Reviewerly/exHarmony huggingface dataset:
Interpretable Topic Extraction and Word Embedding Learning Using Non-Negative Tensor DEDICOM

Unsupervised topic extraction is a vital step in automatically extracting concise contentual information from large text corpora. Existing topic extraction methods lack the capability of linking relations between these topics which would further help text understanding. Therefore we propose utilizing the Decomposition into Directional Components (DEDICOM) algorithm which provides a uniquely interpretable matrix factorization for symmetric and asymmetric square matrices and tensors. We constrain DEDICOM to row-stochasticity and non-negativity in order to factorize pointwise mutual information matrices and tensors of text corpora. We identify latent topic clusters and their relations within the vocabulary and simultaneously learn interpretable word embeddings. Further, we introduce multiple methods based on alternating gradient descent to efficiently train constrained DEDICOM algorithms. We evaluate the qualitative topic modeling and word embedding performance of our proposed methods on several datasets, including a novel New York Times news dataset, and demonstrate how the DEDICOM algorithm provides deeper text analysis than competing matrix factorization approaches.
BDS-GCN: Efficient Full-Graph Training of Graph Convolutional Nets with Partition-Parallelism and Boundary Sampling

Graph Convolutional Networks (GCNs) have emerged as the state-of-the-art model for graph-based learning tasks. However, it is still challenging to train GCNs at scale, limiting their applications to real-world large graphs and hindering the exploration of deeper and more sophisticated GCN architectures. While it can be natural to leverage graph partition and distributed training for tackling this challenge, this direction has only been slightly touched on previously due to the unique challenge posed by the GCN structures, especially the excessive amount of boundary nodes in each partitioned subgraph, which can easily explode the required memory and communications for distributed training of GCNs. To this end, we propose BDS-GCN, a method that adopts unbiased boundary sampling strategy to enable efficient and scalable distributed GCN training while maintaining the full-graph accuracy. Empirical evaluations and ablation studies validate the effectiveness of the proposed BDS-GCN, e.g., boosting the throughput by up-to 500% and reducing the memory usage by up-to 58% for distributed GCN training, while achieving the same accuracy, as compared with the state-of-the-art methods. We believe our BDS-GCN would open up a new paradigm for enabling GCN training at scale. All code will be released publicly upon acceptance.
Adaptive Self-training for Neural Sequence Labeling with Few Labels

Neural sequence labeling is an important technique employed for many Natural Language Processing (NLP) tasks, such as Named Entity Recognition (NER), slot tagging for dialog systems and semantic parsing. Large-scale pre-trained language models obtain very good performance on these tasks when fine-tuned on large amounts of task-specific labeled data. However, such large-scale labeled datasets are difficult to obtain for several tasks and domains due to the high cost of human annotation as well as privacy and data access constraints for sensitive user applications. This is exacerbated for sequence labeling tasks requiring such annotations at token-level. In this work, we develop techniques to address the label scarcity challenge for neural sequence labeling models. Specifically, we develop self-training and meta-learning techniques for training neural sequence taggers with few labels. While self-training serves as an effective mechanism to learn from large amounts of unlabeled data -- meta-learning helps in adaptive sample re-weighting to mitigate error propagation from noisy pseudo-labels. Extensive experiments on six benchmark datasets including two for massive multilingual NER and four slot tagging datasets for task-oriented dialog systems demonstrate the effectiveness of our method. With only 10 labeled examples for each class for each task, our method obtains 10% improvement over state-of-the-art systems demonstrating its effectiveness for the low-resource setting.
Google’s (Forgotten) Monopoly – Ad Technology Services on the Open Web

This paper focuses on online display advertising, whereby publishers display advertisements on their website against remuneration. This form of advertising represents a critically important source of revenues for publishers, from large news organisations to online game producers to blogs, offering valuable content to Internet users. Given the importance of online display advertising to publishers, it is no wonder that this area has been the subject of intense discussion among stakeholders and has raised the attention of competition authorities. In particular, concerns have expressed that publishers do not receive their fair share of advertising revenues due to the large fees that are captured by the “ad tech” companies intermediating between advertisers and publishers. This paper provides an overview of the online display advertising landscape, and explores whether Google, the leading ad tech providers, has engaged in potential exclusionary and exploitative conduct.
Deep Concatenated Residual Networks for Improving Quality of Halftoning-Based BTC Decoded Image

This paper presents a simple technique for improving the quality of the halftoning-based block truncation coding (H-BTC) decoded image. The H-BTC is an image compression technique inspired from typical block truncation coding (BTC). The H-BTC yields a better decoded image compared to that of the classical BTC scheme under human visual observation. However, the impulsive noise commonly appears on the H-BTC decoded image. It induces an unpleasant feeling while one observes this decoded image. Thus, the proposed method presented in this paper aims to suppress the occurring impulsive noise by exploiting a deep learning approach. This process can be regarded as an ill-posed inverse imaging problem, in which the solution candidates of a given problem can be extremely huge and undetermined. The proposed method utilizes the convolutional neural networks (CNN) and residual learning frameworks to solve the aforementioned problem. These frameworks effectively reduce the impulsive noise occurrence, and at the same time, it improves the quality of H-BTC decoded images. The experimental results show the effectiveness of the proposed method in terms of subjective and objective measurements.
Reverse Image Search for the Fashion Industry Using Convolutional Neural Networks

A.I. is becoming a hot topic in the rapidly developing digital world of today. Its applications are extensive and ever yet further growing as our understanding of it increases. Reverse image search is another interesting upcoming topic, of which its applications make our lives significantly easier in a number of business sectors and even in our day to day lives. Its implementation is when instead of text, pictures are used as search input, and similar images are returned as the result to help users of a system easier find what they are looking for. In this paper we trained our own Convolutional Neural Network(CNN), and designed an image search engine for use in the fashion industry.
Ternary Echo Hiding in Audio Files

We propose a new family of echo hiding procedures for working with ternary watermarked audio files. Most attention is paid to the case when the image is used as a watermark. The possibility of using a melody for this purpose is also mentioned. A human is supposed to be a detector to check for a watermark in an audio file. The approach employs infinite impulse response (IIR) filters of a particular form that allow multiple symbols to be inserted into the container's fragment. The suggested method surpasses the classical implementation of echo hiding parameters regarding the method's payload and resistance to various attacks.
Privacy Violation Issues in Re-publication of Modification Datasets

Privacy preservation models are often proposed to address privacy violation issues in datasets that are focused on performing one-time data releasing. Thus, if datasets are allowed to update (modify) the data of them when the new data become available and released on performing multiple times, privacy preservation models could be insufficient. For this reason, the aims of this work are to identify the vulnerabilities of privacy preservation models in dynamic datasets which are based on data updating (data modifying), and further propose a new algorithm that can address privacy violation issues in the re-publication of modified datasets.
Scene Detection in De Boer Historical Photo Collection

This paper demonstrates how transfer learning can be used to improve scene detection applied to a historical press photo collection.After applying transfer learning to a pre-trained Places-365 ResNet-50 model, we achieve a Top-1 accuracy of .68 and a Top-5 accuracy of .89 on our data set, which consists of 132 categories.In addition to describing our annotation and training strategy, we also reflect on the use of transfer learning and the evaluation of computer vision models for heritage institutes.
Learning to Abstract and Predict Human Actions

Human activities are naturally structured as hierarchies unrolled over time. For action prediction, temporal relations in event sequences are widely exploited by current methods while their semantic coherence across different levels of abstraction has not been well explored. In this work we model the hierarchical structure of human activities in videos and demonstrate the power of such structure in action prediction. We propose Hierarchical Encoder-Refresher-Anticipator, a multi-level neural machine that can learn the structure of human activities by observing a partial hierarchy of events and roll-out such structure into a future prediction in multiple levels of abstraction. We also introduce a new coarse-to-fine action annotation on the Breakfast Actions videos to create a comprehensive, consistent, and cleanly structured video hierarchical activity dataset. Through our experiments, we examine and rethink the settings and metrics of activity prediction tasks toward unbiased evaluation of prediction systems, and demonstrate the role of hierarchical modeling toward reliable and detailed long-term action forecasting.
Detecting Emotion of Users’ Analyzing Social Media Bengali Comments Using Deep Learning Techniques

Bengali is one of the most spoken languages in the world. Nowadays, many people share their thoughts, emotions, and ideas through social media using the Bengali language. Facebook comments are one of the sources of sharing opinions among them. The goal of this study is to classify positive and negative emotions accurately with the help of the Facebook comments dataset. For this work, different deep learning models called Stacked Long short-term memory (LSTM), Stacked LSTM with 1D convolution, CNN with pre-trained word embeddings and, RNN with pre-trained word embedding have been used. The RNN model with pre-trained word embeddings has performed the best among all models with 98.3% accuracy. Other models do not perform well as the RNN model.
An Empirical Study on the Curriculum Design for Dance Teachers in China

This study examines the employment situation of dance teachers after undergraduate study to reflect the curriculum design and training philosophy of dance teacher education in higher education in China.This study further suggests a dance education model that focuses on popularizing dancing, in another word, "an education through dancing".Through this, this study also discusses "dancing lessons for quality education", a new educational ideal and model proposed by department of education in China.
De Finettian Logics of Indicative Conditionals Part II: Proof Theory and Algebraic Semantics

Abstract In Part I of this paper, we identified and compared various schemes for trivalent truth conditions for indicative conditionals, most notably the proposals by de Finetti (1936) and Reichenbach (1935, 1944) on the one hand, and by Cooper ( Inquiry , 11 , 295–320, 1968) and Cantwell ( Notre Dame Journal of Formal Logic , 49 , 245–260, 2008) on the other. Here we provide the proof theory for the resulting logics and , using tableau calculi and sequent calculi, and proving soundness and completeness results. Then we turn to the algebraic semantics, where both logics have substantive limitations: allows for algebraic completeness, but not for the construction of a canonical model, while fails the construction of a Lindenbaum-Tarski algebra. With these results in mind, we draw up the balance and sketch future research projects.
Comparing Dependency-based Compositional Models with Contextualized Word Embeddings

In this article, we compare two different strategies to contextualize the meaning of words in a sentence: both distributional models that make use of syntax-based methods following the Principle of Compositionality and Transformer technology such as BERT-like models. As the former methods require controlled syntactic structures, the two approaches are compared against datasets with syntactically fixed sentences, namely subject-predicate and subject-predicate-object expressions. The results show that syntax-based compositional approaches working with syntactic dependencies are competitive with neural-based Transformer models, and could have a greater potential when trained and developed using the same resources.
Deep Learning-based Hand Pose Estimation from 2D Image

Human hands, the most significant part of a human body, are used as a healthy and secure medium in modern technology for the development of human-computer interaction. There are various technical applications like virtual reality, touch-free writing, aerial handwriting, sign language which are performed based on the user's hand gestures. The ability to recognize hand shape and motion can be the basis for understanding the hand gesture control. In the above context, we propose a deep learning technique for hand gesture estimation that identifies hand pose from input images that reveal depth information. This system uses the convolutional neural network (CNN) to detect 3D hand gestures with the help of palm joints and fingertips. We analyzed the positions of 2D joints and fingertips and determined the depth information. From the experimental results, the proposed system is able to present an accurate estimate of the depth information from 2D hand images.
Efficient Depth Data Coding Method Based on Plane Modeling for Intra Prediction

In this article, we propose a depth data coding method by plane modeling. The plane modeling is a prediction method based on a plane estimation from depth pixels in a block. The plane modeling improves the intra-picture prediction for depth data comparing with intra modes of conventional coding standards. The plane modeling coefficients, which are the information about the estimated plane, are required to be provided during the depth data coding. The plane modeling coefficients are predicted from neighboring depth pixels. A prediction error of the plane modeling coefficients is calculated through the plane modeling for the selected pixels. If the prediction error is below a certain threshold, then the plane modeling coefficients are applied to the plane modeling for the block. From the simulation results, we verify that the proposed method achieves up to 6.76% bit rate saving in the same coding distortion condition compared to VVC test model.
Process Hierarchy for GSD Based User-Centric Requirements Elicitation Frameworks

The user-centric requirements elicitation frameworks facilitate the product users during the requirements elicitation process, which positively contributes in the improvement of whole requirements elicitation process. The applications of user-centric requirements elicitation frameworks in global software development projects have demonstrated the improvements in the quality of requirements elicitation process. This article presents a comprehensive hierarchical structure of internal processes running during the requirements elicitation process in user-centric frameworks in GSD contexts. The article presents a multi-layered hierarchy of different internal processes involved during the whole activity of requirements elicitation. The presented structure will help the software development industry to better optimize and manage the requirements elicitation process for globally distributed software development projects. The presented process hierarchy will also help the software engineers to perform the requirements elicitation process with more rigor and comprehensiveness.
A Novel RE Teams Selection Process For User-Centric Requirements Elicitation Frameworks Based On Big-Five Personality Assessment Model

The requirements elicitation team selection process is considered as the key factor for planning the activities of work tasks performed by the requirements engineering teams. Consequently, the selection of most appropriate requirements elicitation teams for user-centric requirements elicitation frameworks plays a critical role in improving the quality of requirements elicitation frameworks. This paper presents an innovative process for requirements elicitation team's selection for user-centric requirements elicitation frameworks. The presented process uses big-five personality assessment model to select the most appropriate requirements elicitation teams for such frameworks. The presented team selection process takes input from the software development industry data advertised for the induction of requirements analysts and computes the most relevant big-five personality traits for requirements engineering teams. The big-five personality traits predicted by the proposed process have been benchmarked with the predictions of the already available processes. The results obtained by using the proposed process have been found substantially improved to better support the requirements engineering works.
Easy Approach to a Responsive Website Design Using Artisteer Application Software

Easy approach to a responsive website design using artisteer application software - Progressively, the unique mobile internet
Design of an Aerial Manipulation System with Robotic Claw for Search and Rescue Operations

The concept of Aerial Manipulation refers to the design of Unmanned Aerial Vehicle to work as a combined system in collaboration with an object grasping device. Over the past two decades there have been tremendous discoveries regarding the different methodologies used for aerial manipulation which yield the best results in terms of flight time, stability, object grasping and releasing. The Quadcopter is intended to serve as an aid in Search and Rescue Operations where the Drone's presence will function as a better alternative to human resources. The focus of the proposed work is to enable the Drone to have a simple claw type grasping mechanism which is specially designed keeping in mind important designing specifications of the UAV such as stability, payload lifting capability etc. which will be single handedly controlled via the Flight Controller which is an ARM Cortex M4 based Microcontroller and with the help of an open source Autopilot powered GUI to set the navigation points via the telemetry module.
Research on Application of Intelligent Tracing System for IVD Reagents Based on Blockchain Technology

In recent years, the IVD industry has developed rapidly based on the increasing market demand, and plays an important role in disease prevention, clinical diagnosis, health monitoring and guiding treatment. Therefore, followed quality and safety issues are highly concerned. The unique advantages of blockchain technology, decentralization, distrust and non-tampering, can write into trusted node data in every link covering production, circulation and usage of IVD reagents, and establish a distributed ledger with full backup, which makes the anti-conterfeiting and traceability for IVD reagents possible. We discuss whole process intelligent tracing system for IVD reagents based on blockchain technology. Through the strong mechanism of pre-supervision and post-punishment, the source of reagents can be traced, quality and responsibility can be investigated, and the medical inspection quality and diagnostic safety can be guarded.
A Blockchain-based Crowdsourced Task Assessment Framework using Smart Contract

In today's world, crowdsourcing is a highly rising paradigm where mass people are engaged in solving a problem.Though this system has a lot of advantages, yet people are not interested in working on this platform.Thus, we survey people to find out the constraints of this platform and the main reason behind their unwillingness.59% of people think that security and privacy is the major challenge of a crowdsourcing platform.Therefore, we propose a blockchain-based crowdsourced system which can provide security and privacy to the user's information.We also have used a smart contract to verify the task so that the users get the exact output that they have wanted.We implemented our system and compared the performance with the existing systems.Our proposed approach outperforms the current methods in terms of cost and properties.
A lossless self-recovery watermarking scheme with JPEG-LS compression

Due to the rapid development of application for medical images in recent years, the security of medical images becomes more vital. In this paper, a fragile watermarking scheme for medical images is proposed. The proposed scheme first divides the whole medical image into ROI (Regions of Interest) and RONI (Regions of Non-interest). Then the JPEG-LS algorithm is applied to ROI to get the compressed data which will be embedded into the whole medical image. And then, image encryption methods are used to assure the invisibility of the medical image. At last, a POB number system enhances the security of the scheme. With the use of the scheme in this paper, we can judge whether the medical image is tampered or not, and can also recover the original medical images in specific conditions without any loss. Moreover, if the medical image is tampered, we can also localize the tampered regions. Experimental results show that the suggested scheme achieves a better performance on security, invisibility and robustness in comparison to the state-of-the-art methods for medical images.
Optical Character Recognition Using Deep Learning Techniques for Printed and Handwritten Documents

Despite decades of research, developing optical character recognition (OCR) systems with capabilities comparable to that of a human remains an open challenge. A large scale of documents in the form of the image is needed to be entered into computer databases which takes a lot of memory as compared to editable text and there can be errors while interpretation of data from an image. This project aims to use OCR to convert handwritten or printed documents into editable text. Documents are scanned to image format as an input to a doc_class_net which is a full-size image classifier that classifies the input image into four different classes viz. printed, semi-printed, handwritten discrete, and handwritten cursive. The OCR model predicts and then decodes the text in the image and gives the output as an editable text. We have applied OCR to printed text images using the Pytesseract. For handwritten text images, the text is predicted using a self-developed convolutional recurrent neural network (CRNN) named CL-9 (7 CNN layers and 2 LSTM layers). The accuracy of the doc_class_net classifier and line_class_net classifier(line-wise classifier) was 88.03 % and 82.1 % respectively. The overall accuracy for printed, handwritten discrete and handwritten cursive obtained is 94.79 %, 75.2 %, and 65.7 % respectively. OCR has real-time applications in various fields like medical prescriptions, smart libraries, and tax returns. Using this method books, magazines, and any other form of documents can be digitized and made accessible very efficiently.