Miracle-dz/UniFAR-FTUs huggingface dataset:
A Repository of State of the Art and Competitive Baseline Summaries for Generic News Summarization

In the period since 2004, many novel sophisticated approaches for generic multi-document summarization have been developed. Intuitive simple approaches have also been shown to perform unexpectedly well for the task. Yet it is practically impossible to compare the existing approaches directly, because systems have been evaluated on different datasets, with different evaluation measures, against different sets of comparison systems. Here we present a corpus of summaries produced by several state-of-the-art extractive summarization systems or by popular baseline systems. The inputs come from the 2004 DUC evaluation, the latest year in which generic summarization was addressed in a shared task. We use the same settings for ROUGE automatic evaluation to compare the systems directly and analyze the statistical significance of the differences in performance. We show that in terms of average scores the state-of-the-art systems appear similar but that in fact they produce very different summaries. Our corpus will facilitate future research on generic summarization and motivates the need for development of more sensitive evaluation measures and for approaches to system combination in summarization.
Real-Time Detection of Malware Downloads via Large-Scale URL->File->Machine Graph Mining

In this paper we propose Mastino, a novel defense system to detect malware download events. A download event is a 3-tuple that identifies the action of downloading a file from a URL that was triggered by a client (machine). Mastino utilizes global situation awareness and continuously monitors various network- and system-level events of the clients' machines across the Internet and provides real time classification of both files and URLs to the clients upon submission of a new, unknown file or URL to the system. To enable detection of the download events, Mastino builds a large download graph that captures the subtle relationships among the entities of download events, i.e. files, URLs, and machines. We implemented a prototype version of Mastino and evaluated it in a large-scale real-world deployment. Our experimental evaluation shows that Mastino can accurately classify malware download events with an average of 95.5% true positive (TP), while incurring less than 0.5% false positives (FP). In addition, we show the Mastino can classify a new download event as either benign or malware in just a fraction of a second, and is therefore suitable as a real time defense system.
Modeling outer products of features for image classification

Recent studies have shown that sparse coding is an efficient method for feature quantization in image classification tasks. However, sparse coding can only capture linear statistical regularities among the features. In the paper, we show that features can be quantized in a nonlinear way by modeling their outer products. Experiments on some public datasets show that the proposed method can achieve comparable or better results than sparse coding.
Managing the Brand: Administrative Structure of the Hidden Profession of Collegiate Trademark Licensing

This study analyzes the current state of collegiate trademark licensing departments through first-hand accounts from current or past licensing professionals. Semi-structured interviews were conducted to understand current issues that are facing the licensing industry, particularly focusing on internal organizational structure of collegiate trademark licensing departments at institutes of higher education. Eleven participants, including licensing agents, licensees, licensing directors, and licensing experts were interviewed and the following themes emerged. Collegiate trademark licensing is a hidden profession, with little education available about the industry. Programs have greatly evolved over recent decades, but institutional infrastructure still lags in the appropriate assets and resources to sustain a growing program. Licensing directors have strong professional relationships with others in the field, but often have difficulty receiving buy-in to licensing objectives from their internal peers. A strong sense of ‘university’ versus ‘athletics’ exists, causing confusion and discrepancy in managing the multi-faceted licensing objectives.
Evaluating and Comparing the IGraph Community Detection Algorithms

Complex networks became a very important tool in machine learning field, helping researchers to investigate and mine data. They can model real dynamic networks, aiding to unveil information's about the systems they model. Communities are notable groups that may exist in a complex network and the community detection problem is the focus of attention of many researchers. The igraph library implements a good set of community detection algorithms, allowing researchers to easily apply them to data mining tasks. But each algorithm uses a different approach, leading to different performances. In this paper, the community detection algorithms implemented in the igraph library are investigated and ranked according to their performances in a set of different scenarios. Results show walktrap and multi-level got the highest scores while leading eigenvector and spinglass got the lowest ones. These findings are an important contribution for aiding researchers to select or discard algorithms in their own experiments using igraph library.
Study on Database System about Emotional Diseases Caused by Anger-Out and Anger-In

The disease causing by negative emotions including anger-out and anger-in is called emotional disease. In this paper the technology of database management is introduced into the research of emotional disease mechanism. The aim of this paper is to create database system of emotional diseases which provide experimental data for epidemiology, clinic retrospection and foresighted research, change the actuality that store data on EXCEL table which is inconvenient to query, statistic and analyze. The method of this research is, Firstly, collect questionnaire data and scale data via batch input. Secondly, class these data logically. Finally, provide convenient function of query, statistic, analysis, import, export and report printing through reasonable organizing data and scientific analyzing data. The function of batch data input, query, statistic and analysis fast are realized in this paper. These results provide plenty experimental data for the research of emotional diseases. The database system of emotional diseases has applied practicality. The result shows that it is more scientific, reasonable and convenient methods to organize data by database than EXECL table. It is a more scientific way of organizing data. The statistic result is important to the recovery and cure of Emotional Diseases. The deeply study in statistic, analysis and data digging will be done to provide important and scientific academic guidance for solving all kind problems causing by anger-out and anger-in and improving level of prevention and cure of emotional diseases.
A Jigsaw Puzzle Solving Guide on Mobile Devices

In this report we present our work on designing and implementing a mobile phone application that helps people solve jigsaw puzzles by locating the image of a single patch on the complete picture. Details of the algorithm and implementation are discussed and test results are presented. Keywords-component; Jigsaw Puzzle; Mobile Application; Template Matching; Image Segmentation; SURF; RANSAC
Methods for efficient semi-automatic pronunciation dictionary bootstrapping

In this paper we propose efficient methods which contribute to a rapid and economic semi-automatic pronunciation dictionary development and evaluate them on English, German, Spanish, Vietnamese, Swahili, and Haitian Creole. First we determine optimal strategies for the word selection and the period for the grapheme-to-phoneme model retraining. In addition to the traditional concatenation of single phonemes most commonly associated with each grapheme, we show that web-derived pronunciations and cross-ligual grapheme-to-phoneme models can help to reduce the initial editing effort. Furthermore we show that our phoneme-level combination of the output of multiple grapheme-to-phoneme converters reduces the editing effort more than the best single converters. Totally, we report on average 15% relative editing effort reduction with our phonemelevel combination compared to conventional methods. An additional reduction of 6% relative was possible by including initial pronunciations from Wiktionary for English, German, and Spanish.
Cell membrane electroporation-Part 3: the equipment

As described in Part 1, a cell membrane can be made permeable to various molecules by carrying out a procedure called electroporation [1]. This procedure is being successfully used in biology, biotechnology, and medicine [2], [3]. It requires electroporators and electrodes. An electroporator generates short HV pulses of specific shape, amplitude, duration, number, and repetition frequency [4], and the pulses are applied to the target cells or load through the electrodes [5]. The energy delivered to the load is governed by the number of pulses and the pulse voltage, current, and duration. In biomedical applications that energy can be several joules; in biotechnology, where electroporation is used for treatment of agricultural products and water, it can be several kilojoules.
Constrained Motif Discovery

The goal of motif discovery algorithms is to efficiently find unknown recurring patterns in time series. Most available algorithms cannot utilize domain knowledge in any way which results in quadratic or at least sub-quadratic time and space complexity. For large time series datasets for which domain knowledge can be available this is a severe limitation. In this paper we define the Constrained Motif Discovery problem which enables utilization of domain knowledge into the motif discovery process. We also show that most unconstrained motif discovery problems be converted into constrained motif discovery problem using a change point detection algorithm. We provide two algorithms for solving this problem and compare their performance to state-of-the-art motif discovery algorithms on a large set of synthetic time series. The proposed algorithms can provide linear time and constant space complexity. The proposed algorithms provided four to ten folds increase in speed compared to two state of the art motif discovery algorithms without loss of accuracy and provided better noise robustness in high noise levels.
TerminAPTor: Highlighting Advanced Persistent Threats through Information Flow Tracking

Long lived attack campaigns known as Advanced Persistent Threats (APTs) have emerged as a serious security risk. These attack campaigns are customised for their target and performed step by step during months on end. The major difficulty in detecting an APT is keeping track of the different steps logged over months of monitoring and linking them. In this article, we describe TerminAPTor, an APT detector which highlights the traces left by attackers in the monitored system during the different stages of an attack campaign. TerminAPTor tackles this challenge by resorting to Information Flow Tracking (IFT). Our main contribution is showing that IFT can be used to highlight APTs. Additionally, we describe a generic representation of APTs and validate our IFT-based APT detector.
Bortezomib with chemotherapy is highly active in advanced B-precursor acute lymphoblastic leukemia: Therapeutic Advances in Childhood Leukemia & Lymphoma (TACL) Study.

Therapy of relapsed pediatric acute lymphoblastic leukemia (ALL) is hampered by low remission rates and high toxicity, especially in second and subsequent relapses. Our phase 1 study, T2005-003, showed that the combination of bortezomib with vincristine, dexamethasone, pegylated asparaginase, and doxorubicin had acceptable toxicity. We report the phase 2 expansion of this combination in patients with relapsed ALL who failed 2-3 previous regimens. Twenty-two patients with relapsed ALL were treated with bortezomib combined with this regimen; their ages ranged from 1 to 22 years, and they had either B-precursor ALL (n = 20) or T-cell ALL (n = 2). Grade 3 peripheral neuropathy developed in 2 (9%) patients. After 3 patients died from bacterial infections, treatment with vancomycin, levofloxacin, and voriconazole prophylaxis resulted in no further infectious mortality in the last 6 patients. Fourteen patients achieved complete remission (CR), and 2 achieved CR without platelet recovery, for an overall 73% response rate, meeting predefined criteria allowing for early closure. B-precursor patients faired best, with 16 of 20 (80%) CR + CR without platelet recovery, whereas the 2 patients with T-cell ALL did not respond. Thus, this combination of bortezomib with chemotherapy is active in B-precursor ALL, and prophylactic antibiotics may be useful in reducing mortality. Bortezomib merits further evaluation in combination therapy in pediatric B-precursor ALL. This study is registered at http://www.clinicaltrials. gov as NCT00440726.
Approximation Algorithms for Combinatorial Optimization

In combinatorial optimization, the most important challenges are presented by problems belonging to the class NP-hard. For such problems no algorithm is known that can solve all instances in polynomial time. It is also strongly believed that no polynomial algorithm is capable of doing this. Although it is very difficult to solve exactly any of the NP-hard problems, some of them are amenable to methods that return approximate solutions in polynomial time. An important alternative to exact methods are algorithms that compute not the optimum, but some approximation that may be useful in practice. The area of approximation algorithms has the objective of providing general techniques for development of algorithms for NP-hard problems with proven guarantee of approximation. In other words, for these algorithms it can be proved that the solution returned will be within a relative distance from the optimum. In this article, we present some of the most interesting techniques used in the field of approximation algorithms, as well as problems that have been approximately solved using these algorithms.
Mother or Motherland: Can a Government Have an Impact on Educational Attainment of the Population? Preliminary Evidence from India

In this paper, using data from the 61st round of the (Indian) National Sample Survey, we examine the relative impacts of personal-household and state-level characteristics (including government policy) on the likelihood of transition from one educational level to the next. Our analysis suggests that the most important factors driving these transition likelihoods are personal and household characteristics like gender and education of household heads. However, state-level characteristics and government policies have a significant impact on these transition likelihoods as well, especially for transitions from the lowest levels of education to somewhat higher levels. The odds of making the transition to higher education, especially tertiary education, are systematically lower for women than for men, for individuals in rural areas than those in urban areas, and for Muslims than for Hindus. An important conclusion of our analysis is that there is significant scope for government policy to address educational gaps between various demographic and other groups in the country.
Open-source, low-cost, compliant, modular, underactuated fingers: Towards affordable prostheses for partial hand amputations

In this paper we present a series of design directions for the development of affordable, compliant, modular, underactuated robot fingers, that can be used as prostheses by amputees that suffer from various partial hand amputations (index to pinky fingers are considered). Our design is based on parametric models that have been derived from hand anthropometry studies. Various interfaces have been considered in order to control the prosthesis, depending on the type and level of amputation. More precisely: 1) An Electromyography (EMG) based interface is used to control the robot fingers employing the EMG signals of the human forearm muscles 2) A flex sensors based interface is used to record the motion of the intact finger/fingers and predict the motion of the prosthesis implementing a synergistic behavior in an efficient manner, 3) A body powered interface is used for those that want to achieve even lower cost, with robust intuitive operation. Following the proposed design directions, an amputee will be able to replicate our fingers and develop personalized, affordable, light-weight but yet efficient prostheses.
Two New Classes of Hamiltonian Graphs

We prove that a triangular grid without local cuts is (almost) always Hamiltonian. This suggests an efficient scheme for rendering triangulated manifolds by graphics hardware. We also show that the Hamiltonian Cycle problem is NP-Complete for planar subcubic graphs of arbitrarily high girth. As a by-product we prove that there exist tri-Hamiltonian planar subcubic graphs of arbitrarily high girth. 1 Definitions and Notation We say that a graph G = (V,E) is induced by a set S ⊂ R if V = S, and E = {{i, j} | i, j ∈ S, |i− j| = 1}. Let Z∆ be the infinite triangular lattice, i.e., the set of vertices of the tiling of R with unit equilateral triangles. A triangular grid graph, or triangular grid, is a plane graph induced by a subset of vertices of Z∆. Let G = (V,E) be a triangular grid without degree-1 vertices. A bounded face f of G is called a hole if f is not a unit equilateral triangle. Let h denote the number of holes in G. A vertex v ∈ V is called a cut if its removal disconnects G; v is a local cut v is a cut or if the number of holes in G \ v is less than h. A vertex v ∈ V is called boundary if its degree is less than 6. The non-boundary vertices of G (degree-6 vertices) are called internal vertices and denoted by V6. The subgraph B of G induced by its boundary vertices, V \ V6, is a set of cycles B = {C, C1, . , Ch}. The cycle C ∈ B that separates G from its unbounded face is called the outer boundary. Each cycle in B \ C = {C1 . Ch} bounds a hole, i.e., is the boundary of a hole of G. Having no local cut means that the cycles in B are simple and vertex-disjoint. Let the cost of B be the number of edges in it. For g ∈ N we denote by Gg the class of planar bipartite maximum-degree-3 graphs of girth g. Our Contributions We prove that triangular grids without local cuts are always Hamiltonian, with the exception of one special graph, “The Star of David” (Fig. 1). Our proof is constructive and allows one to find the Hamiltonian cycle in linear time. This has application in computer graphics as it gives an efficient scheme for outputting triangulation data. We prove that for arbitrarily high g, the Hamiltonian cycle problem is NP-complete for graphs from Gg . We also prove that for arbitrarily high g there exist graphs in Gg that have exactly 3 Hamiltonian cycles. 2 Triangular Grids without Local Cuts are Hamiltonian The crucial observations that we use are as follows: 1) One can attach to the cycles in B all internal vertices at the cost of 1 per ∗Applied Math and Statistics, Stony Brook University. †Helsinki Institute for Information Technology. Figure 1: The only non-Hamiltonian triangular grid without local cuts: the Star of David. Cj Ci
Jacobi-Davidson Style QR and QZ Algorithms for the Reduction of Matrix Pencils

Recently the Jacobi--Davidson subspace iteration method has been introduced as a new powerful technique for solving a variety of eigenproblems. In this paper we will further exploit this method and enhance it with several techniques so that practical and accurate algorithms are obtained. We will present two algorithms, JDQZ for the generalized eigenproblem and JDQR for the standard eigenproblem, that are based on the iterative construction of a (generalized) partial Schur form. The algorithms are suitable for the efficient computation of several (even multiple) eigenvalues and the corresponding eigenvectors near a user-specified target value in the complex plane. An attractive property of our algorithms is that explicit inversion of operators is avoided, which makes them potentially attractive for very large sparse matrix problems. We will show how effective restarts can be incorporated in the Jacobi--Davidson methods, very similar to the implicit restart procedure for the Arnoldi process. Then we will discuss the use of preconditioning, and, finally, we will illustrate the behavior of our algorithms by a number of well-chosen numerical experiments.
Optical and Infrared Nondetection of the z = 10 Galaxy behind Abell 1835

Gravitational lensing by massive galaxy clusters is a powerful tool for the discovery and study of high-redshift galaxies, including those at z ≥ 6 likely responsible for cosmic reionization. Pelló et al. recently used this technique to discover a candidate gravitationally magnified galaxy at z = 10 behind the massive cluster lens Abell 1835 (z = 0.25). We present new Keck and Spitzer Space Telescope observations of the z = 10 candidate (hereafter #1916, following Pelló et al.'s nomenclature) together with a reanalysis of archival optical and near-infrared imaging from the Hubble Space Telescope and Very Large Telescope, respectively. Our analysis extends from the atmospheric cutoff at λobs ≃ 0.35 μm out to λobs ≃ 5 μm. The z = 10 galaxy is not detected in any of these data, including an independent reduction of Pelló et al.'s discovery H- and K-band imaging. We conclude that there is no statistically reliable evidence for the existence of #1916. We also assess the implications of our results for ground-based near-infrared searches for gravitationally magnified galaxies at z ≳ 7. The broad conclusion is that such experiments remain feasible, assuming that space-based optical and mid-infrared imaging are available to break the degeneracy with low-redshift interlopers (e.g., z ~ 2-3) when fitting spectral templates to the photometric data.
POPSTAR at RepLab 2013: Polarity for Reputation Classification

This paper describes our participation in the Polarity for Reputation classification task of RepLab 2013. Our system leveraged on a set of components previously developed for a Twitter message polarity classifier. Following a super- vised approach, a Logistic Regression classifier is trained from annotated data. A refined language model is used to represent tweets in terms of a vocabulary consisting only of the most informative terms with word features weighted using a measure from the Information Retrieval field. To help reduce the sparseness of the feature vector, the model is enriched with another, more compact, represen- tation of the words. Finally, we extract features to capture the use of informal and affective language. Our approach ranked in the top three for all the metrics, showing that the strategies for Twitter Sentiment Analysis are useful for the task of Polarity for Reputation classification.
ITERATED TABU SEARCH FOR THE TRAVELING SALESMAN PROBLEM: NEW RESULTS

In this paper, we present some new results obtained for the traveling salesman problem (TSP) by using the iterated tabu search (ITS) meta-heuristic. ITS is a promising extension to the ordinary tabu search scheme. It seeks near-optimal solutions by combining intensification (standard tabu search) and diversification (perturbation of solutions) in a proper way. For the TSP, the main effect is achieved due to decomposition of the solution neighbourhood structure and considerably speeding-up the tabu search process, which is used, namely, in the role of intensification. This fast-iterated tabu search (FITS) technique resulted in quite encouraging solutions for the TSP instances from the TSP instance library TSPLIB. FITS obviously outperformed the other heuristic algorithms used in the experimentation, especially, on the smaller TSP instances.