Miracle-dz/UniFAR-FTUs huggingface dataset:
Protein Ubiquitylation and Sumoylation Site Prediction Based on Ensemble and Transfer Learning

Ubiquitylation, a typical post-translational modification (PTM), plays an important role in signal transduction, apoptosis and cell proliferation. A ubiquitylation like PTM, sumoylation also may affect gene mapping, expression and genomic replication. Over the past two decades, machine learning has been widely employed in protein ubiquitylation and sumoylation site prediction tools. These existing tools require feature engineering, but failed to provide general interpretable features and probably underutilized the growing amount of data. This prompted us to propose a deep learning-based model that integrates multiple convolution and fully-connected layers of seven supervised learning sub-models to extract deep representations from protein sequences and physico-chemical properties (PCPs). Especially, we divided PCPs into 6 clusters and customized deep networks accordingly for handling the high correlations among one cluster. A stacking ensemble strategy was applied to combine these deep representations to make prediction. Furthermore, with the advantage of transfer learning, our deep learning model can work well on protein sumoylation site prediction as well after fine-tuning. On the high-quality annotated database Swiss-Prot, our model outperformed several well-known ubiquitylation and sumoylation site prediction tools. Our code is freely available at https://github.com/ruiwcoding/DeepUbiSumoPre.
Artificial dust deposition chamber for investigation of soiling loss in photovoltaic modules

Natural dust deposition is a long process requiring several days to obtain dust deposition of required density. This makes it imperative to have an indoor dust deposition system which can accelerate the process of dust deposition for testing purposes. This paper presents an artificial dust deposition system using no liquid carriers, which can deposit dust uniformly over a 20 x 20 cm2 area with a non uniformity of 5%. Uniformity of the deposited dust was assessed by comparing Jsc values among the glass samples at the center and at 5 points within a sample. Dust used for deposition was collected from PV modules, located at IIT Bombay. Deposition was conducted at three different densities namely, low (0.15 mg/cm2), medium (0.55 mg/cm2) and high (0.9mg/cm2), to check for uniformity. Jsc losses were calculated using Quantum Efficiency measurements, for 11 different dust densities ranging from 0.15 to 2 mg/cm2. Particle size distribution of the dust from bulk and the dust deposited over the glass surface using this system are comparable with a mean of 17 um.
Admittance control for collaborative dual-arm manipulation

Human-robot collaboration is an appealing solution to increase the flexibility of production lines. In this context, we propose a kinematic control strategy for dual-arm robotic platforms physically collaborating with human operators. Based on admittance control, our approach aims at improving the performance of object transportation tasks by acting on two levels: estimating and compensating gravity effects on one side, and considering human intention in the cooperative task space on the other. An experimental study using virtual reality reveals the effectiveness of our method in terms of reduced human energy expenditure.
Resource Allocation in NOMA-Enhanced Full-Duplex Symbiotic Radio Networks

This paper studies a full-duplex symbiotic radio network, where a full-duplex access point (FAP) transmits downlink orthogonal frequency division multiplexing signals to a legacy user (LU) and simultaneously receives signals backscattered from multiple passive backscatter devices (BDs) with capability of radio-frequency energy harvesting. A non-orthogonal-multiple-access enhanced dynamic-time-division-multiple-access (NOMA-DTDMA) transmission scheme is proposed to exploit the channel dynamics and further improve the spectrum efficiency. In order to maximize the throughput performance and ensure BD fairness, we maximize the minimum throughput among all BDs by jointly optimizing the FAP’s subcarrier power allocation, the BDs’ backscatter time allocation and power reflection coefficients, subject to the LU’s throughput requirement, the BDs’ harvested energy requirements, and other practical constraints. An efficient iterative algorithm is proposed to solve the formulated non-convex problem, by utilizing the block coordinated descent and successive convex optimization techniques. The convergence and complexity of the proposed algorithm are also analyzed. Numerical results show that the proposed NOMA-DTDMA scheme significantly outperforms the benchmark scheme of dynamic TDMA in terms of both throughput performance and BD fairness. Also, the trade-off performances between the BDs’ throughput and the LU’s throughput requirement as well as the BDs’ harvested energy requirements are numerically verified.
River Water Pollution Monitoring using Multiple Sensor System of WSNs (Case: Siak River, Indonesia)

The river is a natural phenomenon that commonly available in the tropical region because of rain intensity. Many peoples and community like to live along the riverside for a few decades ago. The river used by the community for transportation and daily activities uses river water. In this research objective to design and develop a new system with multiple sensors system to monitor river water pollution because most of the people use it. Wireless Sensor Networks (WSNs) used in this design and development because of advantages WSNs system, multiple sensor nodes installed for detection of water pollution such as water temperature, pH, electrical conductivity (EC) and dissolved oxygen (DO). The system designed to be able to monitor river water pollution parameters and send the information to the data center (backend system). Arduino microcontroller used to process and filtering the data before sending to the backend system, only valid and valuable information to collect and keep in the database. Results show system be able to detect polluted water with indicating parameters and shows in a graph. Based on analysis can be concluded that polluted water indicator mostly from residence waste and industry. Furthermore, WSNs sensors will deploy in some area then compare the results each other.
A Resource Consumption and Performance Overhead Optimized Reduction Circuit on FPGAs

Many scientific and engineering applications involve massive vector operations (such as dot product and matrix multiplication) which can be calculated efficiently by using reduction circuit. However, the low performance and large resource consumption of the reduction circuit limit the ability of the system. In this paper, an optimized reduction circuit with high performance and low resource consumption is proposed, which can handle multiple sets of arbitrary size without pipeline stalling. A new reduction scheduling algorithm is proposed, which consumes fewer cycles and buffer size compared with other methods. Moreover, in order to achieve a high clock frequency, the reduction circuit implements novel status and buffer management modules. The proposed design using a deeply pipelined double-precision floating-point adder as reduction operator is implemented on FPGAs, which achieves at least 1.59 times improvement on area-time product compared with the reported methods.
A Ransomware Detection Method Using Fuzzy Hashing for Mitigating the Risk of Occlusion of Information Systems

Today, a significant threat to organisational information systems is ransomware that can completely occlude the information system by denying access to its data. To reduce this exposure and damage from ransomware attacks, organisations are obliged to concentrate explicitly on the threat of ransomware, alongside their malware prevention strategy. In attempting to prevent the escalation of ransomware attacks, it is important to account for their polymorphic behaviour and dispersion of inexhaustible versions. However, a number of ransomware samples possess similarity as they are created by similar groups of threat actors. A particular threat actor or group often adopts similar practices or codebase to create unlimited versions of their ransomware. As a result of these common traits and codebase, it is probable that new or unknown ransomware variants can be detected based on a comparison with their originating or existing samples. Therefore, this paper presents a detection method for ransomware by employing a similarity preserving hashing method called fuzzy hashing. This detection method is applied on the collected WannaCry or WannaCryptor ransomware corpus utilising three fuzzy hashing methods SSDEEP, SDHASH and mvHASH-B to evaluate the similarity detection success rate by each method. Moreover, their fuzzy similarity scores are utilised to cluster the collected ransomware corpus and its results are compared to determine the relative accuracy of the selected fuzzy hashing methods.
UV-stable surface passivation for crystalline silicon cells in solar modules with UV light transmitting encapsulation materials

We investigate the ultraviolet (UV) stability of dielectric passivation layers on n+-type industry-typical phosphorus- diffused emitters with saturation current densities J0e in the range of 36 fA cm–2 to 67 fA cm–2. We prepare symmetrical silicon wafer test structures with various types of passivation layers and derive their saturation current densities from carrier lifetime measurements after exposure to different types of UV lamps. Our results reveal that UV illumination of emitters with a typical industrial silicon nitride (SiNx) passivation layer strongly increases the surface recombination. The illumination by narrow band lamps with intensity peak at 312 nm for a UV dose of 80 kWh m–2 significantly increases the J0e for these samples from 67 fA cm–2 to 507 fA cm–2. In contrast, a passivation layer stack consisting of a thermally grown silicon oxide and SiNx improves the UV stability of the samples. For this passivation layer stack, the J0e only marginally increases from 36 fA cm–2 to 46 fA cm–2. The application of this thermal SiOy/SiNx passivation layer to bifacial passivated emitter and rear cells (PERC+) results in a stable conversion efficiency after exposure to a UV dose of 24 kWh m–2. Our development of UV-stable PERC+ cells with SiOy passivation layers enables to apply UV light transmitting encapsulation materials for solar modules in order to increase the annual energy yield in the field.
Fraudulent News Detection using Machine Learning Approaches

The rampant spread of fake news on social media has skyrocketed over the years. Fake news has become a notorious devil affecting the overall demographic of the nation. Its not only regular users who are worried but also the marketers who raised concerns about the impact of fake news on trade. Online sources for news consumption are a double edged sword. Fake news is increasingly becoming a menace to our society. It is typically generated for commercial interests to attract viewers and also to collect advertising revenue. However, media giants with potentially malicious agendas have been known to produce fake news in order to influence events and policies around the world. This paper addresses a classifier that can predict whether a piece of news is legit and not just a botched up fact. The proposed model train itself using data sets having headlines of news of multiple years to predict whether a news article is true to its word. The proposed work provides a convenient hassle-free platform for everyone and aims to spread calm by decreasing rumors and misunderstandings in the society.
Cost-Efficient Outsourced Decryption of Attribute-Based Encryption Schemes for Both Users and Cloud Server in Green Cloud Computing

To reduce a user’s decryption cost and protect the private information from being leaked, Green et al. proposed an approach oursourcing the decryption of the attribute based encryption (ABE) scheme to the cloud server. Later, almost all ABE schemes with outsourced decryption (ABE-OD) used their model or approach. However, the cloud server needs to repeat the outsourced decryption service of the same ciphertext for distinct users satisfying the same access policy in these schemes. Green computing is the atmosphere conscientious and recyclable utilization of resources. The green cloud networks can reduce their cost or energy requirements by adapting its performance, optimizing resources management and services. The method is not efficient for the cloud server in the green cloud networks. In this article, to take into account recyclable utilization of resources for the cloud server, we put forward a new and secure approach to reduce total overhead of the cloud server when many users satisfying an access policy require the outsourced decryptions for the same ciphertext besides decreasing the decryption computation cost for users. Compared with the existing ABE-OD schemes, our total overhead of the cloud server is independent of the number of the users who satisfy an access policy and request the outsourcing decryption service. Finally, we extend our approach to a RCCA-secure ABE-OD scheme.
PV system performance evaluation by clustering production data to normal and non-normal operation.

The purpose of this study is the development of an algorithmic tool to cluster the points of a scatterplot of "System Yield vs. Reference Yield" of photovoltaic systems, which tends to follow a linear regression. From such clustering one can distinguish data points that comply with the linearity (inliers) and the data points which do not (outliers). The tool then is applied to the scatterplot for two main purposes: a) to detect and exclude any data input anomalies or b) to detect and separate measurements where the PV system is functioning properly from the measurements that show that the photovoltaic (PV) system is malfunctioning.
Small Scale Feature Propagation Using Deep Residual Learning for Diabetic Retinopathy Classification

Automated diagnosis of diabetic retinopathy from fundus images involves detecting both small- and large-scale lesions, which makes this a difficult task for deep learning applications. In this paper we investigate the effects of small scale feature propagation for improving diabetic retinopathy classification. To accomplish this, we have utilized a publicly available dataset with 88,702 images, which contains unbalanced number of examples for different classes. A linear equation for class-specific gradient weighting has been proposed and has found to be beneficial. Three different residual architectures with residual and skip connections have been tested and their efficacy for this task is examined. The residual connections have been verified to improve the results for detecting small scale features for deep architectures. Skip connections within the current experimental setting have been found to be detrimental for the overall performance, potential solutions and their resulting effects have been discussed.
Prediction of Indonesia Presidential Election Results for the 2019-2024 Period Using Twitter Sentiment Analysis

The main purpose of this research is to predict the election of the President and Vice President of the Republic of Indonesia from 2019–2024 through the mining process of public opinion on twitter and test it accurately with classification algorithms namely Support Vector Machine (SVM) with selection features of Particle Swarm Optimization (PSO) and Genetic Algorithms (GA). As the fourth largest democracy and the fifth largest twitter user in the world, twitter in Indonesia is very influential as a place for people to fight each other's arguments. Public talk about the election of two candidates for President and Vice President of the Republic of Indonesia for the 2019–2024 on Twitter, became an interesting topic for Twitter users with a variety of public sentiment both positive and negative. Therefore, there is a need for a method that helps to see public opinion effectively. The researchers used tweets in Indonesian as research data with keywords #jokowi2periode and #2019tetapjokowi, and #2019prabowosandi and #2019gantipresiden with a total of 4000 tweets. This study uses a classification technique, namely SVM Algorithm. This method is chosen based on many of the best classification techniques commonly used for the analysis of opinion sentiments. It is because of SVM has weaknesses for selecting the appropriate parameters or features. In this study, researchers made improvements to previous research using a feature selection comparison method, PSO & GA. The results of this study are in the form of a prediction of the pairs of candidates for the President and the Vice President of Indonesia for the period of 2019–2024 who have more positive sentiments. Based on public opinion on Twitter, the pair Prabowo Subianto-Sandiaga Uno is predicted to be elected as President and Vice President of Indonesia for the period of 2019–2024 with the most positive sentiment, reaching 830 out of 1000 tweets entered. And the SVM method of the combination of PSO is the best method with accuracy reaching 86.20% and the AUC value reaching 0.934.
Automated High-Productivity Microinjection System for Adherent Cells

Automated microinjection systems for suspension cells have been studied for years. Nevertheless, microinjection systems for adherent cells still suffer from laborious manual operations and low productivity. This paper presents a new automated microinjection system with high productivity for adherent cells. This system enhances productivity through four approaches. First, cells are detected automatically to replace manual selections. Second, the injection paths of detected cells are optimized rapidly to save time. Third, the penetration depth is adjusted adaptively according to the moving plane of the dish holder plate. Finally, constant outflow-based injection is adopted to minimize clogging. The first three approaches aim to improve the injection speed, and the last one aims to extend the usage time of micropipettes. Experiments of massive injections on MC3T3-E1 cells are performed to evaluate cell detection efficiency, injection speed, success rate, and survival rate. Results confirm that the system allows injections of over 1500 cells in one hour without much training and preparation a priori.
Civil Servant's E-Govemment Adoption Levels: Are age and context matters?

This study examines the differences of e-government adoption by civil servants among age groups and between mandatory and voluntary context using UTAUT model. It used a non-probability sampling technique and an online survey to collect the data. A one-way ANOVA using SPSS was conducted to analyze the data. The study finds that most employees have the highest positive adoption levels in effort expectancy. Furthermore, there are significant mean differences between employee's age group of performance expectancy for mandatory system and facilitating conditions for voluntary system. However, there is no statistically different on civil servant's adoption level between mandatory and voluntary context.
Optimized Polynomial Multiplier Over Commutative Rings on FPGAs: A Case Study on BIKE

In this paper, we present two constant-time FPGAbased polynomial multipliers for post-quantum secure key encapsulation mechanisms based on quasi-cyclic codes, which are among round 2 candidates in the NIST PQC standardization process. The pipelined hardware architecture for polynomial multiplications proposed in this work are fully parameterized in terms of the size of the polynomial, and can be further tuned flexibly to achieve a trade-off between time and area depending on individual needs. We also present a case study on the BIKE key generators which use these two polynomial multiplier architectures as building blocks. Compared with the state-of-the-art hardware implementation of BIKE, the design proposed in this work is around 9× faster in terms of run-time while maintaining an over 6× smaller time-area product.
Adversarial Mobility Learning for Human Trajectory Classification

Understanding human mobility is one of the important but challenging tasks in Location-based Social Networks (LBSN). Recently, a user mobility mining task called Trajectory User Linking (TUL) has become an essential and popular topic, aiming at identifying user identities through exploiting their mobility patterns. Existing methods mainly focus on learning sequential mobility patterns by capturing long-short term dependencies among historical check-ins. However, users have personalized moving preferences, which have not been considered in previous work. Besides, how to leverage the prior knowledge behind human mobility needs to be further investigated. In this work, we present a novel semi-supervised method, called AdattTUL, to make adversarial mobility learning for human trajectory classification, which is an end-to-end framework modeling human moving patterns. AdattTUL integrates multiple human preferences of check-in behaviors and involves an attention mechanism to dynamically capture the complex relationships of user check-ins from trajectory data. In addition, AdattTUL leverages an adversarial network to help in regularizing the data distribution of human trajectories. Extensive experiments conducted on real-world LBSN datasets show that AdattTUL significantly improves the TUL performance.
The Development of Digital Marketing Strategy for Tourism Startup: A Case Study of Atourin

The development of startup companies is supported by the growth of internet users and also positive developments and large market customers have attracted investment interest both from within and outside the country. With the business process that is unique and highly dependent on the effectiveness of product positioning and promotion methods, a startup named Atourin requires the development of marketing strategies to improve competitiveness to develop its business model. This can help to meet the periodic targets as a reference for Atourin’s business development, which will be information that is accountable to investors. This research was conducted with a qualitative approach that was included in the case study by interviewing Atourin’s top level management and observing Atourin’s performance and conducting market and competitive analysis relevant to the Atourin’s business model to provide recommendations to companies in the form of marketing-based strategies information technology tailored to the context of Atourin. This study describes various strategies and tactics in utilizing information technology-based marketing to achieve the stated objectives, which produce eight strategies and fourteen tactics which generally describes strategies in attracting visitors who have an interest in tourism to visit the Atourin site and become Atourin’s active users. The results of this study can be used as a reference in the formation of information technology-based marketing strategies that are in line with Atourin’s business needs.