Legal Judgment Elements Extraction Approach with Law Article-aware Mechanism

Legal judgment elements extraction (LJEE) aims to identify the different judgment features from the fact description in legal documents automatically, which helps to improve the accuracy and interpretability of the judgment results. In real court rulings, judges usually need to scan both the fact descriptions and the law articles repeatedly to find out the relevant information, and it is hard to acquire the key judgment features quickly, so legal judgment elements extraction is a crucial and challenging task for legal judgment prediction. However, most existing methods follow the text classification framework, which fails to model the attentive relations of the law articles and the legal judgment elements. To address this issue, we simulate the working process of human judges, and propose a legal judgment elements extraction method with a law article-aware mechanism, which captures the complex semantic correlations of the law article and the legal judgment elements. Experimental results show that our proposed method achieves significant improvements than other state-of-the-art baselines on the element recognition task dataset. Compared with the BERT-CNN model, the proposed “All labels Law Articles Embedding Model (ALEM)” improves the accuracy, recall, and F1 value by 0.5, 1.4 and 1.0, respectively.

A hierarchical RCNN for vehicle and vehicle license plate detection and recognition

<span>Vehicle and vehicle license detection obtained incredible achievements during recent years that are also popularly used in real traffic scenarios, such as intelligent traffic monitoring systems, auto parking systems, and vehicle services. Computer vision attracted much attention in vehicle and vehicle license detection, benefit from image processing and machine learning technologies. However, the existing methods still have some issues with vehicle and vehicle license plate recognition, especially in a complex environment. In this paper, we propose a multivehicle detection and license plate recognition system based on a hierarchical region convolutional neural network (RCNN). Firstly, a higher level of RCNN is employed to extract vehicles from the original images or video frames. Secondly, the regions of the detected vehicles are input to a lower level (smaller) RCNN to detect the license plate. Thirdly, the detected license plate is split into single numbers. Finally, the individual numbers are recognized by an even smaller RCNN. The experiments on the real traffic database validated the proposed method. Compared with the commonly used all-in-one deep learning structure, the proposed hierarchical method deals with the license plate recognition task in multiple levels for sub-tasks, which enables the modification of network size and structure according to the complexity of sub-tasks. Therefore, the computation load is reduced.</span>

On-Road Driver Emotion Recognition Using Facial Expression

With the development of intelligent automotive human-machine systems, driver emotion detection and recognition has become an emerging research topic. Facial expression-based emotion recognition approaches have achieved outstanding results on laboratory-controlled data. However, these studies cannot represent the environment of real driving situations. In order to address this, this paper proposes a facial expression-based on-road driver emotion recognition network called FERDERnet. This method divides the on-road driver facial expression recognition task into three modules: a face detection module that detects the driver’s face, an augmentation-based resampling module that performs data augmentation and resampling, and an emotion recognition module that adopts a deep convolutional neural network pre-trained on FER and CK+ datasets and then fine-tuned as a backbone for driver emotion recognition. This method adopts five different backbone networks as well as an ensemble method. Furthermore, to evaluate the proposed method, this paper collected an on-road driver facial expression dataset, which contains various road scenarios and the corresponding driver’s facial expression during the driving task. Experiments were performed on the on-road driver facial expression dataset that this paper collected. Based on efficiency and accuracy, the proposed FERDERnet with Xception backbone was effective in identifying on-road driver facial expressions and obtained superior performance compared to the baseline networks and some state-of-the-art networks.

Distance-Entropy: An Effective Indicator for Selecting Informative Data

Smart agriculture is inseparable from data gathering, analysis, and utilization. A high-quality data improves the efficiency of intelligent algorithms and helps reduce the costs of data collection and transmission. However, the current image quality assessment research focuses on visual quality, while ignoring the crucial information aspect. In this work, taking the crop pest recognition task as an example, we proposed an effective indicator of distance-entropy to distinguish the good and bad data from the perspective of information. Many comparative experiments, considering the mapping feature dimensions and base data sizes, were conducted to testify the validity and robustness of this indicator. Both the numerical and the visual results demonstrate the effectiveness and stability of the proposed distance-entropy method. In general, this study is a relatively cutting-edge work in smart agriculture, which calls for attention to the quality assessment of the data information and provides some inspiration for the subsequent research on data mining, as well as for the dataset optimization for practical applications.

Recruitment of a long-term memory supporting neural network during repeated maintenance of a multi-item abstract visual image in working memory

Humans can flexibly transfer information between different memory systems. Information in visual working memory (VWM) can for instance be stored in long-term memory (LTM). Conversely, information can be retrieved from LTM and temporarily held in WM when needed. It has previously been suggested that a neural transition from parietal- to midfrontal activity during repeated visual search reflects transfer of information from WM to LTM. Whether this neural transition indeed reflects consolidation and is also observed when memorizing a rich visual scene (rather than responding to a single target), is not known. To investigate this, we employed an EEG paradigm, in which abstract six-item colour-arrays were repeatedly memorized and explicitly visualized, or merely attended to. Importantly, we tested the functional significance of a potential neural shift for longer-term consolidation in a subsequent recognition task. Our results show a gradually enhanced- and sustained modulation of the midfrontal P170 component and a decline in parietal CDA, during repeated WM maintenance. Improved recollection/visualization of memoranda upon WM-cueing, was associated with contralateral parietal- and right temporal activity. Importantly, only colour-arrays previously held in WM, induced a greater midfrontal P170-response, together with left temporal- and late centro-parietal activity, upon re-exposure. These findings provide evidence for recruitment of an LTM-supporting neural network which facilitates visual WM maintenance.

Culture shapes preschoolers’ emotion recognition but not emotion comprehension: a cross-cultural study in Germany and Singapore

Contemporary approaches suggest that emotions are shaped by culture. Children growing up in different cultures experience culture-specific emotion socialization practices. As a result, children growing up in Western societies (e.g., US or UK) rely on explicit, semantic information, whereas children from East Asian cultures (e.g., China or Japan) are more sensitive towards implicit, contextual cues when confronted with others’ emotions. The aim of the present study was to investigate two aspects of preschoolers’ emotion understanding (emotion recognition and emotion comprehension) in a cross-cultural setting. To this end, Singaporean and German preschoolers were tested with an emotion recognition task employing European-American and East Asian child’s faces and the Test of Emotion Comprehension (TEC; Pons et al., 2004). In total, 129 German and Singaporean preschoolers (mean age 5.34 years) participated. Results indicate that preschoolers were able to recognize emotions of child’s faces above chance level. In line with previous findings, Singaporean preschoolers were more accurate in recognizing emotions from facial stimuli compared to German preschoolers. Accordingly, Singaporean preschoolers outperformed German preschoolers in the Recognition component of the TEC. The overall performance in TEC did not differ between the two samples. Findings of this study provide further evidence that emotion understanding is culturally shaped in accordance with culture-specific emotion socialization practices.

A Framework for Pedestrian Attribute Recognition Using Deep Learning

The pedestrian attribute recognition task is becoming more popular daily because of its significant role in surveillance scenarios. As the technological advances are significantly more than before, deep learning came to the surface of computer vision. Previous works applied deep learning in different ways to recognize pedestrian attributes. The results are satisfactory, but still, there is some scope for improvement. The transfer learning technique is becoming more popular for its extraordinary performance in reducing computation cost and scarcity of data in any task. This paper proposes a framework that can work in surveillance scenarios to recognize pedestrian attributes. The mask R-CNN object detector extracts the pedestrians. Additionally, we applied transfer learning techniques on different CNN architectures, i.e., Inception ResNet v2, Xception, ResNet 101 v2, ResNet 152 v2. The main contribution of this paper is fine-tuning the ResNet 152 v2 architecture, which is performed by freezing layers, last 4, 8, 12, 14, 20, none, and all. Moreover, data balancing techniques are applied, i.e., oversampling, to resolve the class imbalance problem of the dataset and analysis of the usefulness of this technique is discussed in this paper. Our proposed framework outperforms state-of-the-art methods, and it provides 93.41% mA and 89.24% mA on the RAP v2 and PARSE100K datasets, respectively.

The teaching and learning of listening and vocabulary in EFL classes at a Chinese university

<p><b>Listening is an important skill for second language learners of any language. To develop listening skills effectively, research suggests using a more process-oriented than product-oriented approach to teaching listening. That is, placing greater emphasis on developing learner awareness and strategic competence than on answering listening comprehension questions. The present study investigates how listening is taught by two teachers in the context of Chinese tertiary English foreign language (EFL) classes, where listening tends to be taught as a discreet skill. Another focus of the research is how the relationship between vocabulary and listening is understood and addressed in this context. While it is well known that vocabulary knowledge is needed for and can be learnt through listening, less is known about how the vocabulary support is provided and vocabulary knowledge is gained in such listening classes.</b></p> <p>This research involved three main areas of investigation. The first area investigated the teaching of listening. It involved a content analysis of listening materials in the textbook (e.g., listening texts and listening activities), followed by classroom observations of listening instruction practices, and post-lesson interviews with the teachers and their learners about their beliefs about teaching and learning listening. Findings showed that a product-oriented approach dominated the textbook materials, the classroom practices and the beliefs of the teachers and learners.</p> <p>The second area concerns the vocabulary demands of these listening classes. This involved a corpus-based analysis of the frequency and kinds of vocabulary in the textbook, followed by measurement of the learners’ vocabulary size (i.e., the Vocabulary Size Test by Nation & Beglar, 2007) and knowledge (i.e., a recognition task in the Yes/No format). The corpus analyses results showed that: (1) vocabulary knowledge of 3000-word families was required to comprehend the textbook; (2) high frequency vocabulary made up the majority of the words in the textbook. The VST results showed that, on average, the learners’ written receptive size ranged from 5000 to 7000-word families. The pre-lesson Yes/No task results showed that the students had difficulty recognizing a substantial number of the words they met in the textbook.</p> <p>The third area investigated the nature of vocabulary support and vocabulary learning in the listening class. Firstly, an analysis of the teachers’ classroom practices from observation data relating to vocabulary was carried out. Secondly, interview data from the teachers was examined for evidence of their beliefs about vocabulary and listening. Thirdly, post-lesson interview data with learners and data from a post-test repeat of the vocabulary recognition task were examined to find out more about the learners’ perceptions of vocabulary in listening class and the vocabulary learning gains they made in these classes. Findings revealed that the learners relied on the glossaries to prepare for listening classes. They also expected vocabulary instruction from the teachers, so long as it did not distract from listening activity completion. Both teachers primarily used translation to provide vocabulary support, but differed markedly in the amount of vocabulary support they provided. In both classes, significant vocabulary gains were found in a comparison of the pre-and-post lesson Yes/No task results. The vocabulary-related episodes in the listening classes were a notable influence on these learning gains.</p> <p>This research has pedagogical implications for the EFL listening classroom. The findings highlight the mutually reinforcing influences of textbook design and teacher beliefs on how listening is taught. These influences, in turn, shape how learners perceive the process of developing their L2 listening skills. With respect to vocabulary and listening, the findings also suggest that even where the lexical demands of listening appear to be well within the vocabulary level of the learners, there is considerable potential for vocabulary learning from listening classes. Teachers and learners alike are likely to benefit from systematically building on this potential. Future research could further investigate L2 learners’ behaviors and perceptions in the listening class, and examine their vocabulary knowledge in the spoken form.</p>

The teaching and learning of listening and vocabulary in EFL classes at a Chinese university

<p><b>Listening is an important skill for second language learners of any language. To develop listening skills effectively, research suggests using a more process-oriented than product-oriented approach to teaching listening. That is, placing greater emphasis on developing learner awareness and strategic competence than on answering listening comprehension questions. The present study investigates how listening is taught by two teachers in the context of Chinese tertiary English foreign language (EFL) classes, where listening tends to be taught as a discreet skill. Another focus of the research is how the relationship between vocabulary and listening is understood and addressed in this context. While it is well known that vocabulary knowledge is needed for and can be learnt through listening, less is known about how the vocabulary support is provided and vocabulary knowledge is gained in such listening classes.</b></p> <p>This research involved three main areas of investigation. The first area investigated the teaching of listening. It involved a content analysis of listening materials in the textbook (e.g., listening texts and listening activities), followed by classroom observations of listening instruction practices, and post-lesson interviews with the teachers and their learners about their beliefs about teaching and learning listening. Findings showed that a product-oriented approach dominated the textbook materials, the classroom practices and the beliefs of the teachers and learners.</p> <p>The second area concerns the vocabulary demands of these listening classes. This involved a corpus-based analysis of the frequency and kinds of vocabulary in the textbook, followed by measurement of the learners’ vocabulary size (i.e., the Vocabulary Size Test by Nation & Beglar, 2007) and knowledge (i.e., a recognition task in the Yes/No format). The corpus analyses results showed that: (1) vocabulary knowledge of 3000-word families was required to comprehend the textbook; (2) high frequency vocabulary made up the majority of the words in the textbook. The VST results showed that, on average, the learners’ written receptive size ranged from 5000 to 7000-word families. The pre-lesson Yes/No task results showed that the students had difficulty recognizing a substantial number of the words they met in the textbook.</p> <p>The third area investigated the nature of vocabulary support and vocabulary learning in the listening class. Firstly, an analysis of the teachers’ classroom practices from observation data relating to vocabulary was carried out. Secondly, interview data from the teachers was examined for evidence of their beliefs about vocabulary and listening. Thirdly, post-lesson interview data with learners and data from a post-test repeat of the vocabulary recognition task were examined to find out more about the learners’ perceptions of vocabulary in listening class and the vocabulary learning gains they made in these classes. Findings revealed that the learners relied on the glossaries to prepare for listening classes. They also expected vocabulary instruction from the teachers, so long as it did not distract from listening activity completion. Both teachers primarily used translation to provide vocabulary support, but differed markedly in the amount of vocabulary support they provided. In both classes, significant vocabulary gains were found in a comparison of the pre-and-post lesson Yes/No task results. The vocabulary-related episodes in the listening classes were a notable influence on these learning gains.</p> <p>This research has pedagogical implications for the EFL listening classroom. The findings highlight the mutually reinforcing influences of textbook design and teacher beliefs on how listening is taught. These influences, in turn, shape how learners perceive the process of developing their L2 listening skills. With respect to vocabulary and listening, the findings also suggest that even where the lexical demands of listening appear to be well within the vocabulary level of the learners, there is considerable potential for vocabulary learning from listening classes. Teachers and learners alike are likely to benefit from systematically building on this potential. Future research could further investigate L2 learners’ behaviors and perceptions in the listening class, and examine their vocabulary knowledge in the spoken form.</p>

Effects of mild-to-moderate sensorineural hearing loss and signal amplification on vocal emotion recognition in middle-aged–older individuals

Previous research has shown deficits in vocal emotion recognition in sub-populations of individuals with hearing loss, making this a high priority research topic. However, previous research has only examined vocal emotion recognition using verbal material, in which emotions are expressed through emotional prosody. There is evidence that older individuals with hearing loss suffer from deficits in general prosody recognition, not specific to emotional prosody. No study has examined the recognition of non-verbal vocalization, which constitutes another important source for the vocal communication of emotions. It might be the case that individuals with hearing loss have specific difficulties in recognizing emotions expressed through prosody in speech, but not non-verbal vocalizations. We aim to examine whether vocal emotion recognition difficulties in middle- aged-to older individuals with sensorineural mild-moderate hearing loss are better explained by deficits in vocal emotion recognition specifically, or deficits in prosody recognition generally by including both sentences and non-verbal expressions. Furthermore a, some of the studies which have concluded that individuals with mild-moderate hearing loss have deficits in vocal emotion recognition ability have also found that the use of hearing aids does not improve recognition accuracy in this group. We aim to examine the effects of linear amplification and audibility on the recognition of different emotions expressed both verbally and non-verbally. Besides examining accuracy for different emotions we will also look at patterns of confusion (which specific emotions are mistaken for other specific emotion and at which rates) during both amplified and non-amplified listening, and we will analyze all material acoustically and relate the acoustic content to performance. Together these analyses will provide clues to effects of amplification on the perception of different emotions. For these purposes, a total of 70 middle-aged-older individuals, half with mild-moderate hearing loss and half with normal hearing will perform a computerized forced-choice vocal emotion recognition task with and without amplification.