Reviewerly/exHarmony huggingface dataset:
Dynamic Heuristic Set Selection for Cross-Domain Selection Hyper-heuristics

Selection hyper-heuristics have proven to be effective in solving various real-world problems. Hyper-heuristics differ from traditional heuristic approaches in that they explore a heuristic space rather than a solution space. These techniques select constructive or perturbative heuristics to construct a solution or improve an existing solution respectively. Previous work has shown that the set of problem-specific heuristics made available to the hyper-heuristic for selection has an impact on the performance of the hyper-heuristic. Hence, there have been initiatives to determine the appropriate set of heuristics that the hyper-heuristic can select from. However, there has not been much research done in this area. Furthermore, previous work has focused on determining a set of heuristics that is used throughout the lifespan of the hyper-heuristic with no change to this set during the application of the hyper-heuristic. This paper investigates dynamic heuristic set selection (DHSS) which applies dominance to select the set of heuristics at different points during the lifespan of a selection hyper-heuristic. The DHSS approach was evaluated on the benchmark set for the CHeSC cross-domain hyper-heuristic challenge. DHSS was found to improve the performance of the best performing hyper-heuristic for this challenge.
Towards a Driver's Gaze Zone Classifier using a Single Camera Robust to Temporal and Permanent Face Occlusions

Although exists several drivers' gaze direction classifiers to prevent traffic accidents caused by inattentive driving, making this classification while the driver's face is temporarily or permanently occluded remains exceptionally challenging. For example, drivers using masks, sunglasses, or scarves and daily light variations are non-ideal conditions that recurrently appear in an everyday driving scenario and are frequently overlooked by the existing classifiers. This paper presents a single camera framework gaze zone classifier that operates robustly even during non-uniform lighting, non-frontal face pose, and faces undergo temporal or permanent occlusions. The usage of a normalized dense aligned face pose vector, the classification result of a pre-processed right eye area pixels, and the classification result of a pre-processed left eye area pixels is the cornerstone of the feature vector used in our model. The key of this paper is double-folded: firstly, the usage of a normalized dense alignment for a robust face, landmark, and head-pose direction detection and secondly, the processing of the right and left eye images using computer vision and deep learning techniques for refining, modifying, and finally labeling eyes information. Experiments on a challenging dataset involving non-uniform lighting, non-frontal face pose, and faces with temporal or permanent occlusions show each feature's importance towards making a robust gaze zone classifier under unconstrained driving situations.
Semantic-Driven Context Aggregation Network for Underwater Image Enhancement

Recently, underwater image enhancement has attracted broad attention due to its potential in ocean exploitation. Unfortunately, limited to the hand-crafted subjective ground truth for matching low-quality underwater images, existing techniques are less robust for some unseen scenarios and may be unfriendly to semantic-related vision tasks. To handle these issues, we aim at introducing the high-level semantic features extracted from a pre-trained classification network into the image enhancement task for improving robustness and semantic-sensitive potency. To be specific, we design an encoder-aggregation-decoder architecture for enhancement, in which a context aggregation residual block is tailored to improve the representational capacity of the original encoder-decoder. Then we introduce a multi-scale feature transformation module that transforms the extracted multi-scale semantic-level features, to improve the robustness and endow the semantic-sensitive property for the encoder-aggregation-decoder network. In addition, during the training phase, the pre-trained classification network is fixed to avoid introducing training costs. Extensive experiments demonstrate the superiority of our method against other state-of-the-art methods. We also apply our method into the salient object detection task to reveal our excellent semantic-sensitive ability.
Towards a Human-AI Hybrid Framework for Inter-Researcher Similarity Detection

Understanding the intellectual landscape of scientific communities and their collaborations has become an indispensable part of research per se. In this regard, measuring similarities among scientific documents can help researchers to identify groups with similar interests as a basis for strengthening collaboration and university-industry linkages. To this end, we intend to evaluate the performance of hybrid crowd-computing methods in measuring the similarity between document pairs by comparing the results achieved by crowds and artificial intelligence (AI) algorithms. That said, in this paper we designed two types of experiments to illustrate some issues in calculating how similar an automatic solution is to a given ground truth. In the first type of experiments, we created a crowdsourcing campaign consisting of four human intelligence tasks (HITs) in which the participants had to indicate whether or not a set of papers belonged to the same author. The second type involves a set of natural language processing (NLP) processes in which we used the TF-IDF measure and the Bidirectional Encoder Representation from Transformers (BERT) model. The results of the two types of experiments carried out in this study provide preliminary insight into detecting major contributions from human-AI cooperation at similarity calculation in order to achieve better decision support. We believe that in this case decision makers can be better informed about potential collaborators based on content-based insights enhanced by hybrid human-AI mechanisms.
Leakage Resilient Cheating Detectable Secret Sharing Schemes

A secret sharing scheme generates shares of a secret that will be distributed among a set of participants such that the shares of qualified subsets of participants can reconstruct the secret, and shares of non-qualified subsets leak no information about the secret. Secret sharing is a fundamental cryptographic primitive in multiparty computation, threshold cryptography, and secure distributed systems. Leakage resilient secret sharing models side channel leakages from all the shares to the adversary, rendering the adversary more powerful. In CRYPTO’19 Srinivasan and Vasudevan (SV) proposed compilers that convert a secret sharing for a general access structure to a leakage resilient secret sharing for the same access structure in two leakage models: local leakage and strong local leakage. In this paper we consider cheater detectable secret sharing that provides security against active (cheating) attackers that modify their controlled shares with the goal of modifying the reconstructed secret. We extend the SV compilers to convert a linear secret sharing for a general access structure to a cheater detectable secret sharing for the same access structure when the adversary has access to the shares of a non-qualified subset and the leaked information from all other shares. Our extensions add a precoding step to the SV compilers that use Algebraic Manipulation Detection (AMD) codes, and work for both well established models of cheater detection known as \(\textsf {OKS}\) and \(\textsf {CDV}\) models, using weak and strong AMD codes, respectively. To prove our results we formalize two security notions for leakage resilient cheating detectable secret sharing, and prove relation between them, which can be of independent interest. We discuss directions for future work.
Does Media Richness Influence the User Experience of Chatbots: A Pilot Study

From a user’s perspective, this pilot study investigates the contributors and irritants related to the media content format used by chatbots to assist users in an online setting. In this study, we use automated facial expression analysis (AFEA), which analyses users’ facial expressions and captures the valence of their lived experience. A questionnaire and a single-question interview were also used to measure the users’ perceived experience. All measures taken together allowed us to explore the effects of three media content formats (i.e., an interactive question and answer (Q&A), a video, and a link referring to a webpage) used in chatbots on both the lived and perceived experiences of users. In line with Media Richness Theory (MRT), our results show that an interactive Q&A might be an optimal chatbot design approach in providing users with sought-after information or assistance with transactions. Moreover, important avenues for future research emerge from this study and will be discussed.
Evaluating the Prediction Bias Induced by Label Imbalance in Multi-label Classification

Prediction bias is a well-known problem in classification algorithms, which tend to be skewed towards more represented classes. This phenomenon is even more remarkable in multi-label scenarios, where the number of underrepresented classes is usually larger. In light of this, we hereby present the Prediction Bias Coefficient (PBC), a novel measure that aims to assess the bias induced by label imbalance in multi-label classification. The approach leverages Spearman's rank correlation coefficient between the label frequencies and the F-scores obtained for each label individually. After describing the theoretical properties of the proposed indicator, we illustrate its behaviour on a classification task performed with state-of-the-art methods on two real-world datasets, and we compare it experimentally with other metrics described in the literature.
Painting Art Style Rendering System Based on Information Intelligent Technology

For a long time, the art of painting has been very mysterious to most ordinary people. Throughout the ages, countless artists have devoted their entire lives to paintings. Many masters have formed their own unique painting styles. With the rapid development of computer intelligence technology, ordinary people can also pry into the mystery of artistic style. Based on this, the purpose of this article is to study the painting art style rendering system based on information intelligence technology. This article first summarizes the basic theory of information intelligence technology, and then extends the core technology of information intelligence. Combining with the current situation of rendering the artistic style of painting in our country, it analyzes the existing problems and shortcomings. On this basis, it uses the core technology of information intelligence to supplement and improve it, and further research and analyze the painting art style rendering system. This article systematically expounds the functional module design, demand design and database design of the painting art style rendering system based on information intelligence technology. And use comparative analysis method, field investigation method and other research forms to carry out experimental research on the style rendering system. Studies have shown that the painting style rendering system based on information intelligence technology is superior to traditional painting style rendering methods in many aspects, especially in terms of rendering efficiency more than 20% higher, which fully reflects the art of painting based on information intelligence technology studied in this article. The excellent performance of the style rendering system and the problems of the traditional moral painting style rendering methods need to be resolved urgently.
Channel Redundancy and Overlap in Convolutional Neural Networks with Channel-Wise NNK Graphs

Feature spaces in the deep layers of convolutional neural networks (CNNs) are often very high-dimensional and difficult to inter-pret. However, convolutional layers consist of multiple channels that are activated by different types of inputs, which suggests that more insights may be gained by studying the channels and how they relate to each other. In this paper, we first analyze theoretically channel-wise non-negative kernel (CW-NNK) regression graphs, which allow us to quantify the overlap between channels and, indirectly, the intrinsic dimension of the data representation manifold. We find that redundancy between channels is significant and varies with the layer depth and the level of regularization during training. Additionally, we observe that there is a correlation between channel overlap in the last convolutional layer and generalization performance. Our experimental results demonstrate that these techniques can lead to a better understanding of deep representations.
Role of Deep Learning in Disaster Prediction

Natural disasters can be very destructive to mankind so predicting it can be a very powerful tool since it supports the modification of the loss of damage to mankind and nature. Predicting events required continues processing of large amount of data with respect to time. With the help of good quality datasets, deep learning (DL) mechanism can become capable of predicting the existence of several natural disasters. It can be the difference between life and death for thousands of people. The concept of DL is not new. This paper discusses basic introduction of DL along with its role in disaster prediction system. It also encompasses different ideas to develop the system which may helpful to give any future direction to new researchers to start their research.
Reuse Opportunities in JavaScript applications

JavaScript nowadays is among the most popular programming languages, used for developing web and IoT applications. Currently, the majority of JavaScript applications is reusing third-party components to acquire various functionalities. In this paper we isolate popular reused components and explore the type of functionality that is mostly being reused. Additionally, we examine whether the client applications adapt to the most recent versions of the reused components, and further study the reuse intensity of pairs of components that coexist in client applications. For this purpose, we performed a case study on 9389 components reused by 430 JavaScript applications hosted in GitHub. The results show that Compiler and Testing Frameworks are the most common types of functionality being reused, while the majority of client applications tend to adopt the recent versions of the reused components.
Dynamic VNF Resource Scaling and Migration: A Machine Learning Approach

In this chapter, VNF scalability via joint resource scaling and migration is studied in a local network segment, to meet a probabilistic delay requirement in the presence of non-stationary traffic with changing traffic statistics. A change point detection scheme determines boundaries between stationary traffic segments in an online manner as new traffic samples arrive, and provides a triggering signal for VNF scalability. Under the fBm traffic model assumption, the resource demand for a probabilistic delay guarantee is predicted for each newly detected stationary traffic segment, based on traffic parameter learning with Gaussian process regression and fBm resource provisioning model. Given the predicted resource demand, a dynamic VNF migration problem is formulated as a Markov decision process (MDP) with variable-length decision epochs, to maximize the long-term reward integrating load balancing, migration cost, and resource overloading penalty. The MDP is solved by a reinforcement leaning (RL) approach. Specifically, a penalty-aware deep \(\mathcal {Q}\)-learning algorithm demonstrating advantages in reducing training loss and increasing cumulative reward is employed to learn the adaptive VNF migration actions under the dynamics in change points, resource demand, and background traffic.
Robust learning-based x-ray image denoising—potential pitfalls, their analysis and solutions

Purpose:Since guidance based on x-ray imaging is an integral part of interventional procedures, continuous efforts are taken towards reducing the exposure of patients and clinical staff to ionizing radiation. Even though a reduction in the x-ray dose may lower associated radiation risks, it is likely to impair the quality of the acquired images, potentially making it more difficult for physicians to carry out their procedures.Method:We present a robust learning-based denoising strategy involving model-based simulations of low-dose x-ray images during the training phase. The method also utilizes a data-driven normalization step-based on an x-ray imaging model-to stabilize the mixed signal-dependent noise associated with x-ray images. We thoroughly analyze the method's sensitivity to a mismatch in dose levels used for training and application. We also study the impact of differing noise models used when training for low and very low-dose x-ray images on the denoising results.Results:A quantitative and qualitative analysis based on acquired phantom and clinical data has shown that the proposed learning-based strategy is stable across different dose levels and yields excellent denoising results, if an accurate noise model is applied. We also found that there can be severe artifacts when the noise characteristics of the training images are significantly different from those in the actual images to be processed. This problem can be especially acute at very low dose levels. During a thorough analysis of our experimental results, we further discovered that viewing the results from the perspective of denoising via thresholding of sub-band coefficients can be very beneficial to get a better understanding of the proposed learning-based denoising strategy.Conclusion:The proposed learning-based denoising strategy provides scope for significant x-ray dose reduction without the loss of important image information if the characteristics of noise is accurately accounted for during the training phase.
Requirements for Online User Feedback Management in RE Tasks

This extended abstract summarises the paper titled Specifying Requirements for Data Collection and Analysis in Data-Driven RE. A Research Preview, which as been presented at the 27th International Working Conference REFSQ 2021 [1].
A Calibration-Free In-Memory True Random Number Generator Using Voltage-Controlled MRAM

In this paper, we propose an in-memory True Random Number Generator (TRNG) using Voltage-Controlled MRAM that doesn't require calibration of the writing pulse's width and amplitude. Previous solution using Spin Transfer Torque (STT) MRAM requires calibration for every MTJ, thus making the multi-row random number generation inside the memory impossible. We also propose a 100% relative throughput digital bias correction circuit that doesn't degrade bit rate. The VC- MTJs are fabricated in CMOS BEOL compatible process with an 80 nm diameter and high TMR ratio of 160%. MRAM array circuits and bias correction circuits are fabricated in 65 nm CMOS technology and wire-bonded with the VC-MTJ devices. Multiple VC-MTJs are tested and shown to pass all NIST randomness tests.
Small Superset and Big Subset Obfuscation

Let \(S = \{1,\dots ,n\}\) be a set of integers and X be a subset of S. We study the boolean function \(f_X(Y)\) which outputs 1 if and only if Y is a small enough superset (resp., big enough subset) of X. Our purpose is to protect X from being known when the function is evasive, yet allow evaluations of \(f_X\) on any input \(Y\subseteq S\). The corresponding research area is called function obfuscation. The two kinds of functions are called small superset functions (SSF) and big subset functions (BSF), respectively. In this paper, we obfuscate SSF and BSF in a very simple and efficient way. We prove both input-hiding security and virtual black-box (VBB) security based on the subset product problem.
Romanian printed language, statistical independence and the type II statistical error

The paper revisits the notion of statistical independence for printed Romanian with a focus for the case when the language is considered as a chain of words. The analysis is carried out on a literary corpus of approx. 6 million words. We aim to improve the perception of the concept of statistical independence for natural texts and to use this concept to evaluate the numerical properties of the printed language.One main objective is to correlate the statistical independence results with the type II statistical error and at the same time to expand and circle back on previous results of the authors. We investigated different scenarios in relation with different word clusters involved in the process of creating Artificial Words (corroborated with the statistical independence evaluation). The Artificial Words consist of groups of the low probability words (based on previous findings on the type II statistical error in word probability investigation) and the results support the d = 100 minimum statistical independence distance.
Acute myeloid leukemia diagnosis using deep learning

Background Early disease detection has a great impact on saving lives. One of the top deadliest diseases in the world is leukemia. Once detected, its treatment is immediately required. Standard morphologic diagnosis of leukemia by hematologists is done by examining the patient’s peripheral blood (PB) and bone marrow (BM) under microscope. However, manual recognition is prone to variations such as experience and tiredness where the percentage of error in diagnosis is between 30%–40%. Therefore, it is necessary to have a robust automated system for leukemia detection that is not influenced by human variations. Machine learning is attracting interest in the biomedical field as it improves sensitivity and specificity of the disease diagnosis, influencing the objectivity of the decision-making process.Objectives Testing deep learning technique that use microscopic images to identify leukemia based on a pretrained deep convolutional neural network which is an approach of machine learning algorithms.Material and method Dataset used was collected from Clinical Pathology lab, Assiut University Hospital, Egypt, containing binary balanced dataset: leukemic and normal blood, each sample composed of PB and BM smears images. We analyzed 412 digital images; 206 images of leukemia (AML types only) & 206 images of normal blood. 1-Training & testing phase: testing different types of pre-trained convolutional neural networks models ex. Alexnet, VGG16, GoogleNet, ResNet101, and Inception-v3. We divided images into two groups; 80% of images assigned as “training-set”, each of the models trained on the images to extract features to differentiate leukemic and normal images, remaining 20% images were assigned as “testing-set” or so called “never seen” by the models. 2-Evaluation of sensitivity/specificity/accuracy: evaluating ability of the models to detect target; based on sensitivity, specificity and accuracy of detection. 3. We computed the testing execution time per image in dataset.Results Comparing the ability of models to differentiate leukemia vs normal images showed that Inception-v3 model had the highest accuracy (99%) in detection and classification of AML. In terms of sensitivity and specificity, the Inception-v3 can detect all leukemic cases of AML, with 100% sensitivity; and specificity of 97.8%. Inception-v3 required only 0.2273 seconds to test each image in AML-IDB.Conclusion Inception-v3 outperforms other pretrained convolutional neural networks in diagnosis of leukemia, this model of Machine learning algorithm can be used in the context of lab diagnosis acting as a second opinion after manual evaluation of leukemia.