Miracle-dz/UniFAR-FTUs huggingface dataset:
BeaTicket - Beacon Based Ticketing System

In this paper, we have implemented the Beacon Based Ticketing System, which uses the bluetooth of the smartphone that senses the Beacon in the bus and interacts with the backend server to issue a ticket to the user. All the ticket generation and completion process is done in the background without the interaction of the user. Our model employs Contactless approach for Ticket Generation unlike the smartcard or token that need to be touched against the terminal. The basic model of Beacon based Ticketing System consists of 3 devices: Beacon, Bluetooth equipped smartphone and server. Whenever a passenger will board a bus, beacon attached to the bus will broadcast the information which will be detected by an application installed on the user’s smartphone. There is a unique ID for every beacon and that beacon will be used to uniquely determine the bus number. The UID will be send to the server which will check for the minimum balance in the wallet which is integrated into the application itself and generate the ticket correspondingly. Now when the passenger de-boards the bus, the information is sent again to the server which deducts the money from the wallet and sends the trip summary back to the user’s device. This system increases the efficiency of the process and provides comfort and ease to the passengers. This model provides Offline Ticket Generation system, it allows the user to travel without internet access, and as soon it becomes available, the locally stored board and de-board time are sent to the server, which determines the respective locations, and accordingly the fare is deducted from the wallet. The Trip History, stores the records of all previous trips and displays them to the user in reverse chronological order. We have successfully implemented a Bus Ticketing System based on Beacon Technology. The Ticket Generation can be done both offline and online and payment will be made using the wallet provided to each user, that is integrated in the application itself. Offline Ticket Generation allowed the user to travel even without internet access. Our model also provided Contactless feature so that the user does not need to touch the smartphone against any terminal or scan any barcode. Optimizer feature notifies the user if the bus is nearby so that the user’s waiting time is minimised at the bus stop.
Annotating and Modeling Shallow Semantics Directly from Text

Annotating and Modeling Shallow Semantics Directly from Text Luheng He Chair of the Supervisory Committee: Associate Professor Luke Zettlemoyer Computer Science and Engineering One key challenge to understanding human language is to find out the word to word semantic relations, such as “who does what to whom”, “when”, and “where”. Semantic role labeling (SRL) is the widely studied challenge of recovering such predicate-argument structure. SRL is designed to be consistent across syntactic alternations , which can potentially benefit downstream applications such as information extraction, machine translation, and summarization. However, the performance of SRL system is limited by the amount of training data and its dependence on the intermediate syntactic representation. In this thesis, our goal is to develop annotation frameworks and learning models for recovering semantic structures directly from text, in an end-to-end manner. We first introduce question-answer driven semantic role labeling (QA-SRL), an annotation framework that allows us to gather SRL information from non-expert annotators. Different from the traditional SRL formalisms (e.g. PropBank), this new task does not depend on predefined syntactic structure or frame ontology. It is simple and intuitive enough that we can train any native speaker to provide annotation, as long as they can understand the meanings of sentences. We also develop two general-purpose, syntax-independent neural models that lead to significant performance gains, including a over 40% error reduction over long-standing pre-neural performance levels on PropBank. Our first model, DeepSRL, uses highway BiLSTMs to make local BIO-tagging decisions for each token. While significantly out-performing previous systems, DeepSRL cannot jointly process multiple predicates or incorporate span-level features. To address these limitations, we further introduce a span-based neural model called the Labeled Span Graph Networks (LSGNs). Inspired by a recent state-of-the-art coreference resolution model, LSGNs build contextualized representations for all spans in the input text, and use lightweight classifiers to make independent edge labeling decisions. With LSGNs, we are able to model all predicate words and argument spans jointly, the first end-to-end result we know of. LSGNs also lead to a unified view for many NLP structures involving span-labeling or span-span relations. In addition to SRL and coreference resolution, LSGNs also achieve state-of-the-art performance when applied to named entity recognition (NER) without any feature engineering. This opens up exciting future directions to build a single, unified model for end-to-end, document-level semantic analysis.
High Capacitive and Confidentiality Based Image Steganography and Watermarking using Private Stego-Key

: Steganography and watermarking are two important areas of research and it involve a number of applications. These two areas of research are important especially when reliable and secure information exchange is required. Steganography methods usually do not need to provide strong and safe security against removing or modification of the hidden message. Watermarking methods need to be very robust to attempts to remove or modify a hidden message. A data-hider may compress the least significant bits of the encrypted image using a data-hiding key to create a sparse space to accommodate some additional useful data. With an encrypted image containing additional data, if a receiver has the data-hiding key, then he can able to extract the additional data though he does not know the image content. If the receiver has the encryption key, he can decrypt the received data to obtain an image similar to the original one, but cannot extract the additional data. If the receiver has both the data-hiding key and the encryption key, he can extract the additional data and recover the original content without any error. To enhance the embedding capacity of image steganography and watermarking for human vision, a novel adaptive number of least significant bits substitution method with private stego-key based on gray-level ranges is proposed in this paper.
Towards a taxonomy of cyber threats against target applications

Abstract As the adoption of ubiquitous computing is growing with rapid pace, its security is becoming a major concern due to nimble and creative cyber attackers. The evolving threats are continuously challenging and reminding security professionals as well as corporations to come up with interconnected and integrated approach to security. In this paper, Taxonomy of Cyber Threats is prepared in terms of Industrial, OS, Mobile OS, Web Browser, Network and Social Network applications which will assist Information Security bodies such as CERT in describing and categorizing the attacks consistently. The proposed taxonomy consists of eight aspects which effectively comprehends the taxonomy in order to deal with the intrinsic issues in different target sectors. For better manifestation, the taxonomy is applied to some well known cyber threats. Finally, an analysis of Cyber Threats is conducted for that Symantec Threat database is used and some important findings are presented.
Stand-alone Type Intelligente Solar Streetlight Control System

This paper proposes a new solar-powered streetlamp light control system that improves the efficiency of energy consumption of conventional solar-powered streetlamps. The proposed new solar-powered streetlamp light control system is efficiently recharged using the solar-power recharging controller, having the maximum power point tracking function and controls the lamp using the vehicle detection system data applying the audio recognition technology. The test result confirmed that the streetlamps were lighted in accordance with the detected vehicle data and that the performance was 25% higher than the conventional solar-powered recharging controller. The system is expected to help solve the problem of energy efficiency of conventional solar-powered streetlamp system, ensuring traffic safety, and preventing the risk of crimes when walking at nighttime.
ADVANCED MORPHOLOGICAL APPROACH IN AEROSPACE DESIGN DURING CONCEPTUAL STAGE

This paper presents an advanced morphological approach supporting designers and developers in their search for as well as synthesis and analysis of new engineering solutions during the conceptual design stage. The proposed method is based on the cluster analysis and the set theory, the set of rules and engineering implementations maximizing the gain of the products potential. The number of possible combinations using the standard morphological technology is extremely large. We present a mathematical framework that handles this problem. The method was evaluated with case studies of new engineering solutions in aerospace, ecology and adaptive soundproofing system. The case studies verified the significant potential of the proposed approach in comparison with the methods presently in use.
Joint Antenna Array Mode Selection and User Assignment for Full-Duplex MU-MISO Systems

This paper considers a full-duplex (FD) multiuser multiple-input single-output system where a base station simultaneously serves both uplink (UL) and downlink (DL) users on the same time-frequency resource. The crucial barriers in implementing FD systems reside in the residual self-interference and co-channel interference. To accelerate the use of FD radio in future wireless networks, we aim at managing the network interference more effectively by jointly designing the selection of half-array antenna modes (in the transmit or receive mode) at the base station with time phases and user assignments. The first problem of interest is to maximize the overall sum rate subject to quality-of-service requirements, which is formulated as a highly non-concave utility function followed by non-convex constraints. To address the design problem, we propose an iterative low-complexity algorithm by developing new inner approximations, and its convergence to a stationary point is guaranteed. To provide more insights into the solution of the proposed design, a general max–min rate optimization is further considered to maximize the minimum per-user rate while satisfying a given ratio between UL and DL rates. Furthermore, a robust algorithm is devised to verify that the proposed scheme works well under channel uncertainty. The simulation results demonstrate that the proposed algorithms exhibit fast convergence and substantially outperform existing schemes.
Privacy-Protected Route-Based Spatial-Textual Location Search in Road Networks

Location search and recommendation have received significant attention in recent years. To protect the users’ privacy, we propose and study a novel privacy-protected route-based spatial-textual location (PPRSTL) query in road networks. Given a set of locations <inline-formula> <tex-math notation="LaTeX">$O$ </tex-math></inline-formula> with the textual description, a query route <inline-formula> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> with a set of query keywords to describe user’s preference, the PPRSTL query finds the location with the highest spatial-textual similarity to the query route <inline-formula> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula>. We believe that such type of query is very useful in many mobile applications such as location recommendation and discovery, and location-based services in general. The problem is challenging due to three reasons: (1) how to model the spatial and textual similarity practically; (2) how to constrain the search space in a comparatively small range in the spatial and textual domains, and; (3) how to protect the users’ privacy when returning the query results. To overcome these challenges, we define a boolean spatial-textual measure to evaluate the similarity in the spatial and textual domains, and a two-phase search mechanism to protect users’ privacy. We develop two expansion search algorithms that follow the filter-and-refinement paradigm, to compute the queries efficiently. Two pairs of upper and lower bounds are defined to prune the search space effectively. In addition, we adopt an expansion center selection method to further enhance the query efficiency. Finally, we conduct extensive experiments on real and synthetic spatial data sets to verify the performance of the developed algorithms.
Management Report for Marketing in Higher Education Based On Data Warehouse and Data Mining

Elements of globalization in the world of education has expanded and developed, and made an Higher Education institutions market has been developed as a global phenomenon, so that Higher Education institutions aware to show their existence in global and high competition. The objective of this study was to apply data warehouse and data mining techniques that can be used by universities to obtain relevant information about their current condition, and to track the institution's development, which is required by the management to monitor organization performance in marketing area, and to support information in decision making process. The research method began with the collection of data and information, analyzes the current condition in marketing area, design the data warehouse model and mining the data. The obtained results were the data warehouse model and evaluation model using data mining technique to support the management in marketing decision-making process.
An Efficient Single-Stage Based Power Electronic Interface for Plug-in Electric Vehicles

In this research, a new power converter has been presented for plug-in electric vehicle applications, which is derived from the conventional secondary ended primary inductance converter (SEPIC). The proposed converter operates for all modes of vehicles [plug-in charging mode (PCM), propulsion mode (PM) and regenerative braking mode (RBM)] with high voltage conversion ratio. The proposed structure operates as a SEPIC in PCM and as a conventional single switch buck/boost converter in PM and RBM. Therefore, universal voltage range, i.e., 85–265 V can be used to charge the battery as well as dc-link voltage can be varied in wide range. A comparative study of the proposed converter has been investigated with existing integrated converters. Furthermore, an analytical loss analysis of the semiconductor devices has been investigated to select the power stage switches as well as efficiency calculation of the converter in each mode. The proposed concept is validated through strong simulation and hardware results.
Issues and Implications of Scrum on Global Software Development

Global Software Development (GSD) is progressively becoming an ordinary practice in the software business; also there is an increasing awareness of agile practices applying in offshore, global and distributed Software Development (DSD) projects. Global software development aims at bringing together the current international software industry and to make optimal use of globally existing talent, whereas possibly reduce cost, time and effort to market. Although the idea seems quite promising at first look large geographical spread, cultural differences and multiple time zones in global software development environment leads to many drastic issues and challenges like team management, collaboration, communication, infrastructure, cost and quality management etc. Agile methodologies come into play when dealing with such type of problems. The aim of this paper is to explain the benefits of using scrum methodology in a distributed softwaredevelopment environment. The paper also highlights the challenges that are faced by the agile team in the distributed environment of a global software development project. Theprovided information can be useful for Global Software Development experts to get familiarized with various challenging factors that may affect communication, collaboration and coordination related processes in GSD and also with the use of various Scrum practices.
Digital nudges and dark patterns: The angels and the archfiends of digital communication

Nudging is simply guiding people behaviors by the use of user-interface and design elements in digital environments. Today, many decisions are made in online environments. Gaining insights about digital nudging can greatly help communicators, policy makers, and designers lead users to make the most desirable choice for them and/or for the wealth of the society as well. Digital nudges can be used in many digital environments like e-mail, SMS, push notifications, mobile apps, social media, gamification, e-commerce, e-government, location services, corporate digital information systems, and many other digital interfaces that include any kind of decision-making processes. This study is a descriptive study and more of a qualitative nature and aims to identify the digital nudging concept, dark patterns, and usage of digital nudges in real-life applications. It also proposes a brief digital nudging process schema to be used for designing behavioral digital interventions.
THE GENUINE NEEDS OF CONFERENCE ATTENDEES: AN ANALYSIS BY THE MODERN QUALITY FUNCTION DEPLOYMENT

The primary purpose of this research is to understand the genuine needs of conference attendees regarding conference participation within a multi-dimensional perspective and to identify the priority of those needs via modern QFD methodology. The findings support the early studies on the fact that academic development and networking are the most important needs towards conferences. Following these two primary need categories, organization-related needs are also given importance by the participants of this research. Offering free time for leisure and recreation activities within conference programs is valued more than pre-arranged social activities by attendees. It is anticipated that the research offers insight into the development and improvement of conference services through the identification of value-adding attributes, which would have practical implications for conference organizers and destination marketers. Unlike most of the extant literature in event management, this research employs the in-depth interviews, focus group, KJ method and AHP within modern QFD methodology.
The effect of user variables on academic websites usability : An empirical study

Abstract The aim of the study is to delineate quantitative usability evaluation of 50-academic websites using System Usability Scale. 600-participants are enquired about usability of these academic websites using SUS a 10-question based survey created through Google form. The study utilizes one of different applications of SUS on academic websites in which, the users are not implored to execute particular tasks on these websites before giving the adjective rating for usability to specific website but are rather queried to gauge usability hinged on their experience with a given website. Results manifest that the end-users gave lower SUS scores for 38-websites, whereas experts gave lower SUS scores for only 10-websites. It is observed that female participants gave lower SUS scores for only 7-websites. Further, results show that the mobile and desktop users gave lower SUS scores for 35 and 9-websites respectively. The participants of age group 20-25 have rated 36-websites with lower SUS as compared to greater age groups. 8-websites fall into the low marginal section of acceptability range whereas 42-websites are rated at the lower level and fall into non-acceptability range. Results conclude that end-users and participants of age group 20-25 face more usability problems on websites which needs to be resolved. Female participants show more satisfaction with websites against male counterparts. Further, when websites have been evaluated on mobile phones then participants also face more usability problems. The study manifests explicit distinctions among websites and provides wholistic approach for presenting usability scores for academic websites while considering different user variables.
Parallel Euclidean distance matrix computation on big datasets

We propose, in this paper, three parallel algorithms to accelerate the Euclidean matrix computation on parallel computers. The first algorithm, designed for shared memory computers and GPU, uses a linear index to fill the block lower triangular part of the distance matrix. The linear index/subscripts conversion is obtained with triangular number and avoid loops over blocks of columns and rows. The second algorithm (designed for distributed memory computer) in addition to linear index uses circular shift on a 1D periodic topology. The distance matrix is computed iteratively and we show that the number of iterations required is about half the number of processors involved. Numerical experiments are carried-out to demonstrate the performances of the proposed algorithms.
Applying queue theory for modeling of cloud computing: A systematic review

The cloud computing paradigm is an important service in the Internet for sharing and providing resources in a cost‐efficient way. Modeling of a cloud system is not an easy task because of the complexity and large scale of such systems. Cloud reliability could be improved by modeling the various aspects of cloud systems, including scheduling, service time, wait time, and hardware and software failures. The aim of this study is to survey research studies done on the modeling of cloud computing using the queuing system in order to identify where more emphasis should be placed in both current and future research directions. This paper follows the goal by investigating the articles published between 2008 and January 2017 in journals and conferences. A systematic mapping study combined with a systematic literature review was performed to find the related literature, and 71 articles were selected as primary studies that were classified in relation to the focus, research type, and contribution type. We classified the modeling techniques of cloud computing using the queuing theory in seven categories based on their focus area: (1) performance, (2) quality of service, (3) workflow scheduling, (4) energy savings, (5) resource management, (6) priority‐based servicing, and (7) reliability. A majority of the primary articles focus on performance (37%), 15% of them focus on resource management, 14% of them focus on quality of service, 13% of them focus on workflow scheduling, 13% of them focus on energy savings, 4% of them focus on priority‐based servicing for requests, and 4% of them focus on reliability. This work summarizes and classifies the research efforts conducted on applying queue theory for modeling of cloud computing (AQTMCC), providing a good starting point for further research in this area.