Reviewerly/exHarmony huggingface dataset:
ViewSeeker: An Interactive View Recommendation Framework

View recommendations have emerged as a powerful tool to assist data analysts in exploring and understanding big data. Existing view recommendation approaches proposed a variety of utility functions in selecting useful views. However, the suitability of the utility functions and their tunable parameters for an analysis is usually dependent on the analysis context, such as the user, the data and the analysis task. In order to provide context-aware view recommendation, we formulate a new Interactive View Recommendation (IVR) paradigm, where the system interacts with the user to discover the utility functions that are most suitable in the current analysis context. We further develop an IVR framework, coined ViewSeeker, which leverages user feedback on intelligently selected example views to discover the most suitable utility functions. Finally, we implemented a prototype of ViewSeeker and verified its efficiency and effectiveness using two real-world datasets.
Semantic Partitioning and Machine Learning in Sentiment Analysis

This paper investigates sentiment analysis in Arabic tweets that have the presence of Jordanian dialect. A new dataset was collected during the coronavirus disease (COVID-19) pandemic. We demonstrate two models: the Traditional Arabic Language (TAL) model and the Semantic Partitioning Arabic Language (SPAL) model to envisage the polarity of the collected tweets by invoking several, well-known classifiers. The extraction and allocation of numerous Arabic features, such as lexical features, writing style features, grammatical features, and emotional features, have been used to analyze and classify the collected tweets semantically. The partitioning concept was performed on the original dataset by utilizing the hidden semantic meaning between tweets in the SPAL model before invoking various classifiers. The experimentation reveals that the overall performance of the SPAL model competes over and better than the performance of the TAL model due to imposing the genuine idea of semantic partitioning on the collected dataset.
Identifying Soft Biometric Features from a Combination of Keystroke and Mouse Dynamics

In this preliminary paper, we investigate the use of keystroke and mouse dynamics as a means of identifying soft biometric features. We present evidence that combining features from both provides a more accurate means of identifying all of the soft biometric traits investigated, regardless of the machine learning method used. The data presented in this paper gives a thorough breakdown of accuracy scores from multiple machine learning methods and numbers of features used.
Automated Face Recognition by Smart Security System Using AI &amp; ML Algorithms

Face recognition has a very important role in various applications, from security, surveillance to authentication. For safety most of the household is having CC cameras such that they could recognize the persons from it. These CCTV are allocated for having safety and to know who visited their houses. In few highly secured places where allowance to any unknown intruder is strictly prohibited. Thereby, this paper deals with a system which could recognize the face of the intruder through surveillance camera using ML and AI based algorithms. The design specified is successfully implemented using HOG feature extraction and SVM classification algorithms and it classifies the faces for a video stream given as input. The major objective entitled to this paper is to recognize the faces of people from the video by HOG feature extractor and classify them using SVM and train the machine to tell who is the person working for the organization and who are the intruder.
Prediction of leukemia by classification and clustering techniques

Leukemia is a kind of blood cancer that impacts the white blood cells and damages the bone marrow. Typically the complete blood count (CBC) and bone marrow are affected. It can be a fatal disease if not identified at the earliest stage. Usually, manual microscopic assessment of stained sample slides is used for analysis of leukemia, but manual diagnostic strategies are time consuming, less accurate, and prone to errors due to diverse human elements such as pressure, fatigue, and so on. To avoid possible faults and errors and to assist pathologists, clustering and classification techniques are required, which are being used in every medical field to obtain better outcomes. This chapter emphasizes clustering and classification techniques applied to detection of leukemia. The proposed work consists of two phases: Phase I deals with the collection of the dataset and visualization of datasets, and Phase II deals with machine learning and data mining techniques for the prediction of leukemia. We would expect that the proposed techniques would show better performance than other existing techniques. The proposed techniques could be utilized for other diseases as well.
Multi-Exit Semantic Segmentation Networks

Semantic segmentation arises as the backbone of many vision systems, spanning from self-driving cars and robot navigation to augmented reality and teleconferencing. Frequently operating under stringent latency constraints within a limited resource envelope, optimising for efficient execution becomes important. At the same time, the heterogeneous capabilities of the target platforms and the diverse constraints of different applications require the design and training of multiple target-specific segmentation models, leading to excessive maintenance costs. To this end, we propose a framework for converting state-of-the-art segmentation CNNs to Multi-Exit Semantic Segmentation (MESS) networks: specially trained models that employ parametrised early exits along their depth to i) dynamically save computation during inference on easier samples and ii) save training and maintenance cost by offering a post-training customisable speed-accuracy trade-off. Designing and training such networks naively can hurt performance. Thus, we propose a novel two-staged training scheme for multi-exit networks. Furthermore, the parametrisation of MESS enables co-optimising the number, placement and architecture of the attached segmentation heads along with the exit policy, upon deployment via exhaustive search in <1 GPUh. This allows MESS to rapidly adapt to the device capabilities and application requirements for each target use-case, offering a train-once-deploy-everywhere solution. MESS variants achieve latency gains of up to 2.83 $$\times $$ with the same accuracy, or 5.33 pp higher accuracy for the same computational budget, compared to the original backbone network. Lastly, MESS delivers orders of magnitude faster architectural customisation, compared to state-of-the-art techniques.
A Semantic Encoding Out-of-Distribution Classifier for Generalized Zero-Shot Learning

Generalized zero-shot learning (GZSL) poses a challenging problem in that it aims to recognize both seen classes that have appeared in the training stage and unseen classes that have not appeared during training. By utilizing a gating mechanism as the binary classifier, gating methods can decompose GZSL into a conventional ZSL problem and a supervision learning task, thereby leading to outstanding performance by GZSL. However, unseen classes contain many confusing visual samples that distribute too close to the seen class boundaries and are prone to misclassification. To solve this problem, we propose a novel semantic encoding out-of-distribution classifier (SE-OOD) for GZSL. Our method first utilizes semantically consistent mapping to project all the visual samples to their corresponding semantic attributes. Then, both the projected visual samples and original semantic attributes are encoded to their latent representations for distribution alignment. After separating the unseen samples from seen samples in the learned latent space, two domain classifiers are adopted to perform ZSL and supervised classification tasks. Extensive experiments are conducted on four benchmarks, and the results show that our proposed SE-OOD can outperform the state-of-the-arts by a large margin.
On Properties and Optimization of Information-theoretic Privacy Watchdog

We study the problem of privacy preservation in data sharing, where S is a sensitive variable to be protected and X is a non-sensitive useful variable correlated with S. Variable X is randomized into variable Y, which will be shared or released according to p <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Y |X</inf> (y|x). We measure privacy leakage by information privacy (also known as log-lift in the literature), which guarantees mutual information privacy and differential privacy (DP). Let ${\mathcal{X}}_\varepsilon ^c \subseteq {\mathcal{X}}$ contain elements in the alphabet of for which the absolute value of log-lift (abs-log-lift for short) is greater than a desired threshold ϵ. When elements $x \in {\mathcal{X}}_\varepsilon ^c$ are randomized into $y \in {\mathcal{Y}},$ we derive the best upper bound on the abs-log-lift across the resultant pairs (s, y). We then prove that this bound is achievable via an X-invariant randomization p(y|x) = R(y) for $x,y \in {\mathcal{X}}_\varepsilon ^c$. However, the utility measured by the mutual information I(X; Y) is severely damaged in imposing a strict upper bound ϵ on the abs-log-lift. To remedy this and inspired by the probabilistic (ϵ, δ)-DP, we propose a relaxed (ϵ, δ)-log-lift framework. To achieve this relaxation, we introduce a greedy algorithm which exempts some elements in ${\mathcal{X}}_\varepsilon ^c$ from randomization, as long as their abs-log-lift is bounded by ϵ with probability 1 − δ. Numerical results demonstrate efficacy of this algorithm in achieving a better privacy-utility tradeoff.
Atoms of Confusion in Java

Although writing code seems trivial at times, problems arise when humans misinterpret what the code actually does.One of the potential causes are "atoms of confusion", the smallest possible patterns of misinterpretable source code.Previous research has investigated the impact of atoms of confusion in C code.Results show that developers make significantly more mistakes in code where atoms are present.In this paper, we replicate the work of Gopstein et al. to the Java language.After deriving a set of atoms of confusion for Java, we perform a two-phase experiment with 132 computer science students (i.e., novice developers).Our results show that participants are 2.7 up to 56 times more likely to make mistakes in code snippets affected by 7 out of the 14 studied atoms of confusion, and when faced with both versions of the code snippets, participants perceived the version affected by the atom of confusion to be more confusing and/or less readable in 10 out of the 14 studied atoms of confusion.
Estimating (and fixing) the Effect of Face Obfuscation in Video Recognition

Recent research has shown that faces can be obfuscated in large-scale datasets with a minimal performance impact on image classification and downstream tasks like object recognition. In this paper, we investigate the role of face obfuscation in video classification datasets and quantify a more significant reduction in performance caused by face blurring. To reduce such performance effects, we propose a generalized distillation approach in which a privacy-preserving action recognition network is trained with privileged information given by face identities. We show, through experiments performed on Kinetics-400, that the proposed approach can fully close the performance gap caused by face anonymization.
On the Robustness and Generalizability of Face Synthesis Detection Methods

In recent years, significant progress has been made within human face synthesis. It is now possible, and easy for anyone, to generate credible high-resolution images of non-existing people. This calls for effective detection methods. In this paper, three state-of-the-art deep learning-based methods are evaluated with respect to their robustness and generalizability, which are two factors that must be taken into consideration for methods intended to be deployed in the wild. The robustness experiments show that it is possible to achieve near-perfect performance when discriminating between real and synthetic facial images that have been post-processed heavily with various perturbation techniques; especially when similar perturbations are incorporated during training of the detection models. The generalization experiments show that already trained detection models can achieve high performance on images from sources not known during training, provided that the models are fine-tuned on such images. One model achieved an average accuracy of 96.8% after being fine-tuned on 3 training images from each unknown source considered (one real and one synthetic source). However, additional images were required when fine-tuning using a different approach aimed at preventing catastrophic forgetting. Furthermore, in general, no method generalized well without fine-tuning. Hence, the limited generalization capability remains a shortcoming that must be overcome before the detection methods can be utilized in the wild.
A-PoA: Anonymous Proof of Authorization for Decentralized Identity Management

Self-sovereign Identity Management (SSIM) pro-motes self-control of credentials without relying on external administration. However, the state-of-the-art SSIM based on Decentralized Identifiers and Verifiable Credentials (VCs) defined by the World Wide Web Consortium does not enable credential holders to verify whether a Credential Issuing Authority (CIA) legitimately issued a credential.As a remedy, our work constructs a secure authentication protocol, called A-PoA, to provide decentralized and anonymous authorization of CIAs. We leverage a cryptographic accumulator to enable the Root Authority (registering a Credential Schema) with the ability to authorize a CIA (registering a Credential Definition) to issue a credential. The proof of accumulator membership relies on a non-interactive zero-knowledge proof. This allows a credential holder or validator node to verify the validity of a CIA, while the CIA remains anonymous. Our security analysis shows the integrity and confidentiality of our protocol against hostile network participants and our experimental evaluation shows constant verification times independent of the number of authenticated CIAs. Hence, A-PoA introduces the missing building block to develop SSIM-capable and VC-compatible ecosystems acting as a drop-in replacement for traditional Public Key Infrastructure schemes.
VXSlate: Exploring Combination of Head Movements and Mobile Touch for Large Virtual Display Interaction

Virtual Reality (VR) headsets can open opportunities for users to accomplish complex tasks on large virtual displays using compact and portable devices. However, interacting with such large virtual displays using existing interaction techniques might cause fatigue, especially for precise manipulation tasks, due to the lack of physical surfaces. To deal with this issue, we explored the design of VXSlate, an interaction technique that uses a large virtual display as an expansion of a tablet. We combined a user's head movements as tracked by the VR headset, and touch interaction on the tablet. Using VXSlate, a user head movements positions a virtual representation of the tablet together with the user's hand, on the large virtual display. This allows the user to perform fine-tuned multi-touch content manipulations. In a user study with seventeen participants, we investigated the effects of VXSlate on users in problem-solving tasks involving content manipulations at different levels of difficulty, such as translation, rotation, scaling, and sketching. As a baseline for comparison, off-the-shelf touch-controller interactions were used. Overall, VXSlate allowed participants to complete the task with completion times and accuracy that are comparable to touch-controller interactions. After an interval of use, VXSlate significantly reduced users' time to perform scaling tasks in content manipulations, as well as reducing perceived effort. We reflected on the advantages and disadvantages of VXSlate in content manipulation on large virtual displays and explored further applications within the VXSlate design space.
Tensorlayer 3.0: A Deep Learning Library Compatible With Multiple Backends

TensorLayer 3.0 is a deep learning library refactored based on TensorLayer 2.0. It is compatible with multiple deep learning frameworks, designed for researchers and engineers. The library provides an extensive collection of customised neural layers enabling users to build advanced AI models quickly. TensorLayer 3.0 designed backend layers to unify the low-level APIs operators of multiple frameworks, and model abstraction layers to be compatible with model building using different frameworks. Therefore, the library has the combined advantage of low coupling, easy to extend and great compatibility with other frameworks. The TensorLayer community has accumulated a large number of users, and there are many open-source examples to help users get started quickly.
Semankey: A Semantics-Driven Approach for Querying RDF Repositories Using Keywords

The Web of Data aims at linking Internet data repositories. Semantic Web technologies make data easily readable by computer agents, enabling the automation of complex tasks and facilitating data integration. They facilitate the achievement of the Web of Data in which users can query the connected datasets in the search engine style, i.e. by using keywords. However, querying semantic repositories in a friendly way, not requiring the mastering of query languages such as SPARQL, is still a challenging task. In this work, we present Semankey, an approach for the automatic building of SPARQL queries from a list of keywords entered by the user. Semankey identifies semantic entities in the keywords by using a domain ontology to interpret the query meaning and automatically builds a set of queries by connecting the entities through the relationships described in the ontology and by applying query size-based heuristics. The main contributions of Semankey are the use of query filters and the generation of multiple SPARQL queries derived from the different interpretations of the given input, according to the underlying domain ontology. We used the data from the Question Answering over Linked Data challenge for evaluating our approach in different execution modes and for analyzing the query trees generated, obtaining a precision of 0.52 and a recall of 0.60 when considering the best answer provided per test case.
An Approach to Accuracy Assessment of ASTER Derived Mineral Maps

An accuracy assessment of a classified thematic map is critical for the success of a remote sensing project. The identification and quantification of accuracy sources for classified mineral maps derived from satellite images enable confident decisions to be made for further exploration operations. Nineteen rock samples were collected from the different lithological units of the hydrothermally altered Kuh Panj Cu porphyry occurrence within the south-eastern part of Iran. SPECIM hyperspectral imaging was applied to the rock samples, followed by X-ray diffraction (XRD) analysis to compare the SPECIM mineral maps. The SPECIM results were then interpreted for mineralogy and compositional mapping extracted from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images. The spectral angle mapper (SAM) and real value−area (RV−A) fractal methods were applied on SPECIM and ASTER images to interpret the mineralogy and derived classified map products. Two methods, including confusion matrix and one minus standard deviation over mean, were used to assess the accuracy of the classified SPECIM and ASTER derived mineral maps. Performing sensitivity studies were investigated, including the effect of spatially displacing ASTER mineral mapping and changing the SAM-derived threshold values on the accuracy of the ASTER mineral map. The sensitivity analyses demonstrated that selecting an optimum SAM-derived threshold value is more important than spatial displacement. Finally, accuracy sources were summarized in an accuracy budget table. The results demonstrated 89 and 88% accuracy for SPECIM and 88 and 92% accuracy for ASTER mineral maps via the confusion matrix and one minus standard deviation over mean methods, respectively. The accuracy budget helped to evaluate and compare all sources of accuracy with their accuracy values.
Sentiment Analysis and Classification of COVID-19 Tweets

The ruinous COVID-19 (Coronavirus disease) pandemic is the paramount issue prevalent in our world today. Social media serves as a common platform, where thoughts, experiences and essential information pertaining to COVID-19 could be shared. The primary objective of this project is to analyze using NLP (Natural Language Processing) techniques, the sentiments of users across various countries based on tweets posted during the pandemic, which could be beneficial for healthcare and government organizations to assess and address the needs of individuals and formulate policies accordingly. The sentiments of tweets are determined to be positive or negative based on analysis results obtained. Unlike existing research, a proper comparative sentiment analysis of COVID-19 related tweets has been performed to obtain conclusions regarding suitability of sentiment classification models and their respective accuracies. According to the proposed approach in this paper, a convolutional neural network and recurrent neural network is constructed for sentiment analysis based on text, which will help identify the growth in fear sentiment and negative sentiment with assured higher accuracy.
Improving Online Performance Prediction for Semantic Segmentation

In this work we address the task of observing the performance of a semantic segmentation deep neural network (DNN) during online operation, i.e., during inference, which is of high importance in safety-critical applications such as autonomous driving. Here, many high-level decisions rely on such DNNs, which are usually evaluated offline, while their performance in online operation remains unknown. To solve this problem, we propose an improved online performance prediction scheme, building on a recently proposed concept of predicting the primary semantic segmentation task’s performance. This can be achieved by evaluating the auxiliary task of monocular depth estimation with a measurement supplied by a LiDAR sensor and a subsequent regression to the semantic segmentation performance. In particular, we propose (i) sequential training methods for both tasks in a multi-task training setup, (ii) to share the encoder as well as parts of the decoder between both task’s networks for improved efficiency, and (iii) a temporal statistics aggregation method, which significantly reduces the performance prediction error at the cost of a small algorithmic latency. Evaluation on the KITTI dataset shows that all three aspects improve the performance prediction compared to previous approaches.