Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards migrating computation to distributed memory caches

Memcached and other in-memory distributed key-value stores play a critical role in large-scale web applications, by reducing traffic to persistent storage and providing an easy-to-access look-aside cache in which programmers can store arbitrary data. These caches typically have a narrow interface, consisting only of gets, sets, and compare-and-set. In the worst case, this interface can cause significant inefficiencies as clients get large data items, perform small changes, and then set the updated items back into the cache. We extend memcached to allow clients to execute code directly in the cache. An idealized evaluation on micro-benchmarks based on workload traces from a Cable/Internet service provider shows compelling performance, leading to a recommendation that further research be conducted to make in-cache fetch-and-phi safe and programmer-friendly, and that researchers consider whether a truly distributed cloud platform should make it easier for programmers to execute custom code at all levels of the software stack.
The importance of process knowledge for cross project learning: evidence from a UK hospital

A core prescription from the knowledge management movement is that the successful management of organizational knowledge will prevent farms from "reinventing the wheel. " Information and Communication Technologies (ICTs) are seen as a key enabler of this process. Our findings challenge this logic. They suggest instead that knowledge is embedded within organizational processes and it is through the continual enactment of these processes that knowledge is created, renewed and transferred. Evidence from a cataract project underway at a Midlands Hospital substantiates our findings. We suggest therefore that attention should be directed at the development and use of ICTs to support the relational aspects of project working and the production of process knowledge facilitating processes of knowledge exploration.
IMPLEMENTATION OF OPAQUE PREDICATES FOR FPGA DESIGNS HARDWARE OBFUSCATION

Obfuscation is a technique, which makes design less intelligible in order to complicate reverse engineering. In this paper, we introduce new approach to hardware obfuscation by inserting constant generators (CGs). CG is circuit which output permanently produces same value but which schematic is not minimized during synthesis. CGs can be used to create primitives, embed watermarks and introduce fictive interdependencies in circuit. They help to hide actual design performance information by forcing synthesizing tools to generate deceiving delay reports through unused paths.
Cross-Rack-Aware Updates in Erasure-Coded Data Centers

The update performance in erasure-coded data centers is often bottlenecked by the constrained cross-rack bandwidth. We propose CAU, a cross-rack-aware update mechanism that aims to mitigate the cross-rack update traffic in erasure-coded data centers. CAU builds on three design elements: (i) selective parity updates, which select the appropriate parity update approach based on the update pattern and the data layout to reduce the cross-rack update traffic; (ii) data grouping, which relocates and groups updated data chunks in the same rack to further reduce the cross-rack update traffic; and (iii) interim replication, which stores a temporary replica for each newly updated data chunk. We evaluate CAU via trace-driven analysis, local cluster experiments, and Amazon EC2 experiments. We show that CAU enhances state-of-the-arts by mitigating the cross-rack update traffic as well as maintaining high update performance in both local cluster and geo-distributed environments.
Derivation of New Readability Formulas (Automated Readability Index, Fog Count and Flesch Reading Ease Formula) for Navy Enlisted Personnel

Abstract : Three readability formulas were recalculated to be more suitable for Navy use. The three formulas are the Automated Readability Index (ARI), Fog Count, and Flesch Reading Ease Formula. They were derived from test results of 531 Navy enlisted personnel enrolled in four technical training schools. Personnel were tested for their reading comprehension level according to the comprehension section of the Gates-McGinitie reading test. At the same time, they were tested for their comprehension of 18 passages taken from Rate Training Manuals. Scores on the reading test and training material passages allowed the calculation of the grade level of the passages. This scaled reading grade level is based on Navy personnel reading Navy training material and comprehending it.
Coalition and opposition MPs on Facebook

Social Media platforms are today the main spheres in which politicians make political and personal statements, confront other public figures and interact with the public. In the current study, the Facebook pages of all Israeli MPs were scraped and analyzed for the entire period of the 19th Israeli parliament service (between 2013-2015), in order to find similarities and differences between the posting behavior and acceptance of coalition and opposition members. We found that popular posts published by members of coalition and opposition differ in terms of scope of publication, scope of user engagement (posts by coalition members were more engaged-with), content and format (posts by members of opposition more varied in format, more mobilizing, critical, opinionative and negative, less formal but also less personal). The implications for the character of Facebook as a key parliamentary discursive arena are discussed.
Using reasoning markers to select the more rigorous software practitioners' online content when searching for grey literature

Background: Blog articles have potential value as a source of practitioner generated evidence in grey literature reviews: they could complement already accepted sources (e.g. interviews and focus groups). To be valuable to research, blog articles need to be relevant, rigorous and evidence-based. Objective: This paper focuses on the rigour of blog articles. We develop, evaluate and partially validate a set of reasoning markers that can be used to search for rigorous blog articles. We then demonstrate how these markers can be used in the online search of grey literature for software testing. Method: We identify discourse markers from literature and then select those that are explicit indicators of reasoning. We evaluate the set against a corpus of persuasive essays, and validate false negatives to refine our set further. We then demonstrate the use of the set in a search of grey literature on software testing. Results: The set of markers is reasonably successful at detecting reasoning within the corpus, achieving a precision of 89.6% in the first pass and 91.1% after refining our set. However, recall is low due to specifically focusing only on explicit indicators of reasoning (31.3% in the first pass and 38.7% in the second). Our overall F1-Score is 46.4% in the first pass and 54.3% in the second. This is acceptable for the time being as the current focus is on the quality of results retrieved from a keyword-based search. Improving the recall of results is left for future research. Conclusion: Our work provides a set of discourse markers that can be used to indicate the presence of reasoning in a blog article, and therefore provides an indication of the more rigorous blog article content. We intend to extend the work through considering the presence of evidence, and improving the relevance of blog articles found.
On Applicability of Cross-project Defect Prediction Method for Multi-Versions Projects

Context: Cross-project defect prediction (CPDP) research has been popular, and many CPDP methods have been proposed so far. As the straightforward use of Cross-project (CP) data was useless, those methods filter, weigh, and adapt CP data for a target project data. This idea would also be useful for a project having past defect data. Objective: To evaluate the applicability of CPDP methods for multi-versions projects. The evaluation focused on the relationship between the performance change and the proximity of older release data to a target project. Method: We conducted experiments that compared the predictive performance between using older version data with and without Nearest Neighbor (NN) filter, a classic CPDP method. Results: NN-filter could not make clear differences in predictive performance. Conclusions: NN-filter was not helpful for improving predictive performance with older release data.
Context-Centric Pricing: Early Pricing Models for Software Crowdsourcing Tasks

In software crowdsourcing, task price is one of the most important incentive to attract broad worker participation and contribution. Underestimating or overestimating a task's price may lead to task starvation or resource inefficiency. Nevertheless, few studies have addressed pricing support in software crowdsourcing. In this study, we propose Context-Centric Pricing approach to support software crowdsourcing pricing based on limited information available at early planning phase, i.e. textual task requirements. In the proposed approach, the global models include a list of 6 pricing factors and employ different natural language processing techniques for prediction; in addition, local models can be derived w.r. t. more relevant context, i.e. a set of similar tasks identified based on Topic modeling and we evaluate 7 predictive models. The proposed approach is evaluated on 450 software tasks extracted from TopCoder, the largest software crowdsourcing platform. The results show that: 1) the proposed models can be used at early crowdsourcing planning phase, when information on traditional metrics are not available; 2) the best model achieves 65% in accuracy measure; 3) the local model exhibits 27% increases superior to the global model. The proposed work can stimulate future research into crowdsourcing pricing estimation and inform ideas for crowdsourcing decision-makers.
MDL-based context-dependent subword modeling for speech recognition

Context-dependent phone units, such as triphones, have recently come to be used to model subword units in speech recognition systems that are based on the use of hidden Markov models(HMMs). While most such systems employ clustering of the HMM parameters(e.g., subword clustering and state clustering)to control the HMM size, so as to avoid poor recognition accuracy due to a lack of training data, none of them provide any effective criteria for determining the optimal number of clusters.This paper proposes a method in which state clustering is accomplished by way of phonetic decision trees and in which the minimum description length(MDL)criterion is used to optimize the number of clusters.Large-vocabulary Japanese-language recognition experiments show that this method achieves higher accuracy than the maximum-likelihood approach.
Re-thinking Traceability: A Prototype to Record and Revisit the Evolution of Design Artefacts

Keeping track of design processes is a cumbersome task due to the apparently unconstrained and unstructured nature of creative work. Traceability is fundamental to revisit and reflect on the design narratives that describe artefact evolution. In this paper, we aim to identify what characteristics are necessary to facilitate traceability of creative design processes. For this end, we use a functional prototype to connect artefacts, design rationale, and decisions in a shared workspace. We evaluated this prototype for 15 weeks with six pairs of students engaged in a user-centered design project. Our findings show that having a lean repository of artefacts annotated with design rationale can facilitate tracking progress in different phases of the process. We found that creating a record of the participants' design work is useful to reflect on and for team agreement, ensure consistency of evolving artefacts, and help in planning future steps in the design project.
A Stable Penalty Method for the Compressible Navier-Stokes Equations: III. Multidimensional Domain Decomposition Schemes

The purpose of this paper is to present asymptotically stable open boundary conditions for the numerical approximation of the compressible Navier-Stokes equations in three spatial dimensions. The treatment uses the conserwtion form of the NavierStokes equations and utilizes linearization and localization at the boundaries based on these variables. The proposed boundary conditions are applied through a penalty procedure, thus ensuring correct behavior of the scheme as the Reynolds number tends to infinity. The versality of this method is demonstrated for the problem of a compressible flow past a circular cylinder. i JSH was supported by Rise National Laboratory, by UNIeC, the Danish Computing Center for Research and Education and by the Danish Science Academy. DG was supported by National Aeronautics and Space Administration under NASA Contract No. NAS1-19480 while the second author was in residence at the Institute for Computer Applications in Science and Engineering (ICASE), NASA Langley Research Center, Hampton, VA. Research was also supported by AFOSR grant 930090, DARPA grant N00014-91-J-4016, NSF grant DMS-9211820 i
The gem5 simulator

The gem5 simulation infrastructure is the merger of the best aspects of the M5 [4] and GEMS [9] simulators. M5 provides a highly configurable simulation framework, multiple ISAs, and diverse CPU models. GEMS complements these features with a detailed and exible memory system, including support for multiple cache coherence protocols and interconnect models. Currently, gem5 supports most commercial ISAs (ARM, ALPHA, MIPS, Power, SPARC, and x86), including booting Linux on three of them (ARM, ALPHA, and x86). The project is the result of the combined efforts of many academic and industrial institutions, including AMD, ARM, HP, MIPS, Princeton, MIT, and the Universities of Michigan, Texas, and Wisconsin. Over the past ten years, M5 and GEMS have been used in hundreds of publications and have been downloaded tens of thousands of times. The high level of collaboration on the gem5 project, combined with the previous success of the component parts and a liberal BSD-like license, make gem5 a valuable full-system simulation tool.
Facial Communicative Signals: valence recognition in task-oriented human-robot Interaction

In this dissertation, we investigate facial communicative signals (FCSs) in terms of valence recognition in task-oriented human-robot interaction. Facial communicative signals mainly comprise head gestures, eye gaze, and facial expressions. We review important psychological findings about the human display and perception of FCSs. Based on this discussion, several conclusions are drawn that motivate the presented work. We investigate a FCS recognition in terms of positive or negative valence in an object-teaching scenario where human subjects teach objects to a robot. The correct or wrong answer of the robot when queried for the object name is used to define the ground truth data for the FCSs the humans displayed in turn during their reaction to this answer. Thus, the facial display the human showed after the robot classified an object correctly is treated as an example of the positive or success class. Similarly, the FCSs shown after a wrong answer constitutes an example of the negative or failure class. We evaluated to which degree humans can infer whether the answer of the robot was correct or not from looking at these facial displays only. Furthermore, we present a simple static baseline approach for the automatic classification of these facial displays in terms of valence. It is based on feature extraction with active appearance models (AAMs) and a classification with support vector machines (SVMs). The method does not consider temporal dynamics, but uses a simple majority voting scheme over the classification results for the single frames. This simple static approach yielded baseline results for a more sophisticated dynamic approach. The dynamic approach is based on the selection of discriminative reference subsequences as prototypes in a nearest-neighbor-based classification scheme. The temporal dynamics are considered by means of dynamic time warping (DTW), which is used to compare sequences of AAM feature vectors. In the conducted evaluation, this dynamic FCS recognition approach outperformed the static baseline approach and achieved human level classification accuracies in a person-specific classification.
A distance routing effect algorithm for mobility (DREAM)

1 Introduction h this paper we introduce a new routing protocol for ad hoc networks built around two novel observations. One, called the distance eflect, usw the fmt that the greater the distance separating two nodes, the slower they appear to be moving with respect to each other. Accor@gly, the location information in routing tables can be updated as a function of the distance separating nodes without compromising the routing accuracy. The second idea is that of triggering the sending of location updates by the moving nodes autonomously, based ody on a node's mobility rate. htuitively, it is clear that in a direction routing dgorithrn, routing information about the slower moving nodes needs to be updated less frequently than that about hig~y mobtie nodw. h this way e~ node can optimize the frequency at which it sends updates to the networks and correspondingly r~ duce the bandwidth and energy used, leading to a fully distributed and self-optimizing system. B~ed on thwe routing tablw, the proposed direction algorithm sends messages in the " recorded dwectionn of the destination node, guaranteeing detivery by following the direction with a given probability. We show by detailed simda-tion that our protocol always delivers more than 80% of the data messages by following the direction computed, without using any recovery procedure. In addition, it mintilzes the overhead used for maintaining routes using the two new principlw of update message frequency and distance. Lastly, the dgorithrn is fully distributed, provides loop-free paths, and is robust, since it suppfies multiple routes. Pemlissiontomakedigitalorhsrdcopiesof allorpartof this\vorkfor personal or classroom use is granted without fee provided that copies are not mzde or dis~. buted for prolit or commercial ad~arrtageand that copies bcwrthis notice and the full citation on the first page. To copy othm}tise, to republish, to post on senrers or to redistribute to lists, requires prior specific permission an&'ora fee. 76 Rom a routing perspective, an ad hoc network is a packet radio network in which the mobile nodes perform the routing functions. Generdy, routing is multi-hop since nodes may not be within the wireless transmission range of one another and thus depend on each other to forward packets to a given destination. Since the topology of an ad hoc network changes frequently, a routing protocol should be a distributed algorithm that computes multiple, cycle free routes while keeping the communication overhead to a minimum (see, e.g., [4]). One way to …
The Complete Problem Solver

This unique volume returns in its second edition, revised and updated with the latest advances in problem solving research. It is designed to provide readers with skills that will make them better problem solvers and to give up-to-date information about the psychology of problem solving. Professor Hayes provides students and professionals with practical, tested methods of defining, representing, and solving problems. Each discussion of the important aspects of human problem solving is supported by the most current research on the psychology problem solving. "The Complete Problem Solver, Second Edition" features: *Valuable learning strategies; *Decision making methods; *Discussions of the nature of creativity and invention, and*A new chapter on writing. "The Complete Problem Solver" utilizes numerous examples, diagrams, illustrations, and charts to help any reader become better at problem solving. See the order form for the answer to the problem below.
Design and Application of Hybrid Intelligent Systems

Intelligent Systems cover a broad area of knowledge-based systems, computational intelligence, soft computing, and their hybrid combinations. Research and development in intelligent systems have enabled us to not only solve a range of problems which were previously considered too difficult but also have enabled a larger number of other problems to be tackled more effectively. This volume focuses on the recent research developments on intelligent systems in a hybrid environment and its applications in business systems, image processing, Internet modeling, control/automation and data mining. The different contributions presented in this volume were accepted for the Third International Conference on Hybrid Intelligent Systems (HIS'03).
Zygomatic Smile Detection: The Semi-Supervised Haar Training of a Fast and Frugal System: A Gift to OpenCV Community

Five different OpenCV-compatible XML haarcascades of zygomatic smile detectors as well as five SMILEsamples from which these detectors were derived had been trained and are presented hereby as a new open source SMILEsmileD package. Samples have been extended in an incremental learning fashion, exploiting previously trained detector in order to add and label new elements of positive example set. After coupling with already known face detector, overall AUC performance ranges between 77%-90.5% when tested on JAFFE dataset and <1ms per frame speed of smile detection is achieved when tested on webcam-obtained videos. Observed results indicate that a semi-supervised incremental method which implements an existing haar-feature based classifier in order to to extend a sample from which a new classifier will be derived can be a method of 1) performance augmentation and 2) reduction of the amount of work dedicated to manual labeling of regions of interest in the positive example sample.
Statistical verification of power grids considering process-induced leakage current variations

Transistor threshold voltages (V/sub th/) have been reduced as part of on-going technology scaling. The smaller V/sub th/ values feature increased variations due to underlying process variations, with a strong within-die component. Correspondingly, given the exponential dependence of leakage on V/sub th/, circuit leakage currents are increasing significantly and have strong within-die statistical variations. With these leakage currents loading the power grid, the grid develops correspondingly large statistical voltage drops. This leakage-induced voltage drop is an unavoidable background level of noise on the grid. Any additional non-leakage currents due to circuit activity will lead to voltage drop which is to be added to this background noise. We propose a technique for checking whether the statistical voltage drop on every node is within user-specified bounds, given user-specified statistics of the leakage currents.
Vector restoration based static compaction of test sequences for synchronous sequential circuits

The authors propose a new procedure for static compaction that belongs to the class of procedures that omit test vectors from a given test sequence in order to reduce its size without reducing the fault coverage. The previous procedures that achieved high levels of compaction using this technique attempted to omit test vectors from a given test sequence one at a time or in consecutive subsequences. Consequently, the omission of each vector or subsequence required extensive simulation to determine the effects of each vector omission on the fault coverage. The proposed procedure first omits (almost) all the test vectors from the sequence, and then restores some of them as necessary to achieve the required fault coverage. The decision to restore a vector requires simulation of a single fault. Thus, the overall computational effort of this procedure is significantly lower. The loss of compaction compared to the scheme that omits the vectors one at a time or in subsequences is small in most cases. Experimental results are presented to support these claims.