Reviewerly/exHarmony huggingface dataset:
Bioremediation of heavy metals from artisanal crude oil refinery (Kpo-Fire) impacted soil using Bacillus flexus and Pseudomonas aeruginosa in Ngie Community, Degema Local Government Area, Rivers State, Nigeria

The existence of heavy metals in “kpo-fire” impacted soil creates significant risks to human health and the ecosystem. In this study, the efficacy of the elimination of heavy metal from “kpo-fire” impacted soil was evaluated using bacterial treatments. The organisms (Bacillus flexus and Pseudomonas aeruginosa) used in the bioremediation setup were isolated from the impacted soil. Heavy metal analysis was carried out using an Atomic Absorption Spectrophotometer. The experimental setup involved the recreation of the contaminated soil sample in three (3) vessels labeled as: Flask A containing 300g of un-amended sample (control) to monitor natural process; Flask B containing 300g of sterilized sample; Flask C containing 300g of sample with Pseudomonas aeruginosa and Bacillus flexus. Soil baseline physicochemical composition was determined to have a pH of 6.18, Temperature of 29.2oC, Total Organic Carbon 7.58 mg/kg and Phosphate concentration 37.56 mg/kg. At the end of the investigation, experimental setup C, containing bacterial inocula was observed to possess the best bio-removal rates for Mercury (99.32%), Cadmium (77.59%), Boron (72.84%) and Arsenic (93.43%) after a 42-day period of study. Also, the concentrations declined from 1.05264mg/kg to 0.00621mg/kg for Mercury; Cadmium declined from 5.93mg/kg to 1.16mg/kg; Boron declined from 3.61mg/kg to 0.82mmg/kg and Arsenic declined from 2.78mg/kg to 0.16mg/kg. Molecular characterization revealed the contaminated soil had predominance of isolates with genomic molecular weight of 1,500 bp and the phylogenetic construct showed the bacterial isolates were Pseudomonas aeruginosa (MT023359), Bacillus flexus (MT023375) and Lysinibacillus macroides (MT023377). Statistical analysis revealed a strong positive correlation between the bacterial biomass and heavy metal removal. The synergistic parts played by bacterial consortia in the bio-removal of heavy metals from the polluted soil have been established and these potentials can be harnessed as a roadmap for eco-recovery of impacted environment in the Niger Delta. Bacillus flexus and Pseudomonas aeruginosa in consortium are efficient in remediation of kpo-fire contaminated soil.
Error-resilient Floquet geometric quantum computation

We propose a geometric quantum computation (GQC) scheme, called Floquet GQC (FGQC), where error-resilient geometric gates based on periodically driven two-level systems can be constructed via a non-Abelian geometric phase proposed in a recent study [V. Novi\ifmmode \check{c}\else \v{c}\fi{}enko and G. Juzeli\ifmmode \bar{u}\else \={u}\fi{}nas, Phys. Rev. A 100, 012127 (2019)]. Based on Rydberg atoms, we give possible implementations of universal FGQC single-qubit gates and a nontrivial FGQC two-qubit gate. By using numerical simulation, we evaluate the performance of the FGQC Z and X gates in the presence of both decoherence and a certain kind of systematic control error. For the currently available coherence time of the Rydberg state, ${T}_{2}\ensuremath{\approx}32\phantom{\rule{0.28em}{0ex}}\ensuremath{\mu}\mathrm{s}$, the numerical results show that the X and Z gate fidelities are about $0.900$ and $0.899$, respectively. In addition, we find that FGQC is robust against global control error; both analytical demonstration and numerical evidence are given. As the coherence time of various qubits grows, FGQC may provide a promising error-resilient quantum computation scheme in the future.
Constrained feature selection for semisupervised color-texture image segmentation using spectral clustering

Color-texture image segmentation remains a challenging problem due to extensive color-texture variability. Thus, the limited prior knowledge that is expressed by pairwise constraints can be exploited to guide the segmentation process. We propose a new semisupervised method by combining constrained feature selection and spectral clustering (SC) to perform color-texture image segmentation. The pairwise constraints are used by the constraint feature selection to choose the most relevant features among an available set of color and texture features. For this purpose, an innovative constraint score is developed to evaluate a subset of features at one time. A specific constrained SC algorithm involving the pairwise constraints is then applied to regroup the pixels into clusters. Experimental results on four benchmark datasets show that the proposed constraint score outperforms the main state-of-the-art constraint scores and that our semisupervised segmentation method is competitive compared with supervised, semisupervised, and unsupervised state-of-the-art segmentation methods.
STUDENTS' POINT OF VIEW ON THE USE OF WHATSAPP GROUP TO ELEVATE WRITING ABILITY

English has been an important language nowadays. The role of the English language also has become the qualification of many people. However, many students state that English is not an easy language to master especially in Indonesia. Teachers also find it as a challenge in teaching English in Indonesia. They must have the appropriate technique and media for the students. One of the media that can be used by teachers to teach and engage students’ participation in learning English is WhatsApp. This application is also popular among the students as instructional learning mobile medium, including as a teaching medium to teach writing for students. Besides it is easy to operate, many students are familiar with it. This research aimed to find out students’ perspectives towards the use of WhatsApp group in improving students’ writing ability. This study applied a descriptive qualitative study with 37 students of the English Education study program as the participants. The data instrument used in this research were questionnaire and interview. The findings show that teaching writing by using the WhatsApp group can bring positive effects and benefit during the teaching and learning process. Besides that, it can also develop students’ writing ability. This result is supported by students’ opinions which state many good responses to this media rather than bad responses. Moreover, the result also shows that the WhatsApp group is one of the appropriate learning platforms for education in Indonesia.
Development of Android-based harmonic oscillation pocket book for senior high school students

A research has been conducted to develop a physic learning media in form of android-based pocket book. We designed Harmonic Oscillation pocket book and test its feasibility and effectiveness in improving cognitive learning outcomes of students. This study using research and development method adapted from ADDIE model. The results revealed that this software has been successfully developed and is very feasible to be used as a physics learning media. This Android-based Harmonic Oscillation pocket book is effective in enhancing students cognitive learning outcomes and students showed positive tendency response toward the software.
Towards An Autonomous Landing System in Presence of Uncertain Obstacles in Indoor Environments

The landing task is fundamental to Micro air vehicles (MAVs) when attempting to land in an unpredictable environment (e.g., presence of static obstacles or moving obstacles).The MAV should immediately detect the environment through its sensors and decide its actions for landing.This paper addresses the problem of the autonomous landing approach of a commercial AR.Drone 2.0 in presence of uncertain obstacles in an indoor environment.A localization methodology to estimate the drone's pose based on the sensor fusion techniques which fuses IMU and Poxyz signals is proposed.In addition, a visionbased approach to detect and estimate the velocity, position of the moving obstacle in the drone's working environment is presented.To control the drone landing accurately, a cascade control based on an Accelerated Particle Swarm Optimization algorithm (APSO) is designed.The simulation and experimental results demonstrate that the obtained model is appropriate for the measured data.
Exhaustive Description of the System Architecture and Prototype Implementation of an IoT-Based eHealth Biometric Monitoring System for Elders in Independent Living

In this paper, we present an exhaustive description of an extensible e-Health Internet-connected embedded system, which allows the measurement of three biometric parameters: pulse rate, oxygen saturation and temperature, via several wired and wireless sensors residing to the realm of Noncommunicable Diseases (NCDs) and cognitive assessment through Choice Reaction Time (CRT) analysis. The hardware used is based on ATMEGA AVR + MySignals Hardware printed circuit board (Hardware PCB), but with multiple upgrades (including porting from ATMEGA328P to ATMEGA2560). Multiple software improvements were made (by writing high-level device drivers, text-mode and graphic-mode display driver) for increasing functionality, portability, speed, and latency. A top-level embedded application was developed and benchmarked. A custom wireless AT command firmware was developed, based on ESP8266 firmware to allow AP-mode configuration and single-command JavaScript Object Notation (JSON) data-packet pushing towards the cloud platform. All software is available in a git repository, including the measurement results. The proposed eHealth system provides with specific NCDs and cognitive views fostering the potential to exploit correlations between physiological and cognitive data and to generate predictive analysis in the field of eldercare.
e-SimNet: a visual similar product recommender system for E-commerce

&lt;span&gt;Visual similarity recommendations have an immense role in E-commerce portals. Fetching the appropriate similar products and suggesting to the buyers based on the product image's visual features is complex. Here in our research, we presented an efficient E-commerce similar product network model (e-SimNet) for visually similar recommendations. To achieve our objective, we have performed image feature extraction and generating embeddings using deep learning techniques and built an Index tree using the approximate nearest neighbor oh yeah (ANNOY) algorithm. Further, we have fetches top-N the near similar items using distance measure. We have benchmarked our model in terms of accuracy, error rate, and results show that better than other state-of-the-art approaches with 96.22% of accuracy.&lt;/span&gt;
Improving Asynchronous Interview Interaction with Follow-up Question Generation

The user experience of an asynchronous video interview system, conventionally is not reciprocal or conversational.Interview applicants expect that, like a typical face-to-face interview, they are innate and coherent.We posit that the planned adoption of limited probing through follow-up questions is an important step towards improving the interaction.We propose a follow-up question generation model (followQG) capable of generating relevant and diverse follow-up questions based on the previously asked questions, and their answers.We implement a 3D virtual interviewing system, Maya, with capability of follow-up question generation.Existing asynchronous interviewing systems are not dynamic with scripted and repetitive questions.In comparison, Maya responds with relevant follow-up questions, a largely unexplored feature of virtual interview systems.We take advantage of the implicit knowledge from deep pre-trained language models to generate rich and varied natural language follow-up questions.Empirical results suggest that followQG generates questions that humans rate as high quality, achieving 77% relevance.A comparison with strong baselines of neural network and rule-based systems show that it produces better quality questions.The corpus used for fine-tuning is made publicly available.
Gated Graph Attention Network for Cancer Prediction

With its increasing incidence, cancer has become one of the main causes of worldwide mortality. In this work, we mainly propose a novel attention-based neural network model named Gated Graph ATtention network (GGAT) for cancer prediction, where a gating mechanism (GM) is introduced to work with the attention mechanism (AM), to break through the previous work’s limitation of 1-hop neighbourhood reasoning. In this way, our GGAT is capable of fully mining the potential correlation between related samples, helping for improving the cancer prediction accuracy. Additionally, to simplify the datasets, we propose a hybrid feature selection algorithm to strictly select gene features, which significantly reduces training time without affecting prediction accuracy. To the best of our knowledge, our proposed GGAT achieves the state-of-the-art results in cancer prediction task on LIHC, LUAD, KIRC compared to other traditional machine learning methods and neural network models, and improves the accuracy by 1% to 2% on Cora dataset, compared to the state-of-the-art graph neural network methods.
IoT System for Vital Signs Monitoring in Suspicious Cases of Covid-19

Currently the world is going through a pandemic caused by Covid-19, the World Health Organization recommends to stay isolated from the rest of the people.This research shows the development of a prototype based on the internet of things, which aims to measure three very important aspects: heart rate, blood oxygen saturation and body temperature, these will be measured through sensors that will be connected to a NodeMCU module that integrates a Wi-Fi module, which will transmit the data to an IoT platform through which the data can be displayed, achieving real-time monitoring of the vital signs of the patient suspected of Covid-19.
The Usefulness of Bibles in Low-Resource Machine Translation

Bibles are available in a wide range of languages, which provides valuable parallel text between languages since verses can be aligned accurately between all the different translations. How well can such data be utilized to train good neural machine translation (NMT) models? We are particularly interested in low-resource languages of high morphological complexity, and attempt to answer this question in the current work by training and evaluating Basque-English and Navajo-English MT models with the Transformer architecture. Different tokenization methods are applied, among which syllabification turns out to be most effective for Navajo and it is also good for Basque. Another additional data resource which can be potentially available for endangered languages is a dictionary of either word or phrase translations, thanks to linguists’ work on language documentation. Could this data be leveraged to augment Bible data for better performance? We experiment with different ways to utilize dictionary data, and find that word-to-word mapping translation with a word-pair dictionary is more effective than low-resource techniques such as backtranslation or adding dictionary data directly into the training set, though neither backtranslation nor word-to-word mapping translation produce improvements over using Bible data alone in our experiments.
Exploratory Analysis of Electronic Health Records using Topic Modeling

The rapid growth of electronic health record (EHR) systems brings an increase in available information about patients in hospitals. This massive amount of text information presents an opportunity to extract unknown information about medical history, medication, diseases, allergies, among others. Extracting the main topics that represent the subjects covered by a text collection can give valuable insights. To this end, approaches for topic modeling have been used to tackle such problems as information discovery and topic extraction with thematic information. In this context, this work presents an exploratory analysis of a collection of electronic health records from an intensive care unit (ICU). The collection is split into two sub-collections: discharged patients and patients who progressed to death. We apply an LDA-based approach to discover the latent topics from the collections. The analyses show that some topics are more recurrent in the deceased patients (the death collection), like renal diseases, and others are more recurrent in the discharge collection, for example, diabetes. The results of the analyses can be useful for improving intensive care services since the topics can be a guide to understanding the patterns in discharge and death situations.
Benchmarking of Transformer-Based Pre-Trained Models on Social Media Text Classification Datasets

Free text data from social media is now widely used in natural language processing research, and one of the most common machine learning tasks performed on this data is classification. Generally speaking, performances of supervised classification algorithms on social media datasets are lower than those on texts from other sources, but recently-proposed transformer-based models have considerably improved upon legacy state-of-the-art systems. Currently, there is no study that compares the performances of different variants of transformer-based models on a wide range of social media text classification datasets. In this paper, we benchmark the performances of transformer-based pre-trained models on 25 social media text classification datasets, 6 of which are health-related. We compare three pre-trained language models, RoBERTa-base, BERTweet and ClinicalBioBERT in terms of classification accuracy. Our experiments show that RoBERTa-base and BERTweet perform comparably on most datasets, and considerably better than ClinicalBioBERT, even on health-related datasets.
Translation Learning Tool for Local Language to Bahasa Indonesia using Knuth-Morris-Pratt Algorithm

During the COVID-19 pandemic time, it is a requirement to deliver online learning since students can not have face-to-face meetings and they depend on gadgets such as a computer, mobile phone, iPad, and laptop to continue their study. One of the subjects is local language learning as this subject is a requirement in some provinces in Indonesia as a gesture concerning local wisdom. However, there is a lack of support for learning the local language since the local languages have a mouth to mouth learning knowledge without any dictionary support. This paper proposed the idea of using the Knuth-Morris-Pratt algorithm to translate the Palembang language to Bahasa Indonesia to help students to learn the Palembang language with the application.
Safe Multi-Agent Pathfinding with Time Uncertainty

In many real-world scenarios, the time it takes for a mobile agent, e.g., a robot, to move from one location to another may vary due to exogenous events and be difficult to predict accurately. Planning in such scenarios is challenging, especially in the context of Multi-Agent Pathfinding (MAPF), where the goal is to find paths to multiple agents and temporal coordination is necessary to avoid collisions. In this work, we consider a MAPF problem with this form of time uncertainty, where we are only given upper and lower bounds on the time it takes each agent to move. The objective is to find a safe solution, which is a solution that can be executed by all agents and is guaranteed to avoid collisions. We propose two complete and optimal algorithms for finding safe solutions based on well-known MAPF algorithms, namely, A* with Operator Decomposition (A* + OD) and Conflict-Based Search (CBS). Experimentally, we observe that on several standard MAPF grids the CBS-based algorithm performs better. We also explore the option of online replanning in this context, i.e., modifying the agents' plans during execution, to reduce the overall execution cost. We consider two online settings: (a) when an agent can sense the current time and its current location, and (b) when the agents can also communicate seamlessly during execution. For each setting, we propose a replanning algorithm and analyze its behavior theoretically and empirically. Our experimental evaluation confirms that indeed online replanning in both settings can significantly reduce solution cost.
A threshold group signature scheme suitable for the Internet of Things

Summary With the development of information technology, the Internet of Things terminals are vulnerable to threats such as eavesdropping, tampering, and counterfeiting. The application of threshold group signature technology can effectively ensure the security of the system in applications such as battlefield intelligent decision‐making, connected vehicles, and intelligent manufacturing. However, the existing threshold group signature schemes lack the two‐way trusted authentication mechanism between group members and group manager, and group manager can easily become a security risk for the entire signature system. To solve this problem, this paper proposes a threshold group signature scheme based on elliptic curve that achieves mutual authentication of group members and group manager. The security analysis proves that the proposed scheme is anonymous, traceable, and can resist collusion attacks and frame attacks. The results of performance analysis and comparison also show that under the same security strength, the scheme proposed has shorter signature length, lower calculation amount for signature generation and signature verification, which can effectively reduce the communication and calculation overhead of the IoT terminal, and can be better applied to the Internet of Things application scenarios.
Fault Injectors for TensorFlow: Evaluation of the Impact of Random Hardware Faults on Deep CNNs

Today, Deep Learning (DL) enhances almost every industrial sector, including safety-critical areas. The next generation of safety standards will define appropriate verification techniques for DL-based applications and propose adequate fault tolerance mechanisms. DL-based applications, like any other software, are susceptible to common random hardware faults such as bit flips, which occur in RAM and CPU registers. Such faults can lead to silent data corruption. Therefore, it is crucial to develop methods and tools that help to evaluate how DL components operate under the presence of such faults. In this paper, we introduce two new Fault Injection (FI) frameworks InjectTF and InjectTF2 for TensorFlow 1 and TensorFlow 2, respectively. Both frameworks are available on GitHub and allow the configurable injection of random faults into Neural Networks (NN). In order to demonstrate the feasibility of the frameworks, we also present the results of FI experiments conducted on four VGG-based Convolutional NNs using two image sets. The results demonstrate how random bit flips in the output of particular mathematical operations and layers of NNs affect the classification accuracy. These results help to identify the most critical operations and layers, compare the reliability characteristics of functionally similar NNs, and introduce selective fault tolerance mechanisms.