Reviewerly/exHarmony huggingface dataset:
Development of payload capacity enhanced robust video watermarking scheme based on symmetry of circle using lifting wavelet transform and SURF

Using simple image and video editing software it is relatively easy to pirate the digital contents at the cost of huge revenue loss to the content owner. Entertainment is the key industry that is overwhelmed by huge financial loss due to digital piracy. WhatsApp, Facebook, Instagram, Pinterest and Twitter are some of the massive social media platforms that are used by millions of users. Many of the users share digital content without giving credit to the owner. Digital watermarking is an efficient technique to protect the digital rights and legal compensation of content owners. Due to the poor selection of watermarking locations, most of the state-of-art methods were fall short to preserve the relationships among the host data. Hence such methods were not efficient to provide safeguards against digital piracy. Therefore, this paper proposes a digital video watermarking technique that analyzes, extracts and processes the useful patterns or features of data based on the symmetry of the circle using 2D-Lifting Wavelet Transform (2D-LWT) and Speeded-Up Robust Features (SURF). SURF method is used to obtain five invariant feature points on an approximation part of 2D-LWT utilizing the luminance component of each video frame. The invariant points serve as centers of the circles used to find the embed locations. The maximum intensity value point in all the points on the circumference of the 1/8 part of the circle is then calculated to serve as an embedding location. Three other points in different quadrants are simply obtained using the concept of symmetry of the circle. After applying temporal desynchronization, compression and insertion of noise attacks the video is assessed through subjective and objective evaluation metrics. The results reveal a high degree of correlation (close to one) and good visual transparency which proves that the proposed scheme is superior for practical applications.
Analysis and modeling of dialect information in Ao, a low resource language

Ao is a Tibeto-Burman language spoken in Nagaland, India. It is a low resource, tonal language with three lexical tones, namely, high, mid, and low. However, tone assignment on lexical words may differ among the three dialects of Ao, namely, Chungli, Mongsen, and Changki. In this work, an acoustic study is conducted on the three tones in the three dialects of Ao. It was found that the acoustic characteristics of the tones in the Changki dialect are markedly different from that of the Chungli and the Mongsen dialects. Hence, in the latter part of the work, automatic dialect identification (DID) in the Ao dialects is attempted with Mel Frequency Cepstral Coefficients, Shifted Delta Cepstral coefficients, and F0 features using the Gaussian Mixture models. It is confirmed that in both text-dependent and text-independent DID, the F0 features improve the accuracy of classification.
The hierarchical task network planning method based on Monte Carlo Tree Search

Since the hierarchical task network (HTN) planning depends on the domain knowledge of the problem, the planning result relies on the writing order of the decomposition method. Besides, the solution obtained by planning is usually a general feasible solution, which means there are shortcomings in the ability of finding the optimal solution. In order to reduce the dependence of HTN planning on domain knowledge and obtain a better planning solution, Pyhop-m, an HTN planning algorithm based on Monte Carlo Tree Search(MCTS) is proposed. In the planning process, a planning tree is built by MCTS to guide the HTN planner to choose the best decomposition method. Experiments illustrates that whether in the static or dynamic environment, Pyhop-m is superior to the existing Pyhop and heuristic-based Pyhop-h in plan length, planning success rate and optimal solution rate. Under the 95% confidence level, the confidence intervals of Pyhop-m algorithm to achieve the planning success rate and the optimal solution rate in the dynamic environment are [75.82%,89.18%] and [88.67%,93.95%], which are significantly higher than those of Pyhop-h with [58.19%,77.81%] and [69.91%,80.69%], respectively. Moreover, it can solve the planning problem with uncertain action executions by repeatedly simulating and evaluating the leaf nodes of the planning tree. It can be concluded that Pyhop-m can not only make the planning result independent of the writing order of the decomposition methods, but also search out the global optimal solution.
The development of body representations: an associative learning account

Representing one's own body is of fundamental importance to interact with our environment, yet little is known about how body representations develop. One account suggests that the ability to represent one's own body is present from birth and supports infants' ability to detect similarities between their own and others' bodies. However, in recent years evidence has been accumulating for alternative accounts that emphasize the role of multisensory experience obtained through acting and interacting with our own body in the development of body representations. Here, we review this evidence, and propose an integrative account that suggests that through experience, infants form multisensory associations that facilitate the development of body representations. This associative account provides a coherent explanation for previous developmental findings, and generates novel hypotheses for future research.
Rare Disease Identification from Clinical Notes with Ontologies and Weak Supervision

The identification of rare diseases from clinical notes with Natural Language Processing (NLP) is challenging due to the few cases available for machine learning and the need of data annotation from clinical experts. We propose a method using ontologies and weak supervision. The approach includes two steps: (i) Text-to-UMLS, linking text mentions to concepts in Unified Medical Language System (UMLS), with a named entity linking tool (e.g. SemEHR) and weak supervision based on customised rules and Bidirectional Encoder Representations from Transformers (BERT) based contextual representations, and (ii) UMLS-to-ORDO, matching UMLS concepts to rare diseases in Orphanet Rare Disease Ontology (ORDO). Using MIMIC-III US intensive care discharge summaries as a case study, we show that the Text-to-UMLS process can be greatly improved with weak supervision, without any annotated data from domain experts. Our analysis shows that the overall pipeline processing discharge summaries can surface rare disease cases, which are mostly uncaptured in manual ICD codes of the hospital admissions.
Data Augmentation for Fairness in Personal Knowledge Base Population

Cold start knowledge base population (KBP) is the problem of populating a knowledge base from unstructured documents. While neural networks have led to improvements in the different tasks that are part of KBP, the overall F1 of the end-to-end system remains quite low. This problem is more acute in personal knowledge bases, which present additional challenges with regard to data protection, fairness and privacy. In this work, we use data augmentation to populate a more complete personal knowledge base from the TACRED dataset. We then use explainability techniques and representative set sampling to show that the augmented knowledge base is more fair and diverse as well.
A Complex Encryption System Design Implemented by AES

With the rapid development of internet technology and the increasing popularity of e-commerce, data encryption technology plays a very important role in data security.Information security has two aspects: security protocol and cryptographic algorithm and the latter is the foundation and core technology of information security.Advanced Encryption Standard (AES) encryption algorithm is one of the most commonly used algorithms in symmetric encryption algorithms.Such algorithms face issues when used in the context of key management and security functions.This paper focuses on the systematic analysis of these issues and summarizes AES algorithm implementation, comprehensive application and algorithm comparison with other existing methods.To analyze the performance of the proposed algorithm and to make full use of the advantages of AES encryption algorithm, one needs to reduce round key and improve the key schedule, as well as organically integrate with RSA algorithm.Java language is used to implement the algorithm due to its large library, then to show the efficiency of the proposed method we compare different parameters, such as encryption/decryption speed, entropies and memory consumption...) with a classic algorithm.Based on the results of the comparison between AES and the hybrid AES algorithm, the proposed algorithm shows good performance and high security.It therefore can be used for key management and security functions, particularly for sharing sensitive files through insecure channel.This analysis provides a reference useful for selecting different encryption algorithms according to different business needs.
Applied Optimization and Swarm Intelligence: A Systematic Review and Prospect Opportunities

Optimization is one of the most studied fields within the wider area of artificial intelligence. In the current literature, hundreds of works can be found focused on solving many diverse problems of this kind by resorting to a vast spectrum of solvers. In this context, Swarm Intelligence methods have gained significant popularity in the related community, maintaining a constant momentum in recent years, and having been applied to problems coming from a wide variety of real-world contexts. This chapter contributes to this line by presenting a systematic overview of Swarm Intelligence solvers applied to different branches of optimization problems. To do that, we have focused our attention on four of the most intensively studied application fields: transportation, energy, medicine, and industry. Apart from this systematic review, we also share in this paper our envisioned status of this area, by identifying the most interesting opportunities. These open challenges should stimulate the scientific efforts made by the community in the upcoming years.
Elbert: Fast Albert with Confidence-Window Based Early Exit

Despite the great success in Natural Language Processing (NLP) area, large pre-trained language models like BERT are not well-suited for resource-constrained or real-time applications owing to the large number of parameters and slow inference speed. Recently, compressing and accelerating BERT have become important topics. By incorporating a parameter-sharing strategy, ALBERT greatly reduces the number of parameters while achieving competitive performance. Nevertheless, ALBERT still suffers from a long inference time. In this work, we propose the ELBERT, which significantly improves the average inference speed compared to ALBERT due to the proposed confidence-window based early exit mechanism, without introducing additional parameters or extra training overhead. Experimental results show that ELBERT achieves an adaptive inference speedup varying from 2× to 10× with negligible accuracy degradation compared to AL-BERT on various datasets. Besides, ELBERT achieves higher accuracy than existing early exit methods used for accelerating BERT under the same computation cost. Furthermore, to understand the principle of the early exit mechanism, we also visualize the decision-making process of it in ELBERT. Our code is publicly available online. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Capacity of Quantum Private Information Retrieval With Colluding Servers

Quantum private information retrieval (QPIR) is a protocol in which a user retrieves one of multiple files from n non-communicating servers by downloading quantum systems without revealing which file is retrieved. As variants of QPIR with stronger security requirements, symmetric QPIR is a protocol in which no other files than the target file are leaked to the user, and t-private QPIR is a protocol in which the identity of the target file is kept secret even if at most t servers may collude to reveal the identity. The QPIR capacity is the maximum ratio of the file size to the size of downloaded quantum systems, and we prove that the symmetric t-private QPIR capacity is min{1,2( n- t)/ n} for any 1 ≤ t <; n. We construct a capacity-achieving QPIR protocol by the stabilizer formalism and prove the optimality of our protocol. The proposed capacity is greater than the classical counterpart.
From the Curtain to Kansas: Conducting Wizard-of-Oz Studies in the Wild

Conversational agents have been touted for their potential to support individuals over time as health coaches or personal assistants, but have yet to live up to this potential. Wizard-of-oz (WOz) methods enable researchers to test early prototypes of conversational applications before they are fully implemented, with a human “wizard” filling in the gaps in functionality. Current WOz methods, however, are more commonly used for studies in a lab setting, rather than deployment studies, which more accurately capture users’ interactions in-the-wild. We argue for the need for WOz methods for deployment studies that address key challenges, namely the need for easy-to-prototype technology that works reliably in the wild, as well as the user's expectations for 24/7 availability. We describe an initial approach that begins to address these challenges, as well as the insights gleaned from a two-week WOz study of t2.coach, a conversational agent health coach for diabetes self-management. We argue that the findings from our WOz study could not have been identified from a lab usability study with the same prototype, and the need for the research community to further develop methods for WOz deployment studies.
Time-Varying Graph Signal Inpainting Via Unrolling Networks

We propose an interpretable graph neural network based on algorithm unrolling to reconstruct a time-varying graph signal from partial measurements. The proposed graph unrolling networks expand algorithm unrolling to the graph-time domain and provide an interpretation of the architecture design from a signal processing perspective. We unroll an iterative inpainting algorithm by mapping each iteration to a single network layer. The feed-forward process is thus equivalent to iteratively reconstructing a time-varying graph signal. We train this network through unsupervised learning, where the input time-varying graph signal is used to supervise the training. By leveraging the learning ability of neural networks, we adaptively capture appropriate priors from input data, instead of manually choosing signal priors. To validate the proposed methods, we conduct experiments on three real-world datasets and demonstrate that our networks achieve smaller reconstruction errors than conventional inpainting algorithms and state-of-the-art graph neural networks.
Chaogate Parameter Optimization using Bayesian Optimization and Genetic Algorithm

Chaotic circuits have found application in various research areas, including cryptography. However, more effort has to be made to achieve the properties required for such circuits when it comes to their circuit design. We identify and optimize for regions of chaos in a simple three-transistor system known as a chaogate. We use simulations to study the dynamical behavior of the system treated as a one-dimensional map, and then maximize its chaotic and cryptographic behavior using artificial intelligence. We propose several useful metrics for the chaogate, such as the maximum Lyapunov exponent, and measure these metrics over the transistor parameter space. Finally, we apply Bayesian optimization and Genetic Algorithm to identify various chaogate designs in different technology nodes, which we visualize, compare, and use to propose future research.
AttendAffectNet: Self-Attention based Networks for Predicting Affective Responses from Movies

In this work, we propose different variants of the self-attention based network for emotion prediction from movies, which we call AttendAffectNet. We take both audio and video into account and incorporate the relation among multiple modalities by applying self-attention mechanism in a novel manner into the extracted features for emotion prediction. We compare it to the typically temporal integration of the self-attention based model, which in our case, allows to capture the relation of temporal representations of the movie while considering the sequential dependencies of emotion responses. We demonstrate the effectiveness of our proposed architectures on the extended COGNIMUSE dataset [1], [2] and the MediaEval 2016 Emotional Impact of Movies Task [3], which consist of movies with emotion annotations. Our results show that applying the self-attention mechanism on the different audio-visual features, rather than in the time domain, is more effective for emotion prediction. Our approach is also proven to outperform many state-of-the-art models for emotion prediction. The code to reproduce our results with the models' implementation is available at: https://github.com/ivyha010/AttendAffectNet.
Anchor: The achieved goal to replace the subgoal for hierarchical reinforcement learning

Hierarchical reinforcement learning (HRL) extends traditional reinforcement learning methods to complex tasks, such as the continuous control task with long horizon. As an effective paradigm for HRL, the subgoal-based HRL method uses subgoals to provide intrinsic motivation which helps the agent to reach the desired goal. However, it is tough to determine the subgoal. In this paper, we present a new concept called anchor to replace the subgoal. Our anchor is selected from the achieved goals of the agent. By the anchor, we propose a new HRL method which encourages the agent to move fast away from the corresponding anchor in the right direction of reaching the desired goal. Specifically, for moving fast, our new method uses an intrinsic reward computed by the distance between the current achieved goal and the corresponding anchor. Meanwhile, for moving in the right direction, it weights the intrinsic reward by the extrinsic rewards collected in the process of moving away from the corresponding anchor. The experiments demonstrate the effectiveness of the proposed method on the continuous control task with long horizon.
Triple Sequence Generative Adversarial Nets for Unsupervised Image Captioning

Labelling image-sentence is expensive and some unsupervised image captioning methods show promising results on caption generation. However, the generated captions are not very relevant to images due to the excessive dependence on the corpus. In order to overcome that drawback, we focus on the correspondence between image and sentence to construct an image caption with better mapping relation. In this paper, we present a novel triple sequence generative adversarial net including an image generator, a discriminator, and a sentence generator. The image generator is used to generate the image regions for words. Meanwhile, the sentence corpus guides the sentence generator based on the generated image regions. The discriminator judges the relevance between the words in the sentence and the generated image regions. In the experiments, we use a large number of unpaired images and sentences to train our model on the unsupervised and unpaired setting. The experimental results demonstrate that our method achieves significant improvements as compared to all baselines.
Teachers’ Perceptions around Digital Games for Children in Low-resource Schools for the Blind

Most children who are blind live in low-resource settings and attend schools that have poor technical infrastructure, overburdened teachers, and outdated curriculum. Our work explores the role digital games can play to develop digital skills of such children in the early grades. Recognizing the critical role of teachers in introducing children to technology, we conducted a mixed-methods study to examine which attributes of digital games teachers find useful for children and what challenges they perceive in integrating digital games in schools for the blind. Our findings indicate that teachers prefer games that align well with curriculum objectives, promote learning, improve soft skills, and increase engagement with computers. Despite being overburdened and lacking technological support, teachers expressed strong enthusiasm to integrate these games in school curriculum and schedule. We conclude by discussing design implications for designers of accessible games in low-resource settings.
Image Captioning using Convolutional Neural Networks and Recurrent Neural Network

Image Caption is a concept of gathering the right description of the given image on the internet use Computer Vision and natural language processing. The following is achieved using the Deep learning techniques called as convolution neural network and recurrent neural network. The dataset used for implementation is called as the Flickr8_k Dataset. The model uses the combination of convolution neural network which helps in extraction whereas the recurrent neural network helps in generation of the right text.
Combining Query Reduction and Expansion for Text-Retrieval-Based Bug Localization

Automated text-retrieval-based bug localization (TRBL) techniques normally use the full text of a bug report to formulate a query and retrieve parts of the code that are buggy. Previous research has shown that reducing the size of the query increases the effectiveness of TRBL. On the other hand, researchers also found improvements when expanding the query (i.e., adding more terms). In this paper, we bring these two views together to reformulate queries for TRBL. Specifically, we improve discourse-based query reduction strategies, by adopting a combinatorial approach and using task phrases from bug reports, and combine them with a state-of-the-art query expansion technique, resulting in 970 query reformulation strategies. We investigate the benefits of these strategies for localizing buggy code elements and define a new approach, called Qrex, based on the most effective strategy. We evaluated the reformulation strategies, including Qrex, on 1,217 queries from different software systems to retrieve buggy code artifacts at three code granularities, using five state-of-the-art automated TRBL approaches. The results indicate that Qrex increases TRBL effectiveness by 4% - 12.6%, compared to applying query reduction and expansion in isolation, and by 32.1%, compared to the no-reformulation baseline.