Reviewerly/exHarmony huggingface dataset:
Model validation failure in class imbalance problems

For a classification task, multiple classification models can be built from the training set in various ways. In general, the best-performing model is selected for deployment through a model validation procedure. However, even if the dataset is sufficiently large, model validation is difficult when the minority class is too rare in an absolute sense in the validation set. Under such an extreme absolute rarity condition, the validation performance of a model is more affected by randomness in the model so that it would misleadingly estimate the generalization ability of the model. In this regard, even a random guessing model, which will eventually fail to accurately classify new data, can yield a considerably high validation performance by chance. This implies that the selected model may not perform well during its deployment. In this study, the effect of absolute rarity on the inherent difficulty of model validation is investigated. We demonstrate that the higher degree of absolute rarity in the validation set as well as comparing a larger number of models during model validation contribute to an increased likelihood of model validation failure. Finally, a practical guideline is suggested to evaluate model validation results.
Awareness of and awareness that: their combination and dynamics

Abstract The paper proposes a logical framework representing the notion of explicit knowledge as the combination of awareness of and awareness that. The setting, semantically combining neighbourhood models with ideas from awareness logic, separates the mere fact of entertaining some information (being aware of$\varphi$) from the acknowledgement that the information is indeed the case (being aware that$\varphi$ holds). The text discusses not only the main properties these concepts obtain under the given representation, but also several of the epistemic actions that can be defined, and the way they affect the agent’s awareness (and thus her knowledge).
Behavioral Management for Employees based on Blockchain and Smart Contracts

With the continuing innovation of information and network technologies, on the one hand, social media has gradually become a necessary tool for internal communication within the organization, resulting in great changes in the behavioral patterns of employees; on the other hand, emerging technologies such as blockchain and smart contracts have enlightened many outstanding issues in organizational management, such as opaque data, tamable information and incomplete execution. Responding to the impact of new media on organizational management model, combined with the emerging technologies--blockchain and smart contracts, this paper aims to explore the behavioral management model of employees for trusted data within virtual cyberspace. It is devoted to the description modeling of employees' behavior and the guidance of employees' behavior based on smart contracts. We also validate our proposed method in a real-world scenario, and the experimental results prove our analysis, as well as the effectiveness of our approach. This paper is a basic research in the new application field, and is expected to offer helpful guidance and reference for future research efforts and industrial applications.
Outlier detection for power data based on contractive auto-encoder

With the continuous development of data science, big data technology has been widely used in the power industry. Since the operation of the power grid is related to the whole industrial production and the stability of household electricity consumption, abnormality detection in power big data is particularly important, and the outlier data is the most important abnormal data. There are many traditional methods for power data outlier detection. However, with the continuous deepening of the construction of intelligent power grids and ubiquitous power Internet of Things platforms, the scale of power data is increasing, and the data dimension is also rising. The traditional outlier detection method is difficult to achieve better results in the face of massive power big data. In view of the large scale and high latitude of power big data, in this paper, we proposes a power data outlier detection method based on contractive auto-encoder, which learns a low-dimensional representation of high-dimensional data by learning an encoder-decoder structure and implements outlier detection. Experiments show that the proposed method can effectively realize the outlier detection of power data and achieve higher accuracy.
Compact and Efficient Multitask Learning in Vision, Language and Speech

Across-domain multitask learning is a challenging area of computer vision and machine learning due to the intra-similarities among class distributions. Addressing this problem to cope with the human cognition system by considering inter and intra-class categorization and recognition complicates the problem even further. We propose in this work an effective holistic and hierarchical learning by using a text embedding layer on top of a deep learning model. We also propose a novel sensory discriminator approach to resolve the collisions between different tasks and domains. We then train the model concurrently on textual sentiment analysis, speech recognition, image classification, action recognition from video, and handwriting word spotting of two different scripts (Arabic and English). The model we propose successfully learned different tasks across multiple domains.
A mentalist agent for identifying characters using dynamic query strategies

We consider the problem of finding the minimal sequence of questions needed to identify an unknown element from a set of cardinality M. This task is common meet in games such as Guess Who, 20 Questions or Akinator. Our scenario is to identify a person based on features extracted from an image. The assumption is that the user thinks at any person described on the DBpedia. We do not store previous expert knowledge or user profile. The sequence of questions is built based on heuristics that favor the most relevant features: information gain, gain ration, probabilistic entropy. As we deal with features that are automatically extracted from images, the data is noisy. We test the performance of the method using simulated dialogues between software agent and human agent.
Space Debris Tracking Via Generalized Labeled Multi-Bernoulli Random Finite Sets

In this paper we present an algorithm for detection and tracking space debris under complex space background via (δ-generalized labeled multi-Bernoulli (δ-GLMB) random finite sets (RFS). It includes two parts, position extractor for suspected targets and tracking filter. The core strategy of this position extractor is morphological method. The filter is implemented by Sequential Monte Carlo (SMC) method and each iteration of (δ-GLMB multi-target tracking filter includes two operations: update and prediction. The processing results of measured data show that the algorithm has strong detection and tracking ability for dim and small targets under complex space background. To the best of our knowledge, this is the first time that the (δ-GLMB multitarget tracking filter has been successfully applied in the field of space debris target detection and tracking.
Efficient and de-shadowing approach for multiple vehicle tracking in aerial video via image segmentation and local region matching

Multiple vehicle tracking in aerial video has vital applications in intelligent transportation system. However, this is challenging owing to the demand for higher tracking efficiency. Moreover, it is particularly challenging to handle shadows and occlusions in complex traffic scenes. Currently, most approaches mainly address the multiple vehicle tracking problem based on consecutive frame pairs. However, they are inefficient and ineffective with respect to the tracking of vehicles that are shadowed or occluded by trees, buildings, or large vehicles. Therefore, in terms of consecutive multiframe images, we propose an efficient deshadowing approach for multiple vehicle tracking in aerial video via image segmentation and local region matching. By processing a series of frames at a time, this approach will address the occurrence of shadows and occlusions in multiple vehicle tracking. First, image segmentation and road segmentation are combined to construct the region adjacency graph within the road (RAGR) for each frame. Local region matching is then performed among the RAGRs based on the distance transform. In particular, to improve the region matching efficiency, a local matching criterion is proposed by introducing region of interest for the node of the RAGR. Finally, based on the motion dissimilarities and varying spatial distances of the background and moving vehicles, a batch-oriented foreground separation is achieved to separate vehicles (the foreground) from the background. Experiments and comparative analysis conducted on UAV123 and DARPA VIVID data sets demonstrate that the proposed approach for multiple vehicle tracking in aerial video can generate satisfactory and competitive results.
Enable Traditional Laptops with Virtual Writing Capability Leveraging Acoustic Signals

Abstract Human–computer interaction through touch screens plays an increasingly important role in our daily lives. Besides smartphones and tablets, laptops are the most prevalent mobile devices for both work and leisure. To satisfy the requirements of some applications, it is desirable to re-equip a typical laptop with both handwriting and drawing capability. In this paper, we design a virtual writing tablet system, VPad, for traditional laptops without touch screens. VPad leverages two speakers and one microphone, which are available in most commodity laptops, to accurately track hand movements and recognize writing characters in the air without additional hardware. Specifically, VPad emits inaudible acoustic signals from two speakers in a laptop and then analyzes energy features and Doppler shifts of acoustic signals received by the microphone to track the trajectory of hand movements. Furthermore, we propose a state machine-based trajectory optimization method to correct the unexpected trajectory and employ a stroke direction sequence model based on probability estimation to recognize characters users write in the air. Experimental results show that VPad achieves the average error of 1.55 cm for trajectory tracking and the accuracy over 90% of character recognition merely through built-in audio devices on a laptop.
Unsupervised Multi-Topic Labeling for Spoken Utterances

Systems such as Alexa, Cortana, and Siri appear rather smart. However, they only react to predefined wordings and do not actually grasp the user's intent. To overcome this limitation, a system must grasp the topics the user is talking about. Therefore, we apply unsupervised multi-topic labeling to spoken utterances. Although topic labeling is a well-studied task on textual documents, its potential for spoken input is almost unexplored. Our approach for topic labeling is tailored to spoken utterances; it copes with short and ungrammatical input. The approach is two-tiered. First, we disambiguate word senses. We utilize Wikipedia as pre-labeled corpus to train a naïve-bayes classifier. Second, we build topic graphs based on DBpedia relations. We use two strategies to determine central terms in the graphs, i.e. the shared topics. One focuses on the dominant senses in the utterance and the other covers as many distinct senses as possible. Our approach creates multiple distinct topics per utterance and ranks results. The evaluation shows that the approach is feasible; the word sense disambiguation achieves a recall of 0.799. Concerning topic labeling, in a user study subjects assessed that in 90.9% of the cases at least one proposed topic label among the first four is a good fit. With regard to precision, the subjects judged that 77.2% of the top ranked labels are a good fit or good but somewhat too broad (Fleiss' kappa k = 0.27).
The Data Scientist on LinkedIn: Job Advertisement Corpus Processing with NooJ

For organizations using big data, one of the most important element to reach tangible results is exploiting human resources: it is not possible to manage data without using them intelligently. Considering the human intervention in relation to big data, means calling into question the so-called “data scientist”. Moving from the above, the main aim of this study is using the linguistic software environment NooJ to process a large corpus of job advertisements for data scientist in Italy collected on the business-networking site LinkedIn. Creating specific linguistic resources with NooJ, we are able to identify the most required skills by companies and organizations. Searching the ideal candidate to hire, companies pay attention equally to technical skills and soft skills, in particular, as the capacity to work in team and communicate concerns. Finally, our research confirmed that studying the context in which the single words are inserted represents a key step in the process of information extraction by texts.
A Personalised Hybrid Learning Object Recommender System

In this work, we present a hybrid Recommender System (RS) for prescribing learning objects to students in a Personalised Learning Environment (PLE). This RS for Learning Objects (LOs) uses explicit and implicit student profiling to filter learning material for recommendation to a student. Further, the student profile consists of learning preferences, the student's confidence level in the required topics as well as the courses she enrolled in. Finally, we factor in implicit and explicit learning object ratings to enhance the recommendations.
Query optimization Approach with Shuffle Intermediate Cache Layer for Spark SQL

Spark SQL is a big data processing tool for structured data query and analysis. However, due to the execution of Spark SQL, there are multiple times to write intermediate data to the disk, which reduces the execution efficiency of Spark SQL. Targeting on the existing issues, we design and implement an intermediate data cache layer between the underlying file system and the upper Spark core to reduce the cost of random disk I/O. By using the query pre-analysis module, we can dynamically adjust the capacity of cache layer for different queries. And the allocation module can allocate proper memory for each node in cluster. This paper develops the SSO (Spark SQL optimizer) module and integrates it into the original Spark system to achieve the above functions. This paper compares the query performance with the existing Spark SQL by experiment data generated by TPC-H tool. The experimental results show that the SSO module can effectively improve the query efficiency, reduce the disk I/O cost and make full use of the cluster memory resources.
Web Service Selection with Correlations: A Feature-Based Abstraction Refinement Approach

In this paper, we address the web service selection problem for linear workflows. Given a linear workflow specifying a set of ordered tasks and a set of candidate services providing different features for each task, the selection problem deals with the objective of selecting the most eligible service for each task, given the ordering specified. A number of approaches to solving the selection problem have been proposed in literature. With web services growing at an incredible pace, service selection at the Internet scale has resurfaced as a problem of recent research interest. In this work, we present our approach to the selection problem using an abstraction refinement technique to address the scalability limitations of contemporary approaches. Experiments on web service benchmarks show that our approach can add substantial performance benefits in terms of space when compared to an approach without our optimization.
Deep URL

Nowadays, many people rely on internet for various information needs, due to the development of advanced technologies. The internet has unlimited web resources, but some contents are not appropriate for all the age groups, especially children under 18. The number of adult websites increases every day thereby posing challenge for existing content-based / black listing approaches, which require entire web page contents for classification purpose / frequent database updates. To overcome the above issues, we propose an URL based deep learning model that not only avoids the unnecessary content downloads, but also handles the dynamic nature of web. As the URL is a sequence of characters, a novel embedding method is proposed for effective URL representation. A Recurrent Convolutional Neural Network based approach is also proposed that can classify the Adult websites by learning the significant features derived only from URLs. By conducting various experiments on the benchmark ODP dataset, we have analyzed the performance of the proposed approach. From the experimental results, it is shown that an accuracy of 87.6% has been achieved which is a significant improvement over the existing approaches.
Fully Homomorphic Key Update and Key Exchange Over Exterior Product Spaces For Cloud Computing Applications

We introduce a fully homomorphic symmetric-key encryption scheme, a homomorphic key update and a key exchange protocols for exchanging secret keys among multiple parties, updating secret keys on existing ciphertexts without prior decryption, and generating secret keys for partial, ephemeral and/or consolidated data, which have useful applications in cloud computing.
An Unsupervised Neural Model for Aspect Based Opinion Mining

The paper approaches aspect based opinion mining, which uses an unsupervised neural network as the opinion classifier. The neural network is an extension of the Growing Hierarchical Self-organizing Maps (GHSOM). In the aspect based sentiment analysis, we exploit the fact that the binary relations of syntactic dependency, extracted from product reviews, very often express relations between an aspect of the reviewed product and an opinion towards that aspect. We use the Growing Hierarchical Self-organizing Maps to classify pairs built according to the dependency relations, more exactly to classify pairs of the form (aspect, opinion bearing word). With this classification, we discover whether the various text mentions of the aspects of the target entity (such as the aspects of a product) are opinionated with positive or negative sentiment in the text of a review. We classify these pairs against a domain specific taxonomy of aspects, which also includes (positive/ negative) opinions associated with the aspects. Since it is based on classification against an ontology, our approach is semantic oriented. We tested our system on a collection of reviews about photo cameras.
Short-Text Sentiment Classification Based on Graph-LSTM

With the rapid development of social networks, short texts make it easier for people to comment on hot events and express their emotions. The information published by users through short texts contains emotional features of different trends, and it is of great significance to dig deep into these features. However, the traditional RNN algorithm, SVM and linear LSTM can only discriminate emotional sentiment because of the short text grammar and the sparse data, which is far from the purpose of opinion mining. Therefore, this paper proposes to apply Graph LSTM to short text classification, mine deeper information, and achieve good results. Finally, the paper compares three different machine learning methods to achieve fine-grained sentiment analysis.
Method for Collecting Relevant Topics from Twitter supported by Big Data

Abstract There is a fast increase of information and data generation in virtual environments due to microblogging sites such as Twitter, a social network that produces an average of 8, 000 tweets per second, and up to 550 million tweets per day. That’s why this and many other social networks are overloaded with content, making it difficult for users to identify information topics because of the large number of tweets related to different issues. Due to the uncertainty that harms users who created the content, this study proposes a method for inferring the most representative topics that occurred in a time period of 1 day through the selection of user profiles who are experts in sports and politics. It is calculated considering the number of times this topic was mentioned by experts in their timelines. This experiment included a dataset extracted from Twitter, which contains 10, 750 tweets related to sports and 8, 758 tweets related to politics. All tweets were obtained from user timelines selected by the researchers, who were considered experts in their respective subjects due to the content of their tweets. The results show that the effective selection of users, together with the index of relevance implemented for the topics, can help to more easily find important topics in both sport and politics.
Lucene-P2: A Distributed Platform for Privacy-Preserving Text-based Search

Information retrieval (IR) plays an essential role in daily life. However, currently deployed IR technologies, e.g., Apache Lucene - open-source search software, are insufficient when the information is protected or deemed to be private. For example, submitting a query to a publicly available search engine (e.g., Bing or Google) requires disclosing potentially delicate facts (e.g., thoughts about abortion), as well as the websites the user considers interesting. Similarly, when a private database contains sensitive information needed by the user, it cannot be searched freely. Over the past decade, various approaches, generally referred to as private information retrieval, have been proposed to obfuscate queries and responses, but they are limited in that the retrieved information is inadequate to compute relevancy. To address such limitations, this paper introduces the necessary techniques to build Lucene-P2 that allows one party to discover whether a second party harbors any relevant textual information without either party disclosing any information.
Civility 101: Free speech, social media, and university faculty

An abstract is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Implementing Graph Based Rank on Online News Media Keyword Extraction

Commonly used method in keyword extraction usually needs a huge collections of articles to determine important and unimportant words. For example, in TF-IDF, one of the most popular term-weighting schemes, a huge collections of documents is needed for stop-words filtering. Other than that, TF-IDF can be used to rank words using its significance in an article. Words with less significant value will be ranked last. In this paper, a new approach in word ranking is applied in keyword extraction process. This approach using a single document to determine its keywords. Online news media articles, taken from TEMPO.CO website, written in Bahasa Indonesia are used in this paper. Online news media is chosen as it has semi-formal language structure and completed with additional information, such as tags or keywords. TEMPO.CO is believed as one of reliable online news media which has good quality news. In addition, after pre-evaluation, article from TEMPO.CO has the most related keywords with its content compared to other articles taken from different online news media portal. Weight from each word is calculated using graph based method. Then, every word is ranked based on its weight. Words placed in five and ten highest percentile will be assumed as keywords. Precision and recall are estimated using keywords found and given from the article. The evaluation gives a better result in using graph based rank compared to TF-IDF. The difference ranges from ten to twenty percent. Although there is an absolute distinction between the two methods, both their average precision and recall values have high standard deviation. This condition is due to the unclear pattern in keyword selection.