Reviewerly/exHarmony huggingface dataset:
Breast Cancer Image Classification via Multi-level Dual-network Features and Sparse Multi-Relation Regularized Learning

Breast cancer is one of the leading causes of cancer death worldwide. Recently, the computer-aided diagnosis and detection technique has been developed for the early diagnosis of breast cancer, but the diagnostic efficiency has still been a challenging issue. For this reason, we aim to improve the breast cancer diagnostic accuracy and reduce the workload of doctors in this paper by devising a deep learning framework based on histological image. Therefore, we develop a model of multi-level feature of dual-network combined with sparse multi-relation regularized learning method, which enhances the classification performance and robustness. Specifically, first, we preprocess the histological images using scale transformation and color enhancement methods. Second, the multi-level features are extracted from preprocessed images using InceptionV3-ML and ResNet-50 networks. Third, the feature selection method via sparse multi-relation regularization is further developed for performance boosting and overfitting reduction. We evaluate the proposed method based on the public ICIAR 2018 Challenge dataset of breast cancer histology images. Experimental results show that our method has achieved promising performance and outperformed the related works.
Emerging Privacy Challenges and Approaches in CAV Systems

The growth of Internet-connected devices, Internet-enabled services and Internet of Things systems continues at a rapid pace, and their application to transport systems is heralded as game-changing. Numerous developing CAV (Connected and Autonomous Vehicle) functions, such as traffic planning, optimisation, management, safety-critical and cooperative autonomous driving applications, rely on data from various sources. The efficacy of these functions is highly dependent on the dimensionality, amount and accuracy of the data being shared. It holds, in general, that the greater the amount of data available, the greater the efficacy of the function. However, much of this data is privacy-sensitive, including personal, commercial and research data. Location data and its correlation with identity and temporal data can help infer other personal information, such as home/work locations, age, job, behavioural features, habits, social relationships. This work categorises the emerging privacy challenges and solutions for CAV systems and identifies the knowledge gap for future research, which will minimise and mitigate privacy concerns without hampering the efficacy of the functions.
Morphological Area Gradient: System-independent Dense Tissue Segmentation in Mammography Images

Breast density has been identified as one of the strongest risk factors for breast cancer. However, the development of reliable and reproducible methods for the automatic dense tissue segmentation has been an important challenge. Due to the complexity of the acquisition process of mammography images, current approaches need to be calibrated for specific mammographic systems or require access to raw mammograms. In this work, we introduce the Morphological Area Gradient (MAG) as a generic measure for mammography images. MAG is generic in the sense that it does not need calibration or access to raw mammograms. At the core of MAG is the derivative of the area of segmented tissue with respect to the pixel intensity. We have found that the high-density regions can be automatically segmented by minimizing the MAG of a mammogram. To verify the performance of MAG, we collected 566 full-field digital mammograms using two different medical devices and a human expert manually annotated the high-density regions in each image. The proposed MAG method yields a median absolute error of 7.6% and a Dices similarity coefficient of 0.83, which are superior to other clinically validated state-of-the-art algorithms.
Consolidation of Belief in Two Logics of Evidence

Recently, several logics have emerged with the goal of modelling evidence in a more relaxed sense than that of justifications. Here, we explore two of these logics, one based on neighborhood models and the other being a four-valued modal logic. We establish grounds for comparing these logics, finding, for any model, a counterpart in the other logic which represents roughly the same evidential situation. Then we propose operations for consolidation, answering our central question: What should the doxastic state of a rational agent be in a given evidential situation? These operations map evidence models to Kripke models. We then compare the consolidations in the two logics, finding conditions under which they are isomorphic. By taking this dynamic perspective on belief formation we pave the way for, among other things, a study of the complexity, and an AGM-style analysis of rationality of these belief-forming processes.
Software reusability dataset based on static analysis metrics and reuse rate information

The widely adopted component-based development paradigm considers the reuse of proper software components as a primary criterion for successful software development. As a result, various research efforts are directed towards evaluating the extent to which a software component is reusable. Prior efforts follow expert-based approaches, however the continuously increasing open-source software initiative allows the introduction of data-driven alternatives. In this context we have generated a dataset that harnesses information residing in online code hosting facilities and introduces the actual reuse rate of software components as a measure of their reusability. To do so, we have analyzed the most popular projects included in the maven registry and have computed a large number of static analysis metrics at both class and package levels using SourceMeter tool [2] that quantify six major source code properties: complexity, cohesion, coupling, inheritance, documentation and size. For these projects we additionally computed their reuse rate using our self-developed code search engine, AGORA [5]. The generated dataset contains analysis information regarding more than 24,000 classes and 2000 packages, and can, thus, be used as the information basis towards the design and development of data-driven reusability evaluation methodologies. The dataset is related to the research article entitled “Measuring the Reusability of Software Components using Static Analysis Metrics and Reuse Rate Information” [1].
InteractivePaper

The use of the minimalistic design in the document editing UI based on novel handwriting recognition technologies for mobile devices with a touch screen is very limited in real-world applications, mainly due to the propagation of errors made by recognition engines for different types of input. It is caused by the iterative nature of recognition for diverse input. Currently, one of the common ways is to pre-configure the input type manually for different types of documents' content that leads to an increase in UI complexity. Bearing this in mind, we revisit UI design issues regarding the unification of recognition flow by introducing the document layout analyzer, to avoid switching between modes during input of handwritten documents. The system for the iterative input of diverse document by using handwritten strokes recognition techniques was presented. Using an evaluation study, we have shown that the proposed approach provides a more interactive experience for the user and increases the input speed by 1.5--2 times compared to the classical approaches.
Field Trial of a Finite-Key Quantum Key Distribution System in the Metropolitan Florence Area

In a society based on the continuous exchange of sensitive data and information, the importance of secure and trustful communications is essential. Quantum key distribution (QKD) makes it possible to share data in an unconditionally secure way exploiting the basic principles of Quantum Mechanics [1]. During the last 30 years, many QKD protocols have been developed and tested, achieving long distance transmission [2] and secret key rates up to hundreds of Mbits per second [3]. However, this technology is still far from a large-scale deployment in existing fiber networks and telecom infrastructures, due to multiple factors: low secret-key rate, limited distance between users, lack of applications, high costs and high requirements in terms of low noise fiber links.
Relational Model for Parameter Description in Automatic Semantic Web Service Composition

Automatic Service Composition is a research direction aimed at facilitating the usage of atomic web services. Particularly, the goal is to build workflows of services that solve specific queries, which cannot be resolved by any single service from a known repository. Each of these services is described independently by their providers that can have no interaction with each other, therefore some common standards have been developed, such as WSDL, BPEL, OWL-S. Our proposal is to use such standards together with JSON-LD to model a next level of semantics, mainly based on binary relations between parameters of services. Services relate to a public ontology to describe their functionality. Binary relations can be specified between input and/or output parameters in service definition. The ontology includes some relations and inference rules that help to deduce new relations between parameters of services. To our knowledge, it is for the first time that parameters are matched not only based on their type, but on a more meaningful semantic context considering such type of relations. This enables the automation of a large part of the reasoning that a human person would do when manually building a composition. Moreover, the proposed model and the composition algorithm can work with multiple objects of the same type, a fundamental feature that was not possible before. We believe that the poor model expressiveness is what is keeping service composition from reaching large-scale application in practice.
Coordination in Collaborative Work by Deep Reinforcement Learning with Various State Descriptions

Cooperation and coordination are sophisticated behaviors and are still major issues in studies on multi-agent systems because how to cooperate and coordinate depends on not only environmental characteristics but also the behaviors/strategies that closely affect each other. On the other hand, recently using the multi-agent deep reinforcement learning (MADRL) has received much attention because of the possibility of learning and facilitating their coordinated behaviors. However, the characteristics of socially learned coordination structures have been not sufficiently clarified. In this paper, by focusing on the MADRL in which each agent has its own deep Q-networks (DQNs), we show that the different types of input to the network lead to various coordination structures, using the pickup and floor laying problem, which is an abstract form related to our target problem. We also indicate that the generated coordination structures affect the entire performance of multi-agent systems.
Frame Rate Optimization in Traffic Sign Detection

Traffic sign plays an important role to ensure maximum transportation safety for people. In an Autonomous Vehicle (AV) system, traffic signs help AV to get more familiar with the road conditions and its surrounding. AV employs Artificial Intelligence (AI) for recognizing its surrounding by detecting and classifying the traffic signs. However, AI has a high computational algorithm which takes a lot of time to finish a detection process and therefore produces a low frame rate which may lead to a fatal accident. Meanwhile, the application of a Graphics Processing Unit (GPU) in AI will increase the system's frame rate but requires an expensive cost. This paper proposes an algorithm to increase the frame rate of a traffic sign detection system without an additional GPU. Our approach is to shift the color and shape segmentation systems right before sending the frames to the main system. Since the number of frames with traffic signs typically less than the ones without traffic signs, we can reduce the computation load of the main system, and therefore a higher frame rate will be achieved. We show that the pre-processing system improves the frame rate of the system to reach 19.4 frames per second.
Application of Statistical Simulation for Measles Epidemic Process Forecasting

Simulation of epidemic processes is actual task, which has high social and economic value. Given research is dedicated to the application of statistical methods to the prediction of the incidence of measles. The structure of the models has been developed. The models show high accuracy.
Niijima

Multilingual data-parallel pipelines, such as Microsoft's Scope and Apache Spark, are widely used in real-world analytical tasks. While the involvement of multiple languages (often including both managed and native languages) provides much convenience in data manipulation and transformation, it comes at a performance cost --- managed languages need a managed runtime, incurring much overhead. In addition, each switch from a managed to a native runtime (and vice versa) requires marshalling or unmarshalling of an ocean of data objects, taking a large fraction of the execution time. This paper presents Niijima, an optimizing compiler for Microsoft's Scope/Cosmos, which can consolidate C#-based user-defined operators (UDOs) across SQL statements, thereby reducing the number of dataflow vertices that require the managed runtime, and thus the amount of C# computations and the data marshalling cost. We demonstrate that Niijima has reduced job latency by an average of 24% and up to 3.3x, on a series of production jobs.
Optimal processing scheme for restoration of phase data corrupted by strong decorrelation noise and dislocations

The presence of speckle noise and dislocations makes phase restoration potentially difficult in quantitative phase imaging and metrology. Unfortunately, there is no appropriate approach to deal with phase data corrupted by high speckle noise and phase dislocations. Usually, processing schemes may deal with low-pass phase filtering, phase unwrapping, or phase inpainting. This paper discusses the efficient processing to deal with noisy phase maps corrupted with phase dislocations. Six processing schemes, combining four operations, are evaluated. The investigation is carried out by realistic numerical simulations in which strong decorrelation phase noise and phase dislocations are generated. As a result, most robust and faster processing is established. The applicability of the optimal scheme is demonstrated through deformation measurement in dental materials.
Construction of irreducible polynomials through rational transformations

Let Fq be the finite field with q elements, where q is a power of a prime. We discuss recursive methods for constructing irreducible polynomials over Fq of high degree using rational transformations. In particular, given a divisor D>2 of q+1 and an irreducible polynomial f∈Fq[x] of degree n such that n is even or D≢2(mod4), we show how to obtain from f a sequence {fi}i≥0 of irreducible polynomials over Fq with deg⁡(fi)=n⋅Di.
An Energy Efficient and Interference Aware Virtual Machine Consolidation Algorithm Using Workload Classification

Inefficient resource usage is one of the greatest causes of high energy consumption in cloud data centers. Virtual Machine (VM) consolidation is an effective method for improving energy related costs and environmental sustainability for modern data centers. While dynamic VM consolidation can improve energy efficiency, virtualisation technologies cannot guarantee performance isolation between co-located VMs resulting in interference issues. We address the problem by introducing a energy and interference aware VM consolidation algorithm. The proposed algorithm utilizes the predictive capabilities of a Machine Learning (ML) model in an attempt to classify VM workloads to make more informed consolidation decisions. Furthermore, using recent workload data from Microsoft Azure we present a comparative study of two popular classification algorithms and select the model with the best performance to incorporate into our proposed approach. Our empirical results demonstrate how our approach improves energy efficiency by 31% while also reducing service violations by 69%.
Apache Hive Performance Improvement Techniques for Relational Data

Hadoop is a widely adapted open-source map reduce implementation for storing and processing extremely large data sets. However, using Hadoop is not easy for end-users, especially for those who were not familiar with the map-reduce approach. Even for simple tasks, like getting raw counts or averages, users have to write map-reduce programs. Apache Hive, which is a data warehouse infrastructure tool to process structured data in Hadoop helps users to easily query, summarize and analyze Big Data with SQL-like expressions called HiveQL. It supports various file formats to import and export data from-and-to the storage file system. Hive's objective is to make it easy and performative when petabytes levels of data needs to be processed. Unlike RDBMS, Hive stores data in a document-based structure so queries with JOINS degrade performance with high resource consumption. However, there are ways to improve performance for the relational data by correctly configuring Hive. In this research, we implement several optimization techniques to improve the query performance and evaluate the results to compare their impact on the outcome. For this study, we used the TPC generated dataset. TPC is a nonprofit corporation founded to define benchmarks for transactional processing in databases. Also, different techniques are discussed in the context of the techniques of aggregation and their variable states are observed.
Survey of Methods in the Spatial Domain Image Steganography based Imperceptibility and Payload Capacity

Image steganography is a technique to hide messages in an image container with the aim of securing the message. Steganography is widely investigated in the spatial domain of images, with the most widely used methods being Least Significant Bit (LSB), Pixel Value Differencing (PVD) and Chinese Remainder Theorem (CRT). Some important aspects of steganography techniques are imperceptibility, payload capacity, data integrity, data security, and robustness, where each of these methods has advantages and disadvantages in each of these aspects. The imperceptibility aspect is strongly influenced by payload capacity, where the greater the payload, the lower the imperceptibility quality and vice versa. This research aims to compare the three methods based on the two most important aspects of the embedding process, namely imperceptibility and payload capacity. Whereas in the extraction process data integrity tests are carried out. Based on the results of testing, it has been proven that the LSB method has the best imperceptibility quality, while for the PVD capacity aspect it becomes the best. But after further testing, message embedding using 2-bits LSB can overcome the problem of overcapacity and can be compared with PVD with the same size of payload capacity. After further testing, it turns out that LSB has a good imperceptibility quality compared to PVD with when tested with the same maximum payload. Whereas for data integrity testing in the extraction process, all methods can be extracted perfectly without losing data bits. Then it can be concluded that the LSB method has the best imperceptibility quality, and by using the 2-bits LSB, capacity the LSB method can offset the payload capacity of the PVD method, whereas the imperceptibility quality in the LSB method is still superior.
HPAKE: Hybrid Precocious Authentication and Key Establishment in IoT

In Internet of Things (IoT), the objects of the physical world communicate with each other through the Sensor Devices via Internet connection. A large number of connected Devices frequently transmit small size data. Machine-to-Machine communication and the information obtained from the devices allow the automation of basic tasks in the Application services. Thus, a restriction of the low-powered devices in IoT and collecting a wide range of data lead to a challenge in designing an appropriate data networking and security. Authentication is the process of determining legitimate communication between the IoT devices. The proposed system, Hybrid Precocious Authentication and Key Establishment (HPAKE) Scheme uses a Hybrid model consisting of Hash Functions and Public Key Encryption along with a Nonce for Machine-to-Machine communication between the IoT devices. The HPAKE enhances the security by authenticating and establishing secure Session Key between the communicating Devices using Elliptic Curve Cryptography (ECC) that eliminates various attacks viz., Eavesdropping, Man- In-The-Middle Attack etc.,. The performance analysis shows the secure Key Distribution using ECC and enhanced authentication while reducing the communication overhead over an Enhanced Authentication and Key Establishment (EAKES6Lo) Scheme and Lightweight Authentication Mechanism (LAM).
Joint Image Deblurring and Matching with Blurred Invariant-Based Sparse Representation Prior

Image matching is important for vision-based navigation. However, most image matching approaches do not consider the degradation of the real world, such as image blur; thus, the performance of image matching often decreases greatly. Recent methods try to deal with this problem by utilizing a two-stage framework—first resorting to image deblurring and then performing image matching, which is effective but depends heavily on the quality of image deblurring. An emerging way to resolve this dilemma is to perform image deblurring and matching jointly, which utilize sparse representation prior to explore the correlation between deblurring and matching. However, these approaches obtain the sparse representation prior in the original pixel space, which do not adequately consider the influence of image blurring and thus may lead to an inaccurate estimation of sparse representation prior. Fortunately, we can extract the pseudo-Zernike moment with blurred invariant from images and obtain a reliable sparse representation prior in the blurred invariant space. Motivated by the observation, we propose a joint image deblurring and matching method with blurred invariant-based sparse representation prior (JDM-BISR), which obtains the sparse representation prior in the robust blurred invariant space rather than the original pixel space and thus can effectively improve the quality of image deblurring and the accuracy of image matching. Moreover, since the dimension of the pseudo-Zernike moment is much lower than the original image feature, our model can also increase the computational efficiency. Extensive experimental results demonstrate that the proposed method performs favorably against the state-of-the-art blurred image matching approach.
Recommender system for board games

Since the beginning of the twenty-first century, renaissance of interest in board games can be observed. As a matter of fact, share of board games within the entertainment market, it is growing constantly. For instance, local clubs where board games are played, as well as shops dedicated to this hobby materialize across Poland (but the trend is global). Since this hobby is relatively new, for example in comparison with watching movies, there are no applications that would recommend games to players. Here, the natural example could be the FilmWeb site, which recommends movies. At the same time, demand for good board game recommendations exists, which is illustrated by the popularity of game reviewers. Moreover, a huge database is available within the BoardGameGeek.com site, which contains information about games, including user-generated reviews. Hence, the idea to fill the existing gap, and to create a board game recommending system. In this note we report on the results of out work in this direction.