Reviewerly/exHarmony huggingface dataset:
MBeacon: Privacy-Preserving Beacons for DNA Methylation Data

The advancement of molecular profiling techniques fuels biomedical research with a deluge of data.To facilitate data sharing, the Global Alliance for Genomics and Health established the Beacon system, a search engine designed to help researchers find datasets of interest.While the current Beacon system only supports genomic data, other types of biomedical data, such as DNA methylation, are also essential for advancing our understanding in the field.In this paper, we propose the first Beacon system for DNA methylation data sharing: MBeacon.As the current genomic Beacon is vulnerable to privacy attacks, such as membership inference, and DNA methylation data is highly sensitive, we take a privacy-by-design approach to construct MBeacon.
Complex Program Induction for Querying Knowledge Bases in the Absence of Gold Programs

Recent years have seen increasingly complex question-answering on knowledge bases (KBQA) involving logical, quantitative, and comparative reasoning over KB subgraphs. Neural Program Induction (NPI) is a pragmatic approach toward modularizing the reasoning process by translating a complex natural language query into a multi-step executable program. While NPI has been commonly trained with the ‘‘gold’’ program or its sketch, for realistic KBQA applications such gold programs are expensive to obtain. There, practically only natural language queries and the corresponding answers can be provided for training. The resulting combinatorial explosion in program space, along with extremely sparse rewards, makes NPI for KBQA ambitious and challenging. We present Complex Imperative Program Induction from Terminal Rewards (CIPITR), an advanced neural programmer that mitigates reward sparsity with auxiliary rewards, and restricts the program space to semantically correct programs using high-level constraints, KB schema, and inferred answer type. CIPITR solves complex KBQA considerably more accurately than key-value memory networks and neural symbolic machines (NSM). For moderately complex queries requiring 2- to 5-step programs, CIPITR scores at least 3× higher F1 than the competing systems. On one of the hardest class of programs (comparative reasoning) with 5–10 steps, CIPITR outperforms NSM by a factor of 89 and memory networks by 9 times. 1
WIQA: A dataset for “What if...” reasoning over procedural text

We introduce WIQA, the first large-scale dataset of "What if..." questions over procedural text.WIQA contains three parts: a collection of paragraphs each describing a process, e.g., beach erosion; a set of crowdsourced influenct graphs for each paragraph, describing how one change affects another; and a large (40k) collection of "What if...?" multiplechoice questions derived from the graphs.For example, given a paragraph about beach erosion, would stormy weather result in more or less erosion (or have no effect)?The task is to answer the questions, given their associated paragraph.WIQA contains three kinds of questions: perturbations to steps mentioned in the paragraph; external (out-of-paragraph) perturbations requiring commonsense knowledge; and irrelevant (no effect) perturbations.We find that state-of-the-art models achieve 73.8% accuracy, well below the human performance of 96.3%.We analyze the challenges, in particular tracking chains of influences, and present the dataset as an open challenge to the community.
Building Ontology-Driven Tutoring Models for Intelligent Tutoring Systems Using Data Mining

Pedagogical (Tutor or Tutoring) Models are an important element of Intelligent Tutoring Systems (ITS) and they can be described by sets of (tutoring) rules. The implementation of a Tutoring Model includes both the formal representation of the aforementioned rules and a mechanism able to interpret such representation and execute the rules. One of the most suitable approaches to formally represent pedagogical rules is to construct semantic web ontologies that are highly interoperable and can be integrated with other models in an ITS like the subject domain and the student model. However, the main drawback of semantic web-based approaches is that they require a considerable human effort to prepare and build relevant ontologies. This paper proposes a novel approach to maintain the benefits of the semantic web-based approach in representing pedagogical rules for an ITS, while overcoming its main drawback by employing a data mining technique to automatically extract rules from real-world tutoring sessions and represent them by means of Web Ontology Language (OWL).
A Classification Framework for Software Defect Prediction Using Multi-filter Feature Selection Technique and MLP

Production of high quality software at lower cost can be possible by detecting defect prone software modules before the testing process.With this approach, less time and resources are required to produce a high quality software as only those modules are thoroughly tested which are predicted as defective.This paper presents a classification framework which uses Multi-Filter feature selection technique and Multi-Layer Perceptron (MLP) to predict defect prone software modules.The proposed framework works in two dimensions: 1) with oversampling technique, 2) without oversampling technique.Oversampling is introduced in the framework to analyze the effect of class imbalance issue on the performance of classification techniques.The framework is implemented by using twelve cleaned NASA MDP datasets and performance is evaluated by using: F-measure, Accuracy, MCC and ROC.According to results the proposed framework with class balancing technique performed well in all of the used datasets.
Do FEARS drive Bitcoin?

Purpose This study examines the prediction power of investor sentiment on Bitcoin return. Design/methodology/approach We construct a Financial and Economic Attitudes Revealed by Search (FEARS) index using search volume from Google's search engine to reveal household-level (“bankruptcy”, “unemployment”, “job search”, etc.) and market-level sentiment (“bankruptcy”, “unemployment”, “job search”, etc.). Findings Using a variety of quantitative methodologies such as the transfer entropy model as well as threshold regression and OLS, GLS and 2SLS estimations, we find that (1) investor sentiment has strong predictive power on Bitcoin, (2) household-level sentiment has larger effects than market-level sentiment and (3) the impact of sentiment is greater in low sentiment regimes than in high sentiment regimes. Based on these information, we build a hypothetical trading strategy that outperforms a simple buy-and-hold strategy both on an absolute and risk-adjusted basis. The results are consistent across cryptocurrencies and regions. Research limitations/implications The findings contribute to the ongoing debate in the literature on the efficiency of cryptocurrency markets. The results reveal that the Bitcoin market is not efficient in the sense of the efficient market hypothesis – asset prices do not fully reflect all available information and we were able to “beat the market”. In addition, it sheds further light on the debate whether Bitcoin can be considered a medium of exchange, i.e. a currency or an investment product. Because investors are reallocating their Bitcoin holdings during times of increased market sentiment due to liquidity needs, they obviously consider bitcoin an investment product rather than a currency. Originality/value This study is the first to examine the impact of investor sentiment measured by FEARS on Bitcoin return.
Unsupervised Deep Learning CAD Scheme for the Detection of Malaria in Blood Smear Microscopic Images

Recent advances in deep learning, coupled with the onslaught of unlabelled medical data have drawn ever-increasing research interests by discovering multiple levels of distributed representations and solving complex medical related problems. Malaria disease detection in early stage requires an accurate and precise diagnosis in order to achieve successful patient remission. This paper proposes a comprehensive computer-aided diagnosis (CAD) scheme for identifying the presence of malaria parasites in thick blood smear images. The parameters of the scheme are pre-trained by functional link artificial neural network followed by sparse stacked autoencoder. The optimum size of the CAD scheme used in this research is 12500-2500-100-50-2, where the input layer has 12500 nodes and Softmax classifier output layer has 2 nodes. Moreover, the 10- fold cross validation reflects that the classification is reliable and is applicable to new patient blood smear images. The proposed CAD scheme has been evaluated using malaria blood smear image data set, achieving a detection accuracy of 89.10%, a sensitivity of 93.90% and specificity of 83.10%. The extensive comparative experiment suggests that the proposed CAD scheme provides richer effectiveness and efficiency for malaria data set compared to other deep learning techniques for better diagnosis decision and management. This work implements a novel approach to fast processing and will be a beneficial tool in disease identification.
Named Entity Recognition for Social Media Texts with Semantic Augmentation

Existing approaches for named entity recognition suffer from data sparsity problems when conducted on short and informal texts, especially user-generated social media content.Semantic augmentation is a potential way to alleviate this problem.Given that rich semantic information is implicitly preserved in pre-trained word embeddings, they are potential ideal resources for semantic augmentation.In this paper, we propose a neural-based approach to NER for social media texts where both local (from running text) and augmented semantics are taken into account.In particular, we obtain the augmented semantic information from a large-scale corpus, and propose an attentive semantic augmentation module and a gate module to encode and aggregate such information, respectively.Extensive experiments are performed on three benchmark datasets collected from English and Chinese social media platforms, where the results demonstrate the superiority of our approach to previous studies across all three datasets.
Learning Collision-Free Space Detection From Stereo Images: Homography Matrix Brings Better Data Augmentation

Collision-free space detection is a critical component of autonomous vehicle perception. The state-of-the-art algorithms are typically based on supervised deep learning. Their performance is dependent on the quality and amount of labeled training data. It remains an open challenge to train deep convolutional neural networks (DCNNs) using only a small quantity of training samples. Therefore, in this article, we mainly explore an effective training data augmentation approach that can be employed to improve the overall DCNN performance, when additional images captured from different views are available. Due to the fact that the pixels in collision-free space (generally regarded as a planar surface) between two images, captured from different views, can be associated using a homography matrix, the target image can be transformed into the reference view. This provides a simple but effective way to generate training data from additional multiview images. Extensive experimental results, conducted with six state-of-the-art semantic segmentation DCNNs on three datasets, validate the effectiveness of the proposed method for enhancing collision-free space detection performance. When validated on the KITTI road benchmark, our approach provides the best results, compared with other state-of-the-art stereo vision-based collision-free space detection approaches.
Spam Emails Detection Based on Distributed Word Embedding with Deep Learning

In recent years, a rapid shift from general and random attacks to more sophisticated and advanced ones can be noticed. Unsolicited email or spam is one of the sources of many types of cybercrime techniques that use complicated methods to trick specific victims. Spam detection is one of the leading machine learning-oriented applications in the last decade. In this work, we present a new methodology for detecting spam emails based on deep learning architectures in the context of natural language processing (NLP). Past works on classical machine learning based spam email detection has relied on various feature engineering methods. Identifying a proper feature engineering method is a difficult task and moreover vulnerable in an adversarial environment. Our proposed method leverage the text representation of NLP and map towards spam email detection task. Various email representation methods are utilized to transform emails into email word vectors, as an essential step for machine learning algorithms. Moreover, optimal parameters are identified for many deep learning architectures and email representation by following the hyper-parameter tuning approach. The performance of many classical machine learning classifiers and deep learning architectures with various text representations are evaluated based on publicly available three email corpora. The experimental results show that the deep learning architectures performed better when compared to the standard machine learning classifiers in terms of accuracy, precision, recall, and F1-score. This is essentially due to the fact that the deep learning architectures facilitate to learn hierarchical, abstract and sequential feature representations of emails. Furthermore, word embedding with deep learning has performed well in comparison to the other classical email representation methods. The word embedding simplify to learn the syntactic, semantic and contextual similarity of emails. This endows word embedding with deep learning methods in spam email filtering in the real environment.
A Systematic Assessment of Syntactic Generalization in Neural Language Models

While state-of-the-art neural network models continue to achieve lower perplexity scores on language modeling benchmarks, it remains unknown whether optimizing for broad-coverage predictive performance leads to human-like syntactic knowledge. Furthermore, existing work has not provided a clear picture about the model properties required to produce proper syntactic generalizations. We present a systematic evaluation of the syntactic knowledge of neural language models, testing 20 combinations of model types and data sizes on a set of 34 English-language syntactic test suites. We find substantial differences in syntactic generalization performance by model architecture, with sequential models underperforming other architectures. Factorially manipulating model architecture and training dataset size (1M-40M words), we find that variability in syntactic generalization performance is substantially greater by architecture than by dataset size for the corpora tested in our experiments. Our results also reveal a dissociation between perplexity and syntactic generalization performance.
Smart Contract Vulnerability Detection: From Pure Neural Network to Interpretable Graph Feature and Expert Pattern Fusion

Smart contracts hold digital coins worth billions of dollars, their security issues have drawn extensive attention in the past years. Towards smart contract vulnerability detection, conventional methods heavily rely on fixed expert rules, leading to low accuracy and poor scalability. Recent deep learning approaches alleviate this issue but fail to encode useful expert knowledge. In this paper, we explore combining deep learning with expert patterns in an explainable fashion. Specifically, we develop automatic tools to extract expert patterns from the source code. We then cast the code into a semantic graph to extract deep graph features. Thereafter, the global graph feature and local expert patterns are fused to cooperate and approach the final prediction, while yielding their interpretable weights. Experiments are conducted on all available smart contracts with source code in two platforms, Ethereum and VNT Chain. Empirically, our system significantly outperforms state-of-the-art methods. Our code is released.
Glanceable AR: Evaluating Information Access Methods for Head-Worn Augmented Reality

Augmented reality head-worn displays (AR HWDs) have the potential to assist personal computing and the acquisition of everyday information. In this research, we propose Glanceable AR, an interaction paradigm for accessing information in AR HWDs. In Glanceable AR, secondary information resides at the periphery of vision to stay unobtrusive and can be accessed by a quick glance whenever needed. We propose two novel hands-free interfaces: "head-glance", in which virtual contents are fixed to the user’s body and can be accessed by head rotation, and "gaze-summon" in which contents can be "summoned" into central vision by eye-tracked gazing at the periphery. We compared these techniques with a baseline heads-up display (HUD), which we call "eye-glance" interface in two dual-task scenarios. We found that the head-glance and eye-glance interfaces are more preferred and more efficient than the gaze-summon interface for discretionary information access. For a continuous monitoring task, the eye-glance interface was preferred. We discuss the implications of our findings for designing Glanceable AR interfaces in AR HWDs.
Developing a Distributed Print Depository System: Challenges and Opportunities

SUMMARY. Barbara DesRosiers and Melissa Trevvett provided background information regarding their ongoing projects to develop print archives to preserve legacy materials and to serve as a backup to electronic journals. The presenters discussed projects currently underway and outlined the factors and issues to be considered when planning to form or join a print archive consortium. They also provided details about their current partnerships with JSTOR, Michigan State University, the University of Illinois, Urbana-Champaign, and Yale University including: the components of the projects, funding and staffing issues, and coordination and communication among the partners. The presenters stressed the importance of legal agreements among partners as a way to minimize risk and provide better management of the program. [Article copies available for a fee from The Haworth Document Delivery Service: 1-800-344HAWORTH. E-mail address: &lt;docdelivery@haworthpress.com&gt; Website: &lt;https://www.HaworthPress.com&gt;]
ISSN: What Is It Good For?

SUMMARY. The International Standard Serial Number (ISSN) is a part of everyday life for serialists of all kinds. The ISSN serves many purposes. An understanding of these purposes, along with an overview of the ISSN program’s history, organization, and current issues affecting standards, are all important points of information for anyone working with serials. This tactics session covered information on the ISSN as presented by two serials catalogers from the National Serials Data Program at the Library of Congress, Esther Simpson and Pamela Simpson. Issues specific to electronic continuing resources were also addressed. [Article copies available for a fee from The Haworth Document Delivery Service: 1-800-HAWORTH. E-mail address: &lt;docdelivery@haworthpress.com&gt; Website: &lt;https://www.HaworthPress.com&gt;]
Cosdes: A Collaborative Spam Detection System With A Novel E-Mail Abstraction Scheme

E-mail communication is indispensable nowadays, but the e-mail spam problem continues growing drastically. In recent years, the notion of collaborative spam filtering with near-duplicate similarity matching scheme has been widely discussed. The primary idea of the similarity matching scheme for spam detection is to maintain a known spam database, formed by user feedback, to block subsequent near-duplicate spams. On purpose of achieving efficient similarity matching and reducing storage utilization, prior works mainly represent each e-mail by a succinct abstraction derived from e-mail content text. However, these abstractions of e-mails cannot fully catch the evolving nature of spams, and are thus not effective enough in near-duplicate detection. This paper proposes a novel e-mail abstraction scheme, which considers e-mail layout structure to represent e-mails. A procedure is presented to generate the e-mail abstraction using HTML content in e-mail, and this newly devised abstraction can more effectively capture the near-duplicate phenomenon of spams. Moreover, a complete spam detection system Cosdes (standing for Collaborative Spam Detection System) is designed, which possesses an efficient near-duplicate matching scheme and a progressive update scheme. The progressive update scheme enables system Cosdes to keep the most up-to-date information for near-duplicate detection.
System Architecture and Data Allocation/Coordination Technologies of Autonomous Decentralized Database System for High Assurance

During the last few years there have been important changes in the way that consumers use the Internet and e-commerce. This exceptional expansion, combined with dynamic interactions with users and providers, is boosting the development of new electronic business models, such as block chain, fintech, bitcoin, Uber, Airbnb, etc. The information systems that support such business processes, usually depend on distributed database systems. These approaches have mostly opted to exploit two important characteristics of this kind of applications: at first, they contain a set of commodity types with a limited inventory. On the other hand, incremental updates are the kind of operations which these items typically involve. In this sense, it is possible to achieve system distribution by using tokens which represent the commodities? instances for sales in e-business. There exist two mainly different mechanisms for distributing tokens: replication and partitioning. Token replication mechanism demands expensive synchronization protocols to provide the consistency of the data. On the other hand, token partitioning, depends on token redistribution strategies which allow dynamic migration of tokens to those servers in which it is needed. Such strategies are developed from the standpoint that the total system can locally be known, and therefore, they fail to address the important problem of how to allocate and coordinate the data to make it adaptive to rapidly changing situations. In this paper, we present Autonomous Decentralized Database System (ADDS) concept and architecture in order to extend the properties of autonomy and decentralization to wide-area distributed database systems. ADDS fundamental technologies confers autonomy and loosely coupling to all the components of the system (databases) and provides a background coordination to adapt the system to evolving situations. Moreover, we present two data allocation and coordination technologies for high response time based on specific situations. Finally, in order to show the effectiveness of our proposal we compare it with conventional partitioning strategies.
An Ontology Based Information Retrieval System

Ontology provide a structured way of describing knowledge. Ontology's are usually repositories of concepts and relations between them, so using them in information retrieval seems to be a reasonable goal. The main objective in this report is to provide efficient means to move from keyword-based to concept-based information retrieval utilizing ontology's for conceptual definitions [1]. In this paper, we present the skeleton of such an IR system which works on a collection of domain specific documents and exploits the use of a domain specific ontology to improve the overall number of relevant documents retrieved. In this system, a user enters a query from which the meaningful concepts are extracted; using these concepts and domain ontology, query expansion is performed. We propose a system that matches the query terms in the ontology/schema graph and exploits the surrounding knowledge to derive an enhanced query. The enhanced query is given to the underlying basic keyword search system LUCENE [2]. In this approach we try to make use of more ontological Knowledge than IS-A and HAS-A relationships and synonyms for information retrieval.