OpenRaiser/Intern-Atlas huggingface dataset:
Detecting Emerging Technologies and their Evolution using Deep Learning and Weak Signal Analysis
Venue: arXiv cs.AI 2022
Year: 2022

Emerging technologies can have major economic impacts and affect strategic stability. Yet, early identification of emerging technologies remains challenging. In order to identify emerging technologies in a timely and reliable manner, a comprehensive examination of relevant scientific and technological (S&T) trends and their related references is required. This examination is generally done by domain experts and requires significant amounts of time and effort to gain insights. The use of domain experts to identify emerging technologies from S&T trends may limit the capacity to analyse large volumes of information and introduce subjectivity in the assessments. Decision support systems are required to provide accurate and reliable evidence-based indicators through constant and continuous monitoring of the environment and help identify signals of emerging technologies that could alter security and economic prosperity. For example, the research field of hypersonics has recently witnessed several advancements having profound technological, commercial, and national security implications. In this work, we present a multi-layer quantitative approach able to identify future signs from scientific publications on hypersonics by leveraging deep learning and weak signal analysis. The proposed framework can help strategic planners and domain experts better identify and monitor emerging technology trends.
Two ways to make your robot proactive: reasoning about human intentions, or reasoning about possible futures
Venue: arXiv cs.AI 2022
Year: 2022

Robots sharing their space with humans need to be proactive in order to be helpful. Proactive robots are able to act on their own initiative in an anticipatory way to benefit humans. In this work, we investigate two ways to make robots proactive. One way is to recognize humans' intentions and to act to fulfill them, like opening the door that you are about to cross. The other way is to reason about possible future threats or opportunities and to act to prevent or to foster them, like recommending you to take an umbrella since rain has been forecasted. In this paper, we present approaches to realize these two types of proactive behavior. We then present an integrated system that can generate proactive robot behavior by reasoning on both factors: intentions and predictions. We illustrate our system on a sample use case including a domestic robot and a human. We first run this use case with the two separate proactive systems, intention-based and prediction-based, and then run it with our integrated system. The results show that the integrated system is able to take into account a broader variety of aspects that are needed for proactivity.
Accelerating Grasp Learning via Pretraining with Coarse Affordance Maps of Objects
Venue: arXiv cs.RO 2022
Year: 2022

Self-supervised grasp learning, i.e., learning to grasp by trial and error, has made great progress. However, it is still time-consuming to train such a model and also a challenge to apply it in practice. This work presents an accelerating method of robotic grasp learning via pretraining with coarse affordance maps of objects to be grasped based on a quite small dataset. A model generated through pre-training is harnessed as an initialization policy to warmly start grasp learning so as to guide a robot to capture more effective rewards at the beginning of training. An object in its coarse affordance map is annotated with a single key point and thereby, the burden of labeling is greatly alleviated. Extensive experiments in simulation and on a real robot are conducted to evaluate the proposed method. The simulation results show that it can significantly accelerate grasp learning by nearly three times over a vanilla Deep Q-Network -based method. Its test on a real UR3 robot shows that it reaches a grasp success rate of 89.5% via only 500 times of grasp tries within about two hours, which is four times faster than its competitor. In addition, it enjoys an outstanding generalization ability to grasp prior-unseen novel objects. It outperforms some existing methods and has the potential to directly apply to a robot for real-world grasp learning tasks.
Building Automation System Data Integration with BIM: Data Structure and Supporting Case Study
Venue: arXiv cs.NI 2022
Year: 2022

Buildings Automation Systems (BAS) are ubiquitous in contemporary buildings, both monitoring building conditions and managing the building system control points. At present, these controls are prescriptive and pre-determined by the design team, rather than responsive to actual building performance. These are further limited by prescribed logic, possess only rudimentary visualizations, and lack broader system integration capabilities. Advances in machine learning, edge analytics, data management systems, and Facility Management-enabled Building Information Models (FM-BIMs) permit a novel approach: cloud-hosted building management. This paper presents an integration technique for mapping the data from a building Internet of Things (IoT) sensor network to an FM-BIM. The sensor data naming convention and timeseries analysis strategies integrated into the data structure are discussed and presented, including the use of a 3D nested list to permit timeseries data to be mapped to the FM-BIM and readily visualized. The developed approach is presented through a case study of an office living lab consisting of a local sensor network mimicking a BAS, which streams to a cloud server via a virtual private network connection. The resultant data structure and key visualizations are presented to demonstrate the value of this approach, which permits the end-user to select the desired timeframe for visualization and readily step through the spatio-temporal building performance data.
Face Detection on Mobile: Five Implementations and Analysis
Venue: arXiv cs.CV 2022
Year: 2022

In many practical cases face detection on smartphones or other highly portable devices is a necessity. Applications include mobile face access control systems, driver status tracking, emotion recognition, etc. Mobile devices have limited processing power and should have long-enough battery life even with face detection application running. Thus, striking the right balance between algorithm quality and complexity is crucial. In this work we adapt 5 algorithms to mobile. These algorithms are based on handcrafted or neural-network-based features and include: Viola-Jones (Haar cascade), LBP, HOG, MTCNN, BlazeFace. We analyze inference time of these algorithms on different devices with different input image resolutions. We provide guidance, which algorithms are the best fit for mobile face access control systems and potentially other mobile applications. Interestingly, we note that cascaded algorithms perform faster on scenes without faces, while BlazeFace is slower on empty scenes. Exploiting this behavior might be useful in practice.
Channel Estimation in RIS-assisted Downlink Massive MIMO: A Learning-Based Approach
Venue: arXiv cs.IT 2022
Year: 2022

For downlink massive multiple-input multiple-output (MIMO) operating in time-division duplex protocol, users can decode the signals effectively by only utilizing the channel statistics as long as channel hardening holds. However, in a reconfigurable intelligent surface (RIS)-assisted massive MIMO system, the propagation channels may be less hardened due to the extra random fluctuations of the effective channel gains. To address this issue, we propose a learning-based method that trains a neural network to learn a mapping between the received downlink signal and the effective channel gains. The proposed method does not require any downlink pilots and statistical information of interfering users. Numerical results show that, in terms of mean-square error of the channel estimation, our proposed learning-based method outperforms the state-of-the-art methods, especially when the light-of-sight (LoS) paths are dominated by non-LoS paths with a low level of channel hardening, e.g., in the cases of small numbers of RIS elements and/or base station antennas.
Characterizing the Action-Generalization Gap in Deep Q-Learning
Venue: arXiv cs.AI 2022
Year: 2022

We study the action generalization ability of deep Q-learning in discrete action spaces. Generalization is crucial for efficient reinforcement learning (RL) because it allows agents to use knowledge learned from past experiences on new tasks. But while function approximation provides deep RL agents with a natural way to generalize over state inputs, the same generalization mechanism does not apply to discrete action outputs. And yet, surprisingly, our experiments indicate that Deep Q-Networks (DQN), which use exactly this type of function approximator, are still able to achieve modest action generalization. Our main contribution is twofold: first, we propose a method of evaluating action generalization using expert knowledge of action similarity, and empirically confirm that action generalization leads to faster learning; second, we characterize the action-generalization gap (the difference in learning performance between DQN and the expert) in different domains. We find that DQN can indeed generalize over actions in several simple domains, but that its ability to do so decreases as the action space grows larger.
A neural prosody encoder for end-ro-end dialogue act classification
Venue: arXiv cs.CL 2022
Year: 2022

Dialogue act classification (DAC) is a critical task for spoken language understanding in dialogue systems. Prosodic features such as energy and pitch have been shown to be useful for DAC. Despite their importance, little research has explored neural approaches to integrate prosodic features into end-to-end (E2E) DAC models which infer dialogue acts directly from audio signals. In this work, we propose an E2E neural architecture that takes into account the need for characterizing prosodic phenomena co-occurring at different levels inside an utterance. A novel part of this architecture is a learnable gating mechanism that assesses the importance of prosodic features and selectively retains core information necessary for E2E DAC. Our proposed model improves DAC accuracy by 1.07% absolute across three publicly available benchmark datasets.
Delay Encryption by Cubing
Venue: arXiv cs.CR 2022
Year: 2022

Delay Encryption (often called Timed-Release Encryption) is a scheme in which a message is sent into the future by ensuring its confidentiality only for a given amount of time. We propose a new scheme based on a novel time-lock puzzle. This puzzle relies on the assumption that repeated squaring is an inherently sequential process. We perform an extensive and practical analysis of many classical and quantum attacks on our scheme and conclude that it is secure given some precautions.
The Fluctuating Two-Ray Fading Model with Independent Specular Components
Venue: arXiv cs.IT 2022
Year: 2022

We introduce and characterize the independent fluctuating two-ray (IFTR) fading model, a class of fading models consisting of two specular components which fluctuate independently, plus a diffuse component modeled as a complex Gaussian random variable. The IFTR model complements the popular fluctuating two-ray (FTR) model, on which the specular components are fully correlated and fluctuate jointly. The chief probability functions of the received SNR in IFTR fading, including the PDF, CDF and MGF, are expressed in closed-form, having a functional form similar to other state-of-the-art fading models. Then, the IFTR model is empirically validated using multiple channels measured in rather diverse scenarios, including line of sight (LOS) millimeter-wave, land mobile satellite (LMS) and underwater acoustic communication (UAC), showing a better fit than the original FTR model and other models previously used in these environments. Additionally, the performance of wireless communication systems operating under IFTR fading is evaluated in closed-form in two scenarios: (i) exact and asymptotic bit error rate for a family of coherent modulations; and (ii) exact and asymptotic outage probability.
Aggregating Pairwise Semantic Differences for Few-Shot Claim Veracity Classification
Venue: arXiv cs.CL 2022
Year: 2022

As part of an automated fact-checking pipeline, the claim veracity classification task consists in determining if a claim is supported by an associated piece of evidence. The complexity of gathering labelled claim-evidence pairs leads to a scarcity of datasets, particularly when dealing with new domains. In this paper, we introduce SEED, a novel vector-based method to few-shot claim veracity classification that aggregates pairwise semantic differences for claim-evidence pairs. We build on the hypothesis that we can simulate class representative vectors that capture average semantic differences for claim-evidence pairs in a class, which can then be used for classification of new instances. We compare the performance of our method with competitive baselines including fine-tuned BERT/RoBERTa models, as well as the state-of-the-art few-shot veracity classification method that leverages language model perplexity. Experiments conducted on the FEVER and SCIFACT datasets show consistent improvements over competitive baselines in few-shot settings. Our code is available.
Some Grammatical Errors are Frequent, Others are Important
Venue: arXiv cs.CL 2022
Year: 2022

In Grammatical Error Correction, systems are evaluated by the number of errors they correct. However, no one has assessed whether all error types are equally important. We provide and apply a method to quantify the importance of different grammatical error types to humans. We show that some rare errors are considered disturbing while other common ones are not. This affects possible directions to improve both systems and their evaluation.
MEWS: Real-time Social Media Manipulation Detection and Analysis
Venue: arXiv cs.CV 2022
Year: 2022

This article presents a beta-version of MEWS (Misinformation Early Warning System). It describes the various aspects of the ingestion, manipulation detection, and graphing algorithms employed to determine--in near real-time--the relationships between social media images as they emerge and spread on social media platforms. By combining these various technologies into a single processing pipeline, MEWS can identify manipulated media items as they arise and identify when these particular items begin trending on individual social media platforms or even across multiple platforms. The emergence of a novel manipulation followed by rapid diffusion of the manipulated content suggests a disinformation campaign.
SubER: A Metric for Automatic Evaluation of Subtitle Quality
Venue: arXiv cs.CL 2022
Year: 2022

This paper addresses the problem of evaluating the quality of automatically generated subtitles, which includes not only the quality of the machine-transcribed or translated speech, but also the quality of line segmentation and subtitle timing. We propose SubER - a single novel metric based on edit distance with shifts that takes all of these subtitle properties into account. We compare it to existing metrics for evaluating transcription, translation, and subtitle quality. A careful human evaluation in a post-editing scenario shows that the new metric has a high correlation with the post-editing effort and direct human assessment scores, outperforming baseline metrics considering only the subtitle text, such as WER and BLEU, and existing methods to integrate segmentation and timing features.
Entity-aware and Motion-aware Transformers for Language-driven Action Localization in Videos
Venue: arXiv cs.CV 2022
Year: 2022

Language-driven action localization in videos is a challenging task that involves not only visual-linguistic matching but also action boundary prediction. Recent progress has been achieved through aligning language query to video segments, but estimating precise boundaries is still under-explored. In this paper, we propose entity-aware and motion-aware Transformers that progressively localizes actions in videos by first coarsely locating clips with entity queries and then finely predicting exact boundaries in a shrunken temporal region with motion queries. The entity-aware Transformer incorporates the textual entities into visual representation learning via cross-modal and cross-frame attentions to facilitate attending action-related video clips. The motion-aware Transformer captures fine-grained motion changes at multiple temporal scales via integrating long short-term memory into the self-attention module to further improve the precision of action boundary prediction. Extensive experiments on the Charades-STA and TACoS datasets demonstrate that our method achieves better performance than existing methods.
AdaVAE: Exploring Adaptive GPT-2s in Variational Auto-Encoders for Language Modeling
Venue: arXiv cs.CL 2022
Year: 2022

Variational Auto-Encoder (VAE) has become the de-facto learning paradigm in achieving representation learning and generation for natural language at the same time. Nevertheless, existing VAE-based language models either employ elementary RNNs, which is not powerful to handle complex works in the multi-task situation, or fine-tunes two pre-trained language models (PLMs) for any downstream task, which is a huge drain on resources. In this paper, we propose the first VAE framework empowered with adaptive GPT-2s (AdaVAE). Different from existing systems, we unify both the encoder\&decoder of the VAE model using GPT-2s with adaptive parameter-efficient components, and further introduce Latent Attention operation to better construct latent space from transformer models. Experiments from multiple dimensions validate that AdaVAE is competent to effectively organize language in three related tasks (language modeling, representation modeling and guided text generation) even with less than $15\%$ activated parameters in training. Our code is available at \url{https://github.com/ImKeTT/AdaVAE}.
E-Mail Assistant -- Automation of E-Mail Handling and Management using Robotic Process Automation
Venue: arXiv cs.LG 2022
Year: 2022

In this paper, a workflow for designing a bot using Robotic Process Automation (RPA), associated with Artificial Intelligence (AI) that is used for information extraction, classification, etc., is proposed. The bot is equipped with many features that make email handling a stress-free job. It automatically login into the mailbox through secured channels, distinguishes between the useful and not useful emails, classifies the emails into different labels, downloads the attached files, creates different directories, and stores the downloaded files into relevant directories. It moves the not useful emails into the trash. Further, the bot can also be trained to rename the attached files with the names of the sender/applicant in case of a job application for the sake of convenience. The bot is designed and tested using the UiPath tool to improve the performance of the system. The paper also discusses the further possible functionalities that can be added on to the bot.
How does Feedback Signal Quality Impact Effectiveness of Pseudo Relevance Feedback for Passage Retrieval?
Venue: arXiv cs.IR 2022
Year: 2022

Pseudo-Relevance Feedback (PRF) assumes that the top results retrieved by a first-stage ranker are relevant to the original query and uses them to improve the query representation for a second round of retrieval. This assumption however is often not correct: some or even all of the feedback documents may be irrelevant. Indeed, the effectiveness of PRF methods may well depend on the quality of the feedback signal and thus on the effectiveness of the first-stage ranker. This aspect however has received little attention before. In this paper we control the quality of the feedback signal and measure its impact on a range of PRF methods, including traditional bag-of-words methods (Rocchio), and dense vector-based methods (learnt and not learnt). Our results show the important role the quality of the feedback signal plays on the effectiveness of PRF methods. Importantly, and surprisingly, our analysis reveals that not all PRF methods are the same when dealing with feedback signals of varying quality. These findings are critical to gain a better understanding of the PRF methods and of which and when they should be used, depending on the feedback signal quality, and set the basis for future research in this area.
Joint Tracking of Groups of Users with Uplink Reference Signals
Venue: arXiv cs.NI 2022
Year: 2022

In cellular networks user equipment (UE) need to be tracked so that they can be reached by incoming data and to keep context information such as encryption keys available for UE originated transmission. Typically UEs measure reference signal transmissions that are broadcasted by the network, and report to the network based on some criterion that allows the network to know the UE location with sufficient accuracy. An alternative approach is to let the UEs send out reference signals that the network can detect to track the UE location. This reduces the need for the UEs to measure and report, while it requires some resources for uplink transmission. In this paper we propose and evaluate a solution for jointly tracking groups of UEs that are moving together. The results show that UEs can be tracked efficiently with low resource consumption.
Energy efficiency of network-coding enabled mobile small cells
Venue: arXiv cs.NI 2022
Year: 2022

Energy efficiency becomes increasingly important due to the limited battery capacity in wireless devices while at the same time user throughput requirements are relentlessly increasing. In this paper, we study an energy efficient cooperation scheme which employs network coding to enhance the energy efficiency for mobile devices. Herein we propose that the mobile devices are clustered into mobile small cells with one of the mobile devices acting as a group head with basic transceiver, coding and relaying functionalities. Group heads coordinate the transmissions from the mobile devices in the mobile small cell to the network's base stations. The objective function of the cooperative scheme is to minimize mobile devices' energy consumption subject to a certain bit error probability. The proposed network-coding based scheme has been evaluated by means of numerical simulations and compared to both a conventional direct transmit scheme, with no cooperation groups, and a cooperative relaying scheme. Results show that, with network-coded cooperation, energy efficiency may significantly increase provided the density of base stations and mobile devices is below a certain value. Above this value none of the compared cooperation schemes may improve energy efficiency, but rather power consumption is reduced only when mobile devices transmit via base stations in their close proximity.
Exploiting Inductive Bias in Transformers for Unsupervised Disentanglement of Syntax and Semantics with VAEs
Venue: arXiv cs.CL 2022
Year: 2022

We propose a generative model for text generation, which exhibits disentangled latent representations of syntax and semantics. Contrary to previous work, this model does not need syntactic information such as constituency parses, or semantic information such as paraphrase pairs. Our model relies solely on the inductive bias found in attention-based architectures such as Transformers. In the attention of Transformers, keys handle information selection while values specify what information is conveyed. Our model, dubbed QKVAE, uses Attention in its decoder to read latent variables where one latent variable infers keys while another infers values. We run experiments on latent representations and experiments on syntax/semantics transfer which show that QKVAE displays clear signs of disentangled syntax and semantics. We also show that our model displays competitive syntax transfer capabilities when compared to supervised models and that comparable supervised models need a fairly large amount of data (more than 50K samples) to outperform it on both syntactic and semantic transfer. The code for our experiments is publicly available.