Reviewerly/exHarmony huggingface dataset:
Where Are You? Localization from Embodied Dialog

We present WHERE ARE YOU? (WAY), a dataset of ~6k dialogs in which two humans – an Observer and a Locator – complete a cooperative localization task. The Observer is spawned at random in a 3D environment and can navigate from first-person views while answering questions from the Locator. The Locator must localize the Observer in a detailed top-down map by asking questions and giving instructions. Based on this dataset, we define three challenging tasks: Localization from Embodied Dialog or LED (localizing the Observer from dialog history), Embodied Visual Dialog (modeling the Observer), and Cooperative Localization (modeling both agents). In this paper, we focus on the LED task – providing a strong baseline model with detailed ablations characterizing both dataset biases and the importance of various modeling choices. Our best model achieves 32.7% success at identifying the Observer’s location within 3m in unseen buildings, vs. 70.4% for human Locators.
Multi-Antenna Channel Interpolation via Tucker Decomposed Extreme Learning Machine

Channel interpolation is an essential technique for providing high-accuracy estimation of the channel state information for wireless systems design where the frequency-space structural correlations of multi-antenna channel are typically hidden in matrix or tensor forms. In this correspondence paper, a modified extreme learning machine (ELM) that can process tensorial data, or ELM model with tensorial inputs (TELM), is proposed to handle the channel interpolation task. The TELM inherits many good properties from ELMs. Based on the TELM, the Tucker decomposed extreme learning machine is proposed for further improving the performance. Furthermore, we establish a theoretical argument to measure the interpolation capability of the proposed learning machines. Experimental results verify that our proposed learning machines can achieve comparable mean squared error (MSE) performance against the traditional ELMs but with 15% shorter running time, and outperform the other methods for a 20% margin measured in MSE for channel interpolation.
The EOS Decision and Length Extrapolation

Extrapolation to unseen sequence lengths is a challenge for neural generative models of language. In this work, we characterize the effect on length extrapolation of a modeling decision often overlooked: predicting the end of the generative process through the use of a special end-of-sequence (EOS) vocabulary item. We study an oracle setting - forcing models to generate to the correct sequence length at test time - to compare the length-extrapolative behavior of networks trained to predict EOS (+EOS) with networks not trained to (-EOS). We find that -EOS substantially outperforms +EOS, for example extrapolating well to lengths 10 times longer than those seen at training time in a bracket closing task, as well as achieving a 40% improvement over +EOS in the difficult SCAN dataset length generalization task. By comparing the hidden states and dynamics of -EOS and +EOS models, we observe that +EOS models fail to generalize because they (1) unnecessarily stratify their hidden states by their linear position is a sequence (structures we call length manifolds) or (2) get stuck in clusters (which we refer to as length attractors) once the EOS token is the highest-probability prediction.
Mixed Information Flow for Cross-Domain Sequential Recommendations

Cross-domain sequential recommendation is the task of predict the next item that the user is most likely to interact with based on past sequential behavior from multiple domains. One of the key challenges in cross-domain sequential recommendation is to grasp and transfer the flow of information from multiple domains so as to promote recommendations in all domains. Previous studies have investigated the flow of behavioral information by exploring the connection between items from different domains. The flow of knowledge (i.e., the connection between knowledge from different domains) has so far been neglected. In this article, we propose a mixed information flow network for cross-domain sequential recommendation to consider both the flow of behavioral information and the flow of knowledge by incorporating a behavior transfer unit and a knowledge transfer unit . The proposed mixed information flow network is able to decide when cross-domain information should be used and, if so, which cross-domain information should be used to enrich the sequence representation according to users’ current preferences. Extensive experiments conducted on four e-commerce datasets demonstrate that the proposed mixed information flow network is able to improve recommendation performance in different domains by modeling mixed information flow. In this article, we focus on the application of mixed information flow network s to a scenario with two domains, but the method can easily be extended to multiple domains.
Scalable Reinforcement Learning of Localized Policies for Multi-Agent Networked Systems

We study reinforcement learning (RL) in a setting with a network of agents whose states and actions interact in a local manner where the objective is to find localized policies such that the (discounted) global reward is maximized. A fundamental challenge in this setting is that the state-action space size scales exponentially in the number of agents, rendering the problem intractable for large networks. In this paper, we propose a Scalable Actor-Critic (SAC) framework that exploits the network structure and finds a localized policy that is a $O(\rho^\kappa)$-approximation of a stationary point of the objective for some $\rho\in(0,1)$, with complexity that scales with the local state-action space size of the largest $\kappa$-hop neighborhood of the network.
Structure-Aware Procedural Text Generation From an Image Sequence

It is an important activity for our society to create new value by combining materials. From daily cooking to manufacturing for industry, we often describe the way to do it as a procedural text. As pointed by some previous studies for natural language understanding, one important property of the procedural text is its dependency of the context, which is the merging operations of materials and can be represented by a graph or tree structure. This paper aims to investigate the impact of explicitly introducing such a structure on the vision and language task of procedural text generation from an image sequence. To this end, we propose (1) a new dataset, which extends a definition of a tree structure merging tree to a vision and language version and (2) a novel structure-aware procedural text generation model, which learns the context dependency efficiently. Experimental results show that the proposed method can boost the performance of traditional versatile methods.
A unifying type-theory for higher-order (amortized) cost analysis

This paper presents λ-amor, a new type-theoretic framework for amortized cost analysis of higher-order functional programs and shows that existing type systems for cost analysis can be embedded in it. λ-amor introduces a new modal type for representing potentials – costs that have been accounted for, but not yet incurred, which are central to amortized analysis. Additionally, λ-amor relies on standard type-theoretic concepts like affineness, refinement types and an indexed cost monad. λ-amor is proved sound using a rather simple logical relation. We embed two existing type systems for cost analysis in λ-amor showing that, despite its simplicity, λ-amor can simulate cost analysis for different evaluation strategies (call-by-name and call-by-value), in different styles (effect-based and coeffect-based), and with or without amortization. One of the embeddings also implies that λ-amor is relatively complete for all terminating PCF programs.
Proactive Container Auto-scaling for Cloud Native Machine Learning Services

Understanding the resource usage behaviors of the ever-increasing machine learning workloads are critical to cloud providers offering Machine Learning (ML) services.Capable of auto-scaling resources for customer workloads can significantly improve resource utilization, thus greatly reducing the cost.Here we leverage the AI4DL framework [1] to characterize workload and discover resource consumption phases.We advance the existing technology to an incremental phase discovery method that applies to more general types of ML workload for both training and inference.We use a timewindow MultiLayer Perceptron (MLP) to predict phases in containers with different types of workload.Then, we propose a predictive vertical auto-scaling policy to resize the container dynamically according to phase predictions.We evaluate our predictive auto-scaling policies on 561 long-running containers with multiple types of ML workloads.The predictive policy can reduce up to 38% of the allocated CPU compared to the default resource provisioning policies by developers.By comparing our predictive policies with commonly used reactive auto-scaling policies, we find that they can accurately predict sudden phase transitions (with an F1-score of 0.92) and significantly reduce the number of out-of-memory errors (350 vs. 20).Besides, we show that the predictive auto-scaling policy maintains the number of resizing operations close to the best reactive policies.
What Matters for On-Policy Deep Actor-Critic Methods? A Large-Scale Study

In recent years, reinforcement learning (RL) has been successfully applied to many different continuous control tasks. While RL algorithms are often conceptually simple, their state-of-the-art implementations take numerous low- and high-level design decisions that strongly affect the performance of the resulting agents. Those choices are usually not extensively discussed in the literature, leading to discrepancy between published descriptions of algorithms and their implementations. This makes it hard to attribute progress in RL and slows down overall progress [Engstrom'20]. As a step towards filling that gap, we implement >50 such ``choices in a unified on-policy deep actor-critic framework, allowing us to investigate their impact in a large-scale empirical study. We train over 250'000 agents in five continuous control environments of different complexity and provide insights and practical recommendations for the training of on-policy deep actor-critic RL agents.
Collaborative Intrusion Detection System in Cognitive Smart City Network (CSC-Net)

Smart environment is about incorporating smart thinking in the environment and implementing the technical intervention that improvise the city's environment. Artificial intelligence (AI) provides solutions in huge technological issues in various aspects of day-to-day life such as autonomous transportation, governance, healthcare, agriculture, maintenance, logistics, and education that are automated, managed, controlled, and accessed remotely with the aid of smart devices. Cognitive computing is denoted as a next-generation AI-dependent method that gives human-computer interactions with personalized services that replicate manual behavior. Simultaneously, massive data is generated from the applications of the smart city like smart transportation, retail industry, healthcare, and governance. It is necessary to obtain a reliable, sustainable, continuous, and secure framework in the cloud centralized infrastructure. In this research article, the authors proposed the architecture of cognitive smart city network (CSC-Net) that defines how data are collected from applications of smart city and scrutinized by cognitive computing. This research article predicts the mobile edge computing solution (MEC) that permits node collaboration between internet of things (IoT) devices for providing secure and reliable communication among smart devices and fog layer, conversely fog layer and cloud layer. This proposed work helps to reduce the excessive traffic flow in smart environment with the support of node to node communication protocols. Collaborative-dependent intrusion detection system (C-IDS) is proposed to solve the data security issues in fog and cloud layers.
Named Entity Recognition for Code Mixed Social Media Sentences

With the increase of internet applications and social media platforms there has been an increase in the informal way of text communication. People belonging to different regions tend to mix their regional language with English on social media text. This has been the trend with many multilingual nations now and is commonly known as code mixing. In code mixing, multiple languages are used within a statement. The problem of named entity recognition (NER) is a well-researched topic in natural language processing (NLP), but the present NER systems tend to perform inefficiently on code-mixed text. This paper proposes three approaches to improve named entity recognizers for handling code-mixing. The first approach is based on machine learning techniques such as support vector machines and other tree-based classifiers. The second approach is based on neural networks and the third approach uses long short-term memory (LSTM) architecture to solve the problem.
Cubical Agda: A dependently typed programming language with univalence and higher inductive types

Abstract Proof assistants based on dependent type theory provide expressive languages for both programming and proving within the same system. However, all of the major implementations lack powerful extensionality principles for reasoning about equality, such as function and propositional extensionality. These principles are typically added axiomatically which disrupts the constructive properties of these systems. Cubical type theory provides a solution by giving computational meaning to Homotopy Type Theory and Univalent Foundations, in particular to the univalence axiom and higher inductive types (HITs). This paper describes an extension of the dependently typed functional programming language Agda with cubical primitives, making it into a full-blown proof assistant with native support for univalence and a general schema of HITs. These new primitives allow the direct definition of function and propositional extensionality as well as quotient types, all with computational content. Additionally, thanks also to copatterns, bisimilarity is equivalent to equality for coinductive types. The adoption of cubical type theory extends Agda with support for a wide range of extensionality principles, without sacrificing type checking and constructivity.
Goldilocks: Just-Right Tuning of BERT for Technology-Assisted Review

Technology-assisted review (TAR) refers to iterative active learning workflows for document review in high recall retrieval (HRR) tasks. TAR research and most commercial TAR software have applied linear models such as logistic regression to lexical features. Transformer-based models with supervised tuning are known to improve effectiveness on many text classification tasks, suggesting their use in TAR. We indeed find that the pre-trained BERT model reduces review cost by 10% to 15% in TAR workflows simulated on the RCV1-v2 newswire collection. In contrast, we likewise determined that linear models outperform BERT for simulated legal discovery topics on the Jeb Bush e-mail collection. This suggests the match between transformer pre-training corpora and the task domain is of greater significance than generally appreciated. Additionally, we show that just-right language model fine-tuning on the task collection before starting active learning is critical. Too little or too much fine-tuning hinders performance, worse than that of linear models, even for a favorable corpus such as RCV1-v2.
Development and use of augmented reality models to teach medicinal chemistry

Students in the doctor of pharmacy curriculum have varied backgrounds in their chemical training and also their ability to make mental conversions from two-dimensional chemical representations, on lecture slides or textbook images, to three-dimensional cognitive understanding. In order to bridge the gap, augmented reality (AR) models were developed to provide an alternative learning medium for the students. AR was selected to take advantage of the ubiquitous presence of smartphones, without incurring the expense of Virtual Reality hardware.AR models were developed and introduced in the classroom in three phases. Student survey responses were used to improve the utility of the models in between phases. Active learning exercises were developed that required both individual and group interactions to complete.An optimized AR model creation workflow was developed that allowed each AR model to be created and posted in about 30 min. Depending on the phase of the study, 69% to 88% of the students found the AR models easy to use and 58% to 83% wanted to see more AR models used in future lectures. A majority (76%) of the students viewed the AR models on their smartphones.Augmented reality modules were created for use in medicinal chemistry courses in the pharmacy curriculum. Models were introduced in phases and included iterative improvements based on student feedback. The AR exercises provided active learning opportunities and were well received. The majority of students would like additional AR modules used in the course.
Reader-Guided Passage Reranking for Open-Domain Question Answering

Current open-domain question answering systems often follow a Retriever-Reader architecture, where the retriever first retrieves relevant passages and the reader then reads the retrieved passages to form an answer.In this paper, we propose a simple and effective passage reranking method, named Reader-guIDEd Reranker (RIDER), which does not involve training and reranks the retrieved passages solely based on the top predictions of the reader before reranking.We show that RIDER, despite its simplicity, achieves 10 to 20 absolute gains in top-1 retrieval accuracy and 1 to 4 Exact Match (EM) gains without refining the retriever or reader.In addition, RIDER, without any training, outperforms state-of-the-art transformer-based supervised rerankers.Remarkably, RIDER achieves 48.3 EM on the Natural Questions dataset and 66.4 EM on the TriviaQA dataset when only 1,024 tokens (7.8 passages on average) are used as the reader input after passage reranking.
CPISNet: Delving into Consistent Proposals of Instance Segmentation Network for High-Resolution Aerial Images

Instance segmentation of high-resolution aerial images is challenging when compared to object detection and semantic segmentation in remote sensing applications. It adopts boundary-aware mask predictions, instead of traditional bounding boxes, to locate the objects-of-interest in pixel-wise. Meanwhile, instance segmentation can distinguish the densely distributed objects within a certain category by a different color, which is unavailable in semantic segmentation. Despite the distinct advantages, there are rare methods which are dedicated to the high-quality instance segmentation for high-resolution aerial images. In this paper, a novel instance segmentation method, termed consistent proposals of instance segmentation network (CPISNet), for high-resolution aerial images is proposed. Following top-down instance segmentation formula, it adopts the adaptive feature extraction network (AFEN) to extract the multi-level bottom-up augmented feature maps in design space level. Then, elaborated RoI extractor (ERoIE) is designed to extract the mask RoIs via the refined bounding boxes from proposal consistent cascaded (PCC) architecture and multi-level features from AFEN. Finally, the convolution block with shortcut connection is responsible for generating the binary mask for instance segmentation. Experimental conclusions can be drawn on the iSAID and NWPU VHR-10 instance segmentation dataset: (1) Each individual module in CPISNet acts on the whole instance segmentation utility; (2) CPISNet* exceeds vanilla Mask R-CNN 3.4%/3.8% AP on iSAID validation/test set and 9.2% AP on NWPU VHR-10 instance segmentation dataset; (3) The aliasing masks, missing segmentations, false alarms, and poorly segmented masks can be avoided to some extent for CPISNet; (4) CPISNet receives high precision of instance segmentation for aerial images and interprets the objects with fitting boundary.
DSLN: Securing Internet of Things Through RF Fingerprint Recognition in Low-SNR Settings

The explosive growth of Internet of Things (IoT) has mandated the security of data access. Although authentication methods can enhance network security, their vulnerability to malicious attacks may be a barrier for the wide deployments in IoT scenarios. To address the security issue, we advocate the use of physical-layer security through radio-frequency (RF) fingerprint recognition. Observing that most RF fingerprint recognition methods show a degradation of performance under low signal-to-noise ratio (SNR) environments, we present a dynamic shrinkage learning network (DSLN) to enhance security for IoT applications, particularly in the setting of low SNR. We design a novel dynamic shrinkage threshold for improving the accuracy of recognition under low-SNR environments. Additionally, we design an identity shortcut for reducing the running time of RF fingerprint recognition. In comparison with convolutional neural network (CNN), recurrent neural network (RNN), and a hybrid CNN+RNN network (CRNN), our proposed DSLN yields accuracy improvements of up to 20%. Moreover, DSLN can reduce the running time by up to 60%, indicating its great potential to a real-time IoT system, e.g., an intelligent automotive system.
Efficient and Privacy-Preserving Decision Tree Classification for Health Monitoring Systems

Due to the increasing healthcare costs and the advance of wireless technology, health monitoring systems have been widely adopted recently. In health monitoring systems, a hospital outsources a clinical decision model to a cloud service provider, which receives biomedical data from remote clients and produces clinical decisions based on the outsourced model. Due to critical privacy concerns, both the clinical decision model and biomedical data should be protected. In this article, we propose an efficient and privacy-preserving decision tree (PPDT) classification scheme for health monitoring systems. Specifically, we first transform a decision tree classifier (i.e., the clinical decision model) into the Boolean vectors. Then, we leverage symmetric key encryption to encrypt the Boolean vectors as encrypted indices. The PPDT classification is achieved by searching the encrypted indices with encrypted tokens. We formulate a leakage function and provide the security definition and simulation-based proof for PPDT. The performance analyses demonstrate that PPDT is very efficient in terms of computation, communication, and storage. Experimental evaluations show that PPDT only requires microsecond-level execution time, kilobyte-level communication costs, and kilobyte-level storage costs on the test data set.