Sintering and characterisation of nano-sized yttria-stabilised zirconia

A study has been conducted on the pressure-less sintering of the ZrC>2- Y2O3 system that contains 3 mol% Y2O3, and the effect that the temperature / time relationship has on the properties of the final product. Experiments were performed on two types of commercially available, nano-sized, ZrC>2 - 3mol% Y2O3 (3Y-TZP) powders. An unstabilised zirconia powder was also investigated for comparison purposes. Particle size analysis of these powder yield particle sizes ranging from 1.29 |xm to 1.78 pm, suggesting that the particles are heavily prone to agglomeration in water. BET specific surface area analysis showed the powders to be nano-sized as determined using equivalent spherical diameter theory. The density of the powders was measured using helium gas pycnometry. DTA/TGA analysis indicates that binder burnout occurs on heating in the range 300-440°C. Shrinkage and densification rate characteristics of the powders during sintering was investigated using dilatometry. One of the powders demonstrated full shrinkage during isothermal sintering; the other did not. The point of maximum densification rate differed by approximately 100°C for these powders. A dramatic expansion associated with the tetragonal to monoclinic transformation on cooling from 1400°C is observed by dilatometry for unstabilised zirconia powder sample but not for stabilised powder samples. This indicates that the sintered samples retain in tetragonal phase on cooling to room temperature. Pressureless conventional sintering and two-step sintering were used to sinter pressed discs. The density of discs which were sintered across a temperature range of 1350°C to 1550°C varies between 5.72 g/cm3 and 6.03 g/cm3, corresponding to 97.40% and 99.10% of theoretical density respectively. Density as high as 98.85% of theoretical was recorded for twostep sintering applying holding time of 16 h. Vickers hardness values increased with increasing sintering temperature. Fracture toughness measurements were carried out. X-ray diffraction analysis confirmed that sintered discs were fully tetragonal. Microstructural analysis of sintered samples was conducted to assess microstructural changes with respect to sintering temperature

TermEval: an automatic metric for evaluating terminology translation in MT

Terminology translation plays a crucial role in domain-specific machine translation (MT). Preservation of domain-knowledge from source to target is arguably the most concerning factor for the customers in translation industry, especially for critical domains such as medical, transportation, military, legal and aerospace. However, evaluation of terminology translation, despite its huge importance in the translation industry, has been a less examined area in MT research. Term translation quality in MT is usually measured with domain experts, either in academia or industry. To the best of our knowledge, as of yet there is no publicly available solution to automatically evaluate terminology translation in MT. In particular, manual intervention is often needed to evaluate terminology translation in MT, which, by nature, is a time-consuming and highly expensive task. In fact, this is unimaginable in an industrial setting where customised MT systems are often needed to be updated for many reasons (e.g. availability of new training data or leading MT techniques). Hence, there is a genuine need to have a faster and less expensive solution to this problem, which could aid the end-users to instantly identify term translation problems in MT. In this study, we propose an automatic evaluation metric, TermEval, for evaluating terminology translation in MT. To the best of our knowledge, there is no gold-standard dataset available for measuring terminology translation quality in MT. In the absence of gold standard evaluation test set, we semi-automatically create a gold-standard dataset from English--Hindi judicial domain parallel corpus. We trained state-of-the-art phrase-based SMT (PB-SMT) and neural MT (NMT) models on two translation directions: English-to-Hindi and Hindi-to-English, and use TermEval to evaluate their performance on terminology translation over the created gold standard test set. In order to measure the correlation between TermEval scores and human judgments, translations of each source terms (of the gold standard test set) is validated with human evaluator. High correlation between TermEval and human judgements manifests the effectiveness of the proposed terminology translation evaluation metric. We also carry out comprehensive manual evaluation on terminology translation and present our observations

The Verbal and Non Verbal Signals of Depression -- Combining Acoustics, Text and Visuals for Estimating Depression Level

Depression is a serious medical condition that is suffered by a large number of people around the world. It significantly affects the way one feels, causing a persistent lowering of mood. In this paper, we propose a novel attention-based deep neural network which facilitates the fusion of various modalities. We use this network to regress the depression level. Acoustic, text and visual modalities have been used to train our proposed network. Various experiments have been carried out on the benchmark dataset, namely, Distress Analysis Interview Corpus - a Wizard of Oz (DAIC-WOZ). From the results, we empirically justify that the fusion of all three modalities helps in giving the most accurate estimation of depression level. Our proposed approach outperforms the state-of-the-art by 7.17% on root mean squared error (RMSE) and 8.08% on mean absolute error (MAE).Comment: 10 pages including references, 2 figure

Investigation of porous glasses based on sodium-borosilicate glass system

This work investigated the development of porous glasses by making additions of zirconia (ZrO2) and zircon (ZrSiO4) to the sodium borosilicate glass system SiO2-B2O3-Na2O. Additions of Zr-based compounds were made in an attempt to yield more alkaline durable porous glasses compared to the silica-rich porous glass structures of the parent sodium borosilicate glass system. Glasses were fabricated using a high-temperature fusion process. X-ray diffraction (XRD) was used to confirm that the glasses were amorphous upon pouring from the melt. The glasses were characterised using differential thermal analysis (DTA) to identify important thermal events, including the glass transition temperature (Tg) and crystallisation temperature (Tx). The occurrence of amorphous phase separation was key to the formation of two-phase glasses and ultimately porous glasses. It was found that the quantity of sodium oxide (Na2O) in the glass composition played an important role in determining whether phase separation occurred via nucleation and growth or spinodal decomposition. Based on the DTA data, a heat treatment temperature of 650 °C was selected for three different durations (14, 24 and 63 hours). The heat-treatment caused the glasses to phase separate into two phases; a silica-rich phase and a sodium borate phase. The sodium borate phase coarsened as a function of heat-treatment time. Fourier transform infrared (FTIR) spectroscopy, together with XRD, was found to be effective as a means of comparing the phase separation and crystallisation characteristics. Glasses heat-treated for longer times showed some evidence of crystal formation. Having formed two-phase glass, acid leaching was used to remove the borate phase and create a porous structure. The leaching process had to be carefully controlled in terms of acid type, acid concentration, leaching time and leaching temperature. For all glasses, a post-leach alkali wash step was needed to remove trapped silica gel. The porous glasses comprised a silica-rich porous skeleton. Scanning electron microscopy (SEM) revealed classic interconnected porous morphologies. The most consistent and best-defined morphologies were observed for the zircon-containing glass. Energy dispersive X-ray (EDX) analysis confirmed the presence of zirconium (Zr) in the porous silica-rich skeleton. Mercury intrusion porosimetry (MIP) was used to characterise the pore characteristics and measure pore volume, pore size, pore distribution, total pore surface area, and bulk and apparent density. In general, the porous glasses exhibited sharp, unimodal pore distributions, but there was also evidence of micropores, believed due to residual silica gel. Mean pore size ranged from 40 nm to 200 nm for the different porous glasses studied. It was observed that mean pore size linearly increased with square root of the heat-treatment time. Total pore surface area increased with decreasing size of pores and ranged from 5 to 35 m2/g depending on glass composition and heat-treatment time. Alkaline resistance tests were carried out. Zircon- and zirconia-containing porous glasses are 3-4 times more alkali durable than the parent sodium borosilicate glass

TermEval: an automatic metric for evaluating terminology translation in MT

Terminology translation plays a crucial role in domain-specific machine translation (MT). Preservation of domain-knowledge from source to target is arguably the most concerning factor for the customers in translation industry, especially for critical domains such as medical, transportation, military, legal and aerospace. However, evaluation of terminology translation, despite its huge importance in the translation industry, has been a less examined area in MT research. Term translation quality in MT is usually measured with domain experts, either in academia or industry. To the best of our knowledge, as of yet there is no publicly available solution to automatically evaluate terminology translation in MT. In particular, manual intervention is often needed to evaluate terminology translation in MT, which, by nature, is a time-consuming and highly expensive task. In fact, this is unimaginable in an industrial setting where customised MT systems are often needed to be updated for many reasons (e.g. availability of new training data or leading MT techniques). Hence, there is a genuine need to have a faster and less expensive solution to this problem, which could aid the end-users to instantly identify term translation problems in MT. In this study, we propose an automatic evaluation metric, TermEval, for evaluating terminology translation in MT. To the best of our knowledge, there is no gold-standard dataset available for measuring terminology translation quality in MT. In the absence of gold standard evaluation test set, we semi-automatically create a gold-standard dataset from English–Hindi judicial domain parallel corpus. We trained state-of-the-art phrase-based SMT (PB-SMT) and neural MT (NMT) models on two translation directions: English-to-Hindi and Hindi-to-English, and use TermEval to evaluate their performance on terminology translation over the created gold standard test set. In order to measure the correlation between TermEval scores and human judgments, translations of each source terms (of the gold standard test set) is validated with human evaluator. High correlation between TermEval and human judgements manifests the effectiveness of the proposed terminology translation evaluation metric. We also carry out comprehensive manual evaluation on terminology translation and present our observations

Multimodal neural machine translation for low-resource language pairs using synthetic data

In this paper, we investigate the effectiveness of training a multimodal neural machine translation (MNMT) system with image features for a lowresource language pair, Hindi and English, using synthetic data. A threeway parallel corpus which contains bilingual texts and corresponding images is required to train a MNMT system with image features. However, such a corpus is not available for low resource language pairs. To address this, we developed both a synthetic training dataset and a manually curated development/test dataset for Hindi based on an existing English-image parallel corpus. We used these datasets to build our image description translation system by adopting state-of-theart MNMT models. Our results show that it is possible to train a MNMT system for low-resource language pairs through the use of synthetic data and that such a system can benefit from image features

Incorporating deep visual features into multiobjective based multi-view search results clustering

Current paper explores the use of multi-view learning for search result clustering. A web-snippet can be represented using multiple views. Apart from textual view cued by both the semantic and syntactic information, a complementary view extracted from images contained in the websnippets is also utilized in the current framework. A single consensus partitioning is finally obtained after consulting these two individual views by the deployment of a multi-objective based clustering technique. Several objective functions including the values of a cluster quality measure evaluating the goodness of partitionings obtained using different views and an agreementdisagreement index, quantifying the amount of oneness among multiple views in generating partitionings are optimized simultaneously using AMOSA. In order to detect the number of clusters automatically, concepts of variable length solutions and a vast range of permutation operators are introduced in the clustering process. Finally a set of alternative partitionings are obtained on the final Pareto front by the proposed multi-view based multi-objective technique. Experimental results by the proposed approach on several bench-mark test datasets with respect to different performance metrics evidently establish the power of visual and text based views in achieving better search result clustering

Federated Split Learning with Only Positive Labels for resource-constrained IoT environment

Distributed collaborative machine learning (DCML) is a promising method in the Internet of Things (IoT) domain for training deep learning models, as data is distributed across multiple devices. A key advantage of this approach is that it improves data privacy by removing the necessity for the centralized aggregation of raw data but also empowers IoT devices with low computational power. Among various techniques in a DCML framework, federated split learning, known as splitfed learning (SFL), is the most suitable for efficient training and testing when devices have limited computational capabilities. Nevertheless, when resource-constrained IoT devices have only positive labeled data, multiclass classification deep learning models in SFL fail to converge or provide suboptimal results. To overcome these challenges, we propose splitfed learning with positive labels (SFPL). SFPL applies a random shuffling function to the smashed data received from clients before supplying it to the server for model training. Additionally, SFPL incorporates the local batch normalization for the client-side model portion during the inference phase. Our results demonstrate that SFPL outperforms SFL: (i) by factors of 51.54 and 32.57 for ResNet-56 and ResNet-32, respectively, with the CIFAR-100 dataset, and (ii) by factors of 9.23 and 8.52 for ResNet-32 and ResNet-8, respectively, with CIFAR-10 dataset. Overall, this investigation underscores the efficacy of the proposed SFPL framework in DCML.Comment: 11 pages, 3 figure

Temporality as seen through translation: a case study on Hindi texts

Temporality has significantly contributed to various aspects of Natural Language Processing applications. In this paper, we determine the extent to which temporal orientation is preserved when a sentence is translated manually and automatically from the Hindi language to the English language. We show that the manually and automatically identified temporal orientation in English translated (both manual and automatic) sentences provides a good match with the temporal orientation of the Hindi texts. We also find that the task of manual temporal annotation becomes difficult in the translated texts while the automatic temporal processing system manages to correctly capture temporal information from the translations

Investigation into use of double-layer grid structures as frequency selective surfaces for buildings

A simple Double–Layer Grid plane wave filter structure is proposed that can provide multiple transmission bands for cellular phone frequencies but with a reflection band for WLAN signals. The approach offers ease of construction making it applicable to building applications. A parametric study using simulation supported by simple experimental data investigates the proposed, novel desig