Miracle-dz/UniFAR-FTUs huggingface dataset:
USING FUZZY LR NUMBERS IN BAYESIAN TEXT CLASSIFIER FOR CLASSIFYING PERSIAN TEXT DOCUMENTS

Text Classification is an important research field in information retrieval and text mining. The main task in text classification is to assign text documents in predefined categories based on documents’ contents and labeled-training samples. Since word detection is a difficult and time consuming task in Persian language, Bayesian text classifier is an appropriate approach to deal with different word formats and new words. Also, fuzzy theory may be used to manage uncertainty in imprecise Persian sentences. In this paper, we utilize L-R type fuzzy numbers in Bayesian text classifier to classify textual Persian documents (Fuzzy Bayesian text classifier). The obtained results on simulated imprecise textual Persian documents show improvements in both recall and precision parameters by using Fuzzy Bayesian text classification approach over Naive Bayesian text classifier.
An anti-car collision system using GPS and 5.8 GHz inter-vehicle communication at an off-sight intersection

To prevent accidents at an intersection where the drivers can not see each other, this paper introduces an effective anti-collision system combining DGPS and gyroscope, to map the present position, and wireless information exchange using an inter-vehicle signal. Instead of using a millimeter frequency wave that is currently used in ITS for radar, the use of a 5.8 GHz frequency band is considered. This band is expected to be used in the future for inter-vehicle communication and its diffraction characteristic is expected. By transmitting a signal with an output power of 15 [dBm] and amplifier gain of 15 [dBm], with both receiver and transmitter 40 [m] from the intersection, out of sight, the received power was -68 [dBm]. The results have shown that the system can be made possible with a small-sized omni-directional antenna.
Robust and efficient data management for a distributed hash table

This thesis presents a new design and implementation of the DHash distributed hash table based on erasure encoding. This design is both more robust and more efficient than the previous replication-based implementation [15]. DHash uses erasure coding to store each block as a set of fragments. Erasure coding increases availability while saving storage and communication costs compared to a replication based design. DHash combines Chord's synthetic coordinates with the the set of fragments to implement server selection on block retrieval. DHash enhances robustness by implementing efficient fragment maintenance protocols. These protocols restore missing or misplaced fragments caused by hosts joining and leaving the system. Experiments with a 270-node DHash system running on the PlanetLab [1] and RON [4] testbeds show that the changes to DHash increase the rate at which the system can fetch data by a factor of six, and decrease the latency of a single fetch by more than a factor of two. The maintenance protocols ensure that DHash is robust without penalizing performance. Even up to large database size, the per host memory footprint is less than 10 MB and the per host network bandwidth is under 2 KB/sec over a wide range of system half-lives. Thesis Supervisor: M. Frans Kaashoek Title: Professor of Computer Science and Engineering Thesis Supervisor: Robert Morris Title: Assistant Professor of Computer Science and Engineering
A METHOD FOR ESTIMATING THE PROBABILITY OF RARE ACCIDENTS IN COMPLEX SYSTEMS

Estimating the probability of failures or accidents with aerospace systems is often necessary when new concepts or designs are introduced, as it is being done for Autonomous Aircraft. If the design is safe, as it is supposed to be, accident cases are very rare. This feature requires some variance reduction technique in the probability estimation and several algorithms exist for that, however some specific model features may cause difficulties in practice, such as the case of system models where independent agents have to autonomously accomplish missions within finite time, and likely with the presence of human agents. For handling these scenarios, this paper presents a novel estimation approach, based on the combination of the well-established variation reduction technique of Interacting Particles System (IPS) with the long-standing optimization algorithm denominated DIviding RECTangles (DIRECT). When combined, these two techniques yield statistically significant results for extremely low probabilities. In addition, this novel approach allows the identification of intermediate events and simplifies the evaluation of sensitivity of the estimated probabilities to certain system parameters.
Optimization of FCFS Based Resource Provisioning Algorithm for Cloud Computing

In our project, we propose an optimized version of the FCFS scheduling algorithm to address the major challenges of task scheduling in cloud. The incoming tasks are grouped on the basis of task requirement like minimum execution time or minimum cost and prioritized (FCFS manner). Resource selection is done on the basis of task constraints using a greedy approach. The proposed model will be implemented and tested on simulation toolkit. We intend to create a module depicting the normal FCFS algorithm in comparison to our optimized version algorithm for resource provisioning in the cloud.
The Challenges of Algorithmically Assigning Fact-checks: A Sociotechnical Examination of Google's Reviewed Claims

In the era of misinformation and machine learning, the fact-checking community is eager to develop automated fact-checking techniques that can detect misinformation and present fact-checks alongside problematic content. This thesis explores the technical elements and social context of one such “claim matching” system, Google’s Reviewed Claims. The Reviewed Claims feature was one of the few user-facing interfaces in the complex socio-technical system between fact-checking organizations, news publishers, Google, and online information seekers. This thesis addresses the following research questions: RQ1: How accurate was Google’s Reviewed Claims feature? RQ2: Is it possible to create a consensus definition for “relevant fact-checks” to enable the development of more successful automated fact-checking systems? RQ3: How do different actors in the fact-checking ecosystem define relevance? I investigate these research questions through a series of methods including qualitative coding, qualitative content analysis, quantitative data analysis, and user studies. To answer RQ1, I qualitatively label the relevance of 118 algorithmically assigned factchecks and find that 21% of fact-checks are not relevant to their assigned article. To address RQ2, I find that three independent raters using a survey are only able to come to “fair-moderate agreement” about whether the algorithmically assigned fact-checks are relevant to the matched articles. A reconciliation process substantially raised their agreement. This indicates that further discussions may create a common understanding of relevance among information seekers. Using raters’ open-ended justification responses, I generated 6 categories of justifications for their explanations. To further evaluate if information seekers shared a common definition of relevance, I asked Amazon Mechanical Turk workers to classify six different algorithmically assigned fact-checks and found that crowd workers were more likely to find the matched content relevant and were unable to agree on the justifications. With regard to RQ3, a sociotechnical analysis finds that the fact-checking ecosystem is fraught with distrust and conflicting incentives between individual actors (news publishers distrust fact-checking organizations and platforms, fact-checking organizations distrust platforms, etc.). Given the distrust among actors, future systems should be interpretable and transparent about their definition of “relevance” as well as the ways in which the claim matching is performed. Fact-checking is dependent on nuance and context, AI is not technically sophisticated enough to account for these variables. As such, human-in-the-loop models seem to be essential to future automated fact-checking approaches. However, the results of this thesis indicate untrained crowd workers may not be the ideal candidates for modeling complex values in sociotechnical systems.
Relational programming in minikanren: techniques, applications, and implementations

The promise of logic programming is that programs can be written relationally, without distinguishing between input and output arguments. Relational programs are remarkably flexible—for example, a relational type-inferencer also performs type checking and type inhabitation, while a relational theorem prover generates theorems as well as proofs and can even be used as a simple proof assistant. Unfortunately, writing relational programs is difficult, and requires many interesting and unusual tools and techniques. For example, a relational interpreter for a subset of Scheme might use nominal unification to support variable binding and scope, Constraint Logic Programming over Finite Domains (CLP(FD)) to implement relational arithmetic, and tabling to improve termination behavior. In this dissertation I present miniKanren, a family of languages specifically designed for relational programming, and which supports a variety of relational idioms and techniques. I show how miniKanren can be used to write interesting relational programs, including an extremely flexible lean tableau theorem prover and a novel constraint-free binary arithmetic system with strong termination guarantees. I also present interesting and practical techniques used to implement miniKanren, including a nominal unifier that uses triangular rather than idempotent substitutions and a novel “walk”-based algorithm for variable lookup in triangular substitutions. The result of this research is a family of languages that supports a variety of relational idioms and techniques, making it feasible and useful to write interesting programs as relations.
The Social and Economic Benefits of Improving Mental Health

Women’s Health Victoria (WHV) welcomes the opportunity to provide input into the Productivity Commission Inquiry into the Social and Economic Benefits of Improving Mental Health. WHV is committed to improving women’s mental health, including through advocacy and awareness raising, primary prevention, capacity building and direct service delivery. As a Victorian statewide women’s health promotion service, we collaborate with health professionals, researchers, policy makers, service providers and community organisations to influence and inform health policy and service delivery for women.
Next Generation Artificial Neural Networks for Civil Engineering

If we measure complexity in these simple terms as the number of primary processing units that can be employed usefully in an application then we can compare today’s general purpose digital computer to the brain of a rabbit comprising in the order of 10 9 neurons, while artificial neural networks have progressed no further than the brain of the humble nematode comprising just 302 neurons. Obviously the number of primary processing units that can be employed usefully in a given application provides a very simplified means of comparing complexity—an artificial neuron is usually a much more complicated processing device than a transistor and, moreover, it is likely that significant aspects of the computational mechanisms underlying biological neural networks are yet to be discovered and could be dependent on processes that operate at a lower level than individual neurons see Bullock 2005 for example. Nevertheless, the comparison clearly demonstrates that a plateau has been reached in the development of this technology and that this plateau is at a low elevation.
A New Host and Range Record for the Gall Crab Fungicola fagei as a Symbiont of the Mushroom Coral Lobactis scutaria in Hawai‘i 1

The coral crab Fungicola fagei (Decapoda: Brachyura: Cryptochiridae) is recorded for the first time from the Hawaiian Islands, where it was discovered in a previously unknown association with the solitary, free-living mushroom coral Lobactis scutaria (Anthozoa: Scleractinia: Fungiidae). The associated crab species was discovered off Hilo on the island of Hawai‘i, where it appeared to be relatively common. It could have been previously overlooked because of its small size (max. ca. 1 cm long) and its hidden life style inside the host coral. Species identification is based on the morphology of the carapace and use of the cytochrome oxidase subunit I (COI ) barcode gene as molecular marker. Fungicola fagei is known from other localities in the Indo-West Pacific region, where it is only hosted by mushroom coral species of the genera Podabacia and Sandalolitha. The record of F. fagei from the Hawaiian Islands represents a northeastward extension of its known geographical distribution range, 6,000 km away from its nearest locality in Vanuatu, despite the availability of host species as eastward as the Cook Islands and French Polynesia. Because these other host species do not occur in the Hawaiian Islands, F. fagei’s occurrence here is only possible because of its association with L. scutaria, uniquely recorded from Hawai‘i.
Template Grounded Examples High level advice Key Elements VariablesSelector Reactive LearnerScriptLesson

Advice is a powerful tool for learning. But advice also presents the challenge of bridging the gap between the high-level representations that easily capture human advice and the low-level representations that systems must operate with using that advice. Drawing inspiration from studies on human motor skills and memory systems, we present an approach that converts human advice into synthetic or imagined training experiences, serving to scaffold the low-level representations of simple, reactive learning systems such as reinforcement learners. Research on using mental imagery and directed attention in motor and perceptual skills motivates our approach. We introduce the concept of a cognitive advice template for generating scripted, synthetic experiences and use saliency masking to further conceal irrelevant portions of training observations. We present experimental results for a deep reinforcement learning agent in a Minecraft-based game environment that show how such synthetic experiences improve performance, enabling the agent to achieve faster learning and higher rates of success.
Development of Portable Rehabilitation Device Using Flexible Spherical Actuator with Built-in Embedded Controller and Valves

This study aims at developing a potable rehabilitation device which can be safe to use during holding it by both hands. In our previous study, a novel flexible pneumatic cylinder that can be used even if it is deformed by external force has been developed. A portable rehabilitation device using the flexible spherical actuator that consists of two ring-shaped flexible pneumatic cylinders was proposed and tested. In this paper, more portable and lower-cost rehabilitation device using the spherical actuator that includes the built-in embedded controller and valves is proposed and tested. The attitude control of the device using the built-in controller is executed. As a result, the portable rehabilitation device that it is possible to give the rehabilitation motions to patients with sequential control can be realized. The whole cost of the device can be reduced by a one-third. 
Measurement of Foreign Exchange Exposure for Selected Indian Firms

Foreign exchange fluctuation is the major source of macroeconomic uncertainty affecting firms in an open economy. Countries with floating exchange rate regime are more exposed to this fluctuation; which would in turn affect their firm’s cash flows. The volatility of the firm’s cash flow due to fluctuation in the foreign exchange is known as Foreign Exchange Exposure. These exposures are usually categorized into three types: first is the transaction exposure, which is defined as the likelihood of incurring exchange gains or losses on transactions already entered into and denominated in a foreign currency. Second, economic exposure, defined as the change in the present value of the firm resulting from any change in the future cash flow of the firm caused by the unexpected change in the exchange rate. The extent to which economic exposure affects Exchange rate fluctuation is the major concern for the firms globally, therefore it is essential for firms to know how much they are exposed to these fluctuations. The study aims to measure the foreign exchange exposure and its determinants for selected non-financial Indian firms. The period of analysis is concentrated from 2000-2015. This study has used a panel data methodology with fixed effects model to measure the firm’s exposure by establishing the relationship with the exchange rate changes and the stock returns for the sample of 85 non-financial Indian firms. Furthermore, the firm-sensitive determinants like, export earning, import payments, net capital flows and size of the firm were used to estimate their effect on the foreign exchange exposure. The findings of the study showed that 55% variation in the stock returns was explained by the variation in the exchange rates. With respect to the determinants, it is found that market capitalization, which is a proxy for the size of the firm, is relatively most significant determinant for the exchange rate exposure. This is followed by net capital flows and trade.
THE SELF-BIAS PLL IN STANDARD CMOS

This paper presents a design of self-biased PLL in standard CMOS technology. The PLL circuit generates clock signal for an integrated power meter. The reference frequency is 32.768 kHz and PLL generates fixed output frequency of 4.19 MHz. Self-bias PLL achieves process technology independence, fixed damping factor, and fixed ratio bandwidth to operating frequency. Both the damping factor and bandwidth to operating frequency ratio are determined entirely by a ratio of capacitances. Self-biasing does not require external biasing, that needs special bandgap bias circuit, all of internal bias voltages and currents are generated within the circuit.
An Approach for Fine-grained Detection of Refactoring Instances using Repository with Syntactic Information

This paper proposes a method for fine-grained detection of refactoring instances from the software repository. The proposed method reduces computation time for the refactoring detection by tracing changes of syntactic information based on the repository that stores the results of syntactic analyses. We implemented the proposed method and applied it to the jEdit project. The result of comparison with the UMLDiff algorithm and Ref-Finder shows that the proposed method provides high preciseness for the refactoring detection. Refactoring instances that are provided by the proposed method are expected to be as valuable data for investigating the effectiveness of refactoring and to investigate refactoring support tool that should be implemented.
Spot and Contract Markets in the Brazilian Wholesale Energy Market

The paper models the interaction between a contract and a spot market whose features are borrowed from the Brazilian Wholesale Energy Market. The spot market is modeled as a random mechanism that chooses quantity and prices of electricity. The contract market is composed of generators, retail suppliers of electricity and dealers. The generators and suppliers are price takers, while the dealers have market power. It is shown that, when the number of generators and suppliers increase, even if there is only a monopolist dealer in the market, the contract prices converge to the expected spot price. Moreover, the quantity of energy traded in the contract market approaches the total amount of energy available in the system when the number of dealers increases without bound.
TESTING AND MODELLING OF THE ANGULAR-DEPENDENT SOLAR HEAT GAIN OF GLAZING WITH VENETIAN BLIND SHADING

As overheating problems in glassy buildings came up more and more, EMPA put a focus on the determination and modelling of the total solar energy transmittance (TSET) of multiple glazing combined with different shading systems within the framework of IEA Task 27 "Performance, durability and sustainability of solar façade components". Experimental data were produced by a calorimetric outdoor test facility near Zurich (Switzerland). In a first stage, average constant TSET values related to the incident global solar radiation were identified for a various configurations and a measuring period of a few days each. As expected the results strongly depend on the slat tilt angle as well as on the solar reflectance of the slat surface. Naturally the solar transmittance of an angle-selective component like a Venetian blind also depends on the directional distribution of the incident radiation, in particular on the sun elevation projected into the plane perpendicular to the slat rotation axis. Therefore, results from different measuring periods of the identical shading-glazing configuration are compared, and an extension of the usual average g-value identification is shown. The experimental results are also compared with weighted average g-values from numerical modeling, which is based on a view factor method extended for circular slats in a louver type shading device. In general the agreement between measured and calculated results is good. Some deviation is explained by a significant nondiffuse Albedo component in the hemispherical radiation field. In another paper it was shown that the simplified convective heat transfer calculation used in this model may fail for mechanically ventilated double envelope façades.
LAYIN: toward a global solution for parasitic coupling modeling and visualization

A CAD tool dedicated to parasitic substrate coupling modeling and visualization is presented. A CIF representation of the layout and a specific technology description are used to extract a simple parasitic substrate coupling model. The output is SPICE compatible and includes a geometrical information that is used to show on the layout the distribution of the equipotential lines produced by a perturbing source. Results are compared with measurements and other simulators to demonstrate the accuracy of the model. <<ETX>>
Nuances of the JavaCard API on the cryptographic smart cards – JCAlgTest project

The main goal of this paper is to describe the JCAlgTest project. The JCAlgTest project is designed for an automatic testing of the degree of support for algorithms listed in the JavaCard API specification by a given smart card. Discussion of the results and trends drawn from the database of 28 different cryptographic smart cards is performed. Additional information about particular algorithm or protocol obtained via timing and power analysis is also described and (partially) available in JCAlgTest database.
Thermal comfort in Bangkok residential buildings , Thailand

A field study of thermal comfort was conducted in Bangkok, Thailand in which 1377 responded to a questionnaire while simultaneous physical measurement-s were taken in both airconditioned and naturally ventilated residential buildings. Sensation voting scales chosen are ASHRAE, McIntyre and air flow scale. A regression model was used to find the relationship between the ASHRAE scale votes and the variables that effect thermal comfort. The computer based statistical package SPSS10 for windows was used to perform the regression analysis. The result of this study shows comfort temperature for Bangkok’s people in residential building are 25 deg C for air conditioning buildings and 28 deg C for natural ventilated buildings.