OpenRaiser/Intern-Atlas huggingface dataset:
SetExpan: Corpus-Based Set Expansion via Context Feature Selection and Rank Ensemble
Venue: arXiv cs.CL 2019
Year: 2019

Corpus-based set expansion (i.e., finding the "complete" set of entities belonging to the same semantic class, based on a given corpus and a tiny set of seeds) is a critical task in knowledge discovery. It may facilitate numerous downstream applications, such as information extraction, taxonomy induction, question answering, and web search. To discover new entities in an expanded set, previous approaches either make one-time entity ranking based on distributional similarity, or resort to iterative pattern-based bootstrapping. The core challenge for these methods is how to deal with noisy context features derived from free-text corpora, which may lead to entity intrusion and semantic drifting. In this study, we propose a novel framework, SetExpan, which tackles this problem, with two techniques: (1) a context feature selection method that selects clean context features for calculating entity-entity distributional similarity, and (2) a ranking-based unsupervised ensemble method for expanding entity set based on denoised context features. Experiments on three datasets show that SetExpan is robust and outperforms previous state-of-the-art methods in terms of mean average precision.
AFO-TAD: Anchor-free One-Stage Detector for Temporal Action Detection
Venue: arXiv cs.CV 2019
Year: 2019

Temporal action detection is a fundamental yet challenging task in video understanding. Many of the state-of-the-art methods predict the boundaries of action instances based on predetermined anchors akin to the two-dimensional object detection detectors. However, it is hard to detect all the action instances with predetermined temporal scales because the durations of instances in untrimmed videos can vary from few seconds to several minutes. In this paper, we propose a novel action detection architecture named anchor-free one-stage temporal action detector (AFO-TAD). AFO-TAD achieves better performance for detecting action instances with arbitrary lengths and high temporal resolution, which can be attributed to two aspects. First, we design a receptive field adaption module which dynamically adjusts the receptive field for precise action detection. Second, AFO-TAD directly predicts the categories and boundaries at every temporal locations without predetermined anchors. Extensive experiments show that AFO-TAD improves the state-of-the-art performance on THUMOS'14.
Physical Layer Encryption using a Vernam Cipher
Venue: arXiv cs.CR 2019
Year: 2019

Secure communication is a necessity. However, encryption is commonly only applied to the upper layers of the protocol stack. This exposes network information to eavesdroppers, including the channel's type, data rate, protocol, and routing information. This may be solved by encrypting the physical layer, thereby securing all subsequent layers. In order for this method to be practical, the encryption must be quick, preserve bandwidth, and must also deal with the issues of noise mitigation and synchronization. In this paper, we present the Vernam Physical Signal Cipher (VPSC): a novel cipher which can encrypt the harmonic composition of any analog waveform. The VPSC accomplished this by applying a modified Vernam cipher to the signal's frequency magnitudes and phases. This approach is fast and preserves the signal's bandwidth. In the paper, we offer methods for noise mitigation and synchronization, and evaluate the VPSC over a noisy wireless channel with multi-path propagation interference.
BOBBY2: Buffer Based Robust High-Speed Object Tracking
Venue: arXiv cs.CV 2019
Year: 2019

In this work, a novel high-speed single object tracker that is robust against non-semantic distractor exemplars is introduced; dubbed BOBBY2. It incorporates a novel exemplar buffer module that sparsely caches the target's appearance across time, enabling it to adapt to potential target deformation. As for training, an augmented ImageNet-VID dataset was used in conjunction with the one cycle policy, enabling it to reach convergence with less than 2 epoch worth of data. For validation, the model was benchmarked on the GOT-10k dataset and on an additional small, albeit challenging custom UAV dataset collected with the TU-3 UAV. We demonstrate that the exemplar buffer is capable of providing redundancies in case of unintended target drifts, a desirable trait in any middle to long term tracking. Even when the buffer is predominantly filled with distractors instead of valid exemplars, BOBBY2 is capable of maintaining a near-optimal level of accuracy. BOBBY2 manages to achieve a very competitive result on the GOT-10k dataset and to a lesser degree on the challenging custom TU-3 dataset, without fine-tuning, demonstrating its generalizability. In terms of speed, BOBBY2 utilizes a stripped down AlexNet as feature extractor with 63% less parameters than a vanilla AlexNet, thus being able to run at a competitive 85 FPS.
Infinite families of near MDS codes holding $t$-designs
Venue: arXiv cs.IT 2019
Year: 2019

An $[n, k, n-k+1]$ linear code is called an MDS code. An $[n, k, n-k]$ linear code is said to be almost maximum distance separable (almost MDS or AMDS for short). A code is said to be near maximum distance separable (near MDS or NMDS for short) if the code and its dual code both are almost maximum distance separable. The first near MDS code was the $[11, 6, 5]$ ternary Golay code discovered in 1949 by Golay. This ternary code holds $4$-designs, and its extended code holds a Steiner system $S(5, 6, 12)$ with the largest strength known. In the past 70 years, sporadic near MDS codes holding $t$-designs were discovered and many infinite families of near MDS codes over finite fields were constructed. However, the question as to whether there is an infinite family of near MDS codes holding an infinite family of $t$-designs for $t\geq 2$ remains open for 70 years. This paper settles this long-standing problem by presenting an infinite family of near MDS codes over $\mathrm{GF}(3^s)$ holding an infinite family of $3$-designs and an infinite family of near MDS codes over $\mathrm{GF}(2^{2s})$ holding an infinite family of $2$-designs. The subfield subcodes of these two families are also studied, and are shown to be dimension-optimal or distance-optimal.
Functional Epsilon Entropy
Venue: arXiv cs.IT 2019
Year: 2019

We consider the problem of coding for computing with maximal distortion, where the sender communicates with a receiver, which has its own private data and wants to compute a function of their combined data with some fidelity constraint known to both agents. We show that the minimum rate for this problem is equal to the conditional entropy of a hypergraph and design practical codes for the problem. Further, the minimum rate of this problem may be a discontinuous function of the fidelity constraint. We also consider the case when the exact function is not known to the sender, but some approximate function or a class to which the function belongs is known and provide efficient achievable schemes.
Point Process Flows
Venue: arXiv cs.LG 2019
Year: 2019

Event sequences can be modeled by temporal point processes (TPPs) to capture their asynchronous and probabilistic nature. We propose an intensity-free framework that directly models the point process distribution by utilizing normalizing flows. This approach is capable of capturing highly complex temporal distributions and does not rely on restrictive parametric forms. Comparisons with state-of-the-art baseline models on both synthetic and challenging real-life datasets show that the proposed framework is effective at modeling the stochasticity of discrete event sequences.
D2D-Enabled Mobile User Edge Caching: A Multi-Winner Auction Approach
Venue: arXiv cs.NI 2019
Year: 2019

In device-to-device (D2D)-enabled caching cellular networks, the user terminals (UTs) collaboratively store and share a large volume of popular contents from the base station (BS) for traffic offloading and delivery delay reduction. In this article, the multi-winner auction based caching placement in D2D-enabled caching cellular networks is investigated for UT edge caching incentive and content caching redundancy reduction. Firstly, a multi-winner once auction for UT edge caching is modeled which auctions multiple contents for multiple UTs. Then the optimization problem for content caching revenue maximization is formulated. Specifically, the "cache conflict" restriction relationship among UTs is used as one of the constraints in the problem to reduce the content caching redundancy in a UT movement scenario. The problem is solved by semidefinite programming (SDP) relaxation to obtain an approximate optimal caching placement. Moreover, the payment strategy of the auction is developed as a Nash bargaining game for personal profit fairness among the UTs who win the auction for content caching. Subsequently, a multi-winner once auction based caching (MOAC) placement algorithm is proposed. In addition, due to the high complexity of MOAC, we further propose a heuristic multi-winner repeated auction based caching placement (MRAC) algorithm, which can greatly reduce the complexity with only tiny performance loss. Simulation results show that the proposed algorithms can reduce the traffic load and average content access delay effectively compared with the existing caching placement algorithms.
Towards Computing Inferences from English News Headlines
Venue: arXiv cs.CL 2019
Year: 2019

Newspapers are a popular form of written discourse, read by many people, thanks to the novelty of the information provided by the news content in it. A headline is the most widely read part of any newspaper due to its appearance in a bigger font and sometimes in colour print. In this paper, we suggest and implement a method for computing inferences from English news headlines, excluding the information from the context in which the headlines appear. This method attempts to generate the possible assumptions a reader formulates in mind upon reading a fresh headline. The generated inferences could be useful for assessing the impact of the news headline on readers including children. The understandability of the current state of social affairs depends greatly on the assimilation of the headlines. As the inferences that are independent of the context depend mainly on the syntax of the headline, dependency trees of headlines are used in this approach, to find the syntactical structure of the headlines and to compute inferences out of them.
Hide the Image in FC-DenseNets to another Image
Venue: arXiv cs.MM 2019
Year: 2019

In the past, steganography was to embed text in a carrier, the sender Alice and the recipient Bob share the key, and the text is extracted by Bob through the key. If more information is embedded, the image is easily distorted. In contrast, if there is less embedded information, the image maintains good visual integrity, but does not meet our requirements for steganographic capacity. In this paper, we focus on tackling these challenges and limitations to improve steganographic capacity. An image steganography method based on Fully Convolutional Dense Network(FC-DenseNet) was proposed by us. The hidden network and the extracted network are trained at the same time. The dataset of the deep neural network is derived from various natural images of ImageNet. The experimental results show that the stego-image after steganography and the secret image extracted from stego-imge have a visually good effect, and the stego-image has high capacity and high peak signal to noise ratio. Image-to-image full size hiding is implemented.
Controlling Utterance Length in NMT-based Word Segmentation with Attention
Venue: arXiv cs.CL 2019
Year: 2019

One of the basic tasks of computational language documentation (CLD) is to identify word boundaries in an unsegmented phonemic stream. While several unsupervised monolingual word segmentation algorithms exist in the literature, they are challenged in real-world CLD settings by the small amount of available data. A possible remedy is to take advantage of glosses or translation in a foreign, well-resourced, language, which often exist for such data. In this paper, we explore and compare ways to exploit neural machine translation models to perform unsupervised boundary detection with bilingual information, notably introducing a new loss function for jointly learning alignment and segmentation. We experiment with an actual under-resourced language, Mboshi, and show that these techniques can effectively control the output segmentation length.
The Bitwise Hashing Trick for Personalized Search
Venue: arXiv cs.IR 2019
Year: 2019

Many real world problems require fast and efficient lexical comparison of large numbers of short text strings. Search personalization is one such domain. We introduce the use of feature bit vectors using the hashing trick for improving relevance in personalized search and other personalization applications. We present results of several lexical hashing and comparison methods. These methods are applied to a user's historical behavior and are used to predict future behavior. Using a single bit per dimension instead of floating point results in an order of magnitude decrease in data structure size, while preserving or even improving quality. We use real data to simulate a search personalization task. A simple method for combining bit vectors demonstrates an order of magnitude improvement in compute time on the task with only a small decrease in accuracy.
Blameworthiness in Security Games
Venue: arXiv cs.AI 2019
Year: 2019

Security games are an example of a successful real-world application of game theory. The paper defines blameworthiness of the defender and the attacker in security games using the principle of alternative possibilities and provides a sound and complete logical system for reasoning about blameworthiness in such games. Two of the axioms of this system capture the asymmetry of information in security games.
Context-Driven Data Mining through Bias Removal and Data Incompleteness Mitigation
Venue: arXiv cs.LG 2019
Year: 2019

The results of data mining endeavors are majorly driven by data quality. Throughout these deployments, serious show-stopper problems are still unresolved, such as: data collection ambiguities, data imbalance, hidden biases in data, the lack of domain information, and data incompleteness. This paper is based on the premise that context can aid in mitigating these issues. In a traditional data science lifecycle, context is not considered. Context-driven Data Science Lifecycle (C-DSL); the main contribution of this paper, is developed to address these challenges. Two case studies (using data-sets from sports events) are developed to test C-DSL. Results from both case studies are evaluated using common data mining metrics such as: coefficient of determination (R2 value) and confusion matrices. The work presented in this paper aims to re-define the lifecycle and introduce tangible improvements to its outcomes.
ELSA: A Throughput-Optimized Design of an LSTM Accelerator for Energy-Constrained Devices
Venue: arXiv cs.LG 2019
Year: 2019

The next significant step in the evolution and proliferation of artificial intelligence technology will be the integration of neural network (NN) models within embedded and mobile systems. This calls for the design of compact, energy efficient NN models in silicon. In this paper, we present a scalable ASIC design of an LSTM accelerator named ELSA, that is suitable for energy-constrained devices. It includes several architectural innovations to achieve small area and high energy efficiency. To reduce the area and power consumption of the overall design, the compute-intensive units of ELSA employ approximate multiplications and still achieve high performance and accuracy. The performance is further improved through efficient synchronization of the elastic pipeline stages to maximize the utilization. The paper also includes a performance model of ELSA, as a function of the hidden nodes and time steps, permitting its use for the evaluation of any LSTM application. ELSA was implemented in RTL and was synthesized and placed and routed in 65nm technology. Its functionality is demonstrated for language modeling-a common application of LSTM. ELSA is compared against a baseline implementation of an LSTM accelerator with standard functional units and without any of the architectural innovations of ELSA. The paper demonstrates that ELSA can achieve significant improvements in power, area and energy-efficiency when compared to the baseline design and several ASIC implementations reported in the literature, making it suitable for use in embedded systems and real-time applications.
Microservices based Framework to Support Interoperable IoT Applications for Enhanced Data Analytics
Venue: arXiv cs.DC 2019
Year: 2019

Internet of things is growing with a large number of diverse objects which generate billions of data streams by sensing, actuating and communicating. Management of heterogeneous IoT objects with existing approaches and processing of myriads of data from these objects using monolithic services have become major challenges in developing effective IoT applications. The heterogeneity can be resolved by providing interoperability with semantic virtualization of objects. Moreover, monolithic services can be substituted with modular microservices. This article presents an architecture that enables the development of IoT applications using semantically interoperable microservices and virtual objects. The proposed framework supports analytic features with knowledge-driven and data-driven techniques to provision intelligent services on top of interoperable microservices in Web Objects enabled IoT environment. The knowledge-driven aspects are supported with reasoning on semantic ontology models and the data-driven aspects are realized with machine learning pipeline. The development of service functionalities is supported with microservices to enhance modularity and reusability. To evaluate the proposed framework a proof of concept implementation with a use case is discussed.
Explainable AI: Deep Reinforcement Learning Agents for Residential Demand Side Cost Savings in Smart Grids
Venue: arXiv cs.LG 2019
Year: 2019

Motivated by recent advancements in Deep Reinforcement Learning (RL), we have developed an RL agent to manage the operation of storage devices in a household and is designed to maximize demand-side cost savings. The proposed technique is data-driven, and the RL agent learns from scratch how to efficiently use the energy storage device given variable tariff structures. In most of the studies, the RL agent is considered as a black box, and how the agent has learned is often ignored. We explain the learning progression of the RL agent, and the strategies it follows based on the capacity of the storage device.
Strengthening some complexity results on toughness of graphs
Venue: arXiv cs.DM 2019
Year: 2019

Let $t$ be a positive real number. A graph is called $t$-tough if the removal of any vertex set $S$ that disconnects the graph leaves at most $|S|/t$ components. The toughness of a graph is the largest $t$ for which the graph is $t$-tough. The main results of this paper are the following. For any positive rational number $t \le 1$ and for any $k \ge 2$ and $r \ge 6$ integers recognizing $t$-tough bipartite graphs is coNP-complete (the case $t=1$ was already known), and this problem remains coNP-complete for $k$-connected bipartite graphs, and so does the problem of recognizing 1-tough r-regular bipartite graphs. To prove these statements we also deal with other related complexity problems on toughness. % In this paper we prove the following. For any positive rational number $t$, deciding whether $τ(G)=t$ is DP-complete and if $t < 1$, this problem remains DP-complete for bipartite graphs. For any integer $k \ge 2$ and positive rational number $t \le 1$, recognizing $t$-tough $k$-connected bipartite graphs is coNP-complete. For any integer $r \ge 5$, recognizing $1/2$-tough $r$-regular graphs is coNP-complete. For any integer $r \ge 6$, recognizing 1-tough $r$-regular bipartite graphs is coNP-complete. For any positive rational number $t < 2/3$ we give a polynomial time algorithm for recognizing 3-regular graphs with toughness $t$. Finally, we prove that every connected 4-regular graph is 1/2-tough.
Smart Monitoring: remote-monitoring technology of power, gas, and water consumption in Smart Cities
Venue: arXiv cs.OH 2019
Year: 2019

This paper describes the remote-collection technology of detailed data (Smart Monitoring) on the consumption and quality of energy resources in public services. In this article, under "energy resources" (hereinafter referred to as resources) we outline electrical power, water (hot and cold), heat, and gas. Data on resource quality refer to the parameters characterizing the consumed resource. We also present an option of the data-acquisition system structure based on Smart Monitoring technology. Particular attention is paid to security in the system and the centralized management of its elements. The data flow in such system carries information about the behavior of energy consumers and the household equipment they use. Data on energy consumption for billing purposes in such a system is just one of many kinds, and not the most important feature. The development of Smart Monitoring technology is aimed at developing the market of IT services and mass services based on analysis of collected detailed data on energy-resource consumption.
MonaLog: a Lightweight System for Natural Language Inference Based on Monotonicity
Venue: arXiv cs.CL 2019
Year: 2019

We present a new logic-based inference engine for natural language inference (NLI) called MonaLog, which is based on natural logic and the monotonicity calculus. In contrast to existing logic-based approaches, our system is intentionally designed to be as lightweight as possible, and operates using a small set of well-known (surface-level) monotonicity facts about quantifiers, lexical items and tokenlevel polarity information. Despite its simplicity, we find our approach to be competitive with other logic-based NLI models on the SICK benchmark. We also use MonaLog in combination with the current state-of-the-art model BERT in a variety of settings, including for compositional data augmentation. We show that MonaLog is capable of generating large amounts of high-quality training data for BERT, improving its accuracy on SICK.
NormGrad: Finding the Pixels that Matter for Training
Venue: arXiv cs.CV 2019
Year: 2019

The different families of saliency methods, either based on contrastive signals, closed-form formulas mixing gradients with activations or on perturbation masks, all focus on which parts of an image are responsible for the model's inference. In this paper, we are rather interested by the locations of an image that contribute to the model's training. First, we propose a principled attribution method that we extract from the summation formula used to compute the gradient of the weights for a 1x1 convolutional layer. The resulting formula is fast to compute and can used throughout the network, allowing us to efficiently produce fined-grained importance maps. We will show how to extend it in order to compute saliency maps at any targeted point within the network. Secondly, to make the attribution really specific to the training of the model, we introduce a meta-learning approach for saliency methods by considering an inner optimisation step within the loss. This way, we do not aim at identifying the parts of an image that contribute to the model's output but rather the locations that are responsible for the good training of the model on this image. Conversely, we also show that a similar meta-learning approach can be used to extract the adversarial locations which can lead to the degradation of the model.