Reviewerly/exHarmony huggingface dataset:
A separation logic for negative dependence

Formal reasoning about hashing-based probabilistic data structures often requires reasoning about random variables where when one variable gets larger (such as the number of elements hashed into one bucket), the others tend to be smaller (like the number of elements hashed into the other buckets). This is an example of negative dependence , a generalization of probabilistic independence that has recently found interesting applications in algorithm design and machine learning. Despite the usefulness of negative dependence for the analyses of probabilistic data structures, existing verification methods cannot establish this property for randomized programs. To fill this gap, we design LINA, a probabilistic separation logic for reasoning about negative dependence. Following recent works on probabilistic separation logic using separating conjunction to reason about the probabilistic independence of random variables, we use separating conjunction to reason about negative dependence. Our assertion logic features two separating conjunctions, one for independence and one for negative dependence. We generalize the logic of bunched implications (BI) to support multiple separating conjunctions, and provide a sound and complete proof system. Notably, the semantics for separating conjunction relies on a non-deterministic , rather than partial, operation for combining resources. By drawing on closure properties for negative dependence, our program logic supports a Frame-like rule for negative dependence and monotone operations. We demonstrate how LINA can verify probabilistic properties of hash-based data structures and balls-into-bins processes.
<scp>Computer‐aided</scp> deep learning model for identification of lymphoblast cell using microscopic leukocyte images

Abstract The conventional technique of leukocyte cell classification involves segmenting the required portion of cells from input image, extracting features of the segmented nuclei, reducing and optimizing these features and then implements the classifier. Thus, designing a good classifier by using such techniques increases the time complexity of the system. In order to resolve such issues, the proposed work implements the deep convolutional neural network (DCNN)‐based models for classifying malignant versus normal WBCs. The proposed system is validated on 108 images of ALL‐IDB 1. Due to limited number of training samples, data augmentation is used to create a similar type of virtual image. In this work, experimentation is carried out for discrimination between normal and infected WBC using DCNN with four different activation functions. By using this method, a set of 6000 samples are generated and used for proper training of the DL model for all activation functions. The performance of each trained model is evaluated in terms of accuracy, recall, precision and F‐measure with the maximum values of 98.1%, 98.3%, 98.3% and 98.3% are achieved, respectively. Finally, it has been concluded that the defined DCNN model and ReLu activation function yield outstanding performance for lymphoblast characterization using microscopic blood images.
Toward explainable and advisable model for self‐driving cars

Abstract Humans learn to drive through both practice and theory, for example, by studying the rules, while most self‐driving systems are limited to the former. Being able to incorporate human knowledge of typical causal driving behavior should benefit autonomous systems. We propose a new approach that learns vehicle control with the help of human advice. Specifically, our system learns to summarize its visual observations in natural language, predict an appropriate action response (eg, “I see a pedestrian crossing, so I stop”), and predict the controls, accordingly. Moreover, to enhance the interpretability of our system, we introduce a fine‐grained attention mechanism that relies on semantic segmentation and object‐centric RoI pooling. We show that our approach of training the autonomous system with human advice, grounded in a rich semantic representation, matches or outperforms prior work in terms of control prediction and explanation generation. Our approach also results in more interpretable visual explanations by visualizing object‐centric attention maps. We evaluate our approach on a novel driving dataset with ground‐truth human explanations, the Berkeley DeepDrive eXplanation (BDD‐X) dataset.
Hybrid Binary Butterfly Optimization Algorithm and Simulated Annealing for Feature Selection Problem

Feature selection is performed to eliminate irrelevant features to reduce computational overheads. Metaheuristic algorithms have become popular for the task of feature selection due to their effectiveness and flexibility. Hybridization of two or more such metaheuristics has become popular in solving optimization problems. In this paper, we propose a hybrid wrapper feature selection technique based on binary butterfly optimization algorithm (bBOA) and Simulated Annealing (SA). The SA is combined with the bBOA in a pipeline fashion such that the best solution obtained by the bBOA is passed on to the SA for further improvement. The SA solution improves the best solution obtained so far by searching in its neighborhood. Thus the SA tries to enhance the exploitation property of the bBOA. The proposed method is tested on twenty datasets from the UCI repository and the results are compared with five popular algorithms for feature selection. The results confirm the effectiveness of the hybrid approach in improving the classification accuracy and selecting the optimal feature subset.
Gentry-Wichs is Tight: a Falsifiable Non-adaptively Sound SNARG

By the impossibility result of Gentry and Wichs, non-falsifiable assumptions are needed to construct (even non-zero-knowledge) adaptively sound succinct non-interactive arguments (SNARGs) for hard languages. It is important to understand whether this impossibility result is tight. While it is known how to construct adaptively sound non-succinct non-interactive arguments for \(\mathsf {NP}\) from falsifiable assumptions, adaptively sound SNARGs for \(\mathsf {NP}\) from non-falsifiable assumptions, and adaptively sound SNARGs for \(\mathsf {P}\) from falsifiable assumptions, there are no known non-adaptively sound SNARGs for \(\mathsf {NP}\) from falsifiable assumptions. We show that Gentry-Wichs is tight by constructing the latter. In addition, we prove it is non-adaptively knowledge-sound in the algebraic group model and Sub-ZK (i.e., zero-knowledge even if the CRS is subverted) under a non-falsifiable assumption.
Improving the Segmentation of Pediatric Low-Grade Gliomas Through Multitask Learning

Brain tumor segmentation is a critical task for tumor volumetric analyses and AI algorithms. However, it is a time-consuming process and requires neuroradiology expertise. While there has been extensive research focused on optimizing brain tumor segmentation in the adult population, studies on AI guided pediatric tumor segmentation are scarce. Furthermore, MRI signal characteristics of pediatric and adult brain tumors differ, necessitating the development of segmentation algorithms specifically designed for pediatric brain tumors. We developed a segmentation model trained on magnetic resonance imaging (MRI) of pediatric patients with low-grade gliomas (pLGGs) from The Hospital for Sick Children (Toronto, Ontario, Canada). The proposed model utilizes deep Multitask Learning (dMTL) by adding tumor's genetic alteration classifier as an auxiliary task to the main network, ultimately improving the accuracy of the segmentation results.
Insulator Detection Method Based on Improved Faster R-CNN with Aerial Images

Insulators are a critical component in power transmission, and the detection of insulators is an essential prerequisite for achieving insulator status and fault diagnosis. An insulator detection method in aerial images based on the improved Faster R-CNN is proposed to address the problems of inaccurate localization and undetected error in the detection of insulators. In this method, the generalized intersection over union (GIoU) is adopted to overcome that the detection is sensitive to various scales insulators in aerial images, and it also improves the accuracy of insulator localization effectively. Meanwhile, the soft non-maximum suppression (Soft-NMS) algorithm is adopted to avoid missing detection of insulators in the post-processing stage because of mutual occlusion in aerial images. The experimental results show that the proposed method can effectively detect insulators in aerial images with complex backgrounds, and the average accuracy is significantly improved compared with different methods.
A Resource-Efficient Smart Contract for Privacy Preserving Smart Home Systems

Due to the proliferation of IoT and the popularity of smart contracts mediated by blockchain, smart home systems have become capable of providing privacy and security to their occupants. In blockchain-based home automation systems, business logic is handled by smart contracts securely. However, a blockchain-based solution is inherently resource-intensive, making it unsuitable for resource-constrained IoT devices. Moreover, time-sensitive actions are complex to perform in a blockchainbased solution due to the time required to mine a block. In this work, we propose a blockchain-independent smart contract infrastructure suitable for resource-constrained IoT devices. Our proposed method is also capable of executing time-sensitive business logic. As an example of an end-to-end application, we describe a smart camera system using our proposed method, compare this system with an existing blockchain-based solution, and present an empirical evaluation of their performance.
Refined Convergence Rates of the Good-Turing Estimator

The Good-Turing (GT) estimator is perhaps the most popular framework for modelling large alphabet distributions. Classical results show that the GT estimator convergences to the occupancy probability, formally defined as the total probability of words that appear exactly k times in the sample. In this work we introduce new convergence guarantees for the GT estimator, based on worst-case MSE analysis. Our results refine and improve upon currently known bounds. Importantly, we introduce a simultaneous convergence rate to the entire collection of occupancy probabilities.
Detecting Continuous Integration Skip Commits Using Multi-Objective Evolutionary Search

Continuous Integration (CI) consists of integrating the changes introduced by different developers more frequently through the automation of build process. Nevertheless, the CI build process is seen as a major barrier that causes delays in the product release dates. One of the main reasons for such delays is that some simple changes (i.e., can be skipped) trigger the build, which represents an unnecessary overhead and particularly painful for large projects. In order to cut off the expenses of CI build time, we propose in this paper, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SkipCI</small> , a novel search-based approach to automatically detect CI Skip commits based on the adaptation of Strength-Pareto Evolutionary Algorithm (SPEA-2). Our approach aims to provide the optimal trade-off between two conflicting objectives to deal with both skipped and non-skipped commits. We evaluate our approach and investigate the performance of both within and cross-project validations on a benchmark of 14,294 CI commits from 15 projects that use Travis CI system. The statistical tests revealed that our approach shows a clear advantage over the baseline approaches with average scores of 92% and 84% in terms of AUC for cross-validation and cross-project validations respectively. Furthermore, the features analysis reveals that documentation changes, terms appearing in the commit message and the committer experience are the most prominent features in CI skip detection. When it comes to the cross-project scenario, the results reveal that besides the documentation changes, there is a strong link between current and previous commits results. Moreover, we deployed and evaluated the usefulness of <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SkipCI</small> with our industrial partner. Qualitative results demonstrate the effectiveness of <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SkipCI</small> in providing relevant CI skip commit recommendations to developers for two large software projects from practitioner’s point of view.
Cost-Sensitive Hypergraph Learning With F-Measure Optimization

The imbalanced issue among data is common in many machine-learning applications, where samples from one or more classes are rare. To address this issue, many imbalanced machine-learning methods have been proposed. Most of these methods rely on cost-sensitive learning. However, we note that it is infeasible to determine the precise cost values even with great domain knowledge for those cost-sensitive machine-learning methods. So in this method, due to the superiority of F-measure on evaluating the performance of imbalanced data classification, we employ F-measure to calculate the cost information and propose a cost-sensitive hypergraph learning method with F-measure optimization to solve the imbalanced issue. In this method, we employ the hypergraph structure to explore the high-order relationships among the imbalanced data. Based on the constructed hypergraph structure, we optimize the cost value with F-measure and further conduct cost-sensitive hypergraph learning with the optimized cost information. The comprehensive experiments validate the effectiveness of the proposed method.
Efficient inference models for classification problems with a high number of fuzzy rules

In data science there are problems that are not visible until you work with a sufficiently large number of data. This is the case, for example, with the design of the inference engine in fuzzy rule-based classification systems. The most common way to implement the winning rule inference method is to use sequential processing that reviews each of the rules in the rule set, to determine the best one and return the associated class. This implementation produces fast response times when the set of rules is small and is applied to a small set of examples. In this paper we explore new versions to implement this inference method, avoiding analyzing all the rules and focusing the analysis on the neighborhood of rules around the example. We study experimentally the conditions where each of them should be applied. Finally, we propose an implementation that combines all the studied versions offering good accuracy results and a significant reduction in the response time.
Optimizing Latent Space Directions for Gan-Based Local Image Editing

Generative Adversarial Network (GAN) based localized image editing can suffer from ambiguity between semantic at-tributes. We thus present a novel objective function to evaluate the locality of an image edit. By introducing the super-vision from a pre-trained segmentation network and optimizing the objective function, our framework, called Locally Effective Latent Space Direction (LELSD), is applicable to any dataset and GAN architecture. Our method is also computationally fast and exhibits a high extent of disentanglement, which allows users to interactively perform a sequence of edits on an image. Our experiments on both GAN-generated and real images qualitatively demonstrate the high quality and advantages of our method.
STranGAN: Adversarially-learnt Spatial Transformer for scalable human activity recognition

We tackle the problem of domain adaptation for inertial sensing-based human activity recognition (HAR) applications -i.e., in developing mechanisms that allow a classifier trained on sensor samples collected under a certain narrow context to continue to achieve high activity recognition accuracy even when applied to other contexts. This is a problem of high practical importance as the current requirement of labeled training data for adapting such classifiers to every new individual, device, or on-body location is a major roadblock to community-scale adoption of HAR-based applications. We particularly investigate the possibility of ensuring robust classifier operation, without requiring any new labeled training data, under changes to (a) the individual performing the activity, and (b) the on-body position where the sensor-embedded mobile or wearable device is placed. We propose STranGAN, a framework that explicitly decouples the domain adaptation functionality from the classification model by learning and applying a set of optimal spatial affine transformations on the target domain inertial sensor data stream by employing adversarial learning, which only requires collecting raw data samples (but no accompanying activity labels) from both source and target domains. STranGAN’s uniqueness lies in its ability to perform practically useful adaptation (a) without any labeled training data and without requiring paired, synchronized generation of source and target domain samples, and (b) without requiring any changes to a pre-trained HAR classifier. Empirical results using three publicly available benchmark datasets indicate that STranGAN(a) is particularly effective in handling on-body position heterogeneity(achieving a 5% improvement in classification F1 score compared to state-of-the-art baselines), (b) offers competitive performance for handling cross-individual variations, and (c) the affine transformation parameters can be analyzed to gain interpretable insights on the domain heterogeneity.
Big Data, Big Data Analytics application to Smart home technologies and services for geriatric rehabilitation

The World Health Organization expects that between 2015 and 2050 the proportion of the global population over 60 years of age will almost double from 12% to 22%. A consequence of an aging population is that more people will require care and additional skilled healthcare staff to support them, resulting in increased healthcare costs. Smart homes can provide a cost-effective solution to the rising healthcare costs of the increasingly elderly population while allowing individuals the opportunity to live independently at home. The wide range of sensors within a Smart home generates large volumes of data, at a high velocity in a variety of formats. Big Data Analytics can be utilized to analyze the Big Data generated to improve the wellbeing of the elderly residents including identification of anomalies in behavior, such as fall detection, and pre-emptive identification of the onset of medical complications. However, there are many challenges of converting existing homes into Smart homes. This chapter discusses ambient-assisted living (AAL), Smart homes, Big Data, and the importance of Big Data Analytics and its application to Smart homes to support AAL for the purpose of geriatric rehabilitation. Several examples are provided throughout this chapter, which highlight the benefits of this technology.
Design Space Exploration of Hybrid Quantum–Classical Neural Networks

The unprecedented success of classical neural networks and the recent advances in quantum computing have motivated the research community to explore the interplay between these two technologies, leading to the so-called quantum neural networks. In fact, universal quantum computers are anticipated to both speed up and improve the accuracy of neural networks. However, whether such quantum neural networks will result in a clear advantage on noisy intermediate-scale quantum (NISQ) devices is still not clear. In this paper, we propose a systematic methodology for designing quantum layer(s) in hybrid quantum–classical neural network (HQCNN) architectures. Following our proposed methodology, we develop different variants of hybrid neural networks and compare them with pure classical architectures of equivalent size. Finally, we empirically evaluate our proposed hybrid variants and show that the addition of quantum layers does provide a noticeable computational advantage.
Towards Just-Enough Documentation for Agile Effort Estimation: What Information Should Be Documented?

Effort estimation is an integral part of activities planning in Agile iterative development. An Agile team estimates the effort of a task based on the available information which is usually conveyed through documentation. However, as documentation has a lower priority in Agile, little is known about how documentation effort can be optimized while achieving accurate estimation. Hence, to help practitioners achieve just-enough documentation for effort estimation, we investigated the different types of documented information that practitioners considered useful for effort estimation. We conducted a survey study with 121 Agile practitioners across 25 countries. Our survey results showed that (1) despite the lower priority of documentation in Agile practices, 98% of the respondents considered documented information moderately to extremely important when estimating effort, (2) 73% of them reported that they would re-estimate a task when the documented information was changed, and (3) functional requirements, user stories, definition of done, UI wireframes, acceptance criteria, and task dependencies were ranked as the most useful types of documented information for effort estimation. Nevertheless, many respondents reported that these useful types of documented information were occasionally changing or missing. Based on our study results, we provide recommendations for agile practitioners on how effort estimation can be improved by focusing on just-enough documentation.
A Decentralized Asynchronous Collaborative Genetic Algorithm for Heterogeneous Multi-agent Search and Rescue Problems

In this paper we propose a version of the Genetic Algorithm (GA) for combined task assignment and path planning that is highly decentralized in the sense that each agent only knows its own capabilities and data, and a set of so-called handover values communicated to it from the other agents over an unreliable low bandwidth communication channel. These handover values are used in combination with a local GA involving no other agents, to decide what tasks to execute, and what tasks to leave to others. We compare the performance of our approach to a centralized version of GA, and a partly decentralized version of GA where computations are local, but all agents need complete information regarding all other agents, including position, range, battery, and local obstacle maps. We compare solution performance as well as messages sent for the three algorithms, and conclude that the proposed algorithms has a small decrease in performance, but a significant decrease in required communication. We compare the performance of our approach to a centralized version of GA, and a partly decentralized version of GA where computations are local, but all agents need complete information regarding all other agents, including position, range, battery, and local obstacle maps. We compare solution performance as well as messages sent for the three algorithms, and conclude that the proposed algorithms has a small decrease in performance, but a significant decrease in required communication. We compare solution performance as well as messages sent for the three algorithms, and conclude that the proposed algorithms has a small decrease in performance, but a significant decrease in required communication.