Miracle-dz/UniFAR-FTUs huggingface dataset:
Addressing Fairness in SMT Multicores with a Progress-Aware Scheduler

Current SMT (simultaneous multithreading) processors co-schedule jobs on the same core, thus sharing core resources like L1 caches. In SMT multicores, threads also compete among themselves for uncore resources like the LLC (last level cache) and DRAM modules. Per process performance degradation over isolated execution mainly depends on process resource requirements and the resource contention induced by co-runners. Consequently, the running processes progress at different pace. If schedulers are not progress aware, the unpredictable execution time caused by unfairness can introduce undesirable behaviors on the system such as difficulties to keep priority-based scheduling. This work proposes a job scheduler for SMT multicores that provides fairness to the execution of multi programmed workloads. To this end, the scheduler estimates per-process standalone performance by periodically creating low-contention co-schedules. These estimates are used to compute the per process progress. Then, those processes with less progress are prioritized to enhance fairness. Experimental results on a Intel Xeon with six dual-threaded SMT cores show that the proposed scheduler reduces unfairness, on average, by 3× over Linux OS. Moreover, thanks to the tread to core allocation policy, the scheduler slightly improves throughput and turnaround time.
MODELING PERFECT BEHAVIOR : A GOAL-DRIVEN LEARNING ANALYSIS

We have taken a formal, foundational approach to problems of reasoning and learning, considering a body of knowledge to-be-acquired as a "desired behavior". Learning is, to us, determination of a "device" that models the specified behavior, precisely and perfectly. With suitable constraints on such a learning system, guidance of the learning process, selection of experiments and descriptions of the goal device can also be precise or, well-defined. Here we briefly discuss our formal approach to reasoning and learning, particularly as it is related to the fundamental issues of goal-driven learning described by Leake and Ram [8]. We also describe some applications we have found (and hope to find) for our theory.
Detecting Fake Sites based on HTML Structure Analysis

Fake sites are serious threats for both consumers and merchants. They mimic authentic sites and attempt to obtain money by fraud, steal personal information such as credit card numbers from consumers, ruin merchants' reputation and so on. Usually, it is difficult for consumers to distinguish between authentic sites and fake sites. In this paper, we propose a method to detect fake sites with high accuracy based on analyzing structures of HTML source codes of websites. For the analysis, we leverage a combination of a tree kernel and the support vector machine (SVM). We first test 32 different types of tree kernels with small datasets, and then, select a few kernels that have exhibited the best accuracy scores. Further, we test them with larger datasets, and finally, focus on a single kernel that has shown the best accuracy and speed. The AUC, F-measure, Accuracy scores of the kernel exceed 0.97 with a large dataset with 3001 instances, and we conclude that the method can perform at the practical level.
A Hybrid Distance Metric Learning for Image Ranking

The Distance Metric Learning (DML) has been in attentive on image retrieval, but many of the previous methods are used for classification and clustering of the images. In this paper, we are focusing on designing the ordinal DML algorithms for image ranking purpose, hence the rank levels among the images can be well measured by us. A new hybrid approach is proposed in this paper in order to improve efficiency of existing system. Proposed approach is a hybrid algorithm of linear and nonlinear distance metric learning methods. First of all we present a linear ordinal Mahalanobis DML model which tries to preserve the local geometry information as well as the ordinal relationship of the data. Then a nonlinear DML method by kernelizing the above model is developed, here most of the real-world image data with nonlinear structures is considered. for further improvemrnt of the ranking performance, we derive a multiple kernel DML approach taken by the idea of multiple-kernel learning which performs different kernel operations on different kinds of features of image. Extensive experimental analysis demonstrates the relevant results.
The Morphological Analysis of Craniofacial Structure

The purpose of this paper is to characterize the morphological information of craniofacial structure. Firstly, the morphological feature parameters of cephalometric tracings are extracted using image processing techniques. Then, based on these parameters, the morphology of craniofacial structure is classified using K-means method. Afterwards, four morphology related landmarks such as Go, Po, N, and Me for each class are analyze. In this study, fifty tracings are investigated for morphological analysis. From experimental results, the craniofacial structures are classified into six clusters. Each cluster has four images, eight images, ten image, seven image, thirteen images, and eight images, respectively. The range of spatial distributions for the landmark Go, Po, S, N, and Me are all within 4 mm.
WADI: a water distribution testbed for research in the design of secure cyber physical systems

The architecture of a water distribution testbed (WADI), and on-going research in the design of secure water distribution system is presented. WADI consists of three stages controlled by Programmable Logic Controllers (PLCs) and two stages controlled via Remote Terminal Units (RTUs). Each PLC and RTU uses sensors to estimate the system state and the actuators to effect control. WADI is currently used to (a) conduct security analysis for water distribution networks, (b) experimentally assess detection mechanisms for potential cyber and physical attacks, and (c) understand how the impact of an attack on one CPS could cascade to other connected CPSs. The cascading effects of attacks can be studied in WADI through its connection to two other testbeds, namely for water treatment and power generation and distribution.
Research on Manufacturing Resource Domain Ontology Integration Based on OWL

At present, cooperation between manufacturing enterprises is becoming important. Many manufacturing enterprises establish their own manufacturing resource domain ontologies. In order to achieve knowledge reuse and sharing among different manufacturing resource domain ontologies, the method of manufacturing resource domain ontology integration was introduced. Three cases-equivalence, parallel, and intersection were proposed. Based on analyzing the characteristics and functions of manufacturing resource domain ontology, a new concept similarity computation algorithm was introduced for the manufacturing resource domain ontology. Both information content-based similarity computation and distance -based similarity computation were considered in this algorithm. At the end, an instance about integration of two part of different manufacturing resource domain ontologies was shown.
Hierarchical cluster based routing protocol with high throughput for Wireless Sensor Networks

Hierarchical routing having cluster based topology is a very propitious approach for improving throughput. Wireless Sensor Networks (WSNs) are distributed networks consisting of sensors deployed randomly to oversee an inaccessible area. Sensors operate on limited battery-power. The goal of WSN is to improve network lifetime and reliability with improved throughput. Limited energy resource is the main driving factor behind increasing the throughput. LEACH is one of the eminent hierarchical routing protocol in WSN. In this paper, an improvement of LEACH has been suggested. In our proposed algorithm, we adopt static clustering with dynamic selection of clusterheads within each cluster. It prevents random and concentrated selection of clusterheads. A negligible increase in energy leads to prominent improvement in the throughput. The protocol has been simulated in Castalia-3.2 and results show improvement in throughput when compared to LEACH and LCTS protocol.
An accurate elementary mathematical library for the IEEE floating point standard

The algorithms used by the IBM Israel Scientific Center for the elementary mathematical library using the IEEE standard for binary floating point arithmetic are described. The algorithms are based on the “accurate tables method. ” This methodology achieves high performance and produces very accurate results. It overcomes one of the main problems encountered in elementary mathematical functions computations: achieving last bit accuracy. The results obtained are correctly rounded for almost all arguement values. Our main idea in the accurate tables method is to use “nonstandard tables,” which are different from the natural tables of equally spaced points in which the rounding error prevents obtaining last bit accuracy. In order to achieve a small error we use the following idea: Perturb the original, equally spaced, points in such a way that the table value (or tables values in case we need several tables) will be very close to numbers which can be exactly represented by the computer (much closer than the usual double percision representation). Thus we were able to control the error introduced by the computer representation of real numbers and extended the accuracy without actually using extended precision arithmetic.
Global Cosmological Parameters Determined Using Classical Double Radio Galaxies

A sample of 20 powerful extended radio galaxies with redshifts between zero and 2 were used to determine constraints on global cosmological parameters. Data for six radio sources were obtained from the VLA archive, analyzed, and combined with the sample of 14 radio galaxies used previously by Guerra & Daly to determine cosmological parameters. The new results are consistent with our previous results, and indicate that the current value of the mean mass density of the universe is significantly less than the critical value. A universe with Ωm of unity in matter is ruled out at 99.0% confidence, and the best-fitting values of Ωm in matter are 0.10 and -0.25 assuming zero space curvature and zero cosmological constant, respectively. Note that identical results obtain when the low-redshift bin, which includes Cygnus A, is excluded; these results are independent of whether the radio source Cygnus A is included. The method does not rely on a zero-redshift normalization. The radio properties of each source are also used to determine the density of the gas in the vicinity of the source, and the beam power of the source. The six new radio sources have physical characteristics similar to those found for the original 14 sources. The density of the gas around these radio sources is typical of gas in present-day clusters of galaxies. The beam powers are typically about 1045 ergs s-1.
The Conquest of American Inflation

Presenting an analysis of the rise and fall of U.S. inflation after 1960, this book examines two broad explanations for the behaviour of inflation and unemployment in this period: the natural-rate hypothesis joined to the Lucas critique and a more traditional econometric policy evaluation modified to include adaptive expectations and learning. The text begins with an explanation of how American policymakers increased inflation in the early 1960s by following erroneous assumptions about the exploitability of the Phillips curve - the inverse-relationship between inflation and unemployment. In subsequent chapters, it connects a sequence of ideas, such as self-confirming equilibria, least-squares and other adaptive or recursive learning algorithms. The author synthesizes results from macroeconomics, game theory, control theory, and other fields to extend both adaptive expectations and rational expectations theory, and he explains postwar inflation in terms of drifting coefficients. He interprets his results in favour of adaptive expectations as the relevant mechanism affecting inflation policy. This book is intended for academics, graduate students, and professional economists.
A Riccati equation in radiative stellar collapse

We model the behavior of a relativistic spherically symmetric shearing fluid undergoing gravitational collapse with heat flux. It is demonstrated that the governing equation for the gravitational behavior is a Riccati equation. We show that the Riccati equation admits two classes of new solutions in closed form. We regain particular models, obtained in previous investigations, as special cases. A significant feature of our solutions is the general spatial dependence in the metric functions which allows for a wider study of the physical features of the model, such as the behavior of the causal temperature in inhomogeneous space-times.
Registration and fusion of retinal images-an evaluation study

We present the results of a study on the application of registration and pixel-level fusion techniques to retinal images. The images are of different modalities (color, fluorescein angiogram), different resolutions, and taken at different times (from a few minutes during an angiography examination to several years between two examinations). We propose a new registration method based on global point mapping with blood vessel bifurcations as control points and a search for control point matches that uses local structural information of the retinal network. Three transformation types (similarity, affine, and second-order polynomial) are evaluated on each image pair. Fourteen pixel-level fusion techniques have been tested and classified according to their qualitative and quantitative performance. Four quantitative fusion performance criteria are used to evaluate the gain obtained with the grayscale fusion.
Learning context in Australian universities - perceptions of Chinese accounting students

The study concentrates on how Mainland Chinese students perceive and respond to the learning in Australian universities. Students' satisfaction with their educational experiences and the Australian learning context such as teaching, teacher/student relationship, assessment, workload and English language is explored in this study. Using focus groups, the results show that teaching, teacher/student relationships and English language are areas of concern.
A Natural Language Interface Concordant with a Knowledge Base

The discordance between expressions interpretable by a natural language interface (NLI) system and those answerable by a knowledge base is a critical problem in the field of NLIs. In order to solve this discordance problem, this paper proposes a method to translate natural language questions into formal queries that can be generated from a graph-based knowledge base. The proposed method considers a subgraph of a knowledge base as a formal query. Thus, all formal queries corresponding to a concept or a predicate in the knowledge base can be generated prior to query time and all possible natural language expressions corresponding to each formal query can also be collected in advance. A natural language expression has a one-to-one mapping with a formal query. Hence, a natural language question is translated into a formal query by matching the question with the most appropriate natural language expression. If the confidence of this matching is not sufficiently high the proposed method rejects the question and does not answer it. Multipredicate queries are processed by regarding them as a set of collected expressions. The experimental results show that the proposed method thoroughly handles answerable questions from the knowledge base and rejects unanswerable ones effectively.
ECG Arrhythmia Classification with Support Vector Machines and Genetic Algorithm

This research is on presenting a new approach for cardiac arrhythmia disease classification. The proposed method combines both Support Vector Machine (SVM) and Genetic Algorithm approaches. First, twenty two features from electrocardiogram signal are extracted. These features are obtained semiautomatically from time-voltage of R, S, T, P, Q features of an Electro Cardiagram signals. Genetic algorithm is used to improve the generalization performance of the SVM classifier. In order to do this, the design of the SVM classifier is optimized by searching for the best value of the parameters that tune its discriminate function, and looking for the best subset of features that optimizes the classification fitness function. Experimental results demonstrate that the approach adopted better classifies ECG signals. Four types of arrhythmias were distinguished with 93% accuracy.
NFC: A review of technology, tags, applications and security

Smart phones are on a course to becoming a need instead of just a want as users are integrating the devices into every aspect of their lives. Smart phones adoption also increases due to the convergence of value-added services in them. One of the technologies which will enhance this convergence is Near Field Communication (NFC). NFC is a short range high frequency wireless communication technology. It exchanges data between two devices having maximum distance of 4 inches (~10 cm). It is based on RFID technology and has different types of operating modes. NFC enabled mobile phone can be used in health care application; but this introduces some attacks on application. Major attacks are relay attack, eavesdropping, data insertion, data modification and denial of service (DOS). These attacks must be prevented for making NFC based application more secure. This paper reviews the NFC technology, its communication modes and operating modes along with applications in different modes; risks in NFC and its possible countermeasure. It also reviews different types of NFC tags.
Optimised Montgomery domain inversion on FPGA

Modular inversion is a critical operation in elliptic curve cryptosystems (ECC). This paper presents a hardware optimised modular inversion algorithm targeted towards an FPGA implementation. It exploits the underlying structure of the device, leading to a fast and efficient design. Arithmetic is performed in the Montgomery domain, which allows an inversion result to be an input to further operations without the need for domain conversion. Results presented show an increase in throughput over existing inverter designs on reconfigurable logic.
A novel approach for real time eye state detection in fatigue awareness system

This paper proposes a novel eye state detection approach to construct an efficient real time driver fatigue awareness system with an ordinary webcam. Eye state detection has given big challenges to researchers as eye block takes only a small part of input image and can show at various appearances for its flexibility. Moreover, light illumination and viewpoint changes cause more confusions and difficulties for PC to robustly extract eye structure such as contours and iris circles. We transfer this tough problem to a classification problem by combining a discriminative feature, namely Color Correlogram, with machine learning method (Standard Adaboost in this paper). The novelty of this work is that we can efficiently and robustly detect eye states in real time with a single ordinary webcam, even in somewhat harsh conditions such as certain lighting changes, head rotation and different objects. Experimental evidence supports this method well and human fatigue conditions are simultaneously measured based on eye states.
Utility Analysis of Assessment Centers using objective profit data

In this study we analyze the utility of an assessment center for selecting sales representatives of an German insurance company. Unlike most other utility studies an objective indicator of job performance yearly sales figures is used. The sales figures allow a simple and valid computation of the standard deviation of performance, the Achilles’ heel of utility analysis. Our results which are mainly based upon the BrodgenCronbach-Gleser model show that the assessment center leads to a very high utility measured in German marks. Thus, this study confirms the results of many previous studies using mostly subjective indicators of job performance: Well designed human resource programs produce considerable benefits.
SCREAM/ICE1 and SCREAM2 Specify Three Cell-State Transitional Steps Leading to Arabidopsis Stomatal Differentiation[W][OA]

Differentiation of specialized cell types in multicellular organisms requires orchestrated actions of cell fate determinants. Stomata, valves on the plant epidermis, are formed through a series of differentiation events mediated by three closely related basic-helix-loop-helix proteins: SPEECHLESS (SPCH), MUTE, and FAMA. However, it is not known what mechanism coordinates their actions. Here, we identify two paralogous proteins, SCREAM (SCRM) and SCRM2, which directly interact with and specify the sequential actions of SPCH, MUTE, and FAMA. The gain-of-function mutation in SCRM exhibited constitutive stomatal differentiation in the epidermis. Conversely, successive loss of SCRM and SCRM2 recapitulated the phenotypes of fama, mute, and spch, indicating that SCRM and SCRM2 together determined successive initiation, proliferation, and terminal differentiation of stomatal cell lineages. Our findings identify the core regulatory units of stomatal differentiation and suggest a model strikingly similar to cell-type differentiation in animals. Surprisingly, map-based cloning revealed that SCRM is INDUCER OF CBF EXPRESSION1, a master regulator of freezing tolerance, thus implicating a potential link between the transcriptional regulation of environmental adaptation and development in plants.
Athanasia: A User-Transparent and Fault-Tolerant System for Parallel Applications

This article presents Athanasia, a user-transparent and fault-tolerant system, for parallel applications running on large-scale cluster systems. Cluster systems have been regarded as a de facto standard to achieve multitera-flop computing power. These cluster systems, as we know, have an inherent failure factor that can cause computation failure. The reliability issue in parallel computing systems, therefore, has been studied for a relatively long time in the literature, and we have seen many theoretical promises arise from the extensive research. However, despite the rigorous studies, practical and easily deployable fault-tolerant systems have not been successfully adopted commercially. Athanasia is a user-transparent checkpointing system for a fault-tolerant Message Passing Interface (MPI) implementation that is primarily based on the sync-and-stop protocol. Athanasia supports three critical functionalities that are necessary for fault tolerance: a light-weight failure detection mechanism, dynamic process management that includes process migration, and a consistent checkpoint and recovery mechanism. The main features of Athanasia are that it does not require any modifications to the application code and that it preserves many of the high performance characteristics of high-speed networks. Experimental results show that Athanasia can be a good candidate for practically deployable fault-tolerant systems in very-large and high-performance clusters and that its protocol can be applied to a variety of parallel communication libraries easily.