Reviewerly/exHarmony huggingface dataset:
Ship HRRP Target Recognition Based on CNN and ELM

A new network structure CNN-ELM combining convolutional neural network (CNN) with extreme learning machine (ELM) is proposed in this paper. It is applied to ship high resolution range profile (HRRP) target recognition and implements automatic extraction of deep features of data. First, the CNN-ELM model is constructed and the network parameters are set. Second, in terms of the problem that HRRP data is one-dimensional, HRRP data are reordered to transform one-dimensional data into two-dimensional data. Then, the CNN-ELM model with training data is trained to obtain network parameters. Last, the recognition of test data with trained network model is targeted. In the experiment, the recognition rate of CNN-ELM reached 99.50%. In order to verify the generalization performance of the new method, the data set were halved and CNN-ELM was compared with different methods. The recognition rate of CNN-ELM reached 91.13% in the case of halving the data set. The experimental results of the measured data show that CNN-ELM has a better recognition performance.
Design of Trusted B2B Market Platforms using Permissioned Blockchains and Game Theory

Trusted collaboration satisfying the requirements of (a) adequate transparency and (b) preservation of privacy of business sensitive information is a key factor to ensure the success and adoption of online business-to-business (B2B) collaboration platforms. Our work proposes novel ways of stringing together game theoretic modeling, blockchain technology, and cryptographic techniques to build such a platform for B2B collaboration involving enterprise buyers and sellers who may be strategic. The B2B platform builds upon three ideas. The first is to use a permissioned blockchain with smart contracts as the technical infrastructure for building the platform. Second, the above smart contracts implement deep business logic which is derived using a rigorous analysis of a repeated game model of the strategic interactions between buyers and sellers to devise strategies to induce honest behavior from buyers and sellers. Third, we present a formal framework that captures the essential requirements for secure and private B2B collaboration, and, in this direction, we develop cryptographic regulation protocols that, in conjunction with the blockchain, help implement such a framework. We believe our work is an important first step in the direction of building a platform that enables B2B collaboration among strategic and competitive agents while maximizing social welfare and addressing the privacy concerns of the agents.
Joint Modeling of Characters, Words, and Conversation Contexts for Microblog Keyphrase Extraction

Millions of messages are produced on microblog platforms every day, leading to the pressing need for automatic identification of key points from the massive texts. To absorb salient content from the vast bulk of microblog posts, this article focuses on the task of microblog keyphrase extraction. In previous work, most efforts treat messages as independent documents and might suffer from the data sparsity problem exhibited in short and informal microblog posts. On the contrary, we propose to enrich contexts via exploiting conversations initialized by target posts and formed by their replies, which are generally centered around relevant topics to the target posts and therefore helpful for keyphrase identification. Concretely, we present a neural keyphrase extraction framework, which has 2 modules: a conversation context encoder and a keyphrase tagger. The conversation context encoder captures indicative representation from their conversation contexts and feeds the representation into the keyphrase tagger, and the keyphrase tagger extracts salient words from target posts. The 2 modules were trained jointly to optimize the conversation context encoding and keyphrase extraction processes. In the conversation context encoder, we leverage hierarchical structures to capture the word‐level indicative representation and message‐level indicative representation hierarchically. In both of the modules, we apply character‐level representations, which enables the model to explore morphological features and deal with the out‐of‐vocabulary problem caused by the informal language style of microblog messages. Extensive comparison results on real‐life data sets indicate that our model outperforms state‐of‐the‐art models from previous studies.
Going beyond BDI for agent-based simulation

Research in multi-agent systems has resulted in agent programming languages and logics that are used as a foundation for engineering multi-agent systems. Research includes reusable agent programming platforms for engineering agent systems with environments, agent behaviour, communication protocols and social behaviour, and work on verification. Agent-based simulation is an approach for simulation that also uses the notion of agents. Although agent programming languages and logics are much less used in agent-based simulation, there are successful examples with agents designed according to the BDI paradigm, and work that combines agent-based simulation platforms with agent programming platforms. This paper analyses and evaluates benefits of using agent programming languages and logics for agent-based simulation. In particular, the paper considers the use of agent programming languages and logics in a case study of simulating emergency care units.
Metalanguage and Knowledgebase for Kazakh Morphology

Currently, the volume of various information resources in the Turkic languages is increasing. Processing of such resources requires thesauri and corpora created using a single metalanguage (tagging language) and the knowledge base of subject areas. This article proposes a meta-language of the morphological concepts of the Turkic languages on the example of the Kazakh language, which was used to create the knowledge base of the morphology of the Kazakh language in the Protégé environment. The results of the work were used to develop software applications for semantic search and knowledge extraction, as well as an assessment of knowledge applications on the morphology of the Kazakh language in the e-learning system.
Android GUI Search Using Hand-Drawn Sketches

GUI design is crucial to mobile apps. In the early stages of mobile app development, having access to visually similar apps can help designers and programmers gain inspiration for revising their designs or even reuse existing GUI code. We propose an intuitive sketch modelling language to draw GUI sketches, and a deep learning based method to search for visually similar apps according to the sketches. Preliminary results show the potential of our approach.
Trade-Off-Oriented Development: Making Quality Attribute Trade-Offs First-Class

Implementing a solution for a design decision that precisely satisfies the trade-off between quality attributes can be extremely challenging. Further, typically quality attribute trade-offs are not represented as first-class entities in development artifacts. Hence, decisions might be sub-optimal and lack requirements traceability as well as changeability. We propose Trade-off-oriented Development (ToD), a new concept to automate the selection and integration of reusable implementations for a given design decision based on quality attribute trade-offs. Implementations that vary in quality attributes and that solve reoccurring design decisions are collected in a design decision library. Developers declaratively specify the quality attribute trade-off, which is then used to automatically select the best fitting implementation. We argue that thereby, software could satisfy the trade-offs more precisely, requirements are traceable and changeable, and advances in implementations automatically improve existing software.
A Framework for Improving the Accessibility of Assessment Tasks

In constructing tests it is vital that sources of construct irrelevant variance are minimized, in order to enable valid interpretations about the test taker. One important source of construct irrelevance is inaccessibility to the test and its items. In this chapter a framework is presented for design and review of test items, or more broadly, assessments tasks, to ensure their accessibility. An application is presented in the context of theory exams to obtain a drivers’ license in the Netherlands.
Relative Saliency and Ranking: Models, Metrics, Data and Benchmarks

Salient object detection is a problem that has been considered in detail and many solutions have been proposed. In this paper, we argue that work to date has addressed a problem that is relatively ill-posed. Specifically, there is not universal agreement about what constitutes a salient object when multiple observers are queried. This implies that some objects are more likely to be judged salient than others, and implies a relative rank exists on salient objects. Initially, we present a novel deep learning solution based on a hierarchical representation of relative saliency and stage-wise refinement. Further to this, we present data, analysis and baseline benchmark results towards addressing the problem of salient object ranking. Methods for deriving suitable ranked salient object instances are presented, along with metrics suitable to measuring algorithm performance. In addition, we show how a derived dataset can be successively refined to provide cleaned results that correlate well with pristine ground truth in its characteristics and value for training and testing models. Finally, we provide a comparison among prevailing algorithms that address salient object ranking or detection to establish initial baselines providing a basis for comparison with future efforts addressing this problem. The source code and data are publicly available via our project page: ryersonvisionlab.github.io/cocosalrank.
Architecture and Mathematical Support of Intelligent Control System For Small UAV

We consider the actual problem of organizing the processing of information and control flows in on-board computers with limited computing resources to support UAV autonomous functioning. The relevance of the problem is determined by the requirement to equip small size unmanned aircraft systems with the hardware and software for processing telemetry target data streams received from technical vision and control systems that can operate in real time. We study the bit-parallel representation of the "rotate" operation intended for fast implementation in on-board specialized computers of an aircraft. The technique of converting an iterative representation to bitwise is demonstrated, which opens the way to parallel computations in the specialized devices based on multi-input adders. The operation "rotate" is essentially used in controlling on-board video cameras and other rotary drives, as well as for calculating trigonometric functions. Technical means for processing data streams are combined into a uniform complex for solving trajectory problems on modern computing platforms.
ConPan: A Tool to Analyze Packages in Software Containers

Deploying software packages and services into containers is a popular software engineering practice that increases portability and reusability. Docker, the most popular containerization technology, helps DevOps practitioners in their daily activities. Despite being successfully and increasingly employed, containers may include buggy and vulnerable packages that put at risk the environments in which the containers have been deployed. Existing quality and security monitoring tools provide only limited support to analyze Docker containers, thus forcing practitioners to perform additional manual work or develop adhoc scripts when the analysis goes beyond security purposes. This limitation also affects researchers desiring to empirically study the evolution dynamics of Docker containers and their contained packages. To overcome this limitation, we present ConPan, an automated tool to inspect the characteristics of packages in Docker containers, such as their outdatedness and other possible flaws (e.g., bugs and security vulnerabilities). ConPan comes with a CLI and API, and the analysis results can be presented to the user in a variety of formats.
Improved LSH for privacy-aware and robust recommender system with sparse data in edge environment

Recommender systems have become a popular and effective way to quickly discover new service items that are probably preferred by prospective users. Through analyzing the historical service usage data produced in the past, a recommender system can infer the potential user preferences and make accurate recommendations accordingly. However, in the edge environment, the service usage data stored in each edge server are often very sparse, which may result in expected cold-start problems. Besides, in the edge environment, the data required to make an optimal service recommendation decision are often stored in different edge clients or servers, which require additional privacy-preservation strategies to secure the sensitive data involved. Considering the above two drawbacks, traditional locality-sensitive hashing (LSH) is improved to be multi-probing LSH and then introduced to aid the recommendation process so as to guarantee the security and robustness of recommender systems. Experiments conducted on well-known dataset prove the effectiveness and efficiency of the work.
Engineering quantum correlations for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>m</mml:mi><mml:mo>×</mml:mo><mml:mi>n</mml:mi></mml:mrow></mml:math> spatially encoded two-photons states

Discretizing transverse linear momentum of photon pairs, generated by spontaneous parametric down conversion (SPDC), is one of the simplest methods for producing bipartite entangled states in high dimensions. So far, it has been employed only to prepare states in dimensions $m\ifmmode\times\else\texttimes\fi{}m$. In this work, we study the generalization for engineering entangled states in dimensions $m\ifmmode\times\else\texttimes\fi{}n$. Our approach relies on the manipulation of the pump beam transverse profile and the phase-matching function of the SPDC process to prepare, behind an $m$- and $n$-slit aperture, different $m\ifmmode\times\else\texttimes\fi{}n$ spatial entangled states. We demonstrate the technique experimentally for some $2\ifmmode\times\else\texttimes\fi{}3$ states. Compared to previous approaches in producing $2\ifmmode\times\else\texttimes\fi{}n$ photonic entanglement, which require either more than two photons or hybrid entanglement, our scheme is less demanding and simpler since we employ only two photons and a single degree of freedom to encode the states.
Practical Use Suggests a Re-evaluation of HDR Objective Quality Metrics

Full-reference objective quality metrics for high dynamic range (HDR) images must be validated in terms of their consistency with subjective evaluation results, and previous studies have found a number of metrics that appear to correlate well with the preference of observers. However, those conclusions were based on experiments that do not correspond to the practical use of a current day HDR professional production scenario. In this work, we carry out subjective evaluation experiments for the popular HDR standards of perceptual quantization (PQ) and Hybrid Log-Gamma (HLG), with a state-of-the-art HDR reference monitor used in broadcasting and post-production, and where all the observers participating in the tests are video experts. We find that the ranking of HDR metrics is now substantially different from what was reported earlier, and also that a simple standard dynamic range (SDR) metric can be applied directly to PQ or HLG encoded signals providing excellent results that surpass those of HDR metrics.
SeSaMe: A Data Set of Semantically Similar Java Methods

In the past, techniques for detecting similarly behaving code fragments were often only evaluated with small, artificial oracles or with code originating from programming competitions. Such code fragments differ largely from production codes. To enable more realistic evaluations, this paper presents SeSaMe, a data set of method pairs that are classified according to their semantic similarity. We applied text similarity measures on JavaDoc comments mined from 11 open source repositories and manually classified a selection of 857 pairs.
Hyperlink Classification via Structured Graph Embedding

We formally define a hyperlink classification problem in web search by classifying hyperlinks into three classes based on their roles: navigation, suggestion, and action. Real-world web graph datasets are generated for this task. We approach the hyperlink classification problem from a structured graph embedding perspective, and show that we can solve the problem by modifying the recently proposed knowledge graph embedding techniques. The key idea of our modification is to introduce a relation perturbation while the original knowledge graph embedding models only corrupt entities when generating negative triplets in training. To the best of our knowledge, this is the first study to apply the knowledge graph embedding idea to the hyperlink classification problem. We show that our model significantly outperforms the original knowledge graph embedding models in classifying hyperlinks on web graphs.
A data-driven M2 approach for evidential network structure learning

The Evidential Network (EN) is a significant model for addressing complex systems under uncertainty, with wide applications in reliability assessment, condition monitoring, and elsewhere. The EN structure is currently often determined by professional experience and expert knowledge, which reduces its objectivity and accuracy. Accordingly, a data-driven model, called M2 approach, is proposed for the EN structure learning, which combines the Maximal Information Coefficient (MIC) and the Additive Noise Model (ANM). More specifically, the MIC is first utilized to generate the initial undirected network structure in line with dependencies of variables, based on which the ANM is then employed to determine the direction of arcs amongst nodes according to information transfer order. A numerical case of three-node EN is applied to validate the feasibility and efficiency of the proposed M2 approach in theory. Further, the experimental results of the turbofan engine degradation dataset, available on the Prognostics Center of Excellence (PCoE) at NASA Ames, illustrate the applicability of the M2 approach in practice.
Detection of structural setting that controlling Hammam Faroun area, using aeromagnetic and seismicity data, Gulf of Suez, Egypt

Hammam Faroun is one of the prominent hot springs; it is situated on the central zone of Gulf of Suez on the western side of Sinai Peninsula, Egypt. The main objective of the present study is to assess the structural setting controlling Hammam Faroun, using the available airborne magnetic and seismicity data, beside other ancillary geological information data. The interpretation of airborne magnetic data of the study area revealed that Hammam Faroun area appeared as a small down-lifted block, pulled down to its confinement between faults given in what is known as half graben and then pulled to the southeast direction and still in a depth of 2.0 km. The main structures controlling the study area mainly trend in the NNE-SSW, NW-SE, NE-SW and WNW-ESE directions. The analyses of the available seismic events ascertained the existence of the NW structural trend, parallel to the Gulf of Suez. These analysis revealed the existence of three seismic clusters, each of them is characterized by micro to light/small earthquakes, of magnitudes in the range of, (0.5 ≥ Ml ≤ 4.3) at the study area. Most events have focal depths that range between 2.0 and 40 km, within the upper Earth crust. The fault plane solutions of six recent events exhibit the variability of focal mechanism from site to site. It's characterized by strike-slip and oblique-slip motions with predominant dip-slip normal component. The stress pattern is convenient, with those of the Gulf of Suez events.
Bridging the gap between linked open data-based recommender systems and distributed representations

Recently, several methods have been proposed for introducing Linked Open Data (LOD) into recommender systems. LOD can be used to enrich the representation of items by leveraging RDF statements and adopting graph-based methods to implement effective recommender systems. However, most of those methods do not exploit embeddings of entities and relations built on knowledge graphs, such as datasets coming from the LOD. In this paper, we propose a novel recommender system based on holographic embeddings of knowledge graphs built from Wikidata, a free and open knowledge base that can be read and edited by both humans and machines. The evaluation performed on three standard datasets such as Movielens 1M, Last.fm and LibraryThing shows promising results, which confirm the effectiveness of the proposed method.
Secure Data Deduplication with Reliable Data Deletion in Cloud

A cloud storage system which incorporates the deletion and deduplication functionalities will have both security and efficiency advantages over exiting solutions which provide only one of them. However, the security models of secure data deletion and data deduplication functionalities are not compatible with each other, which will cause security and efficiency vulnerability under coercive adversaries. To solve these security and efficiency challenges, we define and construct a scheme, whose security relies on the proper erasure of keys in the wrapped key tree and periodical update of the deduplication encryption keys. Moreover, we enhance the efficiency of the proposed scheme by introducing incremental data update, where only the changed part is encrypted/decrypted and uploaded/downloaded in data updating. Further security analysis shows that the proposed scheme is secure against coercive attack. Finally, the practical implementation shows that our scheme is performance efficient in computation, storage and communication for both the cloud storage server and users.
Supporting Analysts by Dynamic Extraction and Classification of Requirements-Related Knowledge

In many software development projects, analysts are required to deal with systems' requirements from unfamiliar domains. Familiarity with the domain is necessary in order to get full leverage from interaction with stakeholders and for extracting relevant information from the existing project documents. Accurate and timely extraction and classification of requirements knowledge support analysts in this challenging scenario. Our approach is to mine real-time interaction records and project documents for the relevant phrasal units about the requirements related topics being discussed during elicitation. We propose to use both generative and discriminating methods. To extract the relevant terms, we leverage the flexibility and power of Weighted Finite State Transducers (WFSTs) in dynamic modelling of natural language processing tasks. We used an extended version of Support Vector Machines (SVMs) with variable-sized feature vectors to efficiently and dynamically extract and classify requirements-related knowledge from the existing documents. To evaluate the performance of our approach intuitively and quantitatively, we used edit distance and precision/recall metrics. We show in three case studies that the snippets extracted by our method are intuitively relevant and reasonably accurate. Furthermore, we found that statistical and linguistic parameters such as smoothing methods, and words contiguity and order features can impact the performance of both extraction and classification tasks.