nils-herrmann/clean_scirepeval huggingface dataset:
Contextualized Bilinear Attention Networks

An agent that can see everyday scenes and fluently communicate with people is one of the ambitious goals of artificial intelligence. To achieve that, it is crucial to exploit visually-grounded information and capture subtle nuances from human conversation. To this end, Visual Dialog (VisDial) task has been introduced. In this paper, we propose a new model for visual dialog. Our model employs Bilinear Attention Network (BAN) and Embeddings from Language Models (ELMo) to exploit visually-grounded information and context of dialogs, respectively. Our proposed model outperforms previous state-of-the-art on VisDial v1.0 dataset by a significant margin (5.33% on recall @10)
Driver Inattention Detection based on Eye Gaze - Road Event Correlation

Current road safety initiatives are approaching the limit of their effectiveness in developed countries. A paradigm shift is needed to address the preventable deaths of thousands on our roads. Previous systems have focused on one or two aspects of driving: environmental sensing, vehicle dynamics or driver monitoring. Our approach is to consider the driver and the vehicle as part of a combined system, operating within the road environment. A driver assistance system is implemented that is not only responsive to the road environment and the driver’s actions but also designed to correlate the driver’s eye gaze with road events to determine the driver’s observations. Driver observation monitoring enables an immediate in-vehicle system able to detect and act on driver inattentiveness, providing the precious seconds for an inattentive human driver to react. We present a prototype system capable of estimating the driver’s observations and detecting driver inattentiveness. Due to the “look but not see” case it is not possible to prove that a road event has been observed by the driver. We show, however, that it is possible to detect missed road events and warn the driver appropriately.
Word Length and the Structure of Short-Term Memory

A number of experiments explored the hypothesis that immediate memory span is not constant, but varies with the length of the words to be recalled.  (1) Memory span is inversely related to word length across a wide range of materials; (2) When number of syllables and number of phonemes are held constant, words of short temporal duration are better recalled than words of long duration; (3) Span could be predicted on the basis of the number of words which the subject can read in approximately 2 sec; (4) When articulation is suppressed by requiring the subject to articulate an irrelevant sound, the word length effect disappears with visual presentation, but remains when presentation is auditory. The results are interpreted in terms of a phonemically-based store of limited temporal capacity, which may function as an output buffer for speech production, and as a supplement to a more central working memory system.
Mixing Complexity and its Applications to Neural Networks

We suggest analyzing neural networks through the prism of space constraints. We observe that most training algorithms applied in practice use bounded memory, which enables us to use a new notion introduced in the study of spacetime tradeoffs that we call mixing complexity. This notion was devised in order to measure the (in)ability to learn using a bounded-memory algorithm. In this paper we describe how we use mixing complexity to obtain new results on what can and cannot be learned using neural networks.
Conditional Image-to-Image Translation

Image-to-image translation tasks have been widely investigated with Generative Adversarial Networks (GANs) and dual learning. However, existing models lack the ability to control the translated results in the target domain and their results usually lack of diversity in the sense that a fixed image usually leads to (almost) deterministic translation result. In this paper, we study a new problem, conditional image-to-image translation, which is to translate an image from the source domain to the target domain conditioned on a given image in the target domain. It requires that the generated image should inherit some domain-specific features of the conditional image from the target domain. Therefore, changing the conditional image in the target domain will lead to diverse translation results for a fixed input image from the source domain, and therefore the conditional input image helps to control the translation results. We tackle this problem with unpaired data based on GANs and dual learning. We twist two conditional translation models (one translation from A domain to B domain, and the other one from B domain to A domain) together for inputs combination and reconstruction while preserving domain independent features. We carry out experiments on men's faces from-to women's faces translation and edges to shoes&bags translations. The results demonstrate the effectiveness of our proposed method.
Forces acting on a biped robot. Center of pressure-zero moment point

In the area of biped robot research, much progress has been made in the past few years. However, some difficulties remain to be dealt with, particularly about the implementation of fast and dynamic walking gaits, in other words anthropomorphic gaits, especially on uneven terrain. In this perspective, both concepts of center of pressure (CoP) and zero moment point (ZMP) are obviously useful. In this paper, the two concepts are strictly defined, the CoP with respect to ground-feet contact forces, the ZMP with respect to gravity plus inertia forces. Then, the coincidence of CoP and ZMP is proven, and related control aspects are examined. Finally, a virtual CoP-ZMP is defined, allowing us to extend the concept when walking on uneven terrain. This paper is a theoretical study. Experimental results are presented in a companion paper, analyzing the evolution of the ground contact forces obtained from a human walker wearing robot feet as shoes.
Deriving Decision Models from Process Models by Enhanced Decision Mining

Optimal decision making during the business process execution is crucial for achieving the business goals of an enterprise. Process execution often involves the usage of the decision logic specified in terms of business rules represented as atomic elements of conditions leading to conclusions. However, the question of using and integrating the processand decision-centric approaches, i.e. harmonization of the widely accepted Business Process Model and Notation (BPMN) and the recent Decision Model and Notation (DMN) proposed by the OMG group, is important. In this paper, we propose a four-step approach to derive decision models from process models on the examples of DMN and BPMN: (1) Identification of decision points in a process model; (2) Extraction of decision logic encapsulating the data dependencies affecting the decisions in the process model; (3) Construction of a decision model; (4) Adaptation of the process model with respect to the derived decision logic. Our contribution also consists in proposing an enrichment of the extracted decision logic by taking into account the predictions of process performance measures corresponding to different decision outcomes. We demonstrate the applicability of the approach on an exemplary business process from the banking domain.
TrPF: A Trajectory Privacy-Preserving Framework for Participatory Sensing

The ubiquity of the various cheap embedded sensors on mobile devices, for example cameras, microphones, accelerometers, and so on, is enabling the emergence of participatory sensing applications. While participatory sensing can benefit the individuals and communities greatly, the collection and analysis of the participators' location and trajectory data may jeopardize their privacy. However, the existing proposals mostly focus on participators' location privacy, and few are done on participators' trajectory privacy. The effective analysis on trajectories that contain spatial-temporal history information will reveal participators' whereabouts and the relevant personal privacy. In this paper, we propose a trajectory privacy-preserving framework, named TrPF, for participatory sensing. Based on the framework, we improve the theoretical mix-zones model with considering the time factor from the perspective of graph theory. Finally, we analyze the threat models with different background knowledge and evaluate the effectiveness of our proposal on the basis of information entropy, and then compare the performance of our proposal with previous trajectory privacy protections. The analysis and simulation results prove that our proposal can protect participators' trajectories privacy effectively with lower information loss and costs than what is afforded by the other proposals.
Dynamics and control of a quadrotor with a cable suspended payload

The dynamics and control of a quadrotor with a cable suspended payload is studied in this paper. The payload whose tether point deviates from the center of mass of the quadrotor is called an off-centered suspended payload. By using the matrix form of Kane's method, the equations of motion are obtained. The system is decomposed into rotational and translational channels. A nonlinear controller is developed to enable stabilization of the quadrotor by using partial linearization of the system. Simulations of a set point regulation are presented to verify the effectiveness of the dynamics modeling control design.
Species loss in fragments of tropical rain forest : a review of the evidence

1. A review of the literature shows that in nearly all cases tropical rain forest fragmentation has led to a local loss of species. Isolated fragments suffer eductions in species richness with time after excision from continuous forest, and small fragments often have fewer species recorded for the same effort of observation than large fragments orareas of continuous forest. 2. Birds have been the most frequently studied taxonomic group with respect o the effects of tropical forest fragmentation. 3. The mechanisms of fragmentation-related extinction i clude the deleterious effects of human disturbance during and after deforestation, the reduction of population sizes, the reduction of immigration rates, forest edge effects, changes in community structure (secondand higher-order effects) and the immigration fexotic species. 4. The relative importance of these mechanisms remains obscure. 5. Animals that are large, sparsely or patchily distributed, orvery specialized and intolerant of the vegetation surrounding fragments, are particularly prone to local extinction. 6. The large number of indigenous pecies that are very sparsely distributed and intolerant of conditions outside the forest make evergreen tropical rain forest particularly susceptible to species loss through fragmentation. 7. Much more research is needed to study what is probably the major threat o global biodiversity.
Say It with Colors: Language-Independent Gender Classification on Twitter

Online Social Networks (OSNs) have spread at stunning speed over the past decade. They are now a part of the lives of dozens of millions of people. The onset of OSNs has stretched the traditional notion of community to include groups of people who have never met in person but communicate with each other through OSNs to share knowledge, opinions, interests and activities. Here we explore in depth language independent gender classification. Our approach predicts gender using five colorbased features extracted from Twitter profiles such as the background color in a user’s profile page. This is in contrast with most existing methods for gender prediction that are language dependent. Those methods use high-dimensional spaces consisting of unique words extracted from such text fields as postings, user names, and profile descriptions. Our approach is independent of the user’s language, efficient, scalable, and computationally tractable, while attaining a good level of accuracy.
Understanding User ’ s Cross-Domain Intentions in Spoken Dialog Systems

Spoken language based intelligent assistants (IAs) have been developed for a number of domains but their functionality has mostly been confined to the scope of a given app. One reason is that it’s is difficult for IAs to infer a user’s intent without access to relevant context and unless explicitly implemented, context is not available across app boundaries. We describe context-aware multi-app dialog systems that can learn to 1) identify meaningful user intents; 2) produce natural language representation for the semantics of such intents; and 3) predict user intent as they engage in multi-app tasks. As part of our work we collected data from the smartphones of 14 users engaged in real-life multi-app tasks. We found that it is reasonable to group tasks into high-level intentions. Based on the dialog content, IA can generate useful phrases to describe the intention. We also found that, with readily available contexts, IAs can effectively predict user’s intents during conversation, with accuracy at 58.9%.
In-Situ AI: Towards Autonomous and Incremental Deep Learning for IoT Systems

Recent years have seen an exploration of data volumes from a myriad of IoT devices, such as various sensors and ubiquitous cameras. The deluge of IoT data creates enormous opportunities for us to explore the physical world, especially with the help of deep learning techniques. Traditionally, the Cloud is the option for deploying deep learning based applications. However, the challenges of Cloud-centric IoT systems are increasing due to significant data movement overhead, escalating energy needs, and privacy issues. Rather than constantly moving a tremendous amount of raw data to the Cloud, it would be beneficial to leverage the emerging powerful IoT devices to perform the inference task. Nevertheless, the statically trained model could not efficiently handle the dynamic data in the real in-situ environments, which leads to low accuracy. Moreover, the big raw IoT data challenges the traditional supervised training method in the Cloud. To tackle the above challenges, we propose In-situ AI, the first Autonomous and Incremental computing framework and architecture for deep learning based IoT applications. We equip deep learning based IoT system with autonomous IoT data diagnosis (minimize data movement), and incremental and unsupervised training method (tackle the big raw IoT data generated in ever-changing in-situ environments). To provide efficient architectural support for this new computing paradigm, we first characterize the two In-situ AI tasks (i.e. inference and diagnosis tasks) on two popular IoT devices (i.e. mobile GPU and FPGA) and explore the design space and tradeoffs. Based on the characterization results, we propose two working modes for the In-situ AI tasks, including Single-running and Co-running modes. Moreover, we craft analytical models for these two modes to guide the best configuration selection. We also develop a novel two-level weight shared In-situ AI architecture to efficiently deploy In-situ tasks to IoT node. Compared with traditional IoT systems, our In-situ AI can reduce data movement by 28-71%, which further yields 1.4X-3.3X speedup on model update and contributes to 30-70% energy saving.
SilhoNet: An RGB Method for 3D Object Pose Estimation and Grasp Planning

Autonomous robot manipulation often involves both estimating the pose of the object to be manipulated and selecting a viable grasp point. Methods using RGB-D data have shown great success in solving these problems. However, there are situations where cost constraints or the working environment may limit the use of RGB-D sensors. When limited to monocular camera data only, both the problem of object pose estimation and of grasp point selection are very challenging. In the past, research has focused on solving these problems separately. In this work, we introduce a novel method called SilhoNet that bridges the gap between these two tasks. We use a Convolutional Neural Network (CNN) pipeline that takes in region of interest (ROI) proposals to simultaneously predict an intermediate silhouette representation for objects with an associated occlusion mask. The 3D pose is then regressed from the predicted silhouettes. Grasp points from a precomputed database are filtered by back-projecting them onto the occlusion mask to find which points are visible in the scene. We show that our method achieves better overall performance than the state-of-the art PoseCNN network for 3D pose estimation on the YCB-video dataset.
Biological and green synthesis of silver nanoparticles ∗

In this work, the synthesis of stable silver nanoparticles by the bioreduction method was investigated. Aqueous extracts of the manna of hedysarum plant and the soap-root (Acanthe phylum bracteatum) plant were used as reducing and stabilizing agents, respectively. UV-Vis absorption spectroscopy was used to monitor the quantitative formation of silver nanoparticles. The characteristics of the obtained silver nanoparticles were studied using X-ray diffraction analysis (XRD), energy-dispersive spectroscopy (EDX), and scanning electron microscopy (SEM). The EDX spectrum of the solution containing silver nanoparticles confirmed the presence of an elemental silver signal without any peaks of impurities. The average diameter of the prepared nanoparticles in solution was about 29-68 nm.
Theoretical and Empirical Developments of the Mindfulness-Acceptance-Commitment ( MAC ) Approach to Performance Enhancement

As long as athletes strive to attain optimal performance states and consistently reach high performance goals, psychological interventions will be used to assist in the development of skill and the maintenance of performance. In the pursuit of these goals, newer evidence-driven models based on mindfulnessand acceptance-based approaches have been designed to achieve these ends. Based upon questionable efficacy data for traditional psychological skills training procedures that emphasize reduction or control of internal processes, mindfulnessand acceptance-based approaches develop skills of nonjudging mindful awareness, mindful attention, and experiential acceptance to aid in the pursuit of valued goals. The most formalized and researched mindfulnessand acceptance-based approach within sport psychology is the manualized Mindfulness-Acceptance-Commitment (MAC) protocol. In the 8 years since the MAC was first developed and presented, and the 5 years since the first publication on the protocol, the MAC program has accumulated a continually growing empirical base for both its underlying theory and intervention efficacy as a performance enhancement intervention. This article reviews the empirical and theoretical foundations of the mindfulnessand acceptance-based approaches in general, and MAC in particular; reviews the accumulated empirical findings in support of the MAC approach for performance enhancement; and presents recent MAC developments and suggested future directions.
Surgical Anatomy of the Lower Face: The Premasseter Space, the Jowl, and the Labiomandibular Fold

The anatomic basis for the jowl has not been fully described. A formal analysis was performed of the sub-superficial musculoaponeurotic system (SMAS) areolar tissue layer, which overlies the lower part of the masseter. For this research, facial dissections were performed on 16 fresh cadavers ages 12 to 89 years, and detailed anatomic observations were made during the course of several hundred rhytidectomy procedures. Tissue samples from varying age groups were examined histologically. The areolar cleavage plane overlying the lower masseter has specific boundaries and is a true space named the “premasseter space.” This space is rhomboidal in shape, lined by membrane, and reinforced by retaining ligaments. The masseter fascia lines the floor, and branches of the facial nerve pass under its deep surface. Histologically, the floor is formed by a thin layer of dense connective tissue, which undergoes minor deterioration in architectural arrangement with age. The roof, lined by a thin transparent and adherent membrane on the underside of the platysma, has a less dense collagen network and contains more elastin. With age, there is a significant reduction in the collagen density of the roof. Expansion of the space with aging, secondary to weakness of the anterior and inferior boundaries, results in formation of the jowl. Medial to the premasseter space is the buccal fat in the masticator space, which descends with aging and contributes to the labiomandibular fold and jowl. Application of the premasseter space in surgery provides significant benefits. The SMAS incision should be forward of the traditional preauricular location to be over the space, not behind. Because the space is a naturally occurring cleavage plane, dissection is bloodless and safe, as all facial nerve branches are outside. The premasseter space should be considered as the preferred dissection plane for lower (cervicofacial) facelifts.
Cognitive behavioral therapy for schizophrenia: an empirical review.

Early case studies and noncontrolled trial studies focusing on the treatment of delusions and hallucinations have laid the foundation for more recent developments in comprehensive cognitive behavioral therapy (CBT) interventions for schizophrenia. Seven randomized, controlled trial studies testing the efficacy of CBT for schizophrenia were identified by electronic search (MEDLINE and PsychInfo) and by personal correspondence. After a review of these studies, effect size (ES) estimates were computed to determine the statistical magnitude of clinical change in CBT and control treatment conditions. CBT has been shown to produce large clinical effects on measures of positive and negative symptoms of schizophrenia. Patients receiving routine care and adjunctive CBT have experienced additional benefits above and beyond the gains achieved with routine care and adjunctive supportive therapy. These results reveal promise for the role of CBT in the treatment of schizophrenia although additional research is required to test its efficacy, long-term durability, and impact on relapse rates and quality of life. Clinical refinements are needed also to help those who show only minimal benefit with the intervention.
A BERT Baseline for the Natural Questions

This technical note describes a new baseline for the Natural Questions (Kwiatkowski et al., 2019). Our model is based on BERT (Devlin et al., 2018) and reduces the gap between the model F1 scores reported in the original dataset paper and the human upper bound by 30% and 50% relative for the long and short answer tasks respectively. This baseline has been submitted to the official NQ leaderboard†. Code, preprocessed data and pretrained model are available‡.
Overcoming catastrophic forgetting with hard attention to the task

Catastrophic forgetting occurs when a neural network loses the information learned in a previous task after training on subsequent tasks. This problem remains a hurdle for artificial intelligence systems with sequential learning capabilities. In this paper, we propose a task-based hard attention mechanism that preserves previous tasks’ information without affecting the current task’s learning. A hard attention mask is learned concurrently to every task, through stochastic gradient descent, and previous masks are exploited to condition such learning. We show that the proposed mechanism is effective for reducing catastrophic forgetting, cutting current rates by 45 to 80%. We also show that it is robust to different hyperparameter choices, and that it offers a number of monitoring capabilities. The approach features the possibility to control both the stability and compactness of the learned knowledge, which we believe makes it also attractive for online learning or network compression applications.