Reviewerly/exHarmony huggingface dataset:
Position Bias Mitigation: A Knowledge-Aware Graph Model for Emotion Cause Extraction

The Emotion Cause Extraction (ECE) task aims to identify clauses which contain emotion-evoking information for a particular emotion expressed in text.We observe that a widely-used ECE dataset exhibits a bias that the majority of annotated cause clauses are either directly before their associated emotion clauses or are the emotion clauses themselves.Existing models for ECE tend to explore such relative position information and suffer from the dataset bias.To investigate the degree of reliance of existing ECE models on clause relative positions, we propose a novel strategy to generate adversarial examples in which the relative position information is no longer the indicative feature of cause clauses.We test the performance of existing models on such adversarial examples and observe a significant performance drop.To address the dataset bias, we propose a novel graph-based method to explicitly model the emotion triggering paths by leveraging the commonsense knowledge to enhance the semantic dependencies between a candidate clause and an emotion clause.Experimental results show that our proposed approach performs on par with the existing stateof-the-art methods on the original ECE dataset, and is more robust against adversarial attacks compared to existing models. 1
Cross-Modal Progressive Comprehension for Referring Segmentation

Given a natural language expression and an image/video, the goal of referring segmentation is to produce the pixel-level masks of the entities described by the subject of the expression. Previous approaches tackle this problem by implicit feature interaction and fusion between visual and linguistic modalities in a one-stage manner. However, human tends to solve the referring problem in a progressive manner based on informative words in the expression, i.e., first roughly locating candidate entities and then distinguishing the target one. In this paper, we propose a cross-modal progressive comprehension (CMPC) scheme to effectively mimic human behaviors and implement it as a CMPC-I (Image) module and a CMPC-V (Video) module to improve referring image and video segmentation models. For image data, our CMPC-I module first employs entity and attribute words to perceive all the related entities that might be considered by the expression. Then, the relational words are adopted to highlight the target entity as well as suppress other irrelevant ones by spatial graph reasoning. For video data, our CMPC-V module further exploits action words based on CMPC-I to highlight the correct entity matched with the action cues by temporal graph reasoning. In addition to the CMPC, we also introduce a simple yet effective Text-Guided Feature Exchange (TGFE) module to integrate the reasoned multimodal features corresponding to different levels in the visual backbone under the guidance of textual information. In this way, multi-level features can communicate with each other and be mutually refined based on the textual context. Combining CMPC-I or CMPC-V with TGFE can form our image or video version referring segmentation frameworks and our frameworks achieve new state-of-the-art performances on four referring image segmentation benchmarks and three referring video segmentation benchmarks respectively. Our code is available at https://github.com/spyflying/CMPC-Refseg.
Deep Automatic Natural Image Matting

Automatic image matting (AIM) refers to estimating the soft foreground from an arbitrary natural image without any auxiliary input like trimap, which is useful for image editing. Prior methods try to learn semantic features to aid the matting process while being limited to images with salient opaque foregrounds such as humans and animals. In this paper, we investigate the difficulties when extending them to natural images with salient transparent/meticulous foregrounds or non-salient foregrounds. To address the problem, a novel end-to-end matting network is proposed, which can predict a generalized trimap for any image of the above types as a unified semantic representation. Simultaneously, the learned semantic features guide the matting network to focus on the transition areas via an attention mechanism. We also construct a test set AIM-500 that contains 500 diverse natural images covering all types along with manually labeled alpha mattes, making it feasible to benchmark the generalization ability of AIM models. Results of the experiments demonstrate that our network trained on available composite matting datasets outperforms existing methods both objectively and subjectively. The source code and dataset are available at https://github.com/JizhiziLi/AIM.
DWUG: A large Resource of Diachronic Word Usage Graphs in Four Languages

Word meaning is notoriously difficult to capture, both synchronically and diachronically.In this paper, we describe the creation of the largest resource of graded contextualized, diachronic word meaning annotation in four different languages, based on 100,000 human semantic proximity judgments.We describe in detail the multi-round incremental annotation process, the choice for a clustering algorithm to group usages into senses, and possible -diachronic and synchronic -uses for this dataset.
Anomaly Detection Framework for Wearables Data: A Perspective Review on Data Concepts, Data Analysis Algorithms and Prospects

Wearable devices use sensors to evaluate physiological parameters, such as the heart rate, pulse rate, number of steps taken, body fat and diet. The continuous monitoring of physiological parameters offers a potential solution to assess personal healthcare. Identifying outliers or anomalies in heart rates and other features can help identify patterns that can play a significant role in understanding the underlying cause of disease states. Since anomalies are present within the vast amount of data generated by wearable device sensors, identifying anomalies requires accurate automated techniques. Given the clinical significance of anomalies and their impact on diagnosis and treatment, a wide range of detection methods have been proposed to detect anomalies. Much of what is reported herein is based on previously published literature. Clinical studies employing wearable devices are also increasing. In this article, we review the nature of the wearables-associated data and the downstream processing methods for detecting anomalies. In addition, we also review supervised and un-supervised techniques as well as semi-supervised methods that overcome the challenges of missing and un-annotated healthcare data.
A Fast Anomaly Diagnosis Approach Based on Modified CNN and Multisensor Data Fusion

In this article, a modified convolutional neural network (CNN) algorithm, namely 1D-GAPCNN-SVM, is proposed to address the early anomaly diagnosis problem. Considering the fact that traditional 2D-CNN based approaches contain too many model parameters and are not suitable for fast diagnosis applications using multisensor 1-D time-series measurements, 1D-CNN is introduced to deal with this problem. To reduce the number of parameters, a 1-D global average pooling layer is designed to substitute the fully connected layer with two or three layers. In order to further improve the diagnosis accuracy, a nonlinear multiclass support vector machine (SVM) is adopted to replace the traditional Softmax classifier as the final discriminator. Raw multisensor 1-D time-series data are directly fed into the diagnosis model, then the diagnosis result can be automatically generated. Two experiments, which are rolling bearing and GPS anomaly detection, have been conducted to demonstrate the effectiveness and the superior performance of the proposed method compared to the conventional SVM, K-nearest neighbor, deep neural network (DNN), and traditional 2D-CNN.
An effective railway intrusion detection method using dynamic intrusion region and lightweight neural network

It is significant to detect railway intrusion for railway safety. Unlike indoors, the railway is a complex environment with many interferences leading to false positives in the intrusion detection algorithm. However, there is a lack of research on effective intrusion detection in complex railway scenes. To solve these problems, the paper proposes RailDet, an effective railway intrusion detection method using dynamic intrusion region and lightweight neural network, which has high accuracy, low false-positives and high speed. RailDet consists of two stages. Firstly, the intrusion localization algorithm is used to obtain dynamic intrusion regions. Secondly, the object recognition based on lightweight neural network processes intrusion regions to obtain the coordinates and category. Finally, RailDet is validated on Chinese railways with various weathers and locations, which achieves 96.90% accuracy, 0.24% false-positive rate and 0.043 s/image. Moreover, the proposed method is very meaningful for the application of deep learning and not limited to railway.
Blockchain in human resource management: a systematic review and bibliometric analysis

This paper aims to develop some specific theoretical themes, which could be useful for HR personnel in adopting and implementing blockchain technology in the HR department. Total 126 published documents during the time interval 2016–2021 from the Scopus and Web of Science databases were explored to classify the relevant 25 records using the PRISMA flow diagram. Complete bibliometric information has been synthesised concerning titles, abstracts, and keywords of each of those 25 publications. VOSviewer was used to generate term co-occurrence network maps based on assembled bibliometric information. Four thematic clusters were evolved from the network maps and based on the thematic clusters; two theoretical themes were developed through associations. Two theoretical themes, i.e., Employee-Systems Interaction (ESI) and Blockchain Framework (BcF) for HR, were identified to become the prime movers for adopting and implementing blockchain in the domain of HRM. Later, for justifying the two theoretical themes and testing the robustness of the results, we reported the key terms thoroughly and critically going through the 25 publications. ESI and BcF for HR will add tremendous theoretical contributions to the existing body of knowledge in the fields of blockchain and strategic HRM. Directions for future research have been mentioned.
TLogic: Temporal Logical Rules for Explainable Link Forecasting on Temporal Knowledge Graphs

Conventional static knowledge graphs model entities in relational data as nodes, connected by edges of specific relation types. However, information and knowledge evolve continuously, and temporal dynamics emerge, which are expected to influence future situations. In temporal knowledge graphs, time information is integrated into the graph by equipping each edge with a timestamp or a time range. Embedding-based methods have been introduced for link prediction on temporal knowledge graphs, but they mostly lack explainability and comprehensible reasoning chains. Particularly, they are usually not designed to deal with link forecasting -- event prediction involving future timestamps. We address the task of link forecasting on temporal knowledge graphs and introduce TLogic, an explainable framework that is based on temporal logical rules extracted via temporal random walks. We compare TLogic with state-of-the-art baselines on three benchmark datasets and show better overall performance while our method also provides explanations that preserve time consistency. Furthermore, in contrast to most state-of-the-art embedding-based methods, TLogic works well in the inductive setting where already learned rules are transferred to related datasets with a common vocabulary.
A Normal Distributed Dwarf Mongoose Optimization Algorithm for Global Optimization and Data Clustering Applications

As data volumes have increased and difficulty in tackling vast and complicated problems has emerged, the need for innovative and intelligent solutions to handle these difficulties has become essential. Data clustering is a data mining approach that clusters a huge amount of data into a number of clusters; in other words, it finds symmetric and asymmetric objects. In this study, we developed a novel strategy that uses intelligent optimization algorithms to tackle a group of issues requiring sophisticated methods to solve. Three primary components are employed in the suggested technique, named GNDDMOA: Dwarf Mongoose Optimization Algorithm (DMOA), Generalized Normal Distribution (GNF), and Opposition-based Learning Strategy (OBL). These parts are used to organize the executions of the proposed method during the optimization process based on a unique transition mechanism to address the critical limitations of the original methods. Twenty-three test functions and eight data clustering tasks were utilized to evaluate the performance of the suggested method. The suggested method’s findings were compared to other well-known approaches. In all of the benchmark functions examined, the suggested GNDDMOA approach produced the best results. It performed very well in data clustering applications showing promising performance.
Multiclass sentiment analysis on COVID-19-related tweets using deep learning models

COVID-19 is an infectious disease with its first recorded cases identified in late 2019, while in March of 2020 it was declared as a pandemic. The outbreak of the disease has led to a sharp increase in posts and comments from social media users, with a plethora of sentiments being found therein. This paper addresses the subject of sentiment analysis, focusing on the classification of users' sentiment from posts related to COVID-19 that originate from Twitter. The period examined is from March until mid-April of 2020, when the pandemic had thus far affected the whole world. The data is processed and linguistically analyzed with the use of several natural language processing techniques. Sentiment analysis is implemented by utilizing seven different deep learning models based on LSTM neural networks, and a comparison with traditional machine learning classifiers is made. The models are trained in order to distinguish the tweets between three classes, namely negative, neutral and positive.
Zero-Shot Emotion Detection for Semi-Supervised Sentiment Analysis Using Sentence Transformers and Ensemble Learning

We live in a digitized era where our daily life depends on using online resources. Businesses consider the opinions of their customers, while people rely on the reviews/comments of other users before buying specific products or services. These reviews/comments are usually provided in the non-normative natural language within different contexts and domains (in social media, forums, news, blogs, etc.). Sentiment classification plays an important role in analyzing such texts collected from users by assigning positive, negative, and sometimes neutral sentiment values to each of them. Moreover, these texts typically contain many expressed or hidden emotions (such as happiness, sadness, etc.) that could contribute significantly to identifying sentiments. We address the emotion detection problem as part of the sentiment analysis task and propose a two-stage emotion detection methodology. The first stage is the unsupervised zero-shot learning model based on a sentence transformer returning the probabilities for subsets of 34 emotions (anger, sadness, disgust, fear, joy, happiness, admiration, affection, anguish, caution, confusion, desire, disappointment, attraction, envy, excitement, grief, hope, horror, joy, love, loneliness, pleasure, fear, generosity, rage, relief, satisfaction, sorrow, wonder, sympathy, shame, terror, and panic). The output of the zero-shot model is used as an input for the second stage, which trains the machine learning classifier on the sentiment labels in a supervised manner using ensemble learning. The proposed hybrid semi-supervised method achieves the highest accuracy of 87.3% on the English SemEval 2017 dataset.
A Novel Loss Function for Optical and SAR Image Matching: Balanced Positive and Negative Samples

Image matching is a primary technology for optical and synthetic aperture radar (SAR) image fusion but often shows limited performance due to the highly nonlinear differences between optical and SAR modalities. Recently, deep neural networks (DNNs) have been investigated to effectively extract nonlinear features for image matching tasks, where DNNs are trained based on the elaborated design of loss functions and a low loss value is often expected to obtain better image matching performance. In this letter, we first theoretically demonstrate that when the value of a state-of-the-art loss function decreases, the corresponding matching performance may not consistently improve due to the imbalanced effect of positive and negative samples. To tackle this issue, we proposed an improved loss function to train DNNs for image matching of SAR and optical images. We theoretically prove that the improved loss function ensures the improvement of the matching performance when the loss value decreases based on Taylor’s series expansion analysis. Experimental results on an open dataset with extensive optical and SAR image pairs show that 1) the proposed loss function is better than the original one in terms of image matching performance and 2) the combination of our loss function and existing multiscale convolutional gradient feature (MCGF)-based network provides better matching performance than other state-of-art approaches.
Aesthetic Considerations for Automated Platformer Design

We describe ANGELINA3, a system that can automatically develop games along a defined theme, by selecting appropriate multimedia content from a variety of sources and incorporating it into a game's design. We discuss these capabilities in the context of the FACE model for assessing progress in the building of creative systems, and discuss how ANGELINA3 can be improved through further work.
Improved Arabic speech recognition system through the automatic generation of fine-grained phonetic transcriptions

This paper aims at determining the best way to exploit the phonological properties of the Arabic language in order to improve the performance of the speech recognition system. One of the main challenges facing the processing of Arabic is the effect of the local context, which induces changes in the phonetic representation of a given text, thereby causing the recognition engine to misclassify it. The proposed solution is to develop a set of language-dependent grapheme-to-allophone rules that can predict such allophonic variations and hence provide a phonetic transcription that is sensitive to the local context for the automatic speech recognition system. The novel aspect of this method is that the pronunciation of each word is extracted directly from a context-sensitive phonetic transcription rather than a predefined dictionary that typically does not reflect the actual pronunciation of the word. The paper also aims at employing the stress feature as one of the supra-segmental characteristics of speech to enhance the acoustic modelling. The effectiveness of applying the proposed rules has been tested by comparing the performance of a dictionary based system against one using the automatically generated phonetic transcription. The research reported an average of 9.3% improvement in the system’s performance by eliminating the fixed dictionary and using the generated phonetic transcription to learn the phone probabilities. Marking the stressed vowels with separate stress markers leads to a further improvement of 1.7%.
Crowd Counting With Limited Labeling Through Submodular Frame Selection

Automated crowd counting is valuable for intelligent transportation systems, as it can help to improve the emergency planning and prevent congestion in transit hubs such as train stations and airports. Semi-supervised crowd counting aims to estimate the number of pedestrians in an ongoing scene using a combination of a small number of labeled frames and a large number of unlabeled ones. However, existing methods do not incorporate ways to effectively select informative frames as labeled training samples, resulting in low accuracy on unseen crowd scenes. We propose a submodular method to select the most informative frames from the image sequences of crowds. Specifically, the method selects the most representative images to guarantee the information coverage, by maximizing the similarities between the group of selected images and the image sequence. In addition, these frames are chosen to avoid redundancies and preserve diversity. Finally, our semi-supervised method incorporates graph Laplacian regularization and spatiotemporal constraints. Extensive experiments on three benchmark data sets demonstrate that our proposed approach achieves higher accuracy compared with the state-of-the-art regression methods and competitive performance with deep convolutional models, especially when the number of labeled data is exceptionally small.
QoE-Driven Adaptive K-Push for HTTP/2 Live Streaming

Dynamic adaptive streaming (DAS) over HTTP has been widely deployed over the Internet. However, due to the pull-based nature of HTTP/1.1, there exists intolerable streaming latency and high request overhead in the current DAS systems. With dynamic k-push, HTTP/2 live streaming promises to achieve low live latency with less overhead and small segment duration. In this paper, we propose a quality of experience (QoE) driven adaptive k-push mechanism (QK-Push) for HTTP/2 live streaming. The client just sends one request to set push length ($K$ ) and bitrate (v) parameters and the server would push back $K$ segments in a batch. To determine k-push parameters, a probabilistic buffer model is first designed to avoid buffer underflow/overflow. Also, three QoE objective functions are designed to ensure the high streaming quality (bitrate), playback continuity, and smoothness. QK-Push casts this multi-objective optimization problem as a Pareto optimal problem. To solve it, a Nash bargaining solution is designed to balance the needs for video quality, bitrate smoothness, and request overhead. Finally, the segments in each push cycle are selected by solving the Nash problem with a discrete space Lagrangian method. We implement an HTTP/2 live streaming prototype system, with the QK-Push algorithm over modified dash.js and media presentation description. To evaluate the performances, the extensive live streaming experiments are carried out over a controllable network test bed and real Internet trace. The results demonstrate that the proposed QK-Push algorithm is able to improve the average bitrate up to 13%, reduce the bitrate oscillations up to 81%, decrease the startup delay up to 58%, and increase the estimate the mean opinion score up to 12% compared to the current HTTP/1.1 system.