Miracle-dz/UniFAR-FTUs huggingface dataset:
Constructing a New Stress Index for Teachers in Malaysia

Teachers in Malaysia are suffering from stress, and this issue must be tackled seriously by all interested parties. As teachers play a role in producing good and effective citizen, their stress must be studied and understood. Therefore, this study advocates for a new stress index for Malaysian teachers to better understand teachers' stress. This paper aims to present the findings on teachers' stressors unearthed during the interview with the teachers regarding teachers’ stress. In this step, twenty -nine teachers from primary and secondary schools were interviewed to validate the initial constructs of teachers’ stressors during the construct definition step, the first step in the Fuzzy- Delphi method. Based on the interview’s findings, the interview has corroborated the construct defined in literature research and added two new sub- themes to the stressors’ construct. The study concluded that conducting a study on stress through the Fuzzy-Delphi method is crucial because the Fuzzy-Delphi method is a rigorous process to ensure that an index developed would yield high validity and reliability.
“Investing for Retirement: Optimal Capital Growth and Dynamic Asset Allocation”, Hans U. Gerber and Elias S.W. Shiu, April 2000

SETHI, S.P. 1996. Optimal Consumption and Investment with Bankruptcy. Hingham, Mass. : Kluwer. SHARPE, W.F. 1994. ‘‘The Sharpe Ratio,’’ Journal of Portfolio Management (Fall):49–58. SZEGÖ, G.P., AND SHELL, K. , EDS. 1972. Mathematical Methods in Investment and Finance: Amsterdam: North-Holland. ZIEMBA, W.T. , AND VICKSON, R.G. , EDS. 1975. Stochastic Optimization Models in Finance. New York: Academic Press.
Synchronization methods in mobile wireless links based on OFDM modulation technique

Synchronization in wireless networks based on OFDM modulation has a huge influence for proper receiver operation. Comparing available techniques used for synchronization purposes, authors propose modification of well-known Schmidl and Cox algorithm through matched filter implementation instead of standard correlator. This solution allows precise detection of the OFDM symbol beginning. Influence of CFO on preamble length as well as different types of coefficients usage methods are investigated in last chapter of the article.
Cell-Free Millimeter-Wave Massive MIMO Systems With Limited Fronthaul Capacity

Network densification, massive multiple-input multiple-output (MIMO), and millimeter-wave (mmWave) bands have recently emerged as some of the physical layer enablers for the future generations of wireless communication networks (5G and beyond). Grounded on prior work on sub-6-GHz cell-free massive MIMO architectures, a novel framework for cell-free mmWave massive MIMO systems is introduced that considers the use of low-complexity hybrid precoders/decoders while factors in the impact of using capacity-constrained fronthaul links. A suboptimal pilot allocation strategy is proposed that is grounded on the idea of clustering by dissimilarity. Furthermore, based on mathematically tractable expressions for the per-user achievable rates and the fronthaul capacity consumption, max–min power allocation and fronthaul quantization optimization algorithms are proposed that, combining the use of block coordinate descent methods with sequential linear optimization programs, ensure a uniformly good quality of service over the whole coverage area of the network. The simulation results show that the proposed pilot allocation strategy eludes the computational burden of the optimal small-scale CSI-based scheme while clearly outperforming the classical random pilot allocation approaches. Moreover, they also reveal the various existing trade-offs among the achievable max–min per-user rate, the fronthaul requirements, and the optimal hardware complexity (i.e., the number of antennas and the number of RF chains).
Voting costs and voter welfare

This note considers the effect of the cost of voting on voters' welfare and their incentive to reduce voting costs in a simple model in which two single-voter teams participate to win an election from two alternatives. When each voter''s benefit of winning is his private information but uncertain to his rival, reductions in the cost of voting have two opposing effects on each voter''s welfare, namely, the direct effect and the indirect effect. And the voters may be worse off as their voting costs decrease. In this case, voters will face a prisoner''s dilemma, as each voter has a unilateral incentive to reduce his cost of voting.
Machine Learning for Stock Selection

Machine learning is an increasingly important and controversial topic in quantitative finance. A lively debate persists as to whether machine learning techniques can be practical investment tools. Although machine learning algorithms can uncover subtle, contextual, and nonlinear relationships, overfitting poses a major challenge when one is trying to extract signals from noisy historical data. We describe some of the basic concepts of machine learning and provide a simple example of how investors can use machine learning techniques to forecast the cross-section of stock returns while limiting the risk of overfitting. Disclosure: The authors report no conflicts of interest. Editor’s Note Submitted 19 July 2018 Accepted 30 January 2019 by Stephen J. Brown
Haptic Navigation Cues on the Steering Wheel

Haptic feedback is used in cars to reduce visual inattention. While tactile feedback like vibration can be influenced by the car's movement, thermal and cutaneous push feedback should be independent of such interference. This paper presents two driving simulator studies investigating novel tactile feedback on the steering wheel for navigation. First, devices on one side of the steering wheel were warmed, indicating the turning direction, while those on the other side were cooled. This thermal feedback was compared to audio. The thermal navigation lead to 94.2% correct recognitions of warnings 200m before the turn and to 91.7% correct turns. Speech had perfect recognition for both. In the second experiment, only the destination side was indicated thermally, and this design was compared to cutaneous push feedback. The simplified thermal feedback design did not increase recognition, but cutaneous push feedback had high recognition rates (100% for 200 m warnings, 98% for turns).
SRv6 interoperability report

Segment Routing (SR) can be applied to the IPv6 data plane by leveraging a new type of routing extension header, called Segment Routing Header (SRH). An SRH contains an ordered list, or sequence, of segments representing topological or service-based instructions, or any combination of those. This draft provides an overview of IPv6 Segment Routing (SRv6) implementations and interoperability. It makes an inventory of the various pieces of hardware and software that have been demonstrated to support the processing of an SRH, and details some interoperability scenarios that have been showcased at a public event.
An evolutionary algorithm for deriving optimal operating policy under uncertainties for Tehran multi-reservoir system

Projecting future inflows under climate change and rapidly growing population has large uncertainty and requires serious attention for proper utilization of limited water resources. Existing algorithms can only optimize the operation policy for a specified scenario (e.g., drought, wet, or normal year; decreased or increased demands) and when established, the system would face serious operational difficulty if the expected scenario does not occur. On the other hand, most of water resources systems involve more than three objectives and demand proper techniques to handle computational complexities in so-called many-objective problems. This paper aims at providing a many-objective optimization algorithm using social choice (SC) and melody search (MeS) algorithms that is able to efficiently derive general system operation rules suitable for all possible future scenarios. In other words, the proposed algorithm overcomes uncertainties in the occurrence of future scenarios and works optimally regardless of future conditions; whether it be variable streamflows and/or increased water demands. To evaluate the performance of the proposed algorithm, a system consisting of five reservoirs in the Tehran region with four objective functions. It is shown that in all cases the general multi-scenario rule derived by the proposed method performs as good as each of the operation rules derived for every specific scenario assuming the occurrence of that scenario. Moreover, the proposed many-objective algorithm is able to handle as many objectives as needed without any computational burden and/or algorithm complexity. Review History: Received: 5 November 2018 Revised: 26 January 2019 Accepted: 27 January 2019 Available Online: 27 January 2019
Learning to Look at LiDAR: The Use of R-CNN in the Automated Detection of Archaeological Objects in LiDAR Data from the Netherlands

Computer-aided methods for the automatic detection of archaeological objects are needed to cope with the ever-growing set of largely digital and easily available remotely sensed data. In this paper, a promising new technique for the automated detection of multiple classes of archaeological objects in LiDAR data is presented. This technique is based on R-CNNs (Regions-based Convolutional Neural Networks). Unlike normal CNNs, which classify the entire input image, R-CNNs address the problem of object detection, which requires correctly localising and classifying (multiple) objects within a larger image. We have incorporated this technique into a workflow, which enables the preprocessing of LiDAR data into the required data format and the conversion of the results of the object detection into geographical data, usable in a GIS environment. The proposed technique has been trained and tested on LiDAR data gathered from the central part of the Netherlands. This area contains a multitude of archaeological objects, including prehistoric barrows and Celtic fields. The initial experiments show that we are able to automatically detect and categorise these two types of archaeological objects and thus proof the added value of this technique.
Missing Low-Rank and Sparse Decomposition Based on Smoothed Nuclear Norm

Recovering low-rank and sparse components from missing observations is an essential problem in various fields. In this paper, we have proposed a method to address the missing low-rank and sparse decomposition problem. We have used the smoothed nuclear norm and the $L_{1}$ norm to impose the low-rankness and sparsity constraints on the components, respectively. Furthermore, we have suggested a linear modeling for the corrupted observations. The problem has been solved with the aid of alternating minimization. Moreover, some simplifications have been applied to the relations to reduce the computational complexity, which makes the algorithm suitable for large-scale problems. To evaluate the proposed method, different simulation scenarios have been devised. The superiority of the suggested scheme over its counterparts has been confirmed on both the recovery accuracy and the convergence speed in various applications.
Clinical outcomes after percutaneous coronary intervention in patients with and without history of diabetes mellitus with different stent size

Background There are different reports on the occurrence of post-revascularization outcomes of diabetic patients in previous studies. Lesion complexity, which is reflected in stent size, influences the occurrence of outcomes. The aim of the present study was to investigate the occurrence of clinical outcomes in patients with history of diabetes (hDM) after percutaneous coronary intervention (PCI) with emphasis on stent length and diameter. Methods In a retrospective single-center cohort approach, among patients with stable coronary artery disease who underwent PCI with first- and second-generation DES, subjects were included from 2003 until 2019. Outcomes including revascularization, myocardial infarction, and death, totally defined as major adverse cardiac events (MACE), were sought in follow-up phase. All the patients whether with and without hDM received aspirin and clopidogrel as DAPT for at least two years and one year, respectively. Results About 29% out of 1630 participants had hDM and 37.8% of patients who experienced MACE had hDM. Unlike age and time-to-event, there was significant difference in gender between hDM and non-hDM groups. However, no difference was seen in type of MACE between these two groups. Also, after adjusting confounder variables, there was no significant difference in MACE incidence between hDM and non-hDM groups with different stent length and diameter (different lesion length and diameter). Conclusions hDM did not affect MACE incidence significantly in different stent length and diameter. We think that using of DES supplemented by long term DAPT and tight control of glycemic status after PCI are the underlying reasons.
Performance Analysis of Advanced Video Coding (H.264)

H.264/MPEG-4 Part 10 or AVC (Advanced Video Coding) is a video compression format, and is currently one of the most commonly used formats for the recording, compression, and distribution of video content. The final drafting work on the first version of the standard was completed in May 2003. This paper provides performance analysis of the ITU-T H.264 standard, presenting PSNR, MSE values to get better insight about the H.264 applications in industries and day to day life. Various test sequences are used to get better perception of the superiority of H.264 than previous video coding standards. Keywords—H.264/AVC, PSNR, and MSE. )
Frequency Electromagnetic field effects on Gram-Positive and Gram-Negative Bacteria

Abstract Objective: The effects of electromagnetic waves on the growth of living organisms and the determination of the threshold of these radiations have remained elusive. Therefore, in this research, we have investigated the growth rate of gram-positive (Staphylococcus aureus) and gram-negative (Escherichia coli) bacteria that had been exposed to the different frequencies of electromagnetic fields. Results: The more frequency increased the slower bacteria grew; however, in gram-positive bacteria such as S. aureus, this effect was seen less. The effect of the 1mT electromagnetic field in the growth of S. aureus was significant between the two groups, nonetheless, in the 2mT electromagnetic field, the effect was not significant between the two groups at different frequencies. Noteworthy, no significant change was observed by increasing the frequency in S. aureus exposed bacteria in comparison to the control group. The study of bacterial growth in terms of frequency in both case and control groups showed an increasing trend. Increasing the frequency from 50 Hz to 150Hz, significantly, enhanced the rate of bacterial growth. On the whole, the magnetic field had an increment effect on the growth of bacteria; in fact, this effect was greater on the gram-negative than on the gram-positive bacteria.
SYS: Secure Your Smartphone using Query Analyzer

Smartphone and tablet users typically store a variety of personal information on their devices, including contact information, photos, SMS, and personal data used by various apps. On Android devices, the data is stored in SQLite databases which apps access by constructing and executing queries, either directly or via Android content provider API calls. Before installing an app that uses a content provider, a user must permit for the app to read and/or write the associated data. Many users grant permissions with little understanding of the risks. Even more experienced users cannot make well-informed decisions, as they are only given very coarse information about what data the app accesses. To provide users with more detailed information about how Android apps access and modify stored data, we came up with SYS, the query Analyzer. SYS analyzes app binary code, performing a lightweight static analysis to determine possible values of string variables that are incorporated into queries. SYS reports on the content providers used and the database tables/attributes accessed and/or updated, allowing users to make more informed decisions about whether to grant permissions. This paper describes SYS’s design and evaluates SYS’s functionality.
Lessons learned while supporting Cyber Situational Awareness

The increasing number of cyberattacks against critical infrastructures has pushed researchers to develop many Visual Analytics solutions to provide valid defensive approaches and improve the situational awareness of the security operators. Applying such solutions to complex infrastructures is often challenging, and existing tools can present limitations and exhibit various issues. In this paper, supported by cybersecurity experts of a world leader company in the military domain, we apply an existing Visual Analytics solution, MAD, to a complex network of a critical infrastructure, highlighting its limitations in this scenario and proposing further solutions to improve the cyber situational awareness in both proactive and reactive risk analyses. The results of this research contribute to characterize the activities performed by domain experts in this domain and their implications for the design of Visual Analytics solutions that aim at supporting them.
Securing Cloud Information Under Key Presentation

Late news uncover an intense assailant which breaks information confidentiality by securing cryptographic keys, by methods for pressure or secondary passages in cryptographic programming. Once the encryption key is uncovered, the main practical measure to protect information confidentiality is to restrain the aggressor's entrance to the ciphertext. This might be accomplished, for instance, by spreading ciphertext obstructs crosswise over servers in various managerial spaces hence expecting that the foe can't bargain every one of them. By and by, if information is scrambled with existing plans, a foe outfitted with the encryption key, can at present trade off a solitary server and unscramble the ciphertext pieces put away in that. In this paper, we contemplate information confidentiality against a foe which knows the encryption key and approaches a vast division of the ciphertext squares. To this end, we propose Bastion, a novel and efficient conspire that ensures information confidentiality regardless of whether the encryption key is spilled and the enemy approaches all ciphertext squares. We break down the security of Bastion, and we assess its execution by methods for a model usage. We additionally talk about commonsense experiences as for the reconciliation of Bastion in business scattered capacity frameworks. Our assessment comes about propose that Bastion is appropriate for reconciliation in existing frameworks since it brings about under 5% overhead contrasted with existing semantically secure encryption
Power Failure Sensitivity Analysis via Grouped ?1/2 Sparsity Constrained Logistic Regression

To supply precise marketing and differentiated service for the electric power service department, it is very important to predict the customers with high sensitivity of electric power failure. To solve this problem, we propose a novel grouped ll1/2 sparsity constrained logistic regression method for sensitivity assessment of electric power failure. Different from the ll1 norm and k-support norm, the proposed grouped ll1/2 sparsity constrained logistic regression method simultaneously imposes the inter-class information and tighter approximation to the nonconvex ll0 sparsity to exploit multiple correlated attributions for prediction. Firstly, the attributes or factors for predicting the customer sensitivity of power failure are selected from customer sheets, such as customer information, electric consuming information, electrical bill, 95598 work sheet, power failure events, etc. Secondly, all these samples with attributes are clustered into several categories, and samples in the same category are assumed to be sharing similar properties. Then, ll1/2 norm constrained logistic regression model is built to predict the customer's sensitivity of power failure. Alternating direction of multipliers (ADMM) algorithm is finally employed to solve the problem by splitting it into several sub-problems effectively. Experimental results on power electrical dataset with about one million customer data from a province validate that the proposed method has a good prediction accuracy.
Research on Flow Calculation Method Based on BP Neural Network

Based on the relationship between flow and pressure in the flow standard facility, a BP neural network calculation model is established in this paper considering the influence of parameters like valve opening and pump frequency. Through the flow measurement experiment, the training and testing sample data are obtained, and then the established BP neural network model is trained and tested. The calculation results show that the established BP neural network model can accurately obtain the instantaneous flow value through the pressure in the pipeline.
A Two-stage Identification based on Seed and Grow Algorithm against Anonymized Social Networks

In a internet based social networking services the digital traces left behind even after anonymization they are more sensitive in privacy breaches. The sociologist tracks the feasibility of such an attack. There is an algorithm called Seed and Grow which identifies users from an anonymized social graph based only on graph structure. The algorithm first indentifies a seed sub graph either planted by the attacker or by any one ant then grows the seed layer based on the attackers existing knowledge of the users social relations. The algorithm identifies and relaxes implicit assumptions taken by previous work and improves the identification effectiveness and accuracy.
A SURVEY OF SENTIMENT ANALYSIS USING SENTIWORDNET ON BAHASA INDONESIA

High internet usage in Indonesia brings a number of data user opinions. Useropinion data can be processed into information that is useful for decision making, datasearching and researching for production and marketing strategies. This researchconducted a survey on some Indonesian Sentiment Analysis research articles. Some of thesestudies have different topics and data sources, butuse Sentiwordnet as a lexical database.The results of this survey show the performance of Sentiwordnet can improve classificationaccuracy in the Sentiment analysis system for Indonesian text.
An Efficient Approach For Subspace Clustering By Using OCA 3DS Seekerc Algorithm

In the clustering domain we have identified several problems, which require the mining of actionable subspaces defined by objects and attributes over a progression of time. These subspaces are actionable which means that they have the ability to propose profitable action for the decision-makers. We suggest mining actionable subspace clusters from sequential data, which are subspaces with high and associated utilities. We propose algorithm OCA 3DS Seeker, which uses a hybrid of SVD, optimization algorithm, and 3D frequent item set mining algorithm to mine OCA 3DSs in an efficient and parameter insensitive way. The algorithm is to mine actionable subspace clusters from 3D dimensional data, which are subspaces with high and correlated utilities. The OCA 3DS Seeker provide high efficiency in financial data and is able to discover to significant clusters and find the optimal centroid values to cluster of data with efficient performance and reducible size. We show that our clustering results are not sensitive to the framework parameters. In this paper we show that clusters with higher utilities correspond to higher action ability, and we can able to use our clusters to perform better than one of the most famous value investment strategies. Keywords— data mining; 3D subspace clustering; singular vector decomposition; financial data mining