Reviewerly/exHarmony huggingface dataset:
Just-In-Time Obsolete Comment Detection and Update

Comments are valuable resources for the development, comprehension and maintenance of software. However, while changing code, developers sometimes neglect the evolution of the corresponding comments, resulting in obsolete comments. Such obsolete comments can mislead developers and introduce bugs in the future, and are therefore detrimental. We notice that by detecting and updating obsolete comments in time with code changes, obsolete comments can be effectively reduced and even avoided. We refer to this task as Just-In-Time (JIT) Obsolete Comment Detection and Update. In this work, we propose a two-stage framework named <b>CUP<inline-formula><tex-math notation="LaTeX">$^\mathrm{2}$</tex-math><alternatives><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="xia-ieq1-3138909.gif" xmlns:xlink="http://www.w3.org/1999/xlink"/></alternatives></inline-formula></b> ( <b>Two</b> -stage <b>C</b> omment <b>UP</b> dater) to automate this task. CUP <inline-formula><tex-math notation="LaTeX">$^\mathrm{2}$</tex-math></inline-formula> consists two components, i.e., an <b>O</b> bsolete <b>C</b> omment <b>D</b> etector named <b>OCD</b> and a <b>C</b> omment <b>UP</b> dater named <b>CUP</b> , each of which relies on a distinct neural network model to perform detection (updates). Specifically, given a code change and a corresponding comment, CUP <inline-formula><tex-math notation="LaTeX">$^\mathrm{2}$</tex-math></inline-formula> first leverages OCD to predict whether this comment should be updated. If the answer is yes, CUP will be used to generate the new version of the comment automatically. To evaluate CUP <inline-formula><tex-math notation="LaTeX">$^\mathrm{2}$</tex-math></inline-formula> , we build a large-scale dataset with over 4 million code-comment change samples. Our dataset focuses on method-level code changes and updates on method header comments considering the importance and widespread use of such comments. Evaluation results show that 1) both OCD and CUP outperform their baselines by significant margins, and 2) CUP <inline-formula><tex-math notation="LaTeX">$^\mathrm{2}$</tex-math></inline-formula> performs better than a rule-based baseline. Specifically, the comments generated by CUP <inline-formula><tex-math notation="LaTeX">$^\mathrm{2}$</tex-math></inline-formula> are identical to the ground truth for 41.8% of the samples that are predicted to be positive by OCD. We believe CUP <inline-formula><tex-math notation="LaTeX">$^\mathrm{2}$</tex-math></inline-formula> can help developers detect obsolete comments, better understand where and how to update obsolete comments and reduce their edits on obsolete comment updates.
Structuring Special Purpose Vehicles for Financing Renewable Generators on a Blockchain Marketplace

Eighteen percent of the world’s population do not have access to electricity, impeding economic, social, and human development. The electricity access challenge can be attributed to the significant investment gap needed to finance new power projects, requiring new and innovative financing options. Independent Power Projects, funded, built, owned, and operated by the private sector and constituted via a special purpose vehicle—a legal entity whose sole purpose is implementing a power project—have become one of the fastest-growing sources of investment in the electricity sector. The limitations of traditional finance sources, including high credit, liquidity, margining, third-party, legal, and process risks, means that funds for these projects are expensive and raised only after a long and arduous process. The present article addresses these challenges by describing a novel decentralized autonomous organization, a blockchain-agnostic special purpose vehicle underpinned by a trio of autonomous mechanisms— <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mobilization, collateralization</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">settlement</i> . These mechanisms enable seamless finance mobilization for the special purpose vehicle from a location-independent crowd, revenue collection from the electricity buyer in a risk-mitigated manner, and disbursal of eventual project revenues to investors.
Sustainability at Auburn University: Assessing Rooftop Solar Energy Potential for Electricity Generation with Remote Sensing and GIS in a Southern US Campus

Achieving sustainability through solar energy has become an increasingly accessible option in the United States (US). Nationwide, universities are at the forefront of energy efficiency and renewable generation goals. The aim of this study was to determine the suitability for the installation of photovoltaic (PV) systems based on their solar potential and corresponding electricity generation potential on a southern US university campus. Using Auburn University located in the southern US as a case study, freely available geospatial data were utilized, and geographic information system (GIS) approaches were applied to characterize solar potential across the 1875-acre campus. Airborne light detection and ranging (lidar) point clouds were processed to extract a digital surface model (DSM), from which slope and aspect were derived. The area and total solar radiation of campus buildings were calculated, and suitable buildings were then determined based on slope, aspect, and total solar radiation. Results highlighted that of 443 buildings, 323 were fit for solar arrays, and these selected rooftops can produce 27,068,555 kWh annually. This study demonstrated that Auburn University could benefit from rooftop solar arrays, and the proposed arrays would account for approximately 21.07% of annual electricity requirement by buildings, equivalent to 14.43% of total campus electricity for all operations. Given increasing open and free access to high-resolution lidar data across the US, methods from this study are adaptable to institutions nationwide, for the development of a comprehensive assessment of solar potential, toward meeting campus energy goals.
Linear Ordering Problem based Classifier Chain using Genetic Algorithm for multi-label classification

One of the most challenging tasks in multi-label classification is to identify label interdependence. Classifier Chain is the most prevalent method that utilizes label interdependence for improving classification accuracy as it requires only the number of classifiers equal to the number of labels. It uses a random sequence of labels. However, the order of labels in these sequences affects the classification performance. Nevertheless, despite many proposals in the literature, deciding the order in which these classifiers provide optimum results is a challenge to date. This paper proposes two methods for the ordering problem of the Classifier chain. The first proposed method termed as Linear Ordering Problem based Classifier Chain (LOP-CC) finds the chain order by considering it as a Linear Ordering Problem (LOP). The LOP utilizes a matrix and finds the optimal permutation of rows and corresponding columns that maximizes the sum of all the elements in the upper triangular matrix. This paper utilizes pairwise conditional entropy for creating the matrix to be used with the LOP and solves it using the Genetic Algorithm. It also proposes an extension to LOP-CC method termed as Linear Ordering Problem based partial Classifier Chain (LOP-pCC). It uses the same order of labels as LOP-CC. However, as opposed to LOP-CC, it utilizes partial sequences in the classifier chain rather than a full sequence. Experimentation performed on ten benchmark datasets consisting of a varying number of labels using different performance metrics demonstrates the proposed methods’ effectiveness compared to the other state-of-the-art classifier chain models.
DET: Depth-Enhanced Tracker to Mitigate Severe Occlusion and Homogeneous Appearance Problems for Indoor Multiple-Object Tracking

Multiple-object tracking has long been a topic of interest since it plays an important role in many computer vision applications. Existing works are mostly designed for outdoor tracking, such as video surveillance and autonomous driving. However, the behaviors of objects in outdoor tracking scenarios do not fully reflect the tracking challenges in indoor tracking environments. In outdoor tracking scenarios, pedestrians and vehicles usually move uniformly from place to place on a simple straight path, and target appearances are usually different. In contrast, in indoor scenarios, such as choreographed performances, the dynamic behaviors of dancers lead to severe occlusions, and similar costumes present a homogeneous appearance problem. These severe occlusion and homogeneous appearance problems in indoor tracking lead to noticeable degradation in the performance of existing works. In this paper, we propose a depth-enhanced tracking-by-detection framework and a semantic matching strategy combined with a scene-aware affinity measurement method to mitigate occlusion and homogeneous appearance problems significantly. In addition, we introduce an indoor tracking dataset and increase the diversity of existing benchmark datasets for indoor tracking evaluation. We conduct experiments on both the proposed indoor tracking dataset and the latest MOT benchmarks, MOT17 and MOT20. The experimental results show that our method consistently outperforms other works on the convincing HOTA metric across the benchmarks and greatly reduces the number of identity switches by 20% compared to that of the second-best tracker, DeepSORT, in our proposed indoor MOT benchmark dataset.
Impediments to Blockchain Adoption

ABSTRACT Satoshi Nakamoto (2008) published a seminal paper on a promising digital currency application and proposed a distributed ledger technology (DLT) to support it. Shortly thereafter, in 2009, bitcoin and the customized DLT that supports it were established. Although the DLT described by Nakamoto (2008), which packages data into blocks that are then cryptographically chained together (i.e., “block chain,” or “blockchain”), possesses features that are desirable for some business applications and/or their auditors, over a dozen years later there is not yet a widescale adoption of blockchain for business operations. This paper explores functionality, data and process integrity, and regulatory concerns as potential explanations for the lag in mainstream business and accounting adoption. We also contextualize some of the concerns that are likely to have delayed blockchain implementation by providing a framework of questions directed at both researchers and practitioners.
Content based Surgical Video Retrieval via Multi-Deep Features Fusion

Medicine has seen a rapid increase in the number of minimally invasive surgeries that benefit patients in various ways due to economic and clinical factors. In most hospitals such surgeries are often recorded and stored. The use of these recordings includes many potential applications like for medical education, patient disease analysis, surgical error analysis and skill assessment, etc. However, manual search in this database of long-term videos is exceptionally laborious and time-consuming. Therefore, there is a critical need for an efficient and effective content-based video search system for surgical videos. On the other hand, recent progress in deep learning showed the convolutional neural network (CNN) as an effective feature representation learning model. In this paper, we exploit the multiple CNNs for feature representation and proposed a multi feature fusion based approach for Content based Surgical Video Retrieval. To validate our approach we choose Surgical Actions 160 dataset, where the results demonstrate the effectiveness of feature fusion and outperforms the other state-of-the-arts.
Automatic Brain Tumor Segmentation, and 3D Reconstruction and Visualization Using Augmented Reality

Augmented reality is the overlay of computer generated images, videos, text, sounds on real world structures. It has previously been used for medical aid diagnosis as a support tool for medical practice/training. Magnetic resonance imaging (MRI) is widely used in medical imaging, especially in the detection of brain tumors. Although it has great advantages, like any other acquisition technique, it suffers from some constraints; such as the problem of noise, which can generate unnecessary and false information, which can escape and deceive the human eye. In this paper, we present an augmented reality (AR) system that simplifies 3D brain tumor reconstruction and visualization. We develop a robust and precise method of segmentation of a large class of tumor in brain MRI images, intending to provide useful information to practitioners, for the diagnosis and therapeutic management. The developed tumor segmentation includes two main components: Pre-processing rather than treatment and segmentation. The pre-processing step involves the operations of reducing the intensity heterogeneity by using image enhancement. The tumor segmentation and detection are provided using the Fuzzy C-Means (FCM) classification algorithm followed by morphological operations. It is based on the hypothesis that the tumor appears with specific gray levels in the image, corresponding to an additional class. The 3D brain tumor reconstruction, using 3D Slicer, was virtually augmented on the real scene. The experimental results using AR visualization show improves significantly In the world of brain tumor.
Understanding the “Pathway” Towards a Searcher’s Learning Objective

Search systems are often used to support learning-oriented goals. This trend has given rise to the “search-as-learning” movement, which proposes that search systems should be designed to support learning. To this end, an important research question is: How does a searcher’s type of learning objective (LO) influence their trajectory (or pathway ) toward that objective? We report on a lab study (N = 36) in which participants gathered information to meet a specific type of LO. To characterize LOs and pathways , we leveraged Anderson and Krathwohl’s (A&amp;K’s) taxonomy [ 3 ]. A&amp;K’s taxonomy situates LOs at the intersection of two orthogonal dimensions: (1) cognitive process (CP) (remember, understand, apply, analyze, evaluate, and create) and (2) knowledge type (factual, conceptual, procedural, and metacognitive knowledge). Participants completed learning-oriented search tasks that varied along three CPs (apply, evaluate, and create) and three knowledge types (factual, conceptual, and procedural knowledge). A pathway is defined as a sequence of learning instances (e.g., subgoals) that were also each classified into cells from A&amp;K’s taxonomy. Our study used a think-aloud protocol, and pathways were generated through a qualitative analysis of participants’ think-aloud comments and recorded screen activities. We investigate three research questions. First, in RQ1, we study the impact of the LO on pathway characteristics (e.g., pathway length). Second, in RQ2, we study the impact of the LO on the types of A&amp;K cells traversed along the pathway. Third, in RQ3, we study common and uncommon transitions between A&amp;K cells along pathways conditioned on the knowledge type of the objective. We discuss implications of our results for designing search systems to support learning.
Information retrieval and question answering: A case study on COVID-19 scientific literature

Biosanitary experts around the world are directing their efforts towards the study of COVID-19. This effort generates a large volume of scientific publications at a speed that makes the effective acquisition of new knowledge difficult. Therefore, Information Systems are needed to assist biosanitary experts in accessing, consulting and analyzing these publications. In this work we develop a study of the variables involved in the development of a Question Answering system that receives a set of questions asked by experts about the disease COVID-19 and its causal virus SARS-CoV-2, and provides a ranked list of expert-level answers to each question. In particular, we address the interrelation of the Information Retrieval and the Answer Extraction steps. We found that a recall based document retrieval that leaves to a neural answer extraction module the scanning of the whole documents to find the best answer is a better strategy than relying in a precise passage retrieval before extracting the answer span.
Application of VR Technology to the Training of Paramedics

The virtual world has long been a focus not only of the gaming sphere, but also of the manufacturing and educational industries. The virtual world and its technology have many advantages, the basic ones being, for example, the use of experiential learning, with which the human brain can remember some things better and faster. It was due to the advantages of virtual reality technology that we decided to create an educational system on safety and health at work, and we focused on the healthcare segment due to the COVID-19 pandemic. Thanks to the cooperation of a professional consortium, we created an educational system for safety and health at work and carried out several extensive laboratory measurements, the results of which we followed up in practical measurements with medical staff. The created system is inherently unique and applicable and can be used across several industries. The article presents three basic types of scenarios as well as an evaluation of satisfaction with the proposed system from test participants, i.e., nurses.
Application of multivariate data analysis to biogeochemical exploration at the Twin Lakes Deposit, Monument Bay Gold Project, Manitoba, Canada

The Twin Lakes Deposit, located in the northwest of the Superior Province, is a promising Archean Greenstone-hosted orogenic gold deposit. Biogeochemical and statistical techniques were used for the first time in this area to assess the geochemical dispersion patterns of Au, As, Bi, Se, Sb, Tl, Fe, Co, Ni, Cr, Mo, Cd, Pb, Zn, Mn, K, P, B, Mg, Ca, Ba, and Cu in black spruce samples collected perpendicularly to the overall trend of shear-hosted gold mineralization. ICP-MS analysis of ashed black spruce bark, twigs, and needles has outlined a biogeochemical response of black spruce to the concealed mineralization. Most elements are accumulated preferentially in twigs and bark, while B, K, Mg, Mn, and P are highly concentrated in needles. High Au and Tl enrichments were identified in all sample types compared to the background level. The scatter plot matrix demonstrated a positive bivariate relationship between Au and Fe, Mg, Cu, Mo, and Zn; and horizontal to slightly negative correlation with Ca, K, Mn, P, and B. Investigations on the spatial distribution pattern of Au and its pathfinder elements using a robust log-transformed inverse distance weighted (IDW) interpolation method revealed the presence of zones of gold enrichment in the north to the northwest of the survey, where Au is accompanied by Tl-As-Cd > Bi, Pb, Se > Sb. According to the Robust RQ-mode principal component analysis (PCA), two main components control the distribution of elements in black spruce, including (1) a geological factor that mostly reflects bedrock composition; and (2) a physiological factor that reflects biological processing of essential elements. Of importance is that Au and its pathfinder elements, including As, Se, Tl, and to a lesser extent, Bi, Cd, and Pb were well-separated as a subgroup of the geological factor with a clear spatial relationship to gold mineralization. This study demonstrates a biogeochemical response in black spruce is associated with gold mineralization at the Monument Bay Gold Project using multivariate statistical methods. The results of this study are promising and can be extrapolated to wider surveys across the Monument Bay Gold Project to vector toward new gold mineralized zones or extensions of the Twin Lakes gold deposit. The results also provide a suite of gold pathfinder elements that support exploration efforts and regional target generation.
Verifiable online/offline multi-keyword search for cloud-assisted Industrial Internet of Things

Attribute-based encryption (ABE) and attribute-based keyword search (ABKS) facilitate fine-grained access and search control for cloud-assisted Industrial Internet of Things (IIoT). However, existing schemes suffer from the following drawbacks: (1) their computational overhead in data outsourcing and retrieval is exceptionally high; (2) they obtain wrong search results if one or more of the queried keywords are wrongly selected; (3) in most existing ABKS, the untrustworthiness of the cloud server is not taken into account, and the correctness of the search results is not verified; (4) existing verifiable ABKS schemes do not support search results verification without the main data, and verifiers have to first download the search results and then check their correctness. In this paper, we design a verifiable online/offline multi-keyword search (VMKS) scheme providing high-level solutions to the aforementioned problems. We use the healthcare setting as a case study, and we demonstrate how our VMKS can be deployed in cloud-assisted Healthcare IIoT (HealthIIoT). We prove the security of our VMKS in the standard model and under the hardness assumption of the decisional bilinear Diffie-Hellman (DBDH) problem. Empirical results demonstrate that our VMKS speeds up encryption and verification processes by more than 70% and 90%, respectively. Moreover, our scheme reduces the communication overhead in the verification phase by more than 80%.
A Sensitive Image Encryption Algorithm Based on a Higher-Dimensional Chaotic Map and Steganography

Chaotic maps have been widely used in image encryption due to their complexity, pseudorandomness and high sensitivity to initial values. In this paper, an image encryption algorithm based on a 4D chaotic map and steganography is proposed. The algorithm consists of two rounds of encryption and one operation of embedding hash. In the first round of encryption, the hash value of plaintext image is used to control the generation of chaotic sequences, which makes the encryption algorithm highly relevant to the content of the image. Then, the hash value is embedded into the intermediate image according to the idea of steganography. In the second round of encryption, the image is encrypted under the control of another set of chaotic sequences to hide the hash value and further enhance the security of the algorithm. Different from other algorithms, our algorithm does not need to additionally transmit the hash value to the decryptor through a special channel. It has good availability and adaptability. Experimental results and security analysis demonstrate that the algorithm has high security performance and can resist various attacks.
Inpainted Image Reconstruction Using an Extended Hopfield Neural Network Based Machine Learning System

This paper considers the use of a machine learning system for the reconstruction and recognition of distorted or damaged patterns, in particular, images of faces partially covered with masks. The most up-to-date image reconstruction structures are based on constrained optimization algorithms and suitable regularizers. In contrast with the above-mentioned image processing methods, the machine learning system presented in this paper employs the superposition of system vectors setting up asymptotic centers of attraction. The structure of the system is implemented using Hopfield-type neural network-based biorthogonal transformations. The reconstruction property gives rise to a superposition processor and reversible computations. Moreover, this paper's distorted image reconstruction sets up associative memories where images stored in memory are retrieved by distorted/inpainted key images.