Reviewerly/exHarmony huggingface dataset:
The Imandra Automated Reasoning System (System Description)

We describe Imandra, a modern computational logic theorem prover designed to bridge the gap between decision procedures such as SMT, semi-automatic inductive provers of the Boyer-Moore family like ACL2, and interactive proof assistants for typed higher-order logics. Imandra’s logic is computational, based on a pure subset of OCaml in which all functions are terminating, with restrictions on types and higher-order functions that allow conjectures to be translated into multi-sorted first-order logic with theories, including arithmetic and datatypes. Imandra has novel features supporting large-scale industrial applications, including a seamless integration of bounded and unbounded verification, first-class computable counterexamples, efficiently executable models and a cloud-native architecture supporting live multiuser collaboration. The core reasoning mechanisms of Imandra are (i) a semi-complete procedure for finding models of formulas in the logic mentioned above, centered around the lazy expansion of recursive functions, (ii) an inductive waterfall and simplifier which “lifts” many Boyer-Moore ideas to our typed higher-order setting. These mechanisms are tightly integrated and subject to many forms of user control.
Feature selection considering Uncertainty Change Ratio of the class label

The topic of feature selection in high-dimensional data sets has attracted considerable attention. Feature selection can reduce the dimension of feature and improve the prediction accuracy of the classification model. Information-theoretical-based feature selection methods intend to obtain classification information regarding class labels from the already-selected feature subset as much as possible. Existing methods focus on the reduced uncertainty of class labels while ignoring the change of the remained uncertainty of class labels. In the process of feature selection, the large reduced uncertainty of class labels does not signify the few remained uncertainty of class labels when different candidate features are given. In this paper, we analyze the difference between the reduced uncertainty of class labels and the remained uncertainty of class labels and propose a new term named Uncertainty Change Ratio that considers the change of uncertainty of class labels. Finally, a novel method named Feature Selection considering Uncertainty Change Ratio (UCRFS) is proposed. To prove the classification superiority of the proposed method, UCRFS is compared to three traditional methods and four state-of-the-art methods on fourteen benchmark data sets. The experimental results demonstrate that UCRFS outperforms seven other methods in terms of average classification accuracy, AUC and F1 score.
Co-designing a Self-Management App Prototype to Support People With Spinal Cord Injury in the Prevention of Pressure Injuries: Mixed Methods Study

Spinal cord injury is a complex chronic health condition that requires individuals to actively self-manage. Therefore, an evidence-based, self-management app would be of value to support individuals with spinal cord injury in the prevention of pressure injuries.The main objectives of this study were to (1) establish a co-design approach for developing a high-fidelity prototype app for the self-management of individuals with spinal cord injury, (2) design the prototype that resulted from this process, and (3) conduct the first usability assessment of the prototype app.We adopted a co-design approach to develop an evidence-based app prototype. Starting from a preliminary content model (based on clinical guidelines for the prevention of pressure injuries) and three research-based user personas, we conducted an ideation workshop involving individuals with spinal cord injury and health care professionals. The ideation workshop formed the basis for two consecutive design sprints. The result of this co-design phase was an interactive app prototype. The prototype was evaluated in two rounds of usability testing (N=4 and N=15, respectively) using a combination of qualitative and quantitative methods.The co-design process resulted in a high-fidelity prototype with two key components: a self-management component and a communication component. The final prototype included a combination of features to support individuals with spinal cord injury in the prevention of pressure injuries, namely a smart camera, pressure injury diary, expert consultation, reminders, and knowledge repository. Findings of the usability testing showed that most participants navigated the app fluently with little back and forth navigation and were able to successfully complete a set of assigned tasks. These positive results are supported by the average system usability score achieved (78.5/100; range 47.5-95.0) and our qualitative analysis of the semistructured interviews. Despite an overall positive evaluation of the app prototype, we identified areas for improvement (eg, inclusion of a search function).Individuals with spinal cord injury often need to navigate competing interests and priorities, paired with uncertainty about the accuracy and relevance of clinical recommendations. Understanding what matters to individuals with spinal cord injury can help guide the design of behavioral interventions that are useful and acceptable to these individuals in their daily lives. This study shows that involving individuals with spinal cord injury and health care professionals in co-designing a self-management app can foster knowledge cocreation at the intersection of lived experience, medical expertise, and technical solutions.
A novel privacy-preserving speech recognition framework using bidirectional LSTM

Abstract Utilizing speech as the transmission medium in Internet of things (IoTs) is an effective way to reduce latency while improving the efficiency of human-machine interaction. In the field of speech recognition, Recurrent Neural Network (RNN) has significant advantages to achieve accuracy improvement on speech recognition. However, some of RNN-based intelligence speech recognition applications are insufficient in the privacy-preserving of speech data, and others with privacy-preserving are time-consuming, especially about model training and speech recognition. Therefore, in this paper we propose a novel P rivacy-preserving S peech R ecognition framework using B idirectional L ong short-term memory neural network, namely PSRBL. On the one hand, PSRBL designs new functions to construct security activation functions by combing with an additive secret sharing protocol, namely a secure piecewise-linear Sigmoid and a secure piecewise-linear Tanh respectively, to achieve privacy-preserving of speech data during speech recognition process running on edge servers. On the other hand, in order to reduce the time spent on both the training and the recognition of the speech model while keeping high accuracy during speech recognition process, PSRBL first utilizes secure activation functions to refit original activation functions in the bidirectional L ong S hort- T erm M emory neural network (LSTM), and then makes full use of the left and the right context information of speech data by employing bidirectional LSTM. Experiments conducted on the speech dataset TIMIT show that our framework PSRBL performs well. Specifically compared with the state-of-the-art ones, PSRBL significantly reduces the time consumption on both the training and the recognition of the speech model under the premise that PSRBL and the comparisons are consistent in the privacy-preserving of speech data.
Diversity driven multi-parent evolutionary algorithm with adaptive non-uniform mutation

Any evolutionary algorithm tends to end up in a local optimum. A new approach based on an evolutionary algorithm named as Diversity Driven Multi-Parent Evolutionary Algorithm with Adaptive non-uniform mutation is presented. In the proposed algorithm, Non-uniform mutation is used to maintain diversity in the explored solutions. Fitness variance, which signifies solution space aggregation, is used to detect the premature convergence of the population to a local optimum. The term multi-parent is used in the context of more than two parents participating in crossover operation. After multi-parent selection for cross-over to generate new solutions, the non-uniform adaptive mutation is performed, which in turn is triggered by the diminishing value of fitness variance of candidate solutions and pushes solutions out of local optimum. Hence, it can be said that the algorithm is driven by the diversity of the population and overcomes the tendency of evolutionary algorithms to stuck in local optimum. The performance of this algorithm is tested on 23 basic benchmarks, CEC05 functions, and CEC17 functions. As CEC17 benchmark functions include constraint problems, a constraint-handling technique is proposed based on the fuzzy set theory. In the proposed constrained handling strategy, constraint violation is also taken as another objective along with the main objective. The decision to accept or discard the solution is based on the fuzzy set theory. The values of constraint violation and objective function are calculated and fuzzified by calculating membership values by considering the main objective and constraint violation as triangular fuzzy functions. The best solutions are selected based on cardinal priority ranking. The obtained results from the proposed algorithm are compared with the results available in the literature. The result indicates that this algorithm is competitive, even with a smaller number of function evaluations.
Gear-Induced Concept Drift in Marine Images and Its Effect on Deep Learning Classification

In marine research, image data sets from the same area but collected at different times allow seafloor fauna communities to be monitored over time. However, ongoing technological developments have led to the use of different imaging systems and deployment strategies. Thus, instances of the same class exhibit slightly shifted visual features in images taken at slightly different locations or with different gear. These shifts are referred to as concept drift in the domains computational image analysis and machine learning as this phenomenon poses particular challenges for these fields. In this paper, we analyse four different data sets from an area in the Peru Basin and show how changes in imaging parameters affect the classification of twelve megafauna morphotypes with a 34-layer ResNet. Images were captured using the ocean floor observation system, a traditional sled-based system, or an autonomous underwater vehicle, which is a modern imaging platform capable of surveying larger regions. ResNet applied on separate individual data sets, i.e. without concept drift, showed that changing object distance was less important than the amount of training data. The results for the image data acquired with the ocean floor observation system showed higher performance values than data collected with the autonomous underwater vehicle. The results from this concept drift studies indicate that collecting image data from many dives with slightly different gear may result in training data well suited for learning taxonomic classification tasks and that data volume can compensate for light concept drift.
Extractive Text Summarization from Web pages using Selenium and TF-IDF algorithm

To obtain an overview of the content present in numerous documents, is a time-consuming task. Similarly, searching for specific information online, from multiple websites and webpages is a monotonous task. To avoid this, automatic text summarization is one of the most widely adopted techniques today to get a concise and brief outline of the information. In this paper, a novel process is proposed to generate an extractive summary of the information based on the user's query by extracting data from multiple websites over the internet. Web-scraping through Selenium is also discussed. The Term Frequency-Inverse Document Frequency (TF-IDF) algorithm is applied for text summarization. The proposed approach is unique and efficient for generating summaries as per the user's request.
Rademacher Complexity for Enhancing the Generalization of Genetic Programming for Symbolic Regression

Model complexity has a close relationship with the generalization ability and the interpretability of the learned models. Simple models are more likely to generalize well and easy to interpret. However, too much emphasis on minimizing complexity can prevent the discovery of more complex yet more accurate solutions. Genetic programming (GP) has a trend of generating overcomplex models that are difficult to interpret while not being able to generalize well. This work proposes a novel complexity measure based on the Rademacher complexity for GP for symbolic regression. The complexity of an evolved model is measured by the maximum correlation between the model and the Rademacher variables on the selected training instances. Taking minimizing the training error and the Rademacher complexity of the models as the two objectives, the proposed GP method has shown to be much superior to the standard GP on generalization performance. Compared with GP equipped with two state-of-the-art complexity measures, the proposed method still has a notable advance on generating a better front consisting of individuals with lower generalization errors and being simpler in the behavioral complexity. Further analyses reveal that compared with the state-of-the-art methods, the proposed GP method evolves models that are much closer to the target models in the model structure, and have better interpretability.
High-fidelity multiphoton-entangled cluster state with solid-state quantum emitters in photonic nanostructures

We propose a complete architecture for deterministic generation of entangled multiphoton states. Our approach utilizes periodic driving of a quantum-dot emitter and an efficient light-matter interface enabled by a photonic crystal waveguide. We assess the quality of the photonic states produced from a real system by including all intrinsic experimental imperfections. Importantly, the protocol is robust against the nuclear spin bath dynamics due to a naturally built-in refocusing method reminiscent to spin echo. We demonstrate the feasibility of producing Greenberger-Horne-Zeilinger and one-dimensional cluster states with fidelities and generation rates exceeding those achieved with conventional ``fusion'' methods in current state-of-the-art experiments. The proposed hardware constitutes a scalable and resource-efficient approach towards implementation of measurement-based quantum computing and quantum communication.
Opportunistic Multi-aspect Fairness through Personalized Re-ranking

As recommender systems have become more widespread and moved into areas with greater social impact, such as employment and housing, researchers have begun to seek ways to ensure fairness in the results that such systems produce. This work has primarily focused on developing recommendation approaches in which fairness metrics are jointly optimized along with recommendation accuracy. However, the previous work had largely ignored how individual preferences may limit the ability of an algorithm to produce fair recommendations. Furthermore, with few exceptions, researchers have only considered scenarios in which fairness is measured relative to a single sensitive feature or attribute (such as race or gender). In this paper, we present a re-ranking approach to fairness-aware recommendation that learns individual preferences across multiple fairness dimensions and uses them to enhance provider fairness in recommendation results. Specifically, we show that our opportunistic and metric-agnostic approach achieves a better trade-off between accuracy and fairness than prior re-ranking approaches and does so across multiple fairness dimensions.
Scaffle: bug localization on millions of files

Despite all efforts to avoid bugs, software sometimes crashes in the field, leaving crash traces as the only information to localize the problem. Prior approaches on localizing where to fix the root cause of a crash do not scale well to ultra-large scale, heterogeneous code bases that contain millions of code files written in multiple programming languages. This paper presents Scaffle, the first scalable bug localization technique, which is based on the key insight to divide the problem into two easier sub-problems. First, a trained machine learning model predicts which lines of a raw crash trace are most informative for localizing the bug. Then, these lines are fed to an information retrieval-based search engine to retrieve file paths in the code base, predicting which file to change to address the crash. The approach does not make any assumptions about the format of a crash trace or the language that produces it. We evaluate Scaffle with tens of thousands of crash traces produced by a large-scale industrial code base at Facebook that contains millions of possible bug locations and that powers tools used by billions of people. The results show that the approach correctly predicts the file to fix for 40% to 60% (50% to 70%) of all crash traces within the top-1 (top-5) predictions. Moreover, Scaffle improves over several baseline approaches, including an existing classification-based approach, a scalable variant of existing information retrieval-based approaches, and a set of hand-tuned, industrially deployed heuristics.
A survey on digital camera identification methods

Digital forensics is a topic that has attracted many attention. One of the most common tasks in digital forensics is imaging sensor identification. It may be understood as recognizing devices origin based on subject that this device produced. Therefore, areas that match digital forensics include among others: digital camera, flatbed scanner or printer identification. In this paper we survey methods and algorithms for digital camera identification. The goal of digital camera identification algorithm is to identify and distinct camera's sensor based on produced images. This topic is especially popular in forensics' community since last years. The paper discusses two concepts for camera identification: individual source camera identification (ISCI) and source camera model identification (SCMI). The ISCI aims to distinguish a certain camera among cameras of both the same and the different camera models, while the SCMI distinguishes a certain camera model among others but cannot distinguish a certain camera among the same camera models. We investigate methods dealing with these concepts that include: camera's photo response non uniformity (PRNU) identification, statistical methods, analysis of camera's optical defects, machine learning and deep models which include convolutional neural networks. We also provide a description of popular image datasets that can be used for camera identification algorithms evaluation.
Effective and Fast DeepFake Detection Method Based on Haar Wavelet Transform

DeepFake using Generative Adversarial Networks (GANs) tampered videos reveals a new challenge in today's life. With the inception of GANs, generating high-quality fake videos becomes much easier and in a very realistic manner. Therefore, the development of efficient tools that can automatically detect these fake videos is of paramount importance. The proposed DeepFake detection method takes the advantage of the fact that current DeepFake generation algorithms cannot generate face images with varied resolutions, it is only able to generate new faces with a limited size and resolution, a further distortion and blur is needed to match and fit the fake face with the background and surrounding context in the source video. This transformation causes exclusive blur inconsistency between the generated face and its background in the outcome DeepFake videos, in turn, these artifacts can be effectively spotted by examining the edge pixels in the wavelet domain of the faces in each frame compared to the rest of the frame. A blur inconsistency detection scheme relied on the type of edge and the analysis of its sharpness using Haar wavelet transform as shown in this paper, by using this feature, it can determine if the face region in a video has been blurred or not and to what extent it has been blurred. Thus will lead to the detection of DeepFake videos. The effectiveness of the proposed scheme is demonstrated in the experimental results where the “UADFV” dataset has been used for the evaluation, a very successful detection rate with more than 90.5% was gained.
Transfer Increment for Generalized Zero-Shot Learning

Zero-shot learning (ZSL) is a successful paradigm for categorizing objects from the previously unseen classes. However, it suffers from severe performance degradation in the generalized ZSL (GZSL) setting, i.e., to recognize the test images that are from both seen and unseen classes. In this article, we present a simple but effective mechanism for GZSL and more open scenarios based on a transfer-increment strategy. On the one hand, a dual-knowledge-source-based generative model is constructed to tackle the missing data problem. Specifically, the local relational knowledge extracted from the label-embedding space and the global relational knowledge, which is the estimated data center in the feature-embedding space, are concurrently considered to synthesize the virtual exemplars. On the other hand, we further explore the training issue for the generative models under the GZSL setting. Two incremental training modes are designed to learn directly the unseen classes from the synthesized exemplars instead of the training classifiers with the seen and synthesized unseen exemplars together. It not only presents an effective unseen class learning but also requires less computing and storage resources in practical application. Comprehensive experiments are conducted based on five benchmark data sets. In comparison with the state-of-the-art methods, both the generating and training processes are considered for virtual exemplars by the proposed transfer-increment strategy, which results in a significant improvement in the conventional and GZSL tasks.