Miracle-dz/UniFAR-FTUs huggingface dataset:
Wear-IT : implications of mobile & wearable technologies to human attention and interruptibility

This thesis explores different ways of leveraging mobile sensing to understand how end users use and interact with their smart technologies, namely smartphones and smartwatches. These topics are extensively explored in other parallel research; however, numerous gaps still exist within the literature. The use of mobile sensing to collect quantified ground-truth information of device use in-the-wild is critical to collect unbiased experiences and usage traces. This thesis covers three main themes: (a) the way our affect influences our smartphone use, and how our smartphone usage can also be analysed from our usage habits; (b) revealing quantified exploration of smartwatch usage traits, and how these relate to smartphone use, and (c) novel ways to mitigate interruptions during smartphone or smartwatch use. The thesis begins by explaining the related work and the overall theme of mobile sensing and how device usage influences attention; it then proceeds to elaborate on the contribution of each included article to the overall scope of the thesis. The thesis then concludes with a summary of how the presented articles tie together in a broader scope. Considering the vast amount of research in this field by this thesis’ author as well as other researchers, this type of work can potentially improve the use of novel wearable technologies in the future. By the end of the thesis, the reader should have a broad understanding of what mobile sensing is, and how it can be applied to comprehensively uncover technology use as well as leveraging mobile sensing to enhance the use of technology.
Not All Reviews Are Equal: Towards Addressing Reviewer Biases for Opinion Summarization

Consumers read online reviews for insights which help them to make decisions. Given the large volumes of reviews, succinct review summaries are important for many applications. Existing research has focused on mining for opinions from only review texts and largely ignores the reviewers. However, reviewers have biases and may write lenient or harsh reviews; they may also have preferences towards some topics over others. Therefore, not all reviews are equal. Ignoring the biases in reviews can generate misleading summaries. We aim for summarization of reviews to include balanced opinions from reviewers of different biases and preferences. We propose to model reviewer biases from their review texts and rating distributions, and learn a bias-aware opinion representation. We further devise an approach for balanced opinion summarization of reviews using our bias-aware opinion representation.
A faceted browsing interface for diverse Large-Scale RDF Datasets

RDF knowledge bases contain information about millions of resources, which can be queried using the standard querying language for RDF: SPARQL. However, this information is not easily accessible because it requires knowledge of SPARQL and the structure of the specific dataset to query; requirements that a common Internet user would not met. We propose a faceted browsing interface for these large-scale datasets that does not require prior knowledge of their structure or SPARQL. Faceted browsing consists of adding filters (known as facets) to display only the elements that meet the restrictions. Existing faceted browsing interfaces for RDF do not scale well for current knowledge bases. We propose a new system that creates indexes for fast and easy searches over the dataset, allowing to compute and suggest facets to the user. To validate the scale and efficiency of the system, Wikidata was chosen as a large-scale dataset over which to conduct performance experiments. Then, a user study was performed to evaluate the usability and responsiveness of the system, obtaining results that shows in which aspects the system performs well and which ones can be improved. A final prototype and questionnaire was submitted to the Wikidata contributors to see how this system could help the community.
Implementation Effort and Parallelism - Metrics for Guiding Hardware/Software Partitioning in Embedded System Design

Implementation Effort and Parallelism – Metrics for Guiding Hardware/Software Partitioning in Embedded System Design Hardware/Software partitioning is the task of deciding onto which architectures the algorithms of an embedded system should be implemented. Many criteria influence this decision. This thesis focuses on two important parameters targeting the implementation effort and the algorithm architecture affinity toward FPGA implementation. In the introductory part of this thesis, we give an overview of the challenges faced by the designers in the industry, and identify hardware/software partitioning as one of the most critical phases in the design trajectory. We discuss typical parameters, the accuracy of different estimation techniques, and the many influencing factors when devising metrics for guiding the partitioning phase. In the last part of the introduction we give an overview of the research efforts related to implementation effort estimation, execution time estimation, and the framework and methodologies which use these techniques. In the main part of the thesis, which is composed of four papers, we present our contributions to the topic of HW/SW Partitioning guidance. In Paper A and Tech Report B we deal with the estimation of the implementation effort for implementing algorithms onto FPGAs. In Paper A we present a metric-based approach for estimating the hardware implementation effort for an application in relation to the number of linearindependent paths of its algorithms. We show that a relation between the paths and the needed effort exists. In Tech Report B we further investigate the issue of accurate implementation effort estimation with respect to real-time constrained systems. Here we measure the implementation hardness of algorithms with respect to the given time constraint.
Retinal vascular segmentation combined with PCNN and morphological matching enhancement

Aiming at the problem of large workload and strong subjectivity for manual retinal vessels extraction, this paper proposes a retinal vessel segmentation method that combines regional growing strategy, pulse coupled neural network (PCNN), a Gaussian filter bank and a Gabor filter. First, 2D Gaussian filter bank and 2D Gabor filter are combined to enhance the shape retinal blood vessel region and strengthen the contrast between the blood vessel and the background. Then, PCNN with fast linking mechanism and region growing idea is implemented to achieve automatic retinal vessel segmentation in which the unprocessed pixel with highest intensity is set as the seed, and the adaptive linking weight and stop conditions are adopted. The experimental results on the DRIVE fundus database show that the average accuracy, sensitivity and specificity are 93.96%, 78.64%, 95.64%, respectively. The segmentation results have less vascular breakpoints and clear micro-vessels. This work has promising application value.
Retrieving Most Relevant Images using Annotation of Text for Understanding Textbook of History

Every day the number data is generated on the internet. It is very challenging to determine the importance of the content. Text picturing helps a lot to understand the concept related to the text. Text picturing is a cognitive aid that can help with text understanding, as it helps users decide if the text deserves closer look by showing relevant pictures along with the text. The writers, bloggers, and editorial person link the text with media content is crucial. Visual content helps readers follow the root of a discussion or identify the core theme of an article. In efforts to arrange and display helpful images, content managers, news writers, and user-interface designers perform page layout, assigning images to concepts. To improve image-retrieval accuracy, a number of issues must be addressed. Readers of businesses reports, newspapers, and social media face the challenge of interpreting large volumes of text in a short amount of time. Students, Teachers, Professors or History enthusiast face the challenge to identify large data in a short amount of time. This can lead to loss of important information. Survey shows that images related to such topics are helpful for the reader to understand and remember the context for a better period of time. There are also people who are not able to take in written matter at a standard pace images sure can boost their grasping ability. This work will help to resolve such cases. The user can easily identify the concept according to the images rather than the text.
AN ENHANCED CURRENT-CONVEYOR BASED INSTRUMENTATION AMPLIFIER WITH HIGH CMRR

In this work, a current-mode instrumentation amplifier with common-mode current cancellation is realized using enhanced CCII topology. Detailed CMRR analysis is performed and parameters for maximizing the CMRR are defined. The proposed structure is simulated using SPICE and realized on a prototyping board. According to the simulation and measurement results the proposed circuit shows superior CMRR performance compared to other well-known current-mode instrument amplifier topologies.
A Proposal Approach for Load Distribution and Resources Sharing in IEEE 802.11 Networks

Recently there is a growing interest in the internet and multimedia wirless networking where the bandwidth and the QoS metrics must profoundly adjusted to the application requirement. However the problem of user bandwidth availability arises as one of the most limit of this solution. In fact, the IEEE 802.11 standards do not provide performed mechanism of loading distribution among different APs of the network. Then an AP can be heavily overloaded leading to station throughput degradation. This paper focuses on the specification of a centralization solution for QoS management in IEEE 802.11 networks. It, mainly, presents the general architecture of the overall system based on a load balancing server over the AP(s) network resources. In addition, the paper addresses a protocol specification for AP and mobiles interactions intended to provide best resources allocation and efficiency on communication metrics. This protocol is being described and simulated with SDL and MSC tools as a first step before experimentation in real environment.
Normalizing Non-canonical Turkish Texts Using Machine Translation Approaches

With the growth of the social web, user-generated text data has reached unprecedented sizes. Non-canonical text normalization provides a way to exploit this as a practical source of training data for language processing systems. The state of the art in Turkish text normalization is composed of a token level pipeline of modules, heavily dependent on external linguistic resources and manually defined rules. Instead, we propose a fully automated, context-aware machine translation approach with fewer stages of processing. Experiments with various implementations of our approach show that we are able to surpass the current best-performing system by a large margin.
A Performance Comparison of VMware GPU Virtualization Techniques in Cloud Gaming

Cloud gaming is an application deployment scenario which runs an interactive gaming application remotely in a cloud according to the commands received from a thin client and streams the scenes as a video sequence back to the client over the Internet, and it is of interest to both research community and industry. The academic community has developed some open-source cloud gaming systems such as GamingAnywhere for research study, while some industrial pioneers such as Onlive and Gaikai have succeeded in gaining a large user base in the cloud gaming market. Graphical Processing Unit (GPU) virtualization plays an important role in such an environment as it is a critical component that allows virtual machines to run 3D applications with performance guarantees. Currently, GPU pass-through and GPU sharing are the two main techniques of GPU virtualization. The former enables a single virtual machine to access a physical GPU directly and exclusively, while the latter makes a physical GPU shareable by multiple virtual machines. VMware Inc., one of the most popular virtualization solution vendors, has provided concrete implementations of GPU pass-through and GPU sharing. In particular, it provides a GPU pass-through solution called Virtual Dedicated Graphics Acceleration (vDGA) and a GPU-sharing solution called Virtual Shared Graphics Acceleration (vSGA). Moreover, VMware Inc. recently claimed it realized another GPU sharing solution called vGPU. Nevertheless, the feasibility and performance of these solutions in cloud gaming has not been studied yet. In this work, an experimental study is conducted to evaluate the feasibility and performance of GPU pass-through and GPU sharing solutions offered by VMware in cloud gaming scenarios. The primary results confirm that vDGA and vGPU techniques can fit the demands of cloud gaming. In particular, these two solutions achieved good performance in the tested graphics card benchmarks, and gained acceptable image quality and response delay for the tested games.
Design and evaluation of a non-verbal voice-controlled cursor for point-and-click tasks

A voice-controlled cursor is an alternative to traditional mouse for users having difficulties in moving fingers, hands, or arms. In this paper, we report a proposed voice-controlled cursor suitable for point-and-click tasks by applying the grid-based concept of accessing locations on a computer display with the use of non-verbal sounds, which are humming sounds with two different pitch values and a short alveolar fricative sound, to select desired grid cell and to perform the mouse click event. It is shown in the experiments that the proposed method can achieve a higher accuracy rate, a shorter response time, and a better user preference than the traditional Mouse Grid that recognizes spoken digits as its main cell selection mechanism.
The Effect of Image Resolution on the Geometric Correction of Remote Sensing Satellite Images

Geometric distortion due to sensor and/or environmental error sources represent a big problem in the final reported accuracy of captured satellite images. Image to image registration is one of commonly used processes of geometric correction of captured images for updating digital maps and GIS databases. This research tested the effect of different raw image resolutions with respect to reference image resolution on accuracy of image registration process. The research algorithm is implemented using different resolution satellite images from two different sensors, IKONOS and LANDSAT8. The reference data is an image of 1.0meter resolution from IKONOS-2 satellite images. Spatial interpolation process is applied using first order polynomial technique with four ground control points from reference image, which are sharp defined and well distributed. Nearest neighbour technique is used for investigating intensity pixel values of resultant new corrected images. Geometric correction gave accurate results when using raw images with resolution same or higher than the reference image. The resolution of raw available images may be selected based on the required applications. Images with resolution lower than reference image can be used with applications that do not need high accuracy. nbspnbsp
Improving Programming Skills: Effort on School Level

Strong Programming skills are need off time now a days. Nations having strong programming skills are leading the world. Developing country like Pakistan Lack in this practice and there are very few resources in schools to integrate new technologies and methods that are introduced worldwide. Programming skills is a big issue in computer sciences field and have very few number of skilled programmers. This paper includes how introducing programming on school level (grade 1-3) will help students to increase programing skills and reasons behind poor programming skills . Secondly, it purpose a method to integrate programming in schools having few resources. Furthermore, discuss why students have least interest in problem solving and coding skills.
Deep Learning-Based Hybrid Approach for Phase Retrieval

We develop a phase retrieval algorithm that utilizes the hybrid-input-output (HIO) algorithm with a deep neural network (DNN). The DNN architecture, which is trained to remove the artifacts of HIO, is used iteratively with HIO to improve the reconstructions. The results demonstrate the effectiveness of the approach with little additional cost.
INTRA-SENTENTIAL SWITCHING USE IN CLASSROOM ACTIVITY

The research focused in the intra-sentential code switching used by the students in English Department of IKIP Siliwangi Bandung. The research was conducted to find out the intra-sentential switching that used by the students, why the students switch code, and the function of intra-sentential switching in classroom activity. The research employed a qualitative descriptive study involing field research. The samples are 3 groups presentation from class A2 English Education 2015 IKIP Siliwangi Bandung. This study explored pattern of intra-sentential code-switching (CS) that are manifest in classroom activity of English-Indonesia-Sunda in class A2 English Education 2015 IKIP Siliwangi Bandung, the reason why students switch thier code and in what situation students switch their code or language. The result of this research are 3 groups presentation were switching their language in type of intra-sentential switching, the reasons they switch their langugae are less vocabulary, to make they easier to express their mind, to make listener or audience easier to understand, and nervous. In presentation, group discussion, speech & daily conversation are situations they switch their code or language.Keywords: Code switching, Intra-sentential switching, classroom activity.
Off-policy Learning for Multiple Loggers

It is well known that the historical logs are used for evaluating and learning policies in interactive systems, e.g. recommendation, search, and online advertising. Since direct online policy learning usually harms user experiences, it is more crucial to apply off-policy learning in real-world applications instead. Though there have been some existing works, most are focusing on learning with one single historical policy. However, in practice, usually a number of parallel experiments, e.g. multiple AB tests, are performed simultaneously. To make full use of such historical data, learning policies from multiple loggers becomes necessary. Motivated by this, in this paper, we investigate off-policy learning when the training data coming from multiple historical policies. Specifically, policies, e.g. neural networks, can be learned directly from multi-logger data, with counterfactual estimators. In order to understand the generalization ability of such estimator better, we conduct generalization error analysis for the empirical risk minimization problem. We then introduce the generalization error bound as the new risk function, which can be reduced to a constrained optimization problem. Finally, we give the corresponding learning algorithm for the new constrained problem, where we can appeal to the minimax problems to control the constraints. Extensive experiments on benchmark datasets demonstrate that the proposed methods achieve better performances than the state-of-the-arts.
Certificateless Short Aggregate Signature Scheme for Mobile Devices

Today, data is exploding. A large amount of data needs to be processed in a timely and an efficient manner. Aggregate signatures are an efficient and secure way to handle large numbers of digital signatures. In an aggregate signature scheme, <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> signatures on <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> messages from <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> users can be combined into a single signature, which can make anyone believes that the <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> messages were indeed signed by the <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> corresponding users. In the recent decade, numerous certificateless aggregate signature (CLAS) schemes have been introduced. In most CLAS schemes currently known, the number of hash-to-point operations and the size of signature increase linearly with the number of signers, so they are not suitable for computing restricted devices, such as mobile devices. In this paper, a new CLAS scheme is constructed, which requires only two pairing operations and the signature contains only one point and state information. It is more efficient than the previous ones and suit for the mobile devices.
Separating the radiance contribution of land surface and atmosphere

The problem of quantitative remote sensing inversion is ill-posed in essence. In order to turn the inversion problem from uncertainty into certainty, we must try to make full use of all the Information we have. Furthermore, reducing the number of parameters to be inversed is another good way. For thermal inversion, separation of land surface and atmospheric contribution (SAS) to the remote sensing signal is a necessity to both atmosphere research and land surface research. Taking advantage of the spatial information generated from the visible and near infrared spectral bands, we presented an algorithm to get the relative contribution to remote sensing signal (radiance) from land surface and atmosphere. The algorithm is based on the difference of the spatial thermal pattern between land surface and atmosphere. As a result, regressive image and difference image is generated. Regressive image contains the contribution from land surface and homogenous part of atmosphere. It has similar spatial pattern with land surface. And difference image contains the contribution of inhomogenous part of atmosphere. It reflects the spatial pattern of atmosphere. The test with MAS (MODIS Airborne Simulator) data shows that SAS algorithm has the ability to remove the thermal abnormal from the original thermal image. The real test proves that most of chippy cirrus cloud has been removed from the observed radiance and little effect remains in the regressive image. From the study, we can draw a conclusion that visible and near infrared image is helpful to thermal inversion. It also reveals that spatial information is one kind of good information source that should be used in remote sensing inversion.
Transmission Power and Effects on Energy Consumption and Performance in MANET

This paper explores the effect that transmission power has on the performance of a Mobile Ad hoc Network (MANET). The goal of this research is to determine if the lifetime of the network can be prolongated by using less energy and thus, resulting in a more energy efficient ‘greener’ architecture. A total of 72 unique simulations are conducted of various configurations covering a large variety of possible scenarios: we examined configurations with a different number of nodes, number of traffic flows, mobility model, transmission power and geographical areas. Results show that there is an optimal transmission power, which enhances greater network performance: moreover, this optimal transmission setting makes the network more energy efficient in terms of depletion of the finite energy sources of the nodes. Our overall findings also confirm that higher transmission power results in less energy consumption