Reviewerly/exHarmony huggingface dataset:
NAREOR: The Narrative Reordering Problem

Many implicit inferences exist in text depending on how it is structured that can critically impact the text's interpretation and meaning. One such structural aspect present in text with chronology is the order of its presentation. For narratives or stories, this is known as the narrative order. Reordering a narrative can impact the temporal, causal, event-based, and other inferences readers draw from it, which in turn can have strong effects both on its interpretation and interestingness. In this paper, we propose and investigate the task of Narrative Reordering (NAREOR) which involves rewriting a given story in a different narrative order while preserving its plot. We present a dataset, NAREORC, with human rewritings of stories within ROCStories in non-linear orders, and conduct a detailed analysis of it. Further, we propose novel task-specific training methods with suitable evaluation metrics. We perform experiments on NAREORC using state-of-the-art models such as BART and T5 and conduct extensive automatic and human evaluations. We demonstrate that although our models can perform decently, NAREOR is a challenging task with potential for further exploration. We also investigate two applications of NAREOR: generation of more interesting variations of stories and serving as adversarial sets for temporal/event-related tasks, besides discussing other prospective ones, such as for pedagogical setups related to language skills like essay writing and applications to medicine involving clinical narratives.
Demo Abstract: Vision-aided 3D Human Pose Estimation with RFID

Radio Frequency (RF) based human pose estimation techniques have been proposed to generate human pose without using a camera, so people will no longer worry about their privacy. Compared with other RF sensing based systems, Radio Frequency Identification (RFID) provides a promising solution for RF based human pose estimation. RFID tags can be used as wearable sensors because of their small size. The interference caused by the multipath effect is much smaller in the RFID system. The cost of RFID systems is also lower than the advanced radar based systems such as FMCW radar. Thus, we propose the RFID-Pose system for tracking the movements of multiple human limbs in realtime [1]. In the proposed system, RFID tags are attached to the target human joints. The movement of the tags are captured by the phase variations in the responses from each tag. The human pose is reconstructed by estimating rotation angles from RFID data and the initial human skeleton. The vision data will not be needed anymore in the testing phase, so the user's privacy can be well protected.
“Killing Me” Is Not a Spoiler: Spoiler Detection Model using Graph Neural Networks with Dependency Relation-Aware Attention Mechanism

Several machine learning-based spoiler detection models have been proposed recently to protect users from spoilers on review websites.Although dependency relations between context words are important for detecting spoilers, current attention-based spoiler detection models are insufficient for utilizing dependency relations.To address this problem, we propose a new spoiler detection model called SDGNN that is based on syntax-aware graph neural networks.In the experiments on two realworld benchmark datasets, we show that our SDGNN outperforms the existing spoiler detection models.
A Little Pretraining Goes a Long Way: A Case Study on Dependency Parsing Task for Low-resource Morphologically Rich Languages

Neural dependency parsing has achieved remarkable performance for many domains and languages.The bottleneck of massive labeled data limits the effectiveness of these approaches for low resource languages.In this work, we focus on dependency parsing for morphological rich languages (MRLs) in a low-resource setting.Although morphological information is essential for the dependency parsing task, the morphological disambiguation and lack of powerful analyzers pose challenges to get this information for MRLs.To address these challenges, we propose simple auxiliary tasks for pretraining.We perform experiments on 10 MRLs in low-resource settings to measure the efficacy of our proposed pretraining method and observe an average absolute gain of 2 points (UAS) and 3.6 points (LAS). 1
Filtration network: A frame sampling strategy via deep reinforcement learning for video captioning

Recently many methods use encoder-decoder framework for video captioning, aiming to translate short videos into natural language. These methods usually use equal interval frame sampling. However, lacking a good efficiency in sampling, it has a high temporal and spatial redundancy, resulting in unnecessary computation cost. In addition, the existing approaches simply splice different visual features on the fully connection layer. Therefore, features cannot be effectively utilized. In order to solve the defects, we proposed filtration network (FN) to select key frames, which is trained by deep reinforcement learning algorithm actor-double-critic. According to behavior psychology, the core idea of actor-double-critic is that the behavior of agent is determined by both the external environment and the internal personality. It avoids the phenomenon of unclear reward and sparse feedback in training because it gives steady feedback after each action. The key frames are sent to combine codec network (CCN) to generate sentences. The operation of feature combination in CCN make fusion of visual features by complex number representation to make good semantic modeling. Experiments and comparisons with other methods on two datasets (MSVD/MSR-VTT) show that our approach achieves better performance in terms of four metrics, BLEU-4, METEOR, ROUGE-L and CIDEr.
Emotion-Aware, Emotion-Agnostic, or Automatic: Corpus Creation Strategies to Obtain Cognitive Event Appraisal Annotations.

Appraisal theories explain how the cognitive evaluation of an event leads to a particular emotion. In contrast to theories of basic emotions or affect (valence/arousal), this theory has not received a lot of attention in natural language processing. Yet, in psychology it has been proven powerful: Smith and Ellsworth (1985) showed that the appraisal dimensions attention, certainty, anticipated effort, pleasantness, responsibility/control and situational control discriminate between (at least) 15 emotion classes. We study different annotation strategies for these dimensions, based on the event-focused enISEAR corpus (Troiano et al., 2019). We analyze two manual annotation settings: (1) showing the text to annotate while masking the experienced emotion label; (2) revealing the emotion associated with the text. Setting 2 enables the annotators to develop a more realistic intuition of the described event, while Setting 1 is a more standard annotation procedure, purely relying on text. We evaluate these strategies in two ways: by measuring inter-annotator agreement and by fine- tuning RoBERTa to predict appraisal variables. Our results show that knowledge of the emotion increases annotators’ reliability. Further, we evaluate a purely automatic rule-based labeling strategy (inferring appraisal from annotated emotion classes). Training on automatically assigned labels leads to a competitive performance of our classifier, even when tested on manual annotations. This is an indicator that it might be possible to automatically create appraisal corpora for every domain for which emotion corpora already exist.
Coverage Path Planning Based on Improved Exact Cellular Decomposition Method in Ocean Survey

In order to improve coverage traversal efficiency of ocean survey for AUV, an improved exact cellular decomposition method is proposed. It combines exact cellular decomposition method and Differential Evolution, which reduce the task execution time and alleviate the problem of energy shortage due to obtaining the optimal cells traversal order. The simulation results verify that the improved exact cellular decomposition method performs better than exact cellular decomposition method in coverage path planning task.
JUNLP@DravidianLangTech-EACL2021: Offensive Language Identification in Dravidian Langauges

Offensive language identification has been an active area of research in natural language processing. With the emergence of multiple social media platforms offensive language identification has emerged as a need of the hour. Traditional offensive language identification models fail to deliver acceptable results as social media contents are largely in multilingual and are code-mixed in nature. This paper tries to resolve this problem by using IndicBERT and BERT architectures, to facilitate identification of offensive languages for Kannada-English, Malayalam-English, and Tamil-English code-mixed language pairs extracted from social media. The presented approach when evaluated on the test corpus provided precision, recall, and F1 score for language pair Kannada-English as 0.62, 0.71, and 0.66, respectively, for language pair Malayalam-English as 0.77, 0.43, and 0.53, respectively, and for Tamil-English as 0.71, 0.74, and 0.72, respectively.
A Literature Survey and Analysis on Social Engineering Defense Mechanisms and Infosec Policies

Social engineering attacks can be severe and hard to detect. Therefore, to prevent such attacks, organizations should be aware of social engineering defense mechanisms and security policies. To that end, the authors developed a taxonomy of social engineering defense mechanisms, designed a survey to measure employee awareness of these mechanisms, proposed a model of Social Engineering InfoSec Policies (SE-IPs), and designed a survey to measure the incorporation level of these SE-IPs. After analyzing the data from the first survey, the authors found that more than half of employees are not aware of social engineering attacks. The paper also analyzed a second set of survey data, which found that on average, organizations incorporated just over fifty percent of the identified formal SE-IPs. Such worrisome results show that organizations are vulnerable to social engineering attacks, and serious steps need to be taken to elevate awareness against these emerging security threats.
H2Rec: Homogeneous and Heterogeneous Network Embedding Fusion for Social Recommendation

Due to the problems of data sparsity and cold start in traditional recommendation systems, social information is introduced.From the perspective of heterogeneity, it reflects the indirect relationship between users, and from the perspective of homogeneity, it reflects the direct relationship between users.At present, most social recommendation is based on the homogeneity or heterogeneity of social networks.Few studies consider both of them at the same time, and the deep structure of social networks is not extensively exploited and comprehensively explore.To address these issues, we propose a unified H2Rec model to fuse homogeneous and heterogeneous information for recommendations in social networks.Considering the rich semantics reflected by metapaths in heterogeneous information and the wealth of social information reflected by homogeneous information, the proposed method uses a random walk strategy to generate node sequences in a homogeneous information network and a random walk strategy guided by metapaths to generate node sequences in a heterogeneous information network (HIN).Finally, we combine the two parts into a unified model for social recommendation.Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed model.
Good Classifiers are Abundant in the Interpolating Regime

Within the machine learning community, the widely-used uniform convergence framework has been used to answer the question of how complex, over-parameterized models can generalize well to new data. This approach bounds the test error of the worst-case model one could have fit to the data, but it has fundamental limitations. Inspired by the statistical mechanics approach to learning, we formally define and develop a methodology to compute precisely the full distribution of test errors among interpolating classifiers from several model classes. We apply our method to compute this distribution for several real and synthetic datasets, with both linear and random feature classification models. We find that test errors tend to concentrate around a small typical value $\varepsilon^*$, which deviates substantially from the test error of the worst-case interpolating model on the same datasets, indicating that bad classifiers are extremely rare. We provide theoretical results in a simple setting in which we characterize the full asymptotic distribution of test errors, and we show that these indeed concentrate around a value $\varepsilon^*$, which we also identify exactly. We then formalize a more general conjecture supported by our empirical findings. Our results show that the usual style of analysis in statistical learning theory may not be fine-grained enough to capture the good generalization performance observed in practice, and that approaches based on the statistical mechanics of learning may offer a promising alternative.
CA‐PMG: Channel attention and progressive multi‐granularity training network for fine‐grained visual classification

Fine-grained visual classification is challenging due to the inherently subtle intra-class object variations. To solve this issue, a novel framework named channel attention and progressive multi-granularity training network, is proposed. It first exploits meaningful feature maps through the channel attention module and captures multi-granularity features by the progressive multi-granularity training module. For each feature map, the channel attention module is proposed to explore channel-wise correlation. This allows the model to re-weight the channels of the feature map according to the impact of their semantic information on performance. Furthermore, the progressive multi-granularity training module is introduced to fuse features cross multi-granularity. And the fused features pay more attention to the subtle differences between images. The model can be trained efficiently in an end-to-end manner without bounding box or part annotations. Finally, comprehensive experiments are conducted to show that the method achieves state-of-the-art performances on the CUB-200-2011, Stanford Cars, and FGVC-Aircraft datasets. Ablation studies demonstrate the effectiveness of each part in our module.
Intelligent Cloud Agents in Multi-participant Conversations for Cyber-Physical Exploitation of Cultural Heritage

Most of all existing solutions for developing conversational systems have been designed for dyadic and one-on-one communication with users. In this work, we present the design of mechanisms and interaction models for enabling dyadic agents to converse in multi-participants chat. We aim at complementing the visit of archaeological sites and distributed museums with a social dimension fostered by intelligent agents in one cyber-physical world with live and remote users who enjoy the assets of the cultural environment. A cloud-based prototype implementation is also provided.
Research and evaluation of the measurement uncertainty with the pipeline robot

Abstract The pipeline robot used to detect and maintain the pipeline is a novel technical means, which changes the single traditional mode of excavation sampling inspection of pipeline. This detection technology not only improved the accuracy of pipeline detection but also is easy to analyze the causes of pipeline defects for pipeline engineering management maintenance staff. It is possible to carry out a review of defect and develop pipeline maintenance solutions. Using this method can eliminate the security hidden danger before repairing or replacing section. Thus, the use of a pipeline can save a lot of maintenance costs and reduce pipeline maintenance cost, which can also ensure the safety of people’s life and property. In addition, this method can also reduce the toxic gas or liquid leakage to protect environmental pollution. Therefore, the research of a pipeline detection robot has an important scientific significance and social value. In this paper, our group has a base of the long-term research with the geometric measurement technology, so the measurement uncertainty of the common pipeline robot is studied. The measurement uncertainty of error about positioning is defined as U =0.59mm+9×10 −2 L, k =2 (L unit is m), and the uncertainty evaluation of angle errors measured, including the forward and backward pitching angle, left-right tilt angle, is U =0.10°+4.6×10 −3 θ ( θ unit is °).
Optical Prior-Based Underwater Object Detection with Active Imaging

Underwater object detection plays an important role in research and practice, as it provides condensed and informative content that represents underwater objects. However, detecting objects from underwater images is challenging because underwater environments significantly degenerate image quality and distort the contrast between the object and background. To address this problem, this paper proposes an optical prior-based underwater object detection approach that takes advantage of optical principles to identify optical collimation over underwater images, providing valuable guidance for extracting object features. Unlike data-driven knowledge, the prior in our method is independent of training samples. The fundamental novelty of our approach lies in the integration of an image prior and the object detection task. This novelty is fundamental to the satisfying performance of our approach in underwater environments, which is demonstrated through comparisons with state-of-the-art object detection methods.
Resource Utilization Prediction in Cloud Computing using Hybrid Model

In cloud environment, maximum utilization of resource is possible with good resource management strategies. Workload prediction plays a vital role in estimating the actual resource required for successful execution of an application on cloud. Most of the existing works concentrated on predicting workloads which either showed clear seasonality/trend or for irregular workload patterns. This paper presents a new perspective in forecasting both seasonal and non-seasonal workloads. To accomplish this, a hybrid prediction model which is a combination of statistical and machine learning technique is proposed. Suppose the seasonality exists in the workload pattern, Seasonal Auto Regressive Integrated Moving Average (SARIMA) model is applied for prediction. For non-seasonal workloads Long Short-Term Memory networks (LSTM) or AutoRegressive Integrated Moving Average (ARIMA) model is used based on the results of normality test. This paper presents a prediction model which forecasts the actual resource required for diverse time intervals of daily, hourly and minutes utilization. The experimental results confirm that accuracy of the prediction of LSTM model outperformed ARIMA for irregular workload patterns. The SARIMA model accurately forecasts the resource usage for forthcoming days. This work actually helps the cloud service provider (CSP) to analyze the workload and predict accordingly to avoid over or under provisioning of the cloud resources.
Using Iterative Discrete Wavelet Transform to Improve Trace Element Analysis by XRF Spectrometer

X-ray fluorescence (XRF) spectrometry has certain difficulties of detecting trace amount material components accurately when measuring material samples composed of variable elements, mainly due to low Signal to Noise Ratio (SNR) issues of the characteristic spectroscopic peaks from the measurement. In this paper, a novel method called background noise reduction using Iterative Discrete Wavelet Transform (IDWT) methodology for trace element material analysis by advanced X-ray fluorescence spectrometer is proposed to improve SNR, thereby decreasing the Limit of Detection (LOD) for elemental qualitative analysis, and then achieve a more accurate quantitative analysis of trace elemental concentration. This paper utilized handheld X-ray fluorescence spectrometer to obtain the content of Sulphur in petroleum and 4 major pollution elements in soil. A total of 81 standard samples were collected and measured. The hardware parameters of the instrument were adjusted to optimize the SNR before background noise reduction. Experimental results illustrate that X-ray tube parameters have great influences on the calibration regression. Different X-ray tube voltages were tested and the optimal results were achieved at 5kV. Furthermore, IDWT algorithm was implemented and the optimal results were achieved by wavelet base “db5” and “sym4” with 7 level decomposition. The calibration regression curves were established for the Sulphur in petroleum. The regression R 2 values after IDWT were increased effectively when compared with original data without IDWT. Finally, the experimental results demonstrate a very good linearity between the weight contents of the target material and the XRF spectral characteristic peak intensity, and also it is found the LOD for Sulphur in petroleum can be reduced when combing with the IDWT.
An Approach Based on the Improved SVM Algorithm for Identifying Malware in Network Traffic

Due to the growth and popularity of the internet, cyber security remains, and will continue, to be an important issue. There are many network traffic classification methods or malware identification approaches that have been proposed to solve this problem. However, the existing methods are not well suited to help security experts effectively solve this challenge due to their low accuracy and high false positive rate. To this end, we employ a machine learning-based classification approach to identify malware. The approach extracts features from network traffic and reduces the dimensionality of the features, which can effectively improve the accuracy of identification. Furthermore, we propose an improved SVM algorithm for classifying the network traffic dubbed Optimized Facile Support Vector Machine (OFSVM). The OFSVM algorithm solves the problem that the original SVM algorithm is not satisfactory for classification from two aspects, i.e., parameter optimization and kernel function selection. Therefore, in this paper, we present an approach for identifying malware in network traffic, called Network Traffic Malware Identification (NTMI). To evaluate the effectiveness of the NTMI approach proposed in this paper, we collect four real network traffic datasets and use a publicly available dataset CAIDA for our experiments. Evaluation results suggest that the NTMI approach can lead to higher accuracy while achieving a lower false positive rate compared with other identification methods. On average, the NTMI approach achieves an accuracy of 92.5% and a false positive rate of 5.527%.
Multi‐modal broad learning for material recognition

Material recognition plays an important role in the interaction between robots and the external environment. For example, household service robots need to replace humans in the home environment to complete housework, so they need to interact with daily necessities and obtain their material performance. Images provide rich visual information about objects; however, it is often difficult to apply when objects are not visually distinct. In addition, tactile signals can be used to capture multiple characteristics of objects, such as texture, roughness, softness, and friction, which provides another crucial way for perception. How to effectively integrate multi-modal information is an urgent problem to be addressed. Therefore, a multi-modal material recognition framework CFBRL-KCCA for target recognition tasks is proposed in the paper. The preliminary features of each model are extracted by cascading broad learning, which is combined with the kernel canonical correlation learning, considering the differences among different models of heterogeneous data. Finally, the open dataset of household objects is evaluated. The results demonstrate that the proposed fusion algorithm provides an effective strategy for material recognition.