OpenRaiser/Intern-Atlas huggingface dataset:
On-chip Few-shot Learning with Surrogate Gradient Descent on a Neuromorphic Processor
Venue: arXiv cs.NE 2019
Year: 2019

Recent work suggests that synaptic plasticity dynamics in biological models of neurons and neuromorphic hardware are compatible with gradient-based learning (Neftci et al., 2019). Gradient-based learning requires iterating several times over a dataset, which is both time-consuming and constrains the training samples to be independently and identically distributed. This is incompatible with learning systems that do not have boundaries between training and inference, such as in neuromorphic hardware. One approach to overcome these constraints is transfer learning, where a portion of the network is pre-trained and mapped into hardware and the remaining portion is trained online. Transfer learning has the advantage that pre-training can be accelerated offline if the task domain is known, and few samples of each class are sufficient for learning the target task at reasonable accuracies. Here, we demonstrate on-line surrogate gradient few-shot learning on Intel's Loihi neuromorphic research processor using features pre-trained with spike-based gradient backpropagation-through-time. Our experimental results show that the Loihi chip can learn gestures online using a small number of shots and achieve results that are comparable to the models simulated on a conventional processor.
Sub-query Fragmentation for Query Analysis and Data Caching in the Distributed Environment
Venue: arXiv cs.DB 2019
Year: 2019

When data stores and users are distributed geographically, it is essential to organize distributed data cache points at ideal locations to minimize data transfers. To answer this, we are developing an adaptive distributed data caching framework that can identify suitable data chunks to cache and move across a network of community cache locations.
End-to-End Defect Detection in Automated Fiber Placement Based on Artificially Generated Data
Venue: arXiv cs.CV 2019
Year: 2019

Automated fiber placement (AFP) is an advanced manufacturing technology that increases the rate of production of composite materials. At the same time, the need for adaptable and fast inline control methods of such parts raises. Existing inspection systems make use of handcrafted filter chains and feature detectors, tuned for a specific measurement methods by domain experts. These methods hardly scale to new defects or different measurement devices. In this paper, we propose to formulate AFP defect detection as an image segmentation problem that can be solved in an end-to-end fashion using artificially generated training data. We employ a probabilistic graphical model to generate training images and annotations. We then train a deep neural network based on recent architectures designed for image segmentation. This leads to an appealing method that scales well with new defect types and measurement devices and requires little real world data for training.
Requirements Engineering for Global Systems: Cultural, Regulatory and Technical Aspects
Venue: arXiv cs.SE 2019
Year: 2019

In this paper we present a formal framework for analysis and optimisation of the requirements specifications of systems developed to apply in several countries. As different countries typically have different regulations/laws as well as different cultural restrictions, the corresponding specific requirements might differ in each particular case. Our framework provides a basis for (1) systematic and formal analysis of the diversity and interdependencies within the sets of the requirements, to avoid non-compliance, contradictions and redundancies; (2) corresponding systematic process for change management in the case of global system development.
Internal Calculi for Separation Logics
Venue: arXiv cs.LO 2019
Year: 2019

We present a general approach to axiomatise separation logics with heaplet semantics with no external features such as nominals/labels. To start with, we design the first (internal) Hilbert-style axiomatisation for the quantifier-free separation logic. We instantiate the method by introducing a new separation logic with essential features: it is equipped with the separating conjunction, the predicate ls, and a natural guarded form of first-order quantification. We apply our approach for its axiomatisation. As a by-product of our method, we also establish the exact expressive power of this new logic and we show PSpace-completeness of its satisfiability problem.
Group, Extract and Aggregate: Summarizing a Large Amount of Finance News for Forex Movement Prediction
Venue: arXiv cs.CL 2019
Year: 2019

Incorporating related text information has proven successful in stock market prediction. However, it is a huge challenge to utilize texts in the enormous forex (foreign currency exchange) market because the associated texts are too redundant. In this work, we propose a BERT-based Hierarchical Aggregation Model to summarize a large amount of finance news to predict forex movement. We firstly group news from different aspects: time, topic and category. Then we extract the most crucial news in each group by the SOTA extractive summarization method. Finally, we conduct interaction between the news and the trade data with attention to predict the forex movement. The experimental results show that the category based method performs best among three grouping methods and outperforms all the baselines. Besides, we study the influence of essential news attributes (category and region) by statistical analysis and summarize the influence patterns for different currency pairs.
Base64 encoding and decoding at almost the speed of a memory copy
Venue: arXiv cs.DC 2019
Year: 2019

Many common document formats on the Internet are text-only such as email (MIME) and the Web (HTML, JavaScript, JSON and XML). To include images or executable code in these documents, we first encode them as text using base64. Standard base64 encoding uses 64~ASCII characters: both lower and upper case Latin letters, digits and two other symbols. We show how we can encode and decode base64 data at nearly the speed of a memory copy (memcpy) on recent Intel processors, as long as the data does not fit in the first-level (L1) cache. We use the SIMD (Single Instruction Multiple Data) instruction set AVX-512 available on commodity processors. Our implementation generates several times fewer instructions than previous SIMD-accelerated base64 codecs. It is also more versatile, as it can be adapted---even at runtime---to any base64 variant by only changing constants.
How Does Language Influence Documentation Workflow? Unsupervised Word Discovery Using Translations in Multiple Languages
Venue: arXiv cs.CL 2019
Year: 2019

For language documentation initiatives, transcription is an expensive resource: one minute of audio is estimated to take one hour and a half on average of a linguist's work (Austin and Sallabank, 2013). Recently, collecting aligned translations in well-resourced languages became a popular solution for ensuring posterior interpretability of the recordings (Adda et al. 2016). In this paper we investigate language-related impact in automatic approaches for computational language documentation. We translate the bilingual Mboshi-French parallel corpus (Godard et al. 2017) into four other languages, and we perform bilingual-rooted unsupervised word discovery. Our results hint towards an impact of the well-resourced language in the quality of the output. However, by combining the information learned by different bilingual models, we are only able to marginally increase the quality of the segmentation.
Iterated Decomposition of Biased Permutations Via New Bounds on the Spectral Gap of Markov Chains
Venue: arXiv cs.DS 2019
Year: 2019

The spectral gap of a Markov chain can be bounded by the spectral gaps of constituent "restriction" chains and a "projection" chain, and the strength of such a bound is the content of various decomposition theorems. In this paper, we introduce a new parameter that allows us to improve upon these bounds. We further define a notion of orthogonality between the restriction chains and "complementary" restriction chains. This leads to a new Complementary Decomposition theorem, which does not require analyzing the projection chain. For $ε$-orthogonal chains, this theorem may be iterated $O(1/ε)$ times while only giving away a constant multiplicative factor on the overall spectral gap. As an application, we provide a $1/n$-orthogonal decomposition of the nearest neighbor Markov chain over $k$-class biased monotone permutations on [$n$], as long as the number of particles in each class is at least $C\log n$. This allows us to apply the Complementary Decomposition theorem iteratively $n$ times to prove the first polynomial bound on the spectral gap when $k$ is as large as $Θ(n/\log n)$. The previous best known bound assumed $k$ was at most a constant.
R-SQAIR: Relational Sequential Attend, Infer, Repeat
Venue: arXiv cs.LG 2019
Year: 2019

Traditional sequential multi-object attention models rely on a recurrent mechanism to infer object relations. We propose a relational extension (R-SQAIR) of one such attention model (SQAIR) by endowing it with a module with strong relational inductive bias that computes in parallel pairwise interactions between inferred objects. Two recently proposed relational modules are studied on tasks of unsupervised learning from videos. We demonstrate gains over sequential relational mechanisms, also in terms of combinatorial generalization.
Semi-Automatic Crowdsourcing Tool for Online Food Image Collection and Annotation
Venue: arXiv cs.IR 2019
Year: 2019

Assessing dietary intake accurately remains an open and challenging research problem. In recent years, image-based approaches have been developed to automatically estimate food intake by capturing eat occasions with mobile devices and wearable cameras. To build a reliable machine-learning models that can automatically map pixels to calories, successful image-based systems need large collections of food images with high quality groundtruth labels to improve the learned models. In this paper, we introduce a semi-automatic system for online food image collection and annotation. Our system consists of a web crawler, an automatic food detection method and a web-based crowdsoucing tool. The web crawler is used to download large sets of online food images based on the given food labels. Since not all retrieved images contain foods, we introduce an automatic food detection method to remove irrelevant images. We designed a web-based crowdsourcing tool to assist the crowd or human annotators to locate and label all the foods in the images. The proposed semi-automatic online food image collection system can be used to build large food image datasets with groundtruth labels efficiently from scratch.
As You Are, So Shall You Move Your Head: A System-Level Analysis between Head Movements and Corresponding Traits and Emotions
Venue: arXiv cs.HC 2019
Year: 2019

Identifying physical traits and emotions based on system-sensed physical activities is a challenging problem in the realm of human-computer interaction. Our work contributes in this context by investigating an underlying connection between head movements and corresponding traits and emotions. To do so, we utilize a head movement measuring device called eSense, which gives acceleration and rotation of a head. Here, first, we conduct a thorough study over head movement data collected from 46 persons using eSense while inducing five different emotional states over them in isolation. Our analysis reveals several new head movement based findings, which in turn, leads us to a novel unified solution for identifying different human traits and emotions through exploiting machine learning techniques over head movement data. Our analysis confirms that the proposed solution can result in high accuracy over the collected data. Accordingly, we develop an integrated unified solution for real-time emotion and trait identification using head movement data leveraging outcomes of our analysis.
Decoupling Hierarchical Recurrent Neural Networks With Locally Computable Losses
Venue: arXiv cs.LG 2019
Year: 2019

Learning long-term dependencies is a key long-standing challenge of recurrent neural networks (RNNs). Hierarchical recurrent neural networks (HRNNs) have been considered a promising approach as long-term dependencies are resolved through shortcuts up and down the hierarchy. Yet, the memory requirements of Truncated Backpropagation Through Time (TBPTT) still prevent training them on very long sequences. In this paper, we empirically show that in (deep) HRNNs, propagating gradients back from higher to lower levels can be replaced by locally computable losses, without harming the learning capability of the network, over a wide range of tasks. This decoupling by local losses reduces the memory requirements of training by a factor exponential in the depth of the hierarchy in comparison to standard TBPTT.
Hear "No Evil", See "Kenansville": Efficient and Transferable Black-Box Attacks on Speech Recognition and Voice Identification Systems
Venue: arXiv cs.CR 2019
Year: 2019

Automatic speech recognition and voice identification systems are being deployed in a wide array of applications, from providing control mechanisms to devices lacking traditional interfaces, to the automatic transcription of conversations and authentication of users. Many of these applications have significant security and privacy considerations. We develop attacks that force mistranscription and misidentification in state of the art systems, with minimal impact on human comprehension. Processing pipelines for modern systems are comprised of signal preprocessing and feature extraction steps, whose output is fed to a machine-learned model. Prior work has focused on the models, using white-box knowledge to tailor model-specific attacks. We focus on the pipeline stages before the models, which (unlike the models) are quite similar across systems. As such, our attacks are black-box and transferable, and demonstrably achieve mistranscription and misidentification rates as high as 100% by modifying only a few frames of audio. We perform a study via Amazon Mechanical Turk demonstrating that there is no statistically significant difference between human perception of regular and perturbed audio. Our findings suggest that models may learn aspects of speech that are generally not perceived by human subjects, but that are crucial for model accuracy. We also find that certain English language phonemes (in particular, vowels) are significantly more susceptible to our attack. We show that the attacks are effective when mounted over cellular networks, where signals are subject to degradation due to transcoding, jitter, and packet loss.
Patterns of Multiplex Layer Entanglement across Real and Synthetic Networks
Venue: arXiv cs.SI 2019
Year: 2019

Real world complex networks often exhibit multiplex structure, connecting entities from different aspects of physical systems such as social, transportation and biological networks. Little is known about general properties of such networks across disciplines. In this work, we first investigate how consistent are connectivity patterns across 35 real world multiplex networks. We demonstrate that entanglement homogeneity and intensity, two measures of layer consistency, indicate apparent differences between social and biological networks. We also investigate trade, co-authorship and transport networks. We show that real networks can be separated in the joint space of homogeneity and intensity, demonstrating the usefulness of the two measures for categorization of real multiplex networks. Finally, we design a multiplex network generator, where similar patterns (as observed in real networks), are emerging over the analysis of 11{,}905 synthetic multiplex networks with various topological properties.
Content Delivery Through Hybrid Architecture in Video on Demand System
Venue: arXiv cs.NI 2019
Year: 2019

Peer-to-Peer (P2P) network needs architectural modification for smooth and fast transportation of video content. The viewer imports chunk video objects through the proxy server. The enormous growth of user requests in a small session of time creates huge load on the VOD system. The situation requires either the proxy server streamed video-content fully or partly to the viewers. The missing chunk at the proxy server is imported from the connected peer nodes. Peers exchange chunks among themselves according to some chunk selection policy. Peer node randomly contacts another peer to download a missing chunk from the buffers during each time slot. In video streaming, when the relevant frame is required at the viewer ends that should be available at the respective proxy server. The video watcher also initiates various types of interactive operations like a move forward or skips some finite number of frames that create congestion inside the VOD system. To elevate the situation it needs an effective content delivery mechanism for smooth transportation of content. The proposed hybrid architecture is composed of P2P and mesh architecture that effectively enhances the search mechanism and content transportation in the VOD system.
Opportunistic Selection of Vehicular Data Brokers as Relay Nodes to the Cloud
Venue: arXiv cs.NI 2019
Year: 2019

The Internet of Things (IoT) revolution and the development of smart communities have resulted in increased demand for bandwidth due to the rise in network traffic. Instead of investing in expensive communications infrastructure, some researchers have proposed leveraging Vehicular Ad-Hoc Networks (VANETs) as the data communications infrastructure. However VANETs are not cheap since they require the deployment of expensive Road Side Units (RSU)s across smart communities. In this research, we propose an infrastructure-less system that opportunistically utilizes vehicles to serve as Local Community Brokers (LCBs) that effectively substitute RSUs for managing communications between smart devices and the cloud in support of smart community applications. We propose an opportunistic algorithm that strives to select vehicles in order to maximize the LCBs' service time. The proposed opportunistic algorithm utilizes an ensemble of online selection algorithms by running all of them together in passive mode and selecting the one that has performed the best in recent history. We evaluate our proposed algorithm using a dataset comprising real taxi traces from the city of Shanghai in China and compare our algorithm against a baseline of 9 Threshold Based Online (TBO) algorithms. A number of experiments are conducted and our results indicate that the proposed algorithm achieves up to 87% more service time with up to 10% fewer vehicle selections compared to the best-performing existing TBO online algorithm.
Gamification of In-Classroom Diagram Design for Science Students
Venue: arXiv cs.HC 2019
Year: 2019

Merging the content of learning with the motivation of games can be a successful combination, if done properly and supported by the appropriate tool. Towards this goal, we developed Diagramatic an environment used to gamify the in-classroom activity of designing diagrams during a lecture. Using Diagramatic the professor, instead of lecturing about diagrams or showing examples of such diagrams, can design short games where the students could play by competing during the lecture. Diagramatic is a complete environment offering to the professor a design application to create games and a management application. The management application is used for monitoring the games while students play, as well as to present the results to the students after the end of each game, or to evaluate these results after classroom time. The students may use the mobile application on their mobiles to practice by designing diagrams outside of the classroom, as well as to play a game during classroom time, but only after the professor starts this game. The environment handles the communication from students' mobiles to the professor's applications and vice versa, while the students submit their diagrams or receive the correct ones, so to proceed to follow up games. The current version of Diagramatic is tailored for designing flow graphs used for path testing into a higher education software engineering course, but the environment can be used in any similar case requiring the design of diagrams (e.g. math, physics, chemistry).
FuseMODNet: Real-Time Camera and LiDAR based Moving Object Detection for robust low-light Autonomous Driving
Venue: arXiv cs.CV 2019
Year: 2019

Moving object detection is a critical task for autonomous vehicles. As dynamic objects represent higher collision risk than static ones, our own ego-trajectories have to be planned attending to the future states of the moving elements of the scene. Motion can be perceived using temporal information such as optical flow. Conventional optical flow computation is based on camera sensors only, which makes it prone to failure in conditions with low illumination. On the other hand, LiDAR sensors are independent of illumination, as they measure the time-of-flight of their own emitted lasers. In this work, we propose a robust and real-time CNN architecture for Moving Object Detection (MOD) under low-light conditions by capturing motion information from both camera and LiDAR sensors. We demonstrate the impact of our algorithm on KITTI dataset where we simulate a low-light environment creating a novel dataset "Dark KITTI". We obtain a 10.1% relative improvement on Dark-KITTI, and a 4.25% improvement on standard KITTI relative to our baselines. The proposed algorithm runs at 18 fps on a standard desktop GPU using $256\times1224$ resolution images.
Design Smell Analysis for Developing and Established Open Source Java Software
Venue: arXiv cs.SE 2019
Year: 2019

Software design smells are design attributes which violate the fundamental design principles. Design smells are a key cause of design debt. Although the activities of design smell identification and measurement are predominantly considered in current literature, those which identify and communicate which design smells occur more frequently in newly developing software and which ones are more dominant in established software have been studied to a limited extent. This research describes a mechanism for identifying the design smells that are more prevalent in developing and established software respectively. A tool is provided which is used for design smell detection by analyzing large volumes of source code. More specifically, 164,609 Lines of Code (LoC) and 5,712 class files of six developing and 244,930 LoC and 12,048 class files of five established open-source Java software are analyzed. Obtained results show that out of the 4,020 occurrences of smells that were made for nine preselected types of design smells, 1,643 design smells were detected for developing software, which mainly consisted of four specific types of smells. For established software, 2,397 design smells were observed which predominantly consisted of four other types of smells. The remaining design smell was equally prevalent in both developing and established software. Desirable precision values ranging from 72.9% to 84.1% were obtained for the tool.
Geovisual Analytics and Interactive Machine Learning for Situational Awareness
Venue: arXiv cs.HC 2019
Year: 2019

The first responder community has traditionally relied on calls from the public, officially-provided geographic information and maps for coordinating actions on the ground. The ubiquity of social media platforms created an opportunity for near real-time sensing of the situation (e.g. unfolding weather events or crises) through volunteered geographic information. In this article, we provide an overview of the design process and features of the Social Media Analytics Reporting Toolkit (SMART), a visual analytics platform developed at Purdue University for providing first responders with real-time situational awareness. We attribute its successful adoption by many first responders to its user-centered design, interactive (geo)visualizations and interactive machine learning, giving users control over analysis.