Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploiting User Posts for Web Document Summarization

Relevant user posts such as comments or tweets of a Web document provide additional valuable information to enrich the content of this document. When creating user posts, readers tend to borrow salient words or phrases in sentences. This can be considered as word variation. This article proposes a framework that models the word variation aspect to enhance the quality of Web document summarization. Technically, the framework consists of two steps: scoring and selection. In the first step, the social information of a Web document such as user posts is exploited to model intra-relations and inter-relations in lexical and semantic levels. These relations are denoted by a mutual reinforcement similarity graph used to score each sentence and user post. After scoring, summaries are extracted by using a ranking approach or concept-based method formulated in the form of Integer Linear Programming. To confirm the efficiency of our framework, sentence and story highlight extraction tasks were taken as a case study on three datasets in two languages, English and Vietnamese. Experimental results show that: (i) the framework can improve ROUGE-scores compared to state-of-the-art baselines of social context summarization and (ii) the combination of the two relations benefits the sentence extraction of single Web documents.
Smoothed analysis of the low-rank approach for smooth semidefinite programs

We consider semidefinite programs (SDPs) of size n with equality constraints. In order to overcome scalability issues, Burer and Monteiro proposed a factorized approach based on optimizing over a matrix Y of size $n$ by $k$ such that $X = YY^*$ is the SDP variable. The advantages of such formulation are twofold: the dimension of the optimization variable is reduced and positive semidefiniteness is naturally enforced. However, the problem in Y is non-convex. In prior work, it has been shown that, when the constraints on the factorized variable regularly define a smooth manifold, provided k is large enough, for almost all cost matrices, all second-order stationary points (SOSPs) are optimal. Importantly, in practice, one can only compute points which approximately satisfy necessary optimality conditions, leading to the question: are such points also approximately optimal? To this end, and under similar assumptions, we use smoothed analysis to show that approximate SOSPs for a randomly perturbed objective function are approximate global optima, with k scaling like the square root of the number of constraints (up to log factors). Moreover, we bound the optimality gap at the approximate solution of the perturbed problem with respect to the original problem. We particularize our results to an SDP relaxation of phase retrieval.
POSTER: An Intelligent Framework to Parallelize Hadoop Phases

Hadoop-stock is a reliable, scalable, and open source implementation of the MapReduce framework to process data-intensive applications in a distributed and parallel environment. In a common environment between multiple users with various types of applications, due to the lower number of resources than the number of jobs, there will be multi-wave jobs. Shuffling as the longest phase of running a job has the most adverse effect (network traffic) on the job execution time. On one hand, due to the dependency of shuffle phase to reduce task, the shuffle phase could not start until the reduce task being scheduled. On the other hand, the static scheduling of reduce tasks results in loss of reduce slots. This paper presents our ongoing effort in the designing an intelligent service in which the sort/merge and shuffle phases are completely independent of map and reduce phases and could act in parallel with map and reduce phases. This parallelism mitigates the job completion time.
TaxoGen: Unsupervised Topic Taxonomy Construction by Adaptive Term Embedding and Clustering

Taxonomy construction is not only a fundamental task for semantic analysis of text corpora, but also an important step for applications such as information filtering, recommendation, and Web search. Existing pattern-based methods extract hypernym-hyponym term pairs and then organize these pairs into a taxonomy. However, by considering each term as an independent concept node, they overlook the topical proximity and the semantic correlations among terms. In this paper, we propose a method for constructing topic taxonomies, wherein every node represents a conceptual topic and is defined as a cluster of semantically coherent concept terms. Our method, TaxoGen, uses term embeddings and hierarchical clustering to construct a topic taxonomy in a recursive fashion. To ensure the quality of the recursive process, it consists of: (1) an adaptive spherical clustering module for allocating terms to proper levels when splitting a coarse topic into fine-grained ones; (2) a local embedding module for learning term embeddings that maintain strong discriminative power at different levels of the taxonomy. Our experiments on two real datasets demonstrate the effectiveness of TaxoGen compared with baseline methods.
Roaming Objects: Encoding Digital Histories of Use into Shared Objects and Tools

An increasing number of non-profit groups and organizations have formed "libraries" of shared things to leverage the collaborative use of underutilized resources (e.g., power tools) for the benefit of local communities. Their key challenges are the transience and anonymity of their members, and how to nurture creative interactions among them. We designed and developed Roaming Objects, an interactive system aimed at supporting the capture and sharing of equipment-use experiences among these members. We deployed the system for two months in a tool-sharing cooperative to explore how it may help to address these challenges. We offer insights into how resource sharing cooperatives and collectives could be better supported, by proposing design opportunities that facilitate sharing both physical objects and digital information about their use.
Spatial measures of software complexity

This paper introduces a set of simple software complexity metrics that has been inspired by developments within cognitive psychology. Complexity measures are constructed by analysing the distance be­ tween components of a program. The greater the distance between program fragments, the greater the resulting spatial complexity of a program. Suggestions are made as to how spatial complexity measures can be tailored to individual programmer teams. Using these metrics, the complexity of a software system can be adjusted using subjective measures of programmer experience and knowledge. A related set of simple object-oriented metrics based around the same principles of are also suggested. Finally, a number of further research possibilities are suggested.
Learning Perceptual Embeddings with Two Related Tasks for Joint Predictions of Media Interestingness and Emotions

Integrating media elements of various medium, multimedia is capable of expressing complex information in a neat and compact way. Early studies have linked different sensory presentation in multimedia with the perception of human-like concepts. Yet, the richness of information in multimedia makes understanding and predicting user perceptions in multimedia content a challenging task both to the machine and the human mind. This paper presents a novel multi-task feature extraction method for accurate prediction of user perceptions in multimedia content. Differentiating from the conventional feature extraction algorithms which focus on perfecting a single task, the proposed model recognizes the commonality between different perceptions (e.g., interestingness and emotional impact), and attempts to jointly optimize the performance of all the tasks through uncovered commonality features. Using both a media interestingness dataset and a media emotion dataset for user perception prediction tasks, the proposed model attempts to simultaneously characterize the individualities of each task and capture the commonalities shared by both tasks, and achieves better accuracy in predictions than other competing algorithms on real-world datasets of two related tasks: MediaEval 2017 Predicting Media Interestingness Task and MediaEval 2017 Emotional Impact of Movies Task.
Non-Local Sparse Image Inpainting for Document Bleed-Through Removal

Bleed-through is a frequent, pervasive degradation in ancient manuscripts, which is caused by ink seeped from the opposite side of the sheet. Bleed-through, appearing as an extra interfering text, hinders document readability and makes it difficult to decipher the information contents. Digital image restoration techniques have been successfully employed to remove or significantly reduce this distortion. This paper proposes a two-step restoration method for documents affected by bleed-through, exploiting information from the recto and verso images. First, the bleed-through pixels are identified, based on a non-stationary, linear model of the two texts overlapped in the recto-verso pair. In the second step, a dictionary learning-based sparse image inpainting technique, with non-local patch grouping, is used to reconstruct the bleed-through-contaminated image information. An overcomplete sparse dictionary is learned from the bleed-through-free image patches, which is then used to estimate a befitting fill-in for the identified bleed-through pixels. The non-local patch similarity is employed in the sparse reconstruction of each patch, to enforce the local similarity. Thanks to the intrinsic image sparsity and non-local patch similarity, the natural texture of the background is well reproduced in the bleed-through areas, and even a possible overestimation of the bleed through pixels is effectively corrected, so that the original appearance of the document is preserved. We evaluate the performance of the proposed method on the images of a popular database of ancient documents, and the results validate the performance of the proposed method compared to the state of the art.
RFC-Directed Differential Testing of Certificate Validation in SSL/TLS Implementations

Certificate validation in Secure Socket Layer or Transport Layer Security protocol (SSL/TLS) is critical to Internet security. Thus, it is significant to check whether certificate validation in SSL/TLS is correctly implemented. With this motivation, we propose a novel differential testing approach which is directed by the standard Request For Comments (RFC). First, rules of certificates are extracted automatically from RFCs. Second, low-level test cases are generated through dynamic symbolic execution. Third, high-level test cases, i.e. certificates, are assembled automatically. Finally, with the assembled certificates being test cases, certificate validations in SSL/TLS implementations are tested to reveal latent vulnerabilities or bugs. Our approach named RFCcert has the following advantages: (1) certificates of RFCcert are discrepancy-targeted since they are assembled according to standards instead of genetics; (2) with the obtained certificates, RFCcert not only reveals the invalidity of traditional differential testing but also is able to conduct testing that traditional differential testing cannot do; and (3) the supporting tool of RFCcert has been implemented and extensive experiments show that the approach is effective in finding bugs of SSL/TLS implementations.
Automatic Software Repair: A Survey

Debugging software failures is still a painful, time consuming, and expensive process. For instance, recent studies showed that debugging activities often account for about 50% of the overall development cost of software products [3]. There are many factors contributing to the cost of debugging, but the most impacting one is the extensive manual effort that is still required to identify and remove faults. So far, the automation of debugging activities essentially resulted in the development of techniques that provide useful insights about the possible locations of faults, the inputs and states of the application responsible for the failures, as well as the anomalous operations executed during failures. However, developers must still put a relevant effort on the analysis of the failed executions to exactly identify the faults that must be fixed. In addition, these techniques do not help the developers with the synthesis of an appropriate fix. Recently, researchers focused on a new class of approaches, namely program repair techniques [1, 4], whose key idea is to try to automatically repair software systems by producing an actual fix that can be validated by the testers before it is finally accepted, or that can be adapted to properly fit the system. The benefit of using these techniques is that the fix both explains the reason of the failure and provides a possible solution to the problem, thus alleviating the effort necessary to identify and correct faults. Since program repair techniques have the potential to dramatically reduce the debugging effort, they attracted the interest of many researchers who produced several approaches for repairing different classes of faults under different conditions and hypotheses. The proposed algorithms, techniques, and heuristics have been integrated, experimented, and studied, producing a heterogeneous and articulated research framework where automatic repair techniques are proliferating. Important results have been already achieved, but at the same time these results revealed the existence of relevant challenges that should be faced.
Building Bayesian Neural Networks with Blocks: On Structure, Interpretability and Uncertainty

We provide simple schemes to build Bayesian Neural Networks (BNNs), block by block, inspired by a recent idea of computation skeletons. We show how by adjusting the types of blocks that are used within the computation skeleton, we can identify interesting relationships with Deep Gaussian Processes (DGPs), deep kernel learning (DKL), random features type approximation and other topics. We give strategies to approximate the posterior via doubly stochastic variational inference for such models which yield uncertainty estimates. We give a detailed theoretical analysis and point out extensions that may be of independent interest. As a special case, we instantiate our procedure to define a Bayesian {\em additive} Neural network -- a promising strategy to identify statistical interactions and has direct benefits for obtaining interpretable models.
A data-driven CHC solver

We present a data-driven technique to solve Constrained Horn Clauses (CHCs) that encode verification conditions of programs containing unconstrained loops and recursions. Our CHC solver neither constrains the search space from which a predicate's components are inferred (e.g., by constraining the number of variables or the values of coefficients used to specify an invariant), nor fixes the shape of the predicate itself (e.g., by bounding the number and kind of logical connectives). Instead, our approach is based on a novel machine learning-inspired tool chain that synthesizes CHC solutions in terms of arbitrary Boolean combinations of unrestricted atomic predicates. A CEGAR-based verification loop inside the solver progressively samples representative positive and negative data from recursive CHCs, which is fed to the machine learning tool chain. Our solver is implemented as an LLVM pass in the SeaHorn verification framework and has been used to successfully verify a large number of nontrivial and challenging C programs from the literature and well-known benchmark suites (e.g., SV-COMP).
Genome-Wide Exon-Capture Approach Identifies Genetic Variants of Norway Spruce Genes Associated With Susceptibility to Heterobasidion parviporum Infection

Root and butt rot caused by members of the Heterobasidion annosum species complex is the most economically important disease of conifer trees in boreal forests. Wood decay in the infected trees dramatically decreases their value and causes considerable losses to forest owners. Trees vary in their susceptibility to Heterobasidion infection, but the genetic determinants underlying the variation in the susceptibility are not well-understood. We performed the identification of Norway spruce genes associated with the resistance to Heterobasidion parviporum infection using genome-wide exon-capture approach. Sixty-four clonal Norway spruce lines were phenotyped, and their responses to H. parviporum inoculation were determined by lesion length measurements. Afterwards, the spruce lines were genotyped by targeted resequencing and identification of genetic variants (SNPs). Genome-wide association analysis identified 10 SNPs located within 8 genes as significantly associated with the larger necrotic lesions in response to H. parviporum inoculation. The genetic variants identified in our analysis are potential marker candidates for future screening programs aiming at the differentiation of disease-susceptible and resistant trees.
NUMA-Caffe

Convolution Neural Networks (CNNs), a special subcategory of Deep Learning Neural Networks (DNNs), have become increasingly popular in industry and academia for their powerful capability in pattern classification, image processing, and speech recognition. Recently, they have been widely adopted in High Performance Computing (HPC) environments for solving complex problems related to modeling, runtime prediction, and big data analysis. Current state-of-the-art designs for DNNs on modern multi- and many-core CPU architectures, such as variants of Caffe, have reported promising performance in speedup and scalability, comparable with the GPU implementations. However, modern CPU architectures employ Non-Uniform Memory Access (NUMA) technique to integrate multiple sockets, which incurs unique challenges for designing highly efficient CNN frameworks. Without a careful design, DNN frameworks can easily suffer from long memory latency due to a large number of memory accesses to remote NUMA domains, resulting in poor scalability. To address this challenge, we propose NUMA-aware multi-solver-based CNN design, named NUMA-Caffe, for accelerating deep learning neural networks on multi- and many-core CPU architectures. NUMA-Caffe is independent of DNN topology, does not impact network convergence rates, and provides superior scalability to the existing Caffe variants. Through a thorough empirical study on four contemporary NUMA-based multi- and many-core architectures, our experimental results demonstrate that NUMA-Caffe significantly outperforms the state-of-the-art Caffe designs in terms of both throughput and scalability.
Baselines and a datasheet for the Cerema AWP dataset

This paper presents the recently published Cerema AWP (Adverse Weather Pedestrian) dataset for various machine learning tasks and its exports in machine learning friendly format. We explain why this dataset can be interesting (mainly because it is a greatly controlled and fully annotated image dataset) and present baseline results for various tasks. Moreover, we decided to follow the very recent suggestions of datasheets for dataset, trying to standardize all the available information of the dataset, with a transparency objective.
Fast and Portable Vector DSP Simulation Through Automatic Vectorization

Vector DSPs are quite common in embedded SoCs used in compute-intensive domains such as imaging and wireless communication. To achieve short time-to-market, it is crucial to provide system architects and SW developers with fast and accurate instruction set simulators of such DSPs. To this end, a methodology for accelerating the simulation of vector instructions in vector DSPs is proposed. The acceleration is achieved by enabling automatic translation of the vector instructions in a given vector DSP binary into host SIMD instructions. The key advantage of the proposed methodology is its independence from the host architecture. Empirical evaluation, using a set of commercial vector DSPs, shows that the proposed methodology provides a 4x average reduction in simulation time of a vector instruction and a 2x average reduction in simulation time of a whole application.
Comparative Analysis of Gut Microbiota Changes in Père David's Deer Populations in Beijing Milu Park and Shishou, Hubei Province in China

This study used 16S rRNA high-throughput sequencing technology to examine the differences in gut microbiota between the Père David's deer populations in the Beijing and Shishou areas of China in order to understand the effects of ex situ conservation on the intestinal microflora in the Père David's deer. Results: On the phylum level, the main bacteria found in the Père David's deer populations from both areas were similar: Firmicutes and Bacteroidetes. However, the relative abundances of the two groups were significantly different. Alpha diversity results indicated that there was a difference in the evenness of the microflora between the two groups, and the beta diversity results further indicated that there was a significant difference in the microflora structure between the two groups. Conclusions: During the ex situ conservation process of the Père David's deer, their food sources may change, resulting in differences in the gut microbiota. The intestinal microflora in the Père David's deer from the same area are clustered. Therefore, the impact of changes in food on the gut microbiota of the Père David's deer should be taken into consideration during ex situ conservation.
Fanaticism, racism, and rage online: Corrupting the digital sphere

economies constantly evolving around the engagements of the networked user. In this sense, Metagaming can also be read as a case study of how power is exercised and operates in the digital era and how we may disrupt its operations. Echoing Michel Foucault’s dialectic of power and Scott Lash’s conceptualization of power as a generative force, it could be argued that where there is resistance (alternative play-practices), strategic attempts to discourage or encourage/exploit such practices will be implemented by the many direct and indirect architects of those playworlds. Thus, for metagames to effectively intervene in the economies of play, they must always be critically reflected on and constantly remade. In this way, metagaming can be conceptualized as a form of “generative resistance” that can clue us into the viral and invisible forces that structure how we play with and how we are being played by digital media more generally.
Neutron Imaging at Compact Accelerator-Driven Neutron Sources in Japan

Neutron imaging has been recognized to be very useful to investigate inside of materials and products that cannot be seen by X-ray. New imaging methods using the pulsed structure of neutron sources based on accelerators has been developed also at compact accelerator-driven neutron sources and opened new application fields in neutron imaging. The world’s first dedicated imaging instrument at pulsed neutron sources was constructed at J-PARC in Japan owing to the development of such new methods. Then, usefulness of the compact accelerator-driven neutron sources in neutron science was recognized and such facilities were newly constructed in Japan. Now, existing and new sources have been used for neutron imaging. Traditional imaging and newly developed pulsed neutron imaging such as Bragg edge transmission have been applied to various fields by using compact and large neutron facilities. Here, compact accelerator-driven neutron sources used for imaging in Japan are introduced and some of their activities are presented.