Reviewerly/exHarmony huggingface dataset:
Uncertainty Estimation via Response Scaling for Pseudo-Mask Noise Mitigation in Weakly-Supervised Semantic Segmentation

Weakly-Supervised Semantic Segmentation (WSSS) segments objects without heavy burden of dense annotation. While as a price, generated pseudo-masks exist obvious noisy pixels, which result in sub-optimal segmentation models trained over these pseudo-masks. But rare studies notice or work on this problem, even these noisy pixels are inevitable after their improvements on pseudo-mask. So we try to improve WSSS in the aspect of noise mitigation. And we observe that many noisy pixels are of high confidences, especially when the response range is too wide or narrow, presenting an uncertain status. Thus, in this paper, we simulate noisy variations of response by scaling the prediction map in multiple times for uncertainty estimation. The uncertainty is then used to weight the segmentation loss to mitigate noisy supervision signals. We call this method URN, abbreviated from Uncertainty estimation via Response scaling for Noise mitigation. Experiments validate the benefits of URN, and our method achieves state-of-the-art results at 71.2% and 41.5% on PASCAL VOC 2012 and MS COCO 2014 respectively, without extra models like saliency detection. Code is available at https://github.com/XMed-Lab/URN.
Continual Learning in Task-Oriented Dialogue Systems

Continual learning in task-oriented dialogue systems allows the system to add new domains and functionalities over time after deployment, without incurring the high cost of retraining the whole system each time.In this paper, we propose a first-ever continual learning benchmark for task-oriented dialogue systems with 37 domains to be learned continuously in both modularized and end-to-end learning settings.In addition, we implement and compare multiple existing continual learning baselines, and we propose a simple yet effective architectural method based on residual adapters.We also suggest that the upper bound performance of continual learning should be equivalent to multitask learning when data from all domain is available at once.Our experiments demonstrate that the proposed architectural method and a simple replay-based strategy perform better, by a large margin, compared to other continuous learning techniques, and only slightly worse than the multitask learning upper bound while being 20X faster in learning new domains.We also report several trade-offs in terms of parameter usage, memory size and training time, which are important in the design of a task-oriented dialogue system.The proposed benchmark is released to promote more research in this direction 1 .
Choreography Automata

Automata models are well-established in many areas of computer science and are supported by a wealth of theoretical results including a wide range of algorithms and techniques to specify and analyse systems. We introduce choreography automata for the choreographic modelling of communicating systems. The projection of a choreography automaton yields a system of communicating finite-state machines. We consider both the standard asynchronous semantics of communicating systems and a synchronous variant of it. For both, the projections of well-formed automata are proved to be live as well as lock- and deadlock-free.
Effective Sequence-to-Sequence Dialogue State Tracking

Sequence-to-sequence models have been applied to a wide variety of NLP tasks, but how to properly use them for dialogue state tracking has not been systematically investigated.In this paper, we study this problem from the perspectives of pre-training objectives as well as the formats of context representations.We demonstrate that the choice of pre-training objective makes a significant difference to the state tracking quality.In particular, we find that masked span prediction is more effective than auto-regressive language modeling.We also explore using Pegasus, a span prediction-based pre-training objective for text summarization, for the state tracking model.We found that pre-training for the seemingly distant summarization task works surprisingly well for dialogue state tracking.In addition, we found that while recurrent state context representation works also reasonably well, the model may have a hard time recovering from earlier mistakes.We conducted experiments on the MultiWOZ 2.1-2.4,WOZ 2.0, and DSTC2 datasets with consistent observations.
An Effective Non-Commutative Encryption Approach with Optimized Genetic Algorithm for Ensuring Data Protection in Cloud Computing

Nowadays, succeeding safe communication and protection-sensitive data from unauthorized access above public networks are the main worries in cloud servers. Hence, to secure both data and keys ensuring secured data storage and access, our proposed work designs a Novel Quantum Key Distribution (QKD) relying upon a non-commutative encryption framework. It makes use of a Novel Quantum Key Distribution approach, which guarantees high level secured data transmission. Along with this, a shared secret is generated using Diffie Hellman (DH) to certify secured key generation at reduced time complexity. Moreover, a non-commutative approach is used, which effectively allows the users to store and access the encrypted data into the cloud server. Also, to prevent data loss or corruption caused by the insiders in the cloud, Optimized Genetic Algorithm (OGA) is utilized, which effectively recovers the data and retrieve it if the missed data without loss. It is then followed with the decryption process as if requested by the user. Thus our proposed framework ensures authentication and paves way for secure data access, with enhanced performance and reduced complexities experienced with the prior works.
Graph Neural Network for Source Code Defect Prediction

Predicting defective software modules before testing is a useful operation that ensures that the time and cost of software testing can be reduced. In recent years, several models have been proposed for this purpose, most of which are built using deep learning-based methods. However, most of these models do not take full advantage of a source code as they ignore its tree structure or they focus only on a small part of a code. To investigate whether and to what extent information from this structure can be beneficial in predicting defective source code, we developed an end-to-end model based on a convolutional graph neural network (GCNN) for defect prediction, whose architecture can be adapted to the analyzed software, so that projects of different sizes can be processed with the same level of detail. The model processes the information of the nodes and edges from the abstract syntax tree (AST) of the source code of a software module and classifies the module as defective or not defective based on this information. Experiments on open source projects written in Java have shown that the proposed model performs significantly better than traditional defect prediction models in terms of AUC and F-score. Based on the F-scores of the existing <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">state-of-the-art</i> models, the model has shown comparable predictive capabilities for the analyzed projects.
A federated deep learning framework for privacy preservation and communication efficiency

Deep learning has achieved great success in many applications. However, its deployment in practice has been hurdled by two issues: the privacy of data that has to be aggregated centrally for model training and high communication overhead due to transmission of large amount of data usually geographically distributed. Addressing both issues is challenging and most existing works could not provide an efficient solution. In this paper, we develop FedPC, a Federated Deep Learning Framework for Privacy Preservation and Communication Efficiency. The framework allows a model to be learned on multiple private datasets while not revealing any information of training data, even with intermediate data. The framework also minimizes the amount of data exchanged to update the model. We formally prove the convergence of the learning model when training with FedPC and its privacy-preserving property. We perform extensive experiments to evaluate the performance of FedPC in terms of the approximation to the upper-bound performance (when training centrally) and communication overhead. The results show that FedPC maintains the performance approximation of the models within 8.5% of the centrally-trained models when data is distributed to 10 computing nodes. FedPC also reduces the communication overhead by up to 42.20% compared to existing works.
Initial coin offerings (ICOs): Why do they succeed?

Recent literature has addressed initial coin offering (ICO) projects, which are an innovative form of venture financing through cryptocurrencies using blockchain technology. Many features of ICOs remain unexplored, leaving much room for additional research, including the success factors of ICO projects. We investigate the success of ICO projects, with our main purpose being to identify factors that influence a project's outcome. Following a literature review, from which several potential variables were collected, we used a database comprising 428 ICO projects in the banking/financial sector to regress several econometric models. We confirmed the impacts of several variables and obtained particularly valuable results concerning project and campaign variables. We confirmed the importance of a well-structured and informative whitepaper. The proximity to certain markets with high availability of financial and human capital is also an important determinant of the success of an ICO. We also confirm the strong dependency on cryptocurrency and the impact of cryptocurrency valuations on the success of a project. Furthermore, we confirm the importance of social media in ICO projects, as well as the importance of human capital characteristics. Our research contributes to the ICO literature by capturing most of the success factors previously identified and testing their impacts based on a large database. The current research contributes to the building of systems theory and signaling theory by adapting their frameworks to the ICO environment. Our results are also important for regulators, as ICOs are mainly unregulated and have vast future potential, and for investors, who can benefit from our analysis and use it in their due diligence.
Enhanced Invisibility and Robustness of Digital Image Watermarking Based on DWT-SVD

The propagation of digital media over the Internet has helped improve digitization, which has given an excessive lead to copyright issues. Digital watermarking techniques have been applied to address copyright issues. In research, a system is being developed to handle veracious types of watermarked attacks, for obtaining extreme security and an adequate level of visibility and robustness. The discrete wave transform (DWT) and singular value decomposition (SVD) approaches were applied to analyze veracious types of attacks. The DWT method was used to embed the host image in four levels; this level was processed using the SVD method. The peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) were applied to measure the invisibility, and the normalization correlation (NC) was used to examine the robustness of watermarked images. The empirical results showed that the proposed DWT-SVD achieved superior accuracy in identifying the various attacks. The proposed DWT-SVD performance was confirmed during the training process, and the proposed system was shown to have high invisibility and robustness against various types of attacks on watermarked images. Finally, the results of the proposed system were compared to existing systems, and it was shown that DWT-SVD achieved better performance in terms of pixel-value modification attacks.
Identifying and Monitoring the Daily Routine of Seniors Living at Home

As the population in the Western world is rapidly aging, the remote monitoring solutions integrated into the living environment of seniors have the potential to reduce the care burden helping them to self-manage problems associated with old age. The daily routine is considered a useful tool for addressing age-related problems having additional benefits for seniors like reduced stress and anxiety, increased feeling of safety and security. In this paper, we propose a solution for identifying the daily routines of seniors using the monitored activities of daily living and for inferring deviations from the routines that may require caregivers' interventions. A Markov model-based method is defined to identify the daily routines, while entropy rate and cosine functions are used to measure and assess the similarity between the daily monitored activities in a day and the inferred routine. A distributed monitoring system was developed that uses Beacons and trilateration techniques for monitoring the activities of older adults. The results are promising, the proposed techniques can identify the daily routines with confidence concerning the activity duration of 0.98 and the sequence of activities in the interval of [0.0794, 0.0829]. Regarding deviation identification, our method obtains 0.88 as the best sensitivity value with an average precision of 0.95.
A Multi-User Collaborative AR System for Industrial Applications

Augmented reality (AR) applications are increasingly being used in various fields (e.g., design, maintenance, assembly, repair, training, etc.), as AR techniques help improve efficiency and reduce costs. Moreover, collaborative AR systems extend applicability, allowing for collaborative environments for different roles. In this paper, we propose a multi-user collaborative AR system (aptly called the “multi-user collaborative system”, or MUCSys); it is composed of three ends—MUCStudio, MUCView, and MUCServer. MUCStudio aims to construct industrial content with CAD model transformation, simplification, database update, marker design, scene editing, and exportation, while MUCView contains sensor data analysis, real-time localization, scene loading, annotation editing, and virtual–real rendering. MUCServer—as the bridge between MUCStudio and MUCView—presents collaborative and database services. To achieve this, we implemented the algorithms of local map establishment, global map registration, optimization, and network synchronization. The system provides AR services for diverse industrial processes via three collaborative ways—remote support, collaborative annotation, and editing. According to the system, applications for cutting machines were presented to improve efficiency and reduce costs, covering cutting head designs, production line sales, and cutting machine inspections. Finally, a user study was performed to prove the usage experience of the system.
Adaptive Fractional Image Enhancement Algorithm Based on Rough Set and Particle Swarm Optimization

This paper proposes a new image enhancement algorithm. At first, the paper uses the combination of rough set and particle swarm optimization (PSO) algorithm to distinguish the smooth area, edge and texture area of the image. Then, according to the results of image segmentation, an adaptive fractional differential filter is used to enhance the image. Finally, the experimental results show that the image enhanced by this algorithm has clear edge, rich texture details, and retains the information of the smooth area of the image.
The alegality of blockchain technology

Abstract Similar to the early days of the Internet, today, the effectiveness and applicability of legal regulations are being challenged by the advent of blockchain technology. Yet, unlike the Internet, which has evolved into an increasingly centralized system that was largely brought within the reach of the law, blockchain technology still resists regulation and is thus described by some as being “alegal”, i.e., situated beyond the boundaries of existing legal orders and, therefore, challenging them. This article investigates whether blockchain technology can indeed be qualified as alegal and the extent to which such technology can be brought back within the boundaries of a legal order by means of targeted policies. First, the article explores the features of blockchain-based systems, which make them hard to regulate, mainly due to their approach to disintermediation. Second, drawing from the notion of alegality in legal philosophy, the article analyzes how blockchain technology enables acts that transgress the temporal, spatial, material, and subjective boundaries of the law, thereby introducing the notion of “alegality by design”—as the design of a technological artifact can provide affordances for alegality. Third, the article discusses how the law could respond to the alegality of blockchain technology through innovative policies encouraging the use of regulatory sandboxes to test for the “functional equivalence” and “regulatory equivalence” of the practices and processes implemented by blockchain initiatives.
Breast cancer diagnosis in an early stage using novel deep learning with hybrid optimization technique

Breast cancer is one of the primary causes of death that is occurred in females around the world. So, the recognition and categorization of initial phase breast cancer are necessary to help the patients to have suitable action. However, mammography images provide very low sensitivity and efficiency while detecting breast cancer. Moreover, Magnetic Resonance Imaging (MRI) provides high sensitivity than mammography for predicting breast cancer. In this research, a novel Back Propagation Boosting Recurrent Wienmed model (BPBRW) with Hybrid Krill Herd African Buffalo Optimization (HKH-ABO) mechanism is developed for detecting breast cancer in an earlier stage using breast MRI images. Initially, the MRI breast images are trained to the system, and an innovative Wienmed filter is established for preprocessing the MRI noisy image content. Moreover, the projected BPBRW with HKH-ABO mechanism categorizes the breast cancer tumor as benign and malignant. Additionally, this model is simulated using Python, and the performance of the current research work is evaluated with prevailing works. Hence, the comparative graph shows that the current research model produces improved accuracy of 99.6% with a 0.12% lower error rate.
Evaluating feature combination strategies for hate-speech detection in Spanish using linguistic features and transformers

Abstract The rise of social networks has allowed misogynistic, xenophobic, and homophobic people to spread their hate-speech to intimidate individuals or groups because of their gender, ethnicity or sexual orientation. The consequences of hate-speech are devastating, causing severe depression and even leading people to commit suicide. Hate-speech identification is challenging as the large amount of daily publications makes it impossible to review every comment by hand. Moreover, hate-speech is also spread by hoaxes that requires language and context understanding. With the aim of reducing the number of comments that should be reviewed by experts, or even for the development of autonomous systems, the automatic identification of hate-speech has gained academic relevance. However, the reliability of automatic approaches is still limited specifically in languages other than English, in which some of the state-of-the-art techniques have not been analyzed in detail. In this work, we examine which features are most effective in identifying hate-speech in Spanish and how these features can be combined to develop more accurate systems. In addition, we characterize the language present in each type of hate-speech by means of explainable linguistic features and compare our results with state-of-the-art approaches. Our research indicates that combining linguistic features and transformers by means of knowledge integration outperforms current solutions regarding hate-speech identification in Spanish.
Towards On-Device Dehydration Monitoring Using Machine Learning from Wearable Device’s Data

With the ongoing advances in sensor technology and miniaturization of electronic chips, more applications are researched and developed for wearable devices. Hydration monitoring is among the problems that have been recently researched. Athletes, battlefield soldiers, workers in extreme weather conditions, people with adipsia who have no sensation of thirst, and elderly people who lost their ability to talk are among the main target users for this application. In this paper, we address the use of machine learning for hydration monitoring using data from wearable sensors: accelerometer, magnetometer, gyroscope, galvanic skin response sensor, photoplethysmography sensor, temperature, and barometric pressure sensor. These data, together with new features constructed to reflect the activity level, were integrated with personal features to predict the last drinking time of a person and alert the user when it exceeds a certain threshold. The results of applying different models are compared for model selection for on-device deployment optimization. The extra trees model achieved the least error for predicting unseen data; random forest came next with less training time, then the deep neural network with a small model size, which is preferred for wearable devices with limited memory. Embedded on-device testing is still needed to emphasize the results and test for power consumption.
Deep Learning and TextBlob Based Sentiment Analysis for Coronavirus (COVID-19) Using Twitter Data

The novel Coronavirus (COVID-19) has affected the normal life of people around the world and has forced them to maintain social distance, self-quarantine and closing of many businesses. So, the people wish to share the information regarding the pandemic through social media e.g. Twitter. The key objective of this paper is to identify the sentiment associated with the pandemic by utilizing the tweets regarding the Coronavirus (COVID-19) and by using the python libraries to perform the task. We analyze the sentiment of people when Coronavirus has attained a peak level using the machine, deep learning techniques and TextBlob. We have classified the sentiments into positive and negative classes using the Machine Learning (NB, SVM, Logistic regression) approaches and deep learning-based Bi-LSTM model. The Bi-LSTM approach has achieved better accuracy (0.87) compared to the traditional machine learning models for Twitter sentiment classification. From the overall analysis, we could conclude that people are confident and they express more positivity towards the recovery from the COVID-19 pandemic. Such an analysis will help the policy and decision-makers to address the needs of the public appropriately.
FFR_FD: Effective and fast detection of DeepFakes via feature point defects

DeepFakes are widespread on social networks, and they result in severe information concerns. Although various detection methods have been proposed, there are still practical limitations. Previous specific artifact-based methods were insufficient to capture fine-grained features, which limited their effectiveness against advanced DeepFakes. Current DNN-based detectors tend to trade high costs for performance improvement, and are not efficient enough, given that DeepFakes can be created easily by mobile apps, and DNN-based models require expensive computational resources. Furthermore, most methods lack generalizability under the cross-dataset scenario. In this work, we instead mine the more subtle and generalized defects of DeepFakes and propose the fused facial region_feature descriptor (FFR_FD), which is only a vector of the discriminative feature description, for effective and fast DeepFake detection. We show that DeepFake faces have fewer feature points than real ones, especially in facial regions. FFR_FD capitalizes on such key observations, and thus has strong generalizability. We train a random forest classifier with FFR_FD to achieve efficient detection. Extensive experiments on six large-scale DeepFake datasets demonstrate the effectiveness of our lightweight method. Our model generalizes well on the challenging Celeb-DF (v2) dataset, with 0.706 AUC, which is superior to most state-of-the-art methods.