Reviewerly/exHarmony huggingface dataset:
StyPath: Style-Transfer Data Augmentation for Robust Histology Image Classification

The classification of Antibody Mediated Rejection (AMR) in kidney transplant remains challenging even for experienced nephropathologists; this is partly because histological tissue stain analysis is often characterized by low inter-observer agreement and poor reproducibility. One of the implicated causes for inter-observer disagreement is the variability of tissue stain quality between (and within) pathology labs, coupled with the gradual fading of archival sections. Variations in stain colors and intensities can make tissue evaluation difficult for pathologists, ultimately affecting their ability to describe relevant morphological features. Being able to accurately predict the AMR status based on kidney histology images is crucial for improving patient treatment and care. We propose a novel pipeline to build robust deep neural networks for AMR classification based on StyPath, a histological data augmentation technique that leverages a light weight style-transfer algorithm as a means to reduce sample-specific bias. Each image was generated in \(1.84 \pm 0.03\) s using a single GTX TITAN V gpu and pytorch, making it faster than other popular histological data augmentation techniques. We evaluated our model using a Monte Carlo (MC) estimate of Bayesian performance and generate an epistemic measure of uncertainty to compare both the baseline and StyPath augmented models. We also generated Grad-CAM representations of the results which were assessed by an experienced nephropathologist; we used this qualitative analysis to elucidate on the assumptions being made by each model. Our results imply that our style-transfer augmentation technique improves histological classification performance (reducing error from 14.8% to 11.5%) and generalization ability.
Two-level clustering of UML class diagrams based on semantics and structure

The reuse of software design has been an important issue of software reuse. UML class diagrams are widely applied in software design and has become DE factor standard. As a result, the reuse of UML class diagrams has received more attention. With the increasing number of class diagrams stored in reuse repository, their retrieval becomes a time-consuming job. The clustering can narrow down retrieval range and improve the retrieval efficiency. But few efforts have been done in clustering UML class diagrams. This paper tries to propose a clustering approach for UML class diagrams. This paper proposes a two-level clustering of UML class diagrams, namely, semantic clustering and structural clustering. The UML class diagrams stored in reuse repository are clustered into a few domains based on semantics in the first level and a few categories based on structure in the second level. We propose a clustering algorithm named CUFS, in which the idea of partitioning and hierarchical clustering is combined and feature similarity is proposed for the similarity measure between two clusters in order to merge clusters. A better feature representation of a cluster, namely, feature class diagram, is proposed in this paper. In order to form each sub-cluster, the semantic and structural similarities between UML class diagrams are defined, respectively. A series of experimental results show that, the proposed feature similarity measure not only speeds up the clustering process, but also expresses the closeness degree between clusters for merging clusters. The proposed algorithm shows a good clustering quality and efficiency under the condition of different size and distribution of UML class diagrams. It is concluded that the proposed two-level clustering method considers both semantics and structure contained in a class diagram, which can flexibly adapt to different clustering requirements. Also, the proposed clustering algorithm performs better than other related algorithms, regardless of in semantic, structural and hybrid clustering.
Improving prostate cancer classification in H&amp;E tissue micro arrays using Ki67 and P63 histopathology

Histopathology of Hematoxylin and Eosin (H&E)-stained tissue obtained from biopsy is commonly used in prostate cancer (PCa) diagnosis. Automatic PCa classification of digitized H&E slides has been developed before, but no attempts have been made to classify PCa using additional tissue stains registered to H&E. In this paper, we demonstrate that using H&E, Ki67 and p63-stained (3-stain) tissue improves PCa classification relative to H&E alone. We also show that we can infer PCa-relevant Ki67 and p63 information from the H&E slides alone, and use it to achieve H&E-based PCa classification that is comparable to the 3-stain classification. Reported improvements apply to classifying benign vs. malignant tissue, and low grade (Gleason group 2) vs. high grade (Gleason groups 3,4,5) cancer. Specifically, we conducted four classification tasks using 333 tissue samples extracted from 231 radical prostatectomy patients: regression tree-based classification using either (i) 3-stain features, with a benign vs malignant area under the curve (AUC = 92.9%), or (ii) real H&E features and H&E features learned from Ki67 and p63 stains (AUC = 92.4%), as well as deep learning classification using either (iii) real 3-stain tissue patches (AUC = 94.3%) and (iv) real H&E patches and generated Ki67 and p63 patches (AUC = 93.0%) using a deep convolutional generative adversarial network. Classification performance was assessed with Monte Carlo cross validation and quantified in terms of the Area Under the Curve, Brier score, sensitivity, and specificity. Our results are interpretable and indicate that the standard H&E classification could be improved by mimicking other stain types.
Composition of Safety Constraints for Fixed-Wing Collision Avoidance Amidst Limited Communications

This paper considers how to ensure that a system of fixed-wing unmanned aerial vehicles (UAVs) can avoid collisions. To do so, a novel method is developed for creating a barrier function, which is similar to a Lyapunov function and can be used to ensure that a system can stay safe for all future times. After introducing the general approach, it is shown how to ensure that collision avoidance for two vehicles can be guaranteed for all future times. The construction is then extended to the case of arbitrarily many vehicles by addressing how to satisfy multiple safety objectives simultaneously. This is done while ensuring that output actuator commands are within specified limits. Because this formulation requires communication of control values and may therefore reduce throughput of other important messages, it is then shown how to reformulate the solution without this significant communication overhead while still ensuring that safety is maintained and actuator limits are respected. The theoretical developments of this paper are validated in the simulator SCRIMMAGE with a simulation of 20 UAVs that maintain safe distances from each other even though their nominal paths would otherwise cause a collision.
Ethical Issues with Simulating the Bridge Problem in VR

We aim to generate a dilemma for virtual reality-based research that we motivate through an extended case study of Thomson’s (Yale Law J 94(6):1395–1415, 1985) Bridge variant of the trolley problem. Though the problem we generate applies more broadly than the Bridge problem, we believe it makes a good exemplar of the kind of case we believe is problematic. First, we argue that simulations of these thought experiments run into a practicality horn that makes it practically impossible to produce them. These problems revolve around concepts that we call “perspectival fidelity” and “context realism.” Moral dilemmas that include features present in the Bridge variant will, as a result, be practically impossible to simulate. We also argue that, should we be wrong about the practical impossibility of creating a VR simulation of Bridge, such a simulation must face an ethical horn which renders these simulations ethically impermissible to develop or use. For these reasons, we argue that it is virtually impossible to simulate the bridge problem (and other thought experiments with similar features) both practically and ethically in VR.
Robocentric Conversational Group Discovery

Detecting people interacting and conversing with each other is essential to equipping social robots with autonomous navigation and service capabilities in crowded social scenes. In this paper, we introduced a method for unsupervised conversational group detection in images captured from a mobile robot's perspective. To this end, we collected a novel dataset called Robocentric Indoor Crowd Analysis (RICA). The RICA dataset features over 100,000 RGB, depth, and wide- angle camera images as well as LIDAR readings, recorded during a social event where the robot navigated between participants and captured interactions among groups using its on-board sensors. Using the RICA dataset, we implemented an unsupervised group detection method based on agglomerative hierarchical clustering. Our results show that incorporating the depth modality and using normalised features in the clustering algorithm improved group detection accuracy by a margin of 3% on average.
A New Motion Data Structuring for Human Activity Recognition Using Convolutional Neural Network

Human activity recognition is an essential field to study for healthcare services and smooth human-machine interaction. For human motion data collection, wearable sensors are the common devices utilized. In this paper, a new double-channel motion data structuring method for classification is proposed for the data collected from the inertial sensor of a smartphone. It was shaped as a virtual image in a way to extract deep features of the temporal and spatial dependencies among motion signals. The time-series raw data underwent Fourier and wavelet transformations as alternative forms of input data. The virtual images from the three types of representations were made to fit a Convolutional Neural Network model for classification. The proposed model was evaluated using our dataset and other public datasets, where it performed well with all datasets. The trained model showed excellent results when tested on a computer and smartphone for real-time recognition. An exclusive iOS application with data handling and real-time recognition functions was developed for the smartphone. The model has the attributes of lower computational cost and better accuracy, which makes it a sound model for a practical purpose.
Exploring Multi Feature Optimization for Summarizing Clinical Trial Descriptions

Documenting Clinical Trial Descriptions of patients can help doctors with diagnostics and treatment plans and can be used for future reference. However, with the rapid growth of population, manually checking all previous files of a patient is not feasible. We address this challenge by providing summaries of clinical trial descriptions. We present a framework for automatically summarizing Clinical Trial Descriptions, which takes advantage of different features in semantic and syntactic space. We propose a multi objective optimization technique which uses position and similarity of the sentences. The similarity is established based on TF-IDF and WMD. We evaluate the proposed method on clinical trial dataset, and compare the results against human gold standard summaries using ROUGE metrics. Our approach is unsupervised in nature which has an advantage over supervised models in the advent of new diseases where a large quantity of quality data is not available. We provide a detailed ablation study to show the contribution of each feature in our approach and release our code on GitHub. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Recent Advances On Ontology Similarity Metrics: A Survey

With the advent of Semantic Web and the recent advances in the field of knowledge discovery more effort is placed not only on constructing or automatically discovering ontologies but also on determining similarities between ontologies. The latter is usually achieved by a higher order metric taking as input two ontologies in an enhanced graph form and yielding a scalar as a result. This input format allows a considerable degree of flexibility, as the inherently distributed nature of graphs allows the construction of elaborate schemes. Since it is impossible to capture all the essential parameters of an ontology in a single number and given that ontological requirements vary across different domains, it follows that a plethora of such metrics exist. This survey examines the most representative categories of ways for evaluating the similarity between ontology pairs with emphasis placed on the domain of digital culture.
SENET: A Semantic Web for Supporting Automation of Software Engineering Tasks

The use of Natural Language (NL) interfaces to allow devices and applications to respond to verbal commands or free-form textual queries is becoming increasingly prevalent in our society. To a large extent, their success in interpreting and responding to a request is dependent upon rich underlying ontologies and conceptual models that understand the technical or domain specific vocabulary of diverse users. The effective use of NL interfaces in the Software Engineering (SE) domains requires its own ontology models focusing upon software related terms and concepts. While many SE glossaries exist, they are often incomplete and tend to define the vocabulary for specific sub-fields without capturing associations between terms and phrases. This limits their usefulness for supporting NL-related tasks. In this paper we propose an approach for constructing and evolving a semantic network of software engineering concepts and phrases. Our approach starts with a set of existing SE glossaries, uses the existing glossary terms and explicitly defined associations as a starting point, uses machine learning-based techniques to dynamically identify and document additional associations between terms, leverages the network to interpret NL queries in the SE domain, and finally augments the resulting semantic network with feedback provided by users. We evaluate the viability of our approach within the sub-domain of Agile Software Development, focusing on requirements related queries, and show that the semantic network enhances the ability of an NL interface to correctly interpret and execute user queries.
MUDRAKSHARA - A Voice for Deaf/Dumb People

Human beings communicate with each other using words of different spoken languages, but what will happen if a person is deaf/dumb and not able to hear or speak. The deaf/dumb people need to communicate using a certain sign language. As sign language is not understandable by everyone, a translator is needed to make up communication between normal and deaf/dumb people. Practically, translators are not available all the time hence a beneficial impact on the social lives of these hearing/speech disabled people can be brought by a system that recognizes hand gestures and performs the task same as translators. The propound system “Mudrakshara” is an ASL interpreter developed using concepts of machine learning like neural network methodologies and image processing techniques. It identifies hand gestures in the images obtained from video that is captured by a web `cam recorder and gives the meaning of signs made by hearing/speech disabled people thus making communication complete. In this article, an approach is proposed to convert video of hand gestures made by deaf/dumb people into text and audio format.
Code-based Analysis Approach to Detect and Prevent SQL Injection Attacks

Now-a-days web applications are everywhere. Usually these applications are developed by database program which are often written in popular host programming languages such as C, C++, C#, Java, etc., with embedded Structured Query Language (SQL). These applications are used to access and process crucial data with the help of Database Management System (DBMS). Preserving the sensitive data from any kind of attacks is one of the prime factors that needs to be maintained by the web applications. The SQL injection attacks is one of the important security threat for the web applications. In this paper, we propose a code-based analysis approach to automatically detect and prevent the possible SQL Injection Attacks (SQLIA) in a query before submitting it to the underlying database. This approach analyses the user input by assigning a complex number to each input element. It has two part (i) input clustering and (ii) safe (non-malicious) input identification. We provide a details discussion of the proposal w.r.t the literature on security and execution overhead point of view.
Information Weighted Consensus With Interacting Multiple Model Over Distributed Networks

Distributed estimation approach is becoming increasingly popular in the sensor networks community. In this brief, an information weighted consensus with interacting multiple models is proposed for distributed networks. Firstly, the multiple models predict the state estimate individually at each node. Then, the measured data across nodes are fused effectively through the local communication among neighboring nodes. Afterward, the fused data are employed to update the state estimates predicted by multiple models at each node. Finally, a novel model probability calculation criterion is presented to obtain the global state estimate at each node. The effectiveness of the proposed method is demonstrated on a target tracking task.
An Edge Decentralized Security Architecture for Industrial IoT Applications

The deployment of Industrial IoT Applications are quickly spreading around companies and organizations, mainly involving the use of edge computing. At the same time these applications are improving the production process, they introduces new security concerns that can damage the whole system. Lack of knowledge about security aspects of IoT and designers’ current understanding that security techniques applied to cloud computing or embedded systems in general can be adapted to IoT, constitute the main points of failure. IoT security needs new methods and architectures designed specifically for IoT, not adaptations. In this paper, we propose a new edge security architecture for industrial IoT, that combines the concepts of Blockchain and Context-Aware Security. We present how such technologies can be integrated in order to mitigate the security issues related to IoT environments. The proposed architecture was deployed in an Additive Manufacturing Units use case.
Collaborative Filtering Algorithm Based on Dynamic Trust Attenuation

The traditional recommendation algorithm has the problems of cold start and poor interpretability, and different neighbors in the collaborative filtering algorithm have different degrees of influence. It also ignores that user interest will drift with time. To solve the above problems, we propose a collaborative filtering algorithm based on dynamic trust attenuation (DTA-CF). Based on the traditional collaborative filtering recommendation algorithm, it examines the common score and time factor to adjust the neighbor selection mechanism and introduces the concept of trust attenuation to redefine the effect of neighbors. Experiments on the real MovieLens movie data set show that the algorithm is reasonable and effective, the accuracy and rationality have been greatly improved, and the recommendation error has been reduced.
A Novel Lattice-Based Threshold Ring Signature Scheme

In this paper, we present a new TRSS (threshold ring signature scheme) which depends on the worst-case hardness of a lattice problem. We apply Fiat-Shamir transform to a lattice-based identification scheme to obtain the proposed scheme. The hardness of the identification scheme which is the hybrid combination of two different designs is based on inhomogeneous short integer solution (ISIS) problem. We give security proof for the proposed TRSS. Comparing the proposed TRSS and the others which are quantum secure, the size of key pairs and the length of the signature are nearly same. Therefore, the proposed scheme is an alternative to the threshold ring signature schemes in post-quantum area.
Provably secure verifier-based password authenticated key exchange based on lattices

Verifier-based Password Authenticated Key Exchange (VPAKE) protocol enables users to generate a session key over insecure channels, which can limit the impact of server's information leakage. However, most existing VPAKE protocols are based on the integer factorization problem and the discrete logarithm problem; they cannot resist attack by quantum computers. In this chapter, we propose a new VPAKE protocol based on lattices. The protocol is constructed by using Chosen-Ciphertext Attacks (CCA) secure public-key encryption scheme, which is based on the learning with errors problem and an associated approximate smooth projective hash. Furthermore, this protocol uses a new randomized password hashing scheme based on lattices. This scheme enables ASCII-based passwords and a zero-knowledge password policy check; it allows users to prove the compliance of their password without revealing any information. Meanwhile, through explicit mutual authentication between the users and the servers, the protocol can resist undetectable online dictionary attacks. We then prove the security of this protocol. Our new protocol only involves three-round interactions with mutual explicit authentication. In addition, it avoids vulnerability of cryptosystem based on the integer factorization problem, and it is robust against quantum attacks.
Multivariate Adaptive Gaussian Mixture for Scene Level Anomaly Modeling

Scene changes that typically occur in a real-world setting degrade anomaly detection performance over the long run. Most of the existing methods ignore the challenge of temporal concept drift in video surveillance. In this paper, we propose an unsupervised end-to-end framework for adaptive scene level anomaly detection. We utilize multivariate Gaussian mixtures for adaptive scene learning. The mixture represents the possible distribution of normal and abnormal events shown till now. The distribution adapts itself according to the slow scene changes. We introduce a Mahalanobis distance-based contribution factor to update mixture parameters on the arrival of each new event. A detailed discussion and experiments are conducted to decide optimum local as well as global temporal context. The existing public datasets for anomaly detection are of very short duration (maximum of 1.5 hours) to be used for evaluating adaptive approaches. Therefore we also collected a longer duration dataset of continuous 10 hours duration. We achieved a promising performance of 85.14% AUC and 21.26% EER on this data.