Miracle-dz/UniFAR-FTUs huggingface dataset:
A non-parametric LDA-based induction method for sentiment analysis

Sentiment analysis is the process of analyzing the characteristics of opinions, feelings, and emotions which are expressed in textual data. This paper presents a novel approach for generation of a lexical resource named PersianClues used for sentiment analysis in Persian language. Moreover, a novel unsupervised LDA-based sentiment analysis method called LDASA is proposed. In order to generate the PersianClues, at the first phase, an automatic translation approach is used to translate the existing English clues to Persian. Next, iterative refinement approach is used to correct the erroneous clues resulted from previous step. Then, topic-based polar sets are achieved from these clues and finally, each document is categorized into its related polarity using a classification algorithm. To evaluate this method, three resources about hotels, cell phones and digital cameras have been manually gathered from the e-shopping websites and the results of sentiment analysis on these resources are compared with a baseline named SVM-Unigrams. The experimental results demonstrate an improvement of 9% on average in polarity classification accuracy of the base system.
An Architecture for a WWW Workload Generator

1 Overview SURGE (Scalable URL Reference Generator) is a WWW workload generator which is based on analytical models of WWW use. It relies on the fact that a great deal of prior work has been done in the analysis of WWW transactions, and that models for many of the important characteristics of WWW use have been developed. The goal of SURGE is to provide a scalable framework which, from the server's perspective, makes document requests which mimic a set of real users. SURGE is intended to be used for benchmarking, network traac generation and simulation. This paper contains descriptions of the details of the SURGE framework, the additional models which we developed, and the statistical methods we used to parameterize the models. 2 The SURGE Framework 2.1 Structure of SURGE SURGE is a scalable software framework within which a collection of models for the various components of WWW use are combined. From the top down, SURGE software resides on a set of clients which are connected to a WWW server as illustrated in Figure 1. Each SURGE client executes a set of threads, each of which is an ON/OFF source 15, 9]. Each thread requests a document set which is then transfered by the server (ON time). After receiving the document set, a thread sleeps for some amount of time (OFF time). This ON/OFF characteristic is an important diierence between SURGE and other benchmarks such as 1, 2, 3, 4, 5] (14] also includes OFF times).
Clinical and Microbiological Determinants of Outcome in Staphylococcus aureus Bacteraemia

Staphylococcus aureus bacteraemia (SAB) is commonly complicated by metastatic infection or relapse after treatment. Objectives. The study aim was to determine the role of bacterial, host, and management factors in development of complicated SAB. Methods. A prospectively-conducted observational study gathered data on predisposition, management and outcome of 100 consecutive SAB cases. Antibiotic susceptibilities and genetic lineage of bacterial isolates were determined. Further clinical and microbiological data were gathered on two retrospective series from 1999–2000 (n = 57) and 2004 (n = 116). Results. In the prospective cases, 27% met our definition of complicated disease. Expressed as RR and 95% CI, complicated disease was associated with diabetes (1.58, 1.00–2.48), injecting-drug use (5.48, 0.88–33.49), community-onset of symptoms (1.4, 1.02–1.92), and symptom duration ≥48 hours prior to starting effective antibiotic therapy (2.10, 1.22–3.61). Uncomplicated disease was associated with the presence of a central line (0.69, 0.55–0.88) and prompt removal of a primary focus (0.71, 0.57–0.90). Neither methicillin resistance nor genetic lineage was associated with complicated disease, but methicillin resistance was associated with higher mortality. Conclusions. This study demonstrates that clinical rather than microbial factors are the major determinants of SAB outcome and underscores the importance of early treatment.
A framework to identify influencers in signed social networks

Social networks are defined as a graphical data structure, which captures complex social interactions between users of a social network. Signed social networks are weighted representations of the social network with the emphasis of capturing both positive and negative interactions (edges) between actors of the network. Ad-hoc communities in a social network, as a corollary can be treated as the logical grouping of social actors that share common interests, ideas, or beliefs. In this work, we leverage these known constructs in social networks to effectively identify influencers (i.e. a subset of actors that exert their influence over a community), aka, seeds. Traditional approaches largely rely on degree of connectivity in identifying influencers of a community. We hypothesize that there are other measures to identify influences. In this work, our objective is therefore to explore and propose a technique using Principal Component Analysis (PCA) to identify the smallest set of influencers with increasing the possibility of adopting a product. Furthermore, we validate our finding by evaluating the potential of these influencers to identify positive communities in a social network. We believe our approach is novel in choosing our influencers (seeds) and thus by using these seeds, positive and negative edges are established. We exploit resulting positive and negative edges to mine ad-hoc communities of interest.
Decision Threshold Modulation in the Human Brain

Perceptual decisions are made when sensory evidence accumulated over time reaches a decision threshold. Because decisions are also guided by prior information, one important factor that is likely to shape how a decision is adaptively tuned to its context is the predictability of forthcoming events. However, little is known about the mechanisms underlying this contextual regulation of the perceptual decision-making process. Mathematical models of decision making predict two possible mechanisms supporting this regulation: an adjustment of the distance to the decision threshold, which leads to a change in the amount of accumulated evidence required to make a decision, or a gain control of the sensory evidence, leading to a change in the slope of the sensory evidence accumulation. Here, we show that predictability of the forthcoming event reduces the distance to the threshold of the decision. Then, combining model-driven fMRI and the framework of information theory, we show that the anterior cingulate cortex (ACC) adjusts the distance to the decision threshold in proportion to the current amount of predictive information and that the dorsolateral cortex (DLPFC) codes the accumulation of sensory evidence. Moreover, the information flow from the ACC to the DLPFC region that accumulates sensory evidence increases when optimal adjustment of the distance to the threshold requires more complex computations, reflecting the increased weight of ACC's regulation signals in the decision process. Our results characterize the respective contributions of the ACC and the DLPFC to contextually optimized decision making.
Draft Genome Sequence of Salmonella enterica subsp. enterica Serovar Manhattan Strain 111113, from an Outbreak of Human Infections in Northern Italy

ABSTRACT We announce the draft genome sequence of Salmonella enterica subsp. enterica serovar Manhattan strain 111113, isolated from a patient during an outbreak in northern Italy. The genome, which was obtained with Illumina MiSeq technology, is composed of 21 contigs for a total of 4,684,342 bp, with a G+C content of 52.17%.
Haptics Research in TAUCHI : Aiding Visually Impaired Children and Enabling Haptic Interaction

Touch is one of the more recent senses being harnessed in multimodal interaction with personal computers and mobile devices. Haptics has a great potential for helping people with special needs, and making the use of computing devices more natural and concrete for all people. Still, there are great many things that are not known in sufficient detail to make constructing haptic interfaces a simple matter of programming. In this position paper I discuss the projects and ideas that our multimodal interaction research group has been working on within the past few years. In addition, I provide a glance at the upcoming activities that are under way or planned to be investigated in the near future.
BAF and FI-BAF: Efficient and Publicly Verifiable Cryptographic Schemes for Secure Logging in Resource-Constrained Systems

Audit logs are an integral part of modern computer systems due to their forensic value. Protecting audit logs on a physically unprotected machine in hostile environments is a challenging task, especially in the presence of active adversaries. It is critical for such a system to have forward security and append-only properties such that when an adversary compromises a logging machine, she cannot forge or selectively delete the log entries accumulated before the compromise. Existing public-key-based secure logging schemes are computationally costly. Existing symmetric secure logging schemes are not publicly verifiable and open to certain attacks. In this article, we develop a new forward-secure and aggregate signature scheme called Blind-Aggregate-Forward (BAF), which is suitable for secure logging in resource-constrained systems. BAF is the only cryptographic secure logging scheme that can produce publicly verifiable, forward-secure and aggregate signatures with low computation, key/signature storage, and signature communication overheads for the loggers, without requiring any online trusted third party support. A simple variant of BAF also allows a fine-grained verification of log entries without compromising the security or computational efficiency of BAF. We prove that our schemes are secure in Random Oracle Model (ROM). We also show that they are significantly more efficient than all the previous publicly verifiable cryptographic secure logging schemes.
A Model of Boundedly Rational Consumer Choice - An Agent Based Appraoch

The paper presents an extended version of the consumer choice problem. Different from the standard model, prices are not fixed but arise from Walrasian interactions of total demand and a stylized supply function for each of the goods. Three different types of evolutionary algorithms are set up to answer the question whether agents can learn to solve the problem of extended consumer choice. There are three important answers to this question: a) The quality of the results learned crucially depends on the elasticity of supply, which in turn is shown to be a measure of the degree of state dependency of the economic problem. b) It seems to be relatively easy to adhere to the budget constraint, but relatively difficult to reach an optimum with marginal utility per Dollar being equal for each good. c) Agents equipped with some memory are found to perform notably better than agents without memory.
Infrared Candidates for the Intense Galactic X-Ray Source GX 17+2

We present new astrometric solutions and infrared Hubble Space Telescope observations of GX 17+2 (X1813-140), one of the brightest X-ray sources on the celestial sphere. Despite 30 years of intensive study and the existence of a strong radio counterpart with a subarcsecond position, the object remains optically unidentified. The observed X-ray characteristics strongly suggest that it is a so-called Z source, the rare but important category that includes Sco X-1 and Cyg X-2. Use of the USNO-A2.0 catalog enables us to measure the position of optical and infrared objects near the radio source to subarcsecond precision within the International Celestial Reference Frame for direct comparison with the radio position, which we also recompute using modern calibrators. With high confidence we eliminate the V ~ 17.5 star NP Ser, often listed as the probable optical counterpart of the X-ray source, as a candidate. Our HST NICMOS observations show two faint objects within our 0. ″5 radius 90% confidence error circle. Even the brighter of the two, star A, is far fainter than expected (H ≈ 19.8), given multiple estimates of the extinction in this field and our previous understanding of Z sources, but it becomes the best candidate for the counterpart of GX 17+2. The probability of a chance coincidence of an unrelated faint object on the radio position is high. However, if the true counterpart is not star A, it is fainter still, and our conclusion that the optical counterpart is surprisingly underluminous is but strengthened.
The impact of smoke from forest fires on the spectral dispersion of cloud droplet size distributions in the Amazonian region

In this paper, the main microphysical characteristics of clouds developing in polluted and clean conditions in the biomass-burning season of the Amazon region are examined, with special attention to the spectral dispersion of the cloud droplet size distribution and its potential impact on climate modeling applications. The dispersion effect has been shown to alter the climate cooling predicted by the so-called Twomey effect. In biomass-burning polluted conditions, high concentrations of low dispersed cloud droplets are found. Clean conditions revealed an opposite situation. The liquid water content (0.43 ± 0.19 g m−3) is shown to be uncorrelated with the cloud drop number concentration, while the effective radius is found to be very much correlated with the relative dispersion of the size distribution (R2 = 0.81). The results suggest that an increase in cloud condensation nuclei concentration from biomass-burning aerosols may lead to an additional effect caused by a decrease in relative dispersion. Since the dry season in the Amazonian region is vapor limiting, the dispersion effect of cloud droplet size distributions could be substantially larger than in other polluted regions.
MATLAB FOR F EATURE ‐ BASED R ESTAURANT L OGO R ECOGNITION

This paper discusses the implementation of imrestaurant(), a MATLAB function for feature-based restaurant logo recognition. The algorithm works by first applying SIFT to a logo database and then creating a hierarchical vocabulary tree using K-means clustering. The input to the algorithm is a photograph of an image logo, which is first filtered to reduce noise. Then SIFT is applied to the input image, and the output descriptors are pushed to the vocabulary tree. Each image in the logo database is scored using TF-IDF weights and the ten highest scoring database logos are pairwise matched using the ratio test and RANSAC. The restaurant database image with the largest consensus set is searched for on yelp.com. This algorithm was shown to be robust against input image size, background noise, and shearing. Finally, future work in increasing the accuracy of feature detection as well as optical character recognition is discussed.
Decimal floating-point: algorism for computers

Decimal arithmetic is the norm in human calculations, and human centric applications must use a decimal floating point arithmetic to achieve the same results. Initial benchmarks indicate that some applications spend 50% to 90% of their time in decimal processing, because software decimal arithmetic suffers a 100/spl times/ to 1000/spl times/ performance penalty over hardware. The need for decimal floating point in hardware is urgent. Existing designs, however, either fail to conform to modern standards or are incompatible with the established rules of decimal arithmetic. We introduce a new approach to decimal floating point which not only provides the strict results which are necessary for commercial applications but also meets the constraints and requirements of the IEEE 854 standard. A hardware implementation of this arithmetic is in development, and it is expected that this will significantly accelerate a wide variety of applications.
ConceVE

In this article, we present ConceVE, an approach for designing and validating models before they are implemented in a computer simulation. The approach relies on (1) domain-specific languages for model specification, (2) the Alloy Specification Language and its constraint solving analysis capabilities for exploring the state space of the model dynamically, and (3) supporting visualization tools to relay the results of the analysis to the user. We show that our approach is applicable with generic languages such as the Web Ontology Language as well as special XML-based languages such as the Coalition Battle Management Language.
An intelligent mobile-based automatic diagnostic system to identify retinal diseases using mathematical morphological operations

Diabetic retinopathy is considered in terms of the presence of exudates which cause vision loss in the areas affected. This study targets the development of an intelligent mobile-based automatic diagnosis integrated with a microscopic lens to identify retinal diseases at initial stage at any time or place. Exudate detection is a significant step in order obtaining an early diagnosis of diabetic retinopathy, and if they are segmented accurately, laser treatment can be applied effectively. Consequently, precise segmentation is the fundamental step in exudate extraction. This paper proposes a technique for exudate segmentation in colour retinal images using morphological operations. In this method, after pre-processing, the optic disc and blood vessels are isolated from the retinal image. Exudates are then segmented by a combination of morphological operations such as the modified regionprops function and a reconstruction technique. The proposed technique is verified against the DIARETDB1 database and achieves 85.39% sensitivity. The proposed technique achieves better exudate detection results in terms of sensitivity than other recent methods reported in the literature. In future work, our system will be deployed to a mobile platform to allow efficient and instant diagnosis.
A Facebook crawler based on interaction simulation and MHRW-DA

Data acquisition is a precondition to conduct online social network (OSN) research. Based on the interaction simulation, we have implemented a Facebook crawler with unbiased MHRW-DA (Metropolis-Hasting Random Walk with Delayed Acceptance) algorithm, which can obtain the complete friend list of a Facebook user. We make an analysis on the crawled dataset, and find out that (1) 34.2% of Facebook users have changed the default privacy setting, implying that the awareness of privacy protection of Facebook users has been greatly improved; (2) the percentage of female users in those that have changed the default privacy setting is higher than male by 16.8%, implying the awareness of privacy protection of female users is much higher than that of male.
Skousen ' s Analogical Modeling Algorithm : A comparison withLazy

We provide a qualitative and empirical comparison of Skousen's Analogical Modeling algorithm (AM) with Lazy Learning (LL) on a typical Natural Language Processing task. AM incorporates an original approach to feature selection and to the handling of symbolic , unordered feature values. More speciically, it provides a method to dynamically compute an optimally-sized set of nearest neighbours (the analogical set) for each test item, on the basis of which the most plausible category can be selected. We investigate the algorithm's generalisation accuracy and its tolerance to noise and compare it to Lazy Learning techniques on a primary stress assignment task in Dutch. The latter problem is typical for a large amount of classiication problems in Natural Language Processing. It is shown that AM is highly successful in performing the task: it outperforms Lazy Learning in its basic scheme. However, LL can be augmented so that it performs at least as well as AM and becomes as noise tolerant as well.
Robust Path Planning in GPS-Denied Environments Using the Gaussian Augmented Markov Decision Process

As the field of autonomous robotics continues to mature, the a bility for robots to operate reliably in the presence of noise and incomplete information is becoming increasing ly critical. State estimation techniques such as the Kalman filter explicitly model sensor noise and incomplete i nformation by computing a probability distribution over the possible states. The uncertainty of the posterior p obability distribution is a measure of the expected error of the state estimate, but the challenge is how to incorporat e knowledge of uncertainty into the decision making process. Ignoring the uncertainty that accompanies probab ilistic state estimates during planning can lead to plan failures where the estimate is highly uncertain and likely t o be wrong. In this paper we describe a path planning algorithm that choo ses actions based on both the mean and covariance of the state estimate, efficiently planning in the space of Ka lman filter beliefs. We demonstrate that the planner is robust to potential sensor noise and uncertainty in the ro bot’s pose, achieving significantly better performance in GPS-denied environments compared to shortest-path plan ners.
Determinants of Innovative Activity in Canadian Manufacturing Firms: The Role of Intellectual Property Rights

This paper examines how several factors contribute to innovative activity in the Canadian manufacturing sector. First, it investigates the extent to which intellectual property right protection stimulates innovation. Second, it examines the contribution that RD but firms and industries that make more intensive use of patents do not tend to produce more innovations. Second, while R&D is important, developing capabilities in other areas, such as technological competency and marketing, is also important. Third, size effects are significant. The largest firms tend to be more innovative. As for competition, intermediate levels of competition are the most conducive to innovation. Fourth, foreign-controlled firms are not significantly more likely to innovate than domestic-controlled firms once differences in competencies have been taken into account. Fifth, the scientific infrastructure provided by university research is a significant determinant of innovation.
Auditor Independence, Conflict of Interest, and the Unconscious Intrusion of Bias

Information about the financial health of public companies provided by auditors ideally allow s investors to make informed decisions and enhances the efficiency of financial market s. However, under the current system auditors are hired and fired by the companies they audit , which introduces incentives for biases that favor the audited companies. Three experim nts demonstrate bias in auditors' judgments, and show that these biases are not easily cor rected because auditors are not fully aware of them. The first experiment demonstrates that he judgments of professional auditors tend to be biased in favor of their clients. The second and third experiments explore more closely the psychological processes underlying the bias. The results suggest that the closeness of the relationship between auditor and client m ay have a particularly strong biasing influence on auditors' private judgments.