Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of heat moisture treatment on physicochemical and pasting properties of starch extracted from eleven sweet potato varieties.

Starch separated from eleven sweet potato varieties was subjected to heat moisture treatment (HMT) at 30% moisture and 110°C for 3 hours. Following HMT, there were no significant changes in physicochemical properties of the starches. This was attributed to limited moisture content (30%) to influence hydration that could consequently alter granule morphology. Further, chemical parameters such as amylose content, pH and phosphorus were not affected because HMT is a physical treatment. In contrast, HMT caused changes in the packing arrangement of the polymer chains leading to enhanced intragranular binding. Alteration of the internal organisation of the granules caused significant changes in pasting properties of the starches such as pasting time, pasting temperature, peak viscosity, hot paste viscosity, cold paste viscosity, breakdown, set back and stability ratios. The findings provide evidence that functionality of sweet potato starch can be altered to suit a particular industrial need.
Farmers Status, Knowledge & Management Practices on Major Chickpea Insect Pests in Some Selected Zones of Ethiopia

Survey were undertaken in five zones of Oromiya and Amhara regional states, Ethiopia from February to March 2018 to investigate farmers’ status, knowledge, major chickpea production constraints and insect pest management practices of chickpea. The survey involved 293 randomly taken farmers, who are interviewed using a semi-structured questionnaire. Chickpea insect pests were considered as the main, among production constraints of chickpea by most of the interviewed farmers in Ethiopia. The majority of the farmers’ responded that Helicoverpa armigera (Hübner) (Lepidoptera: Noctuidae) is the most prevalent insect pests of chickpea under field condition and Callosobruchus chinensis (L.) (Coleoptera: Bruchidae) in storage. Development Agents (DAs) were the top pest advisory service providers in the current study. A conventional insecticide (namely; Lambda-cyhalotrin and Dimethoate) for the control of H. armigera was the most commonly used pest management methods. Moreover, Two times insecticide applications per cropping season were the most frequent, deployed by most of the interviewed farmers followed by cultural pest management practices. Use of biological control and resistant chickpea varieties against major insect pests were low to negligible in the current survey study in the area. The majority of the farmers began control decision when H. armigera larval stages were smaller and they did not consider larval number per chickpea plant for the decision. These results will be used to formulate future effective and sustainable integrated pest management (IPM) in chickpea for Ethiopian farmers emphasizing ecologically and economically-based approaches.
THE INTERACTION BETWEEN FOREIGN DIRECT INVESTMENT, FOREIGN PORTFOLIO INVESTMENT AND ECONOMIC GROWTH: THE CASE OF SUB- SAHARAN AFRICAN COUNTRIES

Sahra Alti Afrika’da yabanci yatirimlarin ekonomik buyumeyi etkilemesini inceleyen literaturde birbiriyle celisen gorusler mevcuttur. Bazi arastirmacilar Sahra Alti Afrika’da yer alan gelismekte olan ulkelerin sermaye akislarini liberallestirdikleri zaman, yuksek seviyedeki volatilite, artan cari hesap aciklari, gelismis finansal piyasalarin yoklugu ve duzenleyici cercevenin dusuk kalitesinden dolayi ekonomik kayiplara ugrayacaklarini one surmektedirler. Oysa, bazi calismalar Sahra Alti Afrika’daki dusuk seviye birikmis tasarruflarina care olmak icin yabanci yatirimin ekonomik buyumeyi arttirici etkisine odaklanmistir. Calisma, Sahra Alti Afrika’daki ulkeler icin yabanci portfoy yatirimlarinin ve dogrudan yabanci sermaye yatirimlarinin ekonomik buyumeye etkisi konusunda yeni bulgular getirmektedir. Endojenlik sorunu nedeniyle, uc eszamanli denklem sistemini tahmin etmek icin panel VAR metodolojisini kullanilmistir. 1990-2016 arasinda Sahra Alti Afrika’daki 25 ulkeyi analiz ederek, Sahra Alti Afrika’da dogrudan yabanci sermaye yatirimlari ve yabanci portfoy yatirimlarinin tamamlayici oldugunu ve her ikisinin buyumeyi pozitif anlamli etkiledigini bulunmustur.
Improved Speech Enhancement Using a Time-Domain GAN with Mask Learning

Speech enhancement is an essential component in robust automatic speech recognition (ASR) systems. Most speech enhancement methods are nowadays based on neural networks that use feature-mapping or mask-learning. This paper proposes a novel speech enhancement method that integrates time-domain feature mapping and mask learning into a uniﬁed framework using a Generative Adversarial Network (GAN). The proposed framework processes the received waveform and decouples speech and noise signals, which are fed into two short-time Fourier transform (STFT) convolution 1-D layers that map the waveforms to spectrograms in the complex domain. These speech and noise spectrograms are then used to compute the speech mask loss. The proposed method is evaluated using the TIMIT data set for seen and unseen signal-to-noise ratio conditions. It is shown that the proposed method outperforms the speech enhancement methods that use Deep Neural Network (DNN) based speech enhancement or a Speech Enhancement Generative Adversarial Network (SEGAN).
Language Model Data Augmentation Based on Text Domain Transfer

To improve the performance of automatic speech recognition (ASR) for a speciﬁc domain, it is essential to train a language model (LM) using text data of the target domain. In this study, we propose a method to transfer the domain of a large amount of source data to the target domain and augment the data to train a target domain-speciﬁc LM. The proposed method consists of two steps, which use a bidirectional long short-term memory (BLSTM)-based word replacing model and a target domain-adapted LSTMLM, respectively. Based on the learned domain-speciﬁc wordings, the word replacing model converts a given source domain sentence to a confusion network (CN) that includes a variety of target domain candidate word sequences. Then, the LSTMLM selects a target domain sentence from the CN by evaluating its grammatical correctness based on decoding scores. In experiments using lecture and conversational speech corpora as the source and target domain data sets, we conﬁrmed that the proposed LM data augmentation method improves the target conversational speech recognition performance of a hybrid ASR system using an n -gram LM and the performance of N -best rescoring using an LSTMLM.
Dual-Adversarial Domain Adaptation for Generalized Replay Attack Detection

Despite tremendous progress in speaker veriﬁcation recently, replay spooﬁng attacks are still a major threat to these systems. Focusing on dataset-speciﬁc scenarios, anti-spooﬁng systems have achieved promising in-domain performance at the cost of poor generalization towards unseen out-of-domain datasets. This is treated as a domain mismatch problem with a domain adversarial training (DAT) framework, which has previously been applied to enhance generalization. However, since only one domain discriminator is adopted, DAT suffers from the false alignment of cross-domain spoofed and genuine pairs, thus failing to acquire a strong spooﬁng-discriminative capability. In this work, we propose the dual-adversarial domain adaptation (DADA) framework to enable ﬁne-grained alignment of spoofed and genuine data separately by using two domain discriminators, which effectively alleviates the above problem and further improves spooﬁng detection performance. Experiments on the ASVspoof 2017 V.2 dataset and the physical access portion of BTAS 2016 dataset demonstrate that the proposed DADA framework signiﬁcantly outperforms the baseline model and DAT framework in cross-domain evaluation scenarios. It is shown that the newly proposed DADA architecture is more robust and effective for generalized replay attack detection.
Ensemble Approaches for Uncertainty in Spoken Language Assessment

Deep learning has dramatically improved the performance of automated systems on a range of tasks including spoken language assessment. One of the issues with these deep learning approaches is that they tend to be overconﬁdent in the decisions that they make, with potentially serious implications for deployment of systems for high-stakes examinations. This paper examines the use of ensemble approaches to improve both the reliability of the scores that are generated, and the ability to detect where the system has made predictions beyond acceptable errors. In this work assessment is treated as a regression problem. Deep density networks, and ensembles of these models, are used as the predictive models. Given an ensemble of models measures of uncertainty, for example the variance of the predicted distributions, can be obtained and used for detecting outlier predictions. However, these ensemble approaches increase the computational and memory requirements of the sys-tem. To address this problem the ensemble is distilled into a single mixture density network. The performance of the systems is evaluated on a free speaking prompt-response style spoken language assessment test. Experiments show that the ensembles and the distilled model yield performance gains over a single model, and have the ability to detect outliers.
Spotting the Traces of Depression in Read Speech: An Approach Based on Computational Paralinguistics and Social Signal Processing

This work investigates the use of a classiﬁcation approach as a means to identify effective depression markers in read speech, i.e., observable and measurable traces of the pathology in the way people read a predeﬁned text. This is important because the diagnosis of depression is still a challenging problem and reliable markers can, at least to a partial extent, contribute to address it. The experiments have involved 110 individuals and revolve around the tendency of depressed people to read slower and display silences that are both longer and more frequent. The results show that features expected to capture such differences reduce the error rate of a baseline classiﬁer by more than 50% (from 31.8% to 15.5%). This is of particular interest when considering that the new features are less than 10% of the original set (3 out of 32). Furthermore, the results appear to be in line with the ﬁndings of neuroscience about brain-level differences between depressed and non-depressed individuals.
x-Vectors Meet Adversarial Attacks: Benchmarking Adversarial Robustness in Speaker Verification

Automatic Speaker Veriﬁcation (ASV) enables high-security applications like user authentication or criminal investigation. However, ASV can be subjected to malicious attacks, which could compromise that security. The ASV literature mainly studies spooﬁng (a.k.a impersonation) attacks such as voice re-play, synthesis or conversion. Meanwhile, other kinds of attacks, known as adversarial attacks, have become a threat to all kind of machine learning systems. Adversarial attacks introduce an imperceptible perturbation in the input signal that radically changes the behavior of the system. These attacks have been intensively studied in the image domain but less in the speech domain. In this work, we investigate the vulnerability of state-of-the-art ASV systems to adversarial attacks. We consider a threat model consisting in adding a perturbation noise to the test wave-form to alter the ASV decision. We also discuss the methodology and metrics to benchmark adversarial attacks and defenses in ASV. We evaluated three x-vector architectures, which performed among the best in recent ASV evaluations, against fast gradient sign and Carlini-Wagner attacks. All networks were highly vulnerable in the white-box attack scenario, even for high SNR (30-60 dB). Furthermore, we successfully transferred attacks generated with smaller white-box networks to attack a larger black-box network.
Adversarial Dictionary Learning for Monaural Speech Enhancement

In this paper, we propose an adversarial dictionary learning method to train a speaker independent speech dictionary and a universal noise dictionary for improving the generality of the dictionary learning based speech enhancement system. In the learning stage, two discriminators are employed separately to identify the components in speech and noise which are highly correlated with each other. The residuals in the speech and noise magnitude spectral matrices are then utilized to train the speech and noise dictionaries via the alternating direction method of multiplier algorithm, which can effectively reduce the mutual coherence between speech and noise. In the enhancement stage, a new optimization technique is proposed for enhancing the speech based on the low-rank decomposition and sparse coding. Experimental results show that our proposed method achieves better performance in improving the speech quality and intelligibility than the reference methods in terms of three objective performance evaluation measures.
Speech Transformer with Speaker Aware Persistent Memory

End-to-end models have been introduced into automatic speech recognition (ASR) successfully and achieved superior performance compared with conventional hybrid systems, especially with the newly proposed transformer model. However, speaker mismatch between training and test data remains a problem, and speaker adaptation for transformer model can be further improved. In this paper, we propose to conduct speaker aware training for ASR in transformer model. Speciﬁcally, we pro-pose to embed speaker knowledge through a persistent memory model into speech transformer encoder at utterance level. The speaker information is represented by a number of static speaker i-vectors, which is concatenated to speech utterance at each encoder self-attention layer. Persistent memory is thus formed by carrying speaker information through the depth of encoder. The speaker knowledge is captured from self-attention between speech and persistent memory vector in encoder. Experiment results on LibriSpeech, Switchboard and AISHELL-1 ASR task show that our proposed model brings relative 4.7%- 12.5% word error rate (WER) reductions, and achieves superior results compared with other models with the same objective. Furthermore, our model brings relative 2.1%-8.3% WER reductions compared with the ﬁrst persistent memory model used in ASR.
Dysarthric Speech Recognition Based on Deep Metric Learning

We present in this paper an automatic speech recognition (ASR) system for a person with an articulation disorder resulting from athetoid cerebral palsy. Because their utterances are often unstable or unclear, speech recognition systems have difﬁculty recognizing the speech of those with this disorder. For example, their speech styles often ﬂuctuate greatly even when they are repeating the same sentences. For this reason, their speech tends to have great variation even within recognition classes. To alleviate this intra-class variation problem, we propose an ASR system based on deep metric learning. This system learns an embedded representation that is characterized by a small distance between input utterances of the same class, while the distance of the input utterances of different classes is large. Therefore, our method makes it easy for the ASR system to distinguish dysarthric speech. Experimental results show that our proposed approach using deep metric learning improves the word-recognition accuracy consistently. Moreover, we also evaluate the combination of our proposed method and transfer learning from unimpaired speech to alleviate the low-resource problem associated with impaired speech.
Semi-Supervised Learning for Character Expression of Spoken Dialogue Systems

We address character expression for spoken dialogue systems (e.g. extrovert). While conventional studies focused on controlling linguistic expressions, we focus on spoken dialogue behaviors. Speciﬁcally, the proposed model maps three character traits: extroversion, emotional instability, and politeness to four spoken dialogue behaviors: utterance amount, backchan-nel, ﬁller, and switching pause length. It is costly to collect annotated data for training this kind of models. Therefore, we propose a semi-supervised learning approach to utilize not only a character impression data (labeled data) but also a corpus data (unlabeled data). Experimental results show that the proposed model expresses the target character traits through the behaviors more precisely than a baseline model that corresponds to the case of supervised learning only. Besides, we also investigate how to model unlabeled behavior (e.g. speech rate) by utilizing the advantage of semi-supervised learning.
Multi-Modal Fusion with Gating Using Audio, Lexical and Disfluency Features for Alzheimer's Dementia Recognition from Spontaneous Speech

This paper is a submission to the Alzheimer's Dementia Recognition through Spontaneous Speech (ADReSS) challenge, which aims to develop methods that can assist in the automated prediction of severity of Alzheimer's Disease from speech data. We focus on acoustic and natural language features for cognitive impairment detection in spontaneous speech in the context of Alzheimer's Disease Diagnosis and the mini-mental state examination (MMSE) score prediction. We proposed a model that obtains unimodal decisions from different LSTMs, one for each modality of text and audio, and then combines them using a gating mechanism for the final prediction. We focused on sequential modelling of text and audio and investigated whether the disfluencies present in individuals' speech relate to the extent of their cognitive impairment. Our results show that the proposed classification and regression schemes obtain very promising results on both development and test sets. This suggests Alzheimer's Disease can be detected successfully with sequence modeling of the speech data of medical sessions.
Pair Expansion for Learning Multilingual Semantic Embeddings Using Disjoint Visually-Grounded Speech Audio Datasets

We propose a data expansion method for learning a multilingual semantic embedding model using disjoint datasets containing images and their multilingual audio captions. Here, disjoint means that there are no shared images among the multiple language datasets, in contrast to existing works on multilingual semantic embedding based on visually-grounded speech audio, where it has been assumed that each image is associated with spoken captions of multiple languages. Although learning on disjoint datasets is more challenging, we consider it crucial in practical situations. Our main idea is to refer to another paired data when evaluating a loss value regarding an anchor image. We call this scheme “pair expansion”. The motivation behind this idea is to utilize even disjoint pairs by ﬁnding similarities, or commonalities, that may exist in different images. Speciﬁ-cally, we examine two approaches for calculating similarities: one using image embedding vectors and the other using object recognition results. Our experiments show that expanded pairs improve crossmodal and cross-lingual retrieval accuracy compared with non-expanded cases. They also show that similarities measured by the image embedding vectors yield better accuracy than those based on object recognition results.
Don’t Bother Me: How to Handle Content-Irrelevant Objects in Handheld Augmented Reality

In this paper, we applied the concept of diminished reality to remove content-irrelevant pedestrian (i.e., real object) in the context of handheld augmented reality (AR). We prepared three view conditions: in Transparent (TP) condition, we removed the pedestrian entirely; in Semi-transparent (STP) condition, the pedestrian became semi-transparent; lastly, in Default (DF) condition, the pedestrian appeared as is. We conducted a user study to compare the effects of the three conditions on users’ engagement and perception of a virtual pet in the AR content. Our findings revealed that users felt less distracted to the AR content in TP and STP conditions, compared to the DF condition. Furthermore, users felt the virtual pet as more life-like, its behavior more plausible, and felt a higher spatial presence in the real environment, in the TP condition.
Impact of Strategic Planning and Innovation on Organisational Performance of the Saudi Red Crescent Authority

A survey-based empirical examination was made on the impact of strategic planning and innovation on the organisational performance of Saudi Red Crescent Authority. Data was collected from 212 workers in Riyadh and Jeddah. The majority of the sample are educated and experienced. The results of a multiple regression analysis test revealed statistically significant positive associations between each of strategic planning and innovation with organisational performance. Theoretical and practical implications are drawn and recommendations are made as well to assist decision-makers in undertaking strategic planning and emphasising innovation.
Cancer gene therapy by NF-κB-activated cancer cell-specific expression of CRISPR/Cas9 targeting to telomere

NF-κB has been a luring target for cancer therapy due to its over activation in all tumors. In this study, we showed that a gene therapy named as NF-κB-activated gene expression (Nage) could be used to induce cancer cell death in vitro and in vivo by utilizing the NF-κB activity in cancer cells; however, it had no effect on normal cells. In this gene therapy, we constructed a NF-κB-specific promoter by fusing a NF-κB decoy sequence to a minimal promoter, which could be bound by the intracellular over activated NF-κB and thus activate the expression of downstream effector gene in a NF-κB-specific manner. In this study, we firstly demonstrated the cancer cell-specific activation of NF-κB. We then demonstrated the cancer cell specificity of Nage vector expression by introducing a Nage vector that could express a reporter gene ZsGreen in various cell lines. We next demonstrated that a Nage vector that could express CRISPR/Cas9 protein and a telomere-targeting sgRNA could be used to specifically induce death of cancer cells. We finally showed that the Cas9/sgRNA Nage vector packaged into the adeno-associated virus (AAV) could be used to inhibit the growth of xenografted tumors in mouse by intravenously injecting recombinant AAV.
Open-Source Digitally Replicable Lab-Grade Scales

This study provides designs for a low-cost, easily replicable open-source lab-grade digital scale that can be used as a precision balance. The design is such that it can be manufactured for use in most labs throughout the world with open-source RepRap-class material extrusion-based 3-D printers for the mechanical components and readily available open-source electronics including the Arduino Nano. Several versions of the design were fabricated and tested for precision and accuracy for a range of load cells. The results showed the open-source scale was found to be repeatable within 0.05 g with multiple load cells, with even better precision (0.005 g) depending on load cell range and style. The scale tracks linearly with proprietary lab-grade scales, meeting the performance specified in the load cell data sheets, indicating that it is accurate across the range of the load cell installed. The smallest load cell tested (100 g) offers precision on the order of a commercial digital mass balance. The scale can be produced at significant cost savings compared to scales of comparable range and precision when serial capability is present. The cost savings increase significantly as the range of the scale increases and are particularly well-suited for resource-constrained medical and scientific facilities.
Uma Combinação Fatal: Indometacina e Dabigatrana

Although rare, several bleeding complications may occur in patients receiving dabigatran. The risk of bleeding is particularly high in patients with impaired kidney functions or in patients who are on concomitant nephrotoxic drugs.1 We report a case of massive pleuropericardial effusion developed after the initiation of indomethacin treatment in a patient who was receiving dabigatran for deep venous thrombosis.