Reviewerly/exHarmony huggingface dataset:
Investigating Passage-level Relevance and Its Role in Document-level Relevance Judgment

The understanding of the process of relevance judgment helps to inspire the design of retrieval models. Traditional retrieval models usually estimate relevance based on document-level signals. Recent works consider a more fine-grain, passage-level relevance information, which can further enhance retrieval performance. However, it lacks a detailed analysis of how passage-level relevance signals determine or influence the relevance judgment of the whole document. To investigate the role of passage-level relevance in the document-level relevance judgment, we construct an ad-hoc retrieval dataset with both passage-level and document-level relevance labels. A thorough analysis reveals that: 1) there is a strong correlation between the document-level relevance and the fractions of irrelevant passages to highly relevant passages; 2) the position, length and query similarity of passages play different roles in the determination of document-level relevance; 3) The sequential passage-level relevance within a document is a potential indicator for the document-level relevance. Based on the relationship between passage-level and document-level relevance, we also show that utilizing passage-level relevance signals can improve existing document ranking models. This study helps us better understand how users perceive relevance for a document and inspire the designing of novel ranking models leveraging fine-grain, passage-level relevance signals.
Adaptive graph weighting for multi-view dimensionality reduction

Multi-view learning has become a flourishing topic in recent years since it can discover various informative structures with respect to disparate statistical properties. However, multi-view data fusion remains challenging when exploring a proper way to find shared while complementary information. In this paper, we present an adaptive graph weighting scheme to conduct semi-supervised multi-view dimensional reduction. Particularly, we construct a Laplacian graph for each view, and thus the final graph is approximately regarded as a centroid of these single view graphs with different weights. Based on the learned graph, a simple yet effective linear regression function is employed to project data into a low-dimensional space. In addition, our proposed scheme can be well extended to an unsupervised version within a unified framework. Extensive experiments on varying benchmark datasets illustrate that our proposed scheme is superior to several state-of-the-art semi-supervised/unsupervised multi-view dimensionality reduction methods. Last but not least, we demonstrate that our proposed scheme provides a unified view to explain and understand a family of traditional schemes.
Practitioners' Views on Good Software Testing Practices

Software testing is an integral part of software development process. Unfortunately, for many projects, bugs are prevalent despite testing effort, and testing continues to cost significant amount of time and resources. This brings forward the issue of test case quality and prompts us to investigate what make good test cases. To answer this important question, we interview 21 and survey 261 practitioners, who come from many small to large companies and open source projects distributed in 27 countries, to create and validate 29 hypotheses that describe characteristics of good test cases and testing practices. These characteristics span multiple dimensions including test case contents, size and complexity, coverage, maintainability, and bug detection. We present highly rated characteristics and rationales why practitioners agree or disagree with them, which in turn highlight best practices and trade-offs that need to be considered in the creation of test cases. Our findings also highlight open problems and opportunities for software engineering researchers to improve practitioner activities and address their pain points.
Assessing performance of augmented reality-based neurosurgical training

This paper presents a novel augmented reality (AR)-based neurosurgical training simulator which provides a very natural way for surgeons to learn neurosurgical skills. Surgical simulation with bimanual haptic interaction is integrated in this work to provide a simulated environment for users to achieve holographic guidance for pre-operative training. To achieve the AR guidance, the simulator should precisely overlay the 3D anatomical information of the hidden target organs in the patients in real surgery. In this regard, the patient-specific anatomy structures are reconstructed from segmented brain magnetic resonance imaging. We propose a registration method for precise mapping of the virtual and real information. In addition, the simulator provides bimanual haptic interaction in a holographic environment to mimic real brain tumor resection. In this study, we conduct AR-based guidance validation and a user study on the developed simulator, which demonstrate the high accuracy of our AR-based neurosurgery simulator, as well as the AR guidance mode's potential to improve neurosurgery by simplifying the operation, reducing the difficulty of the operation, shortening the operation time, and increasing the precision of the operation.
<i>Deep0Tag</i>: Deep Multiple Instance Learning for Zero-Shot Image Tagging

Zero-shot learning aims to perform visual reasoning about unseen objects. In-line with the success of deep learning on object recognition problems, several end-to-end deep models for zero-shot recognition have been proposed in the literature. These models are successful in predicting a single unseen label given an input image but do not scale to cases where multiple unseen objects are present. Here, we focus on the challenging problem of zero-shot image tagging, where multiple labels are assigned to an image, that may relate to objects, attributes, actions, events, and scene type. Discovery of these scene concepts requires the ability to process multi-scale information. To encompass global as well as local image details, we propose an automatic approach to locate relevant image patches and model image tagging within the Multiple Instance Learning (MIL) framework. To the best of our knowledge, we propose the first end-to-end trainable deep MIL framework for the multi-label zero-shot tagging problem. We explore several alternatives for instance-level evidence aggregation and perform an extensive ablation study to identify the optimal pooling strategy. Due to its novel design, the proposed framework has several interesting features: 1) unlike previous deep MIL models, it does not use any off-line procedure (e.g., Selective Search or EdgeBoxes) for bag generation. 2) During test time, it can process any number of unseen labels given their semantic embedding vectors. 3) Using only image-level seen labels as weak annotation, it can produce a localized bounding box for each predicted label. We experiment with the large-scale NUS-WIDE and MS-COCO datasets and achieve superior performance across conventional, zero-shot, and generalized zero-shot tagging tasks.
Privacy-Preserving Methods for Feature Engineering Using Blockchain: Review, Evaluation, and Proof of Concept

The protection of private data is a key responsibility for research studies that collect identifiable information from study participants. Limiting the scope of data collection and preventing secondary use of the data are effective strategies for managing these risks. An ideal framework for data collection would incorporate feature engineering, a process where secondary features are derived from sensitive raw data in a secure environment without a trusted third party.This study aimed to compare current approaches based on how they maintain data privacy and the practicality of their implementations. These approaches include traditional approaches that rely on trusted third parties, and cryptographic, secure hardware, and blockchain-based techniques.A set of properties were defined for evaluating each approach. A qualitative comparison was presented based on these properties. The evaluation of each approach was framed with a use case of sharing geolocation data for biomedical research.We found that approaches that rely on a trusted third party for preserving participant privacy do not provide sufficiently strong guarantees that sensitive data will not be exposed in modern data ecosystems. Cryptographic techniques incorporate strong privacy-preserving paradigms but are appropriate only for select use cases or are currently limited because of computational complexity. Blockchain smart contracts alone are insufficient to provide data privacy because transactional data are public. Trusted execution environments (TEEs) may have hardware vulnerabilities and lack visibility into how data are processed. Hybrid approaches combining blockchain and cryptographic techniques or blockchain and TEEs provide promising frameworks for privacy preservation. For reference, we provide a software implementation where users can privately share features of their geolocation data using the hybrid approach combining blockchain with TEEs as a supplement.Blockchain technology and smart contracts enable the development of new privacy-preserving feature engineering methods by obviating dependence on trusted parties and providing immutable, auditable data processing workflows. The overlap between blockchain and cryptographic techniques or blockchain and secure hardware technologies are promising fields for addressing important data privacy needs. Hybrid blockchain and TEE frameworks currently provide practical tools for implementing experimental privacy-preserving applications.
Power Quality Disturbance Classification Based on DWT and Multilayer Perceptron Extreme Learning Machine

In order to effectively identify complex power quality disturbances, a power quality disturbance classification method based on empirical wavelet transform and a multi-layer perceptron extreme learning machine (ELM) is proposed. The model uses the discrete wavelet transform (DWT) multi-resolution method to extract classification features. Combined with hierarchical ELM (H-ELM) characteristics, the particle swarm optimization (PSO) single-object feature selection method is used to select the optimal feature set. The hidden layer of the H-ELM classifier in the model is trained by forward training. Once the previous layer is established, the weight of the current layer can be fixed without fine-tuning. Therefore, the training speed can be accelerated, the recognition accuracy is almost independent of the parameter adjustment, and the model has strong robustness. In order to solve the problem of data imbalance in the actual power system, a data enhancement method is proposed to reduce the impact of data imbalance and enhance the generalization performance of the network. The simulation results showed that the proposed method can identify 16 disturbances efficiently and accurately under different noise conditions, and the robustness of the proposed method is verified by the measured data.
Improved Network Traffic Classification Using Ensemble Learning

Despite the large number of research efforts that applied specific machine learning algorithms for network traffic classification, recent work has highlighted limitations and particularities of individual algorithms that make them more suitable to specific types of traffic and scenarios.As such, an important topic in this area is how to combine individual algorithms using meta-learning techniques in order to obtain more robust traffic classification metrics.This paper presents a comparative analysis among meta-learning approaches and individual classifiers to classify network traffic.We investigate and evaluate a range of meta-learning techniques, including Voting, Stacking, Bagging and Boosting.We then propose a new experimental analysis of different meta-learning techniques -also known as ensemble learners -and compare them with their own base classifiers when used individually.Finally, considering the emerging popularity of Neural Networks, we analyze this scenario using the Multi-layer Perceptron classifier.The experiments were performed with data provided by the UCI Machine Learning Repository.The best performance was obtained by an ensemble technique (Bagging), which obtained accuracy of 99.972% and false positive rate of 0.00018%.
Visual question answering via Attention-based syntactic structure tree-LSTM

Due to the various patterns of the image and free-form language of the question, the performance of Visual Question Answering (VQA) still lags behind satisfaction. Existing approaches mainly infer answers from the low-level features and sequential question words, which neglects the syntactic structure information of the question sentence and its correlation with the spatial structure of the image. To address these problems, we propose a novel VQA model, i.e., Attention-based Syntactic Structure Tree-LSTM (ASST-LSTM). Specifically, a tree-structured LSTM is used to encode the syntactic structure of the question sentence. A spatial-semantic attention model is proposed to learn the visual-textual correlation and the alignment between image regions and question words. In the attention model, Siamese network is employed to explore the alignment between visual and textual contents. Then, the tree-structured LSTM and the spatial-semantic attention model are integrated with a joint deep model, in which the multi-task learning method is used to train the model for answer inferring. Experiments conducted on three widely used VQA benchmark datasets demonstrate the superiority of the proposed model compared with state-of-the-art approaches.
On-chip photonic decision maker using spontaneous mode switching in a ring laser

Abstract Efficient and accurate decision making is gaining increased importance with the rapid expansion of information communication technologies including artificial intelligence. Here, we propose and experimentally demonstrate an on-chip, integrated photonic decision maker based on a ring laser. The ring laser exhibits spontaneous switching between clockwise and counter-clockwise oscillatory dynamics; we utilize such nature to solve a multi-armed bandit problem. The spontaneous switching dynamics provides efficient exploration to find the accurate decision. On-line decision making is experimentally demonstrated including autonomous adaptation to an uncertain environment. This study paves the way for directly utilizing the fluctuating physics inherent in ring lasers, or integrated photonics technologies in general, for achieving or accelerating intelligent functionality.
A Novel Hybrid Cuckoo Search- Extreme Learning Machine Approach for Modulation Classification

This paper presents a novel hybrid extreme learning machine (ELM) with cuckoo search algorithm (CSA) for the classification purposes of the digitally modulated signals, such as phase shift keying (PSK), frequency shift keying (FSK), and quadrature amplitude modulation (QAM). Nine modulation schemes having different orders have been considered for this paper. First, the Gabor filter is used to extract the key features from the received signal which are then optimized by the CSA. Finally, the ELM is used to classify the modulation schemes using these optimized features. Our proposed CSA-ELM approach is not only fast convergent and robust but also manifests improved percentage classification accuracy at low SNRs and lower sample size for both AWGN and Rayleigh fading channels.
Policing fraud in England and Wales: examining constables’ and sergeants’ online fraud preparedness

The significant economic and emotional consequences of online fraud have not been equally matched by the policing response to these offenses. This paper examines constables’ and sergeants’ perceived preparation to respond to online fraud through a quantitative analyses of survey data collected from over 1300 officers from 35 local police agencies across England and Wales. The findings demonstrated that constables and sergeants being prepared to respond to online fraud was related to agencies having clear policies and procedures, constables having computer skills and online incident experiences, and being able to relate to fraud victims. The implications of this study for first responder training and resource development to respond to cybercrimes are considered.
A model of the entrance pupil of the human eye

The aperture stop of the iris is subject to refraction by the cornea, and thus an outside observer sees a virtual image: the "entrance pupil" of the eye. When viewed off-axis, the entrance pupil has an elliptical form. The precise appearance of the entrance pupil is a consequence of the anatomical and optical properties of the eye, and the relative positions of the eye and the observer. This paper presents a ray traced model eye that provides the parameters of the entrance pupil ellipse for an observer at an arbitrary location. The model is able to reproduce empirical measurements of the shape of the entrance pupil with good accuracy. I demonstrate that accurate specification of the entrance pupil of a stationary eye requires modeling of corneal refraction, the misalignment of the visual and optical axes, and the non-circularity of the aperture stop. The model, including a three-dimensional ray tracing function through quadric surfaces, is implemented in open-source MATLAB code.
Guiding Autonomous Exploration With Signal Temporal Logic

Algorithms for autonomous robotic exploration usually focus on optimizing time and coverage, often in a greedy fashion. However, obstacle inflation is conservative and might limit mapping capabilities and even prevent the robot from moving through narrow, important places. This letter proposes a method to influence the manner the robot moves in the environment by taking into consideration a user-defined spatial preference formulated in a fragment of signal temporal logic (STL). We propose to guide the motion planning toward minimizing the violation of such preference through a cost function that integrates the quantitative semantics, i.e., robustness of STL. To demonstrate the effectiveness of the proposed approach, we integrate it into the autonomous exploration planner (AEP). Results from simulations and real-world experiments are presented, highlighting the benefits of our approach.
Effective and efficient API misuse detection via exception propagation and search-based testing

Application Programming Interfaces (APIs) typically come with (implicit) usage constraints. The violations of these constraints (API misuses) can lead to software crashes. Even though there are several tools that can detect API misuses, most of them suffer from a very high rate of false positives. We introduce Catcher, a novel API misuse detection approach that combines static exception propagation analysis with automatic search-based test case generation to effectively and efficiently pinpoint crash-prone API misuses in client applications. We validate Catcher against 21 Java applications, targeting misuses of the Java platform's API. Our results indicate that Catcher is able to generate test cases that uncover 243 (unique) API misuses that result in crashes. Our empirical evaluation shows that Catcher can detect a large number of misuses (77 cases) that would remain undetected by the traditional coverage-based test case generator EvoSuite. Additionally, on average, Catcher is eight times faster than EvoSuite in generating test cases for the identified misuses. Finally, we find that the majority of the exceptions triggered by Catcher are unexpected to developers, i.e., not only unhandled in the source code but also not listed in the documentation of the client applications.
Assessing the effects of a full-body motion-based exergame in virtual reality

There is a growing number of commercial exergames tailored for virtual reality (VR) head-mounted displays (HMDs). However, empirical evaluations of the feasibility and effects of such games, especially those requiring full body motions, are still limited. This research investigates the effects of playing a full-body motion VR exergame. We have conducted a study with a game that has two modes, single- and multi-tasking, and with two types of displays, a VR HMD and a 50-inch Large Display, to collect data about users' game experience, simulator sickness, and brainwave responses. Our results indicate that (1) Participants have the same level of game experience and simulator sickness when playing the exergame in VR and Large Display; (2) VR has increased participants' Theta wave; (3) Participants believe multi-tasking is more challenging and show a higher level of simulator sickness than single-tasking; and (4) Participants have a worse game performance in multi-tasking than single-tasking.
A Secure and High Visual-Quality Framework for Medical Images by Contrast-Enhancement Reversible Data Hiding and Homomorphic Encryption

Medical data security is facing great challenges in medical applications due to the open internet and the semi-trusted cloud. For the sake of privacy protection and the security of medical images, this paper proposes a secure and high visual-quality framework for medical images. In this framework, a novel reversible data hiding (RDH) based on lesion extraction embeds privacy data into medical images for privacy protection and image quality improvement, homomorphic encryption based on chaotic map encrypts images for medical image security. The experiments have shown that the proposed framework increases the security of medical data and improves the visual quality of medical images significantly. The proposed RDH in this framework outperforms the other RDH methods with contrast enhancement and the proposed encryption scheme increases image security well.
Tracking user-role evolution via topic modeling in community question answering

Community question answering (CQA) services that enable users to ask and answer questions are popular on the internet. Each user can simultaneously play the roles of asker and answerer. Some work has aimed to model the roles of users for potential applications in CQA. However, the dynamic characteristics of user roles have not been addressed. User roles vary over time. This paper explores user representation by tracking user-role evolution, which could enable several potential applications in CQA, such as question recommendation. We believe this paper is the first to track user-role evolution and investigate its influence on the performance of question recommendation in CQA. Moreover, we propose a time-aware role model (TRM) to effectively track user-role evolution. With different independence assumptions, two variants of TRM are developed. Finally, we present the TRM-based approach to question recommendation, which provides a mechanism to naturally integrate the user-role evolution with content relevance between the answerer and the question into a unified probabilistic framework. Experiments using real-world data from Stack Overflow show that (1) the TRM is valid for tracking user-role evolution, and (2) compared with baselines utilizing role based methods, our TRM-based approach consistently and significantly improves the performance of question recommendation. Hence, our approach could enable several potential applications in CQA.