Reviewerly/exHarmony huggingface dataset:
Histopathological Image and Lymphoma Image Classification using customized Deep Learning models and different optimization algorithms

Medical image classification is a challenging task that is time-consuming, requires deep domain expertise, and is also prone to error. Automated image classification can be used to complement the domain experts for addressing the abovementioned concerns in medical image classification. In this paper, we have used the KIMIA Path 960 dataset, and the NIA curated dataset for image classification. KIMIA Path 960 dataset has 960 images belonging to 20 classes. NIA curated dataset contains lymphoma images belonging to 3 classes. We have used pre-trained Resnet50 model and customized it with adding layers to the pre-trained models for this dataset. In another method, we have used a five-layer CNN for classification. We have also done experiments with different optimization algorithms, namely Adam, AdaMax, AdamW, and Radam, with all the above-mentioned models. Our method using the KIMIAPath 960 dataset and the AdamW optimization algorithm has achieved an accuracy of 99.9%, which is better than the accuracies obtained using Inception Recurrent Residual Convolutional Neural Network (IRRCNN) (98.79%) and Bag of Visual Words (96.5%) approach in other papers. We have also used the same method in the NIA curated dataset of lymphoma images and have achieved the best accuracy of 98.13%, which is better than the papers using the same dataset. We have also observed that the accuracy of the deep neural network is dependent both on the neural network architecture as well as the optimization algorithm used in the neural network architecture.
Multivariate anomaly modeling of primary geochemical halos by U-spatial statistic algorithm development: A case study from the Sari Gunay epithermal gold deposit, Iran

An accurate separation of anomalous areas (values) in primary geochemical halos with heterogeneous spatial variations requires an appropriate algorithm that considers the complex patterns and multivariate relationships of the input geochemical data. There are several problems in multivariate modeling of geochemical compositional data, because such data present spurious values of spatial correlation between the variables. To address this problem, this paper proposes an improved U-spatial statistic algorithm for 3D multivariate anomaly modeling of the geochemical dataset with compositional nature at the Sari Gunay epithermal gold deposit, NW Iran. To perform 3D multivariate modeling, isometric logarithmic ratio (ilr), as an effective method to transform compositional data from the simplex to real space, and minimum/maximum autocorrelation factor (MAF), as a multivariate geostatistical method, were used in an integrated analysis. In comparison to the other separation techniques, this integrated approach has an advantage to control the heterogeneous spatial variations in the input data. In this approach, we improved the U-spatial statistic method by using the variogram tool theory. By using this approach, we identified the primary geochemical haloes associated with Au-As-Sb-Hg front halo elements, Cu-Ag-Pb near-ore halo elements and Ni-Co tail halo elements at the Sari Gunay gold depsoit. The results were validated by using contrast coefficient and exploratory index tools. By examining the 3D multivariate primary anomalous geochemical halos, zones of possible hidden epithermal gold and porphyry copper–gold mineralizations were recognized at this deposit for future exploration.
The emergent potential of mundane media: Playing <i>Pokémon GO</i> in Badalona, Spain

In mid-2016, the streets of cities around the world were populated by digital wayfarers taking part in the augmented reality (AR) game, Pokémon GO. The game popularized the digital overlay technique of AR, in which real-time pedestrian movement is integrated with mobile location-based functionality and network information. In the years that followed, playing Pokémon GO gradually became a mundane activity, fitting into the everyday routines of millions of people across the globe. It is at this juncture – when the gameplay became a habitual and unremarkable practice – that the research discussed here began. Through ethnographic fieldwork conducted in Badalona in Spain in 2018–2019, this article explores how sedimented and mundane media – those that are already embedded in daily life routines and typical scenarios of use – can become possible conduits for informal care, wellbeing and social change through playful inclusion and connection.
State of art Research in Bengali Speech Recognition

Do we want AI as our future working partner or as our personal assistant? if the answer is yes than we must think about the communication between human and machines. Undoubtedly most comfortable communication media is the verbal communication. When it's about human speech it goes to Natural Language processing (NLP) and there are already many advanced works done in this sector specially in English. And some renown big companies already made some operating system-based assistant and robots those or really working near to human, but the concern is almost everything is very rich in English, English is international language but there are some popular languages as well where we need to focus, Bangla is one of them. Bangla is world's 8 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> most popular language almost 163 million people talk in this language which is not ignorable number at all [1]. There are very few quality works in this language even big companies are not that much advanced in Bangla like English or other languages. Here we muster some works in Bangla where researchers tried to solve Bangla speech recognition problems by their own methodologies. We tried to gather most accurate and recent work in this sector.
Efficient Reasoning in Regular Boardgames

We present the technical side of reasoning in Regular Boardgames (RBG) language - a universal General Game Playing (GGP) formalism for the class of finite deterministic games with perfect information, encoding rules in the form of regular expressions. RBG serves as a research tool that aims to aid in the development of generalized algorithms for knowledge inference, analysis, generation, learning, and playing games. In all these tasks, both generality and efficiency are important.In the first part, this paper describes optimizations used by the RBG compiler. The impact of these optimizations ranges from 1.7 to even 33-fold efficiency improvement when measuring the number of possible game playouts per second. Then, we perform an in-depth efficiency comparison with three other modern GGP systems (GDL, Ludii, Ai Ai). We also include our own highly optimized game-specific reasoners to provide a point of reference of the maximum speed. Our experiments show that RBG is currently the fastest among the abstract general game playing languages, and its efficiency can be competitive to common interface-based systems that rely on handcrafted game-specific implementations. Finally, we discuss some issues and methodology of computing benchmarks like this.
Context-aware system for cardiac condition monitoring and management: a survey

Health monitoring assists physicians in the decision-making process, which in turn, improves quality of life. As technology advances, the usage and applications of context-aware systems continue to spread across different areas in patient monitoring and disease management. It provides a platform for healthcare professionals to assess the health status of patients in their care using multiple relevant parameters. In this survey, we consider context-aware systems proposed by researchers for health monitoring and management. More specifically, we investigate different technologies and techniques used for cardiac condition monitoring and management. This paper also proposes 'mCardiac', an enhanced context-aware decision support system for cardiac condition monitoring and management during rehabilitation.
LCDD: Detecting Business Process Drifts Based on Local Completeness

Flexibility and evolution have been a hot topic in the context of business process management. However, contemporary process mining techniques assume processes to be in a steady state, which will result in a mixed-model mined from the event log. Business process drift detection is a family of methods to detect changes by analyzing the event log, but existing methods have some disadvantages in dealing with concept drifts. First, most of these methods detect changes depending on an exploration of a potentially large feature space and are time consuming. Second, with the size of delay period becoming small, the accuracies of some methods will drop rapidly. In this article, we address these problems by proposing a novel drift detection technique called LCDD that significantly differs from existing methods. The core idea is to find out new features and disappeared features from traces after the log fragment meets local completeness. To begin with, we use the relations of direct succession as the lightweight feature, which are compared between two windows (complete window and detection window). Then, we find new direct successions and stable disappeared direct successions by just moving detection window. Finally, the forgetting mechanism is used to abandon some direct successions after finding a change point. An extensive empirical evaluation shows that LCDD is faster and more accurate.
CRATE Exercise Control – A cyber defense exercise management and support tool

The growing dependency on computerized systems in society causes an increased need for a high level of cyber security competence among the professionals tasked with operating and protecting such systems. Cyber defense exercises are important for experience-based learning to train professionals working with critical computer systems. However, arranging realistic exercises require skilled instructors and technicians with the right set of tools. In this paper, we describe an exercise management and support tool called CRATE Exercise Control (CEC). The tool was developed by the Swedish Defence Research Agency (FOI), based on empirical experiences from arranging cyber defense exercises in the cyber range CRATE (Cyber Range And Training Environment), and best practices published by other organizations. We also share experiences made while using CEC during cyber defense exercises, as well as recommendations to consider when designing exercise management and support tools.
BotSpot

Mobile advertising has become inarguably one of the fastest growing industries all over the world. The influx of capital attracts increasing fraudsters to defraud money from advertisers. There are many tricks a fraudster can leverage, among which bot install fraud is undoubtedly the most insidious one due to its ability to implement sophisticated behavioral patterns and emulate normal users, so as to evade from detection rules defined by human experts. In this work, we propose an anti-fraud method based on heterogeneous graph that incorporates both local context and global context via graph neural networks (GNN) and gradient boosting classifier to detect bot fraud installs at Mobvista, a leading global mobile advertising company. Offline evaluations in two datasets show the proposed method outperforms all the competitive baseline methods by at least 2.2% in the first dataset and 5.75% in the second dataset given the evaluation metric [email protected]% Precision. Furthermore, we deploy our method to tackle million-scale data daily at Mobvista. The online performance also shows that the proposed methods consistently detect more bots than other baseline methods.
Named Entity Recognition Method with Word Position

Named entity recognition (also known as entity recognition, entity segmentation and entity extraction) is a sub task of information extraction. It aims to locate and classify named entities in text into predefined categories, such as people, organization, location, time expression, etc. Compared with English, there are more unsolved problems in Chinese named entity recognition. Named entities in English have obvious formal signs, that is, the first letter of every word in entities should be capitalized, and entity boundary recognition is relatively easy. Compared with English, the task of Chinese named entity recognition is more complex, and the recognition of entity boundary is more difficult. In this paper, we propose a named entity method by adding the word position, which embeds the word position of each word into the word vector, in order to better recognize the boundary of Chinese named entity. The experimental results show that the F1 value of the named entity recognition method proposed in this paper increases by about 1%.
Optimal strategies for CSIDH

&lt;p style='text-indent:20px;'&gt;Since its proposal in Asiacrypt 2018, the commutative isogeny-based key exchange protocol (CSIDH) has spurred considerable attention to improving its performance and re-evaluating its classical and quantum security guarantees. In this paper we discuss how the optimal strategies employed by the Supersingular Isogeny Diffie-Hellman (SIDH) key agreement protocol can be naturally extended to CSIDH. Furthermore, we report a software library that achieves moderate but noticeable performance speedups when compared against state-of-the-art implementations of CSIDH-512, which is the most popular CSIDH instantiation. We also report an estimated number of field operations for larger instantiations of this protocol, namely, CSIDH-1024 and CSIDH-1792.&lt;/p&gt;
Superconducting Nanowire Photon-Number-Resolving Detectors Integrated with Current Reservoirs

Photon-number-resolving detectors (PNRDs) play pivotal roles in many quantum-photonic applications; and intrinsic and multiplexed PNRDs have been reported previously. However, for intrinsic PNRDs, the maximum resolvable photon number is limited to a few photons; for the multiplexed PNRDs, the fidelity generally decreases when the to-be-resolved photon number becomes large. Here, to resolve more photons with high fidelity, we report on combined PNRDs, based on a spatial multiplexing configuration of multiple elements, each capable of resolving a few photons. After setting up a model and calculating the fidelity, we propose a possible physical system to realize the combined PNRDs. The proposed detectors are based on the superconducting nanowire multiphoton detectors integrated with current reservoirs that we recently reported, but are designed and configured into PNRDs. Specifically, information about the detected photon numbers is stored in the format of the supercurrent in the current reservoir and then readout by an integrated yTron. We present in detail the operating principle and show a design that can resolve up to 11 photons with high fidelity.
Who is targeted by email-based phishing and malware?

As technologies to defend against phishing and malware often impose an additional financial and usability cost on users (such as security keys), a question remains as to who should adopt these heightened protections. We measure over 1.2 billion email-based phishing and malware attacks against Gmail users to understand what factors place a person at heightened risk of attack. We find that attack campaigns are typically short-lived and at first glance indiscriminately target users on a global scale. However, by modeling the distribution of targeted users, we find that a person's demographics, location, email usage patterns, and security posture all significantly influence the likelihood of attack. Our findings represent a first step towards empirically identifying the most at-risk users.
Are We Secure from Bots? Investigating Vulnerabilities of Botometer

Social media platforms such as Twitter provide an incredibly efficient way to communicate with people. While these platforms have many benefits, they can also be used for deceiving people, spreading misinformation, manipulation, and harassment. Social bots are usually employed for these kind of activities to artificially increase the amount of a particular post. To mitigate the effects of social bots, many bot detection systems are developed. However, the evaluation of these methods are challenging due to lack limited available datasets and the variety of bots people might develop. In this work, we investigate vulnerabilities of state-of-the-art Botometer social bot detection system by creating our own bot scenarios instead of using public datasets. In our experiments, we show that Botometer is not able to detect our social bots, showing that we need more enhanced bot detection models and test collections to better evaluate systems' performances.
Efficient Learning Control of Uncertain Fractional-Order Chaotic Systems With Disturbance

In this brief, the problem of synchronization control is investigated for a class of fractional-order chaotic systems with unknown dynamics and disturbance. The controller is constructed using neural approximation and disturbance estimation where the system uncertainty is modeled by neural network (NN) and the time-varying disturbance is handled using disturbance observer (DOB). To evaluate the estimation performance quantitatively, the serial-parallel estimation model is constructed based on the compound uncertainty estimation derived from NN and DOB. Then, the prediction error is constructed and employed to design the composite fractional-order updating law. The boundedness of the system signals is analyzed. The simulation results show that the proposed new design scheme can achieve higher synchronization accuracy and better estimation performance.
Integration of regularized l1 tracking and instance segmentation for video object tracking

We introduce a tracking-by-detection method that integrates a deep object detector with a particle filter tracker under the regularization framework where the tracked object is represented by a sparse dictionary. A novel observation model which establishes consensus between the detector and tracker is formulated that enables us to update the dictionary with the guidance of the deep detector. This yields an efficient representation of the object appearance through the video sequence hence improves robustness to occlusion and pose changes. Moreover we propose a new state vector consisting of translation, rotation, scaling and shearing parameters that allows tracking the deformed object bounding boxes hence significantly increases robustness to scale changes. Numerical results reported on challenging VOT2016 and VOT2018 benchmarking data sets demonstrate that the introduced tracker, L1DPF-M, achieves comparable robustness on both data sets while it outperforms state-of-the-art trackers on both data sets where the improvement achieved in success rate at IoU-th=0.5 is 11% and 9%, respectively.
Corpus Bootstrapping for Assessment of the Properties of Effectiveness Measures

Bootstrapping is an established tool for examining the behaviour of offline information retrieval (IR) experiments, where it has primarily been used to assess statistical significance and the robustness of significance tests. In this work we consider how bootstrapping can be used to assess the reliability of effectiveness measures for experimental IR. We use bootstrapping of the corpus of documents rather than, as in most prior work, the set of queries. We demonstrate that bootstrapping can provide new insights into the behaviour of effectiveness measures: the precision of the measurement of a system for a query can be quantified; some measures are more consistent than others; rankings of systems on a test corpus likewise have a precision (or uncertainty) that can be quantified; and, in experiments with limited volumes of relevance judgements, measures can be wildly different in terms of reliability and precision. Our results show that the uncertainty in measurement and ranking of system performance can be substantial and thus our approach to corpus bootstrapping provides a key tool for helping experimenters to choose measures and understand reported outcomes.
Developing Combat Behavior through Reinforcement Learning in Wargames and Simulations

Progress in artificial intelligence (AI), particularly deep reinforcement learning (RL), has produced systems capable of performing at or above a professional-human level. This research explored the ability of RL to train AI agents to achieve optimal offensive behavior in small tactical engagements. Agents were trained in a simple, aggregate-level military constructive simulation with behaviors validated with the tactical principles of mass and economy of force. Results showed the combat model and RL algorithm applied had the largest impact on training performance. Additionally, specific training hyper-parameters also contributed to the quality and type of observed behaviors. Future work will seek to validate RL performance in larger and more complex combat scenarios.
Automatic Gaussian Process Model Retrieval for Big Data

Gaussian Process Models (GPMs) are widely regarded as a prominent tool for capturing the inherent characteristics of data. These bayesian machine learning models allow for data analysis tasks such as regression and classification. Usually a process of automatic GPM retrieval is needed to find an optimal model for a given dataset, despite prevailing default instantiations and existing prior knowledge in some scenarios, which both shortcut the way to an optimal GPM. Since non-approximative Gaussian Processes only allow for processing small datasets with low statistical versatility, we propose a new approach that allows to efficiently and automatically retrieve GPMs for large-scale data. The resulting model is composed of independent statistical representations for non-overlapping segments of the given data. Our performance evaluation of the new approach demonstrates the quality of resulting models, which clearly outperform default GPM instantiations, while maintaining reasonable model training time.
Insights on software product line extraction processes

Software Product Lines (SPLs) are rarely developed from scratch. Commonly, they emerge from monolithic architectures when there is a need to create tailored variants, or from existing variants created in an ad-hoc way once their separated maintenance and evolution become challenging. Despite the vast literature about re-engineering systems into SPLs and related technical approaches, there is a lack of detailed analysis about the process itself and the effort that is involved. We provide and analyze empirical data of an existing SPL extraction process: the ArgoUML monolithic architecture transition to ArgoUML-SPL. The analysis relies on information mined from the version control history of the source-code repository and the discussion with developers that took part in the process. The contribution of this study is an in-depth characterization of the process compared to previous works that focused only on the structural results of the final SPL. We made publicly available the dataset and the analysis scripts to be used as baseline for extractive SPL adoption research and practice.