Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving the Performance of Brain-Computer Interface using Deep Learning Algorithms and Event-Related Spectral Perturbation

Brain Computer Interface allows disabled people to communicate with the external world by using their brain signals. The main goal of a BCI is to provide patients who suffer form any neuromuscular disorders whith a communication channel based on their brain signals. In this paper, the aim is to explore the effects of applying deep learning algorithms and Event Related Spectral Perturbation analyses on the performance of different EEG-based BCI paradigms. Two paradigms were investigated: one is based on the Matrix paradigm (known as oddball); and the other one utilizes the Rapid serial visual Presentation (RSVP) for presenting the stimuli. Deep learning algorithms of convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) were utilized to evaluate the two paradigms. Our findings showed that Matrix paradigm is more effective in detecting P300 signal. In terms of classification methods, deep learning of CNN algorithm has shown superiority performance in comparison with the other machine learning algorithms.
Localisation of coconut foliar decay virus in coconut palm

Summary Coconut foliar decay virus (CFDV) occurs at a very low concentration in coconut palm. A 1203 nucleotide segment of the sequenced encapsidated circular single-stranded 1291 nucleotide CFDV-DNA has been amplified and transcribed for use as a 32P cDNA probe for the virus. A rapid method for the extraction of DNA from coconut palm has been devised for a dot-blot hybridisation assay using this probe. An alternative non-radioactive probe has also been developed for future use in CFDV diagnosis. CFDV-DNA was shown to be distributed unevenly in a range of infected palms, necessitating the use of multiple sampling to reliably detect infection in diagnostic tests. Viral DNA was detected in symptomatic and asymptomatic palms of both high and low susceptibility, in disease-free tolerant cultivars, and in palms in remission from disease. Within the same palm, detectability of viral DNA varied little within leaflets, but varied more within and between fronds. CFDV-DNA was detected 6–8 months after insect-mediated inoculation, and symptoms generally appeared after another 1–4 months. In situ hybridisation of rachis tissue showed localisation of DNA within the phloem, but its distribution in the phloem was uneven. CFDV-DNA was detected in tissue adjacent to and within necrotic zones which develop into the petiolar lesions associated with the disease-specific collapse of fronds. Virus was detected in the body of the insect vector, and, where its distribution could be resolved, in the abdomen rather than the head.
Content Based Confidentiality Detection Method for Data Leakage Prevention

protecting confidential data became a challenge for all private and public organizations. According to Gartner report, the majority of data leakages in organizations are due to internal factors. Data Leakage Prevention Systems can protect monitor and identify the confidential data at-rest, inuse and in-motion. This paper presents a Data Leakage Prevention system, to prevent confidential data from leakages using the Term Based Confidentiality Detection Method . The proposed method consists of two phases: training and testing phase. The training phase identifies confidential terms from the documents and testing phase detects the confidentiality of the document.
THE SOCIABLE WEAVER, PART 5: FOOD, FEEDING AND GENERAL BEHAVIOUR

Summary Maclean, G. L. 1973. The Sociable Weaver, Part 5: Food, feeding and general behaviour. Ostrich 44:254-261. The food of the Sociable Weaver during the study period consisted of 78,9% animal material by weight; the rest was mainly seeds, mostly of green grasses. The young are fed entirely on animal food. The most important single source of animal food in the Kalahari is the harvester termite Hodotermes mossambica. Sociable Weavers are independent of drinking water under natural conditions. Members of a colony do not usually feed more than about 1,5 km from the nest mass. Maintenance activities and other general behaviour patterns of the Sociable Weaver are described; they are typically passerine.
Regeneration of Mesophyll Protoplasts Isolated from Dihaploid Clones of Solanum tuberosum

Protoplasts have been isolated from leaves of shoot cultures of six dihaploid clones of Solanum tuberosum L. (2n = 2x = 24). In the KM medium (Kao and Michayluk 1975), sustained cell divisions were obtained in up to 50% of the plated protoplasts of four clones, whereas only a few divisions occurred in the other two clones. The first mitosis appeared 2–8 days after plating, dependent on the clones. In the clones showing sustained cell divisions, a protoplast titre of about 5 × 103 per ml turned out to be optimal. The culture conditions for protoplasts of one of the poorly growing clones, clone H2 140, have been improved using modified KM media, plating at a concentration of as high as 5 × 104 cells per ml, and subsequent diluting at intervals 5 days. The dilutions were carried out with media containing 0.25% agar. Up to 60% of the plated protoplasts underwent divisions within 10 days under these conditions. After about 15 days, the regenerants were transferred onto media inducing organogenesis. Shoots and roots were formed on modified media MS (Murashige and Skoog 1962) and B5 (Gamborg et al. 1968). Plants have been regenerated in four of the investigated clones. Countings of chromosomes revealed a satisfactory stability of the karyotype in shoot culture and protoplast regeneration.
Variation in δ15N and δ13C trophic fractionation: Implications for aquatic food web studies

Use of stable isotope techniques to quantify food web relationships requires a priori estimates of the enrichment or depletion in δ15N and δ13C values between prey and predator (known as trophic fractionation; hereafter Δδ15N and Δδ13C). We conducted a broad‐scale analysis of Δδ15N and Δδ13C from aquatic systems, including three new field estimates. Carnivores had significantly higher Δδ15N values than herbivores. Furthermore, carnivores, invertebrates, and lab‐derived estimates were significantly more variable than their counterparts ( f‐test, p < 0.00001). Δδ13C was higher for carnivores than for herbivores (p = 0.001), while variances did not differ significantly. Excluding herbivores, the average Δδ15N and Δδ13C were 3.4‰ and 0.8‰, respectively. But even with unbiased fractionation estimates, there is variation in isotopic fractionation that contributes to error in quantitative isotope model outputs. We simulated the error variance in δ15N‐based estimates of trophic position and two‐source δ13C diet mixing models, explicitly considering the observed variation in Δδ15N and Δδ13C, along with the other potential error sources. The resultant error in trophic position and mixing model outputs was generally minor, provided that primary consumers were used as baseline indicators for estimating trophic position and that end member d13C values in dietary mixing models were sufficiently distinct.
Real Time Face Recognition in Group Images using LBPH

Face recognition is a beneficial work in computer vision based applications. The goal of the proposed system is to provide complete face recognitions system capable of working a group of images. The faces are detected and verified the identity of an individual using a machine learning algorithm. The haar cascade detects the face from a group of images for training and testing dataset. The dataset contained positive and negative images for training and testing. The LBPH algorithm recognizes the faces from input images. The proposed system detects and recognizes faces with 98% accuracy
Study of Age Estimation Using Fixed Rank Representation (FRR)

As it is an important and challenging problem in computer vision, face age estimation is typically cast as a classification or regression problem over a set of face samples with several ordinal age labels which have intrinsically cross-age correlations across adjacent age dimensions. As an outcome, these such correlations normally lead to age label ambiguities of face samples. Each face sample is associated with a latent label distribution that encodes the cross-age correlation information on label ambiguities. As we propose a totally data-driven distribution learning, approach to adaptively learn the latent label distributions. The proposed approach is capable of effectively discovering the intrinsic age distribution patterns for cross-age correlation analysis on the any prior assumptions on the forms of label distribution learning, this approach is able to flexible model of sample-specific context aware label distribution properties by solving a multi-task problem which jointly optimizes the tasks of age-label distribution learning and age prediction for individuals. Experimental outcomes demonstrate effectiveness of our approach.
MUI-YIN CHINa*, CIA-LING TEOb AND CHIN-HONG PUAHc,d

International trade contributes significantly to driving Malaysia’s economic growth. As intra-industry trade (IIT) has become increasingly important due to changes in the international trade landscape, this study aims to identify the determinants of IIT and to gauge the trade pattern between Malaysia and Singapore in SITC 7 (the machinery and transport equipment manufacturing sub-sector) from 1980 through 2012 using decompositiontype threshold and Autoregressive Distributed Lag (ARDL) methods. In addition, this study will further decompose IIT into horizontal IIT (HIIT) and vertical IIT (VIIT) to examine the current pattern of IIT. The econometric results reveal that the difference in real gross domestic product (GDP) between the two countries is the key determinant of IIT in both the long run and the short run. Meanwhile, the average of foreign direct investment inflow (FDI) in both countries will stimulate IIT in the short run. Furthermore, the decomposition results confirm that the IIT between the two countries is prone to VIIT. The findings of the empirical results suggest that the policy makers of both countries should leverage the ASEAN Economic Community (AEC) and the Regional Comprehensive Economic Partnership Agreement (RCEP) to enhance their trade ties. Intra-Industry Trade Between Malaysia And Singapore In Sitc 7: An Ardl Bound Test Approach *Corresponding author: Tel: + 60341450123 Email : chinmy@acd.tarc. edu.my Int. Journal of Economics and Management 10(1): 109 – 124 (2016)
An Analysis of Methods of Integration of Hyperparameter Optimization on Neuroevolutionary Models

The ﬁeld of automated machine learning has developed numerous methods for the problem of neural architecture search (NAS). One of these is neuroevolution, where evolutionary algorithms are used to evolve model architectures. When employing this approach, the question remains of how best to optimize hyperparameters of the training process such as batch size, learning rate, and the choice of optimizer. To this end, we survey various methods of integrating hyperparameter optimization into a neuroevolutionary pipeline to determine their eﬀect on a wide variety of datasets. We further analyze meta-features of these datasets to see if the eﬃcacy of hyperparameter optimization could be contingent on these meta-features.
A Low Voltage High Speed Segmented Current Steering DAC for Neural Stimulation Application

A multi-phasic neural stimulator using current steering Digital to Analog Converter with low resolution is in great demand to ameliorate symptoms of Parkinson's disease and disorders of consciousness. This article presents 5-bit segmented DAC designed for such applications. In order to simulate the design, cadence virtuoso tool with 180 nm MOS technology is used. The results obtained after simulation indicate that the proposed DAC offers +0.34 LSB INL and +0.36 LSB value for DNL. For the input of 200 MSPS, the power dissipation is about 22 mW when working with 1.8 V of supply voltage. Keywords-component; DAC, DNL, INL, Digital-to-analog converter, CMOS current-steering DAC, SFDR,
Classification of Architectural Designs using Deep Learning

Architecture style of buildings play’s an important role in various aspects. Architectural style or the construction method affects the human health in multiple ways. Many dynasties are ruled India and constructed various types of monuments. So, In this proposed work popular dynasties like Hoysala dynasty, Vijayanagar empire, Mughal empire, Nizam’s of Hyderabad, Chalukya dynasty etc. are considered for creating dataset for the work. The architects of those times had really good knowledge about the different scientific methods to be used for construction. This project aims at classification of different architectural styles. Automatic identification of different architectural styles would facilitate different applications. The dataset is manually created by downloading images from various websites. Deep learning, inception v3 master algorithm are used. Experiments are performed using tenser flow and bottle neck files are created for validation. Good recognition rate is achieved with a fewer data set.
Comparative Analysis of Values in Islamic Texts and Social Studies Education in Turkey.

Social studies education in Turkey is a multidisciplinary field. One of these disciplines is religion. Social studies education can benefit from religion in terms of nurturing effective citizenship. The purpose of this study is to identify and compare the values intersecting the primary religious sources of Islam and the content of social studies education. Document analysis was employed as the method of comparison. The documents represented the verses of the Qur’an and the words of prophet Muhammad. The analysis focused on the values of respect for rights and freedoms, benevolence, justice, honesty, and respect for differences, among a few others. The results of the analysis demonstrate that true Islam espouses all these values and instructs all Muslims to live according to these values. Additionally, learning about Islam from its authentic religious texts dispels misconception of that religion and enhances citizenship education.
Fairness-Aware Instrumentation of Preprocessing~Pipelines for Machine Learning

Surfacing and mitigating bias in ML pipelines is a complex topic, with a dire need to provide system-level support to data scientists. Humans should be empowered to debug these pipelines, in order to control for bias and to improve data quality and representativeness. We propose fair-DAGs , an open-source library that extracts directed acyclic graph (DAG) representations of the data flow in preprocessing pipelines for ML. The library subsequently instruments the pipelines with tracing and visualization code to capture changes in data distributions and identify distortions with respect to protected group membership as the data travels through the pipeline. We illustrate the utility of fair-DAGs with experiments on publicly available ML pipelines.
On the Detection and Matching of Local Structures on Less Textured Objects

: Due to the lack of non-zero gradients around the structures in the less textured scenes, current local feature can hardly be applied in less textured object detection. To deal with this issue, two types of local structures, namely, corner and closed region are proposed in this paper. They are based on purely object contours, which are cleaner and easier to obtain in less textured scenes. Compared to existing detectors, they adapt to object local structures better. In addition, these new type of local structures also bring the advantage that allows us to have different level of abstraction on the object structures. Its effectiveness has
Scalable services, reliability, and consistency

CPS 512 focuses on core concepts in distributed systems, using cloud platforms for megaservices as a motivation and driving example. Well-designed cloud applications are layered above common service platforms that handle the hard problems: tracking groups of participating servers (views), distributing state and functions across a group, coordinating control and ownership of data, storing data reliably, and recovering from server and network failures. The course focuses on the design of these service platforms and their abstractions.
A Hotel Recommender System using Context Based Clustering

The web is one of the largest textual data repositories in the world. There is voluminous data in the digital world. To search for online hotels based on specific requirements of the user is not a very easy job. Ratings and reviews available on different travel websites help to some extent but gives generalized recommendations. A recommender system (RS) which uses reviews is known as content-based and is preferred, to produce a recommendation. Proposed RS maps all requirements of a traveler to features of a hotel and produces person specific recommendation. Phrase-based Recommender System is proposed to reduce efforts and time as compared with a traditional generalized recommender system. The proposed approach makes use of hotel reviews downloaded from TripAdvisor site. The technique initiates with phrase-based feature extraction followed by iterative clustering and ends with feature mapping and exports more relevant recommendations. Betterment of a technique is proved in terms of relevance, accuracy, scalability, and consistency by comparing precision and entropy refinement and corpus size with existing technique.The web is one of the largest textual data repositories in the world. There is voluminous data in the digital world. To search for online hotels based on specific requirements of the user is not a very easy job. Ratings and reviews available on different travel websites help to some extent but gives generalized recommendations. A recommender system (RS) which uses reviews is known as content-based and is preferred, to produce a recommendation. Proposed RS maps all requirements of a traveler to features of a hotel and produces person specific recommendation. Phrase-based Recommender System is proposed to reduce efforts and time as compared with a traditional generalized recommender system. The proposed approach makes use of hotel reviews downloaded from TripAdvisor site. The technique initiates with phrase-based feature extraction followed by iterative clustering and ends with feature mapping and exports more relevant recommendations. Betterment of a technique is proved in terms of relevance, accuracy, scalability, and consistency by comparing precision and entropy refinement and corpus size with existing technique.
T OPIC -F OCUSED E XTRACTIVE S UMMARIZATION

Extractive single-document summarization is the task of condensing a source document into a shorter form while retaining its information content and meaning, by identifying important sections of content in the document and generating them verbatim. However, such systems tend to produce summaries that are often overly generic, with little controllability over the output, and cannot cater to many individuals’ unique information needs in a real-world setting. In this paper, we focus on the task of topic-focused extractive summarization, the task of extractively summarizing a document from some domain with a focus on some particular topic in that domain, with the goal of producing tailored summaries as per user-controlled speciﬁcations. We propose a new BERT-based neural model to learn this task, and build a system which can generate topic-focused summaries for unseen documents in some domain as per the user’s requirements after being trained on a small number of document-summary pairs per domain. We run our system on the CNN/DM and CourtListener datasets, and evaluate it against three baselines (L EAD , K EYWORD and B ERT - S UM ). Our experiments show that when evaluated by humans, we are able to match the coherence of the L EAD baseline while extracting content and consistently outperform ad hoc keyword-based methods; and that when evaluated automatically, we outperform keyword-based methods while providing greater controllability to users.
Development of a Shear Test for Unreinforced and Geosynthetic-Reinforced Hot Mix Asphalt

Inclusion of geosynthetics within hot mix asphalt layers has been reported to be an effective technique to enhance the performance of pavements. The experimental study presented in this paper introduces a new focus for evaluation of unreinforced and geosynthetic-reinforced asphalt specimens. Specifically, a new shear test was developed to evaluate the geosynthetic benefits in geosynthetic-reinforced asphalt specimens subjected to shearing along the cross-geosynthetic direction. In the developed shear test, the cross section of the specimen can be subjected to monotonic or cyclic shear loading. The cracks are expected to initiate and propagate along the cross-geosynthetic direction as the number of loading cycles increases. Following the development of the test equipment, a rigorous test procedure and data analysis protocol was adopted to evaluate unreinforced and geosynthetic-reinforced asphalt specimens. An experimental program was then conducted to compare responses of a control (unreinforced) asphalt specimen with an asphalt specimen that was reinforced using a polyester geosynthetic in its middle. Specifically, the control and geosynthetic-reinforced specimens were tested under monotonic and sinusoidal shear loading. The obtained test results underscored that the geosynthetic reinforcements can effectively enhance the performance of the asphalt under monotonic shear as well as cyclic shear loading. Specifically, the main benefit from geosynthetic inclusion was found in the enhanced residual shear resistance and increased energy absorbed by the geosynthetic-reinforced specimen.
BACKPROPGATION FOR CONTINUOUS THETA NEURON NETWORKS A Dissertation by DAVID FAN

The Theta neuron model is a spiking neuron model which, unlike traditional Leaky-Integrate-and-Fire neurons, can model spike latencies, threshold adaptation, bistability of resting and tonic firing states, and more. Previous work on learning rules for networks of theta neurons includes the derivation of a spike-timing based backpropagation algorithm for multilayer feedforward networks. However, this learning rule is only applicable to a fixed number of spikes per neuron, and is unable to take into account the effects of synaptic dynamics. In this thesis a novel backpropagation learning rule for theta neuron networks is derived which incorporates synaptic dynamics, is applicable to changing numbers of spikes per neuron, and does not explicitly depend on spike-timing. The learning rule is successfully applied to XOR, cosine and sinc function mappings, and comparisons between other learning rules for spiking neural networks are made. The algorithm achieves 97.8 percent training performance and 96.7 percent test performance on the Fischer-Iris dataset, which is comparable to other spiking neural network learning rules. The algorithm also achieves 99.0 percent training performance and 99.14 percent test performance on the Wisconsin Breast Cancer dataset, which is better than the compared spiking neural network learning rules.
Tool Support for Globally Distributed Scrum

Global software development promises advantages compared to collocated software development however, it also brings challenges associated with the introduced distances. To harvest the advantages and handle the challenges, appropriate processes and tools need to be employed. Through the use of agile development methodologies such as Scrum, global software development has successfully implemented such processes. In this position paper we argue, that to fully capitalize on the advantages of Scrum in global software development, appropriate tools needs to be developed. We propose that the use of activity-based tools can help leverage the advantages of Scrum and overcome the challenges of distributed collaboration and illustrate this proposal with examples from an observational study.