Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning to Grasp from a Single Demonstration

Learning-based approaches for robotic grasping using visual sensors typically require collecting a large size dataset, either manually labeled or by many trial and errors of a robotic manipulator in the real or simulated world. We propose a simpler learning-from-demonstration approach that is able to detect the object to grasp from merely a single demonstration using a convolutional neural network we call GraspNet. In order to increase robustness and decrease the training time even further, we leverage data from previous demonstrations to quickly fine-tune a GrapNet for each new demonstration. We present some preliminary results on a grasping experiment with the Franka Panda cobot for which we can train a GraspNet with only hundreds of train iterations.
Attention Incorporate Network: A network can adapt various data size

In traditional neural networks for image processing, the inputs of the neural networks should be the same size such as 224*224*3. But how can we train the neural net model with different input size? A common way to do is image deformation which accompany a problem of information loss (e.g. image crop or wrap). Sequence model(RNN, LSTM, etc.) can accept different size of input like text and audio. But one disadvantage for sequence model is that the previous information will become more fragmentary during the transfer in time step, it will make the network hard to train especially for long sequential data. In this paper we propose a new network structure called Attention Incorporate Network(AIN). It solve the problem of different size of inputs including: images, text, audio, and extract the key features of the inputs by attention mechanism, pay different attention depends on the importance of the features not rely on the data size. Experimentally, AIN achieve a higher accuracy, better convergence comparing to the same size of other network structure
WristOrigami: Exploring Foldable Design for Multi-Display Smartwatch

We present WristOrigami, an origami-inspired design concept and system extending the interaction with smartwatches through a foldable structure with multiple on-wrist displays. The current design provides extra affordances via folding, flipping, and elastic pulling actions on a multi-display smartwatch. To motivate the design of WristOrigami, we developed a taxonomy that could be useful for analyzing and characterizing the origami-inspired multi-display smartwatch interaction. Through a participatory-design study with a set of prototypes with different levels of fidelity, we investigated users' perception of WristOrigami in a wide range of applications with the presented features, and summarized a list of common shape configurations. We summarized our findings into seven design recommendations, to inform the future design of foldable smartwatch interactions. We further developed a set of application demonstrations as proofs-of-concept.
CURD: a dynamic CUDA race detector

As GPUs have become an integral part of nearly every pro- cessor, GPU programming has become increasingly popular. GPU programming requires a combination of extreme levels of parallelism and low-level programming, making it easy for concurrency bugs such as data races to arise. These con- currency bugs can be extremely subtle and di cult to debug due to the massive numbers of threads running concurrently on a modern GPU. While some tools exist to detect data races in GPU pro- grams, they are often prohibitively slow or focused only on a small class of data races in shared memory. Compared to prior work, our race detector, CURD, can detect data races precisely on both shared and global memory, selects an appropriate race detection algorithm based on the synchronization used in a program, and utilizes efficient compiler instrumentation to reduce performance overheads. Across 53 benchmarks, we find that using CURD incurs an aver- age slowdown of just 2.88x over native execution. CURD is 2.1x faster than Nvidia’s CUDA-Racecheck race detector, de- spite detecting a much broader class of races. CURD finds 35 races across our benchmarks, including bugs in established benchmark suites and in sample programs from Nvidia.
Photo Selection for Family Album using Deep Neural Networks

The development of digital cameras and the web booming are the critical reasons for the increasing of digital portraits. However, such kind of daily photos are usually too many to select and organize, which leads to further requirement of better photo management services. In this paper, we are focusing on a significant part of daily photos -- family photos. We collaborate with a family photo service provider, Chikaku Inc., to create a family photo dataset. The dataset contains 12,140 images with corresponding rates (from 1 to 5) measuring if it is suitable to be selected for a family album. According to our experiments on classifying eligibility of the photos as a printed family photo album, the classification accuracy reaches 96.6% on the test set.
Fake accounts, real activism: Political faking and user-generated satire as activist intervention

In this article, I explore user-generated political satire in Italy by focusing on fake political accounts. By fake accounts, I refer to humorous social media accounts that satirize a politician or a political organization through impersonation. I investigate political faking and user-generated satire as an activist intervention. Through in-depth interviews, I explore the motivations and the relationship with Italian politics of a sample of fake account creators. The results show that most of the satirists interviewed here consider satire as a form of activism and even those who do not, still recognize the subversive nature of satire. Furthermore, a majority of the interviewees have complex biographies of activism that predate the creation of the fake accounts. For a smaller number of them, the fake accounts have also provided new possibilities to engage in activism away-from-keyboard (AFK).
Synthesis of bismuth oxyiodides and their composites: characterization, photocatalytic activity, and degradation mechanisms

Bismuth oxyiodides have been prepared using controlled hydrothermal methods. The products are characterized by SEM-EDS, XRD, XPS, FTIR, PL, EPR, and DRS. It is illustrated that BiOI, Bi4O5I2, Bi7O9I3, Bi5O7I, and BiOxIy/BiOpIq composites can be selectively synthesized through a facile solution-based hydrothermal method. UV-Vis spectra display the bismuth oxyiodide materials as indirect semiconductors with an optical bandgap of 1.86–3.316 eV. The photocatalytic efficiency of the powder suspension is evaluated by measuring the Crystal Violet (CV) concentration. This is the first study to demonstrate the superior activities of BiOI, Bi4O5I2, Bi7O9I3, Bi5O7I, and BiOxIy/BiOpIq composites as promising visible-light-responsive photocatalysts. The quenching effects of various scavengers and EPR indicate that the reactive O2˙− plays a major role and ˙OH and h+ play a minor role. The Bi7O9I3/Bi5O7I composite shows the highest photocatalytic activity reaching a maximum rate constant of 0.2225 h−1, which is 6 times higher than that of BiOI and Bi7O9I3 and 4 times higher than that of Bi5O7I.
Horizontal Partition for Scalable Control in Software-Defined Data Center Networks

Distributed control plane architecture has been employed in software-defined data center networks to improve the scalability of control plane. However, since the flow space is partitioned by assigning switches to different controllers, the network topology is also partitioned and the rule setup process has to invoke multiple controllers. Besides, the control load balancing based on switch migration is heavyweight. In this paper, we propose a lightweight load partition method which decouples the flow space from the network topology. The flow space is partitioned with hosts rather than switches as carriers, which supports fine-grained and lightweight load balancing. Moreover, the switches are no longer needed to be assigned to different controllers and we keep all of them controlled by each controller, thus each flow request can be processed by exactly one controller in a centralized style. Evaluations show that our scheme reduces rule setup costs and achieves lightweight load balancing. key words: software-defined networking, control plane, scalability, load partition, load balancing
A Novel Immersive VR Game Model for Recontextualization in Virtual Environments: The μVRModel

In recent years, immersive VR had a great boost in terms of adoption and research perspectives, especially those regarding the serious gaming universe. Within the cultural heritage field, virtual re-contextualization of items is a crucial task to be accomplished by individuals to understand a 3D reconstructed environment as a whole and to assign a meaning and a value to a specific cultural object. Immersive VR and consumer HMDs still present several issues related to motion sickness and locomotion: the interest in real-walking techniques outperforming other locomotion methods is growing year by year, although limited by physical constraints, higher costs, or current technology. In this work, we propose a novel game model (μVR) that combines real-walking techniques and an adaptive, game-driven, multi-scale progression to craft immersive re-contextualization applications. The presented model aims to minimize motion sickness while fully exploiting the physical tracked area and augmenting the understanding of what the user is experiencing at different world scales. We define and formalize the μVR model and its components mathematically for the sake of reproducibility, then we present results from a pilot test planned to validate the model on real users. Results assure the usability and effectiveness of VR model even if further implementation needs to be done.
Focal structures in Prolog

Several studies have suggested that the mental structures of programmers of procedural languages have a close relationship ·with a model of structural knowledge known as Programming Plans. However, it is not clear that this is the case for Prolog, specially because this language has important differences when compared to procedural languages. It does not have obvious syntactic cues to mark blocks of code (begin/end, repeat/until, etc). Also, its powerful primitives (unification and backtracking) and the extensive use of recursion might influence the way programmers comprehend Prolog code in a significant way. This paper reports an experiment that tries to characterise the nature of the mental models that programmers build when comprehending Prolog code by finding out which of several structural models, Programming Plans among them, are most relevant for the case of this programming language. The results suggest that Prolog Schemas, a construct related to data structure relationships, is of central importance to Prolog programmers. This result contrasts with those obtained for procedural languages, where Programming Plans, a concept related to functional information, seems to be the dominant model.
Kairos: Preemptive Data Center Scheduling Without Runtime Estimates

The vast majority of data center schedulers use task runtime estimates to improve the quality of their scheduling decisions. Knowledge about runtimes allows the schedulers, among other things, to achieve better load balance and to avoid head-of-line blocking. Obtaining accurate runtime estimates is, however, far from trivial, and erroneous estimates lead to sub-optimal scheduling decisions. Techniques to mitigate the effect of inaccurate estimates have shown some success, but the fundamental problem remains. This paper presents Kairos, a novel data center scheduler that assumes no prior information on task runtimes. Kairos introduces a distributed approximation of the Least Attained Service (LAS) scheduling policy. Kairos consists of a centralized scheduler and per-node schedulers. The per-node schedulers implement LAS for tasks on their node, using preemption as necessary to avoid head-of-line blocking. The centralized scheduler distributes tasks among nodes in a manner that balances the load and imposes on each node a workload in which LAS provides favorable performance. We have implemented Kairos in YARN. We compare its performance against the YARN FIFO scheduler and Big-C, an open-source state-of-the-art YARN-based scheduler that also uses preemption. Compared to YARN FIFO, Kairos reduces the median job completion time by 73% and the 99th percentile by 30%. Compared to Big-C, the improvements are 37% for the median and 57% for the 99th percentile. We evaluate Kairos at scale by implementing it in the Eagle simulator and comparing its performance against Eagle. Kairos improves the 99th percentile of short job completion times by up to 55% for the Google trace and 85% for the Yahoo trace.
Dissociation of C-Reactive Protein Localizes and Amplifies Inflammation: Evidence for a Direct Biological Role of C-Reactive Protein and Its Conformational Changes

C-reactive protein (CRP) is a member of the pentraxin superfamily that is widely recognized as a marker of inflammatory reactions and cardiovascular risk in humans. Recently, a growing body of data is emerging, which demonstrates that CRP is not only a marker of inflammation but also acts as a direct mediator of inflammatory reactions and the innate immune response. Here, we critically review the various lines of evidence supporting the concept of a pro-inflammatory “CRP system. ” The CRP system consists of a functionally inert circulating pentameric form (pCRP), which is transformed to its highly pro-inflammatory structural isoforms, pCRP* and ultimately to monomeric CRP (mCRP). While retaining an overall pentameric structure, pCRP* is structurally more relaxed than pCRP, thus exposing neoepitopes important for immune activation and complement fixation. Thereby, pCRP* shares its pro-inflammatory properties with the fully dissociated structural isoform mCRP. The dissociation of pCRP into its pro-inflammatory structural isoforms and thus activation of the CRP system occur on necrotic, apoptotic, and ischemic cells, regular β-sheet structures such as β-amyloid, the membranes of activated cells (e.g., platelets, monocytes, and endothelial cells), and/or the surface of microparticles, the latter by binding to phosphocholine. Both pCRP* and mCRP can cause activation of platelets, leukocytes, endothelial cells, and complement. The localization and deposition of these pro-inflammatory structural isoforms of CRP in inflamed tissue appear to be important mediators for a range of clinical conditions, including ischemia/reperfusion (I/R) injury of various organs, cardiovascular disease, transplant rejection, Alzheimer’s disease, and age-related macular degeneration. These findings provide the impetus to tackle the vexing problem of innate immunity response by targeting CRP. Understanding the “activation process” of CRP will also likely allow the development of novel anti-inflammatory drugs, thereby providing potential new immunomodulatory therapeutics in a broad range of inflammatory diseases.
Slant of a Surface Shifts Binocular Visual Direction

We demonstrate how the slant of a surface affects the relative visual direction between binocular stimuli. In two experiments, we measured the visual direction of a binocular stimulus at different distances in the mid-sagittal plane or in the transverse plane at eye level relative to the center of the stimulus field. Experiment 1 showed that when a binocular stimulus (a vertical bar) was presented in front of or behind a surface slanted along the vertical center of the surface, its visual direction shifted toward the surface. Experiment 2 showed that when a binocular stimulus (a horizontal bar) was presented in front of or behind a surface slanted along the horizontal center of the surface, its visual direction also shifted toward the surface. These results indicate that the slant of a surface should be listed among the variables that contribute to the binocular visual direction, as well as the retinal loci of the stimulus, binocular eye position, the location of the visual egocenter, and stimulus properties.
Generative Adversarial Nets for Information Retrieval: Fundamentals and Advances

Generative adversarial nets (GANs) have been widely studied during the recent development of deep learning and unsupervised learning. With an adversarial training mechanism, GAN manages to train a generative model to fit the underlying unknown real data distribution under the guidance of the discriminative model estimating whether a data instance is real or generated. Such a framework is originally proposed for fitting continuous data distribution such as images, thus it is not straightforward to be directly applied to information retrieval scenarios where the data is mostly discrete, such as IDs, text and graphs. In this tutorial, we focus on discussing the GAN techniques and the variants on discrete data fitting in various information retrieval scenarios. (i) We introduce the fundamentals of GAN framework and its theoretic properties; (ii) we carefully study the promising solutions to extend GAN onto discrete data generation; (iii) we introduce IRGAN, the fundamental GAN framework of fitting single ID data distribution and the direct application on information retrieval; (iv) we further discuss the task of sequential discrete data generation tasks, e.g., text generation, and the corresponding GAN solutions; (v) we present the most recent work on graph/network data fitting with node embedding techniques by GANs. Meanwhile, we also introduce the relevant open-source platforms such as IRGAN and Texygen to help audience conduct research experiments on GANs in information retrieval. Finally, we conclude this tutorial with a comprehensive summarization and a prospect of further research directions for GANs in information retrieval.
Distributed Usage Control Enforcement through Trusted Platform Modules and SGX Enclaves

In the light of mobile and ubiquitous computing, sharing sensitive information across different computer systems has become an increasingly prominent practice. This development entails a demand of access control measures that can protect data even after it has been transferred to a remote computer system. In order to address this problem, sophisticated usage control models have been developed. These models include a client side reference monitor (CRM) that continuously enforces protection policies on foreign data. However, it is still unclear how such a CRM can be properly protected in a hostile environment. The user of the data on the client system can influence the client's state and has physical access to the system. Hence technical measures are required to protect the CRM on a system, which is legitimately used by potential attackers. Existing solutions utilize Trusted Platform Modules (TPMs) to solve this problem by establishing an attestable trust anchor on the client. However, the resulting protocols have several drawbacks that make them infeasible for practical use. This work proposes a reference monitor implementation that establishes trust by using TPMs along with Intel SGX enclaves. First we show how SGX enclaves can realize a subset of the existing usage control requirements. Then we add a TPM to establish and protect a powerful enforcement component on the client. Ultimately this allows us to technically enforce usage control policies on an untrusted remote system.
PathwayTools version 13 . 0 : integrated software for pathway / genome informatics and systems biology

PathwayTools is a production-quality software environment for creating a type of model-organism database called a Pathway/Genome Database (PGDB). A PGDB such as EcoCyc integrates the evolving understanding of the genes, proteins, metabolic network and regulatory network of an organism. This article provides an overview of Pathway Tools capabilities. The software performs multiple computational inferences including prediction of metabolic pathways, prediction of metabolic pathway hole fillers and prediction of operons. It enables interactive editing of PGDBs by DB curators. It supports web publishing of PGDBs, and provides a large number of query and visualization tools. The software also supports comparative analyses of PGDBs, and provides several systems biology analyses of PGDBs including reachability analysis of metabolic networks, and interactive tracing of metabolites through a metabolic network. More than 800 PGDBs have been created using PathwayTools by scientists around the world, many of which are curated DBs for important model organisms. Those PGDBs can be exchanged using a peerto-peer DB sharing system called the PGDB Registry.
Teaching Computer Architecture Labs using a MCU Platform

A thorough understanding of concepts and principles of computer architecture and organization must be a primary goal for any computer science curriculum. Instructors face two main issues when delivering labs for these two topics: (i) a steep learning curve; and (ii) low student engagement. We believe these two challenges feed each other viciously. At the end students are discouraged and demotivated to learn and explore further. In an attempt to tackle this challenge without sacrificing the use of real hardware, we propose and demonstrate the use of a microcontroller (MCU) platform for the delivery of labs for computer architecture and organization courses. We also report our experience with the proposed approach at our university.
Perception of Adolescents on the Attitudes of Providers on Their Access and Use of Reproductive Health Services in Delta State, Nigeria

This paper examines the perception of adolescents on the attitudes of providers on their access and use of reproductive health services (ARHS) in Delta State, Nigeria, with a view of assessing the impact of providers’ attitude on the use of adolescents’ reproductive health services in Delta State. The study adopted a survey design to collect primary data using questionnaires and focus group discussions (FGDs) from adolescents in a sample of schools. A sample size of 1500 respondents was taken from 12 schools in six Local Government Areas in three Senatorial Districts in Delta State, Nigeria. The locations of the schools were such that six each were in rural and urban communities respectively. The result from the study was that unfriendly attitudes of providers which keep adolescents waiting, inadequate duration of consultations, judgmental attitudes of some providers, lack of satisfactory services provision and lack of confidentiality will put off adolescents from accessing and using adolescents’ reproductive health services irrespective of their sex, age, class, religion, residence, ethnic group, parents’ education or income levels. The paper concludes that medical personnel take all these issues very seriously when dealing with adolescents to enhance access and use adolescents’ reproductive health services in Delta State and indeed Nigeria.