Miracle-dz/UniFAR-FTUs huggingface dataset:
Proactive multipath routing with a predictive mechanism in software‐defined networks

With the growth of network traffic volume, link congestion cannot be avoided efficiently with conventional routing protocols. By utilizing the single shortest‐path routing algorithm from link state advertisement information, standard routing protocols lack of global awareness and are difficult to be modified in a traditional network environment. Recently, software‐defined network (SDN) provided innovative architecture for researchers to program their own network protocols. With SDN, we can divert heavy traffic to multiple paths in order to resolve link congestion. Furthermore, certain network traffics come in periodic fashion such as peak hours at working days so that we can leverage forecasting for resource management to improve its performance. In this paper, we propose a proactive multipath routing with a predictive mechanism (PMRP) to achieve high‐performance congestion resolution. PMRP has two main concepts: (a) a proactive mechanism where PMRP deploys M/M/1 queue and traffic statistics to simulate weighted delay for possible combinations of multipaths placement of all subnet pairs, and leverage genetic algorithm for accelerating selection of optimized solution, and (b) a predictive mechanism whereby PMRP uses exponential smoothing for demand traffic volumes and variance predictions. Experimental results show a 49% reduction in average delay as compared with single shortest routing, and a 16% reduction in average delay compared with utilization & topology‐aware multipath routing (UTAMP). With the predictive mechanism, PMRP can decrease an additional 20% average delay. Furthermore, PMRP reduces 93% of flow table usage on average as compared with UTAMP.
Definition of distortion power in AC network and analysis of its reasons

This paper studies the conditions under which such non-active power components as reactive power and distortion power appear in the alternating current power network. By simulating with MatLab Simulink, the validity of formulas obtained by analytical way was tested. The formulas define the components of non-active power based on one of the most well-known theories. The results obtained will be useful for mode optimization in the alternating current networks and for developing effective non-active power compensating technologies.
Combined Appetency and Upselling Prediction Scheme in Telecommunication Sector Using Support Vector Machines

Customer Relations Management (CRM) is an essential marketing approach which telecommunication companies use to interact with current and prospective customers. In recent years, researchers and practitioners have investigated customer churn prediction (CCP) as a CRM approach to differentiate churn from non-churn customers. CCP helps businesses to design better retention measures to retain and attract customers. However, a review of the telecommunication sector revealed little to no research works on appetency (i.e. customers likely to purchase new product) and up-selling (i.e. customers likely to buy upgrades) customers. In this paper, a novel up-selling and appetency prediction scheme is presented based on support vector machine (SVM) algorithm using linear and polynomial kernel functions. This study also investigated how using different sample sizes (i.e. training to test sets) impacted the classification performance. Our findings demonstrated that the polynomial kernel function obtained the highest accuracy and the least minimum error in the first three sample sizes (i.e. 80:20, 77:23, 75:25) %. The proposed model is effective in predicting appetency and up-sell customers from a publicly available dataset.
Force Discrimination of Finger Pads Using Improved Hand Haptic Interface

This paper presents improvements to a developed hand haptic interface and the experimental results from a force discrimination test of finger pads. The hand haptic interface combines newly developed one-dimensional (1D) force display devices for each finger pad using the 3D fingertip Haptic Interface Robot called the HIRO III equipped with a 3D hand-pose measurement system. The force display is smaller and lighter than our previous device, and a glove was added to fix multiple force display devices to the human hand. Using the method of constant stimuli, the difference threshold of finger pads on the middle phalanx, proximal phalanx, and metacarpal head of the index finger and thumb were evaluated to obtain the basis for the optimum haptic interface design.
Switching speed improvements in multiphase buck converter via two-shunt voltage-sources

Multiphase Buck converters are well known for their smaller ripple current, smaller filter design and smaller component ratings. The converter operating responses however depend on the value of inductor and load resistance. Increasing the switching frequency can improve the transient responses. However, it reduces the system efficiency and controlling the current response can be complex. This paper proposes an improved multiphase buck converter for a lithium-ion battery charging applications. The proposed system is based on the ‘Bi-level drives’ concept, which requires two input voltage in which one is higher than the other input and is connected in series to the system. By driving the load with a higher input voltage, the transient response is observed to be much faster. Upon reaching the desired reference output, the system will revert to a lower input voltage, thus, the efficiency of the system will not be affected. This scheme requires additional two power switches and a power diode. The operation and the switching scheme will be discussed in this paper.
Understanding the Phishing Ecosystem

In “phishing attacks”, phishing websites mimic trustworthy websites in order to steal sensitive information from end-users. Despite research by both academia and the industry focusing on development of anti-phishing detection techniques, phishing has increasingly become an online threat. Our inability to slow down phishing attacks shows that we need to go beyond detection and focus more on understanding the phishing ecosystem. In this thesis, we contribute in three ways to understand the phishing ecosystem and to offer insight for future anti-phishing efforts. First, we provide a new and comparative study on the life cycle of phishing and malware attacks. Specifically, we use public clickthrough statistics of the Bitly URL shortening service to analyze the click-through rate and timespan of phishing and malware attacks before (and after) they were reported. We find that the efforts against phishing attacks are stronger than those against malware attacks. We also find phishing activity indicating that mitigation strategies are not taking down phishing websites fast enough. Second, we develop a method that finds similarities between the DOMs of phishing attacks, since it is known that phishing attacks are variations of previous attacks. We find that existing methods do not capture the structure of the DOM, and question whether they are failing to catch some of the similar attacks. We accordingly evaluate the feasibility of applying Pawlik and Augsten’s recent implementation of Tree Edit Distance (AP-TED) calculations as a way to compare DOMs and identify similar phishing attack instances. Our method agrees with existing ones that 94% of our phishing database are replicas. It also better discriminates the similarities, but at a higher computational cost. The high agreement between methods strengthens the understanding that most phishing attacks are variations, which affects future anti-phishing strategies. Third, we develop a domain classifier exploiting the history and internet presence of a domain with machine learning techniques. It uses only publicly available information to determine whether a known phishing website is hosted on a legitimate but compromised domain, in which case the domain owner is also a victim, or whether the domain itself is maliciously registered. This is especially relevant due to the recent adoption of the General Data Protection Regulation (GDPR), which prevents certain registration information to be made publicly available. Our classifier achieves 94% accuracy on future malicious domains, while maintaining 88% and 92% accuracy on malicious and compromised datasets respectively from two other sources. Accurate domain classification offers insight with regard to different take-down strategies, and with regard to registrars’ prevention of fraudulent registrations.
A Critical Look at Design Automation Solutions for Collaborative MDO in the AGILE Paradigm

This paper presents a critical discussion on the automated problem formulation and workflow creation approach developed within the European project AGILE to support and accelerate the setup of aircraft MDO workflows in a large, heterogeneous team of experts. The developed framework is based on a methodological approach, called the AGILE paradigm, where a complete MDO system is formulated and executed in five main steps. In Step I the requirements are collected, in Step II a repository of disciplinary tools is established, in Step III the design optimization problem is formulated and structured according to a selected MDO architecture, in Step IV an executable workflow is assembled and finally operated in Step V. All steps have been streamlined and highly automated through the development of a novel set of MDO support applications and data standards, addressed as the AGILE MDO framework. This framework was tested through a series of design campaigns culminating with four design tasks, where a variety of unconventional aircraft configurations is collaboratively designed using MDO. After a brief introduction on the AGILE paradigm and the four design tasks, this paper will focus on a set of AGILE framework core components enabling the automated process to formulate and execute collaborative MDO systems. The strengths and current limitations of these components are discussed, based on the extensive feedback from the heterogeneous set of specialists involved in the four design tasks. Although drastic reductions in the setup time of an MDO system (up to 40 percent) appear to be already achievable, recommendations are provided to improve the flexibility, usability and scalability of the framework.
The Sparsity Adaptive Reconstruction Algorithm Based on Simulated Annealing for Compressed Sensing

This paper proposes a novel sparsity adaptive simulated annealing algorithm to solve the issue of sparse recovery. This algorithm combines the advantage of the sparsity adaptive matching pursuit (SAMP) algorithm and the simulated annealing method in global searching for the recovery of the sparse signal. First, we calculate the sparsity and the initial support collection as the initial search points of the proposed optimization algorithm by using the idea of SAMP. Then, we design a two-cycle reconstruction method to find the support sets efficiently and accurately by updating the optimization direction. Finally, we take advantage of the sparsity adaptive simulated annealing algorithm in global optimization to guide the sparse reconstruction. The proposed sparsity adaptive greedy pursuit model has a simple geometric structure, it can get the global optimal solution, and it is better than the greedy algorithm in terms of recovery quality. Our experimental results validate that the proposed algorithm outperforms existing state-of-the-art sparse reconstruction algorithms.
Mobile Applications to Enhance Versatility of Mobile Learning in Higher Education

In recent times, despite the fact that Mobile learning or m-learning is a relatively new concept, it has become a buzz word in academic, researchers and companies developing learning systems and instructional materials. Unfortunately, standards and platforms like those that exist in elearning are still evolving. One thorny issue is that of m-examinations. Although in e-learning setup supervised examination rooms/center, have worked well the ability to authenticate the candidate doing an examination in unsupervised environment on remote place is limited. Moreover, the requirement of Internet access throughout the examination session and particularly for the student to be able to submit his answers is also a major limitation. This study was not only an effort to remove the two limitations, but also to do so on the mobile learning environment. The study investigated the concept of sending by email a locked examination as an android mobile application. The student would then download and install the App in their smart devices a few minutes, days or even weeks but will not be able to run the App until the instructor sends a key to the registered student’s mobile phone number in the device to unlock the examination App installed in their phones two or three minutes before the examination start time. The approach used in this study was mixed methods. The first method was experimental where a mobile examination application was developed in Android. Then the mobile examination application was send by email to the 60 students who participated so that they could download it in their smart devices. The second method was a questionnaire survey to capture the student’s perception. The findings of the study show that the effect of Organizational Support (OS), Availability of ICTs (ICT) and Effort Expectancy (EE) on Behavioral intention to use M-learning and particularly m-examination was significant. The findings of this research will not only help m-learning practitioners develop m-learning examinations and promote this new IT to potential users, but also provide insights into research on m-examination acceptance.
The design of road conditions mapping system by utilizing openstreetmap spatial data

Land transportation still becomes a vital role in human and material mobility to date. In developing countries, like Indonesia, the development of land transportation infrastructure is growing very fast, but the level of road damage is also still quite high. Therefore, road users need information on road conditions that will be passed. This research proposes the use of openstreetmap data to develop spatial data of road conditions dynamically and inexpensively, because it is open access and free editable. The focus is to design spatial data of road conditions and to present a visualization of road condition maps based on Openstreetmap. The raw data will be exported to the Spatial Database Management Systems (SDBMS) and then redesigned the data so that the system can add the status of road condition on each road segments. The result is a system design that can store spatial data and present a visualization of the road condition map. With this system, anyone can update the condition data of a road and can access the road condition map through the web and mobile applications.
Regulatory Fit in Negotiation: Effects of "Prevention-Buyer" and "Promotion-Seller" Fit

We propose that the psychological effects of performing the buyer versus the seller role in a negotiation depend on regulatory fit (Higgins, 2000) with the demands of the role. When the negotiation emphasizes price, buyers want to pay only what is necessary (minimize monetary loss), which fits prevention focus concerns, whereas sellers want to attain as much money as possible (maximize monetary gain), which fits promotion focus concerns. Study 1 used a hypothetical price negotiation and found that planned demand was greater in the focus-role fit conditions (prevention-buyer; promotion-seller) than in the nonfit conditions. In Study 2, a real price negotiation, buyers adopted a nonloss/loss frame whereas sellers adopted a gain/nongain frame. Negotiators in the focus-role fit conditions subjectively experienced fit, had more demanding opening offers, and, when paired with another negotiator in focus-role fit, impassed more often. Extensions and applications of focus-role fit are discussed.
Nonlinear Controller Design for UAVs with Time-Varying Aerodynamic Uncertainties

Unmanned Aerial Vehicles (UAVs) are here and they are here to stay. Unmanned Aviation has expanded significantly in recent years and research and development in the field of navigation and control have advanced beyond expectations. UAVs are currently being used for defense programs around the world but the range of applications is expected to grow in the near future, with civilian applications such as environmental and aerial monitoring, aerial surveillance and homeland security being some representative examples. Conventional and commercially available small-scale UAVs have limited utilization and applicability to executing specific short-duration missions because of limitations in size, payload, power supply and endurance. This fact has already marked the dawn of a new era of more powerful and versatile UAVs (e.g. morphing aircraft), able to perform a variety of missions. This dissertation presents a novel, comprehensive, step-by-step, nonlinear controller design framework for new generation, non-conventional UAVs with time-varying aerodynamic characteristics during flight. Controller design for such UAVs is a challenging task mainly due to uncertain aerodynamic parameters in the UAV mathematical model. This challenge is tackled by using and implementing μ-analysis and additive uncertainty weighting functions. The technique described herein can be generalized and applied to the class of non-conventional UAVs, seeking to address uncertainty challenges regarding the aircraft’s aerodynamic coefficients.
A Light on Physiological Sensors for Efficient Driver Drowsiness Detection System

The significant advance in bio-sensor technologies hold promise to monitor human physiological signals in real time. In the context of public safety, such technology knows notable research investigations to objectively detect early stage of driver drowsiness that impairs driver performance under various conditions. Seeking for low-cost, compact yet reliable sensing technology that can provide a solution to drowsy state problem is challenging. While some enduring solutions have been available as prototypes for a while, many of these technologies are now in the development, validation testing, or even commercialization stages. The contribution of this paper is to assess current progress in the development of bio-sensors based driver drowsiness detection technologies and study their fundamental specifications to achieve accuracy requirements. Existing market and research products are then ranked following the discussed specifications. The finding of this work is to provide a methodology to facilitate making the appropriate hardware choice to implement efficient yet lowcost drowsiness detection system using existing market physiological based sensors,
The AcousticBrainz Genre Dataset: Multi-Source, Multi-Level, Multi-Label, and Large-Scale

This paper introduces the AcousticBrainz Genre Dataset, a large-scale collection of hierarchical multi-label genre annotations from different metadata sources. It allows researchers to explore how the same music pieces are annotated differently by different communities following their own genre taxonomies, and how this could be addressed by genre recognition systems. Genre labels for the dataset are sourced from both expert annotations and crowds, permitting comparisons between strict hierarchies and folksonomies. Music features are available via the AcousticBrainz database. To guide research, we suggest a concrete research task and provide a baseline as well as an evaluation method. This task may serve as an example of the development and validation of automatic annotation algorithms on complementary datasets with different taxonomies and coverage. With this dataset, we hope to contribute to developments in content-based music genre recognition as well as cross-disciplinary studies on genre metadata analysis
Methods of information systems protection

The subject are mathematical models of building hybrid (complex) cryptosystems based on Mac-Elis crypto-code constructions on damaged codes. The purpose of this work is cryptographic mechanisms design in post-quantum cryptography to provide basic security services. The use of crypto-code structures in the mechanisms of strong authentication based on OTP passwords Development of practical algorithms for their implementation based on the proposed mathematical models. The tasks : analysis of the main threats of using OTP passwords; basics of construction and using multi-channel cryptography systems on damaged codes; a formal description of mathematical models of hybrid crypto-code constructions on damaged codes based in the modified McEliece and Niederreiter crypto-code systems in elliptic curves; development of algorithms for data encryption and decryption at the Niederreiter-McEliece hybrid crypto code constructions (НССС). Conclusion: The comprehensive protection mechanisms proposed in the article ensure the use of a strong authentication protocol in post-quantum cryptography based on OTP passwords. The use of damaged codes extends the possibilities of using crypto-code structures by significantly reducing the power of the alphabet while maintaining the required level of cryptographic resistance.
GEM-NET: Lessons in Multi-Institution Teamwork Using Collaboration Software

The Center for Genetically Encoded Materials (C-GEM) is an NSF Phase I Center for Chemical Innovation that comprises six laboratories spread across three university campuses. Our success as a multi-institution research team demanded the development of a software infrastructure, GEM-NET, that allows all C-GEM members to work together seamlessly—as though everyone was in the same room. GEM-NET was designed to support both science and communication by integrating task management, scheduling, data sharing, and collaborative document and code editing with frictionless internal and public communication; it also maintains security over data and internal communications. In this Article, we document the design and implementation of GEM-NET: our objectives and motivating goals, how each component contributes to these goals, and the lessons learned throughout development. We also share open source code for several custom applications and document how GEM-NET can benefit users in multiple fields and teams that are both small and large. We anticipate that this knowledge will guide other multi-institution teams, regardless of discipline, to plan their software infrastructure and utilize it as swiftly and smoothly as possible.
Identification of the state of suspension elements in railway systems

The running safety and passenger comfort levels in a vehicle are tightly related to the technical state of the suspension elements. The technical state of the suspension depends of the service life time as its components become old and wear out. In this paper, a study on the dynamic behaviour of a railway vehicle is established in relation to the damping elements in one of its suspension stages. An experimental measurement model is developed, obtaining a set of useful signals for the identification of the dynamic parameters of the vehicle and developing a test through the application of the operational modal analysis technique, using least-squares complex exponential method as a basis to validate the numerical model of the multi-body system. Then, the study focuses on developing numerical simulations for the identification of the technical state of the dampers by the registration of dynamic variables under commercial service conditions and on estimating the state of the suspension elements.