Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating Algorithms for Nearest Neighbor Searches in Spatial Databases Using R-Trees

This Master thesis is on the discussion and research of the topic Nearest Neighbors in Spatial Databases using R-trees. Carried out at The Norwegian University of Science and Technology as part of the study program Master of Science, Informatics: Databases and Search during the autumn 2016 and spring 2017. In cooperation with Oracle Norge, MySQL department. The feature of nearest neighbor searches has been a long awaited functionality in MySQL and is why this project was proposed. This thesis looks at several different data structures and algorithms for solving the nearest neighbor problem. The incremental nearest neighbor algorithm implemented for this thesis showed very good results in terms of speed and functionality.
Distributed relay selection method and device

The invention relates to a distributed relay selection method, wherein a relay node participating in cooperative transmission is selected at least from one relay node, and a selection flow comprises the steps that: each relay node respectively estimates the channel state information of a forward link and a backward link of per se; each relay node calculates per se channel quality according to thechannel state information and compares the channel quality obtained from the calculation with a judging threshold; when the channel quality is larger than the threshold value, the relay node is confirmed to participate in the cooperative transmission and sets the timing internal of a per se timer to be a universal time value, wherein the universal time value is smaller than a preset time value; or else, the relay node is confirmed not to participate in the cooperative transmission and sets the timing interval of the per se timer to be a time value larger than the preset time value. The invention also relates to a relay selection device. The invention is suitable for the relay selection in a multi-relay wireless cooperative transmission network, and through distributed selection means, theinvention can effectively reduce the complexity in the process of the relay selection while not lowering the system performance, thereby improving the integral system performance.
Pushing boundaries of face recognition : adversary, heterogeneity, and scale

Due to the unconstrained nature of data capture and non-cooperative subjects, automatic face recognition is still a research challenge for application scenarios such as law enforcement. We observe that challenges of face recognition are broadly rooted into two facets: (1) the non-ideal and possibly adversarial face image samples and (2) the large size and incremental/streaming availability of data. The first facet encompasses various challenges such as intentional or unintentional obfuscation of identity, attempts for spoofing system, user non-cooperation, and large intra-subject variations for heterogeneous face recognition. The second facet caters to challenges arising due to application scenarios such as repeat offender identification and surveillance where the data is either large scale or available incrementally. Along with advancing the face recognition research by addressing the challenges arising from both the aforementioned facets, this dissertation also contributes to the pattern classification research by abstracting the research problems at the classifier level and proposing feature independent solutions to some of the problems. The first contribution addresses the challenge of face obfuscation due to usage of disguise accessories. We collect and benchmark IIIT In and Beyond Visible Spectrum Face Dataset (IBVSD) pertaining to 75 subjects, which has various types of disguises applied on different individuals. It has become one of the most used disguise face dataset in the research community. Since disguised facial regions can lead to erroneous identity prediction, a texture based algorithm is designed to differentiate between biometric and non-biometric facial patches. The proposed approach is embedded with local face recognition algorithm to address the challenge of disguise variations. The approach is further enhanced with the use of thermal spectrum imaging. As the second contribution, the dissertation addresses the challenge of heterogeneous face matching scenarios, such as matching a sketch against a mugshot dataset of digital photographs, cross-spectrum, and crossresolution matching, that arise in a wide range of law enforcement scenarios. Heterogeneous Discriminant Analysis (HDA) is designed to encode multi-view heterogeneity in the classifier to obtain a projection space more suitable for matching. Further, to extend the proposed technique for nonlinear projections, formulation of kernel HDA is proposed. Focusing on application such as identification of repeat offenders, as the third contribution, we develop an approach to efficiently update the face recognition engine to incorporate incremental training data. The proposed Incremental Semi-Supervised Discriminant Analysis (ISSDA) provides mechanism to efficiently, in terms of accuracy and training time, update the discriminatory projection directions. The proposed approach capitalizes on offline unlabeled face image data, which is inexpensive to obtain and generally available in abundance. The fourth contribution of this dissertation is focused on designing
Design of cost-efficient ESD clamp circuits for the power rails of CMOS ASIC's with substrate-triggering technique

Four new device structures for power-rail ESD clamp circuits by using the substrate-triggering technique are investigated in submicron CMOS technology to improve ESD level of the protection device within a smaller silicon area. Experimental results in a 0.6-/spl mu/m CMOS process have verified that the ESD clamp circuit with the double-BJT structure can provide 200% higher ESD robustness in per unit layout area as comparing to the previous design with the NMOS device.
Proceedings of the 20th International Conference on Intelligent User Interfaces Companion

It is with great pleasure that we welcome you to the 2015 ACM International Conference on Intelligent User Interfaces -- ACM IUI 2015 in Atlanta, Georgia, USA. Starting in 1993, ACM IUI is now in its 20th edition and serves as a premier international forum for reporting outstanding research and development on intelligent user interfaces. ACM IUI is where the Human-Computer Interaction (HCI) community meets the Artificial Intelligence (AI) community. We are also very interested in contributions from related fields, such as psychology, behavioral science, cognitive science, computer graphics, design, the arts, etc. At ACM IUI, we focus on the interaction between machine intelligence and human intelligence. While other conferences focus on one side or the other, we address the complex interaction between the two. We welcome research that explores how to make the interaction between computers and people smarter, which may leverage solutions from data mining, knowledge representation, novel interaction paradigms, and emerging technologies. For the 20th edition of the conference, the call for papers attracted a record number of 205 long and short paper submissions, 41 poster paper submissions, 18 demo paper submissions, and 24 student consortium submissions. The final program of the conference includes 3 keynotes, 47 long and short papers (22.9% acceptance rate), 17 posters, 8 demos, 4 workshops, 3 tutorials, and 12 student consortium papers. The program opens with a keynote by Professor Dan Weld on "Intelligent Control of Crowdsourcing", continues with a keynote by Ed Chi on "Blurring of the Boundary Between Interactive Search and Recommendation" on the second day, and ends with a keynote by Rosalind Picard on "Recognizing Stress, Engagement, and Positive Emotion". Four workshops (Personalized Access to Cultural Heritage, Intelligent Digital Games for Empowerment and Inclusion, Interacting with Smart Objects, and Visual Text Analytics) and three tutorials (Speech-based Interaction, Personalization for Behavior Change, and User Affect and Sentiment Modeling) complement the main conference program.
Building Efficient Concurrent Graphs through Composition of Set Objects

In this paper, we propose a generic concurrent directed graph (for shared memory architecture) that is concurrently being updated by threads adding/deleting vertices and edges. The graph is constructed by the composition of the well known concurrent list-based set data structure from the literature. Our construction is generic, in the sense that it can be used to obtain various progress guarantees, depending on the granularity of the underlying concurrent set implementation - either blocking or non-blocking. We prove that the proposed construction is linearizable by identifying its linearization points. Finally, we compare the performance of all the variants of the concurrent graph data-structure along with its sequential implementation. We observe that our concurrent graph data structure mimics the performance of the concurrent list based set.
Processing Well Log Data Using Empirical Mode Decomposition (EMD) and Diffusion Equation

Detection of sharp changes in the mean level of a given data is important to make the most of embedded information. Including well logs, the time series under consideration is generally corrupted by some kind of noise, making it difficult to locate the hidden abrupt changes. A number of studies were conducted to identify discontinuous changes in well logs that may indicate changing lithology or physical properties of formations (e.g. Gaci et al, 2010). Previous works requires priori assumptions such as multi-fractality of data. In this study, we propose methods to segment a well log into distinct stratigraphic or lithologic zones with no priori assumptions on the statistical properties of data or noise. Signal processing schemes presented in this study employ empirical mode decomposition (EMD) and diffusion equation to reduce irregularities or noise in the data and finally Laplacian filters or edge detection is applied to make changes standout. The last step enables us to identify major lithologic changes, which were not obvious in the unprocessed data.
Cookies: HTTP State Management Mechanism

This document defines the HTTP Cookie and Set-Cookie header fields. These header fields can be used by HTTP servers to store state (called cookies) at HTTP user agents, letting the servers maintain a stateful session over the mostly stateless HTTP protocol. Although cookies have many historical infelicities that degrade their security and privacy, the Cookie and Set-Cookie header fields are widely used on the Internet. This document obsoletes RFC 6265.
INSTRUKSI BAHASA PEMROGRAMAN ADT (ABSTRACT DATA TYPE) PADA VIRUS DAN LOOP BATCH

Currently the software is found everywhere , not just on a supercomputer with 25 processors, a computer equipped with a p has software that can synchronize with a PC. Language used to instruct a computer programming language called . There are 2 types of programming languages consists of high-level languages and low-level languages . We know them: Basic, Algol, Cobol, Pascal, PL - 1, RPG, SNOBOL, APL, LISP, GPSs, ADA, DEAL and so that is a high -level language , machine language understood by the computer is the machine language instructions ( Machine language) which is a low-level language , high level language so that we mentioned above in order to be understood by a computer must first be translated by the compiler . From the results of research on Instructions Programming Language Programming Language That Uses the ADT (Abstract Data Type) On Virus And Loop Batch can be summed up in the current trend of virus that the average instruction looping using batch , which often interfere with computer performance, and the addition of our unknown data working because the windows are set automatically at startup Task manager.
1.57 GHz asynchronous and 1.4 GHz dual-modulus 1.2 /spl mu/m CMOS prescalers

We have implemented a dual-modulus prescaler dividing either by 64/65 (128/129) in a 1.2 /spl mu/m standard CMOS process. Using advanced dynamic circuit techniques a maximum frequency of 1.4 GHz has been measured at a supply of 5 V and 6.9 mA. An asynchronous prescaler dividing by 16 has also been implemented. The measured maximum input frequency is 1.57 GHz at a supply of 5 V and only 3 mA. <<ETX>>
Intellectual Property Protection using Obfuscation

1 Definitions of Code Obfuscation 3 1.1 Collberg et al . 4 1.2 Barak et al . 6 1.3 Assertion Obfuscations . 7 1.4 Slicing Obfuscations . 7 1.5 Using Metrics . 8
Continuous speech recognition using multilayer perceptrons with hidden Markov models

A phoneme based, speaker-dependent continuous-speech recognition system embedding a multilayer perceptron (MLP) (i.e. a feedforward artificial neural network) into a hidden Markov model (HMM) approach is described. Contextual information from a sliding window on the input frames is used to improve frame or phoneme classification performance over the corresponding performance for simple maximum-likelihood probabilities, or even maximum a posteriori (MAP) probabilities which are estimated without the benefit of context. Performance for a simple discrete density HMM system appears to be somewhat better when MLP methods are used to estimate the probabilities. <<ETX>>
A Machine Learning Approach to Recipe Text Processing

We propose a machine learning approach to recipe text processing problem aiming at converting a recipe text to a work flow. In this paper, we focus on the natural language processing (NLP) such as word identification, named entity recognition, and s yntac- tic analysis to extract predicate-argument structures (tu ples of a ver- bal expression and its arguments) from a sentence in a recipe text. Predicate-argument structures are subgraphs of the work flo w of a recipe. We solve these problems by methods based on machine learning techniques. The recipe domain is, however, different from the gen- eral domain in which many language resources are available. And we have to adapt NLP systems to the recipe texts by preparing anno- tated data in the recipe domain. To reduce the cost of the adaptation, we adopt a pointwise framework allowing to train analyzers from partially annotated data. The experimental results showed that an adaptation works well for each NLP and with all the adaptations the accuracy of the entire system increased. We can conclude that more adaptation work helps develop an accurate recipe-text-to-flow system.
A comprehensive taxonomy of DDOS attacks and defense mechanism applying in a smart classification

A Distributed denial of service (DDoS) attack uses multiple machines operating in concern to attack a network or site. It is the most important security problem for IT managers. These attacks are very simple organized for intruders and hence so disruptive. The detection and defense of this attack has specific importance among network specialists. In this paper a new and smart taxonomy of DDoS attack and defense mechanism will be introduced. The attacks taxonomy is introduced using both known and potential attack mechanisms. It comprises all types of attacks and provides a comprehensive point of view for DDoS attacks. We introduce a useful tool that can be employed to a sophisticated selection defense method for DDoS attacks. Furthermore a smart taxonomy method of DDOS attacks will be proposed to help selection an appropriate defense mechanism. This method uses some features of DDOS attacks and classifies it to several clusters by Kmean algorithm and labels each cluster with a defense mechanism. If an IDS detects a DDOS attack, proposed system extract attack features and classify it by KNN (K-Nearest-Neighbor) to determine the cluster in which it belongs to. The defense mechanisms taxonomy is using the currently known approaches. Also the comprehensive defense classification will help to find the appropriate strategy to overcome the DDoS attack.
The Effect of Age and Information Design on the Perception of Visual Aesthetics

This paper investigates the interplay of information layout, colour temperature (or colour scheme), aesthetics-type and age in the judgment of aesthetics in mobile website design. Having this knowledge will help designers serve users better through personalization. Hence, using the tourism domain as a case study, we conducted an empirical study (n = 323) on the perception of visual aesthetics , using eight versions of a mobile website, comprising two types of layout (grid and list) and four colour schemes (blue, green, orange and red). The results of our Analysis of Variance show that there is a main effect of aesthetics-type and an interaction between layout, colour temperature and age. Users perceive the presented website designs as more classically aesthetic than expressively aesthetic , with younger people being more critical than older people. Moreover, older people tend to be indifferent about the website colour schemes, while younger people tend to prefer moderate-temperature (green and orange) to extreme-temperature (blue and red) grid-style colour schemes. We discuss the implications of our findings for both age groups. classical aesthetics, expressive aesthetics, layout, colour age.
Improving Automatic Control of an Ankle-Foot Prosthesis Using Machine Learning Algorithms

Most commercially available lower-limb prostheses are designed for walking, not for standing. The Minneapolis VA Health Care System has developed a bimodal prosthetic anklefoot system with distinct modes for walking and standing [1]. With this device, a prosthesis user can select standing or walking mode in order to maximize standing stability or walking functionality, depending on the activity and context. Additionally, the prosthesis was designed to allow for an “automatic mode” to switch between standing and walking modes based on readings from an onboard Inertial Measurement Unit (IMU) without requiring user interaction to manually switch modes. A smartphone app was also developed to facilitate changing between walking, standing and automatic modes. The prosthesis described in [1] was used in a pilot study with 18 Veterans with lower-limb amputations to test static, dynamic, and functional postural stability. As part of the study, 17 Veterans were asked for qualitative feedback on the bimodal ankle-foot system (Table 1).
Research on Motion Control of AUV with Hybrid Actuators

A switch function is presented to smooth control instructions and avoid system vibration during switch operating underwater vehicles with hybrid actuators. And a Modified S-plane Controller (MSC) is proposed by analyzing underwater vehicles dynamics and taking static force and coupling effects between the longitude velocity and other dimensions into account. Besides the advantages of S controller such as simple structure, MSC can solve the motion control of underwater vehicle at high speed which is difficult to control with S-plane controller. The stability of MSC is analyzed with Lyapunov function. Finally, MSC is applied to the motion control of an autonomous underwater vehicle controlled by rudders and thrusters. The feasibility of MSC is demonstrated by the results of velocity control, yaw control and depth control tests.
Combining Knowledge Hunting and Neural Language Models to Solve the Winograd Schema Challenge

Winograd Schema Challenge (WSC) is a pronoun resolution task which seems to require reasoning with commonsense knowledge. The needed knowledge is not present in the given text. Automatic extraction of the needed knowledge is a bottleneck in solving the challenge. The existing state-of-the-art approach uses the knowledge embedded in their pre-trained language model. However, the language models only embed part of the knowledge, the ones related to frequently co-existing concepts. This limits the performance of such models on the WSC problems. In this work, we build-up on the language model based methods and augment them with a commonsense knowledge hunting (using automatic extraction from text) module and an explicit reasoning module. Our end-to-end system built in such a manner improves on the accuracy of two of the available language model based approaches by 5.53% and 7.7% respectively. Overall our system achieves the state-of-the-art accuracy of 71.06% on the WSC dataset, an improvement of 7.36% over the previous best.
Mechanistic representation of soil nitrogen emissions in the Community Multiscale Air Quality (CMAQ) model v 5.1

Abstract. Soils are important sources of emissions of nitrogen-containing (N-containing) gases such as nitric oxide (NO), nitrous acid (HONO), nitrous oxide (N2O), and ammonia (NH3). However, most contemporary air quality models lack a mechanistic representation of the biogeochemical processes that form these gases. They typically use heavily parameterized equations to simulate emissions of NO independently from NH3 and do not quantify emissions of HONO or N2O. This study introduces a mechanistic, process-oriented representation of soil emissions of N species (NO, HONO, N2O, and NH3) that we have recently implemented in the Community Multiscale Air Quality (CMAQ) model. The mechanistic scheme accounts for biogeochemical processes for soil N transformations such as mineralization, volatilization, nitrification, and denitrification. The rates of these processes are influenced by soil parameters, meteorology, land use, and mineral N availability. We account for spatial heterogeneity in soil conditions and biome types by using a global dataset for soil carbon (C) and N across terrestrial ecosystems to estimate daily mineral N availability in nonagricultural soils, which was not accounted for in earlier parameterizations for soil NO. Our mechanistic scheme also uses daily year-specific fertilizer use estimates from the Environmental Policy Integrated Climate (EPIC v0509) agricultural model. A soil map with sub-grid biome definitions was used to represent conditions over the continental United States. CMAQ modeling for May and July 2011 shows improvement in model performance in simulated NO2 columns compared to Ozone Monitoring Instrument (OMI) satellite retrievals for regions where soils are the dominant source of NO emissions. We also assess how the new scheme affects model performance for NOx (NO+NO2), fine nitrate (NO3) particulate matter, and ozone observed by various ground-based monitoring networks. Soil NO emissions in the new mechanistic scheme tend to fall between the magnitudes of the previous parametric schemes and display much more spatial heterogeneity. The new mechanistic scheme also accounts for soil HONO, which had been ignored by parametric schemes.
Some Methods for Posterior Inference in Topic Models

The problem of posterior inference for individual documents is particularly important in topic models. However, it is often intractable in practice. Many existing methods for posterior inference such as variational Bayes, collapsed variational Bayes and collapsed Gibbs sampling do not have any guarantee on either quality or rate of convergence. The online maximum a posteriori estimation (OPE) algorithm has more attractive properties than other inference approaches. In this paper, we introduced four algorithms to improve OPE (namely, OPE1, OPE2, OPE3, and OPE4) by combining two stochastic bounds. Our new algorithms not only preserve the key advantages of OPE but also can sometimes perform significantly better than OPE. These algorithms were employed to develop new effective methods for learning topic models from massive/streaming text collections. Empirical results show that our approaches were often more efficient than the state-of-theart methods. DOI: 10.32913/rd-ict.vol2. no15.687