Abstract

Multi user massive multiple input multiple output (MU-m-MIMO) has emerged as a viable technology for scaling up existing communication systems, and in serving increasing number of users for the next-generation communication systems. Several signal processing algorithms exist for mitigating the performance-limiting artefacts encountered in MU-m-MIMO systems (like inter-symbol interference, inter-channel interfer- ence, and device nonlinearities), among which, reproducing kernel Hilbert space (RKHS) based approaches have emerged to provide effective solutions. However, most of the existing RKHS based detectors for MU-m-MIMO are dictionary-based, which makes it difficult to gauge the memory requirements beforehand, and are prone to error in the presence of noisy observations. Hence, to reduce the computational complexity, a Random Fourier Features (RFF) based parallel detection algorithm is proposed for MU-m-MIMO, that uses decomposed blocks of high dimensional observations, and makes the proposed detector scalable for parallel computation using modern multi- core compute-units at the receivers (which is possible today due to advances in computing). Further, the RFF based explicit feature map to RKHS alleviates the requirement of a dictionary, and facilitates ease of practical implementation. Simulations are performed over realistic MU-m-MIMO systems, which indicates that the proposed approach delivers an acceptable uncoded BER performance, whilst maintaining a finite implementation budget, which makes the proposed approach attractive for implementation. Lastly, the error-rate analysis of the proposed detector is performed, and validated through simulations

Abstract

Most of the applications in speech use mel-frequency spectral coefficients (MFSC) as features as they match the human perceptual mechanism, where the emphasis is given to vocal tract characteristics. But in accent classification, mel-scale distribution of filters may not always be the best representations, e.g., pitch accented languages where the emphasis should be on vocal source information too. Motivated by this, we use end-to-end classification of accents directly from waveforms which will reduce the effort of designing features specific to each corpus. The convolution neural network (CNN) model architecture is designed in such a way that the initial layers exhibit similar operation as in MFSC by initializing the weights using time approximate of MFSC. The entire network along with initial layers is trained to learn accent classification. We observed that learning directly from waveform improved the performance of accent classification when compared to CNN trained on hand-engineered features by 10.94% UAR on the test dataset of common voice corpus. Analyzing the filters after learning, we observed changes in distribution and bandwidths of center frequencies. We further observed the importance of appropriately initializing CNN filters

Abstract

Various representations have been developed for acoustic scene classifications (ASC) task using spectral information. However, there is a wide gap in dealing with acoustic scene representations. In this paper, we propose to use a single frequency filtering (SFF) approach, which provides good temporal and spectral resolution at each instant. Single-frequency filtering cepstral coefficients (SFFCC) with deep neural network (DNN) model as the classifier is used for the experimentation on DCASE 2019 and DCASE 2018 Task 1, development data of subtasks A and B. From the conducted experiments on the development datasets, the usage of the SFFCC features significantly improved ASC performance. This approach has got 35 th team rank out of 46 submissions to the corresponding DCASE 2019 Task 1A challenge with a 52.6% classification accuracy on the evaluation dataset. Also, the effect of raw waveforms taken as features for ASC using DNNs was observed.

Abstract

The past few years have seen several works exploring learning economic solutions from data; these include optimal auction design, function optimization, stable payoffs in cooperative games and more. In this work, we provide a unified learning-theoretic methodology for modeling such problems, and establish tools for determining whether a given solution concept can be efficiently learned from data. Our learning theoretic framework generalizes a notion of function space dimension — the graph dimension — adapting it to the solution concept learning domain. We identify sufficient conditions for efficient solution learnability, and show that results in existing works can be immediately derived using our methodology. Finally, we apply our methods in other economic domains, yielding learning variants of competitive equilibria and Condorcet winners

Abstract

We investigate a growing body of work that seeks to improve recommender systems through the use of review text. Generally, these papers argue that since reviews ‘explain’ users’ opinions, they ought to be useful to infer the underlying dimensions that predict ratings or purchases. Schemes to incorporate reviews range from simple regularizers to neural network approaches. Our initial findings reveal several discrepancies in reported results, partly due to (e.g.) copying results across papers despite changes in experimental settings or data pre-processing. First, we attempt a comprehensive analysis to resolve these ambiguities. Further investigation calls for discussion on a much larger problem about the “importance" of user reviews for recommendation. Through a wide range of experiments, we observe several cases where state-of-the-art methods fail to outperform existing baselines, especially as we deviate from a few narrowly-defined settings where reviews are useful. We conclude by providing hypotheses for our observations, that seek to characterize under what conditions reviews are likely to be helpful. Through this work, we aim to evaluate the direction in which the field is progressing and encourage robust empirical evaluation.

Abstract

We investigate a growing body of work that seeks to improve recommender systems through the use of review text. Generally, these papers argue that since reviews ‘explain’ users’ opinions, they ought to be useful to infer the underlying dimensions that predict ratings or purchases. Schemes to incorporate reviews range from simple regularizers to neural network approaches. Our initial findings reveal several discrepancies in reported results, partly due to (e.g.) copying results across papers despite changes in experimental settings or data pre-processing. First, we attempt a comprehensive analysis to resolve these ambiguities. Further investigation calls for discussion on a much larger problem about the “importance" of user reviews for recommendation. Through a wide range of experiments, we observe several cases where state-of-the-art methods fail to outperform existing baselines, especially as we deviate from a few narrowly-defined settings where reviews are useful. We conclude by providing hypotheses for our observations, that seek to characterize under what conditions reviews are likely to be helpful. Through this work, we aim to evaluate the direction in which the field is progressing and encourage robust empirical evaluation.

Abstract

Learning effective contextual-bandit policies from past actions of a deployed system is highly desirable in many settings (e.g. voice assistants, recommendation, search), since it enables the reuse of large amounts of log data. State-of-the-art methods for such offpolicy learning, however, are based on inverse propensity score (IPS) weighting. A key theoretical requirement of IPS weighting is that the policy that logged the data has "full support", which typically translates into requiring non-zero probability for any action in any context. Unfortunately, many real-world systems produce support deficient data, especially when the action space is large, and we show how existing methods can fail catastrophically. To overcome this gap between theory and applications, we identify three approaches that provide various guarantees for IPS-based learning despite the inherent limitations of support-deficient data: restricting the action space, reward extrapolation, and restricting the policy space. We systematically analyze the statistical and computational properties of these three approaches, and we empirically evaluate their effectiveness. In addition to providing the first systematic analysis of support-deficiency in contextual-bandit learning, we conclude with recommendations that provide practical guidance.

Abstract

Identifying types of online users’ toxic behavior reveals important insights from social media interactions, including whether a user be- comes “radicalized” (more toxic) or “pacified” (less toxic) over time. In this research, we design two metrics to identify toxic user types: F score that captures the changes in a user’s toxicity, and G score that captures the direction of the shift taking place in the user’s toxicity pattern. We apply these metrics to a dataset of 4M user comments from Reddit by defining four toxic user types based on the toxicity scores of a user’s comments: (a) Steady Users whose toxicity scores are steady over time, (b) Fickle-Minded Users that switch between toxic and non-toxic commenting, (c) Pacified Users whose com- menting becomes less toxic in time, and (d) Radicalized Users that become gradually toxic. Findings from the Reddit dataset indicate that fickle-minded users form the largest group (31.2%), followed by pacified (25.8%), radicalized (25.4%), and steadily toxic users (17.6%). The results suggest that the most typical behavior type of toxicity is switching between toxic and non-toxic commenting. This research has implications for preserving the user-friendliness of online communities by identifying continuously toxic users and users in danger of becoming radicalized (in terms of their toxic behavior), and designing interventions to mitigate these behavior types. Using the metrics we have defined, identifying these user types becomes possible. More research is needed to understand why these patterns take place and how they could be mitigated.

Abstract

The paper focuses on the classification of normal and abnormal heart sounds through a low-cost, high-performance PhonoCardioGraphy system using MEMS microphones with good sensitivity for the heart sounds. The proposed algorithm classifies the heart sound components using wavelet decomposition and Shannon energy. The PhonoCardioGraphy system, with the proposed algorithm tested by taking 1756 results, includes both normal and abnormal heart sounds. Experimental results revealed a good sensitivity of 99.17% and detection error rate of 1.5%.

Abstract

Given an image or a video captured from a monocular camera, amodal layout estimation is the task of predicting semantics and occupancy in bird's eye view. The term amodal implies we also reason about entities in the scene that are occluded or truncated in image space. While several recent efforts have tackled this problem, there is a lack of standardization in task specification, datasets, and evaluation protocols. We address these gaps with AutoLay, a dataset and benchmark for amodal layout estimation from monocular images. AutoLay encompasses driving imagery from two popular datasets: KITTI [1] and Argoverse [2]. In addition to fine-grained attributes such as lanes, sidewalks, and vehicles, we also provide semantically annotated 3D point clouds. We implement several baselines and bleeding edge approaches, and release our data and code