Reviewerly/exHarmony huggingface dataset:
Unsupervised framework for depth estimation and camera motion prediction from video

Depth estimation from monocular video plays a crucial role in scene perception. The significant drawback of supervised learning models is the need for vast amounts of manually labeled data (ground truth) for training. To overcome this limitation, unsupervised learning strategies without the requirement for ground truth have achieved extensive attention from researchers in the past few years. This paper presents a novel unsupervised framework for estimating single-view depth and predicting camera motion jointly. Stereo image sequences are used to train the model while monocular images are required for inference. The presented framework is composed of two CNNs (depth CNN and pose CNN) which are trained concurrently and tested independently. The objective function is constructed on the basis of the epipolar geometry constraints between stereo image sequences. To improve the accuracy of the model, a left-right consistency loss is added to the objective function. The use of stereo image sequences enables us to utilize both spatial information between stereo images and temporal photometric warp error from image sequences. Experimental results on the KITTI and Cityscapes datasets show that our model not only outperforms prior unsupervised approaches but also achieving better results comparable with several supervised methods. Moreover, we also train our model on the Euroc dataset which is captured in an indoor environment. Experiments in indoor and outdoor scenes are conducted to test the generalization capability of the model.
Mapping the <i>TOEFL iBT</i>® Test Scores to China's Standards of English Language Ability: Implications for Score Interpretation and Use

The past decade has seen an emerging interest in mapping (aligning or linking) test scores to language proficiency levels of external performance scales or frameworks, such as the Common European Framework of Reference (CEFR), as well as locally developed frameworks, such as China's Standards of English Language Ability (CSE). Such alignment is ultimately a claim about the interpretation of test scores in relation to external levels of language proficiency. To support such a claim, established procedures should be carefully implemented and multiple sources of evidence should be collected. In this research report, we demonstrate the application of a series of steps in building an argument for aligning the scores of the TOEFL iBT® test, an international, large‐scale language proficiency test of English as a foreign language (EFL), to the levels of the CSE. The alignment process comprised the following steps: (a) establishing construct congruence between the TOEFL iBT test and the CSE; (b) establishing recommended minimum test scores (cut scores), set by local experts, to classify language learners into the local proficiency levels; (c) collection of scores by test takers ( N = 1,326) and evaluations of the test takers' proficiency levels by their teachers, based on the local framework; and (d) consideration of the results of other alignment studies in the local context as well as the link between the CEFR and the CSE levels. We conclude with a discussion of the contextual issues that should be considered when interpreting test scores in relation to external proficiency levels. These contextual issues are important considerations because they have the potential to impact score‐based decisions on individuals and institutions. We also discuss the implications for similar alignment research.
Semantics for Cyber-Physical Systems: A cross-domain perspective

Modern life is increasingly made more comfortable, efficient, and sustainable by the smart systems that surround us: smart buildings monitor and adjust temperature levels to achieve occupant comfort while optimizing energy consumption; smart energy grids reconfigure dynamically to make the best use of ad-hoc energy produced by a host of distributed energy producers; smart factories can be reconfigured on the shop-floor to efficiently produce a diverse range of products. These complex systems can only be realized by tightly integrating components in the physical space (sensors, actuators) with advanced software algorithms in the cyber-space, thus creating so-called Cyber-Physical Systems (CPS). Semantic Web technologies (SWT) have seen a natural uptake in several areas based on CPS, given that CPS are data and knowledge intensive while providing advanced functionalities typical of semantics-based intelligent systems. Yet, so far, this uptake has primarily happened within the boundaries of application domains resulting in somewhat disconnected research communities. In this paper, we take a cross-domain perspective by synthesizing our experiences of using SWTs during the engineering and operation of CPSs in smart manufacturing, smart buildings, and smart grids. We discuss use cases that are amenable to the use of SWTs, benefits and challenges of using these technologies in the CPS lifecycle as well as emerging future trends. While non-exhaustive, our paper aims at opening up a dialog between these fields and at putting the foundation for a research area on semantics in CPS.
Optimal policies for information sharing in information system security

Information sharing has become a significant part of information system security endeavor, as many firms have come to realize that it is difficult to defend against the increasingly sophisticated information security attacks with the limited resources of one single firm. The sharing of information security related knowledge and experience helps firms better prepare for the upcoming information security challenges. However, the practice of information sharing is far less prevalent when compared with other information security methods. In this paper, we develop a framework for information sharing decisions of a firm in the context of information system security. We find through analytical and numerical analyses that the optimal level information sharing is a function of the cost, budget and expected cost of perfect protection for a given firm. We also examine the value of information sharing in information security, and identify how such value varies over different investment environments.
ICNN

Modern and recent architectures of vision-based Convolutional Neural Networks (CNN) have improved detection and prediction accuracy significantly. However, these algorithms are extremely computationally intensive. To break the power and performance wall of CNN computation, we reformulate the CNN computation into an iterative process, where each iteration processes a sub-sample of input features with smaller network and ingests additional features to improve the prediction accuracy. Each smaller network could either classify based on its input set or feed computed and extracted features to the next network to enhance the accuracy. The proposed approach allows early-termination upon reaching acceptable confidence. Moreover, each iteration provides a contextual awareness that allows an intelligent resource allocation and optimization for the proceeding iterations. In this article, we propose various policies to reduce the computational complexity of CNN through the proposed iterative approach. We illustrate how the proposed policies construct a dynamic architecture suitable for a wide range of applications with varied accuracy requirements, resources, and time-budget, without further need for network re-training. Furthermore, we carry out a visualization of the detected features in each iteration through deconvolution network to gain more insight into the successive traversal of the ICNN.
An Unknown Radar Emitter Identification Method Based on Semi-Supervised and Transfer Learning

Aiming at the current problem that it is difficult to deal with an unknown radar emitter in the radar emitter identification process, we propose an unknown radar emitter identification method based on semi-supervised and transfer learning. Firstly, we construct the support vector machine (SVM) model based on transfer learning, using the information of labeled samples in the source domain to train in the target domain, which can solve the problem that the training data and the testing data do not satisfy the same-distribution hypothesis. Then, we design a semi-supervised co-training algorithm using the information of unlabeled samples to enhance the training effect, which can solve the problem that insufficient labeled data results in inadequate training of the classifier. Finally, we combine the transfer learning method with the semi-supervised learning method for the unknown radar emitter identification task. Simulation experiments show that the proposed method can effectively identify an unknown radar emitter and still maintain high identification accuracy within a certain measurement error range.
Trust-based resource sharing mechanism in distributed manufacturing

Manufacturing has moved from local towards global economy in the past decades enabling new paradigms to come into practice, such as distributed manufacturing which aims at increasing companies' flexibility with a decentralized system consisting of autonomous entities. In the paper, a distributed collaboration framework of manufacturing agents is introduced, where the members with resource shortages can request resources from others, divide requests among multiple agents, reorganize their production to be able to complete a request and cancel an undertaken task if needed. In a collaboration framework, it is essential to have a commitment to the promises: if participants cannot count on these commitments, the framework's efficiency may decrease. So as to motivate agents to keep their promises and to enable differentiating between reliable and non-reliable partners, here agents consider trustfulness during the selection from resource offering agents' proposals and make decisions considering subjective trust and public reputation values, which are computed based on successfulness of task performing and meeting due dates. In the paper, the impact of the proposed mechanism is investigated with multi-agent simulation. It is shown that considering trustfulness improves the overall system performance; and the improvement depends on the number of participants and the federation's load.
Adaptive Pairwise Comparison for Educational Measurement

Pairwise comparison is becoming increasingly popular as a holistic measurement method in education. Unfortunately, many comparisons are required for reliable measurement. To reduce the number of required comparisons, we developed an adaptive selection algorithm (ASA) that selects the most informative comparisons while taking the uncertainty of the object parameters into account. The results of the simulation study showed that, given the number of comparisons, the ASA resulted in smaller standard errors of object parameter estimates than a random selection algorithm that served as a benchmark. Rank order accuracy and reliability were similar for the two algorithms. Because the scale separation reliability (SSR) may overestimate the benchmark reliability when the ASA is used, caution is required when interpreting the SSR.
Temporal Accumulative Features for Sign Language Recognition

In this paper, we propose a set of features called temporal accumulative features (TAF) for representing and recognizing isolated sign language gestures. By incorporating sign language specific constructs to better represent the unique linguistic characteristic of sign language videos, we have devised an efficient and fast SLR method for recognizing isolated sign language gestures. The proposed method is an HSV based accumulative video representation where keyframes based on the linguistic movement-hold model are represented by different colors. We also incorporate hand shape information and using a small scale convolutional neural network, demonstrate that sequential modeling of accumulative features for linguistic subunits improves upon baseline classification results.
Digital Universities in Russia: Prospects and Problems

The education sector is subject to significant changes due to the increasingly active spread of digital technologies. Usually, the trends in the implementation of digital technologies in educational and research activities are set by commercial organizations – private universities, business schools, corporate universities. But public universities and institutions start to think more and more about digital transformation. By the end of the year, several Russian universities will receive grants for the creation and operation centers of development of digital university models. Anticipating the events, the article considers the model of a digital university, which consists of four blocks – university management information systems, online support for the educational process, key competencies of the digital economy and educational process management based on an individual educational path. The object of the study was the State University of Management and the Financial University under the Government of the Russian Federation. After examination of the current state of university digitalization, we have identified major problems which become an obstacle during digital transformation.
Neural Network based Software Defect Prediction using Genetic Algorithm and Particle Swarm Optimization

In the arena of software engineering, software defects prediction is one of the most attractive research topics. Here the main task is to predict if there is any bug in the software or not. For software testing, software defect detection is important for reducing the time and resources consumed. Accurate estimate of defect software prediction process enables effective discovery and identification of the defects. Such prediction methods are important for the big scale systems, where verification specialists need to focus their attention. In this paper, we proposed a method where the features are selected using Genetic Algorithm (GA). Secondly, make cluster of the selected features using Particle Swarm Optimization (PSO) and then train the model with different Neural Network (NN) methods such as: Feedforward Neural Network (FNN), Recurrent Neural Network (RNN), Artificial Neural Network (ANN), and Deep Neural Network (DNN) and finally calculate accuracy, sensitivity, specificity, precision, negative prediction value, F1 score, and Matthews correlation coefficient. We use five different datasets from NASA promise software engineering repository for our study. From our study, we get the best accuracy result using deep neural network. Experimental consequences show that proposed strategy is a decent technique to predict the software defects.
Multiple scaled contaminated normal distribution and its application in clustering

The multivariate contaminated normal (MCN) distribution represents a simple heavy-tailed generalization of the multivariate normal (MN) distribution to model elliptical contoured scatters in the presence of mild outliers (also referred to as ‘bad’ points herein) and automatically detect bad points. The price of these advantages is two additional parameters: proportion of good observations and degree of contamination. However, in a multivariate setting, only one proportion of good observations and only one degree of contamination may be limiting. To overcome this limitation, we propose a multiple scaled contaminated normal (MSCN) distribution. Among its parameters, we have an orthogonal matrix Γ. In the space spanned by the vectors (principal components) of Γ, there is a proportion of good observations and a degree of contamination for each component. Moreover, each observation has a posterior probability of being good with respect to each principal component. Thanks to this probability, the method provides directional robust estimates of the parameters of the nested MN and automatic directional detection of bad points. The term ‘directional’ is added to specify that the method works separately for each principal component. Mixtures of MSCN distributions are also proposed, and an expectation-maximization algorithm is used for parameter estimation. Real and simulated data are considered to show the usefulness of our mixture with respect to well-established mixtures of symmetric distributions with heavy tails.
Examining virtual reality navigation techniques for 3D network visualisations

Research into how virtual reality (VR) can be a beneficial technology for new and emerging large, complex data visualisations for data scientists is ongoing. In this paper, we evaluate three-dimensional VR navigation technique for data visualisations and test their effectiveness with a large graph visualisation. We evaluate two prominent navigation techniques employed in VR (Teleportation and One-Handed Flying) against two less common methods (Two-Handed Flying and Worlds-In-Miniature) and evaluate their performance and effectiveness through a series of tasks. We found Steering Patterns (One-Handed Flying and Two-Handed Flying) to be faster and preferred by participants for completing searching tasks in comparison to Teleportation. Worlds-In-Miniature was the least physically demanding of the navigations, and was preferred by participants for tasks that required an overview of the graph such as triangle counting.
Rich Features Embedding for Cross-Modal Retrieval: A Simple Baseline

During the past few years, significant progress has been made on cross-modal retrieval, benefiting from the development of deep neural networks. Meanwhile, the overall frameworks are becoming more and more complex, making the training as well as the analysis more difficult. In this paper, we provide a Rich Features Embedding (RFE) approach to tackle the cross-modal retrieval tasks in a simple yet effective way. RFE proposes to construct rich representations for both images and texts, which is further leveraged to learn the rich features embedding in the common space according to a simple hard triplet loss. Without any bells and whistles in constructing complex components, the proposed RFE is concise and easy to implement. More importantly, our RFE obtains the state-of-the-art results on several popular benchmarks such as MS COCO and Flickr 30 K. In particular, the image-to-text and text-to-image retrieval achieve 76.1% and 61.1% (R@1) on MS COCO, which outperform others more than 3.4% and 2.3%, respectively. We hope our RFE will serve as a solid baseline and help ease future research in cross-modal retrieval.
A revised classification scheme of pyrite in the Witwatersrand Basin and application to placer gold deposits

Pyrite is the most abundant ore mineral in gold-bearing quartz pebble conglomerates (QPCs) of the Witwatersrand Basin and similar Archaean sedimentary units. Much of the pyrite in Archaean conglomerates is detrital in origin, implying that it survived weathering, transport, and reworking under anoxic conditions. Detrital pyrite is generally found together with authigenic pyrite formed in situ as a result of syn- to post-depositional processes. As pyrite is frequently associated with gold mineralization, many authors have developed different pyrite classification schemes in order to better understand the nature and origin of the mineralization, although the different classifications cannot always be easily compared as they are largely based on inferred pyrite genesis. We propose a revised, entirely descriptive classification scheme for detrital and authigenic pyrite in Archaean QPCs. The scheme was created in order to provide a consistent framework for the petrographic description of pyrite in clastic sedimentary rocks. Detrital pyrite is subdivided into massive, inclusion-bearing, and coarsely crystalline types. Authigenic pyrite includes euhedral, overgrowth, infill, aggregate, and pseudomorphic types. We evaluate the methods used in pyrite classification, propose formation pathways for detrital and authigenic pyrites, and apply the classification scheme to some well-known occurrences of detrital pyrite-bearing conglomerates.
Heterogeneous tree structure classification to label Java programmers according to their expertise level

Open-source code repositories are a valuable asset to creating different kinds of tools and services, utilizing machine learning and probabilistic reasoning. Syntactic models process Abstract Syntax Trees (AST) of source code to build systems capable of predicting different software properties. The main difficulty of building such models comes from the heterogeneous and compound structures of ASTs, and that traditional machine learning algorithms require instances to be represented as n-dimensional vectors rather than trees. In this article, we propose a new approach to classify ASTs using traditional supervised-learning algorithms, where a feature learning process selects the most representative syntax patterns for the child subtrees of different syntax constructs. Those syntax patterns are used to enrich the context information of each AST, allowing the classification of compound heterogeneous tree structures. The proposed approach is applied to the problem of labeling the expertise level of Java programmers. The system is able to label expert and novice programs with an average accuracy of 99.6%. Moreover, other code fragments such as types, fields, methods, statements and expressions could also be classified, with average accuracies of 99.5%, 91.4%, 95.2%, 88.3% and 78.1%, respectively.
Efficient Execution of Complex Context Queries to Enable Near Real-Time Smart IoT Applications

As the Internet of Things (IoT) is evolving at a fast pace, the need for contextual intelligence has become more crucial for delivering IoT intelligence, efficiency, effectiveness, performance, and sustainability. Contextual intelligence enables interactions between IoT devices such as sensors/actuators, smartphones and connected vehicles, to name but a few. Context management platforms (CMP) are emerging as a promising solution to deliver contextual intelligence for IoT. However, the development of a generic solution that allows IoT devices and services to publish, consume, monitor, and share context is still in its infancy. In this paper, we propose, validate and explain the details of a novel mechanism called Context Query Engine (CQE), which is an integral part of a pioneering CMP called Context-as-a-Service (CoaaS). CQE is responsible for efficient execution of context queries in near real-time. We present the architecture of CQE and illuminate its workflows. We also conduct extensive experimental performance and scalability evaluation of the proposed CQE. Results of experimental evaluation convincingly demonstrate that CoaaS outperforms its competitors in executing complex context queries. Moreover, the advanced functionality of the embedded query language makes CoaaS a decent candidate for real-life deployments.
Next-Item Recommendation via Collaborative Filtering with Bidirectional Item Similarity

Exploiting temporal effect has empirically been recognized as a promising way to improve recommendation performance in recent years. In real-world applications, one-class data in the form of (user, item, timestamp) are usually more accessible and abundant than numerical ratings. In this article, we focus on exploiting such one-class data in order to provide personalized next-item recommendation services. Specifically, we base our work on the framework of time-aware item-based collaborative filtering and propose a simple yet effective similarity measurement called bidirectional item similarity (BIS) that is able to capture sequential patterns even from noisy data. Furthermore, we extend BIS via some factorization techniques and obtain an adaptive version, i.e., adaptive BIS (ABIS), in order to better fit the behavioral data. We also design a compound weighting function that leverages the complementarity between two well-known time-aware weighting functions. With the proposed similarity measurements and weighting function, we obtain two novel collaborative filtering methods that are able to achieve significantly better performance than the state-of-the-art methods, showcasing their effectiveness for next-item recommendation.