OpenRaiser/Intern-Atlas huggingface dataset:
xSense: Learning Sense-Separated Sparse Representations and Textual Definitions for Explainable Word Sense Networks
Venue: arXiv cs.CL 2018
Year: 2018

Despite the success achieved on various natural language processing tasks, word embeddings are difficult to interpret due to the dense vector representations. This paper focuses on interpreting the embeddings for various aspects, including sense separation in the vector dimensions and definition generation. Specifically, given a context together with a target word, our algorithm first projects the target word embedding to a high-dimensional sparse vector and picks the specific dimensions that can best explain the semantic meaning of the target word by the encoded contextual information, where the sense of the target word can be indirectly inferred. Finally, our algorithm applies an RNN to generate the textual definition of the target word in the human readable form, which enables direct interpretation of the corresponding word embedding. This paper also introduces a large and high-quality context-definition dataset that consists of sense definitions together with multiple example sentences per polysemous word, which is a valuable resource for definition modeling and word sense disambiguation. The conducted experiments show the superior performance in BLEU score and the human evaluation test.
Probabilistic Binary Neural Networks
Venue: arXiv cs.LG 2018
Year: 2018

Low bit-width weights and activations are an effective way of combating the increasing need for both memory and compute power of Deep Neural Networks. In this work, we present a probabilistic training method for Neural Network with both binary weights and activations, called BLRNet. By embracing stochasticity during training, we circumvent the need to approximate the gradient of non-differentiable functions such as sign(), while still obtaining a fully Binary Neural Network at test time. Moreover, it allows for anytime ensemble predictions for improved performance and uncertainty estimates by sampling from the weight distribution. Since all operations in a layer of the BLRNet operate on random variables, we introduce stochastic versions of Batch Normalization and max pooling, which transfer well to a deterministic network at test time. We evaluate the BLRNet on multiple standardized benchmarks.
Machine Learning Based Hybrid Precoding for MmWave MIMO-OFDM with Dynamic Subarray
Venue: arXiv cs.IT 2018
Year: 2018

Hybrid precoding design can be challenging for broadband millimeter-wave (mmWave) massive MIMO due to the frequency-flat analog precoder in radio frequency (RF). Prior broadband hybrid precoding work usually focuses on fully-connected array (FCA), while seldom considers the energy-efficient partially-connected subarray (PCS) including the fixed subarray (FS) and dynamic subarray (DS). Against this background, this paper proposes a machine learning based broadband hybrid precoding for mmWave massive MIMO with DS. Specifically, we first propose an optimal hybrid precoder based on principal component analysis (PCA) for the FS, whereby the frequency-flat RF precoder for each subarray is extracted from the principle component of the optimal frequency-selective precoders for fully-digital MIMO. Moreover, we extend the PCA-based hybrid precoding to DS, where a shared agglomerative hierarchical clustering (AHC) algorithm developed from machine learning is proposed to group the DS for improved spectral efficiency (SE). Finally, we investigate the energy efficiency (EE) of the proposed scheme for both passive and active antennas. Simulations have confirmed that the proposed scheme outperforms conventional schemes in both SE and EE.
A Moral Framework for Understanding of Fair ML through Economic Models of Equality of Opportunity
Venue: arXiv cs.LG 2018
Year: 2018

We map the recently proposed notions of algorithmic fairness to economic models of Equality of opportunity (EOP)---an extensively studied ideal of fairness in political philosophy. We formally show that through our conceptual mapping, many existing definition of algorithmic fairness, such as predictive value parity and equality of odds, can be interpreted as special cases of EOP. In this respect, our work serves as a unifying moral framework for understanding existing notions of algorithmic fairness. Most importantly, this framework allows us to explicitly spell out the moral assumptions underlying each notion of fairness, and interpret recent fairness impossibility results in a new light. Last but not least and inspired by luck egalitarian models of EOP, we propose a new family of measures for algorithmic fairness. We illustrate our proposal empirically and show that employing a measure of algorithmic (un)fairness when its underlying moral assumptions are not satisfied, can have devastating consequences for the disadvantaged group's welfare.
Performance Evaluation of the Quorum Blockchain Platform
Venue: arXiv cs.CR 2018
Year: 2018

Quorum is a permissioned blockchain platform built from the Ethereum codebase with adaptations to make it a permissioned consortium platform. It is one of the key contenders in the permissioned ledger space. Quorum supports confidentiality and privacy of smart contracts and transactions, and crash and Byzantine fault tolerant consensus algorithms. In this paper, we characterize the performance features of Quorum. We study the throughput and latency characteristics of Quorum with different workloads and consensus algorithms that it supports. Through a suite of micro-benchmarks, we explore how certain transaction and smart contract parameters can affect transaction latencies.
Now You're Thinking With Structures: A Concept for Structure-based Interactions with Spreadsheets
Venue: arXiv cs.SE 2018
Year: 2018

Spreadsheets are the go-to tool for computerized calculation and modelling, but are hard to comprehend and adapt after reaching a certain complexity. In general, cognition of complex systems is facilitated by having a higher order mental model of the system in question to work with. We therefore present a concept for structure-aware understanding of and interaction with spreadsheets that extends previous work on structure inference in the domain. Following this concept, structural information is used to enrich visualizations, reactively enhance traditional user actions, and provide tools to proactively alter the overall spreadsheet makeup instead of individual cells The intended systems should, in first approximation, not replace common spreadsheet tools, but provide an additional layer of functionality alongside the established interface. In ongoing work, we therefore implemented a tool for structure inference and visualization along the common spreadsheet layout. Based on this framework, we plan to introduce the envisioned proactive and reactive interaction mechanics, and finally provide structure-aware unctionality as an add-in for common spreadsheet processors. We believe that providing the tools for thinking about and interacting with spreadsheets in this manner will benefit users both in terms of productivity and overall spreadsheet quality.
Expert-augmented actor-critic for ViZDoom and Montezumas Revenge
Venue: arXiv cs.LG 2018
Year: 2018

We propose an expert-augmented actor-critic algorithm, which we evaluate on two environments with sparse rewards: Montezumas Revenge and a demanding maze from the ViZDoom suite. In the case of Montezumas Revenge, an agent trained with our method achieves very good results consistently scoring above 27,000 points (in many experiments beating the first world). With an appropriate choice of hyperparameters, our algorithm surpasses the performance of the expert data. In a number of experiments, we have observed an unreported bug in Montezumas Revenge which allowed the agent to score more than 800,000 points.
Event Graphs: Advances and Applications of Second-Order Time-Unfolded Temporal Network Models
Venue: arXiv cs.SI 2018
Year: 2018

Recent advances in data collection and storage have allowed both researchers and industry alike to collect data in real time. Much of this data comes in the form of 'events', or timestamped interactions, such as email and social media posts, website clickstreams, or protein-protein interactions. This of type data poses new challenges for modelling, especially if we wish to preserve all temporal features and structure. We propose a generalised framework to explore temporal networks using second-order time-unfolded models, called event graphs. Through examples we demonstrate how event graphs can be used to understand the higher-order topological-temporal structure of temporal networks and capture properties of the network that are unobserved when considering either a static (or time-aggregated) model. Furthermore, we show that by modelling a temporal network as an event graph our analysis extends easily to consider non-dyadic interactions, known as hyper-events.
SpRRAM: A Predefined Sparsity Based Memristive Neuromorphic Circuit for Low Power Application
Venue: arXiv cs.ET 2018
Year: 2018

In this paper, we propose an efficient predefined structured sparsity-based ex-situ training framework for a hybrid CMOS-memristive neuromorphic hardware for deep neural network to significantly lower the power consumption and computational complexity and improve scalability. The structure is verified on a wide range of datasets including MNIST handwritten recognition, breast cancer prediction, and mobile health monitoring. The results of this study show that compared to its fully connected version, the proposed structure provides significant power reduction while maintaining high classification accuracy.
Collapsed Variational Inference for Nonparametric Bayesian Group Factor Analysis
Venue: arXiv cs.LG 2018
Year: 2018

Group factor analysis (GFA) methods have been widely used to infer the common structure and the group-specific signals from multiple related datasets in various fields including systems biology and neuroimaging. To date, most available GFA models require Gibbs sampling or slice sampling to perform inference, which prevents the practical application of GFA to large-scale data. In this paper we present an efficient collapsed variational inference (CVI) algorithm for the nonparametric Bayesian group factor analysis (NGFA) model built upon an hierarchical beta Bernoulli process. Our CVI algorithm proceeds by marginalizing out the group-specific beta process parameters, and then approximating the true posterior in the collapsed space using mean field methods. Experimental results on both synthetic and real-world data demonstrate the effectiveness of our CVI algorithm for the NGFA compared with state-of-the-art GFA methods.
Unconstraining graph-constrained group testing
Venue: arXiv cs.IT 2018
Year: 2018

In network tomography, one goal is to identify a small set of failed links in a network, by sending a few packets through the network and seeing which reach their destination. This problem can be seen as a variant of combinatorial group testing, which has been studied before under the moniker "graph-constrained group testing." The main contribution of this work is to show that for most graphs, the "constraints" imposed by the underlying network topology are no constraint at all. That is, the number of tests required to identify the failed links in "graph-constrained" group testing is near-optimal even for the corresponding group testing problem with no graph constraints. Our approach is based on a simple randomized construction of tests, to analyze our construction, we prove new results about the size of giant components in randomly sparsified graphs. Finally, we provide empirical results which suggest that our connected-subgraph tests perform better not just in theory but also in practice, and in particular perform better on a real-world network topology.
Evaluation of Preference of Multimedia Content using Deep Neural Networks for Electroencephalography
Venue: arXiv cs.HC 2018
Year: 2018

Evaluation of quality of experience (QoE) based on electroencephalography (EEG) has received great attention due to its capability of real-time QoE monitoring of users. However, it still suffers from rather low recognition accuracy. In this paper, we propose a novel method using deep neural networks toward improved modeling of EEG and thereby improved recognition accuracy. In particular, we aim to model spatio-temporal characteristics relevant for QoE analysis within learning models. The results demonstrate the effectiveness of the proposed method.
Temporal-Spatial Mapping for Action Recognition
Venue: arXiv cs.CV 2018
Year: 2018

Deep learning models have enjoyed great success for image related computer vision tasks like image classification and object detection. For video related tasks like human action recognition, however, the advancements are not as significant yet. The main challenge is the lack of effective and efficient models in modeling the rich temporal spatial information in a video. We introduce a simple yet effective operation, termed Temporal-Spatial Mapping (TSM), for capturing the temporal evolution of the frames by jointly analyzing all the frames of a video. We propose a video level 2D feature representation by transforming the convolutional features of all frames to a 2D feature map, referred to as VideoMap. With each row being the vectorized feature representation of a frame, the temporal-spatial features are compactly represented, while the temporal dynamic evolution is also well embedded. Based on the VideoMap representation, we further propose a temporal attention model within a shallow convolutional neural network to efficiently exploit the temporal-spatial dynamics. The experiment results show that the proposed scheme achieves the state-of-the-art performance, with 4.2% accuracy gain over Temporal Segment Network (TSN), a competing baseline method, on the challenging human action benchmark dataset HMDB51.
A Joint Model of Conversational Discourse and Latent Topics on Microblogs
Venue: arXiv cs.CL 2018
Year: 2018

Conventional topic models are ineffective for topic extraction from microblog messages, because the data sparseness exhibited in short messages lacking structure and contexts results in poor message-level word co-occurrence patterns. To address this issue, we organize microblog messages as conversation trees based on their reposting and replying relations, and propose an unsupervised model that jointly learns word distributions to represent: 1) different roles of conversational discourse, 2) various latent topics in reflecting content information. By explicitly distinguishing the probabilities of messages with varying discourse roles in containing topical words, our model is able to discover clusters of discourse words that are indicative of topical content. In an automatic evaluation on large-scale microblog corpora, our joint model yields topics with better coherence scores than competitive topic models from previous studies. Qualitative analysis on model outputs indicates that our model induces meaningful representations for both discourse and topics. We further present an empirical study on microblog summarization based on the outputs of our joint model. The results show that the jointly modeled discourse and topic representations can effectively indicate summary-worthy content in microblog conversations.
Path Planning Algorithms for a Car-Like Robot visiting a set of Waypoints with Field of View Constraints
Venue: arXiv cs.RO 2018
Year: 2018

This article considers two variants of a shortest path problem for a car-like robot visiting a set of waypoints. The sequence of waypoints to be visited is specified in the first variant while the robot is allowed to visit the waypoints in any sequence in the second variant. Field of view constraints are also placed when the robot arrives at a waypoint, i.e., the orientation of the robot at any waypoint is restricted to belong to a given interval of angles at the waypoint. The shortest path problem is first solved for two waypoints with the field of view constraints using Pontryagin's minimum principle. Using the results for the two point problem, tight lower and upper bounds on the length of the shortest path are developed for visiting n points by relaxing the requirement that the arrival angle must be equal to the departure angle of the robot at each waypoint. Theoretical bounds are also provided on the length of the feasible solutions obtained by the proposed algorithm. Simulation results verify the performance of the bounds for instances with 20 waypoints.
Diversity, Productivity, and Growth of Open Source Developer Communities
Venue: arXiv cs.SE 2018
Year: 2018

The open source development model has become a paradigm shift from traditional in-house/closed-source software development model, with many successes. Traditionally, open source projects were characterized essentially by their individual volunteer developers. Such tradition has changed significantly with the participation of many organizations in particular. However, there exists a knowledge gap concerning how open source developer communities evolve. In this paper, we present some observations on open source developer communities. In particular, we analyze git repositories of 20 well-known open source projects, with over 3 million commit activities in total. The analysis has been carried out in three respects, productivity, diversity and growth using the Spearman's rank correlation coefficient, diversity index and the Gompertz/logistic curves, respectively. We find out that (a) the Spearman's rank correlation coefficient between active contributors and commit activities reveals how changes in the size of the developer community impacts the productivity of the community; (b) the diversity index of an open source developer community reveals the structure of the community; and (c) the growth of open source developer communities can be described using different phases of growth curves as in many organic matters.
Solving Non-identifiable Latent Feature Models
Venue: arXiv cs.LG 2018
Year: 2018

Latent feature models (LFM)s are widely employed for extracting latent structures of data. While offering high, parameter estimation is difficult with LFMs because of the combinational nature of latent features, and non-identifiability is a particularly difficult problem when parameter estimation is not unique and there exists equivalent solutions. In this paper, a necessary and sufficient condition for non-identifiability is shown. The condition is significantly related to dependency of features, and this implies that non-identifiability may often occur in real-world applications. A novel method for parameter estimation that solves the non-identifiability problem is also proposed. This method can be combined as a post-process with existing methods and can find an appropriate solution by hopping efficiently through equivalent solutions. We have evaluated the effectiveness of the method on both synthetic and real-world datasets.
Compressive Massive Random Access for Massive Machine-Type Communications (mMTC)
Venue: arXiv cs.IT 2018
Year: 2018

In future wireless networks, one fundamental challenge for massive machine-type communications (mMTC) lies in the reliable support of massive connectivity with low latency. Against this background, this paper proposes a compressive sensing (CS)-based massive random access scheme for mMTC by leveraging the inherent sporadic traffic, where both the active devices and their channels can be jointly estimated with low overhead. Specifically, we consider devices in the uplink massive random access adopt pseudo random pilots, which are designed under the framework of CS theory. Meanwhile, the massive random access at the base stations (BS) can be formulated as the sparse signal recovery problem by leveraging the sparse nature of active devices. Moreover, by exploiting the structured sparsity among different receiver antennas and subcarriers, we develop a distributed multiple measurement vector approximate message passing (DMMV-AMP) algorithm for further improved performance. Additionally, the state evolution (SE) of the proposed DMMV-AMP algorithm is derived to predict the performance. Simulation results demonstrate the superiority of the proposed scheme, which exhibits a good tightness with the theoretical SE.
Integration of Relational and Graph Databases Functionally
Venue: arXiv cs.DB 2018
Year: 2018

A significant category of NoSQL approaches is known as graph da-tabases. They are usually represented by one property graph. We introduce a functional approach to modelling relations and property graphs. Single-valued and multivalued functions will be sufficient in this case. Then, a typed λ-calculus, i.e., the language of lambda terms, will be used as a data manipulation lan-guage. Some integration options at the query language level are discussed.
Unsupervised Domain Adaptation Based on Source-guided Discrepancy
Venue: arXiv cs.LG 2018
Year: 2018

Unsupervised domain adaptation is the problem setting where data generating distributions in the source and target domains are different, and labels in the target domain are unavailable. One important question in unsupervised domain adaptation is how to measure the difference between the source and target domains. A previously proposed discrepancy that does not use the source domain labels requires high computational cost to estimate and may lead to a loose generalization error bound in the target domain. To mitigate these problems, we propose a novel discrepancy called source-guided discrepancy (S-disc), which exploits labels in the source domain. As a consequence, S-disc can be computed efficiently with a finite sample convergence guarantee. In addition, we show that S-disc can provide a tighter generalization error bound than the one based on an existing discrepancy. Finally, we report experimental results that demonstrate the advantages of S-disc over the existing discrepancies.
Robust Energy Efficient Beamforming in MISOME-SWIPT Systems With Proportional Secrecy Rate
Venue: arXiv cs.IT 2018
Year: 2018

The joint design of beamforming vector and artificial noise covariance matrix is investigated for the multiple-input-single-output-multiple-eavesdropper simultaneous wireless information and power transferring \mbox{(MISOME-SWIPT)} systems. In the MISOME-SWIPT system, the base station delivers information signals to the legitimate user equipments and broadcasts jamming signals to the eavesdroppers. A secrecy energy efficiency (SEE) maximization problem is formulated for the considered \mbox{MISOME-SWIPT} system with imperfect channel state information, where the SEE is defined as the ratio of sum secrecy rate over total power consumption. Since the formulated SEE maximization problem is non-convex, it is first recast into a series of convex problems in order to obtain the optimal solution with a reasonable computational complexity. Two suboptimal solutions are also proposed based on the heuristic beamforming techniques that trade performance for computational complexity. In addition, the analysis of computational complexity is performed for the optimal and suboptimal solutions. Numerical results are used to verify the performance of proposed algorithms and to reveal practical insights.