Reviewerly/exHarmony huggingface dataset:
Deep Imbalanced Data Learning Approach for Video Anomaly Detection

Surveillance video data often exhibit highly imbal-anced data distribution, i.e., majority or normal class instances outnumber the minority or anomalous class instances, which are the point of concern in video anomaly detection (AD). The existing deep learning methods often adopt various ensemble methods consisting of an early or late fusion of the cascade of either deep discriminative or generative learning models. These methods lack the diversity in applying the deep learning algorithms to imbalanced data learning for AD in real-world unlabeled and imbalanced surveillance video data. In this paper, decision level late fusion of two complementary deep learning models is accomplished using a loss function weighted regression model towards imbalanced data learning for video AD. Under the algorithmic level actions, the learning model's architecture consists of two complementary parallel discriminative-generative channels, i.e., a discriminative deep residual network (DRN) channel and a generative deep regression long short-term memory (LSTM) channel. The proposed complementary deep LSTM-DRN-based imbalanced data learning approach improves effectiveness in detecting anomalies compared to state-of-the-art methods.
Depth Estimation Using Feature Pyramid U-Net and Polarized Self-Attention for Road Scenes

Studies have shown that the observed image texture details and semantic information are of great significance for the depth estimation on the road scenes. However, there are ambiguities and inaccuracies in the boundary information of observed objects in previous methods. For this reason, we hope to design a new depth estimation method that can obtain higher accuracy and more accurate boundary information of the detected object. Based on polarized self-attention (PSA) and feature pyramid U-net, we proposed a new self-supervised monocular depth estimation model to extract more accurate texture details and semantic information. Firstly, we add a PSA module at the end of the depth encoder and pose encoder so that the network can extract more accurate semantic information. Then, based on the U-net, we put the multi-scale image obtained by the object detection module FPN (Feature Pyramid network) directly into the decoder. It can guide the model to learn semantic information, thus enhancing the boundary of the image. We evaluated our method on KITTI 2015 datasets and Make3D datasets, and our model achieved better results than previous studies. In order to verify the generalization of the model, we have done monocular, stereo, monocular plus stereo experiments. The experimental results show that our model has achieved better results in several main evaluation indexes and clearer boundary information. In order to compare different forms of PSA mechanism, we did ablation experiments. Compared with no PSA module, after adding the PSA module, better results in evaluating indicators were achieved. We also found that our model is better in monocular training than stereo training and monocular plus stereo training.
CEDAR: communication efficient distributed analysis for regressions

Electronic health records (EHRs) offer great promises for advancing precision medicine and, at the same time, present significant analytical challenges. Particularly, it is often the case that patient-level data in EHRs cannot be shared across institutions (data sources) due to government regulations and/or institutional policies. As a result, there are growing interests about distributed learning over multiple EHRs databases without sharing patient-level data. To tackle such challenges, we propose a novel communication efficient method that aggregates the optimal estimates of external sites, by turning the problem into a missing data problem. In addition, we propose incorporating posterior samples of remote sites, which can provide partial information on the missing quantities and improve efficiency of parameter estimates while having the differential privacy property and thus reducing the risk of information leaking. The proposed approach, without sharing the raw patient level data, allows for proper statistical inference. We provide theoretical investigation for the asymptotic properties of the proposed method for statistical inference as well as differential privacy, and evaluate its performance in simulations and real data analyses in comparison with several recently developed methods.
Renaissance Robot: Optimal Transport Policy Fusion for Learning Diverse Skills

Deep reinforcement learning (RL) is a promising approach to solving complex robotics problems. However, the process of learning through trial-and-error interactions is often highly time-consuming, despite recent advancements in RL algorithms. Additionally, the success of RL is critically dependent on how well the reward-shaping function suits the task, which is also time-consuming to design. As agents trained on a variety of robotics problems continue to proliferate, the ability to reuse their valuable learning for new domains becomes increasingly significant. In this paper, we propose a post-hoc technique for policy fusion using Optimal Transport theory as a robust means of consolidating the knowledge of multiple agents that have been trained on distinct scenarios. We further demonstrate that this provides an improved weights initialisation of the neural network policy for learning new tasks, requiring less time and computational resources than either retraining the parent policies or training a new policy from scratch. Ultimately, our results on diverse agents commonly used in deep RL show that specialised knowledge can be unified into a “Renaissance agent”, allowing for quicker learning of new skills.
BUDA: Budget and Deadline Aware Scheduling Algorithm for Task Graphs in Heterogeneous Systems

Task graphs are widely used to represent data-intensive applications. To efficiently execute these applications on heterogeneous systems, each task must be properly scheduled on the processors of the system. The NP-completeness of the task scheduling problem has motivated researchers to propose various heuristic methods. Recently, Quality of Service (QoS) aware scheduling is becoming an active research area in heterogeneous systems because the end-user has different QoS requirements. Generally, time and cost are the most relevant user concerns. However, it is challenging to find a feasible scheduling plan which minimizes the total execution time of the user’s application (makespan) while satisfying both budget and deadline constraints. In this paper, we present a novel heuristic algorithm called Budget-Deadline-Aware-Scheduling (BUDA) that addresses task graphs scheduling under budget and deadline constraints in heterogeneous systems. The novelty of the BUDA algorithm is based on a Heterogeneous Time-Cost Matrix (HTCM) that is used to prioritize tasks and for processor selection. In addition, we introduce a new Heterogeneous Time-Cost Trade-off factor (HTCT) that tries to adjust the time and cost for the current task among all processors. The experiments based on randomly generated graphs and real-world applications graphs show that the BUDA algorithm outperforms the state-of-the-art algorithms in terms of makespan, time efficiency, and success rate.
A qualitative, network-centric method for modeling socio-technical systems, with applications to evaluating interventions on social media platforms to increase social equality

Abstract We propose and extend a qualitative, complex systems methodology from cognitive engineering, known as the abstraction hierarchy , to model how potential interventions that could be carried out by social media platforms might impact social equality. Social media platforms have come under considerable ire for their role in perpetuating social inequality. However, there is also significant evidence that platforms can play a role in reducing social inequality, e.g. through the promotion of social movements. Platforms’ role in producing or reducing social inequality is, moreover, not static; platforms can and often do take actions targeted at positive change. How can we develop tools to help us determine whether or not a potential platform change might actually work to increase social equality? Here, we present the abstraction hierarchy as a tool to help answer this question. Our primary contributions are two-fold. First, methodologically, we extend existing research on the abstraction hierarchy in cognitive engineering with principles from Network Science. Second, substantively, we illustrate the utility of this approach by using it to assess the potential effectiveness of a set of interventions, proposed in prior work, for how online dating websites can help mitigate social inequality.
Fast Vehicle Detection and Tracking on Fisheye Traffic Monitoring Video Using CNN and Bounding Box Propagation

We design a fast car detection and tracking algorithm for traffic monitoring fisheye video mounted on crossroads. We use ICIP 2020 VIP Cup dataset and adopt YOLOv5 as the object detection base model. The nighttime video of this dataset is very challenging, and the detection accuracy (AP <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">50</inf> ) of the base model is about 54%. We design a reliable car detection and tracking algorithm based on the concept of bounding box propagation among frames, which provides 17.9 percentage points (pp) and 7 pp accuracy improvement over the base model for the nighttime and daytime videos, respectively. To speed up, the grayscale frame difference is used for the intermediate frames in a segment, which can double the processing speed.
Physical-Layer Security, Quantum Key Distribution, and Post-Quantum Cryptography

The growth of data-driven technologies, 5G, and the Internet pose enormous pressure on underlying information infrastructure [...].
An Improved Encryption–Compression-based Algorithm for Securing Digital Images

Nowadays, there is an increasing tendency to upload images to online platforms acting as information carriers for various applications. Unfortunately, the unauthorized utilization of such images is a serious concern that has significantly impacted security and privacy. Although digital images are widely available, the storage of these images requires a large amount of data. This study aims to address these issues by developing an improved encryption–compression-based algorithm for securing digital images that reduces unnecessary hardware storage space, transmission time and bandwidth demand. First, the image is encrypted using chaotic encryption. Then the encrypted image is compressed using wavelet-based compression in order to make efficient use of resources without any information about the encryption key. On the other side, the image is decompressed and decrypted by the receiver. The security assessment of the proposed algorithm is performed in different ways, such as differential and statistical, key sensitivity, and execution time analysis. The experimental analysis proves the security of the method against various possible attacks. Furthermore, the extensive evaluations on a real dataset demonstrate that the proposed solution is secure and has a low encryption overhead compared to similar methods.
Adaptive procedures for discriminating between arbitrary tensor-product quantum states

Discriminating between quantum states is a fundamental task in quantum information theory. Given two quantum states ${\ensuremath{\rho}}_{+}$ and ${\ensuremath{\rho}}_{\ensuremath{-}}$, the Helstrom measurement distinguishes between them with minimal probability of error. However, finding and experimentally implementing the Helstrom measurement can be challenging for quantum states on many qubits. Due to this difficulty, there is great interest in identifying local measurement schemes which are close to optimal. In the first part of this work, we generalize previous work by Acin et al. [Phys. Rev. A 71, 032338 (2005)] and show that a locally greedy scheme using Bayesian updating can optimally distinguish between any two states that can be written as a tensor product of arbitrary pure states. We then show that the same algorithm cannot distinguish tensor products of mixed states with vanishing error probability (even in a large subsystem limit), and introduce a modified locally greedy scheme with strictly better performance. In the second part of this work, we compare these simple local schemes with a general dynamic programming approach which finds both the optimal series of local measurements as well as the optimal order in which subsystems are measured.
q-Learning in Continuous Time

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Data recovery via covert cognizance for unattended operational resilience

One of the important premises of unattended operation, a highly promoted characteristic of fission batteries and advanced microreactors, is the ability to automate the analysis of sensors data used in support of operational monitoring and control. To meet this vision, this work proposes a new monitoring and data recovery paradigm to ensure resilience against data corruption which may be the result of malicious intrusion into the reactor operational network. This is paramount to ensure 100% availability under contingency scenarios such as cyberattacks. In support of this vision, earlier work has presented the concept of covert cognizance and demonstrated its mathematical ability to identify and embed cognizance parameters under the noise-dominated null space of the sensors data. This work extends this concept and applies it in real-time to demonstrate three key characteristics: zero-impact, zero-observability, and data recovery, where the first characteristic is to ensure no impact on operation, the second is immunity to discovery by pattern recognition techniques, and the third is to allow recovery of corrupt or falsified data. Recognizing that fission batteries are designed to operate under steady state most of the time, we elect to employ a small modular reactor model under transient operational conditions to demonstrate the operational resilience enabled by the covert cognizance paradigm. Specifically, the PI controller is augmented with the covert cognizance modules to develop self-awareness and enable automatic data recovery. The developed modules are expected to be equally applicable to a wide range of advanced reactor technologies relying on full or partial unattended control.
What Makes a Good Podcast Summary?

Abstractive summarization of podcasts is motivated by the growing popularity of podcasts and the needs of their listeners. Podcasting is a markedly different domain from news and other media that are commonly studied in the context of automatic summarization. As such, the qualities of a good podcast summary are yet unknown. Using a collection of podcast summaries produced by different algorithms alongside human judgments of summary quality obtained from the TREC 2020 Podcasts Track, we study the correlations between various automatic evaluation metrics and human judgments, as well as the linguistic aspects of summaries that result in strong evaluations.
Prediction of Hedonic and Eudaimonic Characteristics from User Interactions

Content-based recommender (CBR) systems take advantage of item characteristics and user propensities for these characteristics in order to select the items that are better suited for a user. Related work has shown that the characteristics of hedonia (pleasure) and eudaimonia (deeper meaning) account for user preferences in the domain of movies. However, labeling items with hedonic/eudaimonic properties and measuring user propensity for eudaimonic/hedonic experiences could be done only through questionnaires. In this work we present the results of our work-in-progress on the prediction of user propensities for eudaimonic and hedonic experiences from a movie preferences dataset. Our results indicate that a range of classifiers that use ratings of movies as features perform substantially better than the average baseline.
Exploration of Multi-corpus Learning for Hate Speech Classification in Low Resource Scenarios

The dramatic increase in social media has given rise to the problem of online hate speech. Deep neural network-based classifiers have become the state-of-the-art for automatic hate speech classification. The performance of these classifiers depends on the amount of available labelled training data. However, most hate speech corpora have a small number of hate speech samples. In this article, we aim to jointly use multiple hate speech corpora to improve hate speech classification performance in low-resource scenarios. We harness different hate speech corpora in a multi-task learning setup by associating one task to one corpus. This multi-corpus learning scheme is expected to improve the generalization, the latent representations, and domain adaptation of the model. Our work evaluates multi-corpus learning for hate speech classification and domain adaptation. We show significant improvements in classification and domain adaptation in low-resource scenarios.
Pose2UV: Single-Shot Multiperson Mesh Recovery With Deep UV Prior

In this work, we focus on the task of multi-person mesh recovery from a single color image, where the key issue is to tackle the pixel-level ambiguities caused by inter-person occlusions. Overall, there are two main technical challenges when addressing the ambiguities: how to extract valid target features under occlusions and how to reconstruct reasonable human meshes with only a handful of body cues? To deal with these problems, our key idea is to utilize the predicted 2D poses to locate and separate the target person, and reconstruct them with a novel learning-based UV prior. Specifically, we propose a visible pose-mask module to help extract valid target features, then train a dense body mesh prior to promote reconstructing natural mesh represented by the UV position map. To evaluate the performance of our proposed method under occlusions, we further build an in-the-wild 3D multi-person benchmark named as 3DMPB. Experimental results demonstrate that our method achieves state-of-the-art compared with previous methods. The dataset, codes are publicly available on our website.
QUASER

Designing natural language processing (NLP) models that produce predictions by first extracting a set of relevant input sentences, i.e., rationales, is gaining importance for improving model interpretability and producing supporting evidence for users. Current unsupervised approaches are designed to extract rationales that maximize prediction accuracy, which is invariably obtained by exploiting spurious correlations in datasets, and leads to unconvincing rationales. In this paper, we introduce unsupervised generative models to extract dual-purpose rationales, which must not only be able to support a subsequent answer prediction, but also support a reproduction of the input query. We show that such models can produce more meaningful rationales, that are less influenced by dataset artifacts, and as a result, also achieve the state-of-the-art on rationale extraction metrics on four datasets from the ERASER benchmark, significantly improving upon previous unsupervised methods. Our multi-task model is scalable and enables using state-of-the-art pretrained language models to design explainable question answering systems.
Imperative versus declarative collection processing

Java introduced in version 8 with the Stream API means to operate on collections using lambda expressions. Since then, this API is an alternative way to handle collections in a more declarative manner instead of the traditional, imperative style using loops. However, whether the Stream API is beneficial in comparison to loops in terms of usability is unclear. The present paper introduces a randomized control trial (RCT) on the understandability of collection operations performed on 20 participants with the dependent variables response time and correctness. As tasks, subjects had to determine the results for collection operations (either defined with the Stream API or with loops). The results indicate that the Stream API has a significant (p<.001) and large [EQUATION] positive effect on the response times. Furthermore, the usage of the Stream API caused significantly less errors. And finally, the participants perceived their speed with the Stream API higher compared to the loop-based code and the participants considered the code based on the Stream API as more readable. Hence, while existing studies found a negative effect of declarative constructs (in terms of lambda expressions) on the usability of a main stream programming language, the present study found the opposite: the present study gives evidence that declarative code on collections using the Stream API based on lambda expressions has a large, positive effect in comparison to traditional loops.
Cognitive Diagnostic Multistage Testing by Partitioning Hierarchically Structured Attributes

Abstract In cognitive diagnostic models (CDMs), a set of fine‐grained attributes is required to characterize complex problem solving and provide detailed diagnostic information about an examinee. However, it is challenging to ensure reliable estimation and control computational complexity when The test aims to identify the examinee's attribute profile in a large‐scale map of attributes. To address this problem, this study proposes a cognitive diagnostic multistage testing by partitioning hierarchically structured attributes (CD‐MST‐PH) as a multistage testing for CDM. In CD‐MST‐PH, multiple testlets can be constructed based on separate attribute groups before testing occurs, which retains the advantages of multistage testing over fully adaptive testing or the on‐the‐fly approach. Moreover, testlets are offered sequentially and adaptively, thus improving test accuracy and efficiency. An item information measure is proposed to compute the discrimination power of an item for each attribute, and a module assembly method is presented to construct modules anchored at each separate attribute group. Several module selection indices for CD‐MST‐PH are also proposed by modifying the item selection indices used in cognitive diagnostic computerized adaptive testing. The results of simulation study show that CD‐MST‐PH can improve test accuracy and efficiency relative to the conventional test without adaptive stages.
Alignment Rationale for Query-Document Relevance

Deep neural networks are widely used for text pair classification tasks such as as adhoc information retrieval. These deep neural networks are not inherently interpretable and require additional efforts to get rationale behind their decisions. Existing explanation models are not yet capable of inducing alignments between the query terms and the document terms -- which part of the document rationales are responsible for which part of the query? In this paper, we study how the input perturbations can be used to infer or evaluate alignments between the query and document spans, which best explain the black-box ranker's relevance prediction. We use different perturbation strategies and accordingly propose a set of metrics to evaluate the faithfulness of alignment rationales to the model. Our experiments show that the defined metrics based on substitution-based perturbation are more successful in preferring higher-quality alignments, compared to the deletion-based metrics.