Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast Hyperparameter Optimization of Deep Neural Networks via Ensembling Multiple Surrogates

The performance of deep neural networks crucially depends on good hyperparameter configurations. Bayesian optimization is a powerful framework for optimizing the hyperparameters of DNNs. These methods need sufficient evaluation data to approximate and minimize the validation error function of hyperparameters. However, the expensive evaluation cost of DNNs leads to very few evaluation data within a limited time, which greatly reduces the efficiency of Bayesian optimization. Besides, the previous researches focus on using the complete evaluation data to conduct Bayesian optimization, and ignore the intermediate evaluation data generated by early stopping methods. To alleviate the insufficient evaluation data problem, we propose a fast hyperparameter optimization method, HOIST, that utilizes both the complete and intermediate evaluation data to accelerate the hyperparameter optimization of DNNs. Specifically, we train multiple basic surrogates to gather information from the mixed evaluation data, and then combine all basic surrogates using weighted bagging to provide an accurate ensemble surrogate. Our empirical studies show that HOIST outperforms the state-of-the-art approaches on a wide range of DNNs, including feed forward neural networks, convolutional neural networks, recurrent neural networks, and variational autoencoder.
OpenKE: An Open Toolkit for Knowledge Embedding

We release an open toolkit for knowledge embedding (OpenKE), which provides a unified framework and various fundamental models to embed knowledge graphs into a continuous low-dimensional space. OpenKE prioritizes operational efficiency to support quick model validation and large-scale knowledge representation learning. Meanwhile, OpenKE maintains sufficient modularity and extensibility to easily incorporate new models into the framework. Besides the toolkit, the embeddings of some existing large-scale knowledge graphs pre-trained by OpenKE are also available, which can be directly applied for many applications including information retrieval, personalized recommendation and question answering. The toolkit, documentation, and pre-trained embeddings are all released on http://openke.thunlp. org/.
Facilitating Design and Use of Effective Visual Languages in Enterprise Modelling and Information Systems

Enterprise modelling and information systems work often relies heavily on graphical models expressed in visual languages to concisely capture, rigorously model and effectively convey meaning between stakeholders. Recent research has highlighted problems with the effectiveness of popular modelling notations. A physics of notations (PoN) was proposed to address these issues. Application of the PoN has not proven routinely successful. Models are often constructed by experts, but must be well received by non-experts to achieve their goals. This research contends that recent information from the fields of cognition, visualisation and graphic design can be exploited to enhance the return on modelling effort (ROME) and the value of models. Improved meta models, methods for visual language design and enhanced tools can support the definition and use of effective visual languages and the application of the PoN and derivatives.
Statistical NLG for Generating the Content and Form of Referring Expressions

This paper argues that a new generic approach to statistical NLG can be made to perform Referring Expression Generation (REG) successfully. The model does not only select attributes and values for referring to a target referent, but also performs Linguistic Realisation, generating an actual Noun Phrase. Our evaluations suggest that the attribute selection aspect of the algorithm exceeds classic REG algorithms, while the Noun Phrases generated are as similar to those in a previously developed corpus as were Noun Phrases produced by a new set of human speakers.
Trap array: a unified model for scalability evaluation of geometric routing

Scalable routing for large-scale wireless networks needs to find near shortest paths with low state on each node, preferably sublinear with the network size. Two approaches are considered promising toward this goal: compact routing and geometric routing (geo-routing). To date, the two lines of research have been largely independent, perhaps because of the distinct principles they follow. In particular, it remains unclear how they compare to each other in the worst case, despite extensive experimental results showing the superiority of one or another in particular cases. We develop a novel Trap Array topology model that provides a unified framework to uncover the limiting behavior of 10 representative geo-routing algorithms. We present a series of new theoretical results, in comparison to the performance of compact routing as a baseline. In light of their pros and cons, we further design a Compact Geometric Routing (CGR) algorithm that attempts to leverage the benefits of both approaches. Theoretical analysis and simulations show the advantages of the topology model and the algorithm.
Majority Logic Synthesis

The majority function $\langle xyz\rangle$ evaluates to true, if at least two of its Boolean inputs evaluate to true. The majority function has frequently been studied as a central primitive in logic synthesis applications for many decades. Knuth refers to the majority function in the last volume of his seminal The Art of Computer Programming as “probably the most important ternary operation in the entire universe. ” Majority logic sythesis has recently regained signficant interest in the design automation community due to nanoemerging technologies which operate based on the majority function. In addition, majority logic synthesis has successfully been employed in CMOS-based applications such as standard cell or FPGA mapping. This tutorial gives a broad introduction into the field of majority logic synthesis. It will review fundamental results and describe recent contributions from theory, practice, and applications.
Development and CSCW : Standardization and Flexibility in Large-Scale Agile Development

Identifying which agile methods and processes are most effective depends on the goals and aims of an organisation. Agile development promotes an environment of continuous improvement and trust within selforganising teams. Therefore, it is important to allow teams to have the flexibility to customize and tailor their chosen methods. However, in a large-scale agile deployment, there needs to be a degree of process standardization across the organisation; otherwise, different teams will not be able to effectively share knowledge and best practices. This paper addresses this classic CSCW issue of the tensions that arise between process standardization and flexibility in a large-scale agile deployment at the BBC.
Looking glass: a field study on noticing interactivity of a shop window

In this paper we present our findings from a lab and a field study investigating how passers-by notice the interactivity of public displays. We designed an interactive installation that uses visual feedback to the incidental movements of passers-by to communicate its interactivity. The lab study reveals: (1) Mirrored user silhouettes and images are more effective than avatar-like representations. (2) It takes time to notice the interactivity (approx. 1.2s). In the field study, three displays were installed during three weeks in shop windows, and data about 502 interaction sessions were collected. Our observations show: (1) Significantly more passers-by interact when immediately showing the mirrored user image (+90%) or silhouette (+47%) compared to a traditional attract sequence with call-to-action. (2) Passers-by often notice interactivity late and have to walk back to interact (the landing effect). (3) If somebody is already interacting, others begin interaction behind the ones already interacting, forming multiple rows (the honeypot effect). Our findings can be used to design public display applications and shop windows that more effectively communicate interactivity to passers-by.
A Robust Mobile Payment Scheme With Smart Contract-Based Transaction Repository

Recently, the popularity and universality of smart-devices has led to rapid advancement in the development of applications for mobile commerce around the world. Novel mobile payment schemes, such as Apple pay, Android pay, and Samsung pay are becoming an increasingly popular ways to conduct online transactions, no matter what type of smart devices are used. Due to the attendant growth in the importance of security, significant attention has been devoted to the challenge of designing and implementing a robust mobile payment scheme for securing online transactions. In this paper, we demonstrate a robust mobile payment scheme based on sturdy certificateless signatures with bilinear pairing. We elegantly refine the proposed mobile payment scheme to make it suitable for computation-constrained mobile devices. The practicability of the proposed mobile payment scheme is then certified via a rigorous security analysis and thorough performance evaluation using the Raspberry PI as the implementation platform for our proposed scheme. Furthermore, we implement a transaction repository with the aid of smart contract technology. The simulation results, based on Ethereum, demonstrate the feasibility of employing the smart contract technology to secure mobile payments.
Maximizing Broadcast Throughput Under Ultra-Low-Power Constraints

Wireless object-tracking applications are gaining popularity and will soon utilize emerging ultra-low-power device-to-device communication. However, severe energy constraints require much more careful accounting of energy usage than what prior art provides. In particular, the available energy, the differing power consumption levels for listening, receiving, and transmitting, as well as the limited control bandwidth must all be considered. Therefore, we formulate the problem of maximizing the throughput among a set of heterogeneous broadcasting nodes with differing power consumption levels, each subject to a strict ultra-low-power budget. We obtain the oracle throughput (i.e., maximum throughput achieved by an oracle) and use Lagrangian methods to design <italic>EconCast</italic>—a simple asynchronous distributed protocol in which nodes transition between sleep, listen, and transmit states, and dynamically change the transition rates. <italic>EconCast</italic> can operate in groupput or anyput mode to respectively maximize two alternative throughput measures. We show that <italic>EconCast</italic> approaches the oracle throughput. The performance is also evaluated numerically and via extensive simulations and it is shown that <italic>EconCast</italic> outperforms prior art by <inline-formula> <tex-math notation="LaTeX">$6\times $ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">$17\times $ </tex-math></inline-formula> under realistic assumptions. Moreover, we evaluate <italic>EconCast</italic>’s latency performance and consider design tradeoffs when operating in groupput and anyput modes. Finally, we implement <italic>EconCast</italic> using the TI eZ430-RF2500-SEH energy harvesting nodes and experimentally show that in realistic environments it obtains 57%–77% of the achievable throughput.
Poly-logarithmic deterministic fully-dynamic algorithms for connectivity, minimum spanning tree, 2-edge, and biconnectivity

Deterministic fully dynamic graph algorithms are presented for connectivity, minimum spanning tree, 2-edge connectivity, and biconnectivity. Assuming that we start with no edges in a graph with n vertices, the amortized operation costs are O(log 2 n) for connectivity, O(log 4 n) for minimum spanning forest, 2-edge connectivity, and O(log 5 n) biconnectivity.
Modelling Pro-drop with the Rational Speech Acts Model

We extend the classic Referring Expressions Generation task by considering zero pronouns in “pro-drop” languages such as Chinese, modelling their use by means of the Bayesian Rational Speech Acts model (Frank and Goodman, 2012). By assuming that highly salient referents are most likely to be referred to by zero pronouns (i.e., pro-drop is more likely for salient referents than the less salient ones), the model offers an attractive explanation of a phenomenon not previously addressed probabilistically.
Dynamic and Efficient Key Management for Access Hierarchies

Hierarchies arise in the context of access control whenever the user population can be modeled as a set of partially ordered classes (represented as a directed graph). A user with access privileges for a class obtains access to objects stored at that class and all descendant classes in the hierarchy. The problem of key management for such hierarchies then consists of assigning a key to each class in the hierarchy so that keys for descendant classes can be obtained via efficient key derivation. We propose a solution to this problem with the following properties: (1) the space complexity of the public information is the same as that of storing the hierarchy; (2) the private information at a class consists of a single key associated with that class; (3) updates (i.e., revocations and additions) are handled locally in the hierarchy; (4) the scheme is provably secure against collusion; and (5) each node can derive the key of any of its descendant with a number of symmetric-key operations bounded by the length of the path between the nodes. Whereas many previous schemes had some of these properties, ours is the first that satisfies all of them. The security of our scheme is based on pseudorandom functions, without reliance on the Random Oracle Model. Another substantial contribution of this work is that we are able to lower the key derivation time at the expense of modestly increasing the public storage associated with the hierarchy. Insertion of additional, so-called shortcut, edges, allows to lower the key derivation to a small constant number of steps for graphs that are total orders and trees by increasing the total number of edges by a small asymptotic factor such as O(log* n) for an n-node hierarchy. For more general access hierarchies of dimension d, we use a technique that consists of adding dummy nodes and dimension reduction. The key derivation work for such graphs is then linear in d and the increase in the number of edges is by the factor O(logd − 1 n) compared to the one-dimensional case. Finally, by making simple modifications to our scheme, we show how to handle extensions proposed by Crampton [2003] of the standard hierarchies to “limited depth” and reverse inheritance.
NeuralDrop: DNN-based Simulation of Small-Scale Liquid Flows on Solids

Small-scale liquid flows on solid surfaces provide convincing details in liquid animation, but they are difficult to be simulated with efficiency and fidelity, mostly due to the complex nature of the surface tension at the contact front where liquid, air, and solid meet. In this paper, we propose to simulate the dynamics of new liquid drops from captured real-world liquid flow data, using deep neural networks. To achieve this goal, we develop a data capture system that acquires liquid flow patterns from hundreds of real-world water drops. We then convert raw data into compact data for training neural networks, in which liquid drops are represented by their contact fronts in a Lagrangian form. Using the LSTM units based on recurrent neural networks, our neural networks serve three purposes in our simulator: predicting the contour of a contact front, predicting the color field gradient of a contact front, and finally predicting whether a contact front is going to break or not. Using these predictions, our simulator recovers the overall shape of a liquid drop at every time step, and handles merging and splitting events by simple operations. The experiment shows that our trained neural networks are able to perform predictions well. The whole simulator is robust, convenient to use, and capable of generating realistic small-scale liquid effects in animation.
Learning Prototypes for Visual Relationship Detection

Relationships between objects drive our understanding of images, but modelling them poses several challenges due to the combinatorial nature of the problem and the complex structure of natural language. This paper tackles the task of predicting relationships in the form of (subject, predicate, object) triplets from still images. To address these issues, we propose a framework for learning predicate prototypes that aims to capture the multimodal nature of predicate distributions. Concurrently, a network is trained to define a space in which relationships with similar spatial layouts, interacting objects and predicates are clustered together. Finally, at test time, relationships are classified based on the similarity to their train nearest neighbor. We evaluate this approach on two well known datasets and observe comparable performance with state of the art models. Furthermore, we find that coupling prototype learning with a nearest neighbors approach increases the performance from 85.4 % to 87.6 % over a standard classification approach.
Understanding the Impact of Line-of-Sight in the Ergodic Spectral Efficiency of Cellular Networks

In this paper we investigate the impact of line-of-sight Los condition in the ergodic spectral efficiency of cellular networks. To achieve this goal, we have considered the κ-μ shadowed model, which is a general model that provides an excellent fit to a wide set of propagation conditions. To overcome the mathematical complexity of the analysis, we have split the analysis between large and small-scale effects. Building on the proposed framework, we study a number of scenarios that range from heavily-fluctuating LoS to deterministic-LoS. Finally, we shed light on the interplay between fading severity and spectral efficiency by means of the amount of fading.
Task Proposal: The TL;DR Challenge

The TL;DR challenge fosters research in abstractive summarization of informal text, the largest and fastest-growing source of textual data on the web, which has been overlooked by summarization research so far. The challenge owes its name to the frequent practice of social media users to supplement long posts with a “TL;DR”—for “too long; didn’t read”—followed by a short summary as a courtesy to those who would otherwise reply with the exact same abbreviation to indicate they did not care to read a post for its apparent length. Posts featuring TL;DR summaries form an excellent ground truth for summarization, and by tapping into this resource for the first time, we have mined millions of training examples from social media, opening the door to all kinds of generative models.
tidytext: Text Mining and Analysis Using Tidy Data Principles in R

Tidy data sets allow manipulation with a standard set of “tidy” tools, including popular packages such as dplyr (Wickham, Francois, and RStudio 2015), ggplot2 (Wickham, Chang, and RStudio 2016), and broom (Robinson et al. 2015). These tools do not yet, however, have the infrastructure to work fluently with text data and natural language processing tools. In developing this package, we provide functions and supporting data sets to allow conversion of text to and from tidy formats, and to switch seamlessly between tidy tools and existing text mining packages.
What is the best pharmacotherapeutic strategy for HER-2 positive breast cancer?

Amplification of the human epidermal growth factor receptor 2 (HER2) oncogene occurs in about 15% of newly diagnosed, operable breast cancer, and in about 20% of patients with advanced breast cancer [1]. This genetic abnormality, which results in overexpression of the HER2 receptor, was recognized in the 1980s as a potent cancer driver [2]. Efforts at trying to neutralize this driver by pharmacological approaches have produced threemonoclonal antibodies (trastuzumab, pertuzumab, and the antibody-drug conjugated T-DM1) and two tyrosine kinase inhibitors (lapatinib and neratinib) that are available for clinical use [3]. Pharmacological targeting of HER2, with monoclonal antibodies in particular, has dramatically improved the outlook of HER2-positive breast cancer, both in the adjuvant and in the metastatic setting, with notable increases in life expectation (Tables 1 and 2) [4–15].
HUMIR at IEST-2018: Lexicon-Sensitive and Left-Right Context-Sensitive BiLSTM for Implicit Emotion Recognition

This paper describes the approaches used in HUMIR system for the WASSA-2018 shared task on the implicit emotion recognition. The objective of this task is to predict the emotion expressed by the target word that has been excluded from the given tweet. We suppose this task as a word sense disambiguation in which the target word is considered as a synthetic word that can express 6 emotions depending on the context. To predict the correct emotion, we propose a deep neural network model that uses two BiLSTM networks to represent the contexts in the left and right sides of the target word. The BiLSTM outputs achieved from the left and right contexts are considered as context-sensitive features. These features are used in a feed-forward neural network to predict the target word emotion. Besides this approach, we also combine the BiLSTM model with lexicon-based and emotion-based features. Finally, we employ all models in the final system using Bagging ensemble method. We achieved macro F-measure value of 68.8 on the official test set and ranked sixth out of 30 participants.
A Framework for Orchestrating Secure and Dynamic Access of IoT Services in Multi-Cloud Environments

IoT devices have complex requirements but their limitations in terms of storage, network, computing, data analytics, scalability, and big data management require it to be used it with a technology like cloud computing. IoT backend with cloud computing can present new ways to offer services that are massively scalable, can be dynamically configured, and delivered on demand with large scale infrastructure resources. However, a single cloud infrastructure might be unable to deal with the increasing demand of cloud services in which hundreds of users might be accessing cloud resources, leading to a big data problem and the need for efficient frameworks to handle a large number of user requests for IoT services. These challenges require new functional elements and provisioning schemes. To this end, we propose the usage of multi-clouds with IoT which can optimize the user requirements by allowing them to choose best IoT services from many services hosted in various cloud platforms and provide them with more infrastructure and platform resources to meet their requirements. This paper presents a novel framework for dynamic and secure IoT services access across multi-clouds using the cloud on-demand model. To facilitate multi-cloud collaboration, novel protocols are designed and implemented on cloud platforms. The various stages involved in the framework for allowing users access to IoT services in multi-clouds are service matchmaking (i.e., to choose the best service matching user requirements), authentication (i.e., a lightweight mechanism to authenticate users at runtime before granting them service access), and SLA management (including, SLA negotiation, enforcement, and monitoring). SLA management offers benefits like negotiating required service parameters, enforcing mechanisms to ensure that service execution in the external cloud is according to the agreed SLAs and monitoring to verify that the cloud provider complies with those SLAs. The detailed system design to establish secure multi-cloud collaboration has been presented. Moreover, the designed protocols are empirically implemented on two different clouds, including OpenStack and Amazon AWS. Experiments indicate that the proposed system is scalable, authentication protocols result only in a limited overhead compared to standard authentication protocols, and any SLA violation by a cloud provider could be recorded and reported back to the user.