Reviewerly/exHarmony huggingface dataset:
Breast Cancer Detection and Classification using Global Pooling

Breast cancer is one of the leading causes of cancer related death in women. Studies suggest that early detection can bring down the mortality rate to a great extent. Out of different imaging techniques, doctors and radiologists prefer mammography for early detection of breast cancer and so mammography remains the gold standard screening technique. However, due to overwork and lack of expertise,a lot of misclassification is also reported. Computer Aided Diagnosis has helped doctors and radiologists in this regard. With the rapid development of the Convolutional Neural Network image classification has reported major success. Our work uses CNN for classification of ROIs extracted from the mammograms. A novel technique for feature extraction from different convolutional layers of the DCNN using Global Average Pooling and later concatenating all the extracted features before the final classification are proposed. We have experimentally proved that incorporating such techniques can improve classification performance in almost all the pre-trained DCNN structures.
Hyper-Substructure Enhanced Link Predictor

Link prediction has long been the focus in the analysis of network-structured data. Though straightforward and efficient, heuristic approaches like Common Neighbors perform link prediction with pre-defined assumptions and only use superficial structural features. While it is widely acknowledged that a vertex could be characterized by a bunch of neighbor vertices, network embedding algorithms and newly emerged graph neural networks still exploit structural features on the whole network, which may inevitably bring in noises and limits the scalability of those methods. In this paper, we propose an end-to-end deep learning framework, namely hyper-substructure enhanced link predictor (HELP), for link prediction. HELP utilizes local topological structures from the neighborhood of the given vertex pairs, avoiding useless features. For further exploiting higher-order structural information, HELP also learns features from hyper-substructure network (HSN).Extensive experiments on six benchmark datasets have shown the state-of-the-art performance of HELP on link prediction.
Accelerating data filtering for database using FPGA

In the big data era, in order to relieve computational pressure on overloaded CPU caused by ever increasing amount of data, many researches focus on hardware acceleration using FPGA for data-intensive applications. In this paper, a novel FPGA-based storage engine is proposed for DBMS in the cloud with focus on data filtering operation. A hardware data filter is designed which can significantly speedup filtering operations by utilizing parallelism provided by FPGA. Meanwhile, it can support different queries without partial reconfiguration. This FPGA-based storage engine is integrated with DBMS to realize end-to-end acceleration. In addition, an intelligent filtering on/off switch is designed to adaptively decide whether the FPGA-based filter should be employed, based on selectivity estimation. Experimental results show that the proposed solution realizes on average 2.80x computation speedup for data filtering compared with the software baseline, and achieves up to 1.95x improvement in end-to-end evaluation compared with conventional storage engine in low-selectivity cases. Moreover, the FPGA-based solution achieves 2.87x improvement on energy efficiency compared with the similar GPU-based acceleration solution.
TXTing 101: Finding Security Issues in the Long Tail of DNS TXT Records

The DNS TXT resource record is the one with the most flexibility for its contents, as it is a largely unstructured. Although it might be the ideal basis for storing any form of text-based information, it also poses a security threat, as TXT records can also be used for malicious and unintended practices. Yet, TXT records are often overlooked in security research. In this paper, we present the first structured study of the uses of TXT records, with a specific focus on security implications. We are able to classify over 99.54% of all TXT records in our dataset, finding security issues including accidentally published private keys and exploit delivery attempts. We also report on our lessons learned during our large-scale, systematic analysis of TXT records.
Constructing Interpretable Decision Trees Using Parallel Coordinates

The interest in interpretable models that are not only accurate but also understandable is rapidly increasing; often resulting in the machine-learning community turning to decision tree classifiers. Many techniques of growing decision trees use oblique rules to increase the accuracy of the tree and decrease its overall size, but this severely limits understandability by a human user. We propose a new type of oblique rule for decision tree classifiers that is interpretable to human users. We use the parallel coordinates system of visualisation to display both the dataset and rule to the user in an intuitive way. We propose the use of an evolutionary algorithm to learn this new type of rule and show that it produced significantly smaller trees compared to a tree created with axis-parallel rules with minimal loss in accuracy.
Goal Density-based Hindsight Experience Prioritization for Multi-Goal Robot Manipulation Reinforcement Learning

Reinforcement learning for multi-goal robot manipulation tasks is usually challenging, especially when sparse rewards are provided. It often requires millions of data collected before a stable strategy is learned. Recent algorithms like Hindsight Experience Replay (HER) have accelerated the learning process greatly by replacing the original desired goal with one of the achieved points (substitute goals) alongside the same trajectory. However, the selection of previous experience to learn is naively sampled in HER, in which the trajectory selection and the substitute goal sampling is completely random. In this paper, we discuss an experience prioritization strategy for HER that improves the learning efficiency. We propose the Goal Density-based hindsight experience Prioritization (GDP) method that focuses on utilizing the density distribution of the achieved points and prioritizes achieved points which are rarely seen in the replay buffer. These points are used as substitute goals for HER. In addition, we propose an Prioritization Switching with Ensembling Strategy (PSES) method to switch different experience prioritization algorithms during learning, which allows to select the best performance during each learning stage. We evaluate our method with several OpenAI Gym robotic manipulation tasks. The results show that GDP accelerates the learning process in most tasks and can be improved when combining with other prioritization methods using PSES.
Improving Visual Relationship Detection With Two-Stage Correlation Exploitation

Visual relationship detection, as a challenging task used to find and distinguish interactions between object-pairs in one image, has received much attention recently. In this work, we devise a unified visual relationship detection framework with two types of correlation exploitation to address the combination explosion problem in the object-pairs proposing stage and the non-exclusive label problem in the predicate recognition stage. In the object-pairs proposing stage, with the exploitation of relative location correlation between two objects in one pair, one location-embedded rating module (LRM) is developed to effectively select plausible proposals. In the predicate recognition stage, one label-correlation graph module (LGM) is introduced to measure the implicit semantic correlation among predicates; and then assign discrete distributed labels to predicates to improve the precision of top-n recall. Experiments on the two widely used VRD and VG datasets show that our proposed method outperforms current state-of-the-art methods.
A data driven survey of video games

The video game industry is one of the fastest growing of entertainment industries, with a variety of platforms, genres and cutting-edge technologies. Built upon the cultural heritage and supported by the culmination of hardware and software advancements, video games have evolved as a most interactive cultural medium. While PC and console gaming has been the norm during the golden age of video games, mobile technologies have emerged during the last decade, with potential to disrupt the gaming industry as well. In this study, a survey of game scores over the years reveals some general trends in the industry, from the perspective of end users. The average scores, number of titles, as well as a distribution by genre are extracted from Metacritic database and plotted to show an overview of the PC gaming industry since the inception of user scores and reviews on game-related websites and the implications for traditional gaming in the future.
Sample Driven Data Mapping for Linked Data and Web APIs

In order to create the most comprehensive RDF Knowledge Base possible, data integration is essential. Many different data sources are used to extend a given dataset or to correct errors in the data. Nowadays, Web APIs (instead of data dumps) are common external data sources, since many data providers make their data publicly available. However, the classic problems of data integration, i.e., which parts of the datasets can be mapped, remain. In addition, Web APIs are often more restrictive than data dumps and of course slower to access due to latencies and other constraints. In this paper we demonstrate the FiLiPo (Finding Linkage Points) system to automatically find connections (i.e., linkage points) between Web APIs and local Knowledge Bases in a reasonable amount of time. To this end, we developed a sample-driven schema matching system, which models Web API services as parameterized queries. These Web API services return a view definition of their data which subsequently need to be connected to the local database scheme. Furthermore, our approach is able to find valid input values for Web API services automatically (e.g. IDs) and can determine combined linkage points (e.g. first and last name) despite different structures. Our results on six real world API services with two local databases show that our linkage point detection algorithm performs well in terms of precision (0.89 up to 1.0) and recall (0.69 up to 1.0).
Comparison of three models to estimate the slant path atmospheric attenuation in central receiver solar thermal plants under Indian climatic conditions

This paper compares three attenuation models for the distinct meteorological conditions of India and assesses the feasibility of using the satellite data to calculate the slant path extinction coefficient and the slant path transmissivity of the lower atmosphere. The attenuation models are compared for the sites of Pune, Kanpur, and Jaipur. The AERONET data is used with REST2 to model the direct normal irradiance and the total optical depth. The results indicate that the three models give similar transmissivity values for low aerosol concentrations. As the aerosol concentrations increase, the difference in the slant path transmissivity as calculated by two of the models increases with respect to the third reference model. The slant path transmissivity comparison shows that for AERONET AOD at 550 nm (AOD550) less than 0.3, the relative error in the transmissivity values estimated by two of the compared models with respect to the third model is not higher than 10 % for Pune, 18 % for Kanpur, and 12 % for Jaipur. The mean relative error in the slant path transmissivity values obtained from the reference model using MODIS data with respect to the values obtained using AERONET data is 4.8 %, 10.2 %, and 9.2 % for Pune, Kanpur, and Jaipur, respectively. A difference of 0.2 between AERONET AOD550 and MODIS AOD550 values resulted in a difference of 0.05–0.08 between the transmissivity values calculated by the reference model using AERONET AOD550 and using MODIS AOD550 for the three sites.
A Data-Driven Approach to Measure the Usability of Web APIs

Application Programming Interfaces (APIs) are means of communication between applications, hence they can be seen as user interfaces, just with different kind of users, i.e., software or computers. However, the very first consumers of the APIs are humans, namely programmers. Based on the available documentation and the "ease of use" perception (sometimes led by corporate decisions and/or restrictions) they decide to use or not a specific API. In this paper, we propose a data-driven approach to measure web API usability, expressed through the predicted error rate. Following the reviewed state of the art in API usability, we identify a set of usability attributes, and for each of them we propose indicators that web API providers should refer to when developing usable web APIs. Our focus in this paper is on those indicators that can be quantified using the API logs, which indeed reflect the actual behaviour of programmers. Next, we define metrics for the aforementioned indicators, and exemplify them in our use case, applying them on the logs from the web API of District Health Information System (DHIS2) used at World Health Organization (WHO). Using these metrics as features, we build a classifier model to predict the error rate of API endpoints. Besides finding usability issues, we also drill down into the usage logs and investigate the potential causes of these errors.
Binding of Endpoints to Identifiers by On-Chain Proofs

In many applications, identity management (IdM) is used to associate a subject public key with an endpoint at which the subject can be contacted (telephone number, email, etc.). In decentralized applications based on blockchains, it is desirable for the IdM to be decentralized as well. In certain applications, it is paramount to be certain that an endpoint actually belong to a specific subject. Currently, when this is needed, endpoints are either verified by who needs it, which is impractical in blockchain-based applications, or by a centralized authority, which contrasts with the spirit of the blockchain.In this paper, we show two layer-two blockchain-based protocols to prove the association between a subject and an endpoint in a decentralized manner. Our protocols are compatible with a wide variety of endpoints and contribute to fill the gap of the current self sovereign IdM approaches with respect to decentralization. We analyze the security of our proposals and evaluate performances and costs against the common approaches.
A Continuous Data Imputation Mechanism based on Streams Correlation

The increased adoption of the Internet of Things (IoT) for the delivery of intelligent applications over huge volumes of data opens new opportunities to draw conclusions from data and support efficient decision making. For this reason many applications have been developed for data collection and processing. A large part of them are aligned with the requirements of the vast infrastructure of IoT. However, one of the biggest problems occurring at real-time applications is that they are prone to missing values. Missing values can negatively affect the outcomes of any processing activity, thus, they can limit the performance of IoT applications. In this paper, we depart from the relevant literature and propose a data imputation model that is based on the correlation of data reported by different IoT devices. Our aim is to support data imputation using the ‘knowledge’ of a team of IoT devices over their reports for various phenomena. Our scheme adopts a continuous correlation detection methodology applied at real time reports of the involved devices. Hence, any missing value can be replaced by the aggregated outcome of data reported by correlated devices. We provide the description of our approach and evaluate it through a high number of simulations adopting various experimental scenarios.
Using knowledge graphs and behaviour trees for feedback-aware presentation agents

In this paper, we address the problem of how an interactive agent (such as a robot) can present information to an audience and adapt the presentation according to the feedback it receives. We extend a previous behaviour tree-based model to generate the presentation from a knowledge graph (Wikidata), which allows the agent to handle feedback incrementally, and adapt accordingly. Our main contribution is using this knowledge graph not just for generating the system's dialogue, but also as the structure through which short-term user modelling happens. In an experiment using simulated users and third-party observers, we show that referring expressions generated by the system are rated more highly when they adapt to the type of feedback given by the user, and when they are based on previously grounded information as opposed to new information.
Know your roles: Alleviating the academic-professional tension in the case analysis genre

The case analysis, a prominent genre within business administration and information systems (IS) programs, is challenging for students because of competing demands to perform both academic/learner and professional/mock-consultant roles. We examine case analysis student writing that sits in the middle of the pedagogical to professional continuum (Gardner, 2012), where the academic-professional tension is most prominent. The data come from an upper-level IS course at an English-medium American branch campus in the Middle East where we collaborated with IS faculty to scaffold a particular case analysis genre, the case development. In this genre, students analyze their own experiential narrative about a place they visit and make technology-based recommendations to improve the experience. Using systemic functional linguistics (SFL), we unpack the linguistic features of case developments where students attempted to navigate this academic-professional tension. We contrast examples of students who successfully navigated between these two roles with less effective student writing. We conclude by presenting a mentor text that highlights the linguistic features of a case analysis that eases this tension with a productive coexistence of learner and professional roles. Our analysis has implications for helping both students navigate between these roles and teachers conceptualize assignments that explicitly articulate their goals.
Revealing Perceptible Backdoors in DNNs, Without the Training Set, via the Maximum Achievable Misclassification Fraction Statistic

Recently, a backdoor data poisoning attack was proposed, which adds mislabeled examples to the training set, with an embedded backdoor pattern, aiming to have the classifier learn to classify to a target class whenever the backdoor pattern is present in a test sample. We address post-training detection of innocuous perceptible backdoors in DNN image classifiers, wherein the defender does not have access to the poisoned training set. This problem is challenging because without the poisoned training set, we have no hint about the actual backdoor pattern used during training. We identify two properties of perceptible backdoor patterns - spatial invariance and robustness - based upon which we propose a novel detector using the maximum achievable misclassification fraction (MAMF) statistic. We detect whether the trained DNN has been backdoor-attacked and infer the source and target classes. Our detector outperforms other existing detectors experimentally.
Visual Navigation with Asynchronous Proximal Policy Optimization in Artificial Agents

Vanilla policy gradient methods suffer from high variance, leading to unstable policies during training, where the policy’s performance fluctuates drastically between iterations. To address this issue, we analyze the policy optimization process of the navigation method based on deep reinforcement learning (DRL) that uses asynchronous gradient descent for optimization. A variant navigation (asynchronous proximal policy optimization navigation, appoNav) is presented that can guarantee the policy monotonic improvement during the process of policy optimization. Our experiments are tested in DeepMind Lab, and the experimental results show that the artificial agents with appoNav perform better than the compared algorithm.
Reversible Data Hiding in Halftone Images Based on Dynamic Embedding States Group

In many reversible data hiding (RDH) methods for halftone images, the traditional embedding process embeds a 1-bit secret message into each embeddable pixel or pattern. To improve the embedding efficiency and payload, we propose an RDH method used in halftone images based on the dynamic embedding states group (DESG), which can embed at least 1 bit of secret messages per embeddable pixel or pattern. First, by exploiting the statistical features of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4 \times 4$ </tex-math></inline-formula> patterns and the state sequences in each image, the DESG is constructed dynamically, including <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> embedding states with their state patterns and state sequences. Then, secret messages are encoded by matching the longest common subsequence according to the DESG, which are split into several state sequences. The state sequences are embedded by Markov transitions between these <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> changing state patterns. Finally, reversibility is achieved by recording the DESG as the overhead information in RDH. Experiments show that the construction of DESG can improve the embedding efficiency under the same number of embeddable pixels or patterns, and the visual distortion is also significantly reduced by flipping fewer pixels.
Classical Equivalent Quantum Based Efficient Data Preprocessing Algorithm

The machine learning model can infer desired information on processing data sets without being explicitly programmed. It needs refined data to train the perfect model, hence preprocessing is mandatory. The principal component analysis is desired classical existing methods for preprocessing and it requires polynomial time. Now, the research field of computer science is getting influenced by existence of quantum computations, as it supports exponential operations to be performed in parallel over single step of execution. An intrinsic realization of quantum machine provides simultaneous access to either classical or quantum memory. The objective of paper is to understand the importance of data preprocessing and to suggest quantum based solution that takes the advantage of quantum parallelism and thus can obtain computational speedups. So, we contribute to the emergence of quantum computations, processing aspects using quantum accessible memory models and then classical equivalent quantum principal component analysis algorithm. At last we conclude with the mathematical justification over complexity analysis, computational speedups, and prove that quantum algorithms are efficient along with the suggestions of further directions.