Miracle-dz/UniFAR-FTUs huggingface dataset:
Tralfamadore : memory reconstruction, declarative dependency resolution, and parallelism

Tralfamadore is a platform for debugging and analyzing whole software systems, from the operating system up. Tralfamadore employs a novel mechanism for analysis in which, rather than interrupting and examining the system under test as it runs, the system is instead run to completion untouched then examined posthoc. The system under test is run in a virtual machine which records its execution; analysis and debugging tools are then applied to the recorded execution trace data. Tralfamadore thus permits travelling both forward and backward through time while debugging, debugging systems that would not normally be able to be halted (such as those that communicate with time-sensitive external systems), and accurately reproducing a view of a system’s execution even if that system has (maliciously or accidentally) corrupted its internal state. Tralfamadore also has a number of other potentially-interesting applications in aspect-oriented programming, forensic analysis, dynamic analysis, and customer-site bug reporting.
Recognition of para-linguistic information and its application to spoken dialogue system

The human-human interactions in a spoken dialogue seem to use not only linguistic information in the utterances but also some sorts of additional information supporting linguistic information. We call these sorts of additional information "para-linguistic information". In this paper, we present a recognition method of attitudes by prosodic information, and a recognition method of head gestures. In the former method, in order to recognize two attitudes, such as "positive" and "negative", F0 pattern and phoneme alignment are introduced as features. In the latter method, in order to recognize three gestures, such as "nod", "tilt" and "shake", a left-to-right HMM is introduced as the probabilistic model as well as optical flow is introduced as features. Experiment results show that these methods are sufficient to recognize the user attitude as para-linguistic information. Finally, we show a prototype spoken dialogue system using para-linguistic information and how these sorts of information contribute to efficient conversation.
Comprehensive comparison of turbo-code decoders

The decoding of turbo-codes (a novel class of binary parallel concatenated recursive systematic convolutional codes) relies on the application of soft input/soft output decoders. Such decoders can be realized either using maximum-a-posteriori (MAP) symbol estimators or MAP sequence estimators, e.g. the a-priori soft output Viterbi algorithm (APRI-SOVA). The structure of turbo-codes encoders as well as decoders is described. In particular, four different decoder structures suitable for decoding turbo-codes are illustratively described and their error rate performance capabilites compared in both AWGN as well as flat Rayleigh fading channels based on extensive simulation results.
Separation of stationary and time-varying systems and its application to the restoration of gramophone recordings

The motivation for developing the technique described arose from a desire to restore old gramophone recordings which were spectrally distorted during the original recording process. This distortion was caused by the horn used to focus the sound. White noise tests on an original recording horn suggest that its frequency response contains several strong resonances which are modeled as a stationary autoregressive subsystem. The signal to be recovered is modeled as a time-varying autoregressive subsystem. Given knowledge of the parameters of the overall system, it is shown how the two systems can be separated to obtain the parameters of the horn distortion subsystem. An inverse filter can then be designed to recover the original signal. Initial results from experiments on subsystem separation are presented using a simple technique to identify the overall system. Better methods for the overall system identification stage are discussed. <<ETX>>
A new strategy for the application of genetic algorithms to the channel-assignment problem

The channel-assignment problem in cellular radio networks is known to belong to the class of NP-complete optimization problems. So far, this problem has been solved by heuristic assignment strategies or by the application of combinatorial optimization tools like simulated annealing or neural networks. We propose a new powerful approach to the channel-assignment problem by combining the two mentioned groups of solution techniques. The results obtained by the application to a well-known benchmark problem reveal that this absolutely new strategy clearly outperforms the already existing algorithms.
Firefly-based Facial Expression Recognition: Extended Abstract

Automatic facial expression recognition plays an important role in various application domains such as medical imaging, surveillance and human-robot interaction. This research presents a novel facial expression recognition system with modified Local Binary Patterns (LBP) for feature extraction and a modified firefly algorithm (FA) for feature optimization. First, in order to deal with illumination, scaling and rotation variations, we propose a horizontal, vertical and diagonal neighborhood LBP to extract initial discriminative facial features. Then a modified FA is proposed to reduce the dimensionality of the extracted facial features. This FA variant employs Cauchy and Levy distributions to further mutate the best solution identified by the FA to increase exploration in the search space and avoid premature convergence. The overall system is evaluated using two facial expression databases (i.e. CK+, and MMI). The proposed system outperforms other heuristic search algorithms such as Genetic Algorithm, Particle Swarm Optimization, and other existing state-of-the-art facial expression recognition research, significantly.
Non-Orthogonal Multiple Access (NOMA) for cellular future radio access

Radio access technologies for cellular mobile communications are typically characterized by multiple access schemes, e.g., frequency division multiple access (FDMA), time division multiple access (TDMA), code division multiple access (CDMA), and OFDMA. In the 4th generation (4G) mobile communication systems such as Long-Term Evolution (LTE) (Au et al., Uplink contention based SCMA for 5G radio access. Globecom Workshops (GC Wkshps), 2014. doi:10.1109/GLOCOMW.2014. 7063547) and LTE-Advanced (Baracca et al., IEEE Trans. Commun., 2011. doi:10.1109/TCOMM.2011.121410.090252; Barry et al., Digital Communication, Kluwer, Dordrecht, 2004), standardized by the 3rd Generation Partnership Project (3GPP), orthogonal multiple access based on OFDMA or single carrier (SC)-FDMA is adopted. Orthogonal multiple access was a reasonable choice for achieving good system-level throughput performance with simple single-user detection. However, considering the trend in 5G, achieving significant gains in capacity and system throughput performance is a high priority requirement in view of the recent exponential increase in the volume of mobile traffic. In addition the proposed system should be able to support enhanced delay-sensitive high-volume services such as video streaming and cloud computing. Another high-level target of 5G is reduced cost, higher energy efficiency and robustness against emergencies.
The application of hash chains and hash structures to cryptography

In this thesis we study how hash chains and other hash structures can be used in various cryptographic applications. In particular we focus on the applications of entity authentication, signatures and key establishment. We study recursive application of hash functions to create hash chains, hash trees and other hash structures. We collate all these to form a catalogue of structures that we apply to various cryptographic applications. We study existing work on authentication and create many entity authentication schemes based on structures from our catalogue. We present a novel algorithm to find efficient signature schemes from any given hash structure. We study some suggestions for suitable hash structures and define a particular scalable hash structure complete with a simple message to signature map that is the most efficient such scheme of which we know. We explore k-time signature schemes and identify two new properties, which we call perforated and porous. We look at the application of hash structures to key establishment schemes. We compare the existing schemes and make improvements on many. We present a new key establishment scheme, and show a link between certain k-time signatures and certain key establishment schemes. We look at the other applications of hash structures, and suggest areas in which our catalogue could be used for further development.
Bayesian probabilistic reasoning in design

The application of Bayesian reasoning to engineering design is discussed. The decisions involved in the design process can be represented as nodes in a Bayesian network. The conditional probability that a child node in this network will take a desired value, given the values of its parents, can be estimated from a database built up from simulations of past designs, or from suitably encoded empirical principles. Bayesian probabilistic reasoning can then be used to calculate the probability of particular choices leading to a successful design. In this way the most promising alternatives can be investigated first. <<ETX>>
Low Power-Area GDI & PTL Techniques Based Full Adder Designs

Full adder circuit is functional building block of micro processors, digital signal processors or any ALUs. In this paper leakage power is reduced by using less number of transistors with the techniques like GDI (Gate Diffusion Input) and PTL (Pass Transistor Logic) techniques. In this paper 3 designs have been proposed of low power 1 bit full adder circuit with 10Transistors ( using PTL multiplexer) , 8 Transistor (by using NMOS and PMOS PTL devices), 12Transistors (6Transistors to generate carry using GDI technique and 6Transistors to generate sum using tri state inverters). These circuits consume less power with maximum of 73% power saving com-pare to conventional 28T design. The proposed circuit exploits the advantage of GDI technique and pass transistor logic, and sum is generated by tri state inverter logic in all designs. The entire simulations have been done on 180nm single n-well CMOS bulk technology, in virtuoso platform of cadence tool with the supply voltage 1.8V and frequency of 100MHz.
Professional Linux Kernel Architecture

Find an introduction to the architecture, concepts and algorithms of the Linux kernel in Professional Linux Kernel Architecture, a guide to the kernel sources and large number of connections among subsystems. Find an introduction to the relevant structures and functions exported by the kernel to userland, understand the theoretical and conceptual aspects of the Linux kernel and Unix derivatives, and gain a deeper understanding of the kernel. Learn how to reduce the vast amount of information contained in the kernel sources and obtain the skills necessary to understand the kernel sources.
Enhancing Privacy in Personalized web search

Web search engines such as Google, Bing and Yahoo does not prefer user interest to search user query. The result provided by such search engines will general to every user that is the result will not concern to specific user. Personalized web-search engine is a search engine in which we teach the machine to learn users regard, so the search engine can help users to get the useful information for them quickly by using their interest keeping in the database. Personalized websearch engine can sort the results according to users' regard, the results that user likes will be the top of the results. The result of user query will be on the basis of priority and relevance.
Thinking Fast, Thinking Slow! Combining Knowledge Graphs and Vector Spaces

Knowledge graphs and vector space models are both robust knowledge representation techniques with their individual strengths and weaknesses. Vector space models excel at determining similarity between concepts, but they are severely constrained when evaluating complex dependency relations and other logic based operations that are a forte of knowledge graphs. In this paper, we propose the V-KG structure that helps us unify knowledge graphs and vector representation of entities, and allows us to develop powerful inference methods and search capabilities that combine their complementary strengths. We analogize this to thinking ‘fast’ in vector space along with thinking ‘deeply’ and ‘slowly’ by reasoning over the knowledge graph. We have also created a query processing engine that takes complex queries and decomposes them into subqueries optimized to run on the respective knowledge graph part or the vector part of V-KG. We show that the V-KG structure can process specific queries that are not efficiently handled by vector spaces or knowledge graphs alone. We also demonstrate and evaluate the V-KG structure and the query processing engine by developing a system called Cyber-AllIntel for knowledge extraction, representation and querying in an end-to-end pipeline grounded in the cybersecurity informatics domain.
Concepts for cartography-oriented visualization of virtual 3D city models

Virtual 3D city models serve as an effective medium with manifold applications in geoinformation systems and services. To date, most 3D city models are visualized using photorealistic graphics. But an effective communication of geoinformation significantly depends on how important information is designed and cognitively processed in the given application context. One possibility to visually emphasize important information is based on non-photorealistic rendering, which comprehends artistic depiction styles and is characterized by its expressiveness and communication aspects. However, a direct application of non-photorealistic rendering techniques primarily results in monotonic visualization that lacks cartographic design aspects. In this work, we present concepts for cartography-oriented visualization of virtual 3D city models. These are based on coupling non-photorealistic rendering techniques and semantics-based information for a user, context, and media-dependent representation of thematic information. This work highlights challenges for cartography-oriented visualization of 3D geovirtual environments, presents stylization techniques and discusses their applications and ideas for a standardized visualization. In particular, the presented concepts enable a real-time and dynamic visualization of thematic geoinformation.
Correcting infrared satellite estimates of sea surface temperature for atmospheric water vapor attenuation

A new satellite sea surface temperature (SST) algorithm is developed that uses nearly coincident measurements from the microwave special sensor microwave imager (SSM/I) to correct for atmospheric moisture attenuation of the infrared signal from the advanced very high resolution radiometer (AVHRR). This new SST algorithm is applied to AVHRR imagery from the South Pacific and Norwegian sea, which are then compared with simultaneous in situ (ship based) measurements of both skin and bulk SST. In addition, an SST algorithm using a quadratic product of the difference between the two AVHRR thermal infrared channels is compared with the in situ measurements. While the quadratic formulation provides a considerable improvement over the older cross product (CPSST) and multichannel (MCSST) algorithms, the SSM/I corrected SST (called the water vapor or WVSST) shows overall smaller errors when compared to both the skin and bulk in situ SST observations. Compared against drifting buoy data the WVSST and the quadratic SST both exhibit the same general behavior with relatively small differences with the buoy temperatures. <<ETX>>
Appearance Design Evaluation on CNC Machine Tools Based on Fuzzy Synthetic Evaluation Model

In order to guarantee the preciseness and scientific nature of Computerized Numerical Control (CNC) machine tools appearance design, appearance design evaluation has to be made on CNC machine tools so as to change the randomness and uncertainty existed in traditional appearance design, and to establish a scientific method for CNC machine tools appearance design that meets the modern aesthetic needs. The evaluation index of hierarchical structure has been set up from four aspects as stylization, form-orientation, colorization and agreeableness that may influence the appearance design, adopting both qualitative and quantitative methods; weight for each evaluation index has been determined in accordance with Analytic Hierarchy Process ; and a two-level fuzzy synthetic evaluation model for CNC machine tools appearance design evaluation has been made and a comprehensive evaluation has been conducted through fuzzy mathematical method. By analyzing the appearance design of 2MKM95 series CNC precision vertical universal machine tools determined by Tianjin No.2 Machine Tool Corporation, this paper illustrates the preciseness and scientific nature of fuzzy synthetic evaluation model.
Classification of Malicious Web Pages through a J48 Decision Tree, aNaïve Bayes, a RBF Network and a Random Forest Classifier forWebSpam Detection

Web spam is a negative practice carried out by spammers to produce fake searchengines results for improving rank position of their Web pages. It is available on arena of World Wide Web (WWW) in different forms and lacks a consistent definition. The search engines are struggling to eliminate spam pages through machine learning (ML) detectors. Mostly, search engines measure the quality of websites by using different factors (signals) such as, number of visitors, body text, anchor text, back link and forward link etc. information and, and spammers try to induce these signals into their desired pages to subvert ranking function of search engines. This study compares the detection efficiencyof different ML classifiers trained and tested on WebSpam UK2007 data set. The results of our study show that random forest has achieve higher score than other well-known classifiers.
Toward accuracy in end-to-end available bandwidth assessment using packet pair paradigm

To measure available bandwidth, many different active probing techniques have been developed. The packet pair technique is one of the most popular of them. In this paper, we develop a new deterministic model of packet pair delays for end-to-end available bandwidth estimation in IP network paths. This model captures the relationship between the amount of cross traffic and the packet pair gap taking into account the size of each probing pair packet. Based on this deterministic model and considering a particular case obtained when the two packets of the probing pairs are of the same size, we develop an active measurement tool called IGMPS (Improved Gap Model using Packet Size parameter ) that estimates the end-to-end available bandwidth at the bottleneck link of the path. Based on an experimental analysis in various network testbed configurations, we present IGMPS measurement results that verify the tool behavior and illustrate its accuracy. Then, we investigate the quantitative relationship of IGMPS measurement accuracy to several probing parameters such as the packet size and the number of probing packet pairs. Our experiments show that introducing the packet size parameter in the probe gap model formula improves considerably the accuracy of the measurements.
A Microcomputer Relaxation Technique Using Sample Statistics for Describing the Relationship between Human Factors Measures

The relaxation algorithm described in this paper is a method of developing an estimate of the relationship between a dependent variable and an independent variable when the only data available are sample statistics from ranges of the independent variable. An application using a micro-computer is described. The accuracy and sensitivity of the technique is compared with regression analysis.
Pumas@Home 2012 Team Description Paper

This paper describes an autonomous service robot called Justina built by the team Pumas in the laboratory of Biorobotics at the National University of Mexico. The robot is based on the ViRbot architecture for autonomous mobile robots operation. ViRbot defines a human interaction interface based on Natural Language Processing for understanding voice and gesture commands. After this, a Conceptual De- pendence (CD) representation is generated, and an action planner infers a set of rules to perform the commands based on the sensors information and the state of the robot. The aforementioned rules become subtasks that are performed by different submodules that control the hardware and share information by TCP/IP sockets through a Blackboard in or- der to achieve the given command. By using a good representation of the problem domain through CD's and a rule-based system, such as an inference engine, the operation of the robot becomes a more tractable problem and easier to implement.
An automatic offset compensation technique applicable to existing operational amplifier core cell

An automatic offset compensation scheme for two-stage operational amplifiers is described. The presented technique can be applied to an existing OA core cell without modifying its design. Offset voltage is reduced by digitally adjusting the resulting offset current at the node between the first and second OA stages. A background calibration is achieved by using a ping-pong structure that improves the temperature stability and an on-demand calibration is also provided. The proposed circuit has been fabricated in a 0.7 /spl mu/m digital CMOS technology. The measured input offset voltage of the test circuits are less than /spl plusmn/200 /spl mu/V.
Video to Text (V2T) in Wide Area Motion Imagery

Abstract : In this project, the Intelligent Fusion Technology, Inc. (IFT) team has developed a hard (video) and soft (text, voice chat) information fusion approach to automatically generate videos with annotation that can be easily used by future human or machine users. The tracking results following the standard format (.kw18) will also be output in a separated file for interfacing with other modules in the E2AT system integration. In the implementation, each entity corresponds to one tracklet with a unique track ID. Each entity consists of two sets of attributes: common attributes and uncommon attributes. Common attributes are those which will not change over the lifetime of a target track like type and color of the target. Uncommon attributes are those changing over time like target location, direction, and activity. The same sets of attribute definitions are used for entities constructed from both hard and soft data. The association, linkage, fusion, and concatenation can improve the visual tracking results through multi-intelligence information fusion.
The Effect of Viewpoint on Grasp Detection

Many robots today are capable controlling the pose of their visual sensors. A natural problem that these systems face is how to select a pose for their sensor that enables them to perform their task with high reliability. In this work we study the issue of sensor pose selection where the robot’s task is to detect or recognize grasps in visual data. This is an important issue because the reliability of the state-of-the-art in robotic grasping systems is not 100% in situations where the object instances are unknown a priori and where the objects are presented in clutter. A large part of the failures observed in these systems are perceptual errors. In this paper we show that viewpoint does indeed affect grasp detection performance in terms of number of grasps found, accuracy, and detector confidence. Further, we propose a simple method for learning viewpoints that results in finding 4.4 times more correct grasps than a random viewpoint baseline. Finally, we evaluate these ideas on a cluttered, table-top scenario with a diverse set of objects using a Baxter manipulator robot. The robot’s grasp success rate increases by 3-5% when relying on our viewpoint selection method.