Reviewerly/exHarmony huggingface dataset:
Popcorn

Recent conversational recommender systems (CRS) provide a promising solution to accurately capture a user's preferences by communicating with users in natural language to interactively guide them while pro-actively eliciting their current interests. Previous research on this mainly focused on either learning a supervised model with semantic features extracted from the user's responses, or training a policy network to control the dialogue state. However, none of them has considered the issue of popularity bias in a CRS. This paper proposes a human-in-the-loop popularity debiasing framework that integrates real-time semantic understanding of open-ended user utterances as well as historical records, while also effectively managing the dialogue with the user. This allows the CRS to balance the recommendation performance as well as the item popularity so as to avoid the well-known "long-tail'' effect. We demonstrate the effectiveness of our approach via experiments on two conversational recommendation datasets, and the results confirm that our proposed approach achieves high-accuracy recommendation while mitigating popularity bias.
Concise Outlines for a Complex Logic: A Proof Outline Checker for TaDA

Modern separation logics allow one to prove rich properties of intricate code, e.g. functional correctness and linearizability of non-blocking concurrent code. However, this expressiveness leads to a complexity that makes these logics difficult to apply. Manual proofs or proofs in interactive theorem provers consist of a large number of steps, often with subtle side conditions. On the other hand, automation with dedicated verifiers typically requires sophisticated proof search algorithms that are specific to the given program logic, resulting in limited tool support that makes it difficult to experiment with program logics, e.g. when learning, improving, or comparing them. Proof outline checkers fill this gap. Their input is a program annotated with the most essential proof steps, just like the proof outlines typically presented in papers. The tool then checks automatically that this outline represents a valid proof in the program logic. In this paper, we systematically develop a proof outline checker for the TaDA logic, which reduces the checking to a simpler verification problem, for which automated tools exist. Our approach leads to proof outline checkers that provide substantially more automation than interactive provers, but are much simpler to develop than custom automatic verifiers.
Natural language inference for curation of structured clinical registries from unstructured text

Clinical registries-structured databases of demographic, diagnosis, and treatment information-play vital roles in retrospective studies, operational planning, and assessment of patient eligibility for research, including clinical trials. Registry curation, a manual and time-intensive process, is always costly and often impossible for rare or underfunded diseases. Our goal was to evaluate the feasibility of natural language inference (NLI) as a scalable solution for registry curation.We applied five state-of-the-art, pretrained, deep learning-based NLI models to clinical, laboratory, and pathology notes to infer information about 43 different breast oncology registry fields. Model inferences were evaluated against a manually curated, 7439 patient breast oncology research database.NLI models showed considerable variation in performance, both within and across fields. One model, ALBERT, outperformed the others (BART, RoBERTa, XLNet, and ELECTRA) on 22 out of 43 fields. A detailed error analysis revealed that incorrect inferences primarily arose through models' tendency to misinterpret historical findings, as well as confusion based on abbreviations and subtle term variants common in clinical text.Traditional natural language processing methods require specially annotated training sets or the construction of a separate model for each registry field. In contrast, a single pretrained NLI model can curate dozens of different fields simultaneously. Surprisingly, NLI methods remain unexplored in the clinical domain outside the realm of shared tasks and benchmarks. Modern NLI models could increase the efficiency of registry curation, even when applied "out of the box" with no additional training.
Applications of Multi-Agent Deep Reinforcement Learning: Models and Algorithms

Recent advancements in deep reinforcement learning (DRL) have led to its application in multi-agent scenarios to solve complex real-world problems, such as network resource allocation and sharing, network routing, and traffic signal controls. Multi-agent DRL (MADRL) enables multiple agents to interact with each other and with their operating environment, and learn without the need for external critics (or teachers), thereby solving complex problems. Significant performance enhancements brought about by the use of MADRL have been reported in multi-agent domains; for instance, it has been shown to provide higher quality of service (QoS) in network resource allocation and sharing. This paper presents a survey of MADRL models that have been proposed for various kinds of multi-agent domains, in a taxonomic approach that highlights various aspects of MADRL models and applications, including objectives, characteristics, challenges, applications, and performance measures. Furthermore, we present open issues and future directions of MADRL.
Natural Language Processing Meets Quantum Physics: A Survey and Categorization

Recent research has investigated quantum NLP, designing algorithms that process natural language in quantum computers, and also quantum-inspired algorithms that improve NLP performance on classical computers.In this survey, we review representative methods at the intersection of NLP and quantum physics in the past ten years, categorizing them according to the use of quantum theory, the linguistic targets that are modeled, and the downstream application.The literature review ends with a discussion on the key factors to the success that has been achieved by existing work, as well as challenges ahead, with the goal of better understanding the promises and further directions.
A New Theoretical Framework of Pyramid Markov Processes for Blockchain Selfish Mining

In this paper, we provide a new theoretical framework of pyramid Markov processes to solve some open and fundamental problems of blockchain selfish mining under a rigorous mathematical setting. We first describe a more general model of blockchain selfish mining with both a two-block leading competitive criterion and a new economic incentive mechanism. Then we establish a pyramid Markov process and show that it is irreducible and positive recurrent, and its stationary probability vector is matrix-geometric with an explicitly representable rate matrix. Also, we use the stationary probability vector to study the influence of orphan blocks on the waste of computing resource. Next, we set up a pyramid Markov reward process to investigate the long-run average mining profits of the honest and dishonest mining pools, respectively. As a by-product, we build one-dimensional Markov reward processes and provide some new interesting interpretation on the Markov chain and the revenue analysis reported in the seminal work by Eyal and Sirer (2014). Note that the pyramid Markov (reward) processes can open up a new avenue in the study of blockchain selfish mining. Thus we hope that the methodology and results developed in this paper shed light on the blockchain selfish mining such that a series of promising research can be developed potentially.
Automated Traceability for Domain Modelling Decisions Empowered by Artificial Intelligence

Domain modelling abstracts real-world entities and their relationships in the form of class diagrams for a given domain problem space. Modellers often perform domain modelling to reduce the gap between understanding the problem description which expresses requirements in natural language and the concise interpretation of these requirements. However, the manual practice of domain modelling is both time-consuming and error-prone. These issues are further aggravated when problem descriptions are long, which makes it hard to trace modelling decisions from domain models to problem descriptions or vice-versa leading to completeness and conciseness issues. Automated support for tracing domain modelling decisions in both directions is thus advantageous. In this paper, we propose an automated approach that uses artificial intelligence techniques to extract domain models along with their trace links. We present a traceability information model to enable traceability of modelling decisions in both directions and provide its proof-of-concept in the form of a tool. The evaluation on a set of unseen problem descriptions shows that our approach is promising with an overall median F2 score of 82.04%. We conduct an exploratory user study to assess the benefits and limitations of our approach and present the lessons learned from this study.
Cloak &amp; Co-locate: Adversarial Railroading of Resource Sharing-based Attacks on the Cloud

The heterogeneity of resources and the diversity of applications on the cloud motivated the need for resource provisioning systems (RPSs) to meet the users’ performance requirements while maximizing the resource utilization to achieve cost-efficiency. On the other hand, resource sharing-based attacks, such as side-channel, transient execution, rowhammer, and denial of service attacks, exploit shared resources to leak sensitive data or hurt the performance of a victim. Although mounting resource sharing-based attacks on the cloud is trivial once the attacker virtual machine (VM) is co-located with the victim VM, the co-location requirement with the victim limit the practicality of resource sharing-based attacks on the cloud. In this paper, we show that RPSs can be exploited to solve the co-location challenge of resource sharing-based attacks in the cloud. In particular, we propose a new attack, called Cloak & Co-locate, which utilize adversarial evasion attacks to force RPSs to co-locate attackers’ VMs with targeted victims’ VMs. Specifically, Cloak is a fake trace generator (FTG) that is wrapped around an adversary kernel in order to force RPSs to Co-locate it with a specific victim’s VM, while also evading from detection and migration by the RPS.
A Deep Learning Approach for Detection of SQL Injection Attacks Using Convolutional Neural Networks

SQL Injection attacks are one of the major attacks targeting web applications as reported by OWASP. SQL injection, frequently referred to as SQLI, is an arising attack vector that uses malicious SQL code for unauthorized access to data. This can leave the system vulnerable and can result in severe loss of data. In this research work, we have reviewed the different types of SQL Injection attacks and existing techniques for the detection of SQL injection attacks. We have compiled and prepared our own dataset for the study including all major types of SQL attacks and have analyzed the performance of Machine learning algorithms like Naïve Bayes, Decision trees, Support Vector Machine, and K-nearest neighbor. We have also analyzed the performance of Convolutional Neural Networks (CNN) on the dataset using performance measures like accuracy, precision, Recall, and area of the ROC curve. Our experiments indicate that CNN outperforms other algorithms in accuracy, precision, recall, and area of the ROC curve.
Using laser marking to engrave optimal patterns for in‐plane displacement and strain measurement

Abstract The checkerboard constitutes the best pattern for in‐plane displacement and strain measurement because it maximizes image gradient. The use of laser marking to deposit such a pattern on flat surfaces is investigated in this paper. Optimal settings for the parameters influencing the quality of the pattern are given. This pattern being periodic, the images are processed with the localized spectrum analysis, which is one of the spectral techniques used to process such images. It is shown that the metrological performance in terms of measurement resolution is equivalent to the one obtained with transferred checkerboards. Compared to the classic transfer technique generally used to deposit such patterns, the benefit of using laser marking is its ease of use, a much higher print speed and the fact that a thin coat of white paint instead of a thicker layer of white adhesive is employed to ensure a good contrast in the images. Various examples of strain measurements are given, in particular the measurement of the strain field around a knot embedded in a wood specimen subjected to a tensile test.
Mobile Bookkeeper: Personal Financial Management Application with Receipt Scanner Using Optical Character Recognition

Personal financial management is undeniably a worthwhile practice to establish a financial security during a struggling economy and make intelligent monetary decisions regardless of the plethora of spending temptations. Monitoring personal cash flow is part of achieving financial independence, and it is now undemanding to perform because of the available personal budget apps and finance tools. Nevertheless, a missing feature of these technology-driven innovations is the recording, tracking, and monitoring of receipts as well as the generation of personal expenses reports based on these collected pieces of papers. With this application, “Mobile Bookkeeper”, financial enthusiasts can just scan the receipt using the inbuilt camera of any smartphone and details will be automatically transcribed using Optical Character Recognition (OCR). To measure the satisfaction and test the usability of the mobile app, subjective and objective measures via ISO 25062 and ISO 9241 standards were collected, and QUIS 7.0 questionnaire, respectively. The testing results established Mobile Bookkeeper particularly on its receipt scanner feature as a needed mobile finance app. Together with this acceptance is the report highlighting issues and challenges in developing such mobile application especially with OCR integration and its accuracy in text recognition.
Classification-Then-Grounding: Reformulating Video Scene Graphs as Temporal Bipartite Graphs

Today's VidSGG models are all proposal-based methods, i.e., they first generate numerous paired subject-object snippets as proposals, and then conduct predicate classification for each proposal. In this paper, we argue that this prevalent proposal-based framework has three inherent drawbacks: 1) The ground-truth predicate labels for proposals are partially correct. 2) They break the high-order relations among different predicate instances of a same subject-object pair. 3) VidSGG performance is upper-bounded by the quality of the proposals. To this end, we propose a new classification-then-grounding framework for VidSGG, which can avoid all the three overlooked drawbacks. Meanwhile, under this framework, we reformulate the video scene graphs as temporal bipartite graphs, where the entities and predicates are two types of nodes with time slots, and the edges denote different semantic roles between these nodes. This formulation takes full advantage of our new framework. Accordingly, we further propose a novel BIpartite Graph based SGG model: BIG. It consists of a classification stage and a grounding stage, where the former aims to classify the categories of all the nodes and the edges, and the latter tries to localize the temporal location of each relation instance. Extensive ablations on two VidSGG datasets have attested to the effectiveness of our framework and BIG. Code is available at https://github.com/Dawn-LX/VidSGG-BIG.
SensorHub: Multimodal Sensing in Real-Life Enables Home-Based Studies

Observational studies are an important tool for determining whether the findings from controlled experiments can be transferred into scenarios that are closer to subjects' real-life circumstances. A rigorous approach to observational studies involves collecting data from different sensors to comprehensively capture the situation of the subject. However, this leads to technical difficulties especially if the sensors are from different manufacturers, as multiple data collection tools have to run simultaneously. We present SensorHub, a system that can collect data from various wearable devices from different manufacturers, such as inertial measurement units, portable electrocardiographs, portable electroencephalographs, portable photoplethysmographs, and sensors for electrodermal activity. Additionally, our tool offers the possibility to include ecological momentary assessments (EMAs) in studies. Hence, SensorHub enables multimodal sensor data collection under real-world conditions and allows direct user feedback to be collected through questionnaires, enabling studies at home. In a first study with 11 participants, we successfully used SensorHub to record multiple signals with different devices and collected additional information with the help of EMAs. In addition, we evaluated SensorHub's technical capabilities in several trials with up to 21 participants recording simultaneously using multiple sensors with sampling frequencies as high as 1000 Hz. We could show that although there is a theoretical limitation to the transmissible data rate, in practice this limitation is not an issue and data loss is rare. We conclude that with modern communication protocols and with the increasingly powerful smartphones and wearables, a system like our SensorHub establishes an interoperability framework to adequately combine consumer-grade sensing hardware which enables observational studies in real life.
Foresight: a new approach based on the Z-number cognitive map

Abstract Foresight has recently emerged as one of the most attractive and practical fields of study, while being used to draw up a preferable future and formulate appropriate strategies for achieving predetermined goals. The present research aimed at providing a framework for foresight with a primary focus on the role of a cognitive approach and its combination with the concept of fuzzy cognitive map in the environments of uncertainty and ambiguity. The proposed framework consisted of the 3 phases: pre-foresight, foresight, and post-foresight. The main stage (foresight) focused on the role of imagination and intuition in drawing the future in the experts’ minds and depicting their perceptions above perceptions in the form of a fuzzy cognitive map influenced by variables related to the subject under study in order to determine a preferable future. The use of a Z-number concept and integrating it with fuzzy cognitive maps in the foresight-oriented decision-making space, which was mainly saturated with uncertainty and ambiguity, was one of the main strengths of the proposed framework in the current investigation. The present paper focused primarily on the evolution of expert’s knowledge with regard to the topic of foresight. The role of Z-number in various processes, from data collection to illustration, analysis, and aggregation of cognitive maps, was considered for gaining knowledge and understanding into the nature of future. Moreover, an ultimate objective was realized through identifying, aggregating, and selecting the variables from each expert’s perspective and then the relationship between each variable was determined in the main stage of foresight. Finally, the proposed framework was presented and explicated in the form of a case study, which revealed satisfactory results.
LTP: A New Active Learning Strategy for CRF-Based Named Entity Recognition

In recent years, deep learning has achieved great success in many natural language processing tasks, including named entity recognition. The shortcoming is that a large quantity of manually annotated data is usually required. Previous studies have demonstrated that active learning can considerably reduce the cost of data annotation, but there is still plenty of room for improvement. In real applications, we found that existing uncertainty-based active learning strategies have two shortcomings. First, these strategies prefer to choose long sequences explicitly or implicitly, which increases the annotation burden of annotators. Second, some strategies need to revise and modify the model to generate additional information for sample selection, which increases the workload of the developer and increases the training/prediction time of the model. In this paper, we first examine traditional active learning strategies in specific cases of Word2Vec-BiLSTM-CRF and Bert-CRF that have been widely used in named entity recognition on several typical datasets. Then, we propose an uncertainty-based active learning strategy called the lowest token probability (LTP), which combines the input and output of conditional random field (CRF) to select informative instances. LTP is a simple and powerful strategy that does not favor long sequences and does not need to revise the model. We test LTP on multiple real-world datasets, the experiment results show that compared with existing state-of-the-art selection strategies, LTP can reduce about 20% annotation tokens while maintaining competitive performance on both sentence-level accuracy and entity-level F1-score. Additionally, LTP significantly outperformed all other strategies in selecting valid samples, which dramatically reduced the invalid annotation times of the labelers.
Emotion-aware Contrastive Learning for Facial Action Unit Detection

Current AU datasets lack sufficiency and diversity because annotating facial action units (AUs) is laborious. The lack of labeled AU datasets bottlenecks the training of a discriminative AU detector. Compared with AUs, the basic emotional categories are relatively easy to annotate and they are highly correlated to AUs. To this end, we propose an Emotion-aware Contrastive Learning (EmoCo) framework to obtain representations that retain enough AU-related information. EmoCo leverages enormous and diverse facial images without AU annotations while labeled with the six universal facial expressions. EmoCo extends the prevalent self-supervised learning architecture of Momentum Contrast by simultaneously classifying the learned features into different emotional categories and distinguishing features within each emotional category in instance level. In the experiments, we train EmoCo using AffectNet dataset labeled with emotional categories. The EmoCo-learned features outperform other self-supervised learned representations in AU detection tasks on DISFA, BP4D, and GFT datasets. The EmoCo-pretrained models that fine-tuned on the AU datasets outperform most of the state-of-the-art AU detection methods.
pyCSEP: A Python Toolkit For Earthquake Forecast Developers

For government officials and the public to act on real-time forecasts of earthquakes, the seismological community needs to develop confidence in the underlying scientific hypotheses of the forecast generating models by assessing their predictive skill.For this purpose, the Collaboratory for the Study of Earthquake Predictability (CSEP) provides cyberinfrastructure and computational tools to evaluate earthquake forecasts.Here, we introduce pyCSEP, a Python package to help earthquake forecast developers embed model evaluation into the model development process.The package contains the following modules: (1) earthquake catalog access and processing, (2) data models for earthquake forecasts, (3) statistical tests for evaluating earthquake forecasts, and (4) visualization routines.pyCSEP can evaluate earthquake forecasts expressed as expected rates in space-magnitude bins, and simulation-based forecasts that produce thousands of synthetic seismicity catalogs.Most importantly, pyCSEP contains community-endorsed implementations of statistical tests to evaluate earthquake forecasts, and provides well defined file formats and standards to facilitate model comparisons.The toolkit will facilitate integrating new forecasting models into testing centers, which evaluate forecast models and prediction algorithms in an automated, prospective and independent manner, forming a critical step towards reliable operational earthquake forecasting.