Reviewerly/exHarmony huggingface dataset:
MetaCAR: Cross-Domain Meta-Augmentation for Content-Aware Recommendation

Cold-start has become critical for recommendations, especially for sparse user-item interactions. Recent approaches based on meta-learning succeed in alleviating the issue, owing to the fact that these methods have strong generalization, so they can fast adapt to new tasks under cold-start settings. However, these meta-learning-based recommendation models learned with single and spase ratings are easily falling into the meta-overfitting, since the one and only rating <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$r_{ui}$</tex-math></inline-formula> to a specific item <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$i$</tex-math></inline-formula> cannot reflect a user's diverse interests under various circumstances(e.g., time, mood, age, etc), i.e. if <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$r_{ui}$</tex-math></inline-formula> equals to 1 in the historical dataset, but <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$r_{ui}$</tex-math></inline-formula> could be 0 in some circumstance. In meta-learning, tasks with these single ratings are called Non-Mutually-Exclusive(Non-ME) tasks, and tasks with diverse ratings are called Mutually-Exclusive(ME) tasks. Fortunately, a meta-augmentation technique is proposed to relief the meta-overfitting for meta-learning methods by transferring Non-ME tasks into ME tasks by adding noises to labels without changing inputs. Motivated by the meta-augmentation method, in this paper, we propose a cross-domain meta-augmentation technique for content-aware recommendation systems (MetaCAR) to construct ME tasks in the recommendation scenario. Our proposed method consists of two stages: meta-augmentation and meta-learning. In the meta-augmentation stage, we first conduct domain adaptation by a dual conditional variational autoencoder (CVAE) with a multi-view information bottleneck constraint, and then apply the learned CVAE to generate ratings for users in the target domain. In the meta-learning stage, we introduce both the true and generated ratings to construct ME tasks that enables the meta-learning recommendations to avoid meta-overfitting. Experiments evaluated in real-world datasets show the significant superiority of MetaCAR for coping with the cold-start user issue over competing baselines including cross-domain, content-aware, and meta-learning-based recommendations.
Spatial Information Regularized Tensor Decomposition Framework for Super-Resolution Reconstruction of Medical MRI and Radiographs

Super-resolution reconstruction of medical images effectually enhances visual qualities to provide clear visions on anatomical structures. However, the spatial information, which abounds in low-resolution images, has received scant attention in the task of super-resolution, resulting in suppressed reconstruction qualities. This paper brings the spatial information into effective action by virtue of high-dimensional structures of tensor-format data, and presents a method named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Tensor Decomposition based medical Image Super-resolution reconstruction</i> (TDIS). In particular, TDIS employs tensors to preserve rich image spatial information and effectually processes the spatial information contained in image tensors by reformulating transposed convolutional layers using tensor decomposition. Moreover, TDIS provides tensor based error terms to capture spatial differences between generated and target images to reduce visual contrasts between them. Experimental results about reconstructing a range of medical images empirically demonstrate the competence of TDIS compared to the state-of-the-art methods.
ArgumentPrompt: Activating Multi-category of Information for Event Argument Extraction with Automatically Generated Prompts

Event Argument Extraction (EAE) task is a critical and challenging subtask for event extraction. The current mainstream approaches are based on manually designed relevant questions for extracting specific argument roles from the text. However, manually crafting templates take time and fail to cover all possible argument roles. To design better and faster questions related to the EAE task, we embrace automatically generated prompts and propose a method called ArgumentPrompt (AMP): activating multi-category of information for event argument extraction with automatically generated prompts. AMP applies automatically generated prompts to eliminate the demand of the time-consuming and labor-intensive question design for the target extraction. To improve the quality of prompts, we mainly apply multi-category information for prompts and a dual-module to augment the guidance of prompts to language models. Experiments demonstrate that AMP can achieve a performance gain of 1.5% and 3.1% on ACE2005 and RAMS, respectively.
Generating Emotional Responses with DialoGPT-Based Multi-task Learning

Emotion is an essential element for a high quality response, and generating emotional responses is of great significance to a social dialogue system. Traditionally, training an emotional dialogue system needs a large-scale dialogue corpus with emotion labels, which is too expensive for mannual annotation while automatic labeling quality is not guaranteed. Multi-task learning provides a way to learn an emotional response generator through information sharing with emotion recognition tasks. This paper proposes a multi-task learning architecture based on DialoGPT, incorporating response generation with several emotion recognition tasks of different emotion granularities, including both single-label and multi-label classification. Experiments from both automatic evaluation and human evaluation show that the proposed models can generate emotional responses of high quality, outperforming all baseline models in most metrics.
MGCN: A Novel Multi-Graph Collaborative Network for Chinese NER

Named Entity Recognition (NER), one of the most important directions in Natural Language Processing (NLP), is an essential pre-processing step in many downstream NLP tasks. In recent years, most of the existing methods solve Chinese NER tasks by leveraging word lexicons, which has been empirically proven to be useful. Unfortunately, not all word lexicons can improve the performance of the NER. Some self-matched lexical words will either disturb the prediction of character tag, or bring the problem of entity boundaries confusion. Thus, the performance of the NER model will be lowered by such irrelevant lexical words. However, to the best of our knowledge, none of the existing methods can solve these challenges. To address these issues, we present a novel Multi-Graph Collaborative Network (MGCN) for Chinese NER. More specifically, we propose two innovative modules for our methods. Firstly, we build connections among characters to eliminate interferential influences of the noisiness in lexical knowledge. Secondly, by constructing relationship between contextual lexical words, we solve the problem of boundaries confusion. Finally, experimental results on the benchmark Chinese NER datasets show that our methods are not only effective, but also outperform the state-of-the-art (SOTA) results.
Equivocal URLs: Understanding the Fragmented Space of URL Parser Implementations

Uniform Resource Locators (URLs) are integral to the Web and have existed for nearly three decades. Yet URL parsing differs subtly among parser implementations, leading to ambiguity that can be abused by attackers. We measure agreement between widely-used URL parsers and find that each has made design decisions that deviate from parsing standards, creating a fractured implementation space where assumptions of uniform interpretation are unreliable. In some cases, deviations are severe enough that clients using different parsers will make requests to different hosts based on a single, “equivocal” URL. We systematize the thousands of differences we observed into seven pitfalls in URL parsing that application developers should beware of. We demonstrate that this ambiguity can be weaponized through misdirection attacks that evade the Google Safe Browsing and VirusTotal URL classifiers. URL parsing libraries have made a tradeoff to favor permissiveness over strict standards adherence. We hope this work will motivate the systemic adoption of a more unified URL parsing standard–enabling a more secure Web.
Long-Short History of Gradients Is All You Need: Detecting Malicious and Unreliable Clients in Federated Learning

Federated learning offers a framework of training a machine learning model in a distributed fashion while preserving privacy of the participants. As the server cannot govern the clients' actions, nefarious clients may attack the global model by sending malicious local gradients. In the meantime, there could also be unreliable clients who are benign but each has a portion of low-quality training data (e.g., blur or low-resolution images), thus may appearing similar as malicious clients. Therefore, a defense mechanism will need to perform a three-fold differentiation which is much more challenging than the conventional (two-fold) case. This paper introduces MUD-HoG, a novel defense algorithm that addresses this challenge in federated learning using long-short history of gradients, and treats the detected malicious and unreliable clients differently. Not only this, but we can also distinguish between targeted and untargeted attacks among malicious clients, unlike most prior works which only consider one type of the attacks. Specifically, we take into account sign-flipping, additive-noise, label-flipping, and multi-label-flipping attacks, under a non-IID setting. We evaluate MUD-HoG with six state-of-the-art methods on two datasets. The results show that MUD-HoG outperforms all of them in terms of accuracy as well as precision and recall, in the presence of a mixture of multiple (four) types of attackers as well as unreliable clients. Moreover, unlike most prior works which can only tolerate a low population of harmful users, MUD-HoG can work with and successfully detect a wide range of malicious and unreliable clients - up to $$47.5\%$$ and $$10\%$$ , respectively, of the total population. Our code is open-sourced at https://github.com/LabSAINT/MUD-HoG_Federated_Learning .
Browser-Based CPU Fingerprinting

Mounting microarchitectural attacks, such as Spectre or Rowhammer, is possible from browsers. However, to be realistically exploitable, they require precise knowledge about microarchitectural properties. While a native attacker can easily query many of these properties, the sandboxed environment in browsers prevents this. In this paper, we present six side-channel-related benchmarks that reveal CPU properties, such as cache sizes or cache associativities. Our benchmarks are implemented in JavaScript and run in unmodified browsers on multiple platforms. Based on a study with 834 participants using 297 different CPU models, we show that we can infer microarchitectural properties with an accuracy of up to 100%. Combining multiple properties also allows identifying the CPU vendor with an accuracy of 97.5%, and the microarchitecture and CPU model each with an accuracy of above 60%. The benchmarks are unaffected by current side-channel and browser fingerprinting mitigations, and can thus be used for more targeted attacks and to increase the entropy in browser fingerprinting.
An Analysis of COVID-19 related Twitter Data for Asian Hate Speech Using Machine Learning Algorithms

Many people have suffered in this global pandemic of Covid-19 since March 2020. Some suffered from covid-19 disease whereas some suffered from hatred. There are posts and comments on Twitter that reflected Asian hatred blaming them for the coronavirus. Many people expressed their anger and racism through social media and even physically. It is an important task to understand the sentiments of public data to reduce hatred and racism through social media. Many machine learning algorithms can be used to classify Twitter data. In this research, we use two different machine learning classifiers to classify COVID-related Twitter data: Support Vector Machine (SVM) and Random Forest. We compare the performance of both learning methods according to their prediction accuracy, precision, recall, and F1-score. We then predict the tweet label using the data for each month from April to November 2020 using the SVM model. The label used in the dataset are Hate, Counter-hate and Neutral. We analyze the ratio of hate and counter-hate tweets and discuss their possible correlation with events that occurred in those months.
Evaluating Contrastive Learning for Fine-grained Reading Detection

The major drawback of deep learning (DL) algorithms is the necessity of large and labeled data sets in order to achieve peak performance. A DL technique that can overcome this constraint is self-supervised learning, which is applied as non-contrastive self-supervised learning (SSL) and contrastive self-supervised learning (contrastive learning). This paper evaluates a contrastive learning method, which is called simple framework for contrastive learning of visual representations (SimCLR), for the task of fine-grained reading detection. We employ in-the-wild electrooculography (EOG) data sets that describe the eye movement behaviors to evaluate the SimCLR method and compare it against the SSL and pure supervised methods. The results show a maximum performance gain of 3.02 and 3.96 percentage points compared to the SSL and pure supervised methods, respectively, over an equal amount of training data. In addition, the SimCLR method shows a data efficiency of about 80%. The obtained results show a direction for system designers and researchers to handle the lack of large-sized labeled data issues in developing DL models that help to improve user reading habits through eye movement behaviors.
Improving Transparency of Hardware Breakpoints with Virtual Machine Introspection

Hardware breakpoints are used to monitor the behavior of a program on a virtual machine (VM). Although a virtual machine monitor (VMM) can inspect programs on a VM at hardware breakpoints, the programs themselves can detect hardware breakpoints by reading debug registers. Malicious programs may change their behavior to avoid introspection and other security mechanisms if a hardware breakpoint is detected. To prevent introspection evasion, methods for hiding hardware breakpoints by returning a fake value to the VM are proposed. These methods detect the read and write operations of the debug register from the VM and then return the processing to the VM as if their access has succeeded. However, VM introspection remains detectable from the VM by confirming the availability of the debug exception in the address set. While the previous work handles the read and write operations of the debug register, the debug exception is not delivered to the VM program. To address this problem, this study presents a method for making hardware breakpoints compatible with VM introspection. The proposed method uses surplus debug address registers to deliver the debug exception at the hardware breakpoint set by the VM program. If a VM program attempts to write a value to a debug register, the VMM detects and stores the value in a real debug register that is not used for VM introspection. Because debug exception at the hardware breakpoint was delivered to the VM, hardware breakpoints set by the VM were compatible with VM introspection. The evaluation results showed that the proposed method had a low performance overhead.
Blockchain for supply chain traceability: a survey

Blockchain has become a standard for supply chain management, with features like traceability and immutable data, which are valuable in supply chain management systems. This paper will survey the trends in the last few years and discuss gaps in supply chain management systems implemented with Blockchain. We will also propose a new perspective on the role of the Blockchain in supply chain management system and discuss the benefits and pitfalls of implementing it for future research.
Analog Neural Network Model based on Improved Logarithmic Multipliers

Models for artificial intelligence, machine learning, and neural networks are implemented on digital computers with a von Neumann architecture. Few studies have considered analog neural networks. In our previous study, we used multipliers for representing connecting weights in a neural network. The multipliers calculate the product of input signals and their corresponding connecting weights. However, using MOSFET multipliers, their operating range is limited by semiconductor characteristics. The input and output ranges for networks that use these multipliers are thus limited. Furthermore, the circuit operation becomes unstable. Here, we propose a logarithmic four-quadrant multiplier for representing connecting weights. The output of this multiple circuit is a more accurate value compared to the previous circuit. Experiments show that this multiplier exhibits stable operation over a wide range. Therefore, this model can be used directly for input / output of an analog control unit. A model that uses only analog electronic circuits is presented. Its learning time is quite short compared to that for models implemented on a digital computer. We increased the number of units and network layers. We suggest the possibility of a hardware implementation of a deep learning model. Furthermore, this model expects the elucidation of the biomedical learning mechanism.
Treatment Guidance using Sentiment Analysis

Computer science has an impact on every field of activity, including medicine. On one hand, in medicine, automated systems can detect several diseases. On the other hand, they are also involved in the treatment process. Specialized systems can recommend medication, taking into consideration the side effects, give lifestyle suggestions (e.g., sleep, physical exercises), and even indicate to the patients which medics should they go to. One of the main advantages of the automated systems is the ability to process large volumes of data, at higher rates than the human experts. The data may be represented as text, images, or sounds. In this paper, we approach the treatment subject, and focus on data represented as text. We created a system based on sentiment analysis (powered by natural language processing), that provides valuable information regarding conditions/drugs to the user, obtained from the reviews given by other patients.
A Computational System of Psycholinguistic Fuzzy Inference Under Uncertainty

The modern intelligent systems of the Narrow Artificial Intelligence (NAI) cannot independently and continuously think, reason, cognize, and carry out a Logical Inference, based on these computational reasonable functions under: a) uncertainty, b) time changing of the situations and objects of surrounding environment, and c) without using of the human's intellect for retrain (reprogramming) these systems. For a solution of these NAI's problem and in order to create a new reasonable generation of Artificial Intelligence Fuzzy Logic Inference Subsystem (AIFLIS), in this paper is proposed the conception, model, method and subsystem for modeling of the Computational Psycholinguistic and Cognitive Fuzzy Inference in the identified Subject Domain by implementation of the following Computational Brain's computational subsystems: a) identification and preprocessing of the psycholinguistic, lingual, sound, signal. and other meanings of objects, associated with the images of the subject area, and b) situational fuzzy control of computational memory, decision-making, reasoning, thinking, consciousness, awareness, cognition, intuition, wisdom, and others. To implement of the AIFLIS functionality, have been applied the Situational Control, Fuzzy Logic, Psycholinguistics, Data Science, and Informatics.
Biomedical Named Entity Recognition Using Transformers with biLSTM + CRF and Graph Convolutional Neural Networks

One of the applications of Natural Language Processing (NLP) is to process free text data for extracting information. Information extraction has various forms like Named Entity Recognition (NER) for detecting the named entities in the free text. Biomedical named-entity extraction task is about extracting named entities like drugs, diseases, organs, etc. from texts in medical domain. In our study, we improve commonly used models in this domain, such as biLSTM+CRF model, using transformer based language models like BERT and its domain-specific variant BioBERT in the embedding layer. We conduct several experiments on several different benchmark biomedical datasets using a variety of combination of models and embeddings such as BioBERT+biLSTM+CRF, BERT+biLSTM+CRF, Fasttext+biLSTM+CRF, and Graph Convolutional Networks. Our results show a quite visible, 4% to 13%, improvements when baseline biLSTM+CRF model is initialized with pretrained language models such as BERT and especially with domain specific one like BioBERT on several datasets.
An Improved Hybrid Method for Sentiment Analysis

In recent years, the sentiment analysis task has received great attention from communities. To fulfill this task, we proposed in this paper a hybrid method for the French language that combines the strengths of the lexical-based method and those of deep learning-based and machine learning-based methods. This method consists principally of two steps. In the first step, we annotate the learning dataset using an enhanced lexical-based method which is mainly based on a set of new rules. These rules improve significantly the polarity detection by taking into account the contextual information and a set of specific words. Then, we classify the reviews using machine learning (Naïve Bayes, Support Vector Machine) and deep learning (Long Short-Term Memory) algorithms. To realize our method, we collected a dataset composed of 7000 reviews in French from the online purchase site Amazon.fr. We obtained an overall rate of F-measure equal to 96,14%.
Public-key encryption scheme with optimal continuous leakage resilience

In real-world applications, adversaries can launch leakage attacks, such as side-channel attacks, to obtain some information of the secret states (e.g. secret key, random value, etc.) in cryptographic protocols, compromising their security. Moreover, adversaries can obtain more leakage information by performing leakage attacks continuously, and the leakage attacks can be performed at any time by the adversary. Therefore, when designing public-key encryption (PKE) schemes, one should consider the continuous leakage setting. And it is desirable that the designed schemes can resist the leakage attacks. In this paper, to achieve better continuous leakage resilience, a novel construction of continuous leakage-resilient PKE scheme is proposed. The chosen-ciphertext attacks security of our proposed scheme can be proved based on the hardness of the decisional Diffie-Hellman assumption. Our construction tolerates a large key leakage rate, which is one of the best among existing PKE schemes. Moreover, the level of leakage resilience can be adjusted based on the specific requirements in real-world applications. The analyses demonstrate that our proposal also enjoys low computational overheads, outperforming the existing leakage-resilient PKE schemes.