Miracle-dz/UniFAR-FTUs huggingface dataset:
Intelligent Photolithography Corrections Using Dimensionality Reductions

With the shrinking of the IC technology node, optical proximity effects (OPC) and etch proximity effects (EPC) are the two major tasks in advanced photolithography patterning. Machine learning has emerged in OPC/EPC problems because conventional optical-solver-based OPC is time-consuming, and there is no physical model existing for EPC. In this work, we use dimensionality reduction (DR) algorithms to reduce the computation time of complex OPC/EPC problems while the prediction accuracy is maintained. Also, we implement a pure machine learning approach where the input masks are directly mapped to the output etched patterns. While one photolithographic mask can generate many experimental patterns at once, our pure ML-based approach can shorten the trial-and-error period in the photolithographic correction. Additionally, we demonstrate the automation in SEM images preprocessing using feature detection, and this facilitates intelligent manufacturing in semiconductor processing. The input vector dimensions are effectively reduced by two orders of magnitude while the observed mean squared error is not affected significantly. The computation runtime is reduced from 4804 s of the baseline calculation to 10 s–200 s The MSE values changed from the baseline 0.037 to 0.037 for singular value decomposition (SVD), to 0.039 for independent component analysis (ICA), and to 0.035 for factor analysis (FA).
Evaluation of Transportation Reservation Management Module Usage in ERP System at PT. XYZ (a case study approach using Fit Gap Analysis and Technology Acceptance Model)

A successful information systems application should not only be determined by whether the functions of the system are running properly. Most of the times, the development of an information systems application judged only from the technical side, while there are actually many more non-technical factors like the human resources readiness, organizational support, and so on that could affects the level of user’s acceptance towards an information systems application. Therefore, this research will uncover the gaps in Transportation Management System (TRM), a car-rental application of XYZ Transportation Solution’s at PT. XYZ, by using Fit & Gap Analysis for further improvement in the future. Furthermore, this research will also uncover the factors that affect the degree of user acceptance towards that system by using Technology Acceptance Model (TAM) methodology. The result of this research show there are several gaps on the TRM. The factors of Perceived Ease of Use significantly influenced the factor intention to use.
VMAnalyzer: Malware Semantic Analysis using Integrated CNN and Bi-Directional LSTM for Detecting VM-level Attacks in Cloud

Cloud computing is one of the most emerging field in the IT Industry which provides scalable, expandable and almost perfectly elastic software or hardware services to the users. As the scalability and elasticity of cloud computing services increases, it also increases the risk of malicious intervention into cloud. Since the number and types of malware attacks are increasing day by day, it triggers the need of an efficient, robust and scalable malware detection approach for securing virtual domains running in cloud. In this paper, we propose a dynamic analysis approach, called VMAnalyzer which applies deep learning based machine learning techniques for detecting attacks at VM-layer in cloud environment. The VMAnalyzer extracts the ordered sequence of system calls of all the monitored programs and performs the two-layer classification. In layer-1, convolutional neural network (CNN) is applied to extract and select the relevant system call sequences. A number of potentially diverse layers in CNN not only provides the architecture for important feature extraction but also take care of convolution of n-grams with full sequential modelling. The layer-1 output is fed as a input to layer-2 using pipelining. In layer-2, Bi-Directional Long Short Term Memory (LSTM) is applied for learning and detecting the behavior of malicious system call sequences. Our evaluation results demonstrate that our approach outperforms previously used methods for malware detection in cloud. The approach has been validated using University of New Maxico (UNM) dataset and results seem to be promising.
DOA Estimation of Nested Electromagnetic Vector Sensors Array via MUSIC Algorithm

In this paper, we propose the idea of combining the nested array with the electromagnetic vector sensor to achieve the spatial spectrum estimation. The subspace algorithm of (Multiple signal classification) MUSIC algorithm is applied to the Direction of Arrival (DOA) estimation, and the spatial smoothing technique is utilized to the covariance matrix of virtual received signal here so that its rank can be restored. Compared with the uniform scalar sensor array, the proposed method has higher angle estimation performance and higher spatial Degree of Freedom (DOF). Through a nested array of only N physical sensors, O(6N2) DOFs is obtained. Otherwise, the angle estimation performance of a vector sensors array is better than the scalar sensors array.
Algorithm for Power Stabilization in Rotary Drilling Systems

This paper deals with synthesis of a control algorithm for regulation of drilling power in rotary drilling systems. Under some simplifying assumptions, we present a two-steps control design approach where the drilling power is stabilized to a prescribed level through an appropriate control algorithm for angular velocity of the drill bit. Simulation results for the case of nonzero bluntness of the bit are presented that illustrate the validity of the proposed design approach.
A Joint-Selective Robotic Gripper with Actuation Mode Switching

Robotic grippers in research and industry make constant trade-offs between payload capacity, dexterity, cost and performance. We try to address all these problems together by developing a dual mode gripper that can switch between a fully actuated precision mode and a grasping mode. In the underactuated grasping mode, the gripper digits conform to the shape of an object and then switches to the fully actuated precision mode, where each joint is individually locked, leading to a fully actuated gripper for handling the grasped object, thus amplifying its payload capabilities. A design concept is presented that combines all planned requirements-a moderate dexterity, a high force and a compact design of the control unit. This concept is based on a frictional locking of the joints via electromagnets. The resulting gripper was rapidly prototyped and tested for the following characteristics: flexibility in operational environments, payload capacity, accuracy and repeatability of operation. This prototype gripper can grasp objects with a force of up to 70N under active control of all degrees of freedom.
Jerk limited planner for real-time applications requiring variable velocity bounds

The generation of velocity references for autonomous vehicles is a known complex problem. The degree of complexity depends on the requirements to be fulfilled. First of all, in some applications of interest, trajectories need to be planned in real time. Furthermore, for smoothness reasons one may desire them to be jerk limited and, finally, they must satisfy velocity bounds that are variable along the path. Since planners proposed in the literature do not simultaneously fulfill the above mentioned characteristics, this paper proposes a novel generator which is able to efficiently evaluate almost minimum-time references. Despite the heuristic nature of the planner, an extensive test campaign has proved that obtained solutions are only slightly suboptimal w.r. t. the ones provided by a nonlinear solver, i.e., an algorithm for the global optimization whose computational times are incompatible with the online requirement.
Generalized Least Squares Based Channel Estimation for FBMC-OQAM

The presence of intrinsic inter-carrier interference (ICI) and inter-symbol interference (ISI) at the output of the matched filter receiver in filter bank multi-carrier with offset quadrature amplitude modulation (FBMC-OQAM) systems complicates the task of channel estimation (CE). The conventional approach is to use a pilot symbol preamble structure, such as in the interference approximation/cancellation methods (IAM/ICM), together with a simple virtual symbol (VS) based CE scheme. However, this virtual symbol approach ignores the residual interference from the data portion of the FMBC burst as well as the correlation of the noise across the sub-carriers; both of these factors ultimately limit the performance. In this paper, we propose a generalized least squares (GLS) based CE scheme which, when applied to the IAM/ICM pilot structures, takes into account both the correlated noise and data interference effects. Closed-form expressions are derived for the channel estimation mean-squared error (CE-MSE) of the proposed GLS method and for the conventional VS method, and it is shown that the proposed method leads to a significant reduction of the CE-MSE by up to 6 dB when compared with the VS based method. This is demonstrated to result in improved bit error rate (BER) performance, especially when higher-order modulation is employed, making it an attractive channel estimation method for many modern deployment scenarios. The proposed GLS-based channel estimation scheme is parameterized allowing a flexible trade-off between channel frequency selectivity, estimation complexity and CE-MSE gain. Finally, symmetry properties of the intrinsic interference coefficients for a general FBMC-OQAM system are also proved, which facilitate the computation of the noise and interference autocorrelation matrices required by the proposed algorithm.
Evaluation of Collaborative Learning Tools Implementation to Improve Learning Quality in Higher Education

Learning processes are now shifting from traditional learning to the collaborative learning process. Collaborative Learning means group-based environment that combination of lecturing in big class and case study in tutorial class. This research discusses the best practice of collaborative learning to measure collaborative learning implementation in higher education, that improve the quality of collaborative learning implementation. This study contains an evaluation of collaborative learning in courses of information systems based on the subjective perspective of students. The primary data collected during this research consist of 243 respondents as students. The courses that being analyzed in this research are Information System Concept and Management Information System course. Data analysis was using SEM-PLS. This research has 3 (three) hypotheses that already tested, and the result is accept the Content Delivery (CD), but not significant with Planning and Organization (PO), and Learning Methodology (LM). Based on the result, the conclusion for best practice implementation of collaborative learning was to improve the content delivery material and how to deliver the content, such as selecting collaboration tools, capable lecturers, adding more video and fun grouping, also use different learning methods, such as flipped learning, online quiz to make the classroom situation fun and not boring for students.
The Effect of Omni Channel Marketing on the Online Search Behavior of Jakarta Retail Consumers with Theory of Planned Behavior (TPB) Approach

Marketing by using diverse distribution and communication channels or commonly called Omnichannel is an approach that integrates many marketing channels to create an effective and consistent consumer experience. The variety of marketing channels used by consumers, they expect to receive good service, the same shopping experience and are integrated in all types of distribution. This study aims to find out the profile of consumers of shopping online sites in Jakarta and find out any variables from Theory of Planned Behavior (TPB) which describes the online search behavior of omnichannel consumers. In testing the hypothesis in this study used SEM or Structural Equation Model. Respondents in this study were online retail consumers in Jakarta with 125 people. From the results of the study, it is obtained data that the profile is young women under the age of 30 years with the work of workers and employers are consumers of online retail shopping sites in Jakarta. By using the variables in the TPB model, it is known that consumers’ online buying behavior is influenced by the intention to purchase the product where this intention is formed by the presence of variable attitudes towards behavior, subjective norms and perceptions.
Mobile Government Application Success Model: A Case of Halo Polisi

This study has the purpose to evaluate the m-government application called Halo Polisi (in English means Hi Police) and finds out the factors supporting the success of applications with focus on the perspective of public. This study uses the DeLone and McLean information system success model. By using the quantitative data processing method, the questionnaire is distributed online to all respondents who are users of the Halo Polisi application. The result from questionnaire are analyzed using the PLS-SEM. This study shows that the factors supporting the success of the application are system quality, service quality, usage, and user satisfaction where these factors can provide perceived benefits by public.
Private Key and Group Key Generation Using Correlated Sources and Wiretap Broadcast Channel in Presence of One-Way Public Communication

Encryption with shared key is one of the methods to ensure secure communication. To meet the requirements of keys, this paper investigates the problem of generating private key (PK) and group key (GK) using correlated sources and discrete memoryless wiretap broadcast channel (DM-WBC), in presence of one-way public communication. In the model considered, one transmitter (Alice) and three receivers (Bob, Carol and Eve) are connected via a DM-WBC, and a one-way communication over a public noiseless channel, initiated by Alice, is available. Alice and Bob wish to share a PK that is concealed from Carol and Eve, and they also want to generate a GK with Carol that needs to keep secret from Eve. Under this model, the key capacity regions of PK and GK are established by designing joint source-channel coding schemes to achieve these regions. In PK-generation, the considered model can be viewed as a one receiver two-eavesdropper model, and Alice and Bob have access to correlated source sequences. In GK-generation, the considered model can be viewed as a two-receiver one eavesdropper model, and the three legitimate parties (Alice, Bob and Carol) all have the source observations. Furthermore, the security performance of the generated keys are analyzed by analyzing key leakage rate of each key.
Admission of Information Technology in External Audit Profession: Impact of Organizational, Social and Individual Factors

Our research aims to test the variables which impact the acceptance of external auditors on using information technology in assisting their daily audit work. This research uses a technology acceptance model approach that is expanded with the influence of organizational, social and individual factors. Object of this study is external auditors who work in public accounting firm in Jakarta Special Capital Region. In this research we use system usage as dependent variable, while perceived usefulness as intervening variable 1, and perceived ease of use as intervening variable 2. We use organizational factor, social factor and individual factor as independent variables. The results show that both intervening variables have impact on dependent variables, while organizational factor has no impact on both intervening variables, social factor only affect the intervening variable 1 and individual factor affect both intervening variables.
Simulated Annealing for the Multi-Vehicle Cyclic Inventory Routing Problem

This paper studies the Multi-Vehicle Cyclic Inventory Routing Problem (MV-CIRP) as the extension of the Single-Vehicle CIRP (SV-CIRP). The objective is to minimize both distribution and inventory costs at the customers and to maximize the collected rewards simultaneously. The problem is treated as a single objective optimization problem. A subset of customers is selected for each vehicle including the quantity to be delivered to each customer. For each vehicle, a cyclic distribution plan is developed. We construct a mathematical programming model and propose a simulated annealing (SA) metaheuristic for solving both SV-CIRP and MV-CIRP. For SV-CIRP, experimental results on benchmark instances show that SA is comparable to the state-of-the-art algorithms and it is able to improve 12 best known solutions. For the MV-CIRP, the results show that SA performs better than an Iterated Local Search algorithm.
Factors Impacting the Intention to Use M-Payment

Recently m-payment or mobile payment is a payment instrument that is widely accepted in Indonesia, not only because of the convenience offered but also due to an enticing economic benefit. However, how long the m-payment provider will survive to subsidize its promotional costs. Therefore, this study examines factors that affect the use of m-payment without looking at economic benefits but focus on customer behavior. The model in this study involved two individual differences and four m-payment features and equipped with two TAM variables that explain the behavior in adopting the technology. Then, empirical examination is conducted, starts with data collection using a survey from m-payment users. Afterwards, data is analyzed utilizing structural equation models (SEM) method. The result indicates that perceived ease of use (PEOU) and perceived of usefulness (PU) are the most powerful factors that influence the intention to use m-payment. Interestingly, among m-payment characteristics, only compatibility and convenience affect the PEOU while mobility and reachability have no effect on either PEOU or PU. This result also can be an insight for m-payment providers to implement their application development strategies based on their customer behavior.
A Comparison-free Hardware Sorting Engine

This paper proposes a novel compare free hardware sorting engine that sorts N numerous data elements in approximately N clock cycles and detects the largest element in the 1st clock cycle. This sorting engine has been designed using N symmetric cascaded blocks those are built using few fundamental logic components. In this architecture, sorting and storing operations are performed in a pipelined manner. The complete design is synthesized for several data sets from pseudorandomly generated data elements to all unique elements to all the same elements, and also from random to completely sorted data elements. It has been observed that, the engine appears impartial to the input ordering. Synthesis results indicate that the proposed approach consumes reasonably low FPGA resource. The architecture is tested considering delay components of 65-nm standard cell library. For sorting 128 to 64K data elements of size 16, 24, and 32 bits at 125 MHz, this architecture takes per-element sorting delay as approximately 5.3 to 8 ns (1 clock cycle), while it takes 7.72 to 11.8 ns (1 to 2 clock cycles) and 10.16 to 15.5 ns (2 clock cycles), respectively. The engine achieves sorting throughput rate as approximately 125 to 200 Million Elements per second (MEps) (16 bit), 85 to 130 MEps (24 bits) and 65 to 98 MEps (32 bits) for sorting 128 to 64K data elements.
Computing Metric Dimension of Certain Families of Toeplitz Graphs

The position of a moving point in a connected graph can be identified by computing the distance from the point to a set of sonar stations which have been appropriately situated in the graph. Let <inline-formula> <tex-math notation="LaTeX">$Q=\{q_{1},q_{2}, {\dots }, q_{k}\}$ </tex-math></inline-formula> be an ordered set of vertices of a graph <inline-formula> <tex-math notation="LaTeX">$G$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$a$ </tex-math></inline-formula> is any vertex in <inline-formula> <tex-math notation="LaTeX">$G$ </tex-math></inline-formula>, then the code/representation of <inline-formula> <tex-math notation="LaTeX">$a$ </tex-math></inline-formula> w.r. t <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> is the <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-tuple <inline-formula> <tex-math notation="LaTeX">$\left ({r(a,q_{1}), r(a,q_{2}), {\dots }, r(a,q_{k})}\right)$ </tex-math></inline-formula>, denoted by <inline-formula> <tex-math notation="LaTeX">$r(a|Q)$ </tex-math></inline-formula>. If the different vertices of <inline-formula> <tex-math notation="LaTeX">$G$ </tex-math></inline-formula> have the different representations w.r. t <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula>, then <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> is known as a <italic>resolving set/locating set</italic>. A resolving/locating set having the least number of vertices is the <inline-formula> <tex-math notation="LaTeX">${basis}$ </tex-math></inline-formula> for <inline-formula> <tex-math notation="LaTeX">$G$ </tex-math></inline-formula> and the number of vertices in the basis is called <italic>metric dimension</italic> of <inline-formula> <tex-math notation="LaTeX">$G$ </tex-math></inline-formula> and it is represented as <inline-formula> <tex-math notation="LaTeX">${dim}(G)$ </tex-math></inline-formula>. In this paper, the metric dimension of Toeplitz graphs generated by two and three parameters denoted by <inline-formula> <tex-math notation="LaTeX">$T_{n}\langle 1,t\rangle $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$T_{n}\langle 1,2,t\rangle $ </tex-math></inline-formula>, respectively is discussed and proved that it is constant.
An Improved Approach for Detection of Diabetic Retinopathy Using Feature Importance and Machine Learning Algorithms

Diabetic Retinopathy is a human eye disease that causes damage to the eye's retina and may ultimately result in complete blindness. Early detection of diabetic retinopathy is needed to avoid complete blindness. Physical tests, such as visual acuity test, dilation of pupils, optical consistency tomography, is used to detect diabetic retinopathy. However, it is costly in terms of time and might affect the patients. In these consequences, this paper detects the presence of Diabetic Retinopathy in the human eye using a machine learning algorithm. The proposed method applies classification algorithms on several features (e.g., optical disk diameter, lesion-specific (microaneurysms, exudates) or presence of hemorrhages) of an existing Diabetic Retinopathy dataset. Then the features were extracted and used for the final decision making to predict the presence of diabetic retinopathy. The proposed system used Decision Tree, Logistic Regression. Support Vector Machine for the prediction. The proposed method achieved 88% accurate results which is much better than the existing works. Moreover, the proposed method achieves a better score in precision and recall which are 97% and 92%, respectively compared to the existing result 72% and 63%, i.e., more the 25% in each category on average which proves the enormousness of the proposed method.