Reviewerly/exHarmony huggingface dataset:
The security of machine learning in an adversarial setting: A survey

Machine learning (ML) methods have demonstrated impressive performance in many application fields such as autopilot, facial recognition, and spam detection. Traditionally, ML models are trained and deployed in a benign setting, in which the testing and training data have identical statistical characteristics. However, this assumption usually does not hold in the sense that the ML model is designed in an adversarial setting, where some statistical properties of the data can be tampered with by a capable adversary. Specifically, it has been observed that adversarial examples (also known as adversarial input perambulations) elaborately crafted during training/test phases can seriously undermine the ML performance. The susceptibility of ML models in adversarial settings and the corresponding countermeasures have been studied by many researchers in both academic and industrial communities. In this work, we present a comprehensive overview of the investigation of the security properties of ML algorithms under adversarial settings. First, we analyze the ML security model to develop a blueprint for this interdisciplinary research area. Then, we review adversarial attack methods and discuss the defense strategies against them. Finally, relying upon the reviewed work, we provide prospective relevant future works for designing more secure ML models.
DeepStellar: model-based quantitative analysis of stateful deep learning systems

Deep Learning (DL) has achieved tremendous success in many cutting-edge applications. However, the state-of-the-art DL systems still suffer from quality issues. While some recent progress has been made on the analysis of feed-forward DL systems, little study has been done on the Recurrent Neural Network (RNN)-based stateful DL systems, which are widely used in audio, natural languages and video processing, etc. In this paper, we initiate the very first step towards the quantitative analysis of RNN-based DL systems. We model RNN as an abstract state transition system to characterize its internal behaviors. Based on the abstract model, we design two trace similarity metrics and five coverage criteria which enable the quantitative analysis of RNNs. We further propose two algorithms powered by the quantitative measures for adversarial sample detection and coverage-guided test generation. We evaluate DeepStellar on four RNN-based systems covering image classification and automated speech recognition. The results demonstrate that the abstract model is useful in capturing the internal behaviors of RNNs, and confirm that (1) the similarity metrics could effectively capture the differences between samples even with very small perturbations (achieving 97% accuracy for detecting adversarial samples) and (2) the coverage criteria are useful in revealing erroneous behaviors (generating three times more adversarial samples than random testing and hundreds times more than the unrolling approach).
A knowledge-free path planning approach for smart ships based on reinforcement learning

The autonomous navigation of smart ships needs to meet their huge inertia and obey existing complex rules. A smart ship has to realise autonomous driving instead of manual operation, which consists of path planning and controlling. Toward to this goal, this research proposes a path planning and manipulating approach based on Q-learning, which can drive a cargo ship by itself without requiring any input from human experiences. At the very beginning, a ship is modelled with the Nomoto model in a simulation waterway. Then, distances, obstacles and prohibited areas are regularized as rewards or punishments, which are used to judge the performance, or manipulation decisions of the ship. Subsequently, Q-learning is introduced to learn the action–reward model and the learning outcome is used to manipulate the ship's movement. By chasing higher reward values, the ship can find an appropriate path or navigation strategies by itself. After a sufficient number of rounds of training, a convincing path and manipulating strategies will likely be produced. By comparing the proposed approach with the existing methods, it is shown that this approach is more effective in self-learning and continuous optimisation, and therefore closer to human manoeuvring.
A Survey on the New Generation of Deep Learning in Image Processing

During the past decade, deep learning is one of the essential breakthroughs made in artificial intelligence. In particular, it has achieved great success in image processing. Correspondingly, various applications related to image processing are also promoting the rapid development of deep learning in all aspects of network structure, layer designing, and training tricks. However, the deeper structure makes the back-propagation algorithm more difficult. At the same time, the scale of training images without labels is also rapidly increasing, and class imbalance severely affects the performance of deep learning, these urgently require more novelty deep models and new parallel computing system to more effectively interpret the content of the image and form a suitable analysis mechanism. In this context, this survey provides four deep learning model series, which includes CNN series, GAN series, ELM-RVFL series, and other series, for comprehensive understanding towards the analytical techniques of image processing field, clarify the most important advancements and shed some light on future studies. By further studying the relationship between deep learning and image processing tasks, which can not only help us understand the reasons for the success of deep learning but also inspires new deep models and training methods. More importantly, this survey aims to improve or arouse other researchers to catch a glimpse of the state-of-the-art deep learning methods in the field of image processing and facilitate the applications of these deep learning technologies in their research tasks. Besides, we discuss the open issues and the promising directions of future research in image processing using the new generation of deep learning.
Smart anomaly detection in sensor systems: A multi-perspective review

Anomaly detection is concerned with identifying data patterns that deviate remarkably from the expected behaviour. This is an important research problem, due to its broad set of application domains, from data analysis to e-health, cybersecurity, predictive maintenance, fault prevention, and industrial automation. Herein, we review state-of-the-art methods that may be employed to detect anomalies in the specific area of sensor systems, which poses hard challenges in terms of information fusion, data volumes, data speed, and network/energy efficiency, to mention but the most pressing ones. In this context, anomaly detection is a particularly hard problem, given the need to find computing-energy accuracy trade-offs in a constrained environment. We taxonomize methods ranging from conventional techniques (statistical methods, time-series analysis, signal processing, etc.) to data-driven techniques (supervised learning, reinforcement learning, deep learning, etc.). We also look at the impact that different architectural environments (Cloud, Fog, Edge) can have on the sensors ecosystem. The review points to the most promising intelligent-sensing methods, and pinpoints a set of interesting open issues and challenges.
A survey on incorporating domain knowledge into deep learning for medical image analysis

Although deep learning models like CNNs have achieved great success in medical image analysis, the small size of medical datasets remains a major bottleneck in this area. To address this problem, researchers have started looking for external information beyond current available medical datasets. Traditional approaches generally leverage the information from natural images via transfer learning. More recent works utilize the domain knowledge from medical doctors, to create networks that resemble how medical doctors are trained, mimic their diagnostic patterns, or focus on the features or areas they pay particular attention to. In this survey, we summarize the current progress on integrating medical domain knowledge into deep learning models for various tasks, such as disease diagnosis, lesion, organ and abnormality detection, lesion and organ segmentation. For each task, we systematically categorize different kinds of medical domain knowledge that have been utilized and their corresponding integrating methods. We also provide current challenges and directions for future research.
A Literature Survey of Recent Advances in Chatbots

Chatbots are intelligent conversational computer systems designed to mimic human conversation to enable automated online guidance and support. The increased benefits of chatbots led to their wide adoption by many industries in order to provide virtual assistance to customers. Chatbots utilise methods and algorithms from two Artificial Intelligence domains: Natural Language Processing and Machine Learning. However, there are many challenges and limitations in their application. In this survey we review recent advances on chatbots, where Artificial Intelligence and Natural Language processing are used. We highlight the main challenges and limitations of current work and make recommendations for future research investigation.
Pengaruh Metode Mengajar Inquiry dan Minat Membaca Terhadap Kemampuan Menulis Cerita Pendek Siswa Kelas XI SMA Negeri Sekota Samarinda

This research aims to find out the difference between teaching inquiry methods and teaching methods of two stay two strays; differences of students who have high and low interest in reading; as well as the interaction between teaching methods and reading interest in the ability to write short stories. The research method is a 2x2 factorial design experiment research, with the research population of all students of Samarinda City State High School. Research data is obtained through tests of writing short stories and reading interest questionnaires. Based on the analysis of data obtained results, there is a difference between students with teaching method inquiry and two stay two stray from the statistical results Fh = 21.57 &gt; Ft = 4.01; there is a difference in ability between students interested in high reading and low reading interest with statistical results Fh = 15.12 &gt; Ft = 4.01; There is an interaction between teaching methods and reading interest in short story writing skills with statistical results Fh = 6.41 &gt; Ft = 4.01. The conclusion of this study is: the method of teaching inquiry is better than two stay two strays; high reading interest is better than low reading interest; there is also an interaction between teaching inquiry methods and reading interest in short story writing skills.
A Web Based Bayesian Van Hiele Problem Solver For Computer Programming

Computer programming teaching is often based upon the traditional lecture format.However, this methodology may not be the best way to help many students actively understand underlying concepts.This paper formulates an alternative pedagogical approach that encompasses the van Hiele Model, cognitive model, and Bayesian network to design a web-based intelligent van Hiele Problem Solver (IVHPS).The system takes full advantage of Bayesian networks (BNs), which are a formal framework for uncertainty management to provide intelligent navigation support, and to make individualized diagnosis of student solutions in learning computer programming.In addition, we describe the architecture of the system and the roles of seven modules contained in the system.They are all integrated into the environment to increase student satisfaction and achievement by stimulating student motivation and encouraging the perception of problem solving and programming concepts.
A New Model For Author Driven Digital Publishing

Books in print still dominate the college campus.The long-standing model for publishing involves corporate entities that take the work of an author and add value by providing review, typesetting, publishing, marketing, and distribution.This model has been very successful, but is not adapting well as technology moves beyond paper for communication.The most fundamental threat to the traditional publishing model is that the Internet creates a culture where information is perceived to be 'free' and people are reluctant to pay for content.This creates a major dilemma for authors and publishers who seek compensation for work.There have been numerous attempts to adopt non-traditional approaches to publishing that have had some success, but not enough to displace traditional publishing.This paper describes an approach to digital publishing that allows authors to directly deliver content to readers, using advertising revenues for compensation.Paper based copies of books are still available for those who want to purchase them.The details of the method, opportunities, and challenges will be discussed.
Hybrid Hierarchical Multi-Domain Service Function chaining

This document describes a Hybrid Hierarchical Multi-Domain ServiceFunction Chaining (hhSFC) architecture that extends Service FunctionChaining (SFC) to multiple domains with different technology,administration or ownership. The goals of Hybrid Hierarchical SFC areto reduce the complexity of the control plane in a multi-domain sceneand make the negotiation between different domains possible.
"Choo Choos", Robots And Computer Based Instrumentation For Students

This paper describes a versatile and innovative active learning laboratory currently under development that will be used to teach a wide variety of computer science and mathematics subjects.The laboratory, equipped with a model railroad system, autonomous mobile robots and a network of control computers, offers students hands-on experiences with hardware interfacing, data acquisition, real-time programming, client/server programming, and observing actual physical results of numerically computed solutions to problems.The goal of this work is to produce students with experience in designing solutions to challenging problems that have many dimensions and also the ability to effectively implement and test those solutions.This laboratory will enable us to transform a wide range of computer science algorithms and abstract concepts into physical realities.This paper briefly describes the laboratory hardware and overviews an incrementally complex series of assignments supported by the laboratory. BackgroundOur Computer Science program has an enrollment of approximately 250 undergraduates.Although the undergraduate program produces graduates who are able to attend graduate school, many choose to join industry.Our graduates are highly proficient at developing software to solve real problems.The approach we use to achieve this is to emphasize the design and writing of software in a range of areas.Computer Science 4348 -Systems Programming is a three hour senior level course which addresses the design and implementation of system software such as application support libraries and interprocess communication facilities.This course has a particularly strong emphasis on software writing and addresses low-level software development.Prior to funding for this project, students enrolled in this course developed software using a network of DEC workstations.Assignments included logging user activity over a network, simple interprocess communication between Unix machines, use of semaphores to control shared resources, and an extensive client-server development project.We believe the effectiveness of this type of course can be greatly improved by placing it in a setting where students can actively extend the concepts being taught into tangible realities 1, 2 .Therefore, we have begun building a new laboratory equipped with a model railroad system
Assessing The Impact Of Mobile Technology In An Urban Higher Education Environment

Mobile platforms present an excellent opportunity to bridge the digital divide that has an impact on so many students.At Morgan State University, we proudly boast of our leadership as a "Gateway to Opportunity" for many underrepresented students.This paper will address our efforts to: correct and build positive attitudes toward mobile Information Communications Technology (ICT), understand computer usage habits, and experiment with what methods are most effective in using mobile ICT to improve academic performance.Our approach was to insert mobile platforms with differing form factors into a learning environment and investigate their impact on student perception and performance.Outcomes, strategies, and assessment instruments were developed within a generic modular framework to measure the effectiveness of our approach.The preliminary results presented in this paper will show how technology form factor, access, length of time of ownership, and student classification has a positive impact on our student's attitudes and perceptions as well as their academic performances.
Adding Systems Engineering Activities To The Software Curriculum

where he performs teaching and research in the areas of distributed software systems, software process, and modeling software-intensive systems.Prior to joining ASU in 2001, Harry worked several
A Study Of 3 D Stereo Vision System For Improving Visualization Skills

This study compared the effectiveness of a Head Mounted Display (HMD) and anaglyphic glasses on students' learning of 3D designs.The two stereo display systems were used in an introductory design and graphics class.Data related to students' subjective perceptions and their objective performance were collected and analyzed.Results of the study reveal that, subjectively, students perceived the HMD to be more effective for learning of 3D designs.However, the study also found that, objectively, students' did not perform significantly better, when using the HMD.
Model Driven Development of Social Media Environmental Monitoring Applications

The growth of social media and the resulting increase of user generated content attracted people coming from different fields interested in analyzing this kind of data. Recently user generated content has been used for environmental monitoring. In this paper, we propose a model-driven approach for developing social media applications for environmental monitoring that will provide a simplified solution for integrating various data sources, exploiting different platforms and allowing complex data analysis to be performed.
An Intelligent Interactive Tutoring System For Engineering Education

We present the architecture of an Intelligent Interactive Tutoring System (IITS) that is webbased, and can be adapted to courses in engineering, sciences, and mathematics.This research seeks an architecture that can be used by an instructor to make an online learning system for a course the instructor wants to teach in classroom or online, rather than developing an in-depth tutoring system for any specific course.The Intelligent Interactive Tutoring System Shell integrates mathematical tools and an expert-system-type logical analysis/synthesis tool in a webbased environment.The IITS consists of several components including an instructor interface, a student interface, a student model, a student log, a reasoning system, and a mathematical tool interface module, and guides the student through a monitored problem solving session.
NATIONHOOD AND THE QUEST FOR NATIONAL DEVELOPMENT: THE GAMBIA’S EXPERIENCE UNDER THE SECOND REPUBLIC, 1994 – 2015

The task of nation-building today in the world is that which carries along all well-meaning modern nation-states, with Gambia as no exemption. This paper examines the nationhood process and effort in the Gambia between 1994 and 2015. It focuses on national development efforts both in the first and second republics. The paper investigates how human, material and ecological resources are harnessed and deployed towards this noble goal, availing enabling policy-thrusts for effective implementation and goal attainments that attract sustainable socio-economic and political development. The paper beams searchlights on the redoubled efforts under the Yahya Jammeh regime and the pragmatic approaches taken to achieve more meaningfully goal-oriented national development strides that pave ways for practical development giants and strides. An inductive and deductive methods of research that are descriptive and fact-finding are employed for empirical and critical explorations. The paper posits that the strategies of the second republic government, which surpass the first republic’s conservatively-constructed approaches under Jawara served as impetus for the accomplishments recorded from 1994 to 2015 in making the Gambia an economic super power through industrialization of the agro-economic structures and processes of the small nation in the sub-Saharan Africa. Keywords: Nation, Nationhood, National Development, The Gambia, Second Republic, Independence.
Channel Binding Signalling for the Generic Security Services Application Programming Interface

Channel binding is a technique that allows applications to use asecure at a lower layer without having to use authenticationat that lower layer. The concept of binding comes from theGeneric Security Services Application Programming Interface (GSS-API). It turns out that the semantics commonly implemented aredifferent than those specified in the base GSS-API RFC (RFC2743), andthat that specification has a serious bug. This document addressesboth the inconsistency as-implemented and the specification bug. ThisInternet-Draft proposes the addition of a channel bound return flagfor the GSS_Init_sec_context() and GSS_Accept_sec_context() functions.Two behaviors are specified: a default, safe behavior reflectingexisting implementation deployments, and a behavior that is only safewhen the application specifically tells the GSS-API that it (theapplication) supports the new behavior. Additional API elementsrelated to this are also added, including a new security contextestablishment API.
Random scaling factors in Bayesian distributional regression models with an application to real estate data

Distributional structured additive regression provides a flexible framework for modelling each parameter of a potentially complex response distribution in dependence of covariates. Structured additive predictors allow for an additive decomposition of covariate effects with non-linear effects and time trends, unit- or cluster-specific heterogeneity, spatial heterogeneity and complex interactions between covariates of different type. Within this framework, we present a simultaneous estimation approach for multiplicative random effects that allow for cluster-specific heterogeneity with respect to the scaling of a covariate′s effect. More specifically, a possibly non-linear function f( z) of a covariate z may be scaled by a multiplicative and possibly spatially correlated cluster-specific random effect (1+α c ). Inference is fully Bayesian and is based on highly efficient Markov Chain Monte Carlo (MCMC) algorithms. We investigate the statistical properties of our approach within extensive simulation experiments for different response distributions. Furthermore, we apply the methodology to German real estate data where we identify significant district-specific scaling factors. According to the deviance information criterion, the models incorporating these factors perform significantly better than standard models without (spatially correlated) random scaling factors.