Miracle-dz/UniFAR-FTUs huggingface dataset:
Landuse study of the Sentosa island using SPOT images

The objective of this project is to study the change in land cover of Sentosa island and to assess the condition of the vegetation existing on the island using SPOT images. Two SPOT multispectral sub-scenes of the island acquired in Feb 94 and Aug 96 were used in the study. The two images were first co-registered using common ground control points. For each image, the vegetation was characterized by the normalized difference vegetation index (NDVI). The change in the state of vegetation was assessed by the change feature image. Unsupervised classification was done on the image of 1996 and the change feature image separately. The land cover classes in 1996 and the changes between the two images were obtained from the classified images.
A camera-based approach to remote pointing interactions in the classroom

Modern classrooms are brimming with technological resources, from sound systems and multiple large wall displays to the individual computers and cellphones of students. Leveraging this technology presents interesting opportunities in human-computer interaction to potentially create a richer experience that improves the effectiveness of classrooms both for lecturers and students. A key enabler of this experience is the ability for lecturers to manipulate objects on the screen by direct pointing, allowing them to be away from a computer for input. We designed and implemented a remote pointing technique that makes use of a web-camera and a pattern of shapes on the wall display to perform target tracking. A controlled study to evaluate performance and compare the camera-based technique to a traditional mouse for a target selection task in a classroom setting revealed that both devices have comparable error rates but that users are almost twice as fast with the mouse. The increased freedom of movement and immediacy of interaction provided by direct pointing makes the trade-off between speed and convenience reasonable. The technique does not require specialized hardware: the ubiquity of personal pocket cameras and computers makes target tracking with a camera a feasible future option for enabling direct pointing interactions on large wall displays in classroom settings.
How Compass Supports Multi-Feedback Forms and Components Adapted to Learner's Characteristics

In this paper, we present the feedback process of a web-enabled concept mapping learning environment, referred to as COMPASS. Different forms of feedback (text-, graphical- and dialogue-based) are supported and multiple Informative, Tutoring and Reflective Feedback Components (ITRFC) are available, in terms of delivering individualized feedback. The ITRFC aim to stimulate learners to reflect on their beliefs, to guide and tutor them towards the achievement of the learning outcomes addressed by the concept mapping activity, to inform them about their performance and to serve learner’s individual preferences and needs. The adaptive functionality of the feedback process is based on learner’s knowledge level, preferences and interaction behaviour and is implemented through (i) the technology of adaptive presentation that supports the provision of alternative forms of feedback and feedback components, and (ii) the stepwise presentation of the feedback components in the dialogue-based form of feedback. Moreover, COMPASS gives learners the possibility to have control over the feedback presentation process by making the desired selections.
A fast algorithm for backprojection

In the filtered backprojection (FBP) procedure for image reconstruction from projections, backprojection dominates the computation time. The authors propose a simple algorithm which reduces the number of multiplications in the linear interpolation and backprojection stage by 50%, with a small increase in the number of additions. The algorithm performs the interpolation and backprojection of four views together. The resulting image is virtually identical to that obtained by classical FBP. The authors have also generalized the algorithm to backproject more than four view together. This generalized version, however, may increase the number of additions disproportionately, and requires nonuniformly spaced angular sampling. <<ETX>>
A Novel Approach to Malware Detection using Static Classification

Malware, commonly called computer virus, is one of the top security threats to the computer systems around the globe. These are evolving at a very rapid pace and are continually finding new ways to exploit and infect the systems of various enterprises and businesses. Malwares use different techniques to camouflage themselves to make their lifetime longer. In this paper, we present a simple technique based on static features extracted from Windows PE files. The features used are not only extracted from the header part of the malware but also from the payload i.e. body of malware. The static features used are a combination of Function Call Frequency and Opcode Frequency for differentiating malwares from clean files. This combination of features set makes it a new approach for malware detection which provides an accuracy of 97% for a dataset of 1,230 executables files including 800 malware and 430 cleanwares. For classification purpose, we use machine learning algorithms available in WEKA library. Based on the results obtained, we conclude that both features considered in this work play a significant role in distinguishing malicious files from clean ones. KeywordsStatic Malware Analysis; Machine Learning; Classification;
Filtering Template Driven Spam Mails using Vector Space Models

Spam became a big problem to the society. Some spammers are using templates for sending spam. To send a particular promotion they create some template and merge the details of receivers with the template. Similarities can find among these mails and easily ignore the forthcoming spam. Most highvolume spam is sent using tools those randomizes parts of the message - subject, body, sender address etc. The general form of the template that the spammer is using can often guess by inspecting the features of messages. Most of the spam filters are either rule based models or Bayesian models. The main objective in this paper is to find out semantic distance and evaluate the applicability of the two information retrieval techniques, Simple Vector Space Models (VSM) and VSM using Rocchio Classification in the spam context. Both methods are using cosine similarities to identify the spam
Mass Data Query Optimization Based on Multi-objective Co-evolutionary Algorithm

multi-connection database query optimization belongs to a kind of typical complex problem and cost of optimal query strategy obtained from traditional Particle Swarm Optimization Algorithm is relatively high under some conditions and it is easy to fall into local optimal solution. Based on Quantum Particle Swarm Optimization Algorithm, the paper puts forward a kind of optimal algorithm for database query, namely, mass data query algorithm based on multi-objective co-evolutionary algorithm to improve optimization efficiency of database query and optimize performance of algorithm of the paper in solution of database query optimization problems by simulation experiment. The paper puts forward a kind of Gaussian Mutation Quantum Particle Swarm Optimization Algorithm and introduces Gaussian mutation to avoid prematurity phenomenon. Experimental result shows that algorithm of the paper can obtain more optimized query effect when solving multi-list connection database query optimization problems.
Evaluating Homomorphic Encryption Algorithms for Privacy in Wireless Sensor Networks

The Wireless Sensor Networks (WSNs) consist of the sensor nodes those are severely resource constrained. Hence, the protocols devised for the WSNs employ every possible means of optimizing the associated overhead.The WSNs typically, employ in-network processing that is largely based on the data aggregation operations that aggregate the data into a compact representation for further transmission. However, when aggregating a number of different data packets, the integrity of the data values used for aggregation as well as the security of the aggregator node itself, assume prime importance. Even otherwise, due to the ubiquitous and pervasive deployment of the sensor nodes on one hand and due to the resource intensive nature of security algorithms, it is challenging to devise the security protocols for WSNs. Consequently, the protocols for ensuring secure data aggregation also have to be devised and investigated carefully. One of the approaches to ensure secure data aggregation is to use encrypted sensor data for processing, using homomorphic encryption. However, there are several homomorphic encryption algorithms for conventional networks and the feasibility of applying them in resource constrained environment remains an issue to be investigated. In this paper, we attempt at evaluating the performance of various privacy homomorphism algorithms in WSNs. Our work is motivated by identifying a homomorphic encryption algorithm for WSNs that offers security at the minimum overhead. To the best of our knowledge, ours is the first experimental evaluation of the state-of-the-art privacy homomorphic algorithms for WSNs.
Detecting and analyzing bursty events on Twitter

This thesis presents BurstMapper, a system for detecting and characterizing bursts of tweets generated by multiple sources in order to understand interactions between Twitter users and the role of exogenous events (not directly observable on Twitter) in driving tweets. The first stage of the system finds temporal clusters, or bursts of tweets. The second stage characterizes bursts along two dimensions, semantic coherence and causal influence. Semantic coherence measures the semantic relatedness of the tweets in a burst to each other based on a deep neural network derived embedding of tweet contents. Causal influence measures the potential causal interaction between Twitter users using the Hawkes process model. We introduce an annotated corpus of 7,220 tweets produced by five leading candidates in the 2016 U.S. presidential election. Evaluating the system on the annotated corpus shows that with a precision of 75%, tweets caused clearly by specific exogenous events (or responsive tweets hereafter) are detected by the burst detector components of our system. Furthermore, experiments show that the linear combination of semantic coherence and causal influence are predictive of the presence of responsive tweets in a burst, with the Fl-score of 0.76. Examining bursts along the two dimensions reveals that (i) the measures are positively correlated with each other (corr=0.33, p<0.001), (ii) the measures allow us to understand how candidates tend to respond differently to exogenous events, e.g., by attacking opponents or making plan announcements, and (iii) the measures can be used to describe the influence dynamics between candidates over time. Plotting the bursts from a corpus of 1,470 Twitter accounts (the five leading candidates and the users followed by them) shows visual evidence that some user groups (e.g., campaign staffs, journalists, etc.) have a higher levels of semantic coherence and causal interactions. These experiments suggest that the bursts detected by our system provide a useful level of abstraction that summarizes tweet content, providing a solution for coping with massive amount of data on Twitter. Thesis Supervisor: Deb Roy Title: Associate Professor, Program in Media Arts and Sciences
Reformulation, text modeling, and the development of EFL academic writing

This paper mainly investigates the benefits of the implementation of Reformulation and Text Modelling in an EFL writing setting. Reformulation and Text Modeling (henceforth RTM) is intended to help EFL students understand better how to write academic texts to make their texts sound as nativelike as possible. Therefore, RTM was implemented in a writing class in which 35 students participated as the respondents of the study. They were treated with RTM and their essays were then analyzed to examine the effects of the implementation of RTM on their writing products. Besides, this study investigated further the students’ perception towards RTM in EFL writing settings. The findings of this study proved that RTM is beneficial to improve students’ writing performances and students have positive perceptions on RTM. The implications of the findings for language learning are also discussed.
Flexibility-Enhanced HTS System for Disaster Management: Responding to Communication Demand Explosion in a Disaster

Natural disaster interrupts essential services due to the facility damage and lack of power supply. Especially, significant communication outages occurs in a wide area for hierarchical network such as cellular communication system in case core nodes are damaged or congested. In order to provide alternative communication ability, high throughput satellite (HTS) is one of the ideal candidates for disaster management because it provides operative communication for a wide area regardless of the availability of regular terrestrial infrastructures. However, conventional HTS relays data with predetermined beam bandwidth and connection, it is inefficient when the communication demand explodes in a disaster area. Therefore, this paper proposes novel frequency allocation technique with flexibility-enhanced HTS system for disaster management to respond to communication demand explosion. While related research works consider user-link resource allocation, this paper focuses on how to control feeder-link and user-link bandwidths in case that the both links can be assigned at continuous frequency such as Ka-band. Furthermore, our proposal addresses resilient satellite network by selecting optimum gateway based on the traffic demand at neighboring user-link beams. The effectiveness of our proposal is verified through simulation results.
Traffic Sign Detection and Recognition

: Important information can be provided to the drivers by the signs on the road like navigation, safety and warning thus computer based system for automatic recognition and detection of road sign grow popular these days. Designing a traffic sign detection and recognition use computer capabilities to aid the transportation systems can be very useful it is also possible to add robotic assistance like cameras that are equipped with an auto detection system for traffic signs on the road. This research paper presents an overview and some technique for traffic sign detection and recognition, in practice we have implemented Blob Analysis as our method for constructing a computerized system that can detect and recognize traffic signs in an autonomous manner from images that are fed to the system. In this paper we will explain how the implemented approach can be reliable and we will demonstrate the process and experimental results found in the process of implementation.
Voice Controlled Wheelchair using Speaker Dependent Voice Recognition and Improved Formant Frequency Location Method

Voice Recognition is widely spreading technique and has found application in many fields Speaker Dependent voice recognition technique is the preferable technique where high accuracy and low error rate is required with less complexity. Feature extraction plays a key role in the sensitivity or accuracy of the recognition process. This paper focuses on the formant frequency location method and will try to improve accuracy of the most simple statistical feature extraction technique and procedure to control the wheelchair using voice command.
Sensor Fault Detection and Isolation Based on Artificial Neural Networks and Fuzzy Logic Applicated on Induction Motor for Electrical Vehicle

Recently, research has picked up a fervent pace in the area of fault diagnosis of electrical vehicle. Like failures of a position sensor, a voltage sensor, and current sensors. Three-phase induction motors are the “workhorses” of industry and are the most widely used electrical machines. This paper presents a scheme for Fault Detection and Isolation (FDI). The proposed approach is a sensor-based technique using the mains current measurement. Current sensors are widespread in power converters control and in electrical drives. Thus, to ensure continuous operation with reconfiguration control, a fast sensor fault detection and isolation is required. In this paper, a new and fast faulty current sensor detection and isolation is presented. It is derived from intelligent techniques. The main interest of field programmable gate array is the extremely fast computation capabilities. That allows a fast residual generation when a sensor fault occurs. Using of Xilinx System Generator in Matlab / Simulink allows the real-time simulation and implemented on a field programmable gate array chip without any VHSIC Hardware Description Language coding. The sensor fault detection and isolation algorithm was implemented targeting a Virtex5. Simulation results are given to demonstrate the efficiency of this FDI approach.
Clustering of data

In various embodiments, systems, methods, and techniques are disclosed for generating a collection of clusters of related data from a seed. Germs may be generated based on seed generation strategies or rules. For example, clusters may be created by retrieving a seed, adding the seed to a first cluster, retrieving a clustering policy or rules, and adding related data and / or data entities to the cluster based on the clustering strategy become. Different cluster scores can be generated based on attributes of data in a given cluster. Further, cluster meta-valuations may be generated based on different cluster scores associated with a cluster. A ranking of clusters based on cluster metadata can be created. Various embodiments may enable an analyst to gain various insights related to data clusters and may be applied to various tasks including, for example, fraud detection.
Statistical modelling and analysis of ion channel data

The gating behaviour of a single ion channel can be described by a hidden Markov model (HMM), and this forms a basis for statistical analysis of single channel patch clamp data. We analysed extensive high bandwidth (25 kHz and 50 kHz) data from the mechanosensitive channel of large conductance (MscL) in E. coli; the bandwidth refers to the low-pass filter used for the purpose of removing high frequency noise in recordings. The data were modelled using HMMs, and HMMs with a moving average adjustment for filtering. MscL is a multi-level channel and the number of levels is not easily discerned based on the recorded noisy currents. The basic aims of the analysis are to determine the number of conducting levels, together with mean current, mean dwell time and proportion of time at each level. Comprehensive data analyses and comparisons across all our data sets were consistent with seven conductance levels for this channel. Parameter estimates for HMMs with a moving average adjustment for filtering were obtained using an EM (expectation-maximisation) based algorithm which depends on a generalisation of the Baum forward-backward algorithm. The resulting improved algorithm is simpler than that of Khan et al. (2005) because the dimension of the parameter space is much smaller, and consequently the computational complexity is substantially reduced. We present an approach that extends the HMM with an adjustment for filtering to allow for correlated noise, using deconvolution to pre-whiten the noise. This results in a standard HMM. Simulation studies were conducted to investigate and compare the performance of this method to the HMM and HMM with a moving average adjustment for the low-pass filter. The standard errors of parameter estimates for the three types of model were obtained using an algorithm due to Khan (2003).
Complex Dynamic Systems and Interlanguage Variability: Investigating Topic, Syntactic Complexity, and Accuracy in NS-NNS Written Interaction

Studies adopting a Complex Dynamic Systems Theory framework have often fallen into one of two camps: those investigating variable linguistic subsystems (namely, CAF studies) and those concerned with non-linguistic variables displaying the characteristics of dynamic systems, with little concern for their connection to linguistic development. Examining asynchronous interactional data in an 8-week email exchange between a bilingual speaker of Spanish and English and an advanced Chinese learner of English, this study attempts to reconcile these two camps by exploring the extent to which syntactic complexity and article accuracy vary situationally as a function of topic and willingness to communicate (WTC) in L2 written performance over time and by investigating any potential relationships between complexity and accuracy. A qualitative, inductive analysis reveals that topic bears the characteristics of a dynamic system. The results also show that, for both the native speaker (NS) and non-native speaker (NNS), syntactic complexity, measured as subordinate clauses per T-unit and complex nominals per T-unit, varies situationally with topic, shifting not just over time but within a single email. As might be anticipated, article accuracy exhibits the typical instability of a dynamic system for the NNS but not for the NS, whose stable performance may reflect a permanent attractor state. Both the NNS’ and NS’ article accuracy are subject to cross-linguistic influence. Evolving correlations between complexity and accuracy suggest that certain topics and the WTC associated with them may induce trade-offs between the two subsystems.
Augmenting Web Pages and Search Results to Help People Find Trustworthy Information Online

The presence (and, sometimes, prominence) of incorrect and misleading content on the Web can have serious consequences for people who increasingly rely on browsing the Web as their information source for topics such as health, politics, and financial advice. In this paper, we identify and collect several page features (such as popularity among specialized user groups) that are currently difficult or impossible for end-users to assess, yet provide valuable signals regarding credibility. We then present visualizations designed to augment search results and Web pages with the most promising of these features. Our lab evaluation finds that our augmented search results are particularly effective at increasing the accuracy of users’ credibility assessments, highlighting the potential of data aggregation and simple interventions to help people make more informed decisions as they search for information online.
Journal ranking of world scientific corpus by Normalized impact factor

The present paper is devoted to discussion of results of bibliometric analysis of a large set of scientific journals (more than 6 thousand) and scientific fields of knowledge (more than 170) in JCR:SE DBs in 1998–2002 period. The idea of normalized impact factor (K) for evaluation of journals was to compare the traditional impact factor (Ip) of a journal, as indicated in JCR DB, with the average impact factor of the corresponding field of knowledge (Ig). The results of this study are given in tabular form. The tables reflect dynamics of impact factors of fields of knowledge (Ig) and world scientific journals ranked by K.