Miracle-dz/UniFAR-FTUs huggingface dataset:
Social clues powered, personalized software engineering messages classification

Software Engineering is continuously flourished with model, methodologies, tools, and best practices. Only by investing a great deal of time can one keep up with the cutting-edge knowledge. Active and numerous communications on Twitter make it ideal for the task. Even though, content-based information extraction is not quite successful due to the message diversity and implicit user interests. To improve the classification accuracy, we proposed the classification architecture that embraces social clues which consist of the user profile, and user impact. Three features of the messages, the content feature, the user feature and the community feature, are defined according to the social clues and are used to classify the message. The new architecture yield 35 percents of the accuracy improvement from sole content-based classification. By exploiting the social clues, we push the Twitter message classification to the next level.
Performance differences between electroacoustic and electric alone cochlear stimulation using complex tests in noise. A pilot study

Abstract Objectives: Hybrid cochlear implants enable very interesting performances in bisyllabic words recognition, mainly in noisy conditions. In this paper, we studied patients with implants performing some very difficult timbric tasks in quiet and noisy conditions. Methods: We studied three adult patients with hybrid implants (group A-hybrid implants) and three adult patients with traditional ones (group B-traditional implants). We made the following tests (in quiet and noise): bisyllabic words recognition; voice kind recognition; common melodies identification and recognition; musical instrument identification and recognition. Results: Mean results in quiet: test 1: 35% A, 47% B; test 2: 92% A, 42% B; test 3: identification scores 88% A, 100% B, recognition scores: 37.5% A, 50% B; test 4 identification scores 50% A, 20% B, recognition scores 30% A, 20% B. As for tests in noise: test 1: 28% A, 46% B; test 2: 75% A, 25% B; test 3: identification 81% A, 87.5% B and recognition 56% A, 50% B; test 4: identification 60% A, 30% B and recognition 50% A, 20% B. Conclusion: Patients with hybrid implants seem to have better results, mainly in noisy timbric tasks. This could be explained by the better quality of low frequency stimulation and could be an encouragement for further studies and a larger diffusion of this kind of implant in the future.
Proprioceptive Dysfunction in Focal Dystonia: From Experimental Evidence to Rehabilitation Strategies

Dystonia has historically been considered a disorder of the basal ganglia, mainly affecting planning and execution of voluntary movements. This notion comes from the observation that most lesions responsible for secondary dystonia involve the basal ganglia. However, what emerges from recent research is that dystonia is linked to the dysfunction of a complex neural network that comprises basal ganglia–thalamic–frontal cortex, but also the inferior parietal cortex and the cerebellum. While dystonia is clearly a motor problem, it turned out that sensory aspects are also fundamental, especially those related to proprioception. We outline experimental evidence for proprioceptive dysfunction in focal dystonia from intrinsic sensory abnormalities to impaired sensorimotor integration, which is the process by which sensory information is used to plan and execute volitional movements. Particularly, we will focus on proprioceptive aspects of dystonia, including: (i) processing of vibratory input, (ii) temporal discrimination of two passive movements, (iii) multimodal integration of visual-tactile and proprioceptive inputs, and (iv) motor control in the absence of visual feedback. We suggest that these investigations contribute not only to a better understanding of dystonia pathophysiology, but also to develop rehabilitation strategies aimed at facilitating the processing of proprioceptive input.
Jiffy : A Lightweight Jini File System Service

Jiffy is a distributed file system implemented to take advantage of Sun Microsystems’s Jini Technology. This paper contains quantitative results gathered from an experiment that compared the performance of Jiffy against the Andrew File System. The experiment compared the two file systems using several workloads and several simultaneous clients in order to determine the scalability of the system and to locate places that lead to possible overhead. Based on the results of the experiments, we argue that Jiffy is indeed an efficient file system, because it is able to take advantage of clientside caching, callbacks, and session semantics for sharing, each contributing to reduced traffic with the server. Our results show that in many cases Jiffy is able to outperform AFS. We examine possible influences on the data and conclude that for a specific class of clients, a file system built on top of Jini Technology can be extremely efficient and robust.
A Mixed-Model Approach to Association Mapping Using Pedigree Information With an Illustration of Resistance to Phytophthora infestans in Potato

Association or linkage disequilibrium (LD)-based mapping strategies are receiving increased attention for the identification of quantitative trait loci (QTL) in plants as an alternative to more traditional, purely linkage-based approaches. An attractive property of association approaches is that they do not require specially designed crosses between inbred parents, but can be applied to collections of genotypes with arbitrary and often unknown relationships between the genotypes. A less obvious additional attractive property is that association approaches offer possibilities for QTL identification in crops with hard to model segregation patterns. The availability of candidate genes and targeted marker systems facilitates association approaches, as will appropriate methods of analysis. We propose an association mapping approach based on mixed models with attention to the incorporation of the relationships between genotypes, whether induced by pedigree, population substructure, or otherwise. Furthermore, we emphasize the need to pay attention to the environmental features of the data as well, i.e., adequate representation of the relations among multiple observations on the same genotypes. We illustrate our modeling approach using 25 years of Dutch national variety list data on late blight resistance in the genetically complex crop of potato. As markers, we used nucleotide binding-site markers, a specific type of marker that targets resistance or resistance-analog genes. To assess the consistency of QTL identified by our mixed-model approach, a second independent data set was analyzed. Two markers were identified that are potentially useful in selection for late blight resistance in potato.
The MSR-NRC-SRI MT System for NIST Open Machine Translation 2008 Evaluation

1 Mei Yang was an intern with MSR in the summer of 2007 The system combination approach combining system outputs at the word level is similar to the one described in (Rosti et al., 2007). Compared to the previous work, we developed a new method to generate a better alignment between multiple MT hypotheses from different individual systems, which is used to construct a high-quality confusion network. The details of our method will be elaborated in a future paper (He et al., 2008). First, a minimum Bayes risk (MBR) based method is used to select a backbone from the multiple hypotheses, then all the hypotheses are aligned to that backbone to form a confusion network, i.e., a word lattice in which each word is aligned to a list of alternative words (including null). Then, a set of features, including language model scores, word count, and normalized system voting score, are used to decode the confusion network. In training, a confusion network is constructed based on the multiple hypotheses of each sentence in a dev set. Then the corresponding feature weights are trained using Powell’s search to maximize the BLEU score on that dev set. In testing, a confusion network for each sentence in the test set is constructed and these feature weights are applied to decode the final MT output from the confusion network. In this entry, two language models are used, including a 3-gram LM trained on the English part of the parallel training data, and a 5gram LM trained on the whole English Gigaword corpus using a scalable LM toolkit (Nguyen et al., 2007).
Compression and Sieve: Reducing Communication in Parallel Breadth First Search on Distributed Memory Systems

For parallel breadth first search (BFS) algorithm on large-scale distributed memory systems, communication often costs significantly more than arithmetic and limits the scalability of the algorithm. In this paper we sufficiently reduce the communication cost in distributed BFS by compressing and sieving the messages. First, we leverage a bitmap compression algorithm to reduce the size of messages before communication. Second, we propose a novel distributed directory algorithm, cross directory, to sieve the redundant data in messages. Experiments on a 6,144-core SMP cluster show our algorithm outperforms the baseline implementation in Graph500 by 2.2 times, reduces its communication time by 79.0%, and achieves a performance rate of 12.1 GTEPS (billion edge visits per second)
A Methodology for Extrinsically Evaluating Information Extraction Performance

This paper reports a preliminary study addressing two challenges in measuring the effectiveness of information extraction (IE) technology:• Developing a methodology for extrinsic evaluation of IE; and,• Estimating the impact of improving IE technology on the ability to perform an application task.The methodology described can be employed for further controlled experiments regarding information extraction.
Remote attack detection method in IDA: MLSI-based intrusion detection using discriminant analysis

In order to detect intrusions, IDA (Intrusion Detection Agent system) initially monitors system logs in order to discover an MLSI-which is an certain event which in many cases occurs during an intrusion. If an MLSI is found, then IDA judges whether the MLSI is accompanied by an intrusion. We adopt discriminant analysis to analyze information after IDA detects an MLSI in a remote attack. Discriminant analysis provides a classification function that allows IDA to separate intrusive activities from non-intrusive activities. Using discriminant analysis, we can detect intrusions by analyzing only a part of system calls occurring on a host machine, and we can determine whether an unknown sample is an intrusion. In this paper, we explain in detail how we perform discriminant analysis to detect intrusions, and evaluate the classification function. We also describe how to extract a sample from system logs, which is necessary to implement the discriminant analysis function in IDA.
Disruptive Technologies and Potential Cellular Architecture for 5G

The 5G wireless communication system is a new generation mobile communication system beyond 2020. 5GNow is still in the exploration stage. There are some shortcomings in LTE and LTE-A, such as strict synchronism, la- tency in crowded areas, energy consumption, and challenging in IoT, etc. The 5G wireless networks is a will consider sat- isfying better user experience, manifoldness of services, M2M of IoT. In this paper, we discuss some promising key tech- nologies and conclude 5G potential cellular architecture .
Estimating the Optimal Number of Latent Concepts in Source Code Analysis

The optimal number of latent topics required to model the most accurate latent substructure for a source code corpus is an open question in source code analysis. Most estimates about the number of latent topics that exist in a software corpus are based on the assumption that the data is similar to natural language, but there is little empirical evidence to support this. In order to help determine the appropriate number of topics needed to accurately represent the source code, we generate a series of Latent Dirichlet Allocation models with varying topic counts. We use a heuristic to evaluate the ability of the model to identify related source code blocks, and demonstrate the consequences of choosing too few or too many latent topics.
Bayesian Structure Matching

Our recent work has shown that image segmentations can be considered as intermediate-level image descriptors and applied directly and reliably for matching and recognition. A drawback of this approach is that bottom-up oversegmentations have errors and are sometimes unreliable. We propose a Bayesian framework that sums over all possible segmentations weighted by their probability and measures the similarity between images based on the expected value of the mutual information between their segmentations. In effect, our segmentation descriptor can be applie d for recognition without ever segmenting explicitly. Our me tric is so reliable that we obtain state-of-the-art recognit ion results using a simple holistic strategy. Our approach can also measure the similarity between an image and its segmentation directly, thus giving an explicit measure of the segmentation’s faithfulness to the image. We obtain stateof-the-art segmentation results using our metric within a simple greedy merging approach.
Debt Structure, Market Value of Firm, and Recovery Rate

This paper examines the determinants of creditor recoveries from defaulted debt instruments, an important yet under-studied area in investment and risk management. First, we argue that to properly measure a debt instrument’s relative position in a firm’s debt structure, debt pari passu to the instrument must be taken into account. We propose a new measure of seniority and find that it is the most important determinant of recovery rates, explaining more recovery variations than the combination of all commonly used instrument-level variables, including seniority class, collateral type, and percentage above. Second, we find that firm-level variables, especially the trailing 12-month stock returns, are more critical than industryor macroeconomic-level variables, although the latter can also help, for private firms because stock price information is not available for such firms. In contrast with earlier studies, we find that the relative contribution of the industry and macroeconomic variables varies with the sample, model specification, and especially the modeling technique used.
Boundary regularity for polyharmonic maps in the critical dimension

Abstract We consider the Dirichlet problem for intrinsic and extrinsic k-polyharmonic maps from a bounded, smooth domain Ω ⊆ ℝ2k to a compact, smooth Riemannian manifold N ⊆ ℝ l without boundary. For any smooth boundary data, we show that any k-polyharmonic map u ∈ W k,2(Ω, N) is smooth near the boundary ∂Ω.
A Phonotactic Language Model for Spoken Language Identification

We have established a phonotactic language model as the solution to spoken language identification (LID). In this framework, we define a single set of acoustic tokens to represent the acoustic activities in the world's spoken languages. A voice tokenizer converts a spoken document into a text-like document of acoustic tokens. Thus a spoken document can be represented by a count vector of acoustic tokens and token n-grams in the vector space. We apply latent semantic analysis to the vectors, in the same way that it is applied in information retrieval, in order to capture salient phonotactics present in spoken documents. The vector space modeling of spoken utterances constitutes a paradigm shift in LID technology and has proven to be very successful. It presents a 12.4% error rate reduction over one of the best reported results on the 1996 NIST Language Recognition Evaluation database.
Composing Music with BPTT and LSTM Networks: Comparing Learning and Generalization Aspects

Many researchers have used neural networks on music composition. The architecture of the network and the representation of the training data have influence on the results. We propose to compare BPTT and LSTM networks in musical composition task with two different pitch representations. We present the learning algorithms of both networks and the results obtained in the composition of new melodies.
Video Stabilization Using Scale-Invariant Features

Video Stabilization is one of those important video processing techniques to remove the unwanted camera vibration in a video sequence. In this paper, we present a practical method to remove the annoying shaky motion and reconstruct a stabilized video sequence with good visual quality. Here, the scale invariant (SIFT) features, proved to be invariant to image scale and rotation, is applied to estimate the camera motion. The unwanted vibrations are separated from the intentional camera motion with the combination of Gaussian kernel filtering and parabolic fitting. It is demonstrated that our method effectively removes the high frequency 'noise' motion, but also minimize the missing area as much as possible. To reconstruct the undefined areas, resulting from motion compensation, we adopt the mosaicing method with Dynamic Programming. The proposed method has been confirmed to be effective over a widely variety of videos.
Comparing multi-level modeling approaches

As the range of modelling approaches that claim to be “multi-level” diversifies, there is growing debate in the literature about what multi-level modelling actually is and what form supporting languages and infrastructures should take. However, there is no consensus yet on how this debate should be framed and what objective criteria should be used to evaluate different approaches. It is clear from the literature that proponents of different approaches base their arguments on fundamentally different assumptions about what multi-level modelling is and what benefits it should aim to provide. In this position paper we identify some of the core issues that currently hinder progress towards the required consensus and identify some of the terminological differences that have amplified confusion. Referencing various work that represents diverging viewpoints, our goal is to initiate a meta-discussion on what the open questions in multi-level modelling are, how respective proposals to answer them could be evaluated, and which kinds of discussions are expedient in this context.
Alcohol Abuse in Pregnant Women: Effects on the Fetus and Newborn, Mode of Action and Maternal Treatment

Offspring of mothers using ethanol during pregnancy are known to suffer from developmental delays and/or a variety of behavioral changes. Ethanol, may affect the developing fetus in a dose dependent manner. With very high repetitive doses there is a 6–10% chance of the fetus developing the fetal alcoholic syndrome manifested by prenatal and postnatal growth deficiency, specific craniofacial dysmorphic features, mental retardation, behavioral changes and a variety of major anomalies. With lower repetitive doses there is a risk of “alcoholic effects” mainly manifested by slight intellectual impairment, growth disturbances and behavioral changes. Binge drinking may impose some danger of slight intellectual deficiency. It is advised to offer maternal abstinence programs prior to pregnancy, but they may also be initiated during pregnancy with accompanying close medical care. The long term intellectual outcome of children born to ethanol dependent mothers is influenced to a large extent by the environment in which the exposed child is raised.
Recognizing certain repetitions and reversals within strings

Let P1 = {w ε Σ*:w = wR, |w| ≫ 1} be the set of all nontrivial palindromes over Σ. In Part I, we present a linear-time on-line recognition algorithm for P1* ("palstar") on a random-access machine with addition and uniform cost criterion. We also present a lineartime on-line recognition algorithm for P12 on a multitape Turing machine and a recognition algorithm for P12 on a two-way deterministic pushdown automaton. The correctness of these algorithms is based on new "cancellation lemmas" for the languages P1* and P12. In Part II, we present real-time recognition algorithms for the languages {wxyxz ε Σ*: |w|=r|x|, |y|=s|x|, |z|=t|x|} and {wxyxRz ε Σ*: |w|=r|x|, |y|=s|x|, |z|=t|x|} on multitape Turing machines, for arbitrary fixed r, s, and t.
Contribution of Neutrophils to Acute Lung Injury

Treatment of acute lung injury (ALI) and its most severe form, acute respiratory distress syndrome (ARDS), remain unsolved problems of intensive care medicine. ALI/ARDS are characterized by lung edema due to increased permeability of the alveolar-capillary barrier and subsequent impairment of arterial oxygenation. Lung edema, endothelial and epithelial injury are accompanied by an influx of neutrophils into the interstitium and broncheoalveolar space. Hence, activation and recruitment of neutrophils are regarded to play a key role in progression of ALI/ARDS. Neutrophils are the first cells to be recruited to the site of inflammation and have a potent antimicrobial armour that includes oxidants, proteinases and cationic peptides. Under pathological circumstances, however, unregulated release of these microbicidal compounds into the extracellular space paradoxically can damage host tissues. This review focuses on the mechanisms of neutrophil recruitment into the lung and on the contribution of neutrophils to tissue damage in ALI.
Effect of Antibiotic Treatment on Bacteriophage Production by a Cystic Fibrosis Epidemic Strain of Pseudomonas aeruginosa

ABSTRACT Phage production in response to antibiotics varied among four isolates of a Pseudomonas aeruginosa cystic fibrosis (CF) epidemic strain. Whereas ciprofloxacin induced higher levels of phage production, other CF-relevant antibiotics led to reduced production. We detected free phages directly in CF patient sputum samples by both plaque (40% positive) and PCR (76% positive) assays. Our observations suggest that the choice of antibiotics could influence the number of free phages within the CF lung environment.
Detecting Chronic Critics Based on Sentiment Polarity and User’s Behavior in Social Media

There are some chronic critics who always complain about the entity in social media. We are working to automatically detect these chronic critics to prevent the spread of bad rumors about the reputation of the entity. In social media, most comments are informal, and, there are sarcastic and incomplete contexts. This means that it is difficult for current NLP technology such as opinion mining to recognize the complaints. As an alternative approach for social media, we can assume that users who share the same opinions will link to each other. Thus, we propose a method that combines opinion mining with graph analysis for the connections between users to identify the chronic critics. Our experimental results show that the proposed method outperforms analysis based only on opinion mining techniques.