Miracle-dz/UniFAR-FTUs huggingface dataset:
Transformers for Low-Resource Languages: Is Féidir Linn!

The Transformer model is the state-of-the-art in Machine Translation. However and in general and neural translation models often under perform on language pairs with insufficient training data. As a consequence and relatively few experiments have been carried out using this architecture on low-resource language pairs. In this study and hyperparameter optimization of Transformer models in translating the low-resource English-Irish language pair is evaluated. We demonstrate that choosing appropriate parameters leads to considerable performance improvements. Most importantly and the correct choice of subword model is shown to be the biggest driver of translation performance. SentencePiece models using both unigram and BPE approaches were appraised. Variations on model architectures included modifying the number of layers and testing various regularization techniques and evaluating the optimal number of heads for attention. A generic 55k DGT corpus and an in-domain 88k public admin corpus were used for evaluation. A Transformer optimized model demonstrated a BLEU score improvement of 7.8 points when compared with a baseline RNN model. Improvements were observed across a range of metrics and including TER and indicating a substantially reduced post editing effort for Transformer optimized models with 16k BPE subword models. Bench-marked against Google Translate and our translation engines demonstrated significant improvements. The question of whether or not Transformers can be used effectively in a low-resource setting of English-Irish translation has been addressed. Is féidir linn - yes we can.
Organic geochemical and palaeobotanical reconstruction of a late-Holocene archaeological settlement in coastal eastern India

Integration of palaeobotanical (spores, pollen, phytoliths and non-pollen palynomorphs) and organic geochemical proxies, such as stable isotopes of organic carbon (δ13C) and n-alkanes, for studying the evolution and palaeoenvironmental conditions of an archaeological site are rare in India. The evolution of a protohistoric-historic site at Erenda, situated in the eastern coastal region of India, has been studied by using multiple palaeobotanical and organic geochemical proxies assisted with AMS radiocarbon dates. The excavated site lies above Late Pleistocene–Early Holocene Sijua Formation. The absence of anthropogenic evidence in the Sijua Formation likely indicates inhabitable conditions in nearshore/estuarine marshy conditions. The earliest human settlements at the excavation site begin during the first millennium BCE after the initiation of habitable conditions along the coast. The presence of fungal spores and the dominance of C4 phytolith morphotypes indicate prevailing warm and humid climatic conditions and proximity to a freshwater body. The δ13C signature and n-alkane composition indicate the use of C4 grass for the construction of the mud and clay-built huts. The settlers most likely used to consume wild or domestic variety of rice, as evidenced by the presence of bilobate scooped morphotypes. The site was partly abandoned, covered with C3 and C4 vegetation and used as a dumping ground after 663 ± 92 BCE. This implies that people continued to live in the area but possibly moved to a nearby site while using the excavated site as refuse.
Novel sequencing technologies and bioinformatic tools for deciphering the non-coding genome

Abstract High-throughput sequencing techniques have significantly increased the molecular diagnosis rate for patients with monogenic disorders. This is primarily due to a substantially increased identification rate of disease mutations in the coding sequence, primarily SNVs and indels. Further progress is hampered by difficulties in the detection of structural variants and the interpretation of variants outside the coding sequence. In this review, we provide an overview about how novel sequencing techniques and state-of-the-art algorithms can be used to discover small and structural variants across the whole genome and introduce bioinformatic tools for the prediction of effects variants may have in the non-coding part of the genome.
Synthesis of silver nanoparticles (AgNPs) using Ficus retusa leaf extract for potential application as antibacterial and dye decolourising agents

ABSTRACT Synthesis of AgNPs was attempted using aqueous Ficus retusa leaf extract (FRLE). The effects of silver nitrate concentrations, FRLE volume, initial pH and temperature on synthesis were also studied. TEM revealed the size of AgNPs in range of 5–35 nm and crystal structure was confirmed by XRD. FTIR showed the capping of AgNPs by biomolecules of FRLE. Zeta potential of −39.1 indicates good stability of AgNPs. The AgNPs were evaluated for their antibacterial and high catalytic activity in enhancing decolourisation of eriochrome black T dye in presence of sodium borohydride (from 19.74 to 86.05%) and light source (from 41.96 to 80.11%).
Volume added surface barrier discharge plasma excited by bipolar nanosecond pulse power in atmospheric air: optical emission spectra influenced by gap distance

In this paper, volume barrier discharge with different gap distances is added on the discharge border of high-voltage electrode of annular surface barrier discharge for generating volume added surface barrier discharge (V-SBD) excited by bipolar nanosecond high-voltage pulse power in atmospheric air. The excited V-SBDs consist of surface barrier discharge (d=0mm) and volume added surface barrier discharges (d=2mm and 3mm). The optical emission spectra are recorded for calculating emission intensities of N2(C Πu→B Πg) and N2 +(B u å + → X g å ), and simulating rotational and vibrational temperatures. The influences of gap distance of V-SBD on emission intensity and plasma temperature are also investigated and analyzed. The results show that d=0mm structure can excite the largest emission intensity of N2(C Πu→B Πg), while the existence of volume barrier discharge can delay the occurrence of the peak value of the emission intensity ratio of N2 + (B u å + → X g å )/N2 (C Πu→B Πg) during the rising period of the applied voltage pulse and weaken it during the end period. The increasing factor of emission intensity is effected by the pulse repetition rate. The d=3mm structure has the highest threshold voltage while it can maintain more emission intensity of N2(C Πu→B Πg) than that of d=2mm structure. The structure of d=2mm can maintain more increasing factor than that of the d=3mm structure with varying pulse repetition rate. Besides, the rotational temperatures of three V-SBD structures are slightly affected when the gap distance and pulse repetition rate vary. The vibrational temperatures have decaying tendencies of all three structures with the increasing pulse repetition rate.
Deep Learning Models for Pneumonia Identification and Classification Based on X-Ray Images

Diagnosis based on chest X-rays is widely used and approved for the diagnosis of various diseases such as Pneumonia. Manually screening of theses X-ray images technician or radiologist involves expertise and time consuming. Addressing this, we propose an automated approach for the diagnosis of pneumonia by assisting doctors in spotting infected areas in the X-ray images. We propose a deep Convolutional Neural Network (CNN) model for efficiently detecting the presence of pneumonia in the X-ray images. The proposed CNN is designed with 5 convolution blocks followed by 4 fully connected layers. In order to boost the performance of the model, we incorporate batch normalization, dynamic dropout, learning rate decay, L2 regularization weight decay along with Adam optimizer and binary Cross-Entropy loss function while training the model using back propagating algorithm. The proposed model is validated on two publicly accessible benchmark datasets, and the experimental studies conducted on these datasets indicate that the proposed model is efficient. The suggested CNN architecture with specified hyper parameters allows the model to outperform several existing models by achieving accuracy of 97.73% and 91.17% respectively for binary and multi-class classification tasks of pneumonia disease.
Notch Pathway and its Role in Cardiovascular System: Review

The Notch signaling pathway is a core signal transduction mechanism that control diverse cellular processes. Its activity is mandatory for normal embryonic development, as it control the transcription of genes implicated in cellular proliferation, differentiation, migration, and apoptosis. It is considered as a key regulator of neonatal cardiomyocyte development; thus, its abnormality is strongly correlated to cardiovascular congenital malformations. Despite, Notch pathway is considered relatively absent in adult heart; recent studies included evidence of the upregulation of Notch signaling pathway in response to some cardiac insults. In our review, we briefly discuss the possible role of Notch Pathway in adult cardiomyocytes and other cells involved in the cardiovascular health, and its possible contribution to cardiovascular diseases.
SKT: A One-Pass Multi-Sketch Data Analytics Accelerator

Data analysts often need to characterize a data stream as a first step to its further processing. Some of the initial insights to be gained include, e.g., the cardinality of the data set and its frequency distribution. Such information is typically extracted by using sketch algorithms, now widely employed to process very large data sets in manageable space and in a single pass over the data. Often, analysts need more than one parameter to characterize the stream. However, computing multiple sketches becomes expensive even when using high-end CPUs. Exploiting the increasing adoption of hardware accelerators, this paper proposes SKT, an FPGA-based accelerator that can compute several sketches along with basic statistics (average, max, min, etc.) in a single pass over the data. SKT has been designed to characterize a data set by calculating its cardinality, its second frequency moment, and its frequency distribution. The design processes data streams coming either from PCIe or TCP/IP, and it is built to fit emerging cloud service architectures, such as Microsoft’s Catapult or Amazon’s AQUA. The paper explores the trade-offs of designing sketch algorithms on a spatial architecture and how to combine several sketch algorithms into a single design. The empirical evaluation shows how SKT on an FPGA offers a significant performance gain over high-end, server-class CPUs. PVLDB Reference Format: Monica Chiosa, Thomas B. Preußer, and Gustavo Alonso. SKT: A One-Pass Multi-Sketch Data Analytics Accelerator. PVLDB, 14(11): 2369 2382, 2021. doi:10.14778/3476249.3476287
CGM: An Enhanced Mechanism for Streaming Data Collectionwith Local Differential Privacy

Local differential privacy (LDP) is a well-established privacy protection scheme for collecting sensitive data, which has been integrated into major platforms such as iOS, Chrome, and Windows. The main idea is that each individual randomly perturbs her data on her local device, and only uploads the noisy version to an untrusted data aggregator. This paper focuses on the collection of streaming data consisting of regular updates, e.g. , daily app usage. Such streams, when aggregated over a large population, often exhibit strong autocorrelations , e.g. , the average usage of an app usually does not change dramatically from one day to the next. To our knowledge, this property has been largely neglected in existing LDP mechanisms. Consequently, data collected with current LDP methods often exhibit unrealistically violent fluctuations due to the added noise, drowning the overall trend, as shown in our experiments. This paper proposes a novel correlated Gaussian mechanism ( CGM ) for enforcing (ϵ, δ)-LDP on streaming data collection, which reduces noise by exploiting public-known autocorrelation patterns of the aggregated data. This is done through non-trivial modifications to the core of the underlying Gaussian Mechanism; in particular, CGM injects temporally correlated noise, computed through an optimization program that takes into account the given autocorrelation pattern, data value range, and utility metric. CGM comes with formal proof of correctness, and consumes negligible computational resources. Extensive experiments using real datasets from different application domains demonstrate that CGM achieves consistent and significant utility gains compared to the baseline method of repeatedly running the underlying one-shot LDP mechanism.
INVESTIGATION OF THE POTENTIAL UTILIZATIONS OF HUMAN MILK ORIGINATED Lactobacillus gasseri STRAINS IN AQUACULTURE AND SEAFOOD PRODUCTION

In the current study, five Lactobacillus gasseri MA strains from human breast milk were investigated for their usage potentials in aquaculture and seafood products as probiotic and bio-protective. The strains were tested for their susceptibilities to various clinical antibiotics used in fish cultivation, antimicrobial activities against seven fish pathogens, alpha-amylase enzyme activities, and susceptibilities to sodium benzoate which used as a food preservative. The strains were susceptible to Ampicillin, Amoxicillin and Erythromycin antibiotics. Antimicrobial activity test results indicated that the inhibition zone diameters were ranged between 1.66 mm and 11.22 mm against various pathogens originated fish. The highest antimicrobial activity was recorded against the Aeromonas hydrophila ATCC 19570 (11.22 mm) for the MA-2 strain, while the lowest antimicrobial activity was determined against Vibrio anguillarum A4 (1.66 mm) for the MA-6 strain. L. gasseri MA-5, MA-3 and MA-6 showed alpha-amylase activity. The spectrophotometric and live-cell count data showed that all the strains exhibited resistance to sodium benzoate. The results suggest that L. gasseri MA strains could be good sources of probiotic for aquaculture and bio-protectives in seafood products.
On Querying Historical K-Cores

Many real-world relationships between entities can be modeled as temporal graphs, where each edge is associated with a timestamp or a time interval representing its occurrence. K -core is a fundamental model used to capture cohesive subgraphs in a simple graph and have drawn much research attention over the last decade. Despite widespread research, none of the existing works support the efficient querying of historical k -cores in temporal graphs. In this paper, given an integer k and a time window, we study the problem of computing all k -cores in the graph snapshot over the time window. We propose an index-based solution and several pruning strategies to reduce the index size. We also design a novel algorithm to construct this index, whose running time is linear to the final index size. Lastly, we conducted extensive experiments on several real-world temporal graphs to show the high effectiveness of our index-based solution.
Declarative Data Serving: The Future of Machine Learning Inference on the Edge

Recent advances in computer architecture and networking have ushered in a new age of edge computing, where computation is placed close to the point of data collection to facilitate low-latency decision making. As the complexity of such deployments grow into networks of interconnected edge devices, getting the necessary data to be in "the right place at the right time" can become a challenge. We envision a future of edge analytics where data flows between edge nodes are declaratively configured through high-level constraints. Using machine learning model-serving as a prototypical task, we illustrate how the heterogeneity and specialization of edge devices can lead to complex, task-specific communication patterns even in relatively simple situations. Without a declarative framework, managing this complexity will be challenging for developers and will lead to brittle systems. We conclude with a research vision for database community that brings our perspective to the emergent area of edge computing.
Impact of Design Principles and Patterns on Software Flexibility: An Experimental Evaluation Using Flexible Point (FXP)

Corresponding Author: Wan Nurhayati Wan Ab. Rahman Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, 43400, Serdang, Malaysia Email: wnurhayati@upm.edu. my Abstract: Software flexibility is a crucial factor in designing and developing software as it reflects its capability to adapt to changes. It is a topic that has been discussed for a very long time which indicates its importance in software development. However, it is not easy to produce a flexible software design. Design principles provide fundamental concepts in designing good software. On the other hand, design patterns are proven solution to recurring problem. When used correctly, design principles and patterns can be used to improve software flexibility. However, it is necessary to evaluate its impact on software flexibility. For this purpose, this research will conduct an experiment by developing a simple application using Object-Oriented Programming (OOP) based on a case study. Based on the requirements of the case study, two SOLID design principles are chosen such as Single Responsibility Principle (SRP) and Open Closed Principle (OCP) while Strategy and Decorator for the patterns from the GoF. Then, Flexible Points (FXP) is used to measure the differences in software flexibility by comparing the solutions developed before and after applying design principles and patterns. This is aimed to prove that the chosen design principles and patterns have positive impact on software flexibility. Lastly, the result analysis shows that the use of the chosen design principles and patterns indeed improve the flexibility of the software. Therefore, the authors highly recommend adopting design principles and patterns in software development.
How to Get Away with MoRTr: MIMO Beam Altering for Radio Window Privacy

We consider the radio window attack, a privacy threat in which an attacker monitors the wireless link over a period of time, recording the channel state information (CSI) across multiple packets and uses a model to detect, estimate, or classify human movements. To prevent such privacy attacks on a wireless channel, we propose modifying radio training (MoRTr), a novel system for Wi-Fi MIMO-OFDM devices that alters transmitted symbols over time, space and frequency via a pseudo-random process that mimics the changes due to human activity, particularly the training symbols that are used to measure the wireless channel by the receiver. We perform extensive experiments to demonstrate that an attacker is thwarted by the approach. At the same time, we demonstrate that any receiver is able to use its measured CSI to demodulate the data without any significant degradation in performance, despite the fact that the receiver is not measuring the true CSI.
: Dynamic Story Generation through Directed C hatbot Interactions

Natural Language Understanding and Generation are both areas of active research with widespread potential for story telling. This paper proposes an architecture for dynamically generating stories that allows a scene to be constructed and then dynamically written through the interaction of indi­ vidual chatbots. Each chatbot in this environment is meant to mimic either the specific emotional profile of a character or holistically represent all of the character’s attributes. Chatbots are created using the conversation history so that they can understand context, a relevant sentence suggestion provided by a question-answering model to keep generated output on topic, and a fine-tuned version of the GPT-2 transformer-based language model to combine all of this information and generate text. This architecture serves as an en­ semble method of approaching character modeling and also introduces the little-explored concept of emotional style transferring as a method for merg­ ing a story character’s emotional attributes with an independent training corpus. The question-answering model used in this study achieved 65.24% accuracy when tested on the Stanford Question-Answering Dataset and the emotion classification model achieved 57.3% accuracy on the International Survey on Emotion Antecedents and Reactions dataset. While neither of these performances are SOTA for their respective individual tasks, they are used in combination to produce state of the art directed story generation and pave the way for future research.
Dynamic Control of Infeasibility for Nonlinear Programming

An e(cid:27)ective way of solving general nonlinear programming problems is the adoption of composite-step strategies that combine a step tangent to the constraints and a normal step, alternating between reducing the ob-jective function value and the norm of the infeasibility. However, this kind of method requires the control of the iterates in order to prevent one step from destroying the progress of the other. In the Dynamic Control of Infeasibil-ity algorithm, proposed by Bielschowsky and Gomes for equality constrained problems, the steps are controlled through the use of the so called Trust Cylin-ders. We present an extension of this algorithm for solving problems with general constraints. We also show numerical experiments that indicate that the new method has a performance that is comparable to well known nonlinear programing codes
Knowledge accumulating: The general pattern of learning

Artificial Intelligence has been developed for decades with the achievement of great progress. Recently, deep learning shows its ability to solve many real world problems, e.g. image classification and detection, natural language processing, playing GO. Theoretically speaking, an artificial neural network can fit any function and reinforcement learning can learn from any delayed reward. But in solving real world tasks, we still need to spend a lot of effort to adjust algorithms to fit task unique features. This paper proposes that the reason of this phenomenon is the sparse feedback feature of the nature, and a single algorithm, no matter how we improve it, can only solve dense feedback tasks or specific sparse feedback tasks. This paper first analyses how sparse feedback affects algorithm perfomance, and then proposes a pattern that explains how to accumulate knowledge to solve sparse feedback problems.
New visions on metamorphic testing after a quarter of a century of inception

Metamorphic testing (MT) was introduced about a quarter of a century ago. It is increasingly being accepted by researchers and the industry as a useful testing technique. The studies, research results, applications, and extensions of MT have given us many insights and visions for its future. Our visions include: MRs will be a practical means to top up test case generation techniques, beyond the alleviation of the test oracle problem; MT will not only be a standalone technique, but conveniently integrated with other methods; MT and MRs will evolve beyond software testing, or even beyond verification; MRs may be anything that you can imagine, beyond the necessary properties of algorithms; MT research will be beyond empirical studies and move toward a theoretical foundation; MT will not only bring new concepts to software testing but also new concepts to other disciplines; MRs will alleviate the reliable test set problem beyond traditional approaches. These visions may help researchers explore the challenges and opportunities for MT in the next decade.