Reviewerly/exHarmony huggingface dataset:
Towards Image Retrieval with Noisy Labels via Non-deterministic Features

For large image retrieval applications, collecting images from the Internet is a relatively low-cost way to obtain labeled training data. However, images from the Internet are often falsely annotated, which may lead to unsatisfying performances of trained models. To alleviate the impact of label noise, our work adopts non-deterministic features for training deep convolutional neural networks. Suppose that non-deterministic features of images obey multi-dimensional Gaussian distribution, and a feature is represented by its mean and variance. During the training, when images are mapped to non-deterministic features, the model will tend to assign large variance to mislabeled samples, suggesting low confidence levels, instead of causing negative optimization by directly changing estimation of mean value. During the test, mean value of non-deterministic features are used for image retrieval. This method increases redundancy by adding variance term of features, so the model has stronger tolerance to label noise. Experimental results and analysis show that on datasets with label noise, using non-deterministic features leads to better image retrieval results than using deterministic features.
Insulator Detection via CNN for UAS Onboard Computers

This paper proposes the usage of single-stage CNN models for detecting insulators in aerial images and measures their applicability in low-power computing settings that often found in UAS onboard systems. In addition to methods in literature, we also design another network based on YOLOv2 modified with SPP (spatial pyramid pooling) block and CIoU loss as our baseline. Our results shows that while both using SPP block and optimizing the bounding box regression function increases the overall detection accuracy without significant cost, network architectures that is specifically designed for edge devices are much more suitable on said environments. One of such design is SF-YOLO, with computation cost of 3,842 BFLOP (29% lower than YOLOv3 tiny, 86% lower than ours) while retaining AP50 score higher than 0.9, and thus can be further used for autonomous navigation subsystems with proper edge devices.
Efficient Datalog Rewriting for Query Answering in TGD Ontologies

Tuple-generating dependencies (TGDs or existential rules) are an expressive constraint language for ontology-mediated query answering and thus query answering is of high complexity. Existing systems based on first-order rewriting methods can lead to queries too large for DBMS to handle. It is shown that datalog rewriting can result in more compact queries, yet previously proposed datalog rewriting methods are mostly inefficient for implementation. In this paper, we fill the gap by proposing an efficient datalog rewriting approach for answering conjunctive queries over TGDs, and identify and combine existing fragments of TGDs for which our rewriting method terminates. We implemented a prototype system Drewer, and experiments show that it is able to handle a wide range of benchmarks in the literature. Moreover, Drewer shows superior performance over state-of-the-art systems on both the compactness of rewriting and the efficiency of query answering.
Identity-Based Key Agreement for Blockchain-Powered Intelligent Edge

In the new paradigm of blockchain-powered intelligent edge, the key agreement is a significant problem that has not been extensively studied so far. Existing key agreement protocols in the traditional public-key setting are usually too complicated and heavy for edge and end devices. Besides, most protocols in use do not have effective measures to resist side-channel attacks, which are increasingly threatening cloud servers, edge devices, and end devices. Identity (ID)-based protocols can be conveniently implemented in the blockchain-powered intelligent edge. Several leakage-resilient ID-based protocols, which can resist side-channel attacks, have been proposed. However, they all involve time-consuming pairing computations. Besides, none of them address side-channel attacks to the key generation center (KGC). This article designs and realizes two novel ID-based key agreement protocols for the blockchain-powered intelligent edge, including an extended Canetti-Krawczyk (eCK) secure ID-based authenticated key agreement (AKA) protocol and a continuous after-the-fact leakage-resilient eCK (CAFL-eCK) secure ID-based AKA protocol. Both protocols do not involve any heavy pairing computation. Besides, the second one can resist side-channel attacks to the KGC and the communicating parties. A hybrid implementation of the two protocols can achieve high efficiency and strong security at the same time in blockchain-powered intelligent edge environments. This is demonstrated via a use case of a blockchain-powered smart home.
TACTILE – A Mixed Reality-Based System for Cognitive and Physical Training

Older adults are often affected by a decline of mental and physical abilities, which results in frailty, anxiety and reclusiveness. They often live alone; family and friends cannot visit them on a regular basis and thus they suffer from social exclusion and loneliness. TACTILE tackles this problem by fostering an active lifestyle and the wellbeing of the elderlies. TACTILE’s novelty is the use of Mixed Reality that makes the remote partner virtually present. Continuous user feedback collected in three workshops guarantees that the user interaction and experience will meet the end users’ needs. In this paper we describe the TACTILE system and its components, the strong end-user involvement, and the results from the second user workshop, where the second prototype was tested. The system will be validated in final field trials in two test countries – Austria and the Netherlands – with the elderly people and secondary end-users (family members and caregivers).
Exploring Biases and Prejudice of Facial Synthesis via Semantic Latent Space

Deep learning (DL) models are widely used to provide a more convenient and smarter life. However, biased algorithms will negatively influence us. For instance, groups targeted by biased algorithms will feel unfairly treated and even fearful of negative consequences of these biases. This work targets biased generative models' behaviors, identifying the cause of the biases and eliminating them. We can (as expected) conclude that biased data causes biased predictions of face frontalization models. Varying the proportions of male and female faces in the training data can have a substantial effect on behavior on the test data: we found that the seemingly obvious choice of 50:50 proportions was not the best for this dataset to reduce biased behavior on female faces, which was 71% unbiased as compared to our top unbiased rate of 84%. Failure in generation and generating incorrect gender faces are two behaviors of these models. In addition, only some layers in face frontalization models are vulnerable to biased datasets. Optimizing the skip-connections of the generator in face frontalization models can make models less biased. We conclude that it is likely to be impossible to eliminate all training bias without an unlimited size dataset, and our experiments show that the bias can be reduced and quantified. We believe the next best to a perfect unbiased predictor is one that has minimized the remaining known bias.
End-to-End Voice Spoofing Detection Employing Time Delay Neural Networks and Higher Order Statistics

Technological progress and proliferation of sophisticated software has made it easier than ever to spoof a person’s voice and audio in general. Like other biometrics, speaker verification is vulnerable to spoofing attacks. Detecting these attacks using the artifacts present in the recordings is a major challenge. Current trend in spoofing detection is to employ deep learning architectures to perform end-to-end detection by employing a pooling layer which aggregates the frame-level information into utterance-level embeddings. To do so, only the first or first and second order statistics are normally pooled across temporal dimension. In this paper, we investigate the influence of higher order statistics, such as third and fourth order moments, on spoofing detection performance. A Time Delay Neural Network (TDNN) architecture is used on the top of linear frequency cepstral coefficients for carrying out spoofing detection experiments on the ASVspoof2019 challenge logical access and physical access corpora. Experiments results, in terms of equal error rate (EER) and minimum tandem detection cost function (min-tDCF), show that inclusion of higher order statistics is accommodating for improving the performance of spoofing detection systems.
Document Similarity by Word Clustering with Semantic Distance

In information retrieval, Latent Semantic Analysis (LSA) is a method to handle large and sparse document vectors. LSA reduces the dimension of document vectors by producing a set of topics related to the documents and terms statistically. Therefore, it needs a certain number of words and takes no account of semantic relations of words.
Concept Drift Detection via Boundary Shrinking

A change in data distribution, called “concept drift,” often degrades performance of machine-learning models trained beforehand. However, detecting concept drift and identifying where it occurs by hand is a quite costly task and it sometimes overlooks drift until the performance is significantly degraded during operation. In this study, we present an unsupervised concept-drift-detection method with an ensemble of inspector models called “Concept-Drift Detection via Boundary Shrinking (CDDBS)”. To reduce delays in drift detection, we train the inspector models used in CDDBS so that their decision boundaries would be intentionally shrunk in a classification region of a certain class. These shrunk decision boundaries also make it possible to identify where the drift occurs without using true labels because they react individually and sensitively to drift occurring in their corresponding classes. We evaluated the effectiveness of CDDBS by using a simple numerical dataset, several public synthetic benchmark datasets and high-dimensional real images in the CIFAR-10 dataset with synthetic drift. CDDBS outperformed other drift-detection methods in sensitivity of drift detection without increasing the number of false alarms and successfully identified drift-occurring classes without using true labels.
A Novel Approach to End-to-End Facial Recognition Framework with Virtual Search Engine ElasticSearch

Facial recognition has been one of the most intriguing, interesting research topics over years. It involves some specific face-based algorithms such as facial detection, facial alignment, facial representation, and facial recognition as well; however, all of these algorithms are derived from heavy deep learning architectures, which leads to limitations on development, scalability, flawed accuracy, and deployment into publicity with mere CPU servers. It also requires large datasets containing hundreds of thousands of records for training purposes. In this paper, we propose a full pipeline for an effective face recognition application which only uses a small Vietnamese-celebrity datasets and CPU for training that can solve the leakage of data and the need for GPU devices. It is based on a face vector-to-string tokens algorithm then saves face’s properties into Elasticsearch for future retrieval, so the problem of online learning in Facial Recognition is also tackled. In comparison with another popular algorithms on the dataset, our proposed pipeline achieves not only higher accuracy, but also faster inference time for real-time face recognition applications.
Entropy Bounds for Grammar-Based Tree Compressors

The definition of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text {k}^{th}$ </tex-math></inline-formula> -order empirical entropy of strings is extended to node-labelled binary trees: A notion of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text {k}^{th}$ </tex-math></inline-formula> -order empirical entropy for node-labelled binary trees is proposed that is able to capture regularities in both labels and structure of a tree. A suitable binary encoding of tree straight-line programs (that have been used for grammar-based tree compression before) is shown to yield binary tree encodings of size bounded by the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text {k}^{th}$ </tex-math></inline-formula> -order empirical entropy plus some lower order terms. This result is then extended from node-labelled binary trees to node-labelled unranked trees. This generalizes recent results for grammar-based string compression to grammar-based tree compression. Additionally, experimental results with real XML document trees are presented, in which the proposed notion of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text {k}^{th}$ </tex-math></inline-formula> -order empirical tree entropy is computed and compared to the performance of grammar-based tree compressors for those XML document trees.
OrderNet: Sorting High Dimensional Low Sample Data with Few-Shot Learning

Neural networks have shown remarkable classification performances in recent years, often outperforming humans in many tasks. Unfortunately, there are some tasks where conventional neural networks and their training methods have under-performed. One of these areas is learning from a small number of samples which have been partially addressed with the development of few-shot neural networks. Few-shot neural networks contrast traditional classification networks by learning classification tasks with datasets with only a few samples per class; however, these few-shot techniques are trained collectively with a large number of labeled samples. Hence, few-shot learning approaches only address the low sample per class learning problem whereas the task of learning strictly from a low sample size still goes mostly unresolved. In this contribution, we address the challenge of learning from high dimensional low sample data by revising the problem into a data ordering task. Specifically, we have designed OrderNet, a novel network design and training approach that can take a relatively small amount (less than 200 samples) of ordered high dimensional low sample data and organize many more unseen samples. To the best of our knowledge, OrderNet is the first neural network to address the high dimensional low sample data using techniques adopted from few-shot learning. We evaluate OrderNet against its ability to order images of analog clocks by time as well as images of profile pictures by age. Additionally, we demonstrate that OrderNet has superior performance over a conventional regression neural network in the low sample regime.
Content Based-Image Retrieval Using Support Vector Machine

Image retrieval is an important problem in multimedia systems. It is determined as the process of searching and fetching images from a dataset. Content-based Image Retrieval (CBIR) is a significant and challenging field of research in digital image processing. The CBIR system’s essential requirement is to retrieve the relevant information following a query image with higher system output from a large image database. Unfortunately, not all the methods are suitable to be used to get high accuracy of retrieval. Therefore, this research aims to classify the data of query image with the data of image database to get a similar image retrieval using Support Vector Machine (SVM) and validate its accuracy based on the classification for the performance evaluation using the precision-recall measure. The critical point of SVM is to get an optimal hyperplane that separates the data points into two classes. This method was applied to different image databases because the classified-based proposed scheme proved better performance than various existing methods. This project assessed experimental results toward 500 images of the Caltech-256 image dataset to demonstrate the proposed method. For retrieving a similar image following the query image, 20 images from five classes were successfully retrieved by using this method. It will show that the SVM method’s average accuracy from all image classes is 94.79%.
Let Imbalance Have Nowhere to Hide: Class-Sensitive Feature Extraction for Imbalanced Traffic Classification

With the full encryption of network traffic, traffic classification schemes based on machine learning emerge in endlessly. Class imbalance, as a widely-studied challenge in machine learning, has not attracted enough attention in traffic classification researches. The uneven distribution hidden in the real-world traffic will cause performance degradation of the existing schemes. In existing methods, data pre-sampling is easy to introduce noise or lose massive information; the cost matrix of cost-sensitive methods is difficult to design; feature selection methods will filter out lots of “redundant” features and cause unsatisfactory results. In this paper, we propose an effective end-to-end framework for imbalanced traffic classification which avoids the above weaknesses, called DeepFE. We adopt deep neural networks for feature extraction, and model features from the perspective of channels. It can learn class-sensitive feature representation, which is quite helpful to distinguish the minority traffic classes. Moreover, DeepFE can be applied to various tasks because of its unlimited input format, i.e., both the raw bytes and the packet length sequence can be used. We conducted experiments on the public dataset ISCXVPN2016 and a realworld traffic dataset covering 27 applications. The results show that DeepFE achieves excellent results, significantly alleviating the performance degradation caused by imbalance, and surpasses several state-of-the-art methods.
Named Entity Recognition Based on Bi-LSTM and CRF-CEL

Named entity recognition is a fundamental and important task in natural language processing in some specific fields, such as network security, medical diagnosis, law and so on. In these field, there are many complicated named entities with less contextual effective information and longer word length. Therefore, the goal of this study is to propose a model based on Bi-directional Long Short-term Memory Neural Network (Bi-LSTM) and Conditional Random Field (CRF), combined with Complex entity library (CEL), so as to improve the accuracy of complex entity recognition. The BiLSTM-CRF -CEL model that we constructed could achieve a relatively high F1 score as the number of complex entities increases, when the dimensionality is appropriate. It also achieved comparatively high performances among commonly used entity recognition models.
Output Layer Go First: Better Fine-tuning by Bridging the Gap with Pre-training

Pre-trained Language Models (PLMs) have been attracting a lot attention in natural language processing field. A training paradigm of pre-training then fine-tuning is widely adopted for BERT-based architectures. However, due to the task gap between pre-training and fine-tuning, PLMs may suffer from knowledge forgetting during fine-tuning and thus lead to a worse performance than expected. We propose a new fine-tuning method in order to bridge this gap and improve the performance of PLMs. We firstly fine-tune the task-specific output layer of the PLMs while keeping the other layers` parameters constant, and then we fine-tine all layers of the PLMs as usual. Our approach is evaluated on multiple natural language understanding tasks and results to a significant improvement over a strong ELECTRA baseline. Specifically, it gains consistent improvements on tasks in GLUE and SQuAD2.0 with only a little additional computation.
A Novel Hybrid Feature Framework for Multi-View Age Estimation

Facial age estimation has grasped the attention of numerous researchers in recent times. It is a challenging task as a consequence of illumination, pose variations, occlusion, complex background, facial expression, and facial makeup. Estimating the age of an individual with an arbitrary pose is quite a challenging job because most of the age estimation system focuses on the frontal view. In this paper, a novel framework for multi-view age estimation by amalgamating the local and global features is proposed. A novel texture feature, Median Gradient Ternary Pattern is proposed in this paper. The Pseudo Zernike Moment extracts the shape features and the View-based Active Appearance Model constructs an appearance model from the facial images. Further, all three features are combined into a feature vector by executing feature-level fusion. The dimension of the combined feature is reduced using Principal Component Analysis. Multi-class Support Vector Machine is utilized to divide the images into four poses. For each pose, a Support Vector Regression with RBF kernel is applied to train a model for estimating the actual age of an individual. The proposed methodology is performed on two databases, namely, FG-NET and CACD which showcase eminent performance.
Third Knowledge-aware and Conversational Recommender Systems Workshop (KaRS)

In the last few years, a renewed interest of the research community on conversational recommender systems (CRSs) is emerging. This is probably due to the great diffusion of Digital Assistants (DAs) such as Amazon Alexa, Siri, or Google Assistant that are revolutionizing the way users interact with machines. DAs allow users to execute a wide range of actions through an interaction mostly based on natural language messages. However, although DAs are able to complete tasks such as sending texts, making phone calls, or playing songs, they are still at an early stage on offering recommendation capabilities by using the conversational paradigm.
The Miner's Dilemma With Migration

As an important example of the application of blockchain technology, the Bitcoin network has achieved great success. The network is secured by miners who are nowadays organized in mining pools. In this paper, we consider the “block withholding attack” as introduced by Eyal, where mining pools may infiltrate others to decrease their revenues. However, when two mining pools attack each other and neither controls a strict majority, the so-called miner's dilemma arises. Both pools are worse off than without an attack. Knowing this, pools may make implicit non-attack agreements. Having said this, the miner's dilemma is known to emerge only if no pool controls the majority of the mining power. In this work, we extend the original model to include miner migration and show that the miner's dilemma emerges even for pools whose mining power exceeds 50%. We construct a game, where two mining pools attack each other and use simulation analysis methods to analyze the evolution the pools' mining power, infiltration preferences and revenue densities under the influence of different mining pool sizes and miner migration preferences. The results show that underlying game experiences a phase transition fueled by miners' migration preference. Without migration, it is profitable for a large mining pool to attack the other pool. The higher the migration preference of the miners, the more the game transitions into the miner's dilemma and attacking makes both pools worse off. Once the pools violate their implicit non-attack agreements, their revenues decrease.