Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrating multiple state‐of‐the‐art computer‐aided design tools in microelectronics circuit design classes

Demand for microelectronics products has seen a recent explosion due to their increased adaption in high‐performance data storage, networking, and Internet of Things applications. Not only such products need to provide high performance, they are often integrated in mixed signal environments that include both analog and digital circuits. This has posed a challenge to faculty who teach microelectronics design in senior undergraduate and graduate electrical engineering courses. It is becoming increasingly difficult to upgrade microelectronics curricula, so students are enabled with the proper skills to utilize design tools presently common in the industry. This study provides a mechanism to integrate five state of the art design tools in one single design project. The tools are Custom Compiler, Hewlett Simulation Program with Integrated Circuit, verilog compiler simulator, IC Validator, and Design Complier. Students, through a design project, conduct the design, layout, and simulations of an static random‐access memory array. The project utilizes both the full‐custom and the semi‐custom flows. One full design is created and integrated where students do the design and layout of transistors in specific circuits and generate synthesized circuits automatically from a high‐level description language. This study can serve as a resource for senior undergraduate students, graduate students, faculty, and practicing engineers. Finally, it can help electrical engineering programs meet Accreditation Board for Engineering and Technology students’ outcome (k) which is an ability to use the techniques, skills, and modern engineering tools necessary for engineering practice.
Digital Literacy in the Use of Technology-based Information System

Digital literacy is an urgent need for society today, because technological advances that are not balanced by intelligence in using modern technology tools will have a negative impact on human civilization. Surveys conducted by the Indonesian Internet Network Organizing Association (APJII), for example, reveal that more than half of Indonesia's population is now connected to the internet. It was noted that throughout 2016 as many as 132.7 million Indonesians became active internet users from 256.2 million in total population. Digital literacy actually encourages digital citizens to be wiser in using these technological instruments. The problem regarding the ability to develop rational abilities (reason) or the cognitive abilities of the community must also be complemented by social intelligence in the form of digital wisdom. This study is directed to find out clearly about the ability of employing technology employees in the Faculty of Education in the Use of Technology-Based Information Systems established in Universitas Pendidikan Indonesia. Keywords—digital literacy; technology-based information systems
End-to-End Deep Learning Approach for Drug-Target Interaction Prediction

The discovery of potential Drug-Target Interactions is a determining step in the drug discovery and repositioning process, as the effectiveness of the currently available antibiotic treatment, arisen from the increased misuse, is declining. Although putting efforts on the traditional in vivo or in vitro methods, pharmaceutical financial investment has been reduced over the years. Thus, establishing effective computational methods is decisive to find new clinical purposes for the available drugs (leads) in a reasonable amount of time. Successful approaches, including machine and deep learning, have been presented to solve and correctly identify new leads and DTIs, but seldom protein sequences and structured data are used together. In this work, we propose a deep learning architecture model, which exploits the particular ability of Convolutional Neural Networks to automatically surmise and identify important sequential and structural regions and extract 1D representations from protein sequences (amino acid sequences) and compounds SMILES strings. These representations can be interpreted as features that express local dependencies or patterns that can be used in a Fully Connected Neural Network, acting as a binary classifier. The achieved results demonstrate that using CNNs to obtain representations of the data, instead of the traditional descriptors, lead to improved performance. The proposed end-to-end deep learning method outperformed traditional machine learning approaches in the correct classification of both positive and negative interactions, reaching high scores of sensitivity (0.861) and specificity (0.961).
A simulation-based case study for powertrain efficiency improvement by automated driving functions

An increasing level of driving automation and a successive electrification of modern powertrains enable a higher degree of freedom to improve vehicle fuel efficiency and reduce pollutant emissions. Currently, both domains themselves, driving automation as well as powertrain electrification, face the challenge of a rising development complexity with extensive use of virtual testing environments. However, state-of-the-art virtual testing environments typically strictly focus on just one domain and neglect the other. This paper shows the results of a simulation-based case study considering both domains simultaneously. The influence of energy saving automated functionalities on a conventional, a hybrid, and a pure electric powertrain is investigated for a carefully selected inner-city driving scenario. The vehicle simulation models for the different powertrain configurations are calibrated using test bench results and vehicle measurements. A model predictive acceleration controller is developed for realizing the speed optimization function. By considering traffic conditions such as traffic light schedules and a preceding vehicle as the boundary conditions, unnecessary accelerations and decelerations are avoided to reduce the energy demand. The case study is realized by applying this function to the three powertrains variants. As a final result, a clear difference in energy demand is observed: the hybrid powertrain benefits the most in terms of energy demand reduction in the given use case. The results clearly underscore that in future vehicle development programs, the powertrain and the real-world driving functionalities have to be optimized simultaneously to minimize the energy demand during everyday vehicle operation.
Enhancing Exploratory Analysis across Multiple Levels of Detail of Spatiotemporal Events

Crimes, forest fires, accidents, infectious diseases, human interactions with mobile devices (e.g., tweets) are being logged as spatiotemporal events. For each event, its spatial location, time and related attributes are known with high levels of detail (LoDs). The LoD of analysis plays a crucial role in the user’s perception of phenomena. From one LoD to another, some patterns can be easily perceived or different patterns may be detected, thus requiring modeling phenomena at different LoDs as there is no exclusive LoD to study them. Granular computing emerged as a paradigm of knowledge representation and processing, where granules are basic ingredients of information. These can be arranged in a hierarchical alike structure, allowing the same phenomenon to be perceived at different LoDs. This PhD Thesis introduces a formal Theory of Granularities (ToG) in order to have granules defined over any domain and reason over them. This approach is more general than the related literature because these appear as particular cases of the proposed ToG. Based on this theory we propose a granular computing approach to model spatiotemporal phenomena at multiple LoDs, and called it a granularities-based model. This approach stands out from the related literature because it models a phenomenon through statements rather than just using granules to model abstract real-world entities. Furthermore, it formalizes the concept of LoD and follows an automated approach to generalize a phenomenon from one LoD to a coarser one. Present-day practices work on a single LoD driven by the users despite the fact that the identification of the suitable LoDs is a key issue for them. This PhD Thesis presents a framework for SUmmarizIng spatioTemporal Events (SUITE) across multiple LoDs. The SUITE framework makes no assumptions about the phenomenon and the analytical task. A Visual Analytics approach implementing the SUITE framework is presented, which allow users to inspect a phenomenon across multiple LoDs, simultaneously, thus helping to understand in what LoDs the phenomenon perception is different or in what LoDs patterns emerge.
Explicit Instruction of Collocations: An Impact on Learners’ Use and Perceptions

As a part of vocabulary, the correct use of collocations can reflect English language proficiency (Richards, 2008), and explicit instruction is effective for vocabulary learning (Conzett, 2000 and Schmitt, 2000). This study aims to investigate English major students’ use of collocations and their perceptions on explicit learning of collocations after a year of instruction. A test of collocations and an open-ended questionnaire were administered to the fourth year English major students at Khon Kaen University, who were explicitly instructed in the use of some collocations. The mean and Analysis of Variance were utilized to analyze the data from the test scores. A content analysis and the percentage was used to analyze the data from the questionnaire. The test scores revealed that the students used some collocations correctly, and they could use everyday verbs, synonyms and confusable words more accurately than intensifying adverbs. The students perceived that the instruction of collocations should be done explicitly in class since it helped them to learn and become aware of collocation use as well as knowing how to check their collocation use from various sources. การสอนคำเกดรวมดวยวธการสอนทมกระบวนการและขนตอนทชดเจน: ผลกระทบตอการใชและความตระหนกของผเรยน ในฐานะทเปนสวนหนงของคำศพท การใชคำเกดรวมในภาษา องกฤษทถกตองนนจะสะทอนใหเหนถงความสามารถทางภาษา องกฤษของผเรยนได (Richards, 2008) และวธการสอนทม กระบวนการและขนตอนทชดเจนนนมประสทธผลตอการเรยน การสอนคำศพท (Conzett, 2000 and Schmitt, 2000) งานวจยน มจดประสงคทจะศกษาการใชคำเกดรวมและความตระหนกถง คำเกดรวมของนกศกษาวชาเอกภาษาองกฤษทมผลมาจาก วธการ สอนทมกระบวนการและขนตอนทชดเจน หลงจากการสอนนนผาน ไปแลว 1 ป โดยใชแบบทดสอบคำเกดรวมและแบบสอบถาม ปลายเปดกบนกศกษาชนปท 4 คณะมนษยศาสตรและสงคมศาสตร มหาวทยาลยขอนแกน วเคราะหคะแนนทไดจากผลสอบ โดยคาเฉลย และการวเคราะหความแปรปรวน วเคราะหขอมลจากแบบสอบถาม โดยการวเคราะหเนอหาและแปลงเปนคารอยละ พบวานกศกษายง สามารถใชคำเกดรวมบางสวนไดถกตอง และสามารถใชคำเกดรวม กลม everyday verbs และ synonyms and confusable words ได ถกตองมากกวา intensifying adverbs นกศกษาสวนใหญตระหนกวา คำเกดรวมควรมการสอนในหองเรยน เพราะการสอนคำเกดรวมจะ ทำใหนกศกษาเรยนรและตระหนกถงการใชภาษาทถกตอง และยง สงผลใหนกศกษา ไดรวธการและตรวจสอบการใชคำเกดรวมของ ตนเองดวยวธการตางๆ
IoT: Security Challenges and Mitigations

: Nowadays, Internet of things (IoT) is one of the emerging domains which is growing exponentially. IoT is an idea that bridges the smart devices and let the devices to communicate with each other over the internet. IoT is an enormous system of associated gadgets – all of devices accumulate and share the information about how they are utilized and the situations in which they are working. IoT-based innovation will offer propelled dimensions of administrations and for all intents and purposes and it will change the manner in which individuals lead their day by day lives. IoT enhances machine-to-machine(M2M) communication that eliminates the need for human intervention
Scheduling independent stochastic tasks on heterogeneous cloud platforms

This work introduces scheduling strategies to maximize the expected number of independent tasks that can be executed on a cloud platform within a given budget and under a deadline constraint. The cloud platform is composed of several types of virtual machines (VMs), where each type has a unit execution cost that depends upon its characteristics. The amount of budget spent during the execution of a task on a given VM is the product of its execution length by the unit execution cost of that VM. The execution lengths of tasks follow a variety of standard probability distributions (exponential, uniform, half-normal, etc.), which is known beforehand and whose mean and standard deviation both depend upon the VM type. Finally, there is a global available budget and a deadline constraint, and the goal is to successfully execute as many tasks as possible before the deadline is reached or the budget is exhausted (whichever comes first). On each VM, the scheduler can decide at any instant to interrupt the execution of a (long) running task and to launch a new one, but the budget already spent for the interrupted task is lost. The main questions are which VMs to enroll, and whether and when to interrupt tasks that have been executing for some time. We assess the complexity of the problem by showing its NP-completeness and providing a 2-approximation for the asymptotic case where budget and deadline both tend to infinity. Then we introduce several heuristics and compare their performance by running an extensive set of simulations.
Image Recovery in the Infrared Domain via Path-Augmented Compressive Sampling Matching Pursuit

We consider compressive sensing as a means of acquiring high-resolution images from low-cost, low-resolution sensors in the infrared domain. In particular, we reduce errors arising from basis mismatch between the observed image and the signal model by modifying a baseline matching pursuit recovery algorithm. Specifically, we introduce a modification to the analysis step which seeks to find more representative image atoms by searching over a 2-Wasserstein geodesic formed between the two most-correlated atoms at that step. We test our extension by quantifying recovery performance on an ensemble of representative infrared maritime scenes and find improvement over baseline when measured using PSNR, SSIM, and a metric that quantifies global edge recovery performance. We find that the most notable gains occur for very low sparsity levels which favors reduced computational load for the recovery.
Study of SaaS Mode WebGIS based on JavaScript Engine

WebGIS has a wide range of application requirements, but development and application require high labor and resource costs. In order to solve this contradiction, a SaaS mode WebGIS system is proposed. Through the statistical summary of various application scenarios, the system provides a variety of geographic information atomic services to choose from. Tenants can freely combine these services to implement a WebGIS system that meets their needs. The user interface is implemented by an online HTML editing system, and the data structure is self-made through a metadata service, and the business logic is self-made through service assembly and business rules. Business rules are ultimately done through event processing by client-side JavaScript. Through the establishment of an instance, the feasibility of SaaS mode WebGIS system is verified, and users can build WebGIS system according to their own needs
Pluralized non-count nouns across Englishes: A corpus-linguistic approach to variety types

Abstract This corpus-based study of pluralized non-count nouns (informations, advices, etc.) uses collocation-derived measures (determiners vs. bare noun and mass quantifiers) to extract potential candidates of non-count nouns in a bottom-up approach from the British National Corpus (BNC), allowing the detection of grammatical categories from distributional features. We then use this token list to retrieve data on pluralization of non-counts from nine annotated components of the International Corpus of English (ICE). While the distinction between count and non-count nouns is gradient rather than categorical, it is still possible to distinguish between standard and non-standard pluralization of non-counts. Qualitative analyses of our data show that non-standard pluralization of non-count nouns is regularly attested in second-language varieties, including previously unrecorded types; however, it is also occasionally found in first-language varieties. We discuss implications of our corpus results for common explanations of pluralized non-count nouns, such as substrate influence, language learning effects and historical input. By combining a bottom-up corpus-based approach with fine-grained qualitative analyses we can provide a more nuanced view of pluralization of non-counts across ENL and ESL for the investigation of World Englishes.
Incorporating Historical Data via Bayesian Analysis Based on The Logit Model

This thesis presents a Bayesian approach to incorporate historical data. Usually, in statistical inference, a large data size is required to establish a strong evidence. However, in most bioassay experiments, dataset is of limited size. Here, we proposed a method that is able to incorporate control groups data from historical studies. The approach is framed in the context of testing whether an increased dosage of the chemical is associated with increased probability of the adverse event. To test whether such a relationship exists, the proposed approach compares two logit models via Bayes factor. In particular, we eliminate the effect of survival time by using poly-k test. We test the performance of the proposed approach by applying it to six simulated scenarios.
Temporal‐aware and sparsity‐tolerant hybrid collaborative recommendation method with privacy preservation

With the explosive growth of cloud services, how to design effective recommendation models has become more and more important. Temporal information has been proved to be an important factor affecting recommendation performance. Actually, both of user behaviors and QoS performance of services are time‐sensitive, especially in dynamic cloud environment. However, most existing collaborative recommendation methods seldom consider temporal influence to QoS performance. Furthermore, with the ever‐increasing number of security threats in clouds, privacy preservation becomes an important problem to be addressed in recommender systems. Based on these observations, in this paper, we propose a temporal‐aware and sparsity‐tolerant hybrid collaborative recommendation method with privacy preservation, where tensor factorization‐based CF model is integrated into neighborhood‐based CF model to achieve improved recommendation performance. Specifically, temporal influence is considered into recommendation model by distinguishing short‐term QoS metrics from long‐term QoS metrics. A privacy‐aware time aggregation mechanism is adopted to preserve the sensitive time information of users. Moreover, to deal with the sparsity problem, a sparsity‐oriented data QoS rating prediction mechanism based on tensor factorization technique is applied to predict the missing or unrated QoS rating data. Finally, predictions are made based on both stable and temporal nearest neighbors. Experiments are conducted to demonstrate the prediction performance of our proposal.
A probabilistic framework for online structural health monitoring: active learning from machining data streams

A critical issue for data-based engineering is a lack of descriptive labels for the measured data. For many engineering systems, these labels are costly and/or impractical to obtain, and as a result, conventional supervised learning is not feasible. This paper suggests a probabilistic framework for the investigation and labelling of engineering datasets; specifically, acoustic emission data streams recorded online from a turning machine. Two alternative probabilistic measures are suggested to select the most informative observations. During machining operations, these data would then be investigated and annotated by an engineer, in order to maximise the classification performance of a statistical model used to predict tool wear.
SOLVING DIAGONAL SUDOKU PUZZLE 9X9 GRID BY JAVA PROGRAMMING ALGORITHM

Sudoku is one of the well-known puzzle game that has been played worldwide. There are many types of Sudoku puzzle that exist to be played such as diagonal, diagonal even, diagonal odd, is sudoku, lucky seven, parquet and others. This paper focuses on solving the diagonal Sudoku by using Java Programming Algorithm for 9x9 grid. For normal Sudoku, the number 1-9 must be filled up in all grids with no number duplication conflict in the row, column and 3x3 grid inside the Sudoku puzzle itself. While the diagonal Sudoku has one extra condition to be considered that is the number 1-9 must also be filled in the diagonal place which starts from the most upper-left or bottom-left to the most upper-right or bottom-right of the puzzle. There is no conflict with other grid in its diagonal region. In order to solve the puzzle, we have to construct an algorithm and translate it using Java Programming Language. The guessing method with backtrack approach has been selected to construct the algorithm. This Java Programming is designed to solve the diagonal Sudoku puzzle for a 9x9 grid.
COGNITIVE INFORMATION SYSTEMS - ARTIFICIAL INTELLIGENCE & MANAGEMENT DECISIONS

We investigate the interlink between the management decisions and Artificial and Computational Intelligence (ACI) in the context of Cognitive Information Systems (CIS). The analysis is focused on the current trends of application of ACI within the realm of Information Systems (IS) for supporting management decision. One of the research issues is whether there is a difference between CIS and IS based on Artificial Intelligence (AI). Is there any chance to distinguish them from each other? If yes, we should locate the subtle differences and similarities between them. There are various definitions for the recent and the future AI, there are approaches to circumscribe the concept of CIS. The research issue is how the Cognitive Architecture, Science, IS and AI can be reconciled for management information systems (MIS) to support decision making.
Real-Time Optimal Slew Maneuver Planning for Small Satellites

As the small-satellite market grows, so does the demand for large-scale small-satellite missions with diverse payload functions. To facilitate mission planning, and to enable autonomous transition between payload operations, real-time on-board slew maneuver path planning is often required to reorient the spacecraft. The computed trajectory must take into account a variety of kinematic and dynamic constraints on the spacecraft attitude, angular velocity and actuator limits. It is also desirable to compute this trajectory such that it is optimal in some sense. To simplify computation, current applications typically employ a “rest-to-rest” assumption where the maneuver endpoints are assumed to be inertially fixed, leading to long settling times in practice when dynamic endpoints exist. This settling time prohibits command of maneuvers in quick succession, which can limit operational capabilities. By posing the calculation as a convex semidefinite programming optimization problem, this paper presents a computationally attractive pathplanning algorithm that computes an optimal fixed-time trajectory between arbitrary endpoints, while satisfying a variety of common attitude control constraints. In addition, a closed-form iterative solution for a minimum-time maneuver is proposed. Both methods are validated in a simulation case involving the University of Toronto Institute for Aerospace Studies Space Flight Laboratory’s (UTIAS-SFL) next-generation DEFIANT-class spacecraft.