Miracle-dz/UniFAR-FTUs huggingface dataset:
VisualTextualRank : An Extension of VisualRank to Large-Scale Video Shot Extraction Exploiting Tag Co-occurrence ∗

In this paper, we propose a novel ranking method called VisualTextualRank which ranks media data according to the relevance between the data and specified keywords. We apply our method to the system of video shot ranking which aims to automatically obtain video shots corresponding to given action keywords from Web videos. The keywords can be any type of action such as “surfing wave” (sport action) or “brushing teeth” (daily activity). Top ranked video shots are expected to be relevant to the keywords. While our baseline exploits only visual features of the data, the proposed method employs both textual information (tags) and visual features. Our method is based on random walks over a bipartite graph to integrate visual information of video shots and tag information of Web videos effectively. Note that instead of treating the textual information as an additional feature for shot ranking, we explore the mutual reinforcement between shots and textual information of their corresponding videos to improve shot ranking. We validated our framework on a database which was used by the baseline. Experiments showed that our proposed ranking method, VisualTextualRank, improved significantly the performance of the system of video shot extraction over the baseline. key words: shot ranking, tag co-occurence, visual features, bipartite graph
The associations of intimacy and sexuality in daily life

The experience of emotional intimacy is assumed to play a particularly large role in maintaining sexual desire and partnered sexual activity in romantic relationships of longer duration. It is unclear whether the effect of intimacy on sexual contact between partners is direct or indirect, via its impact on sexual desire. Baumeister and Bratslavsky suggested that a certain increment in emotional intimacy causes a greater increment in sexual desire in men than in women. In the present study, we aimed to test the mediating role of sexual desire between perceived intimacy and sexual partner interaction and the gender effect as hypothesized by Baumeister and Bratslavsky. Experience sampling methodology in the participant’s natural environment was used. At 10 quasi-random moments per day, during 7 consecutive days, 134 participants reported their feelings of emotional intimacy, sexual desire, and sexual activity. The direct effect of intimacy on sexual partner interaction was not significant, but an indirect effect via sexual desire was observed. The strength of the association between intimacy and sexual desire diminished over time, from the strongest effect when intimacy, sexual desire, and sexual activity were measured simultaneously to a very small, but significant effect at an average time lag of 3 hr. At still larger time gaps, no effects were found. Men reported a higher average level of sexual desire than women, but the strength of the link between (increases in) intimacy and sexual desire was not different between the genders. The present findings suggest that in both male and female partners in romantic, long-term relationships, higher levels of intimacy are associated with higher sexual desire, which is, in turn, associated with higher odds for partnered sexual activity to occur. The temporal association of increasing intimacy and subsequent sexual desire appears not to be different in women and men.
3D Image-based Indoor Localization Joint With WiFi Positioning

We realize a system that utilizes WiFi to facilitate the image-based localization system, which avoids the confusion caused by the similar decoration inside the buildings. While WiFi-based localization thread obtains the rough location information, the image-based localization thread retrieves the best matching images and clusters the camera poses associated with the images into different location candidates. The image cluster closest to the WiFi localization outcome is selected for the exact camera pose estimation. The usage of WiFi significantly reduces the search scope, avoiding the extensive search of millions of descriptors in a 3D model. In the image-based localization stage, we also propose a novel 2D-to-2D-to-3D localization framework which follows a coarse-to-fine strategy to quickly locate the query image in several location candidates and performs the local feature matching and camera pose estimation after choosing the correct image location by WiFi positioning. The entire system demonstrates significant benefits in combining both images and WiFi signals in localization tasks and great potential to be deployed in real applications.
Diesel: DSL for linear algebra and neural net computations on GPUs

We present a domain specific language compiler, Diesel, for basic linear algebra and neural network computations, that accepts input expressions in an intuitive form and generates high performing code for GPUs. The current trend is to represent a neural network as a computation DAG, where each node in the DAG corresponds to a single operation such as matrix-matrix multiplication, and map the individual operations to hand tuned library functions provided by standard libraries such as CuBLAS and CuDNN. While this method takes advantage of readily available optimized library codes to achieve good performance for individual operations, it is not possible to optimize across operations. As opposed to this, given a computation composed of several operations, Diesel generates (a set) of efficient device functions, where the code is optimized for the computation as a whole, using polyhedral compilation techniques. In addition, there are cases where the code needs to be specialized for specific problem sizes to achieve optimal performance. While standard libraries are written for parametric problem sizes (where problem sizes are provided at runtime), Diesel can accept problem sizes at compile time and generate specialized codes. Experimental results show that the performance achieved by Diesel generated code for individual operations are comparable to the highly tuned versions provided by standard libraries, while for composite computations, Diesel outperforms manually written versions.
A computational approach to support the creation of terminological neologisms in sign languages

This work presents a novel approach to support the creation of terminological neologisms (technical signs) for Science, Technology, Engineering, and Mathematics (STEM) dictionaries in sign languages. We propose the use of computational methods to assist the creation of terminological neologisms that will constitute new STEM dictionaries. More specifically, we propose the use of computer vision algorithms for shape extraction and description from the visual representation (illustration) of a signless technical concept, as well as natural language processing methods for determining the stem of its corresponding oral term. By comparing those pieces of information of the signless technical concept with the corresponding ones of general‐purpose signs in a repository, our approach creates a meaningful new sign from the existing ones with similar features. As far as we know, this is the first computer‐aided proposal to create new signs and deal with the problem of technical vocabulary absence in sign languages, making the process of sign creation more agile and scalable. To validate the created technical signs, an evaluation committee composed by fluent signers considers aspects such as, the phonetic, phonological, and morphological structures of the sign language. Experiments were performed with technical concepts of the architectural drawing area, for which new signs in the Brazilian Sign Language were created. The results are promising with 60% of the new signs being rated on a five‐point scale with a similar satisfaction level of signs of a reference dataset.
Verbal Design: A Participatory Design Approach with Illiterate Patient User Groups

This paper presents a Participatory Design approach focused on applying primarily Verbal Design techniques working alongside illiterate People with Diabetes (PWD) from low socio-economic groups in Pakistan. After gathering a set of initial findings through classic Participatory Design and encountering several challenges, we discuss the development of our Verbal Design Approach in response which uses Narrative Scoping and Persona along with Invisible Design videos to structure and drive discussion and document design. Preliminary work showed that the approach resonated with our illiterate participants.
Object Trajectory Proposal via Hierarchical Volume Grouping

Object trajectory proposal aims to locate category-independent object candidates in videos with a limited number of trajectories,i.e.,bounding box sequences. Most existing methods, which derive from combining object proposal with tracking, cannot handle object trajectory proposal effectively due to the lack of comprehensive objectness measurement through analyzing spatio-temporal characteristics over a whole video. In this paper, we propose a novel object trajectory proposal method using hierarchical volume grouping. Specifically, we first represent a given video with hierarchical volumes by mapping hierarchical regions with optical flow. Then, we filter the short volumes and background volumes, and combinatorially group the retained volumes into object candidates. Finally, we rank the object candidates using a multi-modal fusion scoring mechanism, which incorporates both appearance objectness and motion objectness, and generate the bounding boxes of the object candidates with the highest scores as the trajectory proposals. We validated the proposed method on a dataset consisting of 200 videos from ILSVRC2016-VID. The experimental results show that our method is superior to the state-of-the-art object trajectory proposal methods.
Static Dependency Pair Method in Functional Programs

SUMMARY We have previously introduced the static dependency pair method that proves termination by analyzing the static recursive structure of various extensions of term rewriting systems for handling higher-order functions. The key is to succeed with the formalization of recursive structures based on the notion of strong computability, which is introduced for the termination of typed λ -calculi. To bring the static dependency pair method close to existing functional programs, we also extend the method to term rewriting models in which functional abstractions with patterns are permitted. Since the static dependency pair method is not sound in general, we formulate a class; namely, accessibility, in which the method works well. The static dependency pair method is a very natural reasoning; therefore, our extension di ﬀ ers only slightly from previous results. On the other hand, a soundness proof is dramatically di ﬃ cult.
Application of Correlation Indices on Intrusion Detection Systems: Protecting the Power Grid Against Coordinated Attacks

The future power grid will be characterized by the pervasive use of heterogeneous and non-proprietary information and communication technology, which exposes the power grid to a broad scope of cyber-attacks. In particular, Monitoring-Control Attacks (MCA) --i.e., attacks in which adversaries manipulate control decisions by fabricating measurement signals in the feedback loop-- are highly threatening. This is because, MCAs are (i) more likely to happen with greater attack surface and lower cost, (ii) difficult to detect by hiding in measurement signals, and (iii) capable of inflicting severe consequences by coordinating attack resources. To defend against MCAs, we have developed a semantic analysis framework for Intrusion Detection Systems (IDS) in power grids. The framework consists of two parts running in parallel: a Correlation Index Generator (CIG), which indexes correlated MCAs, and a Correlation Knowledge-Base~(CKB), which is updated aperiodically with attacks' Correlation Indices (CI). The framework has the advantage of detecting MCAs and estimating attack consequences with promising runtime and detection accuracy. To evaluate the performance of the framework, we computed its false alarm rates under different attack scenarios.
Member-level Features + Group ’ s Social Graph Group-level Features Neural Fingerprints Network Multilayer Perceptron Multilayer Perceptron Neural Fingerprints

Event-based social networks have recently emerged as an important complement to online social networks. They enjoy the advantages of both online social networks and offline social communities: offline social events can be conveniently organized online, and users interact with each other face-to-face in the organized offline events. Although previous work has shown that member and structural features are important to the future popularity of the groups, it is not yet clear how different member roles and the interplay between them contribute to group popularity. In this paper, we study a realworld dataset from Meetup — a popular event-based social network platform — and propose a deep neural network based method to predict the popularity of new Meetup groups. Our method uses group-level features specific to event-based social networks, such as time and location of events in a group, as well as structural features internal to a group, such as the estimated member roles in a group and social substructures among members. Empirically, our approach reduces the RMSE of the popularity (measured in RSVPs) of a group’s future events by up to 12%, against the state-of-the-art baselines. Moreover, through case studies, our method also identifies member and structure patterns that are most predictive of a group’s future popularity. Our study provides new understanding about what makes a group successful in event-based social networks.
Deconvolution-Based Global Decoding for Neural Machine Translation

A great proportion of sequence-to-sequence (Seq2Seq) models for Neural Machine Translation (NMT) adopt Recurrent Neural Network (RNN) to generate translation word by word following a sequential order. As the studies of linguistics have proved that language is not linear word sequence but sequence of complex structure, translation at each step should be conditioned on the whole target-side context. To tackle the problem, we propose a new NMT model that decodes the sequence with the guidance of its structural prediction of the context of the target sequence. Our model generates translation based on the structural prediction of the target-side context so that the translation can be freed from the bind of sequential order. Experimental results demonstrate that our model is more competitive compared with the state-of-the-art methods, and the analysis reflects that our model is also robust to translating sentences of different lengths and it also reduces repetition with the instruction from the target-side context for decoding.
Statistical Learning of API Fully Qualified Names in Code Snippets of Online Forums

Software developers often make use of the online forums such as StackOverflow to learn how to use software libraries and their APIs. However, the code snippets in such a forum often contain undeclared, ambiguous, or largely unqualified external references. Such declaration ambiguity and external reference ambiguity present challenges for developers in learning to correctly use the APIs. In this paper, we propose StatType, a statistical approach to resolve the fully qualified names (FQNs) for the API elements in such code snippets. Unlike existing approaches that are based on heuristics, StatType has two well-integrated factors. We first learn from a large training code corpus the FQNs that often co-occur. Then, to derive the FQN for an API name in a code snippet, we use that knowledge and leverage the context consisting of neighboring API names. To realize those factors, we treat the problem as statistical machine translation from source code with partially qualified names to source code with FQNs of the APIs. Our empirical evaluation on real-world code and StackOverflow posts shows that StatType achieves very high accuracy with 97.6% precision and 96.7% recall, which is 16.5% relatively higher than the state-of-the-art approach.
Cell Specific Changes of Autophagy in a Mouse Model of Contusive Spinal Cord Injury

Autophagy is an essential process of cellular waist clearance that becomes altered following spinal cord injury (SCI). Details on these changes, including timing after injury, underlying mechanisms, and affected cells, remain controversial. Here we present a characterization of autophagy in the mice spinal cord before and after a contusive SCI. In the undamaged spinal cord, analysis of LC3 and Beclin 1 autophagic markers reveals important differences in basal autophagy between neurons, oligodendrocytes, and astrocytes and even within cell populations. Following moderate contusion, western blot analyses of LC3 indicates that autophagy increases to a maximum at 7 days post injury (dpi), whereas unaltered Beclin 1 expression and increase of p62 suggests a possible blockage of autophagosome clearance. Immunofluorescence analyses of LC3 and Beclin 1 provide additional details that reveal a complex, cell-specific scenario. Autophagy is first activated (1 dpi) in the severed axons, followed by a later (7 dpi) accumulation of phagophores and/or autophagosomes in the neuronal soma without signs of increased initiation. Oligodendrocytes and reactive astrocytes also accumulate phagophores and autophagosomes at 7 dpi, but whereas the accumulation in astrocytes is associated with an increased autophagy initiation, it seems to result from a blockage of the autophagic flux in oligodendrocytes. Comparison with previous studies highlights the complex and heterogeneous autophagic responses induced by the SCI, leading in many cases to contradictory results and interpretations. Future studies should consider this complexity in the design of therapeutic interventions based on the modulation of autophagy to treat SCI.
Ethical design in ICT application: how satisfy food citizenship needs

A new food behaviour named "Food citizenship" brings to new market trends. Consumers, motivated by personal, environmental, economical and social interests, want to know more information about food, in order to make an awareness and sustainable choice. Leveraging on Industry 4.0 strategically guidelines, a system able to manage and share the food information along the Agrifood Value Chain, is easy to develop but probably goes to the detriment of human factor. The challenge is to communicate this information to the food citizen, intercepting, at the same time, their interests and their value. In order to find a strategy to show the food-product information using ICT technology, tweets about EXPO 2015 are analysed through Social Network and Semantic Analysis. A comparison with the XTC trends definitions allowed the conception and definition of an ethical communication model and IT solution, able to illustrate to customer the food-product in a pertinent way.
Latent Space Representation for Shape Analysis and Learning

We propose a novel shape representation useful for analyzing and processing shape collections, as well for a variety of learning and inference tasks. Unlike most approaches that capture variability in a collection by using a template model or a base shape, we show that it is possible to construct a full shape representation by using the latent space induced by a functional map net- work, allowing us to represent shapes in the context of a collection without the bias induced by selecting a template shape. Key to our construction is a novel analysis of latent functional spaces, which shows that after proper regularization they can be endowed with a natural geometric structure, giving rise to a well-defined, stable and fully informative shape representation. We demonstrate the utility of our representation in shape analysis tasks, such as highlighting the most distorted shape parts in a collection or separating variability modes between shape classes. We further exploit our representation in learning applications by showing how it can naturally be used within deep learning and convolutional neural networks for shape classi cation or reconstruction, signi cantly outperforming existing point-based techniques.
CCAligner: A Token Based Large-Gap Clone Detector

Copying code and then pasting with large number of edits is a common activity in software development, and the pasted code is a kind of complicated Type-3 clone. Due to large number of edits, we consider the clone as a large-gap clone. Large-gap clone can reflect the extension of code, such as change and improvement. The existing state-of-the-art clone detectors suffer from several limitations in detecting large-gap clones. In this paper, we propose a tool, CCAligner, using code window that considers e edit distance for matching to detect large-gap clones. In our approach, a novel e-mismatch index is designed and the asymmetric similarity coefficient is used for similarity measure. We thoroughly evaluate CCAligner both for large-gap clone detection, and for general Type-1, Type-2 and Type-3 clone detection. The results show that CCAligner performs better than other competing tools in large-gap clone detection, and has the best execution time for 10MLOC input with good precision and recall in general Type-1 to Type-3 clone detection. Compared with existing state-of-the-art tools, CCAligner is the best performing large-gap clone detection tool, and remains competitive with the best clone detectors in general Type-1, Type-2 and Type-3 clone detection.
Surface Chemical Characterisation of Pyrite Exposed to Acidithiobacillus ferrooxidans and Associated Extracellular Polymeric Substances

A. ferrooxidans and their metabolic products have previously been explored as a viable alternative depressant of pyrite for froth flotation; however, the mechanism by which separation is achieved is not completely understood. Scanning electron microscopy (SEM), photoemission electron microscopy (PEEM), time-of-flight secondary ion mass spectrometry (ToF-SIMS) and captive bubble contact angle measurements have been used to examine the surface physicochemical properties of pyrite upon exposure to A. ferrooxidans grown in HH medium at pH 1.8. C K-edge near edge X-ray absorption fine structure (NEXAFS) spectra collected from PEEM images indicate hydrophilic lipids, fatty acids and biopolymers are formed at the mineral surface during early exposure. After 168 h, the spectra indicate a shift towards protein and DNA, corresponding to an increase in cell population and biofilm formation on the surface, as observed by SEM. The Fe L-edge NEXAFS show gradual oxidation of the mineral surface from Fe(II) sulfide to Fe(III) oxyhydroxides. The oxidation of the iron species at the pyrite surface is accelerated in the presence of A. ferrooxidans and extracellular polymeric substances (EPS) as compared to HH medium controls. The surface chemical changes induced by the interaction with A. ferrooxidans show a significant decrease in surface hydrophobicity within the first 2 h of exposure. The implications of these findings are the potential use of EPS produced during early attachment of A. ferrooxidans, as a depressant for bioflotation.