Reviewerly/exHarmony huggingface dataset:
Less is more: improving neural-based collaborative filtering by using landmark modeling

Collaborative Filtering (CF) has been extensively studied over the last decade. Among the most successful methods, neural-based methods have changed the research landscape by bringing cutting-edge artificial neural networks (ANN) and big data to offer the best personalization users have ever experienced. However, a significant issue arose from these neural-based methods is that the increase of the model complexity directly impacts the computational cost. In general, this drawback is a consequence of the high dimensionality and sparsity of the input feature space. We believe that reducing sparsity and dimensionality of the input features is essencial to enhance accuracy while keeping computational costs low. This paper investigates an alternative modeling for the CF setting to represent users or items by using landmarks. This modeling drastically decreases the input feature space and eliminate sparsity while maintaining the underlying information needed to achieve great accuracy. Based on that, we propose a novel neural-based CF method via landmark modeling. Experiments on six real-world benchmark CF datasets were conducted by comparing the proposed method to well-known and widely-used CF methods of the state-of-the-art. The results show that the Neural Landmark method outperforms the other methods in both accuracy and computational performance.
Towards ubiquitous requirements engineering through recommendations based on context histories

The growing technological advance is causing constant business changes. The continual uncertainties in project management make requirements engineering essential to ensure the success of projects. The usual exponential increase of stakeholders throughout the project suggests the application of intelligent tools to assist requirements engineers. Therefore, this article proposes Nhatos, a computational model for ubiquitous requirements management that analyses context histories of projects to recommend reusable requirements. The scientific contribution of this study is the use of the similarity analysis of projects through their context histories to generate the requirement recommendations. The implementation of a prototype allowed to evaluate the proposal through a case study based on real scenarios from the industry. One hundred fifty-three software projects from a large bank institution generated context histories used in the recommendations. The experiment demonstrated that the model achieved more than 70% stakeholder acceptance of the recommendations.
Safe and Sample-Efficient Reinforcement Learning for Clustered Dynamic Environments

This letter proposes a safe and sample-efficient reinforcement learning (RL) framework to address two major challenges in developing applicable RL algorithms: satisfying safety constraints and efficiently learning with limited samples. To guarantee safety in real-world complex environments, we use the safe set algorithm (SSA) to monitor and modify the nominal controls, and evaluate SSA+RL in a clustered dynamic environment which is challenging to be solved by existing RL algorithms. However, the SSA+RL framework is usually not sample-efficient especially in reward-sparse environments, which has not been addressed in previous safe RL works. To improve the learning efficiency, we propose three techniques: (1) avoiding behaving overly conservative by adapting the SSA; (2) encouraging safe exploration using random network distillation with safety constraints; (3) improving policy convergence by treating SSA as expert demonstrations and directly learn from that. The experimental results show that our framework can achieve better safety performance compare to other safe RL methods during training and solve the task with substantially fewer episodes.
SAR Ship Instance Segmentation Based on Hybrid Task Cascade

Ship detection in synthetic aperture radar (SAR) images has received extensive attention in recent years. Yet, SAR ship instance segmentation is rarely studied. Therefore, we apply the hybrid task cascade (HTC) delving into the targeted ship in-stance segmentation from SAR imagery. HTC comprehensively considers the coupling relationship between the detection task and segmentation task. Specifically, it replaces the original single detection-head (DH) of the raw Mask R-CNN with three cascaded DHs to further improve performance. We perform many experiments based on the public SAR Ship Detection Dataset (SSDD) dataset for verifying the effectiveness of HTC. Finally, it offers a 65.6% detection <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$AP$</tex> that is superior to Mask R-CNN by 3.4%, and provides a 59.3% segmentation <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$AP$</tex> that is superior to Mask R-CNN by 1.5% as well.
Context-Based Anomaly Detection via Spatial Attributed Graphs in Human Monitoring

Detecting anomalous regions focusing on interesting parts in surveillance images is a significant task in human monitoring. The most challenging problem is that a large number of anomalies in monitoring environments are context-based, which requires combined information between regions and contexts. Existing anomaly detection methods either capture normal patterns from individual regions or detect anomalies based on their defined relations among regions. Therefore, they cannot effectively capture the underlying relationships among regions to discriminate the context-based anomalies. In this paper, we propose to construct spatial attributed graphs to model the regions and contexts and devise a novel graph auto-encoder framework named Context-based Anomaly Detection Auto-Encoder (CADAE) to detect anomalous regions. Specifically, we assume that the context of a region is composed of its spatial relationships with other regions in the same image. The region and its context in an image are represented as a node and its neighborhoods in a spatial attributed graph, respectively. CADAE is first trained to learn normal patterns from nodes in graphs by compressing the representations of the nodes and their neighborhoods, and reconstructing the node attributes and structure information. CADAE then ranks the degrees of the abnormality of testing nodes by reconstruction errors. Unlike existing graph auto-encoders, by adopting a specified sum aggregator, CADAE can discriminate nodes whose neighbors have similar representations and thus capture the diversity of contexts although similar regions often exist in surveillance images. Experimental results on real-world datasets demonstrate the effectiveness of CADAE.
Musical Practices in Software Development: Insights from Gary Marcus’s Guitar Zero

Our goal was to read the book “Guitar zero: The new musician and the science of learning” and find out whether any of the ideas that were presented in this book could be applied to the software development process. After a thorough and comprehensive analysis, we singled out six fundamental musical practices that could be adopted by teams of software engineers to improve their development process. For two of them - team code reviews and personal sprint goal - we performed preliminary experiments, which were aimed at gathering feedback about the usefulness of such practices for software developers.
Probabilistic Coordination of Heterogeneous Teams From Capability Temporal Logic Specifications

This letter explores coordination of heterogeneous teams of agents from high-level specifications. We employ Capability Temporal Logic (CaTL) to express rich, temporal-spatial tasks that require cooperation between many agents with unique capabilities. CaTL specifies combinations of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tasks</i> , each with desired locations, duration, and set of capabilities, freeing the user from considering specific agent trajectories and their impact on multi-agent cooperation. CaTL also provides a quantitative robustness metric of satisfaction based on availability of required capabilities for each task. The novelty of this letter focuses on satisfaction of CaTL formulas under probabilistic conditions. Specifically, we consider uncertainties in robot motion (e.g., agents may fail to transition between regions with some probability) and local probabilistic workspace properties (e.g., if there are not enough agents of a required capability to complete a collaborative task). The proposed approach automatically formulates a mixed-integer linear program given agents, their dynamics and capabilities, an abstraction of the workspace, and a CaTL formula. In addition to satisfying the given CaTL formula, the optimization considers the following secondary goals (in decreasing order of priority): 1) minimize the risk of transition failure due to uncertainties; 2) maximize probabilities of regional collaborative satisfaction (if there is an excess of agents); 3) maximize the availability robustness of CaTL for potential agent attrition; 4) minimize the total agent travel time. We evaluate the performance of the proposed framework and demonstrate its scalability via numerical simulations.
Evolution and differentiation of the cybersecurity communities in three social question and answer sites: A mixed-methods analysis

Cybersecurity affects us all in our daily lives. New knowledge on best practices, new vulnerabilities, and timely fixes for cybersecurity issues is growing super-linearly, and is spread across numerous, heterogeneous sources. Because of that, community contribution-based, question and answer sites have become clearinghouses for cybersecurity-related inquiries, as they have for many other topics. Historically, Stack Overflow has been the most popular platform for different kinds of technical questions, including for cybersecurity. That has been changing, however, with the advent of Security Stack Exchange, a site specifically designed for cybersecurity-related questions and answers. More recently, some cybersecurity-related subreddits of Reddit, have become hubs for cybersecurity-related questions and discussions. The availability of multiple overlapping communities has created a complex terrain to navigate for someone looking for an answer to a cybersecurity question. In this paper, we investigate how and why people choose among three prominent, overlapping, question and answer communities, for their cybersecurity knowledge needs. We aggregated data of several consecutive years of cybersecurity-related questions from Stack Overflow, Security Stack Exchange, and Reddit, and performed statistical, linguistic, and longitudinal analysis. To triangulate the results, we also conducted user surveys. We found that the user behavior across those three communities is different, in most cases. Likewise, cybersecurity-related questions asked on the three sites are different, more technical on Security Stack Exchange and Stack Overflow, and more subjective and personal on Reddit. Moreover, there appears to have been a differentiation of the communities along the same lines, accompanied by overall popularity trends suggestive of Stack Overflow's decline and Security Stack Exchange's rise within the cybersecurity community. Reddit is addressing the more subjective, discussion type needs of the lay community, and is growing rapidly.
More is not Always Better: Insights from a Massive Comparison of Meta-heuristic Algorithms over Real-Parameter Optimization Problems

Much controversy has been lately risen around the design and performance of modern bio-inspired optimization methods, in particular due to the alleged lack of algorithmic novelty in their definition with respect to traditional heuristic solvers. In this work we present a first attempt at shedding empirical evidence over this debate, for which results of a benchmark with unprecedented scales in terms of problems and algorithms are reported and discussed. Specifically, informed conclusions are held in what refers to the claimed superior performance of these bio-inspired solvers and their competitiveness when compared to competition-winning alternatives. Finally, we prove that the tailored selection of a subset of problems and techniques can unfairly bias the comparisons favoring any of such algorithms, ultimately arriving at illusory conclusions about their comparative performance.
Efficient Occupancy Detection System Based on Neutrosophic Weighted Sensors Data Fusion

Recently, a great interest has been dedicated to improving data fusion techniques for indoor occupancy detection.Indoor occupancy detection is extensively used in various applications, such as energy consumption control, surveillance systems, and disaster management.Using environmental sensors to collect data for detecting the occupancy state has the benefit of maintaining privacy.Also, it helps in improving monitoring systems and saving money due to energy consumption control.Nevertheless, sensor data is usually incomplete and noisy, which makes it uncertain and unreliable.These problems affect the detection accuracy.This paper proposes a comprehensive occupancy detection system that depends on a new fusion technique for fusing heterogeneous sensor data, which highly improves occupancy detection efficiency.Using Neutrosophy, the proposed technique handles sensor data uncertainty.Additionally, it improves reliability by fusing multiple sensors data.As it uses only one feature generated from fusing multiple sensors data, training and testing time is reduced.Consequently, the experimental results of applying the proposed fusion technique on a public benchmark dataset exhibit a significant enhancement in binary occupancy detection accuracy.The proposed technique enhanced the worst-case accuracy from 75.1 to 81.3%, 84.7 to 90.7%, 72 to 84.2%, 73.58 to 85.1%, and 65.9 to 78% using Linear Discriminant Analysis (LDA), K-Nearest Neighbors (K-NN), Naive Bayes (NB), Support Vector Machine (SVM), and Random Forest (RF) classifiers, respectively.Using the other six performance metrics, the proposed technique results also outperform some state-of-the-art techniques.
Hybrid Convolution Neural Network in Classification of Cancer in Histopathology Images

Cancer statistics in 2020 reveals that breast cancer is the most common form of cancer among women in India. One in 28 women is likely to develop breast cancer during their lifetime. The mortality rate is 1.6 to 1.7 times higher than maternal mortality rates. According to the US statistics, about 42,170 women in the US are expected to die in 2020 from breast cancer. The chance of survival can be increased through early and accurate diagnosis of cancer. The pathologists manually analyze the histopathology images using high-resolution microscopes to detect the mitotic cells. This is a time-consuming process because there is a minute difference between the normal and mitotic cells. To overcome these challenges, an automatic analysis and detection of breast cancer by using histopathology images play a vital role in prognosis. Earlier researchers used conventional image processing techniques for the detection of mitotic cells. These methods were found to be producing results with low accuracy and time-consuming. Therefore, several deep learning techniques were adopted by researchers to increase the accuracy and minimize the time. The hybrid deep learning model is proposed for selecting abstract features from the histopathology images. In the proposed approach, we have concatenated two different CNN architectures into a single model for effective classification of mitotic cells. Convolution neural network (CNN) automatically detects efficient features without human intervention and classifies cancerous and non-cancerous images using a hybrid fully connected network. It is a computationally efficient, very powerful, and efficient model for performing automatic feature extraction. It detects different phenotypic signatures of nuclei. In order to enhance the accuracy and computational efficiency, the histopathology images are preprocessed, segmented, and feature extracted through CNN and fed into a hybrid CNN for classification. The hybrid CNN is obtained by concatenating two CNN models; together, this is called model leveraging. Model averaging can be improved by weighting the contributions of each sub-model to the combined prediction by the expected performance of the sub-model. The proposed hybrid CNN architecture with data preprocessing with median filter and Otsu-based segmentation technique is trained using 50,000 images and tested using 50,000 images. It provides an overall accuracy of 98.9%.
Chinese Language and Literature Online Resource Classification Algorithm Based on Improved SVM

With the rapid development of network technology and the rapid growth of all kinds of online information resources, a large number of Chinese language and literature resources have emerged on the Internet. Online Chinese language and literature resources are becoming more and more important sources for people to obtain important information. However, existing search engines tend to provide a lot of irrelevant content when searching for information. Therefore, how to quickly and effectively obtain useful resource information and classify Chinese language and literature resources from a large number of information resources is the focus of this paper. This article mainly aims at the current development situation, by consulting a large amount of data, understanding the research status of improved SVM algorithm at home and abroad, showing the idea and advantages of SVM through the algorithm and experimental process, and further improving the SVM algorithm. The improved SVM proposed in this paper greatly improves the efficiency of classification and facilitates the rapid search of information with high matching degree among various online resources of Chinese language and literature.
Are Neural Networks the Best Way for Encrypted Traffic Classification?

Are Neural Networks the best way for encrypted traffic classification? — Currently, the answer is “no”. In the future, maybe, however, much more research is needed to achieve the goal. Many recent papers study how to apply neural networks for traffic classification. Although the accuracy of the best machine learning algorithms is well below 100%, they work surprisingly well, even for encrypted traffic. This paper looks deep inside the encrypted traffic of widely used applications and points out the essential peculiarities of the security protocols. The paper reveals that the abilities of the neural networks to classify encrypted traffic are solidly limited by the amount of exposed unencrypted data. Without it, the algorithms classify traffic randomly. The paper identifies the plain-text data in the encrypted traffic that hints to the neural network algorithms. Based on the findings, the paper describes a spot-on, lightweight, and learning-free method that can easily classify the traffic encrypted with current security protocols. Finally, the paper predicts that the next versions of security protocols may conceal more data and complicate traffic classification. So, the paper analyzes the recently proposed amendments to the encryption protocols and their influence on the performance of the existing traffic classification algorithms.
Study on Thermodynamic Constant of Time Passage

The study of time has a long history and is still ongoing. The term time as formulated by Isaac Newton was created in the 17th century, so naturally it could not relate to thermodynamics. The concept of time introduced by Newton is still popular because it reflects the common human experience of passing. Gottfried W. Leibniz and Immanuel Kant questioned only the absoluteness of time and not its uniform passage. Kant in his profound reflections explicitly linked the sensation of time to man and his sensuality. In this article the definition of time resulting from modern knowledge is introduced justifying the thesis that time is a manifestation of a thermodynamic process, it elapses uniformly, at a rate determined by an appropriate natural constant. The constant has the value of 0.08 [1/year] and indicates the natural connection of the passage of time with the cyclic motion of the planet Earth, and therefore with the calendar. The passage of time is uniform, so the phenomenon of changing the speed of time does not occur.
The Educational Function of English Children’s Movies From the Perspective of Multiculturalism Under Deep Learning and Artificial Intelligence

In children's learning subjects, English courses has its relative particularity compared with Chinese courses and the mathematics. Children's English teaching is often inefficient because of the lack of students' timely consolidation after class. Given this, the present work starts with the analysis of the current situation of children's learning, and introduces the film-assisted English teaching. In the specific teaching links, English teaching is carried out in a three-dimensional teaching mode. Before that, topics of the films are selected for the English teaching, and the films are edited and processed. Initially, the present work expounds the English children's films and their educational functions. Then, children can obtain teaching effect from the films. An English questionnaire is designed to analyze the application effect of English films in children's English teaching. The results show that the film teaching mode improves children's learning interest and motivation, and English film teaching can stimulate students' learning interest. Students are also more active to participate in teaching activities, thus improving their language skills. Under the teaching in the scenes of films, students can perceive the functions of language in certain contexts. Comprehensible language input promotes students' English listening ability and oral expression ability. The films can intuitively show the humanistic style, historical geography, cultures, and customs of English countries, and then cultivate students' western cultural literacy. Practice has proved that the method proposed here can achieve good teaching effect, and it provides certain references for children's English education.
ABID: Angle Based Intrinsic Dimensionality — Theory and analysis

The intrinsic dimensionality refers to the “true” dimensionality of the data, as opposed to the dimensionality of the data representation. For example, when attributes are highly correlated, the intrinsic dimensionality can be much lower than the number of variables. Local intrinsic dimensionality refers to the observation that this property can vary for different parts of the data set; and intrinsic dimensionality can serve as a proxy for the local data complexity of the data set. Most popular methods for estimating the local intrinsic dimensionality are based on distances, and the rate at which the distances to the nearest neighbors increase, a concept known as “expansion dimension”. In this paper we introduce an orthogonal concept, which does not use any distances: we use the distribution of angles among the neighbors with respect to the query point. We derive the theoretical distribution of angles and use this to construct an estimator for intrinsic dimensionality. We derive a regularized version that obeys an upper bound of the true intrinsic dimensionality for idealized data. We generalize the estimator to arbitrary moments and discuss the relation of the regularized second moment to approaches based on principal component analysis. Experimentally, we verify that this measure behaves similarly but complementarily to existing measures of intrinsic dimensionality. By introducing a new idea of intrinsic dimensionality to the research community we hope to contribute to a better understanding of intrinsic dimensionality and to spur new research in this direction.