Abstract

Multi-object tracking has seen a lot of progress recently, albeit with substantial annotation costs for developing better and larger labeled datasets. In this work, we remove the need for annotated datasets by proposing an unsupervised re-identification network, thus sidestepping the labeling costs entirely, required for training. Given unlabeled videos, our proposed method (SimpleReID) first generates tracking labels using SORT [ 3] and trains a ReID network to predict the generated labels using crossentropy loss. We demonstrate that SimpleReID performs substantially better than simpler alternatives, and we recover the full performance of its supervised counterpart consistently across diverse tracking frameworks. The observations are unusual because unsupervised ReID is not expected to excel in crowded scenarios with occlusions, and drastic viewpoint changes. By incorporating our unsupervised SimpleReID with CenterTrack trained on augmented still images, we establish a new state-of-the-art performance on popular datasets like MOT16/17 without using tracking supervision, beating current best (CenterTrack) by 0.2-0.3 MOTA and 4.4-4.8 IDF1 scores. We further provide evidence for limited scope for improvement in IDF1 scores beyond our unsupervised ReID in the studied settings. Our investigation suggests reconsideration towards more sophisticated, supervised, end-to-end trackers [56 , 5] by showing promise in simpler unsupervised alternatives.

Abstract

In this paper, we propose the use of Message Sequence Charts (MSC) as a representation for visualizing narrative text in Hindi. An MSC is a formal representation allowing the depiction of actors and interactions among these actors in a scenario, apart from supporting a rich framework for formal inference. We propose an approach to extract MSC actors and interactions from a Hindi narrative. As a part of the approach, we enrich an existing event annotation scheme where we provide guidelines for annotation of the mood of events (realis vs irrealis) and guidelines for annotation of event arguments. We report performance on multiple evaluation criteria by experimenting with Hindi narratives from Indian History. Though Hindi is the fourth most-spoken first language in the world, from the NLP perspective it has comparatively lesser resources than English. Moreover, there is relatively less work in the context of event processing in Hindi. Hence, we believe that this work is among the initial works for Hindi event processing.

Abstract

Given the recent progress in language modeling using Transformer-based neural models and an active interest in generating stylized text, we present an approach to leverage the generalization capabilities of a language model to rewrite an input text in a target author’s style. Our proposed approach adapts a pre-trained language model to generate author-stylized text by fine-tuning on the author-specific corpus using a denoising autoencoder (DAE) loss in a cascaded encoder-decoder framework. Optimizing over DAE loss allows our model to learn the nuances of an author’s style without relying on parallel data, which has been a severe limitation of the previous related works in this space. To evaluate the efficacy of our approach, we propose a linguistically motivated framework to quantify stylistic alignment of the generated text to the target author at lexical, syntactic and surface levels. The evaluation framework is both interpretable as it leads to several insights about the model, and self-contained as it does not rely on external classifiers, e.g. sentiment or formality classifiers. Qualitative and quantitative assessment indicates that the proposed approach rewrites the input text with better alignment to the target style while preserving the original content better than state-of-the-art baselines.

Abstract

Episodic memories, when reactivated, can be modified or updated by new learning. Since such dynamic memory processes remain largely unexplored in psychiatric disorders, we examined the impact of depression on episodic memory updating. Unipolar and bipolar depression patients, and age/education matched controls, first learned a set of objects (List-1). Two days later, participants in all three groups were either reminded of the first learning session or not followed by the learning of a new set of objects (List-2). Forty-eight hours later, List-1 recall was impaired in unipolar and bipolar patients compared to control participants. Further, as expected, control participants who received a reminder spontaneously recalled items from List-2 during recall of List-1, indicative of an updated List-1 memory. Such spontaneous intrusions were also seen in the unipolar and bipolar patients that received the reminder, suggesting that memory pdating was unaffected in these two patient groups despite impaired recall of List 1. Unexpectedly, we observed a trend towards higher intrusions, albeit statistically insignificant, not only in the reminder but also in the no-reminder subgroups of bipolar patients. We probed this further in a second cohort by testing recall of List-2, which was also impaired in both depression groups. Again bipolar patients showed intrusions, but this time in the reverse order from List-1 into List-2, independent of a reminder. Taken together, despite impaired recall, updating of episodic memories was intact and unidirectional in unipolar depression. In contrast, indiscriminate updating, as evidenced by bidirectional interference between episodic memories, was seen in bipolar depression. These findings reveal a novel distinction between unipolar versus bipolar depression using a reactivation-dependent memory updating paradigm.

Abstract

Software Requirement Specification documents provide natural language descriptions of the core functional requirements as a set of use-cases. Essentially, each use-case contains a set of actors and sequences of steps describing the interactions among them. Goals of use-case reviews and analyses include their correctness, completeness, detection of ambiguities, prototyping, verification, test case generation and traceability. Message Sequence Chart (MSC) have been proposed as a expressive, rigorous yet intuitive visual representation of use-cases. In this paper, we describe a linguistic knowledge-based approach to extract MSCs from use-cases. Compared to existing techniques, we extract richer constructs of the MSC notation such as timers, conditions and alt-boxes. We apply this tool to extract MSCs from several real-life software use-case descriptions and show that it performs better than the existing techniques. We also discuss the benefits and limitations of the extracted MSCs to meet the above goals.

Abstract

Integrating components of vehicular network with the Internet has emerged recently for fulfilling the ever-growing demand for a safe and comfortable driving experience. Road Side Unit (RSU) clouds help in serving requests arriving from vehicles but are limited by available resources. It is also required to reduce the overhead cost corresponding to Virtual Machine (VM) migrations caused by high mobility of vehicles. Also, requests need to be served within their stipulated delay constraint. In this work, we propose a novel algorithm to schedule requests at RSU clouds. Our algorithm efficiently schedules service requests from vehicles while taking care of their delay constraints and VM migrations. The simulation results confirm that our proposed scheduling technique helps in serving more number of requests within their stipulated delay constraint with lesser VM migrations.

Abstract

This paper presents a novel approach designed to answer questions on a reading comprehension passage. It is an end-to-end system which first focuses on comprehending the given passage wherein it converts unstructured passage into a structured data and later proceeds to answer the questions related to the passage using solely the aforementioned structured data. To the best of our knowledge, the proposed design is first of its kind which accounts for entire process of comprehending the passage and then answering the questions associated with the passage. The comprehension stage converts the passage into a Discourse Collection that comprises of the relation shared amongst logical sentences in given passage along with the key characteristics of each sentence. This design has its applications in academic domain , query comprehension in speech systems among others.

Abstract

Energy consumption has dramatically increased in the residential building sector over the past few years. Many researchers today are interested not only in designing energy-efficient buildings but also in evaluating the actual building performance and managing operations for delivering the design intent. Monitoring building performance using currently available sensors and mobile applications poses challenges in terms of cost and standardization. This paper presents a new approach to make sensors affordable and inter-operable by designing open-source hardware and using well-established standards for data communication and storage, thereby making energy monitoring ubiquitous and affordable in the residential sector. The proposed solution uses low-power loggers, Bluetooth Low Energy wireless communication version 4 for data transfer from loggers to mobile phones for further transfer to cloud, oneM2M standard to ensure interoperability, and NGSI-LD for data management.

Abstract

In the last decade, civic crowdfunding has proved to be effective in generating funds for the provision of public projects. However, the existing literature deals only with citizen's with positive valuation and symmetric belief towards the project's provision. In this work, we present novel mechanisms which break these two barriers, i.e., mechanisms which incorporate negative valuation and asymmetric belief, independently. For negative valuation, we present a methodology for converting existing mechanisms to mechanisms that incorporate agents with negative valuations. Particularly, we adapt existing PPR and PPS mechanisms, to present novel PPRN and PPSN mechanisms which incentivize strategic agents to contribute to the project based on their true preference. With respect to asymmetric belief, we propose a reward scheme Belief Based Reward (BBR) based on Robust Bayesian Truth Serum mechanism. With BBR, we propose a general mechanism for civic crowdfunding which incorporates asymmetric agents. We leverage PPR and PPS, to present PPRx and PPSx. We prove that in PPRx and PPSx, agents with greater belief towards the project's provision contribute more than agents with lesser belief. Further, we also show that contributions are such that the project is provisioned at equilibrium.

Abstract

Neural text-to-speech synthesis (NTTS) models have shown significant progress in generating high-quality speech, however they require a large quantity of training data. This makes creating models for multiple styles expensive and time-consuming. In this paper different styles of speech are analysed based on prosodic variations, from this a model is proposed to synthesise speech in the style of a newscaster, with just a few hours of supplementary data. We pose the problem of synthesising in a target style using limited data as that of creating a bi-style model that can synthesise both neutral-style and newscaster-style speech via a one-hot vector which factorises the two styles. We also propose conditioning the model on contextual word embeddings, and extensively evaluate it against neutral NTTS, and neutral concatenative-based synthesis. This model closes the gap in perceived style-appropriateness between natural recordings for newscaster-style of speech, and neutral speech synthesis by approximately two-thirds.