Miracle-dz/UniFAR-FTUs huggingface dataset:
Maximum load path planning for space manipulator in point-to-point task

In this paper, the movement coupling between manipulator and spacecraft is proposed. The dynamic equations of space manipulator are established efficiently by the spatial operator algebra method. The functions of the joint variable are parameterized by polynomial interpolation method and objective function for maximum load carrying capacity is developed. In considering multiple constraints, maximum load path planning for space manipulator is achieved based on genetic algorithm. Simulations are performed in order to illustrate the effectiveness of the algorithm for a 7-DOF space manipulator in point-to-point task.
Sentence-Adapted Factored Language Model for Transcribing Estonian Speech

This work presents a 2-pass recognition method for highly inflected agglutinative languages based on an Estonian large vocabulary recognition task. Morphemes are used as basic recognition units in a standard trigram language model in the first pass. The recognized morphemes are reconstructed back to words using hidden event language model for compound word detection. In the second pass, the vocabulary from N-best sentence candidates from the first pass is used to create an adaptive sentence-specific word-based language model which is applied for rescoring the N-best hypotheses. The sentence specific language model is based on the factored language model paradigm and estimates word probabilities based on the preceding two words and part-of-speech tags. The method achieves a 7.3% relative word error rate improvement over the baseline system that is used in the first pass
On the Amin Method and Its Applications in the Real Options with Interventions under Jump-Diffusion Processes

The paper gives proofs for the monotonicity and convexity of the Amin method to value American option under jump-diffusion processes. We also find that the Amin method is monotone for the free boundary. These properties make the Amin method appropriate for analysis in the real options with intervention, which may be biased for other numerical methods because of lack of monotonicity and convexity. The corresponding numerical results show that the market power is important for the investor in making the decision of intervention.
The Variance Matters: How Party Systems Represent the Preferences of Voters

Cross-national analyses are presented that suggest that changes in the variance of voters' policy preferences—in 12 Western European democracies from 1976 to 1998—are associated with corresponding changes in the variance of policy choices on offer in these party systems. This finding is labeled the Voter Distribution Effects Result. There is also evidence to support a second major finding, the Electoral Laws Result, which states that voter distribution effects, i.e., the effects associated with changes in the variance of voters' policy preferences, are stronger in political systems that feature less proportional electoral rules (e.g., plurality voting systems). These findings have implications for party strategies and for our general understanding of political representation.
Discovery of Environmental Web Resources Based on the Combination of Multimedia Evidence

This work proposes a framework for the discovery of environmental Web resources providing air quality measurements and forecasts. Motivated by the frequent occurrence of heatmaps in such Web resources, it exploits multimedia evidence at different stages of the discovery process. Domain-specific queries generated using empirical information and machine learning driven query expansion are submitted both to the Web and Image search services of a general-purpose search engine. Post-retrieval filtering is performed by combining textual and visual (heatmap-related) evidence in a supervised machine learning framework. Our experimental results indicate improvements in the effectiveness when performing heatmap recognition based on SURF and SIFT descriptors using VLAD encoding and when combining multimedia evidence in the discovery process.
A Statistical Input Pruning Method for Artificial Neural Networks Used in Environmental Modelling

Artificial neural networks (ANNs) provide a useful and effective tool for modelling poorly understood and complex processes, such as those tha t occur in nature. However, developing an ANN to properly model the desired relationship is not a tr ivial task. Selection of the correct causal inputs is one of the most important tasks faced by neural network practi tioners, but as knowledge regarding the relationshi ps modelled by ANNs is generally limited, selecting th e appropriate inputs is also one of the most diffic ult tasks in the development of an ANN. Many of the methods available for assessing the significance of potenti al input variables do not consider the uncertainty or variability associated with the input relevance mea sures used and, consequently, this important factor is ne glected during hypothesis testing. In this paper a modelbased method is presented for pruning ANN inputs, b a ed on the statistical significance of the relatio nship between the input variables and the response variab le. The approach uses Bayesian methods to estimate the input relevance measure such that the uncertainty a ssociated with this parameter can be quantified and hypothesis testing can be carried out in a straight forward and statistical manner. The proposed metho dology is applied to a synthetically generated data set an d it is found to successfully identify the 3 releva nt inputs that were used to generate the data from 15 possible inp ut variables that were originally entered into the ANN.
Automatic Code Generation for SIMD Hardware Accelerators

SIMD hardware accelerators o er an alternative to manycores when energy consumption and performance are critical. For scienti c computing, GPGPUs are used in many computers of the top-500. But embedded processors also use accelerators. However such heterogeneous platforms trade ease of developments for performance: The application code and the data must be split between the host and the accelerator, synchronizations and communications between host and accelerator must be added, and accelerator hardware constraints must be taken into account by the programmer. To ease application development, we present an algorithm to automatically externalize the execution of a parallel loop using a synchronous master/slave protocol. The source-to-source transformation process is incrementally consistent and the transformed code can be executed at any step of the transformation algorithm, which make application and compiler debugging easier and possible without the target hardware. This article details the source-to-source parallel loop externalization algorithm and the new elementary program transformations which are used. Unlike previous work based on annotations or polyhedral model, it relies on interprocedural array region analysis, which embraces more situations than the polyhedral model while still not relying on user input. It has been implemented in the PIPS framework and its results are given at each transformation step for an image processing example.
Querying Heterogeneous and DistributedData Repositories using

The facility that allows users to query heterogeneous and distributed data repositories the majority of users. Moreover, that facility should hide all the intrinsic problems related to the context such as location, organi-zation/structure, query language and semantics of the data in various repositories. In this paper a query processing strategy that focuses on information content is presented. For that, domain speciic ontologies that capture the information content of data repositories are used. Those ontologies are described using a system based on Description Logics. It is also explained the diierent steps followed to provide incremental answers to user queries navigating across ontologies, using pre-deened semantic interontology relationships. Description Logics systems capabilities are exploited to optimize queries and guide the query processing.
Device-free and device-bound activity recognition using radio signal strength

Background: We investigate direct use of 802.15. 4 radio signal strength indication (RSSI) for human activity recognition when 1) a user carries a wireless node (device-bound) and when 2) a user moves in the wireless sensor net (WSN) without a WSN node (device-free). We investigate recognition feasibility in respect to network topology, subject and room geometry (door open, half, closed). Methods: In a 2 person office room 8 wireless nodes are installed in a 3D topology. Two subjects are outfitted with a sensor node on the hip. Acceleration and RSSI are recorded while subject performs 6 different activities or room is empty. We apply machine learning for analysis and compare our results to acceleration data. Results: 10-fold cross-validation with all nodes gives accuracies of 0.896 (device-bound), 0.894 (device-free) and 0.88 (accelerometer). Topology investigation reveals that similar accuracies may be reached with only 5 (device-bound) or 4 (device-free) selected nodes. Applying trained data from one subject to the other and vice-versa shows higher recognition difference on RSSI than on acceleration. Changing of door state has smaller effect on both systems than subject change; with least impact when door is closed. Conclusion: 802.15. 4 RSSI suited for activity recognition. 3D topology is helpful in respect to type of activities. Discrimination of subjects seems possible. Practical systems must adapt no only to long-term environmental dispersion but consider typical geometric changes. Adaptable, robust recognition models must be developed.
Design of a modular, flexible instrument with integrated DC-motors for minimal invasive robotic surgery

This paper proposes a novel concept of a flexible instrument for minimal invasive robotic surgery. The focus of this snake-like manipulator is to reduce the trauma for the patient and a shorter time of hospital stay. Furthermore the performance of complex interventions will be simplified due to the wide range of instruments degrees of freedom. The mechanism of this manipulator is built of several modular segments, which are linked to each other. The two DC-motors inside the segments submit the required two DoFs for generating a 3d pathway. The design of the mechanical parts, sensors and a concept for the control system will be presented in this paper. A simulation environment with an associated mathematical model is also implemented.
Vehicle scheduling base on the improved particle swarm

In order to effectively prevent particle swarm into local optimum and enhance the ability of particles to find the optimal solution, the paper proposes an improved particle swarm algorithm. The paper is on the background of vehicle scheduling, which is using the load priority strategy, and establishes the model for vehicle scheduling problem with load and vehicle scheduling problem with soft time windows. The experimental results show that the improved particle swarm can quickly and efficiently obtain the optimal solution for vehicle routing problem.
Analysis of CMOS Logic and Transmission Gate for 64 Bit Parallel Prefix Adders

Parallel prefix adder network is a type of carry look ahead adder structure. It is widely considered as the fastest adder and used for high performance arithmetic circuits in the digital signal processors. In this article, an introduction to the design of 64 bit parallel prefix adder using transmission technique which acquires least no of nodes with the lowest transistor count and low power consumption is presented. The 64 bit parallel prefix adder is designed and comparison is made between other previously parallel prefix adders. The result shows that the proposed 64 bit parallel prefix adder is slightly better than existing parallel prefix adders and it considerably increases the computation speed.The spice tool is used for analysis with different supply voltages.
Intelligent Machinery . NPL Report of the Controller Universal Turing Machine

In recent years, much research has been devoted to the improvement of the Ethernet; contrarily, few have investigated the evaluation of forward-error correction. In fact, few futurists would disagree with the development of local-area networks , which embodies the technical principles of operating systems. KEPI, our new application for hash tables, is the solution to all of these issues.
Lexico-Syntactic Patterns for Automatic Ontology Building

In this paper, we evaluate different lexico-syntactic patterns in regard to their usefulness for ontology building. Each pattern is analysed individually to determine its respective probability to return the hyponymy relation. We also create different ontologies according to this accuracy criteria to show how it influences the resulting ontology. Using patterns with a success rate over 80% leads to an approximate accuracy of 77% in the final ontology.
VBS Stemmer: A vocabulary-based stemmer

Stemming is referred to a procedure of reducing all words appearing in different morphological variants to a common form. As a matter of fact, it is considered as a functional way in various areas of information-retrieval work and computational linguistics. In this paper, we introduced the Vocabulary Based Stemmer (VBS) as the alternative solution to the stemming problem for the applications which are based on the semantic relation between words or dictionary based and need valid words. The Vocabulary part of VBS stemmer is generated based on WordNet. To validate the VBS Stemmer, part of “Cranfield 1400” test collection being used, and the result shows significant improvements over the previous stemmers
Electrodermal Activity Responses for Quantitative Assessment of Felt Pain

Abstract Accurate assessment of experienced pain is a well-known problem in the clinical practices. Therefore, a proper method for pain detection is highly desirable. Electrodermal activity (EDA) is known as a measure of the sympathetic nervous system activity, which changes during various mental stresses. As pain causes mental stress, EDA measures may reflect the felt pain. This study aims to evaluate changes in skin conductance responses (SCRs), skin potential responses (SPRs), and skin susceptance responses (SSRs) simultaneously as a result of sequences of electrical (painful) stimuli with different intensities. EDA responses as results of painful stimuli were recorded from 40 healthy volunteers. The stimuli with three different intensities were produced by using an electrical stimulator. EDA responses significantly changed (increased) with respect to the intensity of the stimuli. Both SCRs and SSRs showed linear relationship with the painful stimuli. It was found that the EDA responses, particularly SCRs (p < 0.001) and SSRs (p = 0.001) were linearly affected by the intensity of the painful stimuli. EDA responses, in particular SCRs, may be used as a useful indicator for assessment of experienced pain in clinical settings.
Drone, your brain, ring course: accept the challenge and prevail!

Brain Computer Interface systems (BCIs) rely on lengthy training phases that can last up to months due to the inherent variability in brainwave activity between users. We propose a BCI architecture based on the co-learning between the user and the system through different feedback strategies. Thus, we achieve an operational BCI within minutes. We apply our system to the piloting of an AR.Drone 2.0 quadricopter with a series of hoops delimiting an exciting circuit. We show that our architecture provides better task performance than traditional BCI paradigms within a shorter time frame. We further demonstrate the enthusiasm of users towards our BCI-based interaction modality and how they find it much more enjoyable than traditional interaction modalities.
Factors Driving the Adoption of Increasingly Modular or Increasingly Integrated Forms

Many systems migrate towards increasing or decreasing modularity, yet no explicit causal models exist to explain this process. This paper builds upon a general theory of modular systems (Schilling 2000), to explain the migration of systems towards or away from increasingly modular forms. It then applies this model to a specific context: the adoption of modular organizational forms. In many industries, integrated hierarchical organizations are being replaced by non-hierarchical entities that are permeable and interconnected. This paper applies the logic of general systems modularity to explain why in some industries we may expect greater use of modular organizational forms such as contract manufacturing, contingent workers, and alliances than in other industries. The paper then presents data from 330 manufacturing industries that was used to test this model (Schilling & Steensma 2001). An examination of technology trajectories in a wide range of industries indicates that many product systems migrate towards increasing modularity. Product systems that were once tightly integrated may be disaggregated into loosely coupled components that may be mixed and matched, allowing much greater flexibility in end configurations. For instance, personal computers were originally introduced as all-in-one packages (such as Intel’s MCS-4, the Kenback-1, the Apple II or the Commodore PET), but rapidly evolved into modular systems that enable the mixing and matching of components from different vendors. Publishers have also embraced modularity, by utilizing recent information technology advances to enable instructors to assemble their own textbooks from book chapters, articles, cases or their own materials. Even large home appliance manufacturers now offer their products in modular configurations—for example some stoves now offer customers the ability to remove the burners and plug in other cooking devices such as barbecue grills and pancake griddles. Recent research also indicates that a similar phenomenon may be manifest in the emergence of new organizational designs. In many industries, integrated hierarchical organizations have been replaced by non-hierarchical
Towards Semi-Automatic Artifact Rejection for the Improvement of Alzheimer’s Disease Screening from EEG Signals

A large number of studies have analyzed measurable changes that Alzheimer’s disease causes on electroencephalography (EEG). Despite being easily reproducible, those markers have limited sensitivity, which reduces the interest of EEG as a screening tool for this pathology. This is for a large part due to the poor signal-to-noise ratio of EEG signals: EEG recordings are indeed usually corrupted by spurious extra-cerebral artifacts. These artifacts are responsible for a consequent degradation of the signal quality. We investigate the possibility to automatically clean a database of EEG recordings taken from patients suffering from Alzheimer’s disease and healthy age-matched controls. We present here an investigation of commonly used markers of EEG artifacts: kurtosis, sample entropy, zero-crossing rate and fractal dimension. We investigate the reliability of the markers, by comparison with human labeling of sources. Our results show significant differences with the sample entropy marker. We present a strategy for semi-automatic cleaning based on blind source separation, which may improve the specificity of Alzheimer screening using EEG signals.
Optimized on-chip pipelining of memory-intensive computations on the cell BE

Multiprocessors-on-chip, such as the Cell BE processor, regularly suffer from restricted bandwidth to off-chip main memory. We propose to reduce memory bandwidth requirements, and thus increase performance, by expressing our application as a task graph, by running dependent tasks concurrently and by pipelining results directly from task to task where possible, instead of buffering in off-chip memory. To maximize bandwidth savings and balance load simultaneously, we solve a mapping problem of tasks to SPEs on the Cell BE. We present three approaches: an integer linear programming formulation that allows to compute Paretooptimal mappings for smaller task graphs, general heuristics, and a problem speci c approximation algorithm. We validate the mappings for dataparallel computations and sorting.
TAG4VD: a game for collaborative video annotation

Creating descriptive labels for videos is an important task, with application in video retrieval, Web accessibility and computer vision. However automatic creation of such labels is difficult and, alternatively, having professionals manually describing content is too expensive. Engaging end-users in the process of describing multimedia assets may lead to good results and enables creating the sense of participation which is currently one of the key factors to attract customers to a service. The existing approaches are highly successful in terms of number of engaged players and number of collected labels, but hardly create comprehensive tag sets, contributing both with generic or too narrow meaning tags. "Games With A Purpose" are one of the approaches that have been used in an attempt to create comprehensive video descriptions by harnessing the intelligence of human players and have them contributing and collaborating towards a common goal that is recognized if successful. This paper describes a game which implements two mechanisms for collecting data via human-based computation games. Tags introduced by registered players, in a given timecode, are validated based on a collaborative scoring mechanism that eliminates irregular annotations. Additionally, a voting mechanism that enables players to endorse or refuse existing tags, provides an extra instrument to guarantee the quality of the annotations.
Partition Testing vs. Random Testing: The Influence of Uncertainty

This paper compares partition testing and random testing on the assumption that program failure rates are not known with certainty before testing and are, therefore, modeled by random variables. It is shown that under uncertainty, partition testing compares more favorably to random testing than suggested by prior investigations concerning the deterministic case: the restriction to failure rates that are known with certainty systematically favors random testing. In particular, we generalize a result by Weyuker and Jeng (1991) stating equal fault detection probabilities for partition testing and random testing in the case where the failure rates in the subdomains defined by the partition are equal. It turns out that for independent random failure rates with equal expectation, the case above is a boundary case (the worst case for partition testing), and the fault detection probability of partition testing can be up to k times higher than that of random testing, where k is the number of subdomains. Also in a related model for dependent failure rates, partition testing turns out to be consistently better than random testing. The dominance can also be verified for the expected (weighted) number of detected faults as an alternative comparison criterion.
Fine grain 3D integration for microarchitecture design through cube packing exploration

Most previous 3D IC research focused on "stacking" traditional 2D silicon layers, so the interconnect reduction is limited to interblock delays. In this paper, we propose techniques that enable efficient exploration of the 3D design space where each logical block can span more than one silicon layers. Although further power and performance improvement is achievable through fine grain 3D integration, the necessary modeling and tool infrastructure has been mostly missing. We develop a cube packing engine which can simultaneously optimize physical and architectural design for effective utilization of 3D in terms of performance, area and temperature. Our experimental results using a design driver show 36% performance improvement (in BIPS) over 2D and 14% over 3D with single layer blocks. Additionally multi-layer blocks can provide up to 30% reduction in power dissipation compared to the single-layer alternatives. Peak temperature of the design is kept within limits as a result of thermal-aware floorplanning and thermal via insertion techniques.