OpenRaiser/Intern-Atlas huggingface dataset:
Hybrid CMOS-CNFET based NP dynamic Carry Look Ahead Adder
Venue: arXiv cs.ET 2018
Year: 2018

Advanced electronic device technologies require a faster operation and smaller average power consumption, which are the most important parameters in very large scale integrated circuit design. The conventional Complementary Metal-Oxide Semiconductor (CMOS) technology is limited by the threshold voltage and subthreshold leakage problems in scaling of devices. This leads to failure in adapting it to sub-micron and nanotechnologies. The carbon nanotube (CNT) technology overcomes the threshold voltage and subthreshold leakage problems despite reduction in size. The CNT based technology develops the most promising devices among emerging technologies because it has most of the desired features. Carbon Nanotube Field Effect Transistors (CNFETs) are the novel devices that are expected to sustain the transistor scalability while increasing its performance. Recently, there have been tremendous advances in CNT technology for nanoelectronics applications. CNFETs avoid most of the fundamental limitations and offer several advantages compared to silicon-based technology. Though CNT evolves as a better option to overcome some of the bulk CMOS problems, the CNT itself still immersed with setbacks. The fabrication of carbon nanotube at very large digital circuits on a single substrate is difficult to achieve. Therefore, a hybrid NP dynamic Carry Look Ahead Adder (CLA) is designed using p-CNFET and n-MOS transistors. Here, the performance of CLA is evaluated in 8-bit, 16-bit, 32-bit and 64-bit stages with the following four different implementations: silicon MOSFET (Si-MOSFET) domino logic, Si-MOSFET NP dynamic CMOS, carbon nanotube MOSFET (CN-MOSFET) domino logic, and CN-MOSFET NP dynamic CMOS. Finally, a Hybrid CMOS-CNFET based 64-bit NP dynamic CLA is evaluated based on HSPICE simulation in 32nm technology, which effectively suppresses power dissipation without an increase in propagation delay.
Unsupervised Deep Representations for Learning Audience Facial Behaviors
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we present an unsupervised learning approach for analyzing facial behavior based on a deep generative model combined with a convolutional neural network (CNN). We jointly train a variational auto-encoder (VAE) and a generative adversarial network (GAN) to learn a powerful latent representation from footage of audiences viewing feature-length movies. We show that the learned latent representation successfully encodes meaningful signatures of behaviors related to audience engagement (smiling & laughing) and disengagement (yawning). Our results provide a proof of concept for a more general methodology for annotating hard-to-label multimedia data featuring sparse examples of signals of interest.
Beating Fredman-Komlós for perfect $k$-hashing
Venue: arXiv cs.IT 2018
Year: 2018

We say a subset $C \subseteq \{1,2,\dots,k\}^n$ is a $k$-hash code (also called $k$-separated) if for every subset of $k$ codewords from $C$, there exists a coordinate where all these codewords have distinct values. Understanding the largest possible rate (in bits), defined as $(\log_2 |C|)/n$, of a $k$-hash code is a classical problem. It arises in two equivalent contexts: (i) the smallest size possible for a perfect hash family that maps a universe of $N$ elements into $\{1,2,\dots,k\}$, and (ii) the zero-error capacity for decoding with lists of size less than $k$ for a certain combinatorial channel. A general upper bound of $k!/k^{k-1}$ on the rate of a $k$-hash code (in the limit of large $n$) was obtained by Fredman and Komlós in 1984 for any $k \geq 4$. While better bounds have been obtained for $k=4$, their original bound has remained the best known for each $k \ge 5$. In this work, we obtain the first improvement to the Fredman-Komlós bound for every $k \ge 5$. While we get explicit (numerical) bounds for $k=5,6$, for larger $k$ we only show that the FK bound can be improved by a positive, but unspecified, amount. Under a conjecture on the optimum value of a certain polynomial optimization problem over the simplex, our methods allow an effective bound to be computed for every $k$.
Nearly-Optimal Mergesorts: Fast, Practical Sorting Methods That Optimally Adapt to Existing Runs
Venue: arXiv cs.DS 2018
Year: 2018

We present two stable mergesort variants, "peeksort" and "powersort", that exploit existing runs and find nearly-optimal merging orders with practically negligible overhead. Previous methods either require substantial effort for determining the merging order (Takaoka 2009; Barbay & Navarro 2013) or do not have a constant-factor optimal worst-case guarantee (Peters 2001; Auger, Nicaud & Pivoteau 2015; Buss & Knop 2018). We demonstrate that our methods are competitive in terms of running time with state-of-the-art implementations of stable sorting methods.
Learning to Grasp Without Seeing
Venue: arXiv cs.RO 2018
Year: 2018

Can a robot grasp an unknown object without seeing it? In this paper, we present a tactile-sensing based approach to this challenging problem of grasping novel objects without prior knowledge of their location or physical properties. Our key idea is to combine touch based object localization with tactile based re-grasping. To train our learning models, we created a large-scale grasping dataset, including more than 30 RGB frames and over 2.8 million tactile samples from 7800 grasp interactions of 52 objects. To learn a representation of tactile signals, we propose an unsupervised auto-encoding scheme, which shows a significant improvement of 4-9% over prior methods on a variety of tactile perception tasks. Our system consists of two steps. First, our touch localization model sequentially 'touch-scans' the workspace and uses a particle filter to aggregate beliefs from multiple hits of the target. It outputs an estimate of the object's location, from which an initial grasp is established. Next, our re-grasping model learns to progressively improve grasps with tactile feedback based on the learned features. This network learns to estimate grasp stability and predict adjustment for the next grasp. Re-grasping thus is performed iteratively until our model identifies a stable grasp. Finally, we demonstrate extensive experimental results on grasping a large set of novel objects using tactile sensing alone. Furthermore, when applied on top of a vision-based policy, our re-grasping model significantly boosts the overall accuracy by 10.6%. We believe this is the first attempt at learning to grasp with only tactile sensing and without any prior object knowledge.
Computing Coverage Kernels Under Restricted Settings
Venue: arXiv cs.CG 2018
Year: 2018

We consider the Minimum Coverage Kernel problem: given a set $B$ of $d$-dimensional boxes, find a subset of $B$ of minimum size covering the same region as $B$. This problem is $\mathsf{NP}$-hard, but as for many $\mathsf{NP}$-hard problems on graphs, the problem becomes solvable in polynomial time under restrictions on the graph induced by $B$. We consider various classes of graphs, show that Minimum Coverage Kernel remains $\mathsf{NP}$-hard even for severely restricted instances, and provide two polynomial time approximation algorithms for this problem.
Adaptive Selection of Deep Learning Models on Embedded Systems
Venue: arXiv cs.PF 2018
Year: 2018

The recent ground-breaking advances in deep learning networks ( DNNs ) make them attractive for embedded systems. However, it can take a long time for DNNs to make an inference on resource-limited embedded devices. Offloading the computation into the cloud is often infeasible due to privacy concerns, high latency, or the lack of connectivity. As such, there is a critical need to find a way to effectively execute the DNN models locally on the devices. This paper presents an adaptive scheme to determine which DNN model to use for a given input, by considering the desired accuracy and inference time. Our approach employs machine learning to develop a predictive model to quickly select a pre-trained DNN to use for a given input and the optimization constraint. We achieve this by first training off-line a predictive model, and then use the learnt model to select a DNN model to use for new, unseen inputs. We apply our approach to the image classification task and evaluate it on a Jetson TX2 embedded deep learning platform using the ImageNet ILSVRC 2012 validation dataset. We consider a range of influential DNN models. Experimental results show that our approach achieves a 7.52% improvement in inference accuracy, and a 1.8x reduction in inference time over the most-capable single DNN model.
Scripting Relational Database Engine Using Transducer
Venue: arXiv cs.DB 2018
Year: 2018

We allow database user to script a parallel relational database engine with a procedural language. Procedural language code is executed as a user defined relational query operator called transducer. Transducer is tightly integrated with relation engine, including query optimizer, query executor and can be executed in parallel like other query operators. With transducer, we can efficiently execute queries that are very difficult to express in SQL. As example, we show how to run time series and graph queries, etc, within a parallel relational database.
The risk of sub-optimal use of Open Source NLP Software: UKB is inadvertently state-of-the-art in knowledge-based WSD
Venue: arXiv cs.CL 2018
Year: 2018

UKB is an open source collection of programs for performing, among other tasks, knowledge-based Word Sense Disambiguation (WSD). Since it was released in 2009 it has been often used out-of-the-box in sub-optimal settings. We show that nine years later it is the state-of-the-art on knowledge-based WSD. This case shows the pitfalls of releasing open source NLP software without optimal default settings and precise instructions for reproducibility.
Incentivized Delivery Network of IoT Software Updates Based on Trustless Proof-of-Distribution
Venue: arXiv cs.CR 2018
Year: 2018

The prevalence of IoT devices makes them an ideal target for attackers. To reduce the risk of attacks vendors routinely deliver security updates (patches) for their devices. The delivery of security updates becomes challenging due to the issue of scalability as the number of devices may grow much quicker than vendors' distribution systems. Previous studies have suggested a permissionless and decentralized blockchain-based network in which nodes can host and deliver security updates, thus the addition of new nodes scales out the network. However, these studies do not provide an incentive for nodes to join the network, making it unlikely for nodes to freely contribute their hosting space, bandwidth, and computation resources. In this paper, we propose a novel decentralized IoT software update delivery network in which participating nodes referred to as distributors) are compensated by vendors with digital currency for delivering updates to devices. Upon the release of a new security update, a vendor will make a commitment to provide digital currency to distributors that deliver the update; the commitment will be made with the use of smart contracts, and hence will be public, binding, and irreversible. The smart contract promises compensation to any distributor that provides proof-of-distribution, which is unforgeable proof that a single update was delivered to a single device. A distributor acquires the proof-of-distribution by exchanging a security update for a device signature using the Zero-Knowledge Contingent Payment (ZKCP) trustless data exchange protocol. Eliminating the need for trust between the security update distributor and the security consumer (IoT device) by providing fair compensation, can significantly increase the number of distributors, thus facilitating rapid scale out.
Enabling Cross-Event Optimization in Discrete-Event Simulation Through Compile-Time Event Batching
Venue: arXiv cs.PF 2018
Year: 2018

A discrete-event simulation (DES) involves the execution of a sequence of event handlers dynamically scheduled at runtime. As a consequence, a priori knowledge of the control flow of the overall simulation program is limited. In particular, powerful optimizations supported by modern compilers can only be applied on the scope of individual event handlers, which frequently involve only a few lines of code. We propose a method that extends the scope for compiler optimizations in discrete-event simulations by generating batches of multiple events that are subjected to compiler optimizations as contiguous procedures. A runtime mechanism executes suitable batches at negligible overhead. Our method does not require any compiler extensions and introduces only minor additional effort during model development. The feasibility and potential performance gains of the approach are illustrated on the example of an idealized proof-ofconcept model. We believe that the applicability of the approach extends to general event-driven programs.
Trajectory Design for Distributed Estimation in UAV Enabled Wireless Sensor Network
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, we study an unmanned aerial vehicle(UAV)-enabled wireless sensor network, where a UAV is dispatched to collect the sensed data from distributed sensor nodes (SNs) for estimating an unknown parameter. It is revealed that in order to minimize the mean square error (MSE) for the estimation, the UAV should collect the data from as many SNs as possible, based on which an optimization problem is formulated to design the UAV's trajectory subject to its practical mobility constraints. Although the problem is non-convex and NP-hard, we show that the optimal UAV trajectory consists of connected line segments only. With this simplification, an efficient suboptimal solution is proposed by leveraging the classic traveling salesman problem (TSP) method and applying convex optimization techniques. Simulation results show that the proposed trajectory design achieves significant performance gains in terms of the number of SNs whose data are successfully collected, as compared to other benchmark schemes.
Weakly Supervised Domain-Specific Color Naming Based on Attention
Venue: arXiv cs.CV 2018
Year: 2018

The majority of existing color naming methods focuses on the eleven basic color terms of the English language. However, in many applications, different sets of color names are used for the accurate description of objects. Labeling data to learn these domain-specific color names is an expensive and laborious task. Therefore, in this article we aim to learn color names from weakly labeled data. For this purpose, we add an attention branch to the color naming network. The attention branch is used to modulate the pixel-wise color naming predictions of the network. In experiments, we illustrate that the attention branch correctly identifies the relevant regions. Furthermore, we show that our method obtains state-of-the-art results for pixel-wise and image-wise classification on the EBAY dataset and is able to learn color names for various domains.
Novel Deep Learning Model for Traffic Sign Detection Using Capsule Networks
Venue: arXiv cs.CV 2018
Year: 2018

Convolutional neural networks are the most widely used deep learning algorithms for traffic signal classification till date but they fail to capture pose, view, orientation of the images because of the intrinsic inability of max pooling layer.This paper proposes a novel method for Traffic sign detection using deep learning architecture called capsule networks that achieves outstanding performance on the German traffic sign dataset.Capsule network consists of capsules which are a group of neurons representing the instantiating parameters of an object like the pose and orientation by using the dynamic routing and route by agreement algorithms.unlike the previous approaches of manual feature extraction,multiple deep neural networks with many parameters,our method eliminates the manual effort and provides resistance to the spatial variances.CNNs can be fooled easily using various adversary attacks and capsule networks can overcome such attacks from the intruders and can offer more reliability in traffic sign detection for autonomous vehicles.Capsule network have achieved the state-of-the-art accuracy of 97.6% on German Traffic Sign Recognition Benchmark dataset (GTSRB).
Cross-lingual Document Retrieval using Regularized Wasserstein Distance
Venue: arXiv cs.CL 2018
Year: 2018

Many information retrieval algorithms rely on the notion of a good distance that allows to efficiently compare objects of different nature. Recently, a new promising metric called Word Mover's Distance was proposed to measure the divergence between text passages. In this paper, we demonstrate that this metric can be extended to incorporate term-weighting schemes and provide more accurate and computationally efficient matching between documents using entropic regularization. We evaluate the benefits of both extensions in the task of cross-lingual document retrieval (CLDR). Our experimental results on eight CLDR problems suggest that the proposed methods achieve remarkable improvements in terms of Mean Reciprocal Rank compared to several baselines.
Pocket Code: a mobile app for game jams to facilitate classroom learning through game creation
Venue: arXiv cs.CY 2018
Year: 2018

Game jams are a way to create games under fast-paced conditions and certain constraints. The increase in game jam events all over the world, their engaging and creative nature, with the aim of sharing results among players can be seen in the high participation rate of such events (2013: 16,705 participants from 319 jam sites in 63 countries produced 3248 games) . This promising concept can be easily transferred to a classroom setting.
App creation in schools for different curricula subjects - lesson learned
Venue: arXiv cs.CY 2018
Year: 2018

The next generation of jobs will be characterized by an increased demand for people with computational and problem solving skills. In Austria, computer science topics are underrepresented in school curricula hence teaching time for these topics is limited. From primary through secondary school, only a few opportunities exist for young students to explore programming. Furthermore, today's teachers are rarely trained in computer science, which impairs their potential to motivate students in these courses. Within the "No One Left Behind" (NOLB) project, teachers were supported to guide and assist their students in their learning processes by constructing ideas through game making. Thus, students created games that referred to different subject areas by using the programming tool Pocket Code, an app developed at Graz University of Technology (TU-Graz). This tool helps students to take control of their own education, becoming more engaged, interested, and empowered as a result. To ensure an optimal integration of the app in diverse subjects the different backgrounds (technical and non-technical) of teachers must be considered as well. First, teachers were supported to use Pocket Code in the different subjects in school within the feasibility study of the project. Observed challenges and difficulties using the app have been gathered. Second, we conducted interviews with teachers and students to underpin our onsite observations. As a result, it was possible to validate Pocket Codes' potential to be used in a diverse range of subjects. Third, we focused especially on those teachers who were not technically trained to provide them with a framework for Pocket Code units, e.g., with the help of structured lesson plans and predefined templates.
Statically Verifying Continuous Integration Configurations
Venue: arXiv cs.SE 2018
Year: 2018

Continuous Integration (CI) testing is a popular software development technique that allows developers to easily check that their code can build successfully and pass tests across various system environments. In order to use a CI platform, a developer must include a set of configuration files to a code repository for specifying build conditions. Incorrect configuration settings lead to CI build failures, which can take hours to run, wasting valuable developer time and delaying product release dates. Debugging CI configurations is challenging because users must manage configurations for the build across many system environments, to which they may not have local access. Thus, the only way to check a CI configuration is to push a commit and wait for the build result. To address this problem, we present the first approach, VeriCI, for statically checking for errors in a given CI configuration before the developer pushes a commit to build on the CI server. Our key insight is that the repositories in a CI environment contain lists of build histories which offer the time-aware repository build status. Driven by this insight, we introduce the Misclassification Guided Abstraction Refinement (MiGAR) loop that automates part of the learning process across the heterogeneous build environments in CI. We then use decision tree learning to generate constraints on the CI configuration that must hold for a build to succeed by training on a large history of continuous integration repository build results. We evaluate VeriCI on real-world data from GitHub and find that we have 83% accuracy of predicting a build failure.
Under the Underground: Predicting Private Interactions in Underground Forums
Venue: arXiv cs.CR 2018
Year: 2018

Underground forums where users discuss, buy, and sell illicit services and goods facilitate a better understanding of the economy and organization of cybercriminals. Prior work has shown that in particular private interactions provide a wealth of information about the cybercriminal ecosystem. Yet, those messages are seldom available to analysts, except when there is a leak. To address this problem we propose a supervised machine learning based method able to predict which public \threads will generate private messages, after a partial leak of such messages has occurred. To the best of our knowledge, we are the first to develop a solution to overcome the barrier posed by limited to no information on private activity for underground forum analysis. Additionally, we propose an automate method for labeling posts, significantly reducing the cost of our approach in the presence of real unlabeled data. This method can be tuned to focus on the likelihood of users receiving private messages, or \threads triggering private interactions. We evaluate the performance of our methods using data from three real forum leaks. Our results show that public information can indeed be used to predict private activity, although prediction models do not transfer well between forums. We also find that neither the length of the leak period nor the time between the leak and the prediction have significant impact on our technique's performance, and that NLP features dominate the prediction power.
Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems
Venue: arXiv cs.CL 2018
Year: 2018

Automatic machine learning systems can inadvertently accentuate and perpetuate inappropriate human biases. Past work on examining inappropriate biases has largely focused on just individual systems. Further, there is no benchmark dataset for examining inappropriate biases in systems. Here for the first time, we present the Equity Evaluation Corpus (EEC), which consists of 8,640 English sentences carefully chosen to tease out biases towards certain races and genders. We use the dataset to examine 219 automatic sentiment analysis systems that took part in a recent shared task, SemEval-2018 Task 1 'Affect in Tweets'. We find that several of the systems show statistically significant bias; that is, they consistently provide slightly higher sentiment intensity predictions for one race or one gender. We make the EEC freely available.