Reviewerly/exHarmony huggingface dataset:
Boosting Multi-Label Image Classification with Complementary Parallel Self-Distillation

Multi-Label Image Classification (MLIC) appro-aches usually exploit label correlations to achieve good performance. However, emphasizing correlation like co-occurrence may overlook discriminative features and lead to model overfitting. In this study, we propose a generic framework named Parallel Self-Distillation (PSD) for boosting MLIC models. PSD decomposes the original MLIC task into several simpler MLIC sub-tasks via two elaborated complementary task decomposition strategies named Co-occurrence Graph Partition (CGP) and Dis-occurrence Graph Partition (DGP). Then, the MLIC models of fewer categories are trained with these sub-tasks in parallel for respectively learning the joint patterns and the category-specific patterns of labels. Finally, knowledge distillation is leveraged to learn a compact global ensemble of full categories with these learned patterns for reconciling the label correlation exploitation and model overfitting. Extensive results on MS-COCO and NUS-WIDE datasets demonstrate that our framework can be easily plugged into many MLIC approaches and improve performances of recent state-of-the-art approaches. The source code is released at https://github.com/Robbie-Xu/CPSD.
EmmDocClassifier: Efficient Multimodal Document Image Classifier for Scarce Data

Document classification is one of the most critical steps in the document analysis pipeline. There are two types of approaches for document classification, known as image-based and multimodal approaches. Image-based document classification approaches are solely based on the inherent visual cues of the document images. In contrast, the multimodal approach co-learns the visual and textual features, and it has proved to be more effective. Nonetheless, these approaches require a huge amount of data. This paper presents a novel approach for document classification that works with a small amount of data and outperforms other approaches. The proposed approach incorporates a hierarchical attention network (HAN) for the textual stream and the EfficientNet-B0 for the image stream. The hierarchical attention network in the textual stream uses dynamic word embedding through fine-tuned BERT. HAN incorporates both the word level and sentence level features. While earlier approaches rely on training on a large corpus (RVL-CDIP), we show that our approach works with a small amount of data (Tobacco-3482). To this end, we trained the neural network at Tobacco-3482 from scratch. Therefore, we outperform the state-of-the-art by obtaining an accuracy of 90.3%. This results in a relative error reduction rate of 7.9%.
MEIM: Multi-partition Embedding Interaction Beyond Block Term Format for Efficient and Expressive Link Prediction

Knowledge graph embedding aims to predict the missing relations between entities in knowledge graphs. Tensor-decomposition-based models, such as ComplEx, provide a good trade-off between efficiency and expressiveness, that is crucial because of the large size of real world knowledge graphs. The recent multi-partition embedding interaction (MEI) model subsumes these models by using the block term tensor format and provides a systematic solution for the trade-off. However, MEI has several drawbacks, some of which carried from its subsumed tensor-decomposition-based models. In this paper, we address these drawbacks and introduce the Multi-partition Embedding Interaction iMproved beyond block term format (MEIM) model, with independent core tensor for ensemble effects and soft orthogonality for max-rank mapping, in addition to multi-partition embedding. MEIM improves expressiveness while still being highly efficient, helping it to outperform strong baselines and achieve state-of-the-art results on difficult link prediction benchmarks using fairly small embedding sizes. The source code is released at https://github.com/tranhungnghiep/MEIM.
Deep learning kidney segmentation with very limited training data using a cascaded convolution neural network

Deep learning segmentation requires large datasets with ground truth. Image annotation is time consuming and leads to shortages of ground truth data for clinical imaging. This study is to investigate the feasibility of kidney segmentation using deep learning convolution neural network (CNN) models trained with MR images from only a few subjects.A total of 60 subjects from two cohorts were included in this study. The first cohort of 20 subjects from publicly available data was used for training and testing. The second cohort of 40 subjects with renal masses from our institution was used for testing only. A few-shot deep learning approach using 3D augmentation was investigated. T1-weighted images in the first cohort were used for training and testing. Cascaded CNN networks were trained using images from one, three, and six subjects, respectively. Images for the remaining subjects were used for testing. Images in the second cohort were utilized for testing only. Dice and Jaccard coefficients were generated to evaluate the performance of CNN models. Statistical analyses for segmentation metrics among different approaches were performed.Our approach achieved mean Dice coefficients of 0.85 using a single training subject and 0.91 with six training subjects. Compared to a single Unet, the cascaded network significantly improved the results using a single training subject (Dice, 0.759 vs. 0.835; p<0.001) and three subjects (0.864 vs. 0.893; p = 0.015) in the first cohort, and the results for the second cohort (0.821 vs. 0.873; p = 0.008).Our few-shot kidney segmentation approach using 3D augmentation achieved a good performance even using a single Unet. Furthermore, the cascaded network significantly improved the performance of segmentation and was superior to a single Unet in certain cases. Our approach provides a promising solution to segmentation in medical imaging when the number of ground truth masks is limited.
Corporate Social Responsibility and Financial Performance: Evidence from Pakistani Listed Banks

This study intends to examine corporate social responsibility (CSR) in Pakistan's banking sector, CSR disclosure practices and their impact on financial performance.For the study, relevant data was collected from the banks' annual reports, financial websites, the State Bank of Pakistan (SBP) and the Pakistan Stock Exchange (PSE) from 2008 to 2018.The methods utilized in this research study were content analysis and panel data techniques.The results indicate an increase in overall CSR disclosure by all banks in the sample and the findings suggest the involvement of commercial banks in CSR activities, and its proper disclosure has helped to improve their accounting-based financial performance proxied by the return on equity (ROE) and return on assets (ROA).The research findings contribute to a better understanding of the CSR practices in the financial sector of an emerging country, which makes a dynamic effort to develop its financial culture and can encourage rapprochement with Pakistan's financial sector.
NewsBERT: Distilling Pre-trained Language Model for Intelligent News Application

Pre-trained language models (PLMs) like BERT have made great progress in NLP.News articles usually contain rich textual information, and PLMs have the potentials to enhance news text modeling for various intelligent news applications like news recommendation and retrieval.However, most existing PLMs are in huge size with hundreds of millions of parameters.Many online news applications need to serve millions of users with low latency, which poses great challenge to incorporating PLMs in these scenarios.Knowledge distillation techniques can compress a large PLM into a much smaller one and meanwhile keep good performance.However, existing language models are pre-trained and distilled on general corpus like Wikipedia, which have gaps with the news domain and may be suboptimal for news intelligence.In this paper, we propose NewsBERT, which can distill PLMs for efficient and effective news intelligence.In our approach, we design a teacher-student joint learning and distillation framework to collaboratively learn both teacher and student models, where the student model can learn from the learning experience of the teacher model.In addition, we propose a momentum distillation method by incorporating the gradients of teacher model into the update of student model to better transfer the knowledge learned by the teacher model.Thorough experiments on two real-world datasets with three tasks show that NewsBERT can empower various intelligent news applications with much smaller models.
Systematic Overview of Password Security Problems

Alphanumeric passwords are the first line of defense in security for most information systems.Morris and Thompson identified passwords as a weak point in an Information System's security, 35 years ago.Their findings showed that 86% of the passwords were too short, contained lowercase letters only, digits only, were easily found in dictionaries and/or easily compromised.The objective of this paper is to perform a systematic literature review in the area of passwords and passwords security, in order to determine whether alphanumeric passwords are still weak, short and simple.The results show that only 42 out of 63 relevant studies propose a solid solution to deal with the identified problems with alphanumeric passwords, but only 17 have statistically verified it.We find that only 3 studies have a representative sample, which may indicate that the results of the majority of the studies cannot be generalized.We conclude that users and their alphanumeric passwords are still the "weakest link" in the "security chain".Careless security behavior, involving password reuse, writing down and sharing passwords, along with an erroneous knowledge concerning what constitutes a secure password, are the main problems related to the issue of password security.
Video Anomaly Detection for Surveillance Based on Effective Frame Area

Video anomaly detection aims to recognise and analyse the video sequences to classify the normal and abnormal frames. This technology can efficiently reduce the human labour to discover the anomalies in surveillance systems and is widely applied in financial, public security and transport sectors. However, video anomaly detection performance is often degraded by the dataset quality, especially for small objects in video sequences. Besides, the computational cost of the classification model would be required as low as possible. In this paper, we proposed information fusion with a joint model which contains motion estimation, object detection and adversarial learning to detect anomalies in two video datasets: UCSD PED1 and PED2. Experimental results confirm the proposed method outperforms the state-of-the-art methods with the additional advantages in reduced computation cost.
Trends in e-tools and resources for translators and interpreters

Recent years have witnessed the rapid development of translation technology, which has achieved tremendous success in both academia and industry. Due to these rapid advances, it is clear that technology has already profoundly affected the way translation is produced. More recently, translation technology – ranging from translation-specific technologies such as MT to more general-purpose speech technologies and cloud computing – calls into question some of the assumptions about how, by whom, and to what level of quality translation should be done. Commercially viable translation today is almost entirely the computer-aided variety, given the ubiquitous use of computers in text production practices (O’Hagan, 2020).
Mobile robot navigation based on tangent circle algorithm

A difficult issue in mobile robot navigation or path planning in static or dynamic environment is to find the path from the starting point to the target avoiding collisions with obstacles. This paper presents a new approach for solving the problem of mobile robot navigation in dynamic environment based on tangent circle algorithm. To plan the trajectory of the robot, the algorithm relies on the creation of different types of paths between the robot and the target. In fact, during the motion path, the robot switches between a straight line and/or circular trajectory in order to avoid obstacles and reach the goal. The proposed approach is implemented on different simulation scenarios involving state or dynamic obstacles in the environment. Results show that the novel method is efficient and that the mobile robot succeeds to reach its target in a collision-free path in different cases.
Towards recent developments in the methods, metrics and datasets of software fault prediction

The world of software systems is amplified with the changing environment magnifying the demand for quality software. Software fault prediction is a requisite activity ensuring the development of economic, efficient and quality software. It is the procedure for the development of models which help to identify faults in modules during early phases of software development lifecycle. Software fault prediction is one of the most prevalent research disciplines. The existing study in this domain includes numerous modelling techniques and software metrics for the early predictions of software faults. This paper aims to explore some of the prominent studies for software fault prediction in the existing literature. In this paper, software fault prediction papers since 1990 to 2017 are investigated. The paper includes the analysis of the studies having empirical validation and a good source of publication. The paper reflects the methods, metrics, and datasets available in the literature for software fault prediction. In addition, the modelling techniques based on traditional and computational intelligence-based methods are also reviewed. This paper is an endeavour to assemble the existing techniques and metrics of software fault prediction with a motive to assist researchers for easy evaluation of suitable metrics for their own research scenarios.
A Systemic Perspective for Sonification Aesthetics

For more than twenty-five years, the sonification field has been attempting to establish itself as a primary body of knowledge communicating through sound. Despite multiple efforts to embrace the interdisciplinary nature of the field and the subjective nature of sound, we wonder: is the tendency for dealing with such challenges through an objective, functional communication, with a single interpretation criterion, limiting the epistemic boundaries of action? How can a subjectively perceived medium such as sound be embraced in all its aesthetic dimensions? We propose a conceptual transition through the reframing of a sonification as a living system for creating aesthetic experiences. This will be achieved by drawing notions from phenomenology, embodied perception, human-computer interaction and soundscape theory. A systemic sonification distinguishes itself as an ever-evolving system built on dynamic structures that actively responds to changes in its environment and interactions from surrounding beings. Driven by a series of emerging concepts of non-linearity, networks, nested systems and intertwined relationships, the system’s resilience and adaptability grows with each interaction, recentring the human protagonist as the weaver of his/her aesthetic experience through a self-transcendent process that expands the perception field.
DNN Based Moth Search Optimization for Video Forgery Detection

Recently, video tampering process becomes easier due to the rapid advancements in user-friendly editing software and multimedia technology (e.g., Mokey by Imagineer Systems, and Photoshop and Premiere by Adobe). This technologies may highly tamper the original videos, so that the audience gets mislead. Nowadays, MPEG-4 codec is included in a large proportions of video cameras and surveillance systems. Therefore the double compression detection process included as an initial step in the video forensic is receiving a high significance. In this paper, the double compression artifacts is detected by adopting the Markov based features, which identifies the interpolated original videos. The double compressed frames are then segmented by introducing an SLIC super pixel segmentation technique. Here, the feature extraction is performed by applying the scale information that is obtained from the multi-scale Gabor filters. The features of this Gabor scale accurately extract the structural features and also reduce too much of redundancy. This extracted features are then provided to DNN (deep neural network) for forgery detection. In this video forensic process, DNN classifier is included for forgery detection. The CNN classifier is included in various existing forgery detection techniques. But, in our work we include DNN because it contains number of hidden layers which provide accurate results for this forgery detection process. To improve the DNN performance, Moth Search Optimization (MSO) algorithm is introduced in this forgery detection technique. Every nook and corner of this world we can able to find the surveillance cameras for security purpose. But, some fraudsters perform forgeries in this recorded videos for their own benefits. To identify this, a lot of forgery detection techniques are coming into existence. So in this work, we introduce the DNN based MSO to perform the forgery detection in videos.This implementation is processed in python simulation platform. The parametric evaluations are taken in terms of F1-Score, average accuracy, Precision, Recall and. Experimental results will provide improved performance in video forgery detection.
Virtual reality as an urban tourism destination marketing tool

Although there is a breadth of research demonstrating the effectiveness of virtual reality (VR) as a destination marketing tool, there are limited exploratory studies providing empirical evidence from the perspective of tourism marketers in this specific context. The technological, organisational, environmental (TOE) framework has confirmed explanatory power for studies exploring organisational adoption of various technologies; however, the framework's association with VR research is limited. Responding to this dual gap, this exploratory study explores both internal and external factors associated with VR adoption from tourism marketers' perspectives by employing TOE framework as a theoretical base. Interviews were conducted with seven tourism marketers from three urban tourism destinations in the UK. The preliminary study findings indicate that environmental (perceived competitive pressures and global market appeal) factors are positively associated with tourism marketers' VR adoption, while other TOE factors are not. Finally, theoretical contribution, practical implications and avenues for further research are provided.
Sentiment Analysis on Social Media Big Data With Multiple Tweet Words

The main objective of this paper is Analyze the reviews of Social Media Big Data of E-Commerce product’s. And provides helpful result to online shopping customers about the product quality and also provides helpful decision making idea to the business about the customer’s mostly liking and buying products. This covers all features or opinion words, like capitalized words, sequence of repeated letters, emoji, slang words, exclamatory words, intensifiers, modifiers, conjunction words and negation words etc available in tweets. The existing work has considered only two or three features to perform Sentiment Analysis with the machine learning technique Natural Language Processing (NLP). In this proposed work familiar Machine Learning classification models namely Multinomial Naïve Bayes, Support Vector Machine, Decision Tree Classifier, and, Random Forest Classifier are used for sentiment classification. The sentiment classification is used as a decision support system for the customers and also for the business.
Workshop on Context-Aware Recommender Systems

Contextual information has been widely recognized as an important modeling dimension both in social sciences and in computing. In particular, the role of context has been recognized in enhancing recommendation results and retrieval performance. While a substantial amount of existing research has focused on context-aware recommender systems (CARS), many interesting problems remain under-explored. The CARS 2020 workshop provides a venue for presenting and discussing approaches for the next generation of CARS and application domains that may require the use of novel types of contextual information and cope with their dynamic properties in online environments.
Implementation of Smart Agriculture using CloudIoT and its Geotagging on Android Platform

Agriculture is the backbone of several countries like India and most of the economy depends directly or indirectly on agriculture for a living.But these days several crops are significantly low and farmers are difficult to analyze the variations of soil moisture on yield in every day.The solution of the mentioned problem is to place the sensors in the yield and to collect the readings of moisture in every day by implementing the technologies: Internet of Things (IoT), Cloud Computing technology and GSM (Global Service Message) communication.In this paper, explain how these technologies and GSM will help the farmers in areas where there is an acute water problem or where water conservation is essential.The system is used Wi-Fi and GSMbased communications to send the sensor data to Cloud and mobile phones.The hardware is developed using TI"s First Wi-Fi on-chip microcontroller: CC3200 LaunchPad, SIM900A GSM module, soil moisture sensor, motor and mobile phone.The software is developed using Energia IDE.Thing Speak Cloud Computing technology is used in this work for storing the sensor values in.CSV format and also shows the graphical representation of the real-time variation of moisture on mobile using mobile application and Laptop, SIM 900A module is used sent the moisture levels to the farmer"s registered mobile numbers.The system also automatically turns ON/OFF the motor based on the variation of moisture on yields.The whole system is easily implemented on yields and gets the moisture information even in remote areas in the world.
Automatic system for Arabic Sign Language Recognition and translation to spoken One

The deaf-mutes person suffers from inability to speak as well as inability to hear others.This disability creates a barrier that prevents the disabled from integrating with society.Therefore, the owners of this disability attempt to break this barrier by using many methods to try to communicate with others.The most famous of these methods is the sign language which is relying on the body language.The disabled person uses facial expressions and hand gestures to express his needs.In this project, we will try to build a computerized system depending on the depth-measuring cameras and computer vision techniques to capturing and segmenting the pictures of the facial expressions and hand gestures.The segmented gestures are classified and stored in an electronic library after they have been recognized and linked with their corresponding spoken and written Arabic words.The computerized system will be learned with gestures and its corresponding spoken and written Arabic words.A gesture dataset of 40-segmented words that frequently used in the life by the hearing-impaired person was utilized for testing the system.We get in our consideration that each word may has a different occlusion state.The experimental results illustrated that the proposed system has a recognition accuracy over 90%