Miracle-dz/UniFAR-FTUs huggingface dataset:
Closure and Quality Equivalence for Efficient Synthesis of Grasps from Examples

One goal of grasp selection for robotics is to choose contact points that guarantee properties such as force- or form-closure. Many efficient algorithms have been developed to address this problem, but most of these algorithms focus on grasps having a minimal number of contact points. Increasing the number of contacts can dramatically improve the quality and flexibility of grasps that are constructed. However, computation time becomes a problem, as grasp synthesis algorithms that can be generalized to an arbitrary number of contacts typically require time exponential in the number of contacts. This paper presents an efficient algorithm for synthesis of many-contact grasps. The key idea is to geometrically construct families of grasps around a single example such that all grasps within a family meet user-specified design goals. We show that our construction technique can be used to form force-closure grasps, partial force-closure grasps, and grasps above a quality threshold. Our approach requires time polynomial in the number of contacts, making it feasible to handle grasps with relatively large numbers of contacts. Results are shown for three-dimensional grasps with friction having five to twelve contacts and specialized for a variety of tasks. We have used this approach to design grasps for a robot hand and quasi-static manipulation plans for a humanoid robot.
A Semi-fragile Watermarking for ROI Image Authentication

In this paper, a watermarking algorithm for ROI (Regions of Interesting) authentication and tamper detection is proposed. The watermarking is embedded in the DWT (Discrete Wavelet Transform) coefficients of the ROB (Regions of Background). The algorithm uses a hashing to detect whether the ROI of image is tampered, and then uses the watermarking to reconstruct the data of ROI. The experiment results show that the embedded watermark is perceptually invisible and robust to various image processing operations. Most importantly, the tampered place can be identified accurately.
Accuracy of Location Identification with Antenna Polarization on RSSI

Pervasive computing applications, which are location-aware systems becoming increasingly important as part of our daily life. Real-time position localization of moving objects in an indoor environment is an encouraging technology for realizing the vision of creating numerous novel location-aware services and applications in various market segments. An off the shelf development platform that uses Radio Signal Strength Indication (RSSI) based location technique is always used for testing. In this paper we investigated at the affects of polarization on an indoor location tracking system. The target of this paper is to present an optimal design for RSSI location technology. We created a model for determining range form RSSI demonstrated that this model fits our experimental setup. Antenna polarization will affect RSSI and thus range accuracy. The experiments and its errors are carefully analyzed and found that the traditional least squares method of determining the parameters of the range model will give unacceptably high location error. A simple and cheap method of determining polarization angle is introduced with an accelerometer, which also increase the battery life of the node.
Assessment of Impacts of Climate Change on the Water Resources of the Transboundary Jhelum River Basin of Pakistan and India

Pakistan’s economy is significantly reliant on agriculture. However, Pakistan is included in the most water-stressed countries in the world, and its water resources are considerably vulnerable to climate variability and climate change. Therefore, in the present study, the water resources of the Jhelum River basin, which provides water to 6 million hectares of land of Pakistan and hydropower production, were assessed under the scenarios A2 and B2 of HadCM3. A hydrological model, Hydrologic Modeling System (HEC-HMS), was set up, calibrated, and validated for the Jhelum basin, and then streamflow was simulated for three future periods: 2011–2040, 2041–2070, and 2071–2099. The simulated streamflow of each period was compared with the simulated streamflow of the baseline period (1971–2000) to find the changes in the following indicators: mean flow, low flow, median flow, high flow, and center-of-volume dates (CVDs). The results of the study showed an increase of 10%–15% in the mean annual flow as compared to the baseline flow at the end of this century. Winter, spring, and autumn showed an increase in streamflow at most of the sites in all three periods. However, summer (the monsoon season in the basin) showed decreased streamflow at most of the sites. Maximum increase at Azad Pattan was projected in winter in the 2080s, with about 37%–39% increase in flow under both scenarios. Low and median flows were projected to increase, but a decline in high flow was detected in the future under both scenarios. It was also concluded that half of the annual flow in the basin will pass by the Azad Pattan site one week earlier than it does now. On the whole, the Jhelum basin would face more temporal and magnitudinal variations in high, low, and mean flows relative to present conditions. This shows that without a consideration of climate change impacts, proper utilization and management of water resources in the basin will be more difficult.
A HYBRID LEARNING APPROACH TO BLIND DECONVOLUTION OF MIMOSYSTEMSSeungjin

In this paper we present a hybrid learning method for blind deconvolution of linear MIMO systems. We propose a hybrid network that consists of a linear feedforward network followed by a linear feedback network, where each of synapses is represented by an FIR lter. The FIR synapses in the feedforward network are learned by the constant modulus algorithm (CMA) to recover source signals and at the same time, the FIR synapses in the feedback network are updated by spatio-temporal decorrelation algorithms so that diierent sources appear at diierent output nodes. As a spatio-temporal decorrelation task, we consider the extension of anti-Hebbian rule and the natural gradient-based learning algorithm. Useful behavior of the proposed hybrid network is veriied by computer simulation results.
Exploring Human Narrative Intelligence with Narrative Oriented Inquiry

At last year’s conference (see Hiles, �ermak and Chrz, 2009), we outlined an approach to narrative research which we call Narrative Oriented Inquiry . We see this as a dynamic framework for good practice, ie. a psychological approach developed with respect to research on personal narratives which offers a distinctive and critical framework to narrative inquiry. In this paper we want to explore how our approach can be used to explore the narrative intelligence that is at work in our everyday lives (Ricoeur, 1987/1991). We argue that the first steps in narrative data analysis offer crucial insight into how people exhibit this intelligence. Indeed, a relatively straightforward distinction between fabula and sjuzet can be used to reveal the identity positioning at play in a narrator’s constructions. Taking an example from an interview with an elderly man, who is outlining his life journey with particular emphasis on the life events that influenced and changed the course of his career, we demonstrate NOI in action. The data analysis especially focuses upon the subtle ways in which NK participates in his own constructions of self.
Recent advances in memory-based part-of-speech tagging

Memory-based learning algorithms are lazy learners. Examples of a task are stored in memory and processing is largely postponed to the time when new instances of the task need to be solved. This is then done by extrapolating directly from those remembered instances which are most similar to the present ones. Using memory-based learning for Part-of-Speech tagging has a number of advantages over traditional statistical POS taggers: (i) there is no need for an additional smoothing component for sparse data, (ii) even low-frequent or exceptional patterns can contribute to generalization, (iii) the use of a weighted similarity metric allows for an easy integration of diierent information sources, and (iv) both development time and processing speed are very fast (in the order of hours and thousands of words/sec, respectively). In recent work, we have applied the Memory-Based tagger (MBT) to a number of diierent languages and corpora (En-glish, Dutch, Czech, Swedish, and Spanish). Furthermore, we have performed a controlled experimental comparison of MBT with several other POS tagging algorithms.
Boolean modeling in systems biology: an overview of methodology and applications

Mathematical modeling of biological processes provides deep insights into complex cellular systems. While quantitative and continuous models such as differential equations have been widely used, their use is obstructed in systems wherein the knowledge of mechanistic details and kinetic parameters is scarce. On the other hand, a wealth of molecular level qualitative data on individual components and interactions can be obtained from the experimental literature and high-throughput technologies, making qualitative approaches such as Boolean network modeling extremely useful. In this paper, we build on our research to provide a methodology overview of Boolean modeling in systems biology, including Boolean dynamic modeling of cellular networks, attractor analysis of Boolean dynamic models, as well as inferring biological regulatory mechanisms from high-throughput data using Boolean models. We finally demonstrate how Boolean models can be applied to perform the structural analysis of cellular networks. This overview aims to acquaint life science researchers with the basic steps of Boolean modeling and its applications in several areas of systems biology.
Sacred Sports: Moral Responses to Sports Media Content

This study examines moral responses to sports media content. Using two conditions, participants (N = 639) were randomly assigned to read a damaging sports article about their university’s basketball team, either written by an ingroup or outgroup member. Participants then reported their moral outrage to the article, moral cleansing responses, and team support intentions. Findings demonstrated that individuals demonstrated moral outrage, moral cleansing, and team support intentions when their favored team was derogated against, especially when the source of disparagement came from an outgroup member. Results are discussed in light of social identification and moral foundations theories.
A hybrid approach in the evaluation of usability for multimedia objects: case study of the media assets management platform for an advertainment production project toward Beijing Olympics 2008

Many studies done in the study of usability and heuristic evaluations have evolved around the use of scenarios and heuristics. In our study, we aim to define usability attributes using research on scenario-based design methodologies as well as classical heuristic evaluation theories -specifically for hypermedia objects. We then apply these principles on a digital portal developed for use in a video production project for Beijing Olympics 2008, evaluating it for usability using the developed approach, with an aim to also initiate an iterative usability design process for the project.
Weighted Information Gain and User Clicks on Web Search Results

In this poster, we demonstrate that the WIG (Weighted Information Gain) technique, originally proposed for retrieval performance prediction and shown to be effective particularly in Web search environments, has an interesting connection with user clicks on Web search results. Specifically, we observe that high WIG scores generally suggest more clicks. This makes WIG a useful feature for predicting user’s preference for search results, which has potential applications in many important areas such as the automatic tuning of search engine parameters, personalization, sponsored search and others.
Node Failure Localization in Communication Networks via Network Tomography

—We investigate the problem of localizing node failures in a communication network from end-to-end path measurements, under the assumption that a path behaves normally if and only if it does not contain any failed nodes. To uniquely localize node failures, the measurement paths must show different symptoms under different failure events, i.e., for any two distinct sets of failed nodes, there must be a measurement path traversing one and only one of them. This condition is, however, impractical to test for large networks due to the combinatorial numbers of paths and failure sets. Our ﬁrst contribution is a characterization of this condition in terms of easily veriﬁable conditions on the network topology with given monitor placements under three families of probing mechanisms, which differ in whether measurement paths are (i) arbitrarily controllable, (ii) controllable but cycle-free, or (iii) uncontrollable (i.e., determined by the default routing protocol). Our second contribution is a characterization of the maximum identiﬁability of node failures, measured by the maximum number of simultaneous failures that can always be uniquely localized. Speciﬁcally, we bound the maximal identiﬁ-ability from both the upper and the lower bounds which differ by at most one, and show that these bounds can be evaluated in polynomial time. Finally, we quantify the impact of the probing mechanism on the capability of node failure localization by numerically comparing the maximum identiﬁability under different probing
The subcellular distribution and function of MTA1 in cancer differentiation

The functions and mechanisms of metastasis-associated protein 1 (MTA1) in cancer progression are still unclear due to a lagged recognition of the subcellular localization. In the present study, using multiple molecular technologies we confirmed for the first time that MTA1 localizes to the nucleus, cytoplasm and nuclear envelope. MTA1 is primarily localized in the nucleus of normal adult tissues but in the cytoplasm of embryonic tissues. While in colon cancer, both distributions have been described. Further investigation revealed that MTA1 localizes on the nuclear envelope in a translocated promoter region (TPR)-dependent manner, while in the cytoplasm, MTA1 shows an obvious localization on microtubules. Both nuclear and cytoplasmic MTA1 are associated with cancer progression. However, these functions may be associated with different mechanisms because only nuclear MTA1 has been associated with cancer differentiation. Overexpression of MTA1 in HCT116 cells inhibited differentiation and promoted proliferation, whereas MTA1 knockdown resulted in cell differentiation and death. Theses results not only suggest that nuclear MTA1 is a good marker for cancer differentiation diagnosis and a potential target for the treatment of cancers but also reveal the necessity to differentially examine the functions of nuclear and cytoplasmic MTA1.
Single Channel Speech Enhancement Using Outlier Detection

Distortion of the underlying speech is a common problem for single-channel speech enhancement algorithms, and hinders such methods from being used more extensively. A dictionary based speech enhancement method that emphasizes preserving the underlying speech is proposed. Spectral patches of clean speech are sampled and clustered to train a dictionary. Given a noisy speech spectral patch, the best matching dictionary entry is selected and used to estimate the noise power at each time-frequency bin. The noise estimation step is formulated as an outlier detection problem, where the noise at each bin is assumed present only if it is an outlier to the corresponding bin of the best matching dictionary entry. This framework assigns higher priority in removing spectral elements that strongly deviate from a typical spoken unit stored in the trained dictionary. Even without the aid of a separate noise model, this method can achieve significant noise reduction for various non-stationary noises, while effectively preserving the underlying speech in more challenging noisy environments.
S^3 - Statistical Sandhi Splitting

The problem of Sam.dhi-Splitting is central to computational processing of San-skrit texts. Currently the best-known al-gorithm for this task, given a chunk, generates all possible splits and chooses the Maximum-a-Posteriori estimate as the ﬁ-nal answer. Our contributions to the task of Sam . dhi-Splitting are two-fold. Firstly, we improve upon the current algorithm by proposing a principled modiﬁcation of the posterior probability function to achieve better results. Secondly, we pro-pose an algorithm based on Bayesian Word-Segmentation methods. We ﬁnd that the unsupervised version of our algorithm achieves a better precision than the current algorithm with the original probabilistic model. We then present a supervised version of our algorithm that outperforms all previous methods/models.
Network Security Challenges in Android Applications

The digital world is in constant battle for improvement - especially in the security field. Taking into consideration the revelations from Edward Snowden about the mass surveillance programs conducted by governmental authorities, the number of users that raised awareness towards security is constantly increasing. More and more users agree that additional steps must be taken to ensure the fact that communication will remain private as intended in the first place. Taking in consideration the ongoing transition in the digital world, there are already more mobile phones than people on this planet. According to recent statistics there are around 7 billion active cell phones by 2014 out of which nearly 2 billion are smartphones. The use of smartphones by itself could open a great security hole. The most common problem when it comes to Android applications is the common misuse of the HTTPS protocol. Having this in mind, this paper addresses the current issues when it comes to misuse of the HTTPS protocol and proposes possible solutions to overcome this common problem. In this paper we evaluate the SSL implementation in a recent set of Android applications and present some of the most common missuses. The goal of this paper is to raise awareness to current and new developers to actually consider security as one of their main goals during the development life cycle of applications.
[mc]square: A Model Checker for Microcontroller Code

The paper presents details of a model checker for microcontroller-based embedded systems, called [mc] square. The purpose of the tool is to make model checking technology applicable in an embedded systems industry context. Consequently, it does not implement new theory but combines existing techniques to achieve the necessary efficiency and usability in a novel application area. One of the pragmatic requirements has been that model checking must be possible without any kind of manual preprocessing of the code. In its core, [mc]square is an explicit state, CTL model checker which builds the state space from the hardware-specific assembly code. The paper describes the tool features in detail and illustrates its abilities using two realistic examples.
Perceptually Equivalent Judgments Made Visually and via Haptic Sensory-Substitution Devices

ABSTRACT According to the ecological theory of perception–action, perception is primarily of affordances, which are directly perceivable opportunities for behavior. The current study evaluated participants’ ability to use vision and haptic sensory-substitution devices to support perceptual judgments of affordances involving the task of passing through apertures. Sighted participants made perceptual judgments about whether they could walk through apertures of various widths and their level of confidence in each judgment, using unrestricted vision and, when blindfolded, using two haptic sensory-substitution instruments: a cane-like wooden rod and the Enactive Torch, a device that converts distance information into vibrotactile stimuli. The boundary between aperture widths that were judged as pass-through-able versus non-pass-through-able was statistically equivalent across sensory modalities. However, participants were not as confident in their judgments using the rod or Enactive Torch as they were using vision. Additionally, participants’ judgments with the haptic instruments were significantly more accurate than with vision. The results underscore the need to assess sensory-substitution devices in the context of functional behaviors.
Robust Adaptive Control of Linear Induction Motors With Unknown End-Effect and Secondary Resistance

This paper proposes a novel robust adaptive speed/ position tracking control for a linear induction motor (LIM) with both end-effect and secondary resistance unknown. The practical current-fed controlled LIM, with residual current error, is considered, i.e., the traditional ideal current-loop assumption is relaxed. More practical conditions, such as bounded primary voltage and a finite absolute-integral of current tracking error is considered. To overcome the high nonlinearity and nonzero current error, a backstepping method, combining virtual desired variable synthesis, is developed for the speed and position tracking. Then, the controller achieves asymptotic speed and position tracking even with unknown parameters and immeasurable secondary flux. Furthermore, the effect of the residual current error is attenuated in an L2 -gain sense. The experiments for several scenarios are carried out to verify the theoretical result.
A Novel Hybrid Algorithm for Constrained Multi-objective Optimization

A new hybrid Optimization Algorithm is proposed to solve Constrained Multi-objective Optimization Problems (CMOPs). The algorithm is named BBO/DE which combines the exploitation ability of Biogeography-based Optimization (BBO) and the exploration ability of Differential Evolution (DE). Meanwhile distance measures and adaptive penalty functions are adopted to handle the constraints so that optimal solutions in the infeasible space can be searched effectively. In addition, the feasible archive is applied to store the non-dominated feasible solutions obtained so far and is updated based on crowding-distance. Experiment results demonstrate that the proposed hybrid algorithm BBO/DE can approximate the true Pareto front and has better distribution.
Viterbi algorithm motives in turbo decoding

This work addresses the problem of decoding turbo convolutional codes. In particular, it is concerned with the question of how maximum likelihood sequence estimation (MLSE), in the shape of the Viterbi algorithm (VA), can be utilized in the framework of turbo decoding. It is shown that the conventional VA, which is a soft-input hard-output decoder, can be used for iterative decoding of turbo codes. Moreover, it is demonstrated how the VA can be used for obtaining accurate BER estimation as well as an effective stopping criterion.
Knowledge and Attitude Towards European Monetary Union

Abstract In this paper, the relationship between objective knowledge of EU and people's attitudes towards the proposed European Monetary Union (EMU) is analyzed. As a data base, survey data from Eurobarometer 39 (1993) has been employed. First, it is displayed in a cross-country comparison, how the presented knowledge indicator varies between the original 12 EU members. Then the bivariate relationship of the knowledge index with attitudes towards EMU is investigated. In a next step, the robustness of the positive correlation between objective knowledge and attitudes towards EMU is analyzed in a multivariate regression model. As a conclusion, it is argued that a higher level of EU knowledge could influence people's opinion towards further monetary integration positively.