Miracle-dz/UniFAR-FTUs huggingface dataset:
Personality Analysis of Embodied Conversational Agents

People tend to personify machines. Giving machines the ability to actually produce social information can help improve human-machine interactions. Embodied Conversational Agents (ECAs) are virtual software agents that can process and produce speech, facial expressions, gestures and eye gaze, enabling natural, multimodal, human-machine communication. On the one hand, the field of personality psychology provides insights into how we could describe and measure the virtual personality of ECAs. On the other hand, ECAs provide a method to systematically examine how different factors affect the perception of personality. This paper shows that standardized, validated personality questionnaires can be used to evaluate ECAs psychologically, and that state of the art ECAs can manipulate their perceived personality through appearance and behavior.
Convolutional Neural Networks on Embedded Automotive Platforms: A Qualitative Comparison

In the last decade, the rise of power-efficient, heterogeneous embedded platforms paved the way to the effective adoption of neural networks in several application domains. Especially, many-core accelerators (e.g., GPUs and FPGAs) are used to run Convolutional Neural Networks, e.g., in autonomous vehicles, and industry 4.0. At the same time, advanced research on neural networks is producing interesting results in computer vision applications, and NN packages for computer vision object detection and categorization such as YOLO, GoogleNet and AlexNet reached an unprecedented level of accuracy and performance. With this work, we aim at validating the effectiveness and efficiency of most recent networks on state-of-the-art embedded platforms, with commercial-off-the-shelf System-on-Chips such as the NVIDIA Tegra X2 and Xilinx Ultrascale+. In our vision, this work will support the choice of the most appropriate CNN package and computing system, and at the same time tries to "make some order" in the field.
Sinfonia: A new paradigm for building scalable distributed systems

We propose a new paradigm for building scalable distributed systems. Our approach does not require dealing with message-passing protocols, a major complication in existing distributed systems. Instead, developers just design and manipulate data structures within our service called Sinfonia. Sinfonia keeps data for applications on a set of memory nodes, each exporting a linear address space. At the core of Sinfonia is a new minitransaction primitive that enables efficient and consistent access to data, while hiding the complexities that arise from concurrency and failures. Using Sinfonia, we implemented two very different and complex applications in a few months: a cluster file system and a group communication service. Our implementations perform well and scale to hundreds of machines.
Design and Experimental Evaluation of an Odor Sensing Method for a Pocket-Sized Quadcopter

In this study, we design and verify an intake system using the wake of a pocket-sized quadcopter for the chemical plume tracing (CPT) problem. Solving CPT represents an important technique in the field of engineering because it can be used to perform rescue operations at the time of a disaster and to identify sources of harmful substances. An appropriate intake of air when sensing odors plays an important role in performing CPT. Hence, we used the air flow generated by a quadcopter itself to intake chemical particles into two alcohol sensors. By experimental evaluation, we verified that the quadcopter wake intake method has good directivity and can be used to realize CPT. Concretely, even at various odor source heights, the quadcopter had a three-dimensional CPT success rate of at least 70%. These results imply that, although a further development of three-dimensional CPT is necessary in order to conduct it in unknown and cluttered environments, the intake method proposed in this paper enables a pocket-sized quadcopter to perform three-dimensional CPT.
A Lesson on Verification of IoT Software with Frama-C

This paper is a tutorial introduction to Frama- C, a framework for analysis and verification of C programs. We present value analysis, deductive verification and runtime verification of software using Frama-C and in particular its Eva, Wp, and E-Acsl plugins. These techniques are illustrated on examples coming from real-life verification case studies for different modules of Contiki, a lightweight operating system for the Internet of Things.
Diagnostic Accuracy of Rapid Ultrasound in Shock (RUSH) Exam; A Systematic Review and Meta-analysis

Objective: To perform a diagnostic accuracy of the rapid ultrasound in shock (RUSH) to diagnose the etiology of undifferentiated shock in patients presenting to the emergency department (ED). Methods: We searched the Medline via PubMed, Scopus, and ISI Web of Knowledge till July 2017. Two independent reviewers screened studies for eligibility. Our study analysis is planned in accordance with the guidelines for meta–analysis of diagnostic studies. In the systematic search, of 397 references, 295 were excluded on the basis of the title and abstract. For the remaining 102 articles, the full text was retrieved and critically reviewed. After the selection process, five papers were included. Results: The pooled estimate of all data showed that the RUSH protocol exhibited high sensitivity (0.87, 95% Confidence Interval (CI): 0.80-0.92, I2 = 46.7%) and specificity (0.98, 95% C. I.: 0.96-0.99, I2 = 30.8%). The AUC for SROC, a global measure of the RUSH protocol performance, was 0.98 ± 0.01, indicates the high accuracy of the test. Positive and negative likelihood ratios reported from the studies ranged from 9.83 to 51.32 and 0.04 to 0.33, respectively. The pooled estimate of all data showed that the RUSH protocol exhibited high positive likelihood ratio (19.19, 95% C. I.: 11.49-32.06, I2 = 14.1%) and low negative likelihood ratio (0.23, 95% C. I.: 0.15-0.34, I2 = 18.4%). Conclusion: This meta-analysis suggests that RUSH protocol has generally good role to distinguish the states of shock in patients with undifferentiated shock referred to the emergency department.
The Possibility of Matrix Decomposition as Anonymization and Evaluation for Time-sequence Data

Time-sequence data is high dimensional and con- tains a lot of information, which can be utilized in various fields, such as insurance, finance, and advertising. Personal data including time-sequence data is often converted to anonymized datasets, which need to strike a balance between both privacy and utility. In this paper, we consider low-rank matrix decomposition as one of the anonymization methods and evaluate its efficiency. We convert time-sequence datasets to matrices and evaluate both privacy and utility. The record IDs in time-sequence data are changed at regular intervals to reduce re-identification risk. However, since individuals tend to behave in a similar fashion over periods of time, there remains a risk of record linkage even if record IDs are different. Hence, we evaluate the re- identification and linkage risks as privacy risks of time-sequence data. Our experimental results show that matrix decomposition is a viable anonymization method and it can achieve better utility than existing anonymization methods.
Summarization Filter: Consider More About the Whole Query in Machine Comprehension

Machine Comprehension (MC) is a challenging and valuable problem in natural language processing domain. The core method of the MC model is to get appropriate context representation to answer query. Query information such as what the query wants to know is injected into context representation by attention mechanism. The attention mechanism utilizes the relationship between query words and context words but ignores the whole semantics of the query, which is significant to understand what the query really wants to know. Inspired by the human’s reading process, we propose summarization filter mechanism in order to consider more about the whole query in MC. We obtain a summarization vector of the query to represent its whole meaning and fuse it into the gates of some layers. Our mechanism makes the model to better understand the query and give more precise answer. Our model promotes the baseline model’s performance by 0.7 F1 score and 1.1 EM score, which indicates that our mechanism contributes to a decent promotion for MC task.
Osprey: Hyperparameter Optimization for Machine Learning

Osprey is a tool for hyperparameter optimization of machine learning algorithms in Python. Hyperparameter optimization can often be an onerous process for researchers, due to timeconsuming experimental replicates, non-convex objective functions, and constant tension between exploration of global parameter space and local optimization (Jones, Schonlau, and Welch 1998). We’ve designed Osprey to provide scientists with a practical, easyto-use way of finding optimal model parameters. The software works seamlessly with scikit-learn estimators (Pedregosa et al. 2011) and supports many different search strategies for choosing the next set of parameters with which to evaluate a given model, including gaussian processes (GPy 2012), tree-structured Parzen estimators (Yamins, Tax, and Bergstra 2013), as well as random and grid search. As hyperparameter optimization is an embarrassingly parallel problem, Osprey can easily scale to hundreds of concurrent processes by executing a simple command-line program multiple times. This makes it easy to exploit large resources available in high-performance computing environments.
Mining Sequential Patterns from Outsourced Data via Encryption Switching

The increasing demand for data mining in business intelligence has led to a significant growth in the adoption of data mining as a service paradigm which enables companies to outsource their data and mining tasks to a cloud service provider. Despite the popularity of the paradigm, the companies hesitate to enable the cloud providers' access to their data considering customer privacy and intellectual property. In this paper, we propose a privacy-preserving two-party protocol which aims to mine direct sequential patterns from outsourced protected data. We focus on direct sequential pattern mining since it is a widely used primitive in business process analysis. Considering the accuracy and confidentiality, we choose encryption over statistical methods for data protection and processing. To be able to process the encrypted data, we adopt a homomorphic encryption scheme, ElGamal cryptosystem. The novelty of our scheme is that it introduces an encryption switching method that enables us to use both multiplicative and additive homomorphism on ElGamal cryptosystem. The results of our analyses show that our protocol is more efficient than the state-of-the-art proposals in terms of computational cost with a similar communication cost.
Gradients in predation risk in a tropical river system

Abstract The importance of predation risk as a key driver of evolutionary change is exemplified by the Northern Range in Trinidad, where research on guppies living in multiple parallel streams has provided invaluable insights into the process of evolution by natural selection. Although Trinidadian guppies are now a textbook example of evolution in action, studies have generally categorized predation as a dichotomous variable, representing high or low risk. Yet, ecologists appreciate that community structure and the attendant predation risk vary substantially over space and time. Here, we use data from a longitudinal study of fish assemblages at 16 different sites in the Northern Range to quantify temporal and spatial variation in predation risk. Specifically we ask: 1) Is there evidence for a gradient in predation risk? 2) Does the ranking of sites (by risk) change with the definition of the predator community (in terms of species composition and abundance currency), and 3) Are site rankings consistent over time? We find compelling evidence that sites lie along a continuum of risk. However, site rankings along this gradient depend on how predation is quantified in terms of the species considered to be predators and the abundance currency is used. Nonetheless, for a given categorization and currency, rankings are relatively consistent over time. Our study suggests that consideration of predation gradients will lead to a more nuanced understanding of the role of predation risk in behavioral and evolutionary ecology. It also emphasizes the need to justify and report the definition of predation risk being used.
Predicting Off-Target Binding Profiles With Confidence Using Conformal Prediction

Ligand-based models can be used in drug discovery to obtain an early indication of potential off-target interactions that could be linked to adverse effects. Another application is to combine such models into a panel, allowing to compare and search for compounds with similar profiles. Most contemporary methods and implementations however lack valid measures of confidence in their predictions, and only provide point predictions. We here describe a methodology that uses Conformal Prediction for predicting off-target interactions, with models trained on data from 31 targets in the ExCAPE-DB dataset selected for their utility in broad early hazard assessment. Chemicals were represented by the signature molecular descriptor and support vector machines were used as the underlying machine learning method. By using conformal prediction, the results from predictions come in the form of confidence p-values for each class. The full pre-processing and model training process is openly available as scientific workflows on GitHub, rendering it fully reproducible. We illustrate the usefulness of the developed methodology on a set of compounds extracted from DrugBank. The resulting models are published online and are available via a graphical web interface and an OpenAPI interface for programmatic access.
BLP - Boundary Likelihood Pinpointing Networks for Accurate Temporal Action Localization

Despite tremendous progress achieved in temporal action detection, state-of-the-art methods still suffer from the sharp performance deterioration when localizing the starting and ending temporal action boundaries. Although most methods apply boundary regression paradigm to tackle this problem, we argue that the direct regression lacks detailed enough information to yield accurate temporal boundaries. In this paper, we propose a novel Boundary Likelihood Pinpointing (BLP) network to alleviate this deficiency of boundary regression and improve the localization accuracy. Given a loosely localized search interval that contains an action instance, BLP casts the problem of localizing temporal boundaries as that of assigning probabilities on each equally divided unit of this interval. These generated probabilities provide useful information regarding the boundary location of the action inside this search interval. Based on these probabilities, we introduce a boundary pinpointing paradigm to pinpoint the accurate boundaries under a simple probabilistic framework. Compared with other C3D feature based detectors, extensive experiments demonstrate that BLP significantly improves the localization performance of recent state-of-the-art detectors, and achieves competitive detection mAP on both THUMOS’ 14 and ActivityNet datasets, particularly when the evaluation tIoU is high.
Asynchronous decentralized convex optimization through short-term gradient averaging

This paper considers decentralized convex optimization over a network in large scale contexts, where large simultaneously applies to number of training examples, dimensionality and number of networking nodes. We first propose a cen- tralized optimization scheme that generalizes successful existing methods based on gradient averaging, improving their flexibility by making the number of averaged gradients an explicit parameter of the method. We then propose an asynchronous distributed algorithm that implements this original scheme for large decentralized computing networks.
Co-Manage Power Delivery and Consumption for Manycore Systems Using Reinforcement Learning

Maintaining high energy efficiency has become a critical design issue for high-performance systems. Many power management techniques have been proposed for the processor cores such as dynamic voltage and frequency scaling (DVFS). However, very few solutions consider the power losses suffered on the power delivery system (PDS), despite the fact that they have a significant impact on the system overall energy efficiency. With the explosive growth of system complexity and highly dynamic workloads variations, it is challenging to find the optimal power management policies which can effectively match the power delivery with the power consumption. To tackle the above problems, we propose a reinforcement learning-based power management scheme for manycore systems to jointly monitor and adjust both the PDS and the processor cores aiming to improve system overall energy efficiency. The learning agents distributed across power domains not only manage the power states of processor cores but also control the on/off states of on-chip VRs to proactively adapt to the workload variations. Experimental results with realistic applications show that when the proposed approach is applied to a large-scale system with a hybrid PDS, it lowers the system overall energy-delay-product (EDP) by 41% than a traditional monolithic DVFS approach with a bulky off-chip VR.
Computing correlated equilibria in multi-player games

We develop polynomial-time algorithms for finding correlated equilibria—a well-studied notion of rationality that generalizes the Nash equilibrium—in a broad class of succinctly representable multiplayer games, encompassing graphical games, anonymous games, polymatrix games, congestion games, scheduling games, local effect games, as well as several generalizations. Our algorithm is based on a variant of the existence proof due to Hart and Schmeidler, and employs linear programming duality, the ellipsoid algorithm, Markov chain steady state computations, as well as application-specific methods for computing multivariate expectations over product distributions. For anonymous games and graphical games of bounded tree-width, we provide a different polynomial-time algorithm for optimizing an arbitrary linear function over the set of correlated equilibria of the game. In contrast to our sweeping positive results for computing an arbitrary correlated equilibrium, we prove that optimizing over correlated equilibria is NP-hard in all of the other classes of games that we consider.
Verb Variability and Morphosyntactic Priming With Typically Developing 2- and 3-Year-Olds.

Purpose This study was specifically designed to examine how verb variability and verb overlap in a morphosyntactic priming task affect typically developing children's use and generalization of auxiliary IS. Method Forty typically developing 2- to 3-year-old native English-speaking children with inconsistent auxiliary IS production were primed with 24 present progressive auxiliary IS sentences. Half of the children heard auxiliary IS primes with 24 unique verbs (high variability). The other half heard auxiliary IS primes with only 6 verbs, repeated 4 times each (low variability). In addition, half of the children heard prime-target pairs with overlapping verbs (lexical boost), whereas the other half heard prime-target pairs with nonoverlapping verbs (no lexical boost). To assess use and generalization of the targeted structure to untrained verbs, all children described probe items at baseline and 5 min and 24 hr after the priming task. Results Children in the high variability group demonstrated strong priming effects during the task and increased auxiliary IS production compared with baseline performance 5 min and 24 hr after the priming task, suggesting learning and generalization of the primed structure. Children in the low variability group showed no significant increases in auxiliary IS production and fell significantly below the high variability group in the 24-hr posttest. Verb overlap did not boost priming effects during the priming task or in posttest probes. Conclusions Typically developing children do indeed make use of lexical variability in their linguistic input to help them extract and generalize abstract grammatical rules. They can do this quite quickly, with relatively stable, robust learning occurring after a single optimally variable input session. With reduced variability, learning does not occur.
SSKSRIF: Scale and Rotation Invariant Features Based on Spatial Space of Keypoints for Camera-Based Information Spotting

In this paper, we propose a new method to build a scale and rotation invariant descriptor for camera-based heterogeneous contents document image retrieval and spotting systems based on spatial information of keypoints. The descriptor is not only efficient to compute but also has low dimensionality. First, keypoints are extracted from an image using one of the dedicated detectors. And then, stable keypoints are sampled in order to compute descriptors based on geometric invariant from local organizations of the sampled keypoints via Delaunay triangulation graph. For sampling stable keypoints, we propose an approach which can deal with different scale level from scale space. The experimental results show that the proposed descriptor is promising for document image retrieval and spotting systems.
Identifying Opinion-Topics and Polarity of Parliamentary Debate Motions

Analysis of the topics mentioned and opinions expressed in parliamentary debate motions–or proposals–is difficult for human readers, but necessary for understanding and automatic processing of the content of the subsequent speeches. We present a dataset of debate motions with pre-existing ‘policy’ labels, and investigate the utility of these labels for simultaneous topic and opinion polarity analysis. For topic detection, we apply one-versus-the-rest supervised topic classification, finding that good performance is achieved in predicting the policy topics, and that textual features derived from the debate titles associated with the motions are particularly indicative of motion topic. We then examine whether the output could also be used to determine the positions taken by proposers towards the different policies by investigating how well humans agree in interpreting the opinion polarities of the motions. Finding very high levels of agreement, we conclude that the policies used can be reliable labels for use in these tasks, and that successful topic detection can therefore provide opinion analysis of the motions ‘for free’.
Surface Plasmon Resonance-Enhanced Visible-NIR-Driven Photocatalytic and Photothermal Catalytic Performance by Ag/Mesoporous Black TiO2 Nanotube Heterojunctions.

Ag/mesoporous black TiO2 nanotubes heterojunctions (Ag-MBTHs) were fabricated through a surface hydrogenation, wet-impregnation and photoreduction strategy. The as-prepared Ag-MBTHs possess a relatively high specific surface area of ≈85 m2 g-1 and an average pore size of ≈13.2 nm. The Ag-MBTHs with a narrow band gap of ≈2.63 eV extend the photoresponse from UV to the visible-light and near-infrared (NIR) region. They exhibit excellent visible-NIR-driven photothermal catalytic and photocatalytic performance for complete conversion of nitro aromatic compounds (100 %) and mineralization of highly toxic phenol (100 %). The enhancement can be attributed to the mesoporous hollow structures increasing the light multi-refraction, the Ti3+ in frameworks and the surface plasmon resonance (SPR) effect of plasmonic Ag nanoparticles favoring light-harvesting and spatial separation of photogenerated electron-hole pairs, which is confirmed by transient fluorescence. The fabrication of this SPR-enhanced visible-NIR-driven Ag-MBTHs catalyst may provide new insights for designing other high-performance heterojunctions as photocatalytic and photothermal catalytic nanomaterials.