Miracle-dz/UniFAR-FTUs huggingface dataset:
The semantic web, web accessibility, and device independence

Over the past several years there has been a significant increase in awareness of the need for Web accessibility and development of policies relating to Web accessibility in Europe. Techniques for implementation have been standardized and benchmark guidelines (Web Content Accessibility Guidelines i.e. WCAG), recommended.However, as Web protocols and technologies emerge, new opportunities become available and it may be possible to open Web content to yet more people and to develop even more powerful accessibility solutions.New protocols and ingenuity could make it possible to encapsulate additional knowledge during the process of rendering content accessible, without necessarily adding more work for the original author.An implementation by UB Access is the Semantic Web Accessibility Platform (SWAP). SWAP is a semantic web, knowledge based approach to accessibility. SWAP creates alternative renderings of sites, or SWAPviews, which enable people with diverse special needs to smoothly and easily access the content.
EFFICIENT SEPARABLE FILTER ESTIMATION USING RANK-1 CONVOLUTIONAL DICTIONARY LEARNING

Natively learned separable filters for Convolutional Sparse Coding (CSC) have recently been shown to provide equivalent reconstruction performance to their non-separable counterparts (as opposed to approximated separable filters), while reducing computational cost. Furthermore, multiple approaches to optimize the Dictionary Update stage of Convolutional Dictionary Learning (CDL) methods based on the Accelerated Proximal Gradient (APG) framework have recently been proposed.In this paper, we propose a novel separable filter learning method based on the rank-1 decomposition, and test its performance against the existing separable approaches. In adittion, we evaluate how APG-based variations couple with our proposed method in order to improve computational runtime. Our results show that the filters learned through our proposed method match the performance of other natively-learned separable filters, while providing a significant runtime improvement in the learning process through our APG-based implementation.
Evolutionary Testing for Crash Reproduction

Manual crash reproduction is a labor-intensive and time-consuming task. Therefore, several solutions have been proposed in literature for automatic crash reproduction, including generating unit tests via symbolic execution and mutation analysis. However, various limitations adversely affect the capabilities of the existing solutions in covering a wider range of crashes because generating helpful tests that trigger specific execution paths is particularly challenging. In this paper, we propose a new solution for automatic crash reproduction based on evolutionary unit test generation techniques. The proposed solution exploits crash data from collected stack traces to guide search-based algorithms toward the generation of unit test cases that can reproduce the original crashes. Results from our preliminary study on real crashes from Apache Commons libraries show that our solution can successfully reproduce crashes which are not reproducible by two other state-of-art techniques.
A Denotational Semantics of Textually Aligned SPMD Programs

We discuss the benefit of enforcing textual alignment in programming languages proposing unstructured SPMD-like collective operations. Our study is based on a simple language which provides support for global synchronization barriers. A formal definition of textual alignment, based on an operational semantics, is considered. As one can expect the defined property entails the absence of deadlocks. We argue that this property provides an intuitive programming model that makes it easier to perform program analysis and program optimization. To support our claim we provide a compositional denotational semantics which is equivalent to the operational semantics for textually aligned programs.
Worker's reward sensitivity predicting motivation in crowdsourcing: self-approach achievement goals perspective

Current paper investigates direct and indirect effects of workers' reward sensitivity (as represented by Behavioral Approach System) on motivation (measured with work engagement and levels of effort) in crowdsourcing platform. Furthermore, this paper analyses the mediating role of Self-Approach Achievement Goals in channeling 'initial energization' to motivational outcomes. A random sample of 320 taskcn.com workers was used in this study. The findings of this study showed that reward sensitivity was effective directly and indirectly, in positively predicting all motivational outcomes. Interestingly, the predictive relationship only related to self-based approach Achievement Goals and not in relation to task-based approach Achievement Goals. However, workers adopting self-approach and task-approach goals have higher Effort and Work Engagement exerted in crowdsourcing. These results could help crowdsourcing platform managers better understand the participants that working on the tasks, as well as find efficient ways of motivating workers. Targeting workers with higher reward sensitivity is beneficial for platform owners, providing the way to increase workers motivational outcomes at no additional cost, thus facilitating better results.
Guaranteeing deadlines for inter-datacenter transfers

Inter-datacenter wide area networks (inter-DC WAN) carry a significant amount of data transfers that require to be completed within certain time periods, or deadlines. However, very little work has been done to guarantee such deadlines. The crux is that the current inter-DC WAN lacks an interface for users to specify their transfer deadlines and a mechanism for provider to ensure the completion while maintaining high WAN utilization. This paper addresses the problem by introducing a Deadline-based Network Abstraction (DNA) for inter-DC WANs. DNA allows users to explicitly specify the amount of data to be delivered and the deadline by which it has to be completed. The malleability of DNA provides flexibility in resource allocation. Based on this, we develop a system called Amoeba that implements DNA. Our simulations and testbed experiments show that Amoeba, by harnessing DNA's malleability, accommodates 15% more user requests with deadlines, while achieving 60% higher WAN utilization than prior solutions.
Detecting Counterpart Word Pairs across Time

Most search engines require users to input specific words to obtain useful results from the Web. However, this requirement is sometimes challenging for users who want to search for information regarding unknown words. It may be especially difficult to learn about the past without knowing suitable words, such as answering the question "Who was the counterpart of the Prime Minister of the UK in the Ottoman Empire in 1900? " We propose a novel search framework for finding counterpart relationships represented by word pairs across time. This framework detects the counterparts by arithmetic operations as well as Word2Vec. To improve the accuracy, our framework groups news articles to develop context for words. After embedding the words into vector spaces, we map a given relationship to another vector space, then perform arithmetic operations. We show our algorithm outputs better results compared with simply applying Word2Vec.
A State-Space Model for the Dynamic Random Subgraph Model

In recent years, many random graph models have been proposed to extract information from networks. The principle is to look for groups of vertices with homogenous connection profiles. Most of these models are suitable for static networks and can handle different types of edges. This work is motivated by the need of analyzing an evolving network describing email communications between employees of the Enron compagny where social positions play an important role. Therefore, in this paper, we consider the random subgraph model (RSM) which was proposed recently to model networks through latent clusters built within known partitions. Using a state space model to characterize the cluster proportions, RSM is then extended in order to deal with dynamic networks. We call the latter the dynamic random subgraph model (dRSM).
Reflections on NoteCards: seven issues for the next generation of hypermedia systems

NoteCards, developed by a team at Xerox PARC, was designed to support the task of transforming a chaotic collection of unrelated thoughts into an integrated, orderly interpretation of ideas and their interconnections. This article presents NoteCards as a foil against which to explore some of the major limitations of the current generation of hypermedia systems, and characterizes the issues that must be addressed in designing the next generation systems.
SimpleNLG-ZH: a Linguistic Realisation Engine for Mandarin

We introduce SimpleNLG-ZH, a realisation engine for Mandarin that follows the software design paradigm of SimpleNLG (Gatt and Reiter, 2009). We explain the core grammar (morphology and syntax) and the lexicon of SimpleNLG-ZH, which is very different from English and other languages for which SimpleNLG engines have been built. The system was evaluated by regenerating expressions from a body of test sentences and a corpus of human-authored expressions. Human evaluation was conducted to estimate the quality of regenerated sentences.
Unsupervised Parsimonious Cluster-Based Anomaly Detection (PCAD)

Group anomaly detection (AD), i.e. detection of clusters of anomalous samples in a test batch, with the samples in a given such cluster exhibiting a common pattern of atypicality (relative to a null model) has important applications to discovering unknown classes present in a test data batch and, equivalently, to zero-day threat detection in a security context. When the feature space is large, clusters may manifest anomalies on very small feature subsets, which is well-captured by the parsimonious mixture modelling (PMM) framework. Thus, we propose a generalized likelihood ratio test (GLRT-like) group AD framework, with PMMs used for both the null and the alternative hypothesis (that an anomalous cluster is present), and with the Bayesian Information Criterion (BIC) used to adjudicate between these hypotheses. We demonstrate our approach on network traffic data sets, detecting Zeus (web) bots and peer-to-peer traffic as zero-day activities. Our PCAD achieves substantially better detection results than a previous group AD method applied to this domain.
Simulating Crowd Evacuation: From Comfort to Panic Situations

Crowd simulation has its greatest utility in the study of safety measures for crowded events. However, total evacuation time, which is the most important feature in crowd evacuation, can vary depending on the population, the environment, the adopted strategies to decide routes, but also because people can be panicked or not. This paper presents a model to parametrize crowd simulation allowing to increase or decrease the agents stress. We use BioCrowds model and proposed an extension to consider new parameters to deal with crowd relaxing and compression, as comfort and stress. These two new parameters impact the will to go to the goal and the individual panic in trying to save itself. Indeed, our model could be integrated in other crowd simulators. This work discusses some obtained results and also presents a case study regarding a real scenario. We simulate the Hillsborough Disaster happened in 1989 in order to discuss the reliability of our method. Results indicate that our method can simulate in a coherent way the densities observed in the real life event.
UTFPR at WMT 2018: Minimalistic Supervised Corpora Filtering for Machine Translation

We present the UTFPR systems at the WMT 2018 parallel corpus filtering task. Our supervised approach discerns between good and bad translations by training classic binary classification models over an artificially produced binary classification dataset derived from a high-quality translation set, and a minimalistic set of 6 semantic distance features that rely only on easy-to-gather resources. We rank translations by their probability for the “good” label. Our results show that logistic regression pairs best with our approach, yielding more consistent results throughout the different settings evaluated.
Defending against an Internet-based attack on the physical world

We discuss the dangers that scalable Internet functionality may present to the real world, focusing on a simple yet impactful attack that we believe may occur quite soon. We offer and critique various solutions to this class of attack and hope to provide a warning to the Internet community of what is currently possible. The attack is, to some degree, a consequence of the availability of private information on the Web, and the increase in the amount of personal information that users must reveal to obtain Web services.
Species interactions limit the occurrence of urban-adapted birds in cities

Significance Urban environments are expanding worldwide, impacting the populations of many organisms. Understanding how and why species are affected by urbanization is thus an important goal. We examined the role of direct competitive interactions among species on the response of bird species to urbanization. We found evidence that urban-adapted, subordinate species were less widespread in cities than closely related dominant species, but only when dominant and subordinate species live together, which suggests that direct competitive interactions reduce the ability of subordinate species to persist in cities. This result depended on the level of economic development of the country, suggesting that economic development may heighten the effects of competition on subordinate species, thereby reducing species diversity in cities. Urbanization represents an extreme transformation of more natural systems. Populations of most species decline or disappear with urbanization, and yet some species persist and even thrive in cities. What determines which species persist or thrive in urban habitats? Direct competitive interactions among species can influence their distributions and resource use, particularly along gradients of environmental challenge. Given the challenges of urbanization, similar interactions may be important for determining which species persist or thrive in cities; however, their role remains poorly understood. Here, we use a global dataset to test among three alternative hypotheses for how direct competitive interactions and behavioral dominance may influence the breeding occurrence of birds in cities. We find evidence to support the competitive interference hypothesis: behaviorally dominant species were more widespread in urban habitats than closely related subordinate species, but only in taxa that thrive in urban environments (hereafter, urban adapted), and only when dominant and subordinate species overlapped their geographic ranges. This result was evident across diverse phylogenetic groups but varied significantly with a country’s level of economic development. Urban-adapted, dominant species were more widespread than closely related subordinate species in cities in developed, but not developing, countries; countries in economic transition showed an intermediate pattern. Our results provide evidence that competitive interactions broadly influence species responses to urbanization, and that these interactions have asymmetric effects on subordinate species that otherwise could be widespread in urban environments. Results further suggest that economic development might accentuate the consequences of competitive interactions, thereby reducing local diversity in cities.
Talking about other people: an endless range of possibilities

Image description datasets, such as Flickr30K and MS COCO, show a high degree of variation in the ways that crowd-workers talk about the world. Although this gives us a rich and diverse collection of data to work with, it also introduces uncertainty about how the world should be described. This paper shows the extent of this uncertainty in the PEOPLE-domain. We present a taxonomy of different ways to talk about other people. This taxonomy serves as a reference point to think about how other people should be described, and can be used to classify and compute statistics about labels applied to people.
MCTS: Multi-Channel Transmission Simultaneously Using Non-Feedback Fountain Code

Data dissemination is an essential application in Internet of Things (IoT) network. It is generally relayed hop-by-hop due to the limitation of the communication distance of IoT devices. However, multi-hop transmission increases dissemination time, especially in a low duty-cycle network. Channel contention is another factor result in long delay, because most of IoT devices and Wi-Fi equipment work within the same bandwidth (2.4 GHz). To reduce the transmission delay of data dissemination, our group proposes a method of multi-channel transmission simultaneously (MCTS) using no-feedback fountain code. Instead of terminating sending data by feedback as a traditional fountain code protocol, in the MCTS, the sink node pours a certain amount of packets into the network according to the distance of object nodes to guarantee reliable transmission. By making use of channel encoding, the data to ZigBee nodes can be carried by some subcarriers of Wi-Fi in multiple ZigBee channels simultaneously when Wi-Fi sends itself data. This method technically avoids the delay from channel contention. Compared with the flooding method, the delay of the MCTS sending data from the sink node to all ZigBee nodes is decreased to 1/4.
GM-CSF Culture Revisited: Preparation of Bulk Populations of Highly Pure Dendritic Cells from Mouse Bone Marrow

Dendritic cells (DC) loaded with specific peptides are strongly immunogenic for T cells and can be used for cancer immunotherapy. For immunogenic tumors such as melanoma, injection of autologous DC loaded with tumor cell extracts or peptides can induce tumor regression but in only a small proportion of patients. Nevertheless, recent studies on the efficacy of checkpoint blockade for boosting antitumor immunity plus advances in defining tumor neoantigens are stimulating renewed interest in DC immunotherapy. Despite intensive investigation, however, preparation of bulk populations of mature DC has proved difficult, and most preparations contain a significant proportion of potentially tolerogenic immature DC. In this study, we have modified the well-established GM-CSF culture system to prepare substantial quantities of highly pure (>95%) mature DC from mouse bone marrow cells and defined their progenitors. We show that obtaining high yields and purity of DC are heavily dependent on cell density in the cultures and the tempo of addition of growth and maturation stimuli. When loaded with specific peptide, the DC are strongly immunogenic for CD4 and CD8 T cells in vivo and elicit effective antitumor immunity.
Generating E-Commerce Product Titles and Predicting their Quality

E-commerce platforms present products using titles that summarize product information. These titles cannot be created by hand, therefore an algorithmic solution is required. The task of automatically generating these titles given noisy user provided titles is one way to achieve the goal. The setting requires the generation process to be fast and the generated title to be both human-readable and concise. Furthermore, we need to understand if such generated titles are usable. As such, we propose approaches that (i) automatically generate product titles, (ii) predict their quality. Our approach scales to millions of products and both automatic and human evaluations performed on real-world data indicate our approaches are effective and applicable to existing e-commerce scenarios.
Equivalence between nonnegative tensor factorization and tensorial probabilistic latent semantic analysis

This paper establishes a connection between NMF and PLSA on multi-way data, called NTF and T-PLSA respectively. Two types of T-PLSA models are proven to be equivalent to non-negative PARAFAC and non-negative Tucker3. This paper also shows that by running NTF and T-PLSA alternatively, they can jump out of each other's local minima and achieve a better clustering solution.
Detecting and characterizing social spam campaigns

Online social networks (OSNs) are popular collaboration and communication tools for millions of users and their friends. Unfortunately, in the wrong hands, they are also effective tools for executing spam campaigns and spreading malware. Intuitively, a user is more likely to respond to a message from a Facebook friend than from a stranger, thus making social spam a more effective distribution mechanism than traditional email. In fact, existing evidence shows malicious entities are already attempting to compromise OSN account credentials to support these "high-return" spam campaigns. In this paper, we present an initial study to quantify and characterize spam campaigns launched using accounts on online social networks. We study a large anonymized dataset of asynchronous "wall" messages between Facebook users. We analyze all wall messages received by roughly 3.5 million Facebook users (more than 187 million messages in all), and use a set of automated techniques to detect and characterize coordinated spam campaigns. Our system detected roughly 200,000 malicious wall posts with embed- ded URLs, originating from more than 57,000 user accounts. We find that more than 70% of all malicious wall posts advertise phishing sites. We also study the characteristics of malicious accounts, and see that more than 97% are compromised accounts, rather than "fake" accounts created solely for the purpose of spamming. Finally, we observe that, when adjusted to the local time of the sender, spamming dominates actual wall post activity in the early morning hours, when normal users are asleep.
Giant Electro‐Optical Effect through Electrostriction in a Nanomechanical Metamaterial

Electrostriction is a property of all naturally occurring dielectrics whereby they are mechanically deformed under the application of an electric field. It is demonstrated here that an artificial metamaterial nanostructure comprising arrays of dielectric nanowires, made of silicon and indium tin oxide, is reversibly structurally deformed under the application of an electric field, and that this reconfiguration is accompanied by substantial changes in optical transmission and reflection, thus providing a strong electro‐optic effect. Such metamaterials can be used as the functional elements of electro‐optic modulators in the visible to near‐infrared part of the spectrum. A modulator operating at 1550 nm with effective electrostriction and electro‐optic coefficients of order 10−13 m2 V−2 and 10−6 m V−1, respectively, is demonstrated. Transmission changes of up to 3.5% are obtained with a 500 mV control signal at a modulation frequency of ≈6.5 MHz. With a resonant optical response that can be spectrally tuned by design, modulators based on the artificial electrostrictive effect may be used for laser Q‐switching and mode‐locking among other applications that require modulation at megahertz frequencies.
Linking News Sentiment to Microblogs: A Distributional Semantics Approach to Enhance Microblog Sentiment Classification

Social media’s popularity in society and research is gaining momentum and simultaneously increasing the importance of short textual content such as microblogs. Microblogs are affected by many factors including the news media, therefore, we exploit sentiments conveyed from news to detect and classify sentiment in microblogs. Given that texts can deal with the same entity but might not be vastly related when it comes to sentiment, it becomes necessary to introduce further measures ensuring the relatedness of texts while leveraging the contained sentiments. This paper describes ongoing research introducing distributional semantics to improve the exploitation of news-contained sentiment to enhance microblog sentiment classification.