Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementation of Relevance Feedback for Content Based Image Retrieval using Image Mining User Navigation Pattern

: Image retrieval is the basic requirement task in the present scenario. Nowadays, content-based image retrieval (CBIR) is the mainstay of image retrieval systems in which the target image to be retrieved based on the useful features of the given image. Image mining is the arising concept which can be used to extract potential information from the general collection of images. This paper proposes a new method, Navigation-Pattern-based Relevance Feedback (NPRF) image mining which is implemented in MATLAB, to achieve the preciseness of results and effectiveness of CBIR even when large-scale image data is present. In terms of preciseness, feedback iterations are reduced in substantial amount by using the navigation patterns discovered from the user query log. In terms of effectiveness, our proposed search algorithm NPRF Search makes use of the discovered navigation patterns and three kinds of query refinement strategies, Query Point Movement (QPM), Query Reweighting (QR), and Query Expansion (QEX), to converge the search space toward the user‟s intention effectively. By using NPRF method, high quality of image retrieval can be achieved in a small number of feedbacks. The experimental results reveal that NPRF outperforms other existing methods significantly in terms of precision, coverage, and number of feedbacks.
Multi-class Sentiment Analysis on Twitter

—Twitter, Online Social Networks (OSN) moved toward becoming truly the stage for individuals express their contemplations, share their thoughts, opinion on real-time events and up coming occasions. Sentiment analysis is procedure of gathering, conglomeration, and classiﬁcation of data gathered in social networks into a different emotion classes. Sentiment analysis and opinion mining in social networks are current research topics. The state of the art works focused on the binary and ternary classiﬁcations. These binary and ternary classiﬁcations hindrance the actual sentiments present in the tweets of user. However, existence of multiple meanings that might have different sentiment polarity for the same word, the binary classiﬁcation fails to identify meaning and the polarity of slang words. To analyse opinion of user, it is necessary to go deeper in the classiﬁcation and detect the sentiment hidden behind user post. Multi-class sentiment classiﬁcation has always been a challenging task due to complexity of natural languages and the difﬁculty of understanding and mathematically quantifying how humans express their feelings. In this paper, we proposed model that performs the task of multi-class classiﬁcation of online posts of Twitter users. It demonstrates how far it is possible to go with the classiﬁcation, and the limitations of handling the slang words, hashtags used in the tweets. Our implementation demonstrates that proposed approach achieves an accuracy about 45.19% on the multi-class classiﬁcation for the dataset (text emotion). For dataset (Consumer complaint) the proposed approach achieves accuracy of 82.07%. The approach achieve better accuracy due to the number of features extracted from the individual datasets.
University of Nebraska - Lincoln

With the increased popularity of replica-based services in distributed systems such as the Grid, consistency control among replicas becomes more and more important. To this end, IDF (Inconsistency Detection Framework), a two-layered overlay-based architecture, has been proposed as a new way to solve this problem—instead of enforcing a predefined protocol, IDF detects inconsistency in a timely manner when it occurs and resolves it based on applications’ semantics. This paper presents a comprehensive analytical study of IDF to assess its performance and provide insight into its design. More specifically, it develops an analytical model to characterize IDF. Based on this model, we evaluate the successful rate of inconsistency detection within the top layer, which directly impacts the performance of IDF. In addition, this model helps derive a unified formula to characterize a wide range of applications, providing practitioners and protocol designers with quantitative insights into the IDF design that can be potentially optimized to specific applications.
Comparative Proteomic Analysis of the Haemophilus ducreyi Porin-Deﬁcient Mutant 35000HP::P2AB (cid:1)

Haemophilus ducreyi is an obligate human pathogen and the causative agent of the sexually transmitted, genital ulcerative disease chancroid. The genome of strain 35000HP contains two known porin proteins, OmpP2A and OmpP2B. Loss of OmpP2A and OmpP2B expression in the mutant 35000HP::P2AB resulted in no obvious growth defect or phenotype. Comparison of outer membrane proﬁles indicated increased expression of the 58.5-kDa chaperone, GroEL, in the porin-deﬁcient mutant. A proteomics-based comparison resulted in the identiﬁcation of 231 proteins present in membrane-associated protein samples, of which a subset of 56 proteins was differentially expressed at a level of 1.5-fold or greater in the porin-deﬁcient strain 35000HP::P2AB relative to that in 35000HP. Twenty of the differentially expressed proteins were selected for real-time PCR, resulting in the validation of 90% of the selected subgroup. Proteins identiﬁed in these studies suggested a decreased membrane stability phenotype, which was veriﬁed by disk diffusion assay. Loss of OmpP2A and OmpP2B resulted in global protein expression changes which signiﬁcance GraphPad of membrane- Biomarker chromatography-tandem spectrometry afﬁnity-tagged afﬁnity-tagged
PERFORMANCE ANALYSIS OF SINGLE DOCUMENT SUMMARIZATION SYSTEMS

-In recent times, the necessity of generating single document summary has gained popularity among the researchers due to its extensive applicability. Most of the automatic text summarization systems utilize extraction-based techniques for selecting the most significant portions of text to generate coherent summaries. In this paper we will analyze the performance of fuzzified neural network approach with the graph theory approach. In the proposed system, we have developed an efficient automatic text summarization system based neural network and fuzzy logic. In the training phase at first, the feature vector is computed for a set of sentences using the feature extraction technique. After that, the feature vector and their corresponding fuzzy score are used to train the neural network optimally. Later in the testing phase, the input document is subjected to preprocessing and feature extraction techniques. In order to obtain the sentence score for every sentence in the input document, the feature vector is fed to the trained neural network that returns the sentence score for every sentence. Finally, by making use of sentence score, the most important sentences are extracted from the input document. The experimentation is performed with the DUC 2002 dataset and the generated summary is evaluated with the measures such as Precision, recall and f-measure. The comparative results of our proposed approach with the graph theory approach produces better results by means of different compression rates.
P‐1.14: The Influence of Dual‐channel on the Performance of Self‐Align Top‐Gate IGZO Thin Film Transistors

In this work, fully transparent dual‐layer channel self‐aligned top‐gate amorphous indium gallium zinc oxide thin‐film transistors (a‐IGZO TFTs) are fabricated on glass substrates. Dual‐layer channel is consist of indium gallium zinc oxide with different partial pressure of oxygen while sputtering. An oxygen‐rich IGZO was deposited to control the threshold voltage and an oxygen‐poor IGZO was then deposited to modulate device mobility. The device exhibited the excellent performances, including: a field effect mobility of 5.33 cm2/Vs, a suitable threshold voltage of 1.68V, a sub‐threshold swing of 0.54V/dec and a high on/off current ratio. Furthermore, the devices also shows great stability under bias stress.
Start Date of Project : Oct-2016 Duration of Project : 30 Months Organisation Responsible of Deliverable : INTELSPACE Version : 4 . 0 Status : Final

8 Introduction . 9 1. Literature Review . 10 1.1 Business ecosystems . 10 1.2 Network Business Models . 12 1.3 Smart Service Systems . 15 1.4 Industrial Symbiosis (I-S) . 16 1.5 Cyber-Physical Systems . 18 1.6 Service Dominant (S-D) Logic . 19 1.7 Value Networks . 22 1.8 Value Network Analysis . 24 2. Business research – Escalation of the knowledge blocks to NEWBITS Case Studies . 28 2.1 The concept of Business Models in NEWBITS . 28 Literature review on BM . 28 NEWBITS BM Definition. 30 2.2 Escalation of the knowledge blocks to NEWBITS Case Studies . 31 2.3 Business Ecosystem Dynamics . 37 Systemic Business Dynamics . 39 Business Ecosystem Modeling . 41 ABM Business Ecosystem in NEWBITS . 44 Discretization at Business Ecosystem Milestones . 44 3. Justification of the tailored VNA method . 46 3.1 The “Bridging” section . 46 Business Ecosystems . 48 Network Business Models . 50 Smart service systems . 51 Industrial symbiosis . 52 Cyber-Physical Systems . 53 Service Dominant Logic . 55 D4.1 Formalization of NEWBITS modelling method and systemic business dynamics © NEWBITS consortium www.newbits-project.eu Page 5 of 67 Value Networks . 56 3.2 Proposed tailored made VNA . 58 3.3 Justification of tailored made VNA . 61 Bibliography . 64 D4.1 Formalization of NEWBITS modelling method and systemic business dynamics © NEWBITS consortium www.newbits-project.eu Page 6 of 67 List of Figures Figure 1 Simulation model frameworks considering time evolution and abstraction level . 42 Figure 2 Logical sequence from the literature review to justification of Network business models and tailored made VNA . 47 Figure 3 Literature review: an incremental knowledge advancement exercise . 48 Figure 4 Tailored VNA method blocks . 59 Figure 5 Methodological Stages of the Value Network Analysis . 60
The Impact of Integrating Making Activities to Cornerstone Design Courses on Students’ Implicit Theories of Making Ability

A person’s implicit theories in a certain domain are known to have a direct influence on that person’s performance, behaviour, self-esteem, enjoyment and sense of belonging to the domain. This paper explores the role of implicit theory in engineering students’ beliefs about the nature of their making abilities and their self-identification as makers. This is done by assessing if a collaborative project-based engineering design course built on making activities can contribute to influencing students to have a growth mindset about their making abilities. Data from full-time engineering undergraduates were collected during the second week of the fall term. As predicted, the majority of engineering students had a growth mindset about their making abilities mindset, with male students more likely to have a fixed mindset than female students. Moreover, engineering design courses that successfully integrate making activities into the curriculum are shown to induce students to develop a growth mindset in relation to their beliefs about the nature of their making abilities.
Reconsideration of the source model for the 1662 Earthquake

17 The 1662 Hyuga-nada Earthquake is one of the largest earthquakes to have occurred 18 in the Hyuga-nada region, Southwest Japan, rupturing the western part of the Nankai 19 Trough subduction zone. Strong ground motion and a large tsunami with an estimated 20 height of at least 4–5 m were reported along the coast of Miyazaki Prefecture, Kyushu 21 Island, with extensive damage reported across this region. Therefore, developing a more 22 complete picture of the 1662 Hyuga-nada Earthquake will improve our understanding of 23 tsunami risk along the Pacific coast of Southwest Japan. Here we use the most recent 24 geophysical data from the region to propose a novel source model for the 1662 25 Hyuga-nada Earthquake that incorporates our current understanding of the interactions 26 between slow earthquakes and great earthquakes. The source area in our proposed 27 model extends from the focal region of recurrent M7-class interplate earthquakes to the 28 region of slow earthquakes that occur at relatively shallow depths along the plate 29 boundary. Tsunami simulations indicate that the predicted coastal tsunami heights are 30 broadly compatible with previously reported tsunami heights. This new model suggests 31 that the 1662 Hyuga-nada Earthquake was probably a M8-class earthquake, and that previous magnitude determinations may have underestimated the strength of this historic earthquake. However, the model uncertainty remains large, and the calculated tsunami heights for the southern coast of Miyazaki Prefecture are very high. We suggest that future verification, including an analysis of tsunami-related deposits along the coast, will be important to more accurately determine the source area of the 1662 Hyuga-nada 37 Earthquake.
Hand Pose Estimation and Prediction for Virtual Reality Applications

Use of hands is the primary way we interact with the world around us. Recent trends in virtual reality (VR) also reflect the importance of interaction with hands. Mainstream virtual reality headsets such as Oculus Rift, HTC Vive, and the Playstation VR all support and encourage the use of their hand tracking controllers. However, tracking hands is very challenging due to their small size and various occlusions. For example, significant portions of the hand can get occluded when people are holding hands together or holding an object. For this reason, makers of VR headsets let their users hold controllers that are more reliably tracked than tracking hands directly. Another problem of hand tracking is that it often adds latency to the system. Furthermore, networked multiplayer interactions are even more challenging to deliver without users noticing delays due to the addition of network delays. In this thesis, we propose ways to overcome the current limitations of hand use in VR by addressing these challenges. To address difficulty of hand tracking, we present a way to estimate the entire hand pose given a few reliably tracked points on the hand. We used a commonly available multi-touch tablet to track the fingertip positions and estimated the entire hand pose from the tracked fingertip positions using a quadratic encoding method. We show that quadratic encoding method yields smooth motions for smooth changes in the fingertip positions and show some demos of manipulation tasks using the interface. We also present a way to handle changing number of fingertip contacts and show that we can identify unknown fingertips. To address the latency in hand tracking and multiplayer interactions, we propose a method to augment hand pose prediction with eye tracking which will be commonly available in the next generation of VR headsets. We will first motivate our approach by presenting our observations of hand-eye coordination. We show that gaze leads grasping actions by an average of 0.726s, leaves the manipulation area as soon as the action is complete, fixates on the tool tips when a tool is held, and sometimes inspects the object without the hand directly interacting with the inspected area. All of the observations will be used to determine how gaze should be used to predict hand pose during different actions. Then, we present a study on predicting grasp types to show that gaze is effective in predicting hand interactions. We found interesting patterns of gaze during bottle grasps before the hand reaches the bottle. We use neural networks to show that these gaze patterns can be learned in order to improve prediction accuracy. Finally, we conclude with application of grasp type prediction in VR and a user study which evaluates the usefulness and quality of hand pose generated based on grasp type prediction. We found similar gaze patterns in VR as in the real-life experiment. The user study shows potential for usefulness of grasp type prediction in VR applications with rooms to improve in the future.
Influence-directed Explanations for Machine Learning

Increasingly, decisions and actions affecting people’s lives are determined by automated systems processing personal data. Excitement about these systems has been accompanied by serious concerns about their opacity and the threats that they pose to privacy, fairness, and other values. Recognizing these concerns, it is important to make real-world automated decision-making systems accountable for privacy and fairness by enabling them to detect and explain violations of these values. System maintainers may leverage such accounts to repair systems to avoid future violations with minimal impact on the utility goals. In this dissertation, we provide a basis for explaining how machine learning systems use information. These explanations increase trust in the functioning of the system, allowing us to verify that they make not only right decisions but also for justifiable reasons. Further, explanations can be used to support detection of privacy and fairness violations, as well as explain how they came about. We can then leverage this understanding to repair systems to avoid future violations. We identify two major challenges to explaining information use in machine learning systems: (i) converged use, that machine learning systems typically combine a large number of input features, and (ii) indirect use, that these systems can typically infer and use information that is not directly provided to the system. Our approach to explaining how complex machine learning models use information involves answering two questions: (influence) Which factors were influential in determining outcomes? , and (interpretation) What do these factors mean? We first present key results measuring the causal influence of factors in machine learning models. We then examine the following settings: (i) systems with potential indirect use of information, and (ii) convolutional neural networks. For each setting we demonstrate how influence and interpretation combine to account for information use.
Mitigating Memory-Safety Bugs with Efficient Out-of-Process Integrity Checking

Computer programs written in low-level languages with manual memory management, like C and C++, can contain unintentional memory-safety bugs [175] due to developer error. Examples of these bugs include spatial buffer overflows, as well as temporal use-after-frees and double frees, which can be leveraged by attackers to exploit programs by altering their runtime behavior. Indeed, statistics from both Google Chrome [1] and Microsoft [129] show that ~70% of all security vulnerabilities in their codebases involve memory-safety bugs. Past work, as discussed in Chapter 2, has proposed various strategies to eagerly detect or lazily mitigate such bugs. Eager approaches detect memory-safety bugs by checking pointer operations (§2.1), whereas lazy mitigations prevent exploitation by validating program data (§2.4, §2.5). To improve accuracy, mitigations may need to maintain internal state (metadata) about program execution, which must also be protected from corruption. This has been achieved using different techniques, including software-based address space partitioning (§2.2), and hardware-based fine-grained instruction monitoring (§2.3). Nevertheless, these approaches suffer from significant complexity, brittleness, or incompatibility, which reduces their efficiency and effectiveness. In this thesis, we observe that existing mitigations are limited by their decision to maintain internal metadata within the same process. We show that augmenting hardware with a small, secure, and efficient AppendWrite inter-process communication (IPC) primitive allows metadata storage and policy checking to be performed in a separate isolated process, which improves both security and performance. We implement this design in our HerQules [42] framework, which we use to develop new approaches for control-flow integrity and data-flow integrity that are more precise than past work. We evaluate our designs on a variety of real-world programs, including multiple benchmark suites, the NGINX web server, and the Google Chromium web browser.
Machine Learning and Multiagent Preferences

One of the most well known settings dealing with multiagent preferences is voting and social choice. In classical social choice, each of the n agents presents a ranking over the m candidates, and the goal is to find a winning candidate (or a consensus ranking) that is the most “fair” outcome. In this thesis, we consider several variants of this standard setting. For instance, the domain may have an uncountably infinite number of alternatives, and we need to learn each voter’s preferences from a few pairwise comparisons among them. Or, we have a markov decision process, and each voter’s preferences are represented by its reward function. Can we find a consensus policy that everyone would be happy with? Another example is the setting of a conference peer review system, where the agents are the reviewers, and their preferences are given by the defining characteristics they use to accept a paper. Our goal is then to use these preferences to make consensus decisions for the entire conference. We also consider the setting where agents have utility functions over a given set of outcomes, and our goal is to learn a classifier that is fair with respect to these preferences. Broadly speaking, this thesis tackles problems in three areas: (i) fairness in machine learning, (ii) voting and social choice, and (iii) reinforcement learning, each of them handling multiagent preferences with machine learning.