Miracle-dz/UniFAR-FTUs huggingface dataset:
Improvement of Named Entity Tagging by Machine Learning

A Named Entity (NE) is a word, or sequence of words that can be classified as a name of a person, organization, location, date, time, percentage or quantity. Named entities can be valuable in several natural language applications. For instance, automatic text summarization systems can be enriched by using NEs, as they provide important cues for identifying relevant segments in text. Other uses of NE taggers are in the fields of information retrieval (i.e. more accurate Internet search engines), information extraction, automatic speech recognition, question answering and machine translation. There has been a considerable amount of work that aims to improve the performance of NE taggers, however most of these efforts are targeted to build handcrafted NE taggers. While handcrafted systems can achieve good performance they have several disadvantages, such as the need of rebuilding the NE tagger in order to port it to new domains. This proposal presents a research project for building an automated method, based on machine learning, that facilitates the portability of handcrafted NE taggers. The hypothesis underlying this proposal is that the coverage of NE taggers can be increased using machine learning techniques, without the need of rebuilding the linguistic resources of the NE tagger. One motivation for this research is that the task of building a training set is faster and easier than building the linguistic resources for the handcrafted systems. Preliminary results presented here show that it is feasible to prove the correctness of the hypothesis.
An optical and near IR study of the old open cluster NGC 2141

We report CCD optical (B and V passbands) and near IR (J and K bands) observations in the region of the old open cluster NGC 2141. By combining the two sets of photometry (500 stars in common) we derive new estimates of the cluster's fundamental parameters. We conrm that the cluster is 2.5 Gyrs old, but, with respect to previous investigations, we obtain a slightly larger reddening (E(B V )=0 :40), and a slightly shorter distance (3.8 kpc) from the Sun. Finally, we present the Luminosity Function (LF) in the V band, which is another age indicator. We provide a good t for the age range inferred from isochrones by assuming the Kroupa et al. (1993) IMF up to MV = 5.0. We interpret the disagreement at fainter magnitudes as evidence of mass segregation.
Alleviate sparsity problem using hybrid model based on spectral co-clustering and tensor factorization

Most of recommender systems suffer from common challenge named data sparsity. This problem makes an important challenge on the efficiency of collaborative filtering and causes overfitting problem. In this paper, `time' as a third dimension is considered. Therefore, increasing dimensions of data makes this problem more critical. For alleviating this problem, this research applies a framework to block users and items co-occurrences in the similar cluster and adds time to each block. This method imputes appropriate values for missing data based on similar user and item ratings assigned to each block. Preprocess based on optimization is performed on each cluster. After this step, data points in each block are merged together and tensor factorization is used to model relations between users, items and times. Our novel approach has two advantages: (a) it increases the speed of convergence and avoids overfitting the observed data, (b) it reduces sparsity problem and error rate. The evaluation metric demonstrate that our algorithm has good results in practice.
Molecular Biology and Infection of Hepatitis E Virus

Hepatitis E virus (HEV) is a viral pathogen transmitted primarily via fecal-oral route. In humans, HEV mainly causes acute hepatitis and is responsible for large outbreaks of hepatitis across the world. The case fatality rate of HEV-induced hepatitis ranges from 0.5 to 3% in young adults and up to 30% in infected pregnant women. HEV strains infecting humans are classified into four genotypes. HEV strains from genotypes 3 and 4 are zoonotic, whereas those from genotypes 1 and 2 have no known animal reservoirs. Recently, notable progress has been accomplished for better understanding of HEV biology and infection, such as chronic HEV infection, in vitro cell culture system, quasi-enveloped HEV virions, functions of the HEV proteins, mechanism of HEV antagonizing host innate immunity, HEV pathogenesis and vaccine development. However, further investigation on the cross-species HEV infection, host tropism, vaccine efficacy, and HEV-specific antiviral strategy is still needed. This review mainly focuses on molecular biology and infection of HEV and offers perspective new insight of this enigmatic virus.
Design of a hybrid-actuated finger prosthesis with tactile sensing

The mechatronic design of a biomimetic finger prosthesis driven by a new hybrid mechanism is presented. The finger joints are driven by a miniature direct current (DC) motor and a shape memory alloy (SMA) wire. By using a miniature DC motor, a high response rate as well as comparable torque and speed can be achieved at the metacarpophalangeal (MCP) joint. By using SMA to actuate the proximal interphalangeal (PIP) joint, the finger configuration has been miniaturized to anthropomorphic size and weight. Kinematic analysis shows that the finger design can achieve the range of movements of the human counterpart and hence closely resemble its functionality. In order to assess the performance characteristic of the SMA wire, the relationship between the generated displacement and the contraction time has been tested for varying energizing currents. Furthermore, a comparison has been made to ascertain the viability of the conductive rubber sheet for tactile sensing compared to the quantum tunneling composite (QTC) pills.
INTERACTIONS OF REALM BOUNDARIES AND END-TO-ENG NETWORK APPLICATIONS

One of the design principles of the Internet is that the network is made more flexible, and therefore useful, by placing functionality in end applications rather than in network infrastructure. Network gateways that violate this principle are considered harmful. This paper demonstrates that such upper-level gateways exist because of realm-specific performance, security, and protocol needs of certain portions of the Internet. Placing this functionality in end hosts is, conversely, harmful to the flexibility of using the Internet to link disparate networks. Requirements are developed for a protocol to allow end-hosts and gateways to negotiate the functionality of these gateways in terms of the needs of both end applications and network realms.
Fast Reduced Rank Equalizer for HSDPA Systems Based on Lanczos Algorithm

In this paper we propose a Lanczos structure for multi-stage Wiener filtering (MSWF) based space-time equalization for high data-rate WCDMA downlink. An efficient algorithm is derived that replaces the MSWF forward recursion with the Lanczos algorithm. The proposed Lanczos-based implementation provides simultaneously fast convergence and low complexity. Moreover, it does not assume any structure for the received signal covariance matrix. The proposed method is compared in simulations to symbol-level adaptive MSWF and sample matrix inversion based equalization using Cholesky decomposition. Symbol-level adaptive equalization suffers from large eigenvalue spread of the symbol-level covariance matrix. On the other hand, Cholesky-decomposition is computationally complex. The results indicate that significant savings in complexity can be gained using the proposed method without compromising the performance, even in 16-bit fixed point arithmetic
Sentiments on a Grid: Analysis of Streaming News and Views

In this paper we report on constructing a finite state automaton comprising automatically extracted terminology and significant collocation patterns from a training corpus of specialist news (Reuters Financial News). The automaton can be used to unambiguously identify sentiment-bearing words that might be able to make or break people, companies, perhaps even governments. The paper presents the emerging face of corpus linguistics where a corpus is used to bootstrap both the terminology and the significant meaning bearing patterns from the corpus. Much of the current content analysis software systems require a human coder to eyeball terms and sentiment words. Such an approach might yield very good quality results on small text collections but when confronted with a 40-50 million word corpus such an approach does not scale, and a large-scale computer-based approach is required. We report on the use of Grid computing technologies and techniques to cope with this analysis.
Part II: texturing surfaces and geometry creation

Although a variety of algorithms have been developed to synthesize new textures on regular 2D grids, this is seldom the end goal for applications. More often, what is needed is a texture that fits a given surface. In this chapter we will look at methods for performing texture synthesis on surfaces. More specifically, we will assume that a surface description and a texture exemplar has been provided, and that we wish to create a texture on that mesh that is based on the exemplar. Although many of the algorithms are described in terms of polygonal meshes, most of the methods easily extend to other kinds of surfaces (e.g. subdivision surfaces, tensor product patches).
The candidate filament close to the 3C 295 galaxy cluster: optical and X-ray spectroscopy

We present a detailed analysis of the overdensity of X-ray sources close to the 3C 295 galaxy cluster (z = 0.46) to assess whether it is associated with a filament of the large-scale structure of the Universe. We obtained optical spectra of the optical counterparts of eleven sources associated with the filament, finding that one is at z = 0.474. This is a type 1 AGN at 1.5 arcmin from the cluster center. We found three more sources with a redshift in the range 0.37 ÷ 0.53. We extracted the stacked X-ray spectrum of 47 X-ray sources belonging to the putative filament. We found a significant narrow (at the resolution of the Chandra ACIS-I) line at E ∼ 4. 4k eV, the energy of the iron Kα line at the redshift of the cluster. The detection of this line is confirmed at a confidence level of better than 3σ and its energy is constrained to be in the range 6.2−6.47 (at a 90% confidence level), excluding an identification with the 6.7 helium-like iron line from the hot cluster ICM at better than 4σ. We conclude that the detection of the redshifted line is a strong indication that at least several of the excess sources lie at z ∼ 0.46 and that AGNs are efficient tracers of the “filament” connected with the central cluster of galaxies.
A Formal Model for Work Flows

Within this paper, we present a structured, formal model for the specification and analysis of work flows. The model provides a graphical representation supporting a modular description of work flows together with a formal semantics accurately reflecting their execution and laying the foundation for tool support. Methodically relevant concepts such as hierarchy, refinement, model analysis, and model transformation are supported.
A new detection method of singular points of fingerprints based on neural network

Singular points detection, a crucial step for fingerprint identification system, is accurately robust, and reliable. In the processing of the fingerprint image matching and classification, many method use singular points to align two fingerprint images to surmount the problems about rotation and translation. The performances of the fingerprint recognition system rely on the effect of singular points detection. This paper introduces a new method through backpropagation neural network (BPNN) to detect singular points of fingerprint images. The algorithms that directly make use of the gray-scale of fingerprint images to detect singularities are different from the other methods, which are based on the neural network. The algorithm is tested accurately and reliably for a great many of fingerprint images in the FVC2002.
Decision Making in an Agent-based Marketplace

This paper presents a multi-agent market simulator designed for analyzing agent market strategies based on a complete understanding of buyer and seller behaviors, preference models and pricing algorithms, considering user risk preferences and game theory for scenario analysis. This tool studies negotiations based on different market mechanisms and, time and behavior, dependent strategies. The results of the negotiations between agents are analyzed by data mining algorithms in order to extract rules that give agents feedback to improve their strategies. The system also includes agents that are capable of improving their performance with their own experience, by adapting to the market conditions, and capable of considering other agent reactions.
Increasing Entrepreneurial Intentions through Innovations in Pedagogy: European approaches, programmes and tools

As a reaction to the positive effects of entrepreneurship, many universities try to enhance students' entrepreneurial mindsets. Based on existing evaluations of entrepreneurship education, this paper focuses on European innovative pedagogy for classroom entrepreneurship. We highlight innovations in entrepreneurial education from seven European countries: Estonia, Finland, France, Germany, Greece, Poland, and Sweden. Consulting these approaches, the following seams to be particularly crucial: action-/project-based learning, individual and collaborative (team) experiential learning, interdisciplinary grouping, enhancing entrepreneurial skills and knowledge, key qualifications (creativity, autonomy, responsibility, risk-taking), conceptual and informal learning, critical thinking, reflection, meta-cognition, working with innovative ideas, demythologization, learning through "fun", and student-company-university collaboration/networks.
A Hybrid Modeling Approach to Microarchitecture Design Space Exploring

The micro architectural design space of a new processor is too huge for architects to handle with cycle-accurate simulators. Previous researches attack this problem by statistical learning methods such as Artificial Neural Networks (ANN) and statistical sampling solutions such as SimPoint. These approaches greatly reduce the simulation time while keeping the results of CPI precisely. However, all these machine learning and sampling methods are “black boxes”: although we can get CPI accurately, we can’t get detailed information of on-chip components which makes it difficult to find relationships between these components and bottlenecks of a design. Thus these approaches are not sufficient to provide enough intuitions for architects to find potential improvements. This paper proposes a novel “white box” Decision-free Generalized Stochastic Petri Nets (Decision-free GSPN) model. We adopt ANN to estimate certain parameters for GSPN when we consider new design points. Our hybrid approach could predict CPI accurately and produce the usage state of instruction queue (IQ), micro operation queue (uopQ), reservation station (RS), reorder buffer (ROB) and so on precise enough to give architects intuitions into the new design. Our solution takes only several minutes to finish comparing to days when we adopt cycle-accurate software simulator, with less than 8.6% error rate for CPI. We believe the information our method produces is precise enough to give architects more intuitions and insights about how to change a design comparing to previous machine learning methods.
A System to Assess the Semantic Content of Student Essays

This paper presents Apex, a system that can automatically assess a student essay based on its content. It relies on Latent Semantic Analysis, a tool which is used to represent the meaning of words as vectors in a high-dimensional space. By comparing an essay and the text of a given course on a semantic basis, our system can measure how well the essay matches the text. Various assessments are presented to the student regarding the topic, the outline and the coherence of the essay. Our experiments yield promising results.
Results of the 2006 Spoken Term Detection Evaluation

paper presents the pilot evaluation of Spoken Term Detection technologies, held during the latter part of 2006. Spoken Term Detection systems rapidly detect the presence of a term , which is a sequence of words consecutively spoken, in a large audio corpus of heterogeneous speech material. The paper describes the evaluation task posed to Spoken Term Detection systems, the evaluation methodologies, the Arabic, English and Mandarin evaluation corpora, and the results of the evaluation. Ten participants submitted systems for the evaluation.
Integrated Device Scheduling and Processor Voltage Scaling for System-wide Energy Conservation

The challenge in conserving energy in embedded real-time systems is to reduce power consumption while preserving temporal correctness. Previous research has focused on power conservation for either the processor or I/O devices alone. The system-wide energy conservation has received little attention. In this paper, we analyze the problem of system-wide energy-efficient scheduling for hard real-time systems based on the preemptive periodic task model with non-preemptive shared resources. We propose an online system-wide energy-efficient scheduling algorithm Systemwide Energy-Aware EDF (SYS-EDF), which integrates Dynamic Power Management (DPM) for I/O devices and Dynamic Voltage Scaling (DVS) for the processor. An evaluation of SYS-EDF shows that it yields significant energy savings with respect to DVS alone or DPM alone techniques.
Morphologic alterations and immunohistochemical analysis of alpha-fetoprotein and CD34 in chorionic villi of anembryonic pregnancy.

OBJECTIVE To investigate the morphology of chorionic villi using light and electron microscopy, especially the expression of alpha-fetoprotein (AFP) in trophoblastic cells and the process of maturation and margination vasculogenesis proper using CD34 immunohistochemistry in tissues from the first trimester of pregnancy loss due to anembryonic pregnancy in comparison with embryonic pregnancy. METHODS The study consisted of 2 groups: 9 patients with anembryonic pregnancies and 9 patients with embryonic pregnancies between 6 and 10 weeks of gestational age registered at the Department of Gynecology and Obstetrics, University Hospital of Karadeniz Technical University, Turkey, from March 2003 to December 2004. We examined the chorionic villi using light and electron microscopy. For immunohistochemical staining, we used AFP and CD34. RESULTS Microscopically, pathologic changes were shown in syncytiotrophoblast cells of anembryonic pregnancies and AFP was strongly expressed by villous trophoblastic cells compared to embryonic pregnancies. We determined the CD34 positivity in both groups. In anembryonic pregnancies, vascular elements were much fewer in number compared with embryonic pregnancies (p<0.001) and were located in the formed of hemangioblastic cords. CONCLUSION Placental vasculogenesis is a basic feature in all types of pregnancy and a relationship exists between trophoblast cells and vessels in the chorionic villi with the potential to influence each other's functions. Defective chorionic villus vascularization is associated with embryonic death. This study may support the hypothesis, as suggested by previous studies, that anembryonic pregnancy results from early embryonic death and subsequent reabsorption rather than from the nondevelopment of an embryo.
The management of elite athletes’ returning to play: a multidimensional perspective

Returning to sport after an injury can be a difficult process for competitive elite athletes. The purpose of this study was to examine the experience of competitive elite athletes returning to sport after an injury in order to identify critical, and possibly new, factors which can help them to recover and return to play as soon as possible. A number of factors have been identified by several researchers. These factors include medical, physiological, emotional and others. The literature also highlighted the importance of social support, which includes support from people around athletes, such as family, friends, coaches, trainers, teammates and so on. Through this research I wanted to find the most important and helpful to athletes factors. The data of this retrospective qualitative study were collected through interviews. Eleven elite athletes, from six countries and different kind of sports took part in this research. Athletes were interviewed through semi-structured interview and asked to answer some questions and share their experience of recovery from sports injury and returning to play. The collected data were analyzed through the coding method. All similar answers were put together and finally key factors for athletes’ recovery and return to play were identified. This study confirmed the accuracy of a number of helpful factors for athletes’ recovery and return to play which were already known from literature. Moreover the study discovered several new and key factors, like support from government, sport organization employees, and athletes’ own motivation to achieve their goals. The sport manager should pay attention to all these factors when he or she is planning a recovery program for the injured athlete. Keywords: Sport, Olympic Games, injury, management, recovery.
Automated Reasoning for Robot Ethics

Deontic logic is a very well researched branch of mathematical logic and philosophy. Various kinds of deontic logics are considered for different application domains like argumentation theory, legal reasoning, and acts in multi-agent systems. In this paper, we show how standard deontic logic can be used to model ethical codes for multi-agent systems. Furthermore we show how Hyper, a high performance theorem prover, can be used to prove properties of these ethical codes.
Scalable custom instructions identification for instruction-set extensible processors

Extensible processors allow addition of application-specific custom instructions to the core instruction set architecture. However, it is computationally expensive to automatically select the optimal set of custom instructions. Therefore, heuristic techniques are often employed to quickly search the design space. In this paper, we present an efficient algorithm for exact enumeration of all possible candidate instructions given the dataflow graph (DFG) corresponding to a code fragment. Even though this is similar to the "subgraph enumeration" problem (which is exponential), we find that most subgraphs are not feasible candidates for various reasons. In fact, the number of candidates is quite small compared to the size of the DFG. Compared to previous approaches, our technique achieves orders of magnitude speedup in enumerating these candidate custom instructions for very large DFGs.