals based on motor imagery. This article presents an attempt to reach the best classification method based on deep learning methods by comparing two models: Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM), on the same basic data set. The BCI Competition IV dataset 2a was used as the base dataset to test the two classification methods. Experimental results show that the proposed CNN model outperforms the LSTM model, with an accuracy value of 74%, and other stateof-the-art methods.
Capsule Networks with Intersection over Union Loss for Binary Image Segmentation

With the development of Capsule Networks and their adaptation to the task of semantic segmentation, it has become important to determine which hyperparameters perform best for this new type of image processing model. One such parameter is the loss function, for which the baseline is usually cross entropy loss. In recent work on other models, Intersection over Union (IoU) loss has been shown to be effective. This work explores the application of IoU loss to segmentational capsule networks. For this purpose experiments are performed on two datasets: a medical dataset, LUNA16, and a dataset of faces (LFW). Results show marginal to significant improvements when using the IoU loss function as compared to the baseline Binary Cross-Entropy. From this can be concluded that the search for optimal loss functions is not finished and new loss functions may further improve performance of existing models.
Major Products Obtained from the Pyrolysis of Oil Sludge

Oil sludge, if unused, is one of the major industrial wastes that needs to be treated for the refinery or petrochemical industry. It contains a large portion of combustible components with high heating values. Obviously, the conversion of oil sludge to various useful materials such as lower molecular weight organic compounds and carbonaceous residue via pyrolysis not only solves the disposal problem but also matches the appeal of resource utilization. In this study, the oil sludge from the oil storage tank of a typical petroleum refinery plant located in northern Taiwan is used as the raw material of pyrolysis. Its heating value of dry basis and low heating value of wet basis are about 10681 and 5870 kcal/kg, respectively. The pyrolysis of oil sludge is conducted by using nitrogen as carrier gas in the temperature range 378−873 K. The pyrolytic reaction is complex and significant in the range 450−800 K. The residues of pyrolysis of oil sludge exhibit very high viscous form below 623 K (pyrolysis temperatu.
Windows Malware Binaries in C/C++ GitHub Repositories: Prevalence and Lessons Learned

: Does malware lurking in GitHub pose a threat? GitHub is the most popular open source software website, having 188 million repositories. GitHub hosts malware-related projects for research and educational purposes and has also been used by malware to attack users. In this paper, we explore the prevalence of unencrypted, uncompressed binary code malware in Microsoft Windows compatible C and C++ GitHub repositories and characterize the threat. We mined 1,835 repositories for already-compiled malicious ﬁles and data suggesting whether the repository is malware-related. We focused on these repositories because Windows is frequently targeted by malware written in C or C++. These repositories are good resources for attackers and could target Windows users. We extracted all Portable Executable (PE) ﬁles from all commits and queried the malware resource VirusTotal for analysis from its 76 anti-virus engines. Of the 24,395 ﬁles, 4,335 are suspicious, with at least one detection; 440 could be considered malicious, with at least seven detections. We identify topic tags suggesting malware or offensive security content, to differentiate from seemingly benign repositories. 197 of 440 malicious executables were in 27 ostensibly benign repositories. This work illustrates risks in source code repositories and lessons learned in relating GitHub and VirusTotal data.
Optimal Distribution of CNN Computations on Edge and Cloud

In this paper we study the optimal distribution of CNN computations between an edge device and the cloud for a complex IoT application. We propose a pipeline in which we perform experiments with a Jetson Nano and a Raspberry Pi 3B+ as the edge device, and a T2.micro instance from Amazon EC2 as a cloud instance. To answer this generic question, we performed exhaustive experiments on a typical use case, a mobile camerabased street litter detection and mapping application based on a MobilenetV2 model. For our research, we split the computations of the CNN model and divided them over the edge and cloud instances using model partitioning, also including the edge-only and cloud-only configurations. We studied the influence of the specifications of the instances, the input size of the model, the partitioning location of the model and the available network bandwidth on the optimal split position. Depending on the choice of gaining either an economic or performance advantage, we can conclude that a balance between the choice of instances and the calculation mechanism used should be made.
Humic acid complexation to Zn and Cd determined with the new electroanalytical technique AGNES

Environmental context. Humic substances are complex mixtures that play an important role in trace metal bioavailability in soils and aquatic environments. The bioavailability of a metal depends on what chemical forms, or species, it is in. We need to know how much of the metal is present as a free metal ion in solution, and how much is bound up in complexes with humic acids, for example. This work reports the complexation of Cd and Zn to humic acids by means of a simple and robust technique, AGNES (absence of gradients and Nernstian equilibrium stripping). Abstract. AGNES (absence of gradients and Nernstian equilibrium stripping), an emerging electroanalytical technique specifically designed for the determination of the free concentration of heavy metals in aqueous solutions, is here implemented to characterise the binding of CdII and ZnII to a soil humic acid. A set of metal titration experiments were performed by adding Cd or Zn to a purified humic acid (Aldrich) at pH 4, 5, 6 and 7 and measuring the free metal concentration by AGNES. The application of a program with two potential steps along the deposition stage allows for the reduction of the deposition time in the humic titration. The polyelectrolytic effects of the macromolecular ligand were taken into account through the Donnan model. Data free of electrostatic effects were reasonably described by the NICA isotherm, which accounts for heterogeneity, considering just a monomodal distribution (because of the range of pH covered). The obtained affinity parameters indicate a similar strength for Zn and Cd binding to the purified humic acid.
Empirical and Theoretical Evaluation of USE and OCLE Tools

: Validating the conceptual model (CM) is a key activity in ensuring software quality and saving costs, especially when adopting any type of Model-driven Software Engineering methodology, in which standard modelling languages such as UML and tools support for validation become essential. This paper analyses and evaluates the main characteristics of the tools to support test-based validation in CMs. For this, two research approaches were used: (1) an empirical evaluation to compare the effectiveness and fault detection efficiency in a CM and analyses the level of ease of use of two tools used to validate requirements in UML conceptual models, and (2) a complementary theoretical analysis. The study focuses on the class diagram, the most common type of UML diagram, and two tools widely used by the modelling community for test-based validation: USE and OCLE. Theoretical and empirical comparisons were carried out with the aim of selecting an appropriate tool to validate UML-based CMs with OCLE achieving a better score.
Estimating the Probability Density Function of New Fabrics for Fabric Anomaly Detection

Image-based quality control aims at detecting anomalies (i.e. defects) in products. Supervised, data driven approaches have greatly improved Anomaly Detection (AD) performance, but suffer from a major drawback: they require large amounts of annotated training data, limiting their economic viability. In this work, we challenge and overcome this limitation for complex patterned fabrics. Investigating the structure of deep feature representations learned on a large-scale fabric dataset, we find that fabrics form clusters according to their fabric type, whereas anomalies form a cluster on their own. We leverage this clustering behavior to estimate the Probability Density Function (PDF) of new, previously unseen fabrics, in the deep feature representations directly. Using this approach, we outperform supervised and semi-supervised AD approaches trained on new fabrics, requiring only defect-free data for PDF-estimation.
Single Stage Class Agnostic Common Object Detection: A Simple Baseline

This paper addresses the problem of common object detection, which aims to detect objects of similar categories from a set of images. Although it shares some similarities with the standard object detection and co-segmentation, common object detection, recently promoted by \cite{Jiang2019a}, has some unique advantages and challenges. First, it is designed to work on both closed-set and open-set conditions, a.k. a. known and unknown objects. Second, it must be able to match objects of the same category but not restricted to the same instance, texture, or posture. Third, it can distinguish multiple objects. In this work, we introduce the Single Stage Common Object Detection (SSCOD) to detect class-agnostic common objects from an image set. The proposed method is built upon the standard single-stage object detector. Furthermore, an embedded branch is introduced to generate the object's representation feature, and their similarity is measured by cosine distance. Experiments are conducted on PASCAL VOC 2007 and COCO 2014 datasets. While being simple and flexible, our proposed SSCOD built upon ATSSNet performs significantly better than the baseline of the standard object detection, while still be able to match objects of unknown categories. Our source code can be found at \href{https://github.com/cybercore-co-ltd/Single-Stage-Common-Object-Detection}{(URL)}
Aspect Based Sentiment Analysis using French Pre-Trained Models

Aspect Based Sentiment Analysis (ABSA) is a fine-grained task compared to Sentiment Analysis (SA). It aims to detect each aspect evoked in a text and the sentiment associated to each of them. For English language, many works using Pre-Trained Models (PTM) exits and many annotated open datasets are also available. For French Language, many works exits in SA and few ones for ABSA. We focus on aspect target sentiment analysis and we propose an ABSA using French PTM like multilingual BERT (mBERT), CamemBERT and FlauBERT. Three different fine-tuning methods: Fully-Connected, Sentences Pair Classification and Attention Encoder Network, are considered. Using the SemEval2016 French reviews datasets for ABSA, our fine-tuning models outperforms the state-of-the-art French ABSA methods and is robust for the Out-Of-Domain dataset.