Reviewerly/exHarmony huggingface dataset:
A Novel Approach to Cost-Efficient Scheduling of Multi-workflows in the Edge Computing Environment with the Proximity Constraint

The edge computing paradigm is featured by the ability to offload computing tasks from mobile devices to edge clouds and provide high cost-efficient computing resources, storage and network services closer to the edge. A key question for workflow scheduling in the edge computing environment is how to reduce the monetary cost while fulfilling Service-Level-Agreement in terms of performance and quality-of-service requirements. However, it's still a challenge to guarantee user-perceived quality of service of applications deployed upon edge infrastructures due to the fact that such applications are constantly subject to negative impacts, e.g., network congestions, unexpected long message delays, shrinking coverage range of edge servers due to battery depletion. In this paper, we study the multi-workflow scheduling problem and propose a novel approach to Cost-Efficient Scheduling of Multi-Workflows in the Edge Computing Environment With Proximity Constraint. The proposed approach aims at minimizing edge computing costs while meeting user-specified workflow completion deadlines and leverages a discrete firefly algorithm for yielding the scheduling plan. We conduct experimental case studies based on multiple well-known scientific workflow templates and a real-world dataset of edge resource locations as well. Experimental results clearly suggest that our proposed approach outperforms traditional ones in terms of cost and makespan.
Testing the exteroceptive function of nociception: The role of visual experience in shaping the spatial representations of nociceptive inputs

Adequately localizing pain is crucial to protect the body against physical damage and react to the stimulus in external space having caused such damage. Accordingly, it is hypothesized that nociceptive inputs are remapped from a somatotopic reference frame, representing the skin surface, towards a spatiotopic frame, representing the body parts in external space. This ability is thought to be developed and shaped by early visual experience. To test this hypothesis, normally sighted and early blind participants performed temporal order judgment tasks during which they judged which of two nociceptive stimuli applied on each hand's dorsum was perceived as first delivered. Crucially, tasks were performed with the hands either in an uncrossed posture or crossed over body midline. While early blinds were not affected by the posture, performances of the normally sighted participants decreased in the crossed condition relative to the uncrossed condition. This indicates that nociceptive stimuli were automatically remapped into a spatiotopic representation that interfered with somatotopy in normally sighted individuals, whereas early blinds seemed to mostly rely on a somatotopic representation to localize nociceptive inputs. Accordingly, the plasticity of the nociceptive system would not purely depend on bodily experiences but also on crossmodal interactions between nociception and vision during early sensory experience.
Precise wide-range three-dimensional shape measurement method to measure superfine structures based on speckle interferometry

With regard to the three-dimensional (3D) shape measurement, based on speckle interferometry, of an object with a fine structure beyond the diffraction limit of the objective lens, phase distribution of each speckle region in a speckle pattern and a specklegram is discussed. The experimental results confirmed that the phase distribution with respect to the shape of an object exists spatially even in the speckle region of the speckle pattern. However, the measurement results of a superfine structure by speckle interferometry indicated that the spatial continuity of the phase distribution does not exist in each speckle but in the specklegram. This feature was used to confirm that a wide range of 3D shape measurements of an object with a fine structure can be realized by analyzing specklegrams calculated from speckle patterns before and after a lateral shift in speckle interferometry. Furthermore, it is confirmed that the shape measurement of a nonperiodical structure can be performed.
Analysis of backpropagation neural neural network algorithm on student ability based cognitive aspects

Abstract Back propagation is one of the supervised learning and multi-layered training program and uses errors during the process of changing the weight value in the backward process as well as the forward propagation. In the method for predicting cognitive abilities backpropagation the first step is to set the input neuron number, the number of neurons that are hidden, and the number of output neurons. The number of neurons used in the program is 6 neurons consisting of cognitive criteria, 6 hidden neuron layers, and 2 neuron outputs. The highest level of accuracy is in the binary sigmoid and bipolar sigmoid activation functions at the 64th epoch with the accuracy of each function of 82.93% +/- 37.63% and 85.37% +/- 35.34%. The smallest root mean squared error value was found in binary sigmoid of 0.266 with a tolerance of +/- 0.258 on the 100th epoch with an accuracy of 80.49% while for the sigmoid bipolar activation function the smallest root mean squared error value was obtained at the epoch 500 of 0.282 with tolerance +/- 0.353.
Energy calibration via correlation using an adaptive mesh refinement

Energy calibration via correlation has proven to be a powerful calibration method especially in the case of calibration data sets with low counting statistics. This work reports on an evolved version of this approach that uses an adaptive mesh refinement to sample the parameter space which results in the same calibration performance but in a considerably reduced computation time.
3D Human Skeleton Data Compression for Action Recognition

Skeleton-based action recognition continues to open up new application scenarios with the popularity of acquisition devices. This also leads to a rapid increase in the amount of human skeleton data. Currently, there is no skeleton data compression algorithm for the task of action recognition. In order to solve this problem, we propose the first skeleton data compression algorithm, which can compress the skeleton data stream to a small bandwidth while keeping the accuracy of action recognition as high as possible. The proposed compression algorithm is called Motion-based Joints Selection (MJS). It performs compression based on the amount of movement of different joints. In addition, we also explored the combination of MJS and existing lossless compression methods, and found the most suitable one. In the end, we verify that our compression method MJS can achieve promising results on the large dataset NTU-RGB+D.
Current Challenges of Digital Forensics in Cyber Security

The digital age has undoubtedly revolutionized the life and work of people. However, this sheen of digital technology remains challenged by the spate of cybercrimes that imperil the privacy and data of the end-users. The alarming rise in cybercrimes has become a major concern for cyber specialists. In this grim context, digital forensics has emerged as a boon for cyber specialists because it has proven to be an effective means for investigating cyber-attacks. This chapter reviews the existing tools and approaches in the field of digital forensics in cybersecurity. This chapter also discusses the current challenges and problems that are faced by a forensic investigator. In addition, it enlists the different categories of digital forensics. The study concludes by underlining the importance and the need for extensive research in digital forensic tools.
Using argumentation schemes to find motives and intentions of a rational agent

Because motives and intentions are internal, and not directly observable by another agent, it has always been a problem to find a pathway of reasoning linking them to externally observable evidence.This paper proposes an argumentationbased method that one can use to support or attack hypotheses about the motives or intentions of an intelligent autonomous agent based on verifiable evidence.The method is based on a dialectical argumentation approach along with a commitment-based theory of mind.It is implemented using the Carneades Argumentation System, which already has 106 programmed schemes available to it and has an argument search tool.The method uses schemes, notably ones for abductive reasoning, argument from action to intention, argument from action to motive, and some new ones.
Semi-supervised Cross-Modal Hashing with Graph Convolutional Networks

Cross-modal hashing for large-scale approximate neighbor search has attracted great attention recently because of its significant computational and storage efficiency. However, it is still challenging to generate high-quality binary codes to preserve inter-modal and intra-modal semantics, especially in a semi-supervised manner. In this paper, we propose a semi-supervised cross-modal discrete code learning framework. This is the very first work of applying asymmetric graph convolutional networks (GCNs) for scalable cross-modal retrieval. Specifically, the architecture contains multiple GCN branches, each of which is for one data modality to extract modality-specific features and then to generate unified binary hash codes across different modalities, so that the underlying correlations and similarities across modalities are simultaneously preserved into the hash values. Moreover, the branches are built with asymmetric graph convolutional layers, which employ randomly sampled anchors to tackle the scalability and out-of-sample issue in graph learning, and reduce the complexity of cross-modal similarity calculation. Extensive experiments conducted on benchmark datasets demonstrate that our method can achieve superior retrieval performance in comparison with the state-of-the-art methods.
Multi-Label Learning via Feature and Label Space Dimension Reduction

In multi-label learning, each object belongs to multiple class labels simultaneously. In the data explosion age, the size of data is often huge, i.e., large number of instances, features and class labels. The high dimension of both the feature and label spaces has posed great challenges to multi-label learning problems, e.g., high time and memory costs. In this paper, we propose a new framework for multi-label learning with a large number of class labels and features, i.e., Multi-Label Learning via Feature and Label Space Dimension Reduction, namely MLL-FLSDR. Specifically, both the feature space and label space are reduced to low dimensional spaces respectively, in which the local structure of data points is utilized to constrain the geometrical structure on both the learned low dimensional spaces and guarantee the qualities of them. Then, an effective multi-label classifier is constructed from the low dimensional feature space to the latent label space. Last, the final prediction for new test data examples can be obtained by recovering from their prediction results in the latent label space with an encoding matrix learned in the previous stage. Extensive comparison experiments with the state-of-the-art approaches manifest the effectiveness of the proposed method MLL-FLSDR.
Minimum-time Motion-Planning of AUVs under Spatially Varying Ocean Currents

This paper presents a novel method for motion planning of AUVs in the presence of spatially varying ocean currents. The proposed approach uses the concept of Renormalization group theory (RGT) to handle spatially varying ocean currents by merging regions with homogeneous currents (i.e., with similar direction and magnitude). Such regions contain redundant samples and lead to an increased planning time. The removal of redundant samples from the homogeneous regions results in non-uniformly distributed samples for motion planning. Then, a novel variant of the A* search algorithm, which is a hybrid of both sample-based and grid-based methods, is developed to plan the minimum time path between the start and the goal position. The proposed algorithm is guaranteed to provide better quality solutions in terms of the time-cost compared to the standard A* algorithm. Moreover, removal of redundant samples leads to a significant reduction in computational complexity.
Bighead: A Framework-Agnostic, End-to-End Machine Learning Platform

With the increasing need to build systems and products powered by machine learning inside organizations, it is critical to have a platform that provides machine learning practitioners with a unified environment to easily prototype, deploy, and maintain their models at scale. However, due to the diversity of machine learning libraries, the inconsistency between environments, and various scalability requirement, there is no existing work to date that addresses all of these challenges. Here, we introduce Bighead, a framework-agnostic, end-to-end platform for machine learning. It offers a seamless user experience requiring only minimal efforts that span feature set management, prototyping, training, batch (offline) inference, real-time (online) inference, evaluation, and model lifecycle management. In contrast to existing platforms, it is designed to be highly versatile and extensible, and supports all major machine learning frameworks, rather than focusing on one particular framework. It ensures consistency across different environments and stages of the model lifecycle, as well as across data sources and transformations. It scales horizontally and elastically in response to the workload such as dataset size and throughput. Its components include a feature management framework, a model development toolkit, a lifecycle management service with UI, an offline training and inference engine, an online inference service, an interactive prototyping environment, and a Docker image customization tool. It is the first platform to offer a feature management component that is a general-purpose aggregation framework with lambda architecture and temporal joins. Bighead is deployed and widely adopted at Airbnb, and has enabled the data science and engineering teams to develop and deploy machine learning models in a timely and reliable manner. Bighead has shortened the time to deploy a new model from months to days, ensured the stability of the models in production, facilitated adoption of cutting-edge models, and enabled advanced machine learning based product features of the Airbnb platform. We present two use cases of productionizing models of computer vision and natural language processing.
A First Step to the Categorical Logic of Quantum Programs

The long-term goal of our research is to develop a powerful quantum logic which is useful in the formal verification of quantum programs and protocols. In this paper we introduce the basic idea of our categorical logic of quantum programs (CLQP): It combines the logic of quantum programming (LQP) and categorical quantum mechanics (CQM) such that the advantages of both LQP and CQM are preserved while their disadvantages are overcome. We present the syntax, semantics and proof system of CLQP. As a proof-of-concept, we apply CLQP to verify the correctness of Deutsch's algorithm and the concealing property of quantum bit commitment.
The utility of cell blocks for international cytopathology teleconsultation by whole slide imaging

Abstract Introduction Telecytology for second opinion consultation has largely been limited by technical issues, such as the inability to focus well on cellular material. Nevertheless, international telecytology consultation was undertaken at our institution with partners in China and Italy. To overcome issues with scanning cytology slides, we adopted a cell‐block ( CB ) preference for teleconsultation. Methods Telecytology consultation cases received over a 7.5‐year period were retrospectively reviewed. Cytology glass slides were scanned without Z‐stacking using different whole slide scanners. For one referring site, only haematoxylin‐eosin‐stained CB s were scanned, as well as immunostains requested by consultants. For another host centre, aspirate smears were also scanned in some cases. Results A total of 51 non‐gynaecological cases (44 CB only) were evaluated from 48 patients. The specimens included pleural fluids (19), pancreas (14), lymph nodes (6), peritoneal fluids (2) and miscellaneous samples (10). The cytological diagnoses spectrum included 16 (31.37%) cases positive for malignancy, 7 (13.72%) positive for neoplasm, 6 (11.76%) suspicious for malignancy, 10 (19.60%) atypical, 10 (19.60%) negative for malignancy and 2 (3.92%) non‐diagnostic. In 42 (82.35%) cases, immunocytochemistry was requested. Turn‐around‐time ranged from 1.5 to 306 hours. Conclusions Our experience shows that international telecytology for consultation purposes involving non‐gynaecological cases is feasible. A second opinion interpretation was rendered in the majority (64.7%) of cases. Utilising CB only for cytology consultations by whole slide imaging solved focus issues that typically plague evaluation of cytology aspirate smears.
Artificial intelligence in the diagnosis of cardiovascular disease

Artificial intelligence (AI) is a field of computer science that aims to mimic human thought processes. AI techniques have been applied in cardiovascular medicine to explore novel genotypes and phenotypes in existing diseases, improve the quality of patient care, enabling cost-effectiveness, and reducing readmission and mortality rates. The potential of AI in cardiovascular medicine is tremendous; however, ignorance of the challenges may overshadow its potential clinical impact. This paper gives a glimpse of AI's application in cardiovascular clinical care and discusses its potential role in facilitating precision cardiovascular medicine.
Bug Report Summarization

Natural language Processing techniques have been proved very helpful in optimizing the software development process. It has improved the accuracy and speed of different steps of development process. Summarization of software artifacts is one of application of natural language processing techniques to help the developers or testers. Summarization tools and techniques have been applied to many software artifacts in the past like source code, discussions and bug reports. In this paper, we present a systematic literature review of the natural language processing techniques applied for the summarization of bug reports. Bug reports are very important for development process because these have valuable knowledge of the problems and their resolution. By summarizing bug reports, a lot of developer's time can be saved during bug triaging when developers are looking for the similar problems from the past. Bug report summarization is done by various methods and techniques and it have helped the developer to save their time and better understanding of the problem at hand. This survey of the past techniques used for the summarization of bug reports will provide useful and wide background knowledge of this research field to the future researchers.
FSP: a feedback-based saliency propagation method for saliency detection

Propagation-based methods have attracted more and more attention in saliency detection. We present a propagation-based saliency detection framework to overcome some drawbacks in existing methods. First, we construct an effective background-based map by defining a method to remove foreground noises in image boundaries. Second, a saliency propagation method is proposed to further refine the background-based map by introducing a feedback regulation mechanism into saliency propagation. Finally, an optimization method is developed to further enhance the contrast between salient object and background. Extensive experiments on five benchmark datasets are carried out, and experimental results show the superiority of the proposed method than other state-of-the-art methods.
Codedretrieval: Joint Image Compression and Retrieval with Neural Networks

With the explosive increase of image data, the efficiency of both image compression and retrieval becomes unprecedentedly significant. However, these two tasks are usually isolated executed, which waste great computational resources in large-scale image applications. In this work, we propose a joint framework called CodedRetrieval, which can find a general feature expression for both compression and retrieval based on neural network. Additionally, a two stage training strategy is designed to achieve better balance between the two distinct tasks. Experimental results show that our method can achieve competitive perfomance on both compression and retrieval comparing to classic methods, while saving great amount of computation time.
StarTroper, a film trope rating optimizer using machine learning and evolutionary algorithms

Abstract Designing a story is widely considered a crafty yet critical task that requires deep specific human knowledge in order to reach a minimum quality and originality. This includes designing at a high level different elements of the film; these high‐level elements are called tropes when they become patterns. The present paper proposes and evaluates a methodology to automatically synthesize sets of tropes in a way that they maximize the potential rating of a film that conforms to them. We use machine learning to create a surrogate model that maps film ratings from tropes, trained with the data extracted and processed from huge film databases in Internet, and then we use a genetic algorithm that uses that surrogate model as evaluator to optimize the combination of tropes in a film. In order to evaluate the methodology, we analyse the nature of the tropes and their distributions in existing films, the performance of the models and the quality of the sets of tropes synthesized. The results of this proof of concept show that the methodology works and is able to build sets of tropes that maximize the rating and that these sets are genuine. The work has revealed that the methodology and tools developed are directly suitable for assisting in the plots generation as an authoring tool and, ultimately, for supporting the automatic generation of stories, for example, in massively populated videogames.
TIRFaceNet: Thermal IR Facial Recognition

Facial recognition using deep neural networks has attracted increasing attention in recent years. Although great progress has been made in facial recognition systems, there are many problems which hinder the identification process, especially the recognition under low light conditions or at night (full darkness). The goal of this study is to produce a model that identifies persons at night especially unauthorized persons entering a building and to assist the security workgroup in making appropriate decisions. To achieve this goal, we propose a deep convolutional neural network (CNN) based model called TIRFaceNet. Our method realizes pre-processing of thermal and visible images, offers unique features of the face (such as eyes, nose, chin, etc.) which are picked separately and train faces on the deep network. This model analyzes the deep features of a person's face and compares these features with other person's features stored in the dataset to recognize a person's face. In this paper, the proposed TIRFaceNet model achieves a better identification rate compared to other methods. The accuracy of our method is 98.70% ± 0.03 on the DHU database. In the DHUFO database, it is 98.50% ± 0.05. In addition, our method can identify a visible-to-thermal face with less training time (10 h <; 20 h) and with less processing time (0.02 s <; 0.025 s).