Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploiting ankle torque for orbital stabilization in biped robots; a hybrid zero dynamics approach

This paper investigates the rule of ankle torque in stabilization of periodic motions in planar biped with feet. In order to illustrate the main concepts, the walking is considered to be consist of two phases; A fully actuated phase, where the stance foot is flat on the ground, and an instantaneous double support phase. It is shown how the ankle torque can be used effectively within hybrid zero dynamics framework to stabilize the system around a target periodic orbit. This control torque which is designed as an internal command to the swing phase zero dynamics is further exploited to switch among different orbits in one transition step. For the former goal, the Lagrangian property of the swing phase zero dynamics is employed and the ankle torque is applied to regulate the corresponding energy level of the target orbit. For the switching problem, this command is used to steer the trajectory of the system within connecting zero dynamics manifold. The proposed methods are verified through several simulations on a seven-link planar biped robot.
Monitoring of Aging Software Systems Affected by Integer Overflows

Numerical aging-related bugs, which can manifest themselves as the accumulation of floating-point errors and the overflow of integers, represent a known but relatively neglected issue in the field of software aging and rejuvenation. Unfortunately, it is very difficult to avoid and to fix these bugs, since the rules of computer arithmetic and programming languages are often misunderstood or disregarded by programmers. Even though software rejuvenation can potentially mitigate these problems, its adoption is prevented by the lack of approaches for forecasting numerical software aging failures: in order to efficiently plan rejuvenation, the rate of numerical errors has to be known, or at least estimated. In this paper, we focus on software aging phenomena related to integer overflows. We present some examples of integer overflow issues of the MySQL open-source DBMS, and an approach for identifying symptoms of potential integer overflows by on-line monitoring.
An Atomistic Study on Shape-Memory Effect by Shear Deformation and Phase Transformation

An atomistic study on the shape-memory effect is carried out by molecular dynamics simulations with the EAM potential for Ni-Al alloy. As preliminary simulations, the stable structures for several Ni contents at various temperatures are investigated, and reveal that the martensite structure is obtained at low temperature for models with 62% or higher Ni content, while bcc is stable at high temperatures. A 68% Ni model with martensite structure, which consists of several variants in two opposite orientations without macroscopic deformation, is applied for MD simulation under a series of thermo-mechanical conditions of shear loading, unloading, heating and cooling. When shear load is applied, some of the variants with unstable orientation against the load change orientation, and a permanent macroscopic deformation is obtained. When this deformed martensite is heated up, a phase transformation to bcc occurs and the deformation is diminished. The original martensite is regained by cooling. Since the orientation of the variants are random, the macroscopic deformation is not observed, which means that the deformation imposed by the external load recovers the original shape by the heat treatment.
A Multi-lingual Annotated Dataset for Aspect-Oriented Opinion Mining

We present the Trip-MAML dataset, a Multi-Lingual dataset of hotel reviews that have been manually annotated at the sentence-level with Multi-Aspect sentiment labels. This dataset has been built as an extension of an existent English-only dataset, adding documents written in Italian and Spanish. We detail the dataset construction process, covering the data gathering, selection, and annotation. We present inter-annotator agreement figures and baseline experimental results, comparing the three languages. Trip-MAML is a multi-lingual dataset for aspect-oriented opinion mining that enables researchers (i) to face the problem on languages other than English and (ii) to the experiment the application of cross-lingual learning methods to the task.
Indirect Finite Control Set Model Predictive Control of Modular Multilevel Converters

The modular multilevel converter (MMC) is a potential candidate for medium/high-power applications, specifically for high-voltage direct current transmission systems. One of the main challenges in the control of an MMC is to eliminate/minimize the circulating currents while the capacitor voltages are maintained balanced. This paper proposes a control strategy for the MMC using finite control set model predictive control (FCS-MPC). A bilinear mathematical model of the MMC is derived and discretized to predict the states of the MMC one step ahead. Within each switching cycle, the best switching state of the MMC is selected based on evaluation and minimization of a defined cost function. The defined cost function is aimed at the elimination of the MMC circulating currents, regulating the arm voltages, and controlling the ac-side currents. To reduce the calculation burden of the MPC, the submodule (SM) capacitor voltage balancing controller based on the conventional sorting method is combined with the proposed FCS-MPC strategy. The proposed FCS-MPC strategy determines the number of inserted/bypassed SMs within each arm of the MMC while the sorting algorithm is used to keep the SM capacitor voltages balanced. Using this strategy, only the summation of SM capacitor voltages of each arm is required for control purposes, which simplifies the communication among the SMs and the central controller. This paper also introduces a modified switching strategy, which not only reduces the calculation burden of the FCS-MPC strategy even more, but also simplifies the SM capacitor voltage balancing algorithm. In addition, this strategy reduces the SM switching frequency and power losses by avoiding the unnecessary switching transitions. The performance of the proposed strategies for a 20-level MMC is evaluated based on the time-domain simulation studies.
A DSL for Model Mutation and its Applications to Different Domains

Model-Driven Engineering (MDE) is a Software Engineering paradigm that focuses all phases of the software development process in models. Therefore, the automated manipulation of models is essential in MDE. While many Domain-Specific Languages (DSLs) exist to specify model transformation, model simulation, or code generation, there is a lack of DSLs to specify and apply model mutations. A model mutation is a kind of model manipulation that creates a set of variants (or mutants) of a seed model by the application of one or more mutation operators. Model mutation has many applications (e.g., model transformation testing, automated generation of exercises, verification of software testing, etc.). The objective of this thesis is the creation of a DSL for model mutation, and its application to different domains like education, model-based testing and search-based software engineering.
Radiommender: P2P on-line radio with a distributed recommender system

Radiommender is a fully-distributed, peer-to-peer on-line radio. Users can share their music collection and explore music collections of other users. The difference to current file sharing systems is that songs do not need to be searched individually - a distributed recommender system is used to estimate user preference. Recommendation is done with a combination of an implicit voting system that assigns songs to search terms and an affinity network that correlates user interest. The demonstration shows the software functionality and includes the visualization of affinity graphs built by the recommender system.
Applicability of Demographic Recommender System to Tourist Attractions: A Case Study on Trip Advisor

Most of the existing recommender systems for tourism apply knowledge-based and content-based approaches, which need sufficient historical rating information or extra knowledge and suffer from the cold start problem. In this paper, a demographic recommender system is utilized for the recommendation of attractions. This system categorizes the tourists using their demographic information and then makes recommendations based on demographic classes. Its advantage is that the history of ratings and extra knowledge are not needed, so a new tourist can obtain recommendation. Focusing on the attractions on Trip Advisor, we use different machine learning methods to produce prediction of ratings, so as to determine whether these approaches and demographic information of tourists are suitable for providing recommendations. Our preliminary results show that the methods and demographic information can be used to predict tourists' ratings on attractions. But using demographic information alone can only achieve limited accuracy. More information such as textual reviews is required to improve the accuracy of the recommendation.
Inorganic nanolayers: structure, preparation, and biomedical applications

Hydrotalcite-like compounds are two-dimensional inorganic nanolayers also known as clay minerals or anionic clays or layered double hydroxides/layered hydroxy salts, and have emerged as a single type of material with numerous biomedical applications, such as drug delivery, gene delivery, cosmetics, and biosensing. Inorganic nanolayers are promising materials due to their fascinating properties, such as ease of preparation, ability to intercalate different type of anions (inorganic, organic, biomolecules, and even genes), high thermal stability, delivery of intercalated anions in a sustained manner, high biocompatibility, and easy biodegradation. Inorganic nanolayers have been the focus for researchers over the last decade, resulting in widening application horizons, especially in the field of biomedical science. These nanolayers have been widely applied in drug and gene delivery. They have also been applied in biosensing technology, and most recently in bioimaging science. The suitability of inorganic nanolayers for application in drug delivery, gene delivery, biosensing technology, and bioimaging science makes them ideal materials to be applied for theranostic purposes. In this paper, we review the structure, methods of preparation, and latest advances made by inorganic nanolayers in such biomedical applications as drug delivery, gene delivery, biosensing, and bioimaging.
A Verifiable High Level Data Path Synthesis Framework

This work presents a synthesis framework that generates a formally verifiable RTL from a high level language. We develop an estimation model for area, delay and power metrics of arithmetic components for Xilinx Spartan 3 FPGA family. Our estimation model works 300 times faster than Xilinx's toolchain with an average error of 6.57\% for delay and 3.76\% for area estimations. Our framework extracts CDFGs from ANSI-C, LRH(+) [1] and VHDL. CDFGs are verified using the symbolic model checker NuSMV [2] with temporal logic properties. This method guarantees detection of hardware redundancy and word-length mismatch related bugs by static code checking.
A Synthesis of a Knowledge Management Framework for Sports Event Management

Due to rapid social development in Asia, sports events have grown larger and many new countries are also hosting them for their first time. In addition to required increase in expenditures and more efficient management, various instances of inadequate planning highlighted the needs for more effective and better sustainable structures to support knowledge transfer between organizers, from one event to the next. The research presented in this paper aims to facilitate the deployment of systematic knowledge management practices to sports event management, to enable sustainable planning. The research in this paper synthesizes is carried out on the Malaysian Games as an example of a sports event management. Furthermore, we introduce knowledge management (KM) framework that was developed based on studies and observations of processes and activities in this organization. The focus is on knowledge that is key to the success of the Malaysian Games and that which can be used to the development of the organization and in future games.
POSTOPERATIVE HYDROCEPHALUS IN PATIENTS UNDERGOING DECOMPRESSIVE HEMICRANIECTOMY FOR ISCHEMIC OR HEMORRHAGIC STROKE

OBJECTIVEWe have frequently observed the development of postoperative communicating hydrocephalus in patients undergoing decompressive hemicraniectomy. This condition may persist in some patients after cranioplasty and require permanent cerebrospinal fluid (CSF) diversion. To confirm an independent correlation between hemicraniectomy and the development of communicating hydrocephalus, and to detail the frequency and potential clinical factors contributing to this complication, we evaluated our series of patients undergoing hemicraniectomy for life-threatening increases in intracranial pressure secondary to ischemic or hemorrhagic stroke. METHODSA retrospective analysis was performed with a cohort of consecutive patients who underwent emergent hemicraniectomy for medically refractory elevations in intracranial pressure. Patients with known independent risk factors for the development of communicating hydrocephalus were excluded. Clinical and imaging data were reviewed to determine the incidence and type of hydrocephalus after hemicraniectomy, the persistence of hydrocephalus after cranioplasty, and the need for permanent CSF diversion. RESULTSEighty-eight percent of the eligible patients undergoing hemicraniectomy in our cohort developed postoperative communicating hydrocephalus. Half of these patients harbored persistent hydrocephalus after cranioplasty and required placement of a ventriculoperitoneal shunt. We noted a strong correlation between prolonged time to replacement of the bone flap and persistence of hydrocephalus. CONCLUSIONCommunicating hydrocephalus is an almost universal finding in patients after hemicraniectomy. Delayed time to cranioplasty is linked with the development of persistent hydrocephalus, necessitating permanent CSF diversion in some patients. We propose that early cranioplasty, when possible, may restore normal intracranial pressure dynamics and prevent the need for permanent CSF diversion in patients after hemicraniectomy.
A new data hiding method via revision history records on collaborative writing platforms

A new data hiding method via collaboratively-written articles with forged revision history records on collaborative writing platforms is proposed. The hidden message is camouflaged as a stego-document consisting of a stego-article and a revision history created through a simulated process of collaborative writing. The revisions are forged using a database constructed by mining word sequences used in real cases from an English Wikipedia XML dump. Four characteristics of article revisions are identified and utilized to embed secret messages, including the author of each revision, the number of corrected word sequences, the content of the corrected word sequences, and the word sequences replacing the corrected ones. Related problems arising in utilizing these characteristics for data hiding are identified and solved skillfully, resulting in an effective multiway method for hiding secret messages into the revision history. To create more realistic revisions, Huffman coding based on the word sequence frequencies collected from Wikipedia is applied to encode the word sequences. Good experimental results show the feasibility of the proposed method.
The Term Structure of Interest-Rate Futures Prices

We derive general properties of two-factor models of the term structure of interest rates and, in particular, the process for futures prices and rates. Then, as a special case, we derive a no-arbitrage model of the term structure in which any two futures rates act as factors. The term structure shifts and tilts as the factor rates vary. The cross-sectional properties of the model
Computing Entropy for Ortholog Detection

Biological sequences from different species are called orthologs if they evolved from a sequence of a common ancestor species and they have the same biological function. Approximations of Kolmogorov complexity or entropy of biological sequences are already well known to be useful in extracting similarity information between such sequences — in the interest, for example, of ortholog detection. As is well known, the exact Kolmogorov complexity is not algorithmically computable. In practice one can approximate it by computable compression methods. However, such compression methods do not provide a good approximation to Kolmogorov complexity for short sequences. Herein is suggested a new approach to overcome the problem that compression approximations may not work well on short sequences. This approach is inspired by new, conditional computations of Kolmogorov entropy. A main contribution of the empirical work described shows the new set of entropy-based machine learning attributes provides good separation between positive (ortholog) and negative (non-ortholog) data — better than with good, previously known alternatives (which do not employ some means to handle short sequences well). Also empirically compared are the new entropy based attribute set and a number of other, more standard similarity attributes sets commonly used in genomic analysis. The various similarity attributes are evaluated by cross validation, through boosted decision tree induction C5.0, and by Receiver Operating Characteristic (ROC) analysis. The results point to the conclusion: the new, entropy based attribute set by itself is not the one giving the best prediction; however, it is the best attribute set for use in improving the other, standard attribute sets when conjoined with them. Keywords—compression, decision tree, entropy, ortholog, ROC.
Mining direct and indirect fuzzy association rules with multiple minimum supports in large transaction databases

Association rule is an important research topic in data mining and knowledge discovery. Traditional algorithms for mining association rules are built on the binary attributes databases, which has three limitations. Firstly, it can not concern quantitative attributes; secondly, it finds out frequent itemsets based on the single one user-specified minimum support threshold, which implicitly assumes that all items in the data have similar frequency; thirdly, only the direct association rules are discovered. Mining fuzzy association rules has been proposed to address the first limitation. In this paper, we put forward a discovery algorithm for mining both direct and indirect fuzzy association rules with multiple minimum supports to resolve these three limitations.
Smart multifunctional composite materials for improvement of structural and non-structural properties

The principal aim of this thesis is to analyse the effectiveness of multifunctional smart materials as intelligent structures to improve mechanical properties and activate additional non-structural features. In order to investigate these multiple aspects, a comprehensive literature review has been presented focusing on the state of the art in multifunctional and smart materials. From this analysis, five different systems based on different designing solutions and manufacturing techniques were developed and experimentally validated. Multiscaled composites are a typical example of multifunctional materials and are based on the addition of engineered nanoscaled reinforcement to traditional mesoscopic systems. To test the effectiveness of nanomodification, an experimental campaign has been carried out, aimed to the characterisation of a nanocomposite obtained embedding Graphene Nanoplatelets (GNPs) in the polymeric structure of Low Density Polyethylene films at difference concentrations. Nanoscaled fillers were subsequently used to manufacture a threephasic multi-scaled composite based on the inclusion of nanometric SiO2 particles in a traditional carbon fabric/epoxy system. Following a different approach, hybrid structures with embedded Non-Newtonian fluids have been manufactured and tested and the results showed that nonlinear viscosity can be exploited to dynamically enhance material properties during an impact event. The possibility to intervene both on structural and non-structural properties has been investigated with another hybrid system, based on the embodiment of Shape memory Alloys (SMA) wires within a traditional unidirectional CFRP. The study of the impact properties pointed out that the superelasticity effect and the hysteretic stress/strain behaviour of the embedded wires reduce the extent of the internal delamination for samples subjected to low velocity impacts. Moreover, by exploiting the SMAs thermoelectrical properties it is possible to use the embedded metallic network as a strain sensor by measuring the electrical resistance variation and as an embedded heat source to be used for rapid thermographic damage location and evaluation.
Peripheral Neuropathy in Mice Transgenic for a HumanMDR3 P-Glycoprotein Mini-Gene

We have generated mice transgenic for a human MDR3 mini-gene, under control of a hamster vimentin promoter. Expression of the MDR3 transgene was found in mesenchymal tissues, peripheral nerves, and the eye lens. These MDR3 transgenic mice have a slowed motor nerve conduction and dysmyelination of their peripheral nerves. An extensive dysmyelination in some transgenic strains results in a severe peripheral neuropathy with paresis of the hind legs. How expression of the MDR3 transgene causes these abnormalities is unknown. TheMDR3 gene encodes a large glycosylated plasma membrane protein with multiple transmembrane spanning domains, which are involved in the translocation of the phospholipid phosphatidylcholine through the hepatocyte canalicular membrane. The ability of the MDR3 P-glycoprotein to alter phospholipid distribution in the plasma membrane of Schwann cells may cause the damage. It is also possible, however, that the presence of a large glycoprotein in the cell membrane may be sufficient to severely disturb myelination of peripheral nerves.
Power-aware dynamic placement of HPC applications

High Performance Computing applications and platforms have been typically designed without regard to power consumption. With increased awareness of energy cost, power management is now an issue even for compute-intensive server clusters. In this work, we investigate the use of power management techniques for high performance applications on modern power-efficient servers with virtualization support. We consider power management techniques such as dynamic consolidation and usage of dynamic power range enabled by low power states on servers. We identify application performance isolation and virtualization overhead with multiple virtual machines as the key bottlenecks for server consolidation. We perform a comprehensive experimental study to identify the scenarios where applications are isolated from each other. We also establish that the power consumed by HPC applications may be application dependent, non-linear and have a large dynamic range. We show that for HPC applications, working set size is a key parameter to take care of while placing applications on virtualized servers. We use the insights obtained from our experimental study to present a framework and methodology for power-aware application placement for HPC applications.