Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding Neuro-Fuzzy on a class of multinomial malware detection problems

Malware classification has become an important task in protection of privacy and sensitive information from being stolen or modified. A number of malware categories and families emerged over last decade targeting Microsoft Windows since it is the most attractive platform for virus developers. Software for this OS is provided in a format of Portable Executable (PE) files. Majority of commercial anti-virus solutions use signature-based detection, where malware pattern is described by means of unique crisp identifier of corresponding PE file content. Neuro-Fuzzy is one of the prospective Hybrid Intelligence methods suitable for malware detection. Despite the fact that Neuro-Fuzzy is being utilized successfully for binary malware classification, it was not investigated in such complex cases as multinomial classification of malware categories and families. The advantage of this method is ability to produce generalized fuzzy rules model applicable for real-world application. For this study we created a novel large-scale malware dataset that include variety of malware samples. Moreover, reports from VirusTotal and PEframe were collected to PE header features and species naming used by major anti-virus vendors. Finally, we applied tuned Neuro-Fuzzy for handling multinomial problems. This paper contributes as a stepping stone for future analysis of aspects Neuro-Fuzzy methods for real-world malware classification.
The REFLEX galaxy cluster survey VIII. Spectroscopic observations and optical atlas

We present the final data from the spectroscopic survey of the ROSAT-ESO Flux-Limited X-ray (REFLEX) catalog of galaxy clusters. The REFLEX survey covers 4.24 steradians (34% of the entire sky) below a declination of δ =+ 2.5 0 and at high Galactic latitude (|b| > 20 ◦ ). The REFLEX catalog includes 447 entries with a median redshift of 0.08 and is better than 90% complete to a limiting flux fX = 3 × 10 −12 erg s −1 cm −2 (0.1 to 2.4 keV), representing the largest statistically homogeneous sample of clusters drawn from the ROSAT All-Sky Survey (RASS) to date. Here we describe the details of the spectroscopic observations carried out at the ESO 1.5 m, 2.2 m, and 3.6 m telescopes, as well as the data reduction and redshift measurement techniques. The spectra typically cover the wavelength range 3600–7500 A at a two-pixel resolution of ∼14 A, and the measured redshifts have a total rms error of ∼100 km s −1 . In total we present 1406 new galaxy redshifts in 192 clusters, most of which previously did not have any redshift measured. Finally, the luminosity/redshift distributions of the cluster sample and a comparison to the no-evolution expectations from the cluster X-ray luminosity function are presented.
Accelerating defocus blur magnification

A shallow depth-of-field is often used as a creative element in photographs. This, however, comes at the cost of expensive and heavy camera equipment, such as large sensor DSLR bodies and fast lenses. In contrast, cheap small-sensor cameras with fixed lenses usually exhibit a larger depth-of-field than desirable. In this case a computational solution is suggesting, since a shallow depth-of-field cannot be achieved by optical means. One possibility is to algorithmically increase the defocus blur already present in the image. Yet, existing algorithmic solutions tackling this problem suffer from poor performance due to the ill-posedness of the problem: The amount of defocus blur can be estimated at edges only; homogeneous areas do not contain such information. However, to magnify the defocus blur we need to know the amount of blur at every pixel position. Estimating it requires solving an optimization problem with many unknowns. We propose a faster way to propagate the amount of blur from the edges to the entire image by solving the optimization problem on a small scale, followed by edge-aware upsampling using the original image as guide. The resulting approximate defocus map can be used to synthesize images with shallow depth-of-field with quality comparable to the original approach. This is demonstrated by experimental results.
JaVaLI!: understanding real questions

This paper presents a linguistically motivated natural language processing system called JaVaLI! 1, that transforms unrestricted text into logical forms. Special focus is given to ambiguity and linguistic variation problems, which can be handled in different steps of the system processing chain. Our system has been tested in the question interpretation domain and some preliminary results over a corpus of 680 questions in Portuguese are presented.
International collaborations : opportunities, strategies, challenges

PART ONE: Setting the Context. EDITOR'S NOTES (Pamela L. Eddy). 1. Framing Issues of International Education (Leila Tubbeh, Jobila Williams) Addressing important questions at the outset can lead to more successful international partnerships. PART TWO: Case Examples. 2. Institutional Collaborations in Ireland: Leveraging an Increased International Presence (Pamela L. Eddy) Five case studies of collaborations among colleges and universities in Ireland highlight the role of external contexts and internal supporters. 3. Notes from the Field: Lessons Learned in Building a Framework for an International Collaboration (Dan Holland) This chapter identifies the reasons why a small Canadian community college decided to venture into the Chinese education market. 4. International Partnerships: A Game Theory Perspective (Yiyun Jie) Game theory is used to understand the relationship between partners' motivations and preferences for expected outcomes in a collaboration between a Chinese and American university. PART THREE: Changing Roles. 5. Administrative Perspectives on International Partnerships (Marilyn J. Amey) Partnership initiatives must move beyond their initial champions if they are to become more than simply fringe activities. 6. Faculty Perspectives on International Education: The Nested Realities of Faculty Collaborations (Joanne Cooper, Rikki Mitsunaga) Colleges and universities need to understand what entices faculty to begin cross-national collaborations and what sustains their work over time. 7. Leveraging Partnerships to Internationalize the Liberal Arts College: Campus Internationalization and the Faculty (Elizabeth Brewer) Partnerships can internationalize the curriculum through faculty development and have lasting, positive effects on teaching and learning in liberal arts colleges. 8. Student Learning in an International Setting (Darren Kelly) In a reversal of the "getting away from it all" mentality, students' use of the Internet has resulted in their being ambi-located between the host society and the United States. 9. Strategies for Planning for the Future (L. Neal Holly) This chapter provides an outline of key issues for college leaders and faculty to consider as they engage in international partnerships. INDEX.
Policy Brief No. 13. Reclaiming Policy Space: Lessons from Malawi's Fertiliser Subsidy Programme

This case study argues that political context matters in agricultural development issues. No matter what the technical or economic arguments for or against particular policy positions are, it is ultimately the configuration of political interests that influence agricultural policy outcomes on the ground. The case of the on-going debate about fertiliser subsidies in Malawi provides a prime example. Recurrent food crises through the 1990s in Malawi provide the backdrop for the recent debate. The economic reform packages supported by donors from the 1990s insisted on the removal of agricultural subsidies. A targeted input programme, supported by the UK Department for International Development (DFID), persisted, but this too was withdrawn in 2004. This resulted in a substantial political backlash, reflected in the focus of campaigns during the presidential elections of 2004, with all candidates backing a subsidy programme for fertilisers. A simple narrative was presented: hunger and recurrent food crises are best responded to by supporting agriculture, and this means providing subsidies to get agriculture moving with a focus on key crops (notably maize and tobacco). National food security and a reduction on dependence on food imports, as had happened in successive years, required, it was argued, concerted state action. This position gained wide popular backing, and in June 2005 the new president announced the introduction of subsidy programme targeted at resource-constrained, but productive, maize farmers. This budget speech ignited intense political debate. Opposition parties argued for a universal subsidy, extended beyond maize to tobacco as a boost to economic growth and foreign exchange earnings. In the end, the government responded and a universal fertiliser subsidy was agreed with a budget of MK4.7 billion. A coupon-based distribution system was
The isomorphism conjecture holds and one-way functions exist relative to an oracle

We demonstrate an oracle relative to which there are one-way functions but every paddable 1-li-degree collapses to an isomorphism type, thus yielding a relativized failure of the Joseph-Young (1985) conjecture (JYC). We then use this result to construct an oracle relative to which the isomorphism conjecture (IC) is true but one-way functions exist, which answers an open question of Fenner, Fortnow, and Kurtz (1992). Thus, there are now relativizations realizing every one of the four possible states of affairs between the IC and the existence of one-way functions.
Factor Shares and Savings in Endogenous Growth

This paper studies the distributive effects of growth when different agents' income is drawn from accumulated and non-accumulated factors of production in different proportions, notes that political interactions may contribute to determine factor shares and growth when income sources are heterogeneous, and suggests that distributional issues should be taken into account both when formulating growth-oriented policy prescriptions and when interpreting the wide dispersion of growth rates across economies and over time.
Designing a Battery-Less Piezoelectric based Energy Harvesting Interface Circuit with 300 mV Startup Voltage

This paper presents a designing a battery-less piezoelectric based energy harvesting interface circuits with 300mV step-up voltage. A technique (i.e., DC-DC Step-Up converter) has chosen for designing the startup voltage with low voltage energy (i.e., 300mV). The proposed method consumes very little power, and is especially suitable for the ambient environmental source, where energy harvested power is very low. The energy harvesting interface circuit consists of MOSFET bridge ac-dc rectifier, voltage regulator, dc-dc step-up converter and an energy storage device with capacitor at the output terminal, replacing this by external battery. This paper will study results these important issues regarding the efficiencies of the energy harvesting power conversion interface circuits considering the storage device low voltage. The achievement of our development circuit is able to boost up minimum 1.67 V for input DC voltage of 300mV. The overall circuit efficiency is greater than 80 % following the simulation results. This research has focused on the application of Wireless Sensor Network (WSN) and bio-medical device can be operated without battery.
Comparison of RF inductor performance evaluation methods

Various model parameter calculation equations of RF CMOS inductors are compared. The calculation equations are using as variables either Y-, Z-parameters or S-parameters. Results are based on on-wafer RF S-parameter measurements of a 3.75 turn spiral inductor fabricated in AMIS 0.7 µm CMOS DM1P n-well technology. Results obtained with calculation equations for a differentially driven inductor topology show differences evaluating the same model parameter, depending on whether Z- and S-parameters are used. Thus, care must be taken by the designers when estimating an integrated inductor performance to comprehend the operating environment of the device and to choose the corresponding formulas accordingly.
Evaluating Dynamic Strategies: The Free Lunch Was No Banquet

We examine the issues of evaluating the performance of technical trading rules applied to daily equity and exchange rates. First, we document the ability of the rules to predict mean, variance and higher moments of returns. Second, we contrast these often used measures with alternates more closely tied to the profitability of the rules, such as excess return on the buy-and-hold strategy and Sharpe ratio. While, as found by others, the evidence of predictability is statistically strong on very long samples of data, that on profitability is weaker. Third, we turn show that for realistic horizons below 10 years, the rules then do not consistently beat the buy-and-hold strategy. Moreover, when we consider measures of persistence in performance, the evidence is further weakened. Last, we incorporate the potential for data-snooping into the analysis. We use genetic algorithms, in a novel way, to quantify the effect of data-snooping in the evaluation of trading rules. We find that at horizons below 10 years, the bias due to data-snooping is so large that no true economic profitability remains. We also show that even sophisticated algorithms would have failed to yield profits in past 20 years. While we agree with others that "the party may have ended", our results show that any prior free lunch was no banquet.
Mapping a Suburb With a Single Camera Using a Biologically Inspired SLAM System

This paper describes a biologically inspired approach to vision-only simultaneous localization and mapping (SLAM) on ground-based platforms. The core SLAM system, dubbed RatSLAM, is based on computational models of the rodent hippocampus, and is coupled with a lightweight vision system that provides odometry and appearance information. RatSLAM builds a map in an online manner, driving loop closure and relocalization through sequences of familiar visual scenes. Visual ambiguity is managed by maintaining multiple competing vehicle pose estimates, while cumulative errors in odometry are corrected after loop closure by a map correction algorithm. We demonstrate the mapping performance of the system on a 66 km car journey through a complex suburban road network. Using only a web camera operating at 10 Hz, RatSLAM generates a coherent map of the entire environment at real-time speed, correctly closing more than 51 loops of up to 5 km in length.
Image database clustering with SVM-based class personalization

To allow efficient browsing of large image collections, we have to provide a summary of its visual content. We present in this paper a robust approach to organize image databases: the Adaptive Robust Competition (ARC). This algorithm relies on a non-supervised database categorization, coupled with a selection of prototypes in each resulting category. This categorization is performed using image descriptors, which describe the visual appearance of the images. A principal component analysis is performed for every feature to reduce dimensionality. Then, clustering is performed in challenging conditions by minimizing a Competitive Agglomeration objective function with an extra noise cluster to collect outliers. The competition is improved to be adaptive to clusters of various densities. In a second step, we provide the user with tools to correct possible misclassifications and personalize the image categories. The constraints to deal with for such a system are the simplicity of the user feedback and the rapidity to propose a new category based on the user's criteria.
Better Heuristics for a Two-Stage Multi-Machine Assembly Scheduling Problem to Minimize Total Completion Time

We address the two-stage multi-machine assembly scheduling problem to minimize total completion times of all jobs. The first stage consists of m independently working machines where each machine produces its own component. The second stage consists of two independent and identical assembly machines. The processing of a job at the second stage cannot start until its m components, produced by the m machines at the first stage, are complete. This problem has been recently addressed in the literature for which an efficient heuristic, called SAK, was developed for the case when m=2. In this paper, we address the problem for the case m≥2. We propose a hybrid tabu search (HTS) heuristic and show that the overall average error of SAK is more than twice that of HTS while the average CPU time of SAK is five times that of HTS. This clearly indicates that the heuristic HTS is much better than the only existing heuristic available for the problem, i.e., SAK. We also propose two more heuristics, called SDE and NSDE, and show that the overall average error of SDE is about half of that of HTS. Furthermore, we show that the overall average error of NSDE is about one third that of SDE.
Eecient Learning of Selective Bayesian Network Classiiers

In this paper, we present a computation-ally eecient method for inducing selective Bayesian network classiiers. Our approach is to use information-theoretic metrics to ef-ciently select a subset of attributes from which to learn the classiier. We explore three conditional, information-theoretic met-rics that are extensions of metrics used extensively in decision tree learning, namely Quin-lan's gain and gain ratio metrics and Man-taras's distance metric. We experimentally show that the algorithms based on gain ratio and distance metric learn selective Bayesian networks that have predictive accuracies as good as or better than those learned by existing selective Bayesian network induction approaches (K2-AS), but at a signiicantly lower computational cost. We prove that the subset-selection phase of these information-based algorithms has polynomial complexity, as compared to the worst-case exponential time complexity of the corresponding phase in K2-AS.
INFORMATION PROVISION IN ROAD TRANSPORT WITH ELASTIC DEMAND. A WELFARE ECONOMIC APPROACH. IN: TRANSPORT AND INFORMATION SYSTEMS

The aim of this paper is to enhance insight into the welfare economic effects of information provision to a group of drivers, and to gain insights into the mechanisms affecting the impact of providing information. This is done by theoretically analyzing the impact of information provision on network efficiency. In addition, the equity aspects of information provision are considered by answering questions related to this topic. A static economic equilibrium model is presented, allowing for elastic demand. An increasing cost curve represents the costs for travel. The elasticity of demand distinguishes this approach from the existing literature on information provision. The paper concludes with comments and suggested future directions for research.
Recognizing unknown activities using semantic word vectors and twitter timestamps

In this paper, we propose a method for activity recognition, which can estimate new activities which does not appear in the training data, combining word vectors constructed from semantic word vectors and Twitter timestamps. Because traditional activity recognition utilizes supervised machine learning, unknown activity classes which do not appear in the training dataset is unable to be estimated. For this problem, zero-shot machine learning method is proposed, but it requires the preparation of semantic codes. As semantic codes, we utilize word vectors constructed from semantic word vectors and Twitter timestamps. To evaluate the proposed method, we evaluated whether we could estimate unknown activity classes, with the sensor data set collected from 20 households for 4 months, along with the user-generated labels using the web system which can estimate, modify, and add new activity types. As a result, the proposed method could even estimate unknown activity classes. Moreover, by utilizing Twitter timestamps and semantic word vectors from the Japanese Wikipedia in word vectors, the method could estimate 9 unknown activity classes.
Differential Evolution with A New Mutation Operator for Solving High Dimensional Continuous Optimization Problems

In the past decades, different kinds of bio-inspired algorithms have been proposed to solve optimization problems. Although these algorithms have shown good optimization performance in solving lower dimensional problems (D<100), many of them could hardly achieve promising solutions with increasing of dimensions. In this paper, an improved differential evolution algorithm, namely IDE, is proposed to solve high dimensional continuous optimization problems. The proposed approach employs a new mutation operator, which makes a good balance between local and global search. Simulation studies on six benchmark problems provided by CEC 2008 Special Session on Large Scale Global Optimization with D=100, D=500 and D=1000 show that IDE obtains better performance when compared with other five algorithms.
EFFECTS OF SHEAR STRESS IN TELETACTION AND HUMAN PERCEPTION

Ideally, a tactile stimulator presents information through control of surface normal stress and surface shear stresses. Psy-chophysical experiments measured the eeect of shear stress information on the perception of static stimuli applied normal to the surface. Wax gratings in two diierent orientations and various spatial frequencies were used as stimuli. Elastic layers, representing the anti-aliasing lter on a tactile display, were placed over the stimuli. Using an elastic layer which reduces shear stress transmission improved perception compared to standard elastic layers. Spatial resolution diierences due to diierent elastic layers are explained by modulation indices determined from the predicted sub-surface strain using an elastic half-plane model. INTRODUCTION Information about texture, local compliance, and local shape is important in applications such as telesurgery or handling of fragile objects in telerobotics. Figure 1 shows a general connguration of a teletaction system. One possible application is on a robotic laparoscopic telesurgery system. The tactile sensor is mounted on the end eeector (the laparoscopic instrument), and the tactile stimulator display is mounted on the master ma-nipulator (the user interface). The tactile stimulator presents information recorded by the tactile sensor to the user. Ideally, the patterns felt by the user would be indistinguishable from direct contact with the environment. The tactile stimulator needs to generate surface stresses that realistically represent data collected by the tactile sensor. To fully control surface stress, the ideal tactile display system would be an innnite density array of 3 DOF actuators. This paper examines the perceptual diierence between a 1 DOF and 3 DOF tactile display for simple grating patterns. Tactile displays can control either displacements or forces. In a displacement display, an array of pins is shaped into a con
White Matter Hyperintensities Relate to Clinical Progression in Subjective Cognitive Decline

Background and Purpose— In patients with subjective cognitive decline, we assessed whether small vessel disease was associated with clinical progression and cognitive decline. Methods— We included 334 patients with subjective cognitive decline. Follow-up was 3±2 years. Results— Fifty-three (16%) patients progressed clinically to mild cognitive impairment or dementia. White matter hyperintensities were associated with clinical progression and with annual decline on memory, attention, executive functioning, and global cognition. Microbleeds and lacunes were not associated with clinical progression or cognitive decline. Conclusions— In patients with subjective cognitive decline, patients with white matter hyperintensities are at increased risk of clinical progression and cognitive decline.