Reviewerly/exHarmony huggingface dataset:
Designing genetic programming classifiers with feature selection and feature construction

Due to the flexibility of Genetic Programming (GP), GP has been used for feature construction, feature selection and classifier construction. In this paper, GP classifiers with feature selection and feature construction are investigated to obtain simple and effective classification rules. During the construction of a GP classifier, irrelevant and redundant features affect the search ability of GP, and make GP easily fall into local optimum. This paper proposes two new GP classifier construction methods to restrict bad impact of irrelevant and redundant features on GP classifier. The first is to use a multiple-objective fitness function that decreases both classification error rate and the number of selected features, which is named as GPMO. The second is to first use a feature selection method, i.e., linear forward selection (LFS) to remove irrelevant and redundant features and then use GPMO to construct classifiers, which is named as FSGPMO. Experiments on twelve datasets show that GPMO and FSGPMO have advantages over GP classifiers with a single-objective fitness function named GPSO in term of classification performance, the number of selected features, time cost and function complexity. The proposed FSGPMO can achieve better classification performance than GPMO on higher dimension datasets, however, FSGPMO may remove potential effective features for GP classifier and achieve much lower classification performance than GPMO on some datasets. Compared with two other GP-based classifiers, GPMO can significantly improve the classification performance. Comparisons with other classification algorithms show that GPMO can achieve better or comparable classification performance on most selected datasets. Our proposed GPMO can achieve better performance than wrapper-based feature construction methods using GP on applications with insufficient instances. Further investigations show that bloat phenomena exists in the process of GP evolution and overfitting phenomena is not obvious. Moreover, the benefits of GP over other machine learning algorithms are discussed.
Deep Learning of Appearance Affinity for Multi-Object Tracking and Re-Identification: A Comparative View

Recognizing the identity of a query individual in a surveillance sequence is the core of Multi-Object Tracking (MOT) and Re-Identification (Re-Id) algorithms. Both tasks can be addressed by measuring the appearance affinity between people observations with a deep neural model. Nevertheless, the differences in their specifications and, consequently, in the characteristics and constraints of the available training data for each one of these tasks, arise from the necessity of employing different learning approaches to attain each one of them. This article offers a comparative view of the Double-Margin-Contrastive and the Triplet loss function, and analyzes the benefits and drawbacks of applying each one of them to learn an Appearance Affinity model for Tracking and Re-Identification. A batch of experiments have been conducted, and their results support the hypothesis concluded from the presented study: Triplet loss function is more effective than the Contrastive one when an Re-Id model is learnt, and, conversely, in the MOT domain, the Contrastive loss can better discriminate between pairs of images rendering the same person or not.
Variability Model Transformations: Towards Unifying Variability Modeling

A plethora of variability modeling approaches has been developed in the last 30 years. Feature modeling and decision modeling became the most common and well-known groups of variability modeling approaches. Even within these groups, however, there are many different variants of approaches. Also, there are many other approaches such as Orthogonal Variability Modeling, UML-based variability modeling, and many more. Despite past and ongoing efforts, there is no standard variability modeling approach the community can agree on. Many approaches have been developed for a certain purpose and have been demonstrated to be useful for at least that purpose, e.g., domain analysis or automated derivation and configuration of products from a software product line. Still, industry frequently develops their own custom variability management solutions. In this short paper, we discuss our first ideas towards developing a framework for variability model transformations. It would allow researchers and practitioners to experiment with and compare different approaches and tools and switch from one approach or tool to another. We demonstrate the basic feasibility of our idea by transforming a feature model into a decision model. We conclude with a research agenda regarding variability model transformations.
Frequency Domain-Based Perceptual Loss for Super Resolution

We introduce Frequency Domain Perceptual Loss (FDPL), a loss function for single image super resolution (SR). Unlike previous loss functions used to train SR models, which are all calculated in the pixel (spatial) domain, FDPL is computed in the frequency domain. By working in the frequency domain we can encourage a given model to learn a mapping that prioritizes those frequencies most related to human perception. While the goal of FDPL is not to maximize the Peak Signal to Noise Ratio (PSNR), we found that there is a correlation between decreasing FDPL and increasing PSNR. Training a model with FDPL results in a higher average PSRN (30.94), compared to the same model trained with pixel loss (30.59), as measured on the Set5 image dataset. We also show that our method achieves higher qualitative results, which is the goal of a perceptual loss function. However, it is not clear that the improved perceptual quality is due to the slightly higher PSNR or the perceptual nature of FDPL.
Geochemical and mineralogical diagnosis on gold ores: A case study from the Cadillac–Larder Lake Fault Zone, Abitibi, Canada

Numerous orogenic gold deposits have been discovered within the Archean Abitibi greenstone belt of Canada. These include the Francoeur, Wasamac, Astoria, Zulapa, Goldex, Lamaque, Lac Herbin, and Beaufor deposits, all of which are located within the Cadillac–Larder Lake Fault Zone (CLLFZ). These gold deposits are hosted by various lithologies and vary in terms of style of mineralization, sulfide abundance, and hydrothermal alteration. These variations mean that exploration for new deposits has been difficult in this area using common exploration criteria. This study presents the results of a principal component analysis (PCA)-based approach, combining geochemical and mineralogical data to quickly identify different types of gold mineralization within the CLLFZ, including assessing the use of this approach in mineral exploration. The first principal components in both classification methods used during this study (PCE1 and PCM1, representing element geochemical and mineralogical classification respectively) can efficiently discriminate between mineralization and barren/altered rocks. The second principal components (PCE2 and PCM2) can be used to constrain the host rocks (altered vs. barren) and style of mineralization (disseminated vs. vein-hosted), respectively. Finally, PCE3 and PCM3 give more detailed information on the nature of the mineralization in an area, possible reflecting variations in the composition of ore-forming fluids. All of the above indicates that the two PCE and PCM classification methods are complementary, which means that using approaches that combine these methods will provide a comprehensive understanding of the nature of studied samples and/or deposits. Information extracted from the results of the PCA undertaken during this study may be beneficial for mineral exploration in unknown districts, such as glaciated areas, where traditional geological fieldwork is challenging. This study also defines element and mineral mineralization indices based on PCE1 and PCM1, which enables evaluation of the extent and style of gold mineralization in an area. This study indicates that PCA-based automated mineralogical and geochemical classification methods could be highly useful in exploration for gold deposits.
Are Negative Links Really Beneficial to Network Embedding?

In this paper, we start by pointing out the limitations on the validation of existing signed network embedding (NE) methods. To address the limitations, we design the two research questions: (1) are signed NE methods consistently more effective in various types of tasks than unsigned NE methods? (2) in signed NE methods, does the utilization of negative links help provide higher accuracy in various tasks? To answer the questions, we present our evaluation framework consisting of three components: (1) five signed network datasets; (2) six signed and two unsigned NE methods; (3) five types of tasks. Through extensive experiments on our evaluation framework, we demonstrate that additional utilization of negative links really helps only in some tasks related to negative links but not in tasks related to positive links.
Image Segmentation Based Hybrid Watermarking Algorithm for Copyright Protection

Today, most of the information shared across the globe is in multimedia form like audio, video and image. With increased globalization, any type of such information can be broadcasted throughout the world easily and is accessible to multiple users for varied applications. This may lead to fraudulent claiming of data ownership which makes copyright protection a major priority in today's world. Digital Watermarking serves as a prime solution to copyright protection where we embed watermark into digital image which serves as an authenticator. In this paper, we proposed a hybrid algorithm using Image segmentation, SVD (Singular value decomposition), DWT (Discrete wavelet transformation), Homomorphic filtering and RSA encryption technique that helps to overcome various threats. Here, we proposed a new approach of digital watermarking by embedding two segmented watermarks which are obtained using two distinct threshold values on the original watermark. These threshold values are sent as secret keys to the receiver using Diffie-Hellman key exchange algorithm and can be used at the time of copyright verification. This approach of hybrid technique is being tested by applying Gaussian Noise and it gave the best results when compared with the other digital watermarking techniques.
ToxicBot: A Conversational Agent to Fight Online Hate Speech

Acting against online hate speech is an important challenge nowadays. Previous research has specifically focused on the development of NLP methods to automatically detect online hate speech while disregarding further action needed to mitigate hate speech in the future. This paper proposes a system that generates responses to intervene during online conversations with hate speech content. Prior to generation, the system uses a binomial, recurrent network-based classifier with a combination of word and sub-word embeddings to detect hate speech. With this architecture we achieved a F1 score of 0.786. The chatbot is based on a generative approach that uses a pre-trained transformer model and dynamically modifies the history or the persona profile to counteract the user's hate speech. This adaptation provides sentences that the system could use to respond in the presence of aggression and discrimination behaviors.
A video-based vehicle counting system using an embedded device in realistic traffic conditions

One of the most important features of smart cities is efficient traffic monitoring. Currently, many monitoring approaches focus on video-processing techniques using traffic surveillance cameras. However, video analytics for traffic monitoring on edge devices like cameras is a difficult task, due to limited computational resources and variety of unknown traffic scenarios. To overcome these difficulties, we designed and evaluated a real-time vehicle counting system using deep neural networks in an embedded device. Experimental results were carried out to determine the best system configuration parameters and to analyze the impact of changing environmental conditions on our system performance. For urban vehicle counting, our approach could achieve a recall and precision values of 99% within a video processing time of 10 frames per second.
Trust Management in Cyber Physical System using Blockchain

Cyber-physical Systems (CPS) are reshaping the way of interaction with the physical world. Moreover, the true potential of CPS will be realized when a decentralized approach will be taken into account. Blockchain is an up-and-coming technology which can establish trust in CPS, where participants do not trust each other. Blockchain applications eliminate the middle man to provide trust and making the processes more efficient & cheaper. It's decentralized nature and cryptographic algorithm make it immune to attack and becomes a secure technology. When the data is stored on the blockchain it becomes immutable, ensuring trust in data. Nevertheless, this does not guarantee the trustworthiness of the device which is generating the data. We propose a blockchain-based signature storage solution to ensure trust among the participants which is further applicable to a diverse range of blockchain-based CPS applications. We have implemented our system using the Ethereum network and Docker Tools. Our proposed solution guarantees security properties i.e. device identification, authentication, integrity, and non-repudiation. Also, it reduces the storage space and storage cost than the traditional usage of blockchain in CPS.
Quantum Meets Fine-Grained Complexity: Sublinear Time Quantum Algorithms for String Problems

Abstract Longest common substring (), longest palindrome substring (), and Ulam distance () are three fundamental string problems that can be classically solved in near linear time. In this work, we present sublinear time quantum algorithms for these problems along with quantum lower bounds. Our results shed light on a very surprising fact: Although the classic solutions for and are almost identical (via suffix trees), their quantum computational complexities are different. While we give an exact $${{\tilde{O}}}(\sqrt{n})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mover> <mml:mi>O</mml:mi> <mml:mo>~</mml:mo> </mml:mover> <mml:mrow> <mml:mo>(</mml:mo> <mml:msqrt> <mml:mi>n</mml:mi> </mml:msqrt> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> time algorithm for , we prove that needs at least time $$\tilde{\Omega }(n^{2/3})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mover> <mml:mi>Ω</mml:mi> <mml:mo>~</mml:mo> </mml:mover> <mml:mrow> <mml:mo>(</mml:mo> <mml:msup> <mml:mi>n</mml:mi> <mml:mrow> <mml:mn>2</mml:mn> <mml:mo>/</mml:mo> <mml:mn>3</mml:mn> </mml:mrow> </mml:msup> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> even for 0/1 strings.
Modeling Pathfinding for Swarm Robotics

This paper presents a theoretical model for path planning in multi-robot navigation in swarm robotics. The plans for the paths are optimized using two objective functions, namely to maximize the safety distance between the agents and to minimize the mean time to complete a plan. The plans are designed for various vehicle models. The presented path planning model allows us to evaluate both decentralized and centralized planners. In this paper, we focus on decentralized planners and aim to find a set of Pareto-optimal plans, which enables us to investigate the fitness landscape of the problem. For solving the multi-objective problem, we design a modified version of NSGA-II algorithm with adapted operators to find sets of Pareto-optimal paths for several agents using various vehicle models and environments. Our experiments show that small problem instances can be solved well, while solving larger problems is not always possible due to the large complexity.
Design and Analysis of Incentive Mechanism for Ethereum-based Supply Chain Management Systems

Blockchain is becoming more popular because of its decentralized, secured, and transparent nature. Supply chain and its management is indispensable to improve customer services, reduce operating costs and improve financial position of a firm. Integration of blockchain and supply chain is substantial, but it alone is not enough for the sustainability of supply chain systems. The proposed mechanism speaks about the method of rewarding the supply chain parties with incentives so as to improve the security and make the integration of supply chain with blockchain sustainable. The proposed incentive mechanism employs the co-operative approach of game theory where all the supply chain parties show a cooperative behavior of following the blockchain-based supply chain protocols and also this mechanism makes a fair attempt in rewarding the supply chain parties with incentives.
Understanding WiFi Cross-Technology Interference Detection in the Real World

WiFi networks are increasingly subjected to cross-technology interference with emerging IoT and even mobile communication solutions all crowding the 2.4 GHz ISM band where WiFi networks conventionally operate. Due to the diversity of interference sources, maintaining high level of network performance is becoming increasing difficult. Recently, deep learning based interference detection has been proposed as a potentially powerful way to identify sources of interference and to provide feedback on how to mitigate their effects. The performance of such approaches has been shown to be impressive in controlled evaluations. However, little information exists on how they generalize to the complexity of everyday environments. In this paper, we contribute by conducting a comprehensive performance evaluation of deep learning based interference detection. In our evaluation, we consider five orthogonal but complementary metrics: correctness, overfitting, robustness, efficiency, and interpretability. Our results show that, while deep learning indeed has excellent correctness (i.e., detection accuracy), it can be prone to noise in measurements (e.g., struggle when transmission power is dynamically adjusted) and suffers from poor interpretability. Deep learning is also highly sensitive to the quality and quantity of training data, with performance decreasing rapidly when the training and testing measurements come from environments with different characteristics. To compensate for weaknesses of deep learning, as our second contribution we propose a novel signal modeling approach for interference detection and compare it against deep learning. Our results demonstrate that, in terms of errors, there are some differences across the two approaches, with signal modeling being better at identifying technologies that rely on frequency hopping or that have dynamic spectrum signatures but suffering in other cases. Based on our results, we draw guidelines for improving interference detection performance.
Online Multiple Object Tracking Based on Open-Set Few-Shot Learning

How to make an online tracking model effectively adapt to newly appearing objects and object disappearance as well as appearance variations of target objects from few examples is an essential issue in multiple object tracking (MOT). Learning target appearances from few examples is a few-shot classification problem, while identifications of newly appearing objects and object disappearance has the aspect of open-set classification. In this work, we regard online MOT as open-set few-show classification to address both learning from few examples (few-shot classification) and unknown classes such as new objects (open-set classification). Specifically, we develop an embedding neural network, called VOFNet, consisting of convolutional and recurrent parts, to perform open-set few-shot classification. The convolutional part constructs a feature from an example of a target object and the recurrent part determines a representative feature of a target object from few examples. Then VOFNet is trained to provide effective features for open-set few-shot classification. Finally, we develop an online multiple object tracker based on the combination of VOFNet and the bipartite matching. The proposed tracker achieves 49.2 multiple object tracking accuracy (MOTA) with 28.9 frames per second on MOT17 dataset, which shows a significantly better trade-off between the accuracy and the speed than the existing algorithms. For example, the proposed algorithm yields about 3.17 times faster speed with 0.99 times lower accuracy than recent existing MOT algorithm [1].
Perceptually Weighted Mean Squared Error Based Rate-Distortion Optimization for HEVC

The process of rate-distortion (RD) optimization plays a key role for video coding, which aims to achieve a tradeoff between compression efficiency and video quality distortion. Although the conventional objective distortion metric mean squared error performs well in computational complexity, it is not always in accordance with the perceptual quality perceived by human visual system (HVS). Taking the characteristics of HVS into consideration, a perceptually weighted mean squared error (PWMSE) based RD model is proposed in this work. First, a low-pass filter is employed to process the original distortion information in order to simulate visual signal processing and obtain the perceptual distortion. Then, masking modulation in both temporal and spatial domains is introduced into distortion model, and a novel Lagrange multiplier is derived accordingly. The proposed PWMSE based RD model is applied to the high efficiency video coding standard, and comparative experimental results demonstrate its effectiveness. The project page can be found in https://mic.tongji.edu.cn/ab/40/c9778a174912/page.htm.
An Experimental Study on Applying Metamorphic Testing in Machine Learning Applications

Machine learning techniques have been successfully employed in various areas and, in particular, for the development of healthcare applications, aiming to support in more effective and faster diagnostics (such as cancer diagnosis). However, machine learning models may present uncertainties and errors. Errors in the training process, classification, and evaluation can generate incorrect results and, consequently, to wrong clinical decisions, reducing the professionals' confidence in the use of such techniques. Similar to other application domains, the quality should be guaranteed to produce more reliable models capable of assisting health professionals in their daily activities. Metamorphic testing can be an interesting option to validate machine learning applications. Using this testing approach is possible to define relationships that define changes to be made in the application's input data to identify faults. This paper presents an experimental study to evaluate the effectiveness of metamorphic testing to validate machine learning applications. A Machine learning application to verify breast cancer diagnostic was developed, using an available dataset composed of 569 samples whose data were taken from breast cancer images, and used as the software under test, in which the metamorphic testing was applied. The results indicate that metamorphic testing can be an alternative to support the validation of machine learning applications.