Reviewerly/exHarmony huggingface dataset:
Multi-Target Joint Detection; Tracking and Classification Based on Marginal GLMB Filter and Belief Function Theory

This paper proposes a new solution to multi-target joint detection, tracking and classification based on labeled random finite set (RFS) and belief function theory. A class dependent multi-model marginal generalized labeled multi-Bernoulli (MGLMB) filter is developed to analytically calculate the multi-target number, state estimates and model probabilities. In addition, a two-level classifier based on continuous transferable belief model (cTBM) is designed for target classification. To make full use of the kinematic characteristics for classification, both the dynamic modes and states are considered in the classifier, the model dependent class beliefs are computed on the continuous state feature subspace corresponding to different dynamic modes and then fused. As a result that the uncertainty about the classes is well described for decision, the classification results are more reasonable and robust. Moreover, as the estimation and classification problems are jointly solved, the tracking and classification performance are both improved. In the simulation, a scenario contains multi-target with miss detection and dense clutter is used. The performance of multi-target detection, tracking and classification is better than traditional methods based on Bayesian classifier or single model. Simulation results are illustrated to demonstrate the effectiveness and superiority of the proposed algorithm.
Classification with 2-D convolutional neural networks for breast cancer diagnosis

Breast cancer is the most common cancer in women. Classification of cancer/non-cancer patients with clinical records requires high sensitivity and specificity for an acceptable diagnosis test. The state-of-the-art classification model-convolutional neural network (CNN), however, cannot be used with such kind of tabular clinical data that are represented in 1-D format. CNN has been designed to work on a set of 2-D matrices whose elements show some correlation with neighboring elements such as in image data. Conversely, the data examples represented as a set of 1-D vectors-apart from the time series data-cannot be used with CNN, but with other classification models such as Recurrent Neural Networks for tabular data or Random Forest. We have proposed three novel preprocessing methods of data wrangling that transform a 1-D data vector, to a 2-D graphical image with appropriate correlations among the fields to be processed on CNN. We tested our methods on Wisconsin Original Breast Cancer (WBC) and Wisconsin Diagnostic Breast Cancer (WDBC) datasets. To our knowledge, this work is novel on non-image tabular data to image data transformation for the non-time series data. The transformed data processed with CNN using VGGnet-16 shows competitive results for the WBC dataset and outperforms other known methods for the WDBC dataset.
Efficient Handwritten Character Recognition of MODI Script Using Wavelet Transform and SVD

MODI script has historical importance as it was used for writing the Marathi language, until 1950. Due to the complex nature of the script, the character recognition of MODI script is still in infancy. The implementation of more efficient methods at the various stages of the character recognition process will increase the accuracy of the process. In this paper, we present a hybrid method called WT-SVD (Wavelet Transform-Singular Value Decomposition), for the character recognition of MODI script. The WT-SVD method is a combination of singular value decomposition and wavelet transform, which is used for the feature extraction. Euclidean distance method is used for the classification. The experiment is conducted using Symlets and Biorthogonal wavelets, and the results are compared. The method using Biorthogonal wavelet feature extraction achieved the highest accuracy
Kindly bent to free us

Systems programming often requires the manipulation of resources like file handles, network connections, or dynamically allocated memory. Programmers need to follow certain protocols to handle these resources correctly. Violating these protocols causes bugs ranging from type mismatches over data races to use-after-free errors and memory leaks. These bugs often lead to security vulnerabilities. While statically typed programming languages guarantee type soundness and memory safety by design, most of them do not address issues arising from improper handling of resources. An important step towards handling resources is the adoption of linear and affine types that enforce single-threaded resource usage. However, the few languages supporting such types require heavy type annotations. We present Affe, an extension of ML that manages linearity and affinity properties using kinds and constrained types. In addition Affe supports the exclusive and shared borrowing of affine resources, inspired by features of Rust. Moreover, Affe retains the defining features of the ML family: it is an impure, strict, functional expression language with complete principal type inference and type abstraction. does not require any linearity annotations in expressions and supports common functional programming idioms.
BETTER GENERIC OBJECTS COUNTING WHEN ASKING QUESTIONS TO IMAGES: A MULTITASK APPROACH FOR REMOTE SENSING VISUAL QUESTION ANSWERING

Abstract. Visual Question Answering for Remote Sensing (RSVQA) aims at extracting information from remote sensing images through queries formulated in natural language. Since the answer to the query is also provided in natural language, the system is accessible to non-experts, and therefore dramatically increases the value of remote sensing images as a source of information, for example for journalism purposes or interactive land planning. Ideally, an RSVQA system should be able to provide an answer to questions that vary both in terms of topic (presence, localization, counting) and image content. However, aiming at such flexibility generates problems related to the variability of the possible answers. A striking example is counting, where the number of objects present in a remote sensing image can vary by multiple orders of magnitude, depending on both the scene and type of objects. This represents a challenge for traditional Visual Question Answering (VQA) methods, which either become intractable or result in an accuracy loss, as the number of possible answers has to be limited. To this end, we introduce a new model that jointly solves a classification problem (which is the most common approach in VQA) and a regression problem (to answer numerical questions more precisely). An evaluation of this method on the RSVQA dataset shows that this finer numerical output comes at the cost of a small loss of performance on non-numerical questions.
Content format and quality of experience in virtual reality

In this paper, we investigate three forms of virtual reality (VR) content production and consumption. Namely, pre-rendered 360 stereoscopic video, full real-time rendered 3D scenes, and the combination of a real-time rendered 3D environment with a pre-rendered video billboard used to present the central elements of the scene. We discuss the advantages and disadvantages of these content formats and describe the production of a piece of VR cinematic content for the three formats. The cinematic segment presented the interaction between two actors, which the VR user could watch from the virtual room next-door, separated from the action by a one-way mirror. To compare the three content formats, we carried out an experiment with 24 participants. In the experiment, we evaluated the quality of experience, including presence, simulation sickness and the participants’ assessment of content quality, for each of the three versions of the cinematic segment. We found that, in the context of our cinematic segment, combining video and 3D content produced the best experience. We discuss our results, including their limitations and the potential applications.
THE EFFECT OF USING COOPERATIVE SCRIPT ON STUDENT’S READING COMPREHENSION AT GRADE ELEVENT OF SMA NEGRI 2 PEMATANGSIANTAR

This study is about the effect of Cooperative Script on students’ reading comprehension. Problem raised in this research was: What is the effect of using Cooperative Script on student’s reading comprehension at Grade eleven of SMA Negeri 2 Pematangsiantar? To answer the question, the researchers used some theories such as: Harmer (2007), Bolukbas, Keskin, and Polat (2011), Herman (2014) and Barker (2000). Experimental design was conducted to this research. The population of the study was the 2019/202 academic year firstsemester grade XI students of SMA Negeri 2 Pematangsiantar. The samples were two classes, namely XIPMIA3 and XIPIS 1, with the total was 68 students selected by applying random sampling. The sample was divided into two groups. Experimental group (XI PIS 1) was taught by applying Cooperative Script Method while the control group (XI PMIA 3) was taught by applying explaining method. There were two data used in this research. They were pre-test and post-test. The data were analyzed by using t-test formula to show the effect of applying Cooperative Script Method on students’ reading comprehension. After analyzing the data, the finding indicates that t-observed was higher than t-table (6.263 &gt; 1.668)) with the degree of freedom (df) = 66. It means that applying Cooperative Script Methodhas significantly affected students’ reading comprehension.
Effects of Blood Storage on Cell Population Data

Background Cell population data (CPD) are leukocyte morphologic parameters, measured by automated hematology analyzers with VCS technology. Many studies have demonstrated that these parameters may have clinical utility for diagnosing or screening certain pathological conditions. This study is to investigate the effects of peripheral blood stored at room temperature on the CPD values and provide useful information about storage-induced CPD changes. Methods Venous blood samples from healthy donors kept at room temperature (18 - 25°C) for different time intervals were analyzed using a Coulter DxH800 hematology analyzer. The CPD data collected included mean cellular volume, conductivity and multiple angles of light scatters as well as their corresponding standard deviation for neutrophils, lymphocytes, and monocytes. Peripheral blood smears at each time interval were also prepared and examined microscopically. Results Peripheral blood kept at room temperature over time significantly affects the CPD values for neutrophils, lymphocytes, and monocytes. These CPD changes are correlated with the morphologic alterations observed under light microcopy, but detected much earlier. Some changes imitate clinical pathological conditions. For example, aged neutrophils showed decreased median angle light scatters, suggesting cytoplasmic degranulation, which can be seen in the case of myelodysplastic syndrome. Conclusions This study provides valuable information about storage-induced CPD changes that can affect potential clinical application and interpretation of these automated digital morphologic parameters.
Generating visually coherent encrypted images with reversible data hiding in wavelet domain by fusing chaos and pairing function

For secure transmission of digital images, existing cryptographic algorithms transform coherent visual information into a noise-like appearance prompting an adversary of the presence of a possible cipher. This paper proposes an algorithm that produces a visually coherent and meaningful cipher image. The proposed algorithm consists of a permutation-substitution subroutine to obtain a partial cipher. The Arnold-3D map does the permutation, and a delayed logistic map performs the substitution in this subroutine. The hiding of this partial cipher is done in the reference image using an integer wavelet transform. The pixels of the partial cipher are embedded in the four sub-bands of the decomposed reference image as 4 to 1-pixel encoding using Cantor-like pairing function. In addition to the lossless encryption scheme, the integer nature of all the sub-bands in the wavelet decomposition and the invertible pairing function facilitates the perfect reconstruction of the reference image. One of the significant novelty of this work lies in the subtle use of simple pairing functions, which prohibits the unnecessary increase in the size of the cipher, thereby reducing the storage and transmission costs.
Exploiting IoT Data Crossings for Gradual Pattern Mining Through Parallel Processing

Today, with the proliferation of Internet of Things (IoT) applications in almost every area of our society comes the trouble of deducing relevant information from real-time time-series data (from different sources) for decision making. In this paper, we propose a fuzzy temporal approach for crossing such data sets with the ultimate goal of exploiting them for temporal gradual pattern mining. A temporal gradual pattern may take the form: “the higher the humidity, the lower the temperature, almost 15 min later”. In addition, we apply parallel processing on our implementation and measure its computational performance.
Why organisational readiness is vital in the fight against insider threats

A chain is only as strong as its weakest link. It's a well-known phrase, but one that rings particularly true when considering a business's cyber security. In Securonix's 2019 Insider Threat Survey Report, 73% of organisations confirmed that insider attacks are becoming more frequent, and 59% said they think that privileged IT users pose the biggest insider security risk to organisations. 1 Insider threat is on the rise, and it's time for more companies to become aware of the risk they face and prepare correctly. The insider threat is on the rise, and it's time for more companies to become aware of the risk they face and prepare correctly. Organisational readiness is a factor that too many overlook in their pursuit of complete security. The right foundations need to be set before any business can produce a top-drawer security system. Once employees understand the importance of their role in protecting their company and buy into a secure culture, businesses can expect to reap the full benefits of the technology they're installing, argues Mark Rodbert of Idax Identity Analytics.
Collaborative Filtering Recommendation Algorithm Based on User Characteristics and User Interests

Abstract Among many e-commerce platforms, collaborative filtering recommendation algorithm is currently the most widely used recommendation technology. In order to alleviate the deficiencies of the traditional user-based collaborative filtering algorithm in cold start and recommendation accuracy, this paper proposes a collaborative filtering recommendation algorithm based on user characteristics and user interests. The similarity of the algorithm in this paper is composed of user score similarity, user attribute feature similarity and user interest similarity, in which user registration information is used to extract attribute features to calculate user feature similarity; use the number of user evaluations of project attributes to measure users’ interest in different project attributes, use the similarity calculation formula to calculate the interest similarity value between users. The user attribute feature similarity and user interest similarity are combined with the user rating similarity to obtain the final similarity for recommendation. Finally, a simulation experiment is performed on the MovieLens movie data set. Through the experimental results, it can be seen that the improved collaborative filtering algorithm based on user characteristics and user interests not only solves the cold start problem, but also improves the recommendation accuracy.
Multi-Feature Approach for Bug Severity Assignment

When bug reports are submitted through bug tracking systems, they are analysed manually to identify their severity levels. A severity level specifies the negative impact of a bug on a system. With the huge number of submitted reports, setting the severity class manually is tedious and time consuming. Moreover, some bug types are reported more often than other types, which leads to imbalanced bug repositories. This paper proposes a multi-feature approach for automatic severity assignment, which leverages lexical, semantic, and categorical properties of the bug reports. The proposed approach utilizes word embeddings, topic model, vector space model, and an adapted K-Nearest Neighbour technique. Moreover, the impact of utilizing two sampling techniques, namely SMOTE and cluster-based under-sampling (CBU), were investigated. Experiments over two open source repositories, Eclipse and Mozilla, demonstrated that the proposed approach is superior to two previous studies.
Back to the Feature: A Neural-Symbolic Perspective on Explainable AI

We discuss a perspective aimed at making black box models more eXplainable, within the eXplainable AI (XAI) strand of research. We argue that the traditional end-to-end learning approach used to train Deep Learning (DL) models does not fit the tenets and aims of XAI. Going back to the idea of hand-crafted feature engineering, we suggest a hybrid DL approach to XAI: instead of employing end-to-end learning, we suggest to use DL for the automatic detection of meaningful, hand-crafted high-level symbolic features, which are then to be used by a standard and more interpretable learning model. We exemplify this hybrid learning model in a proof of concept, based on the recently proposed Kandinsky Patterns benchmark, that focuses on the symbolic learning part of the pipeline by using both Logic Tensor Networks and interpretable rule ensembles. After showing that the proposed methodology is able to deliver highly accurate and explainable models, we then discuss potential implementation issues and future directions that can be explored.
Character-Independent Font Identification

There are a countless number of fonts with various shapes and styles. In addition, there are many fonts that only have subtle differences in features. Due to this, font identification is a difficult task. In this paper, we propose a method of determining if any two characters are from the same font or not. This is difficult due to the difference between fonts typically being smaller than the difference between alphabet classes. Additionally, the proposed method can be used with fonts regardless of whether they exist in the training or not. In order to accomplish this, we use a Convolutional Neural Network (CNN) trained with various font image pairs. In the experiment, the network is trained on image pairs of various fonts. We then evaluate the model on a different set of fonts that are unseen by the network. The evaluation is performed with an accuracy of 92.27%. Moreover, we analyzed the relationship between character classes and font identification accuracy.
What Leads to a Confirmatory or Disconfirmatory Behavior of Software Testers?

<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Background:</b> The existing literature in software engineering reports adverse effects of confirmation bias on software testing. Confirmation bias among software testers leads to confirmatory behavior, which is designing or executing relatively more specification consistent test cases (confirmatory behavior) than specification inconsistent test cases (disconfirmatory behavior). <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective:</b> We aim to explore the antecedents to confirmatory and disconfirmatory behavior of software testers. Furthermore, we aim to understand why and how those antecedents lead to (dis)confirmatory behavior. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Method:</b> We follow grounded theory method for the analyses of the data collected through semi-structured interviews with twelve software testers. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results:</b> We identified twenty antecedents to (dis)confirmatory behavior, and classified them in nine categories. Experience and Time are the two major categories. Experience is a disconfirmatory category, which also determines which behavior (confirmatory or disconfirmatory) occurs first among software testers, as an effect of other antecedents. Time Pressure is a confirmatory antecedent of the Time category. It also contributes to the confirmatory effects of antecedents of other categories. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusion:</b> The disconfirmatory antecedents, especially that belong to the testing process, e.g., test suite reviews by project team members, may help circumvent the deleterious effects of confirmation bias in software testing. If a team’s resources permit, the designing and execution of a test suite could be divided among the test team members, as different perspectives of testers may help to detect more errors. The results of our study are based on a single context where dedicated testing teams focus on higher levels of testing. The study’s scope does not account for the testing performed by developers. Future work includes exploring other contexts to extend our results.
A Two-Stage Multiple Instance Learning Framework for the Detection of Breast Cancer in Mammograms

Mammograms are commonly employed in the large scale screening of breast cancer which is primarily characterized by the presence of malignant masses. However, automated image-level detection of malignancy is a challenging task given the small size of the mass regions and difficulty in discriminating between malignant, benign mass and healthy dense fibro-glandular tissue. To address these issues, we explore a two-stage Multiple Instance Learning (MIL) framework. A Convolutional Neural Network (CNN) is trained in the first stage to extract local candidate patches in the mammograms that may contain either a benign or malignant mass. The second stage employs a MIL strategy for an image level benign vs. malignant classification. A global image-level feature is computed as a weighted average of patch-level features learned using a CNN. Our method performed well on the task of localization of masses with an average Precision/Recall of 0.76/0.80 and achieved an average AUC of 0.91 on the image-level classification task using a five-fold cross-validation on the INbreast dataset. Restricting the MIL only to the candidate patches extracted in Stage 1 led to a significant improvement in classification performance in comparison to a dense extraction of patches from the entire mammogram.
Educational Data Mining (EDM)on the use of the Internet in the World of Indonesian Education

The purpose of this research is to analyze internet users in the world who are educated in Indonesia by utilizing computer science techniques. The use of the internet in question is the percentage in accessing the internet for ages 5-12 years old, 13-15 years old and 16-18 years old. The data source used is from https://www.bps.go.id/. The method used is kmedoids, while mapping in the form of clusters in this study uses 2 labels namely C1: high cluster for internet use and C2: low cluster for internet use. The results mention that the islands of Sumatra and Java dominate in the use of the internet in all ages. The results state that k-medoids can be applied to the case of mapping internet usage with an accuracy of mentioning above 90% and an error rate below 5%.
Drone-Based Quantum Key Distribution

Current QKD protocols are limited to wired point to point key exchange. Utilizing drones, we work to establish free space quantum channels. Here, we report the progress of the tracking system used on the drones.