Reviewerly/exHarmony huggingface dataset:
Research on the Anomaly Detection Method in Intelligent Patrol Based on Big Data Analysis

The network anomaly detection in intelligent patrol is based on the trigger of a single threshold of network element performance parameters in patrol task, which has a high false alarm rate and low efficiency.In order to effectively and accurately integrate network performance, this paper proposes to mine network element performance data and network element log information in the integrated automatic patrol to detect network anomalies.Because log files have a large amount of data and a variety of types, and log data has a complex structure and contains large implied information.The relationship between network anomalies and time can actively discover through the analysis of the log files.Therefore, big data mining and classification can greatly improve the efficiency of data processing.However, the accuracy of finding network anomalies is insufficient only for log analysis.Therefore, this paper puts forward the performance indexes collected in the log analysis and patrol inspection system and adopts the sequence analysis algorithm to detect network anomalies, so as to improve the accuracy and efficiency of detection.
Analysis of the Click through Rate of Chinese Netizens through Social Software based on DeepFM Algorithm

Abstract Advertising as an important means of marketing, plays an important role in the promotion of commercial services, but its profit efficiency has not been optimistic. How to reduce the push cost and improve the accuracy rate of advertising is the algorithm problem of various enterprises. Chinese netizens have a large base and a large proportion use social software. It is a novel idea to analyze the click-through rate of Chinese netizens’advertisements through the data provided by social software. Based on the idea of lookalike crowd expansion, this paper uses DeepFM algorithm and designs the corresponding experimental process to analyze and predict the clicks of Chinese netizens through social software.
A Knowledge-Based Conceptual Modelling Approach to Bridge Design Thinking and Intelligent Environments

One aspect of knowledge management is concerned with the alignment between what is captured in the heads of people and what is encoded by technology. The alignment of knowledge is necessary as humans possess an efficient ability to design innovation based on business insights, while technological systems are able to operating efficiently in different environments. To support knowledge management, this study presents systematic foundations covering a knowledge-based conceptual modelling approach. On a systematic level, three procedures are presented to facilitate the alignment of knowledge between people and technology: the decomposition of concepts from design thinking in conceptual models, the abstraction of capabilities from intelligent environments in conceptual models, and the (semi-) automated, intelligent transformation of conceptual models. Furthermore, the architecture of ICT infrastructure supporting the three procedures is addressed. These systematic foundations are integrated in the OMiLAB ecosystem and instantiated in two projects. The first project revolves around PRINTEPS, which is a framework to develop practical Artificial Intelligence. The second project revolves around s*IoT, which is a unifying semantic-aware modelling environment for the Internet of Things. Additionally, two concrete cases are presented for both project. Due to employing common systematic foundations, transfer and reuse among the two projects is facilitated.
Exploring mixed integer programming reformulations for virtual machine placement with disk anti-colocation constraints

One of the important problems for datacenter resource management is to place virtual machines (VMs) to physical machines (PMs) such that certain cost, profit or performance objective is optimized, subject to various constraints. In this paper, we consider an interesting and difficult VM placement problem with disk anti-colocation constraints: a VM’s virtual disks should be spread out across the physical disks of its assigned PM. For solutions, we use the mixed integer programming (MIP) formulations and algorithms. However, a challenge is the potentially long computation time of the MIP algorithms. In this paper, we explore how reformulation of the problem can help to reduce the computation time. We develop two reformulations, by redefining the variables, for our VM placement problem and evaluate the computation time of all three formulations. We show that they have vastly different computation time. All three formulations can be useful, but for different problem instances. They all should be kept in the toolbox for tackling the problem. Out of the three, formulation COMB is especially flexible and versatile, and it can solve large problem instances.
Dynamic Task-Specific Factors for Meta-Embedding

Meta-embedding is a technology to create a new embedding by combining different existing embeddings, which captures complementary aspects of lexical semantics. The supervised learning of task-specific meta-embedding is a convenient way to make use of accessible pre-trained word embeddings. However, the weights for different word embeddings are hard to calculate. We introduce the dynamic task-specific factors into meta-embedding (DTFME), which are utilized to calculate appropriate weights of different embedding sets without increasing complexity. Then, we evaluate the performance of DTFME on sentence representation tasks. Experiments show that our method outperforms prior works in several benchmark datasets.
Marine Vertebrate Predator Detection and Recognition in Underwater Videos by Region Convolutional Neural Network

In this paper, we present R-CNN, Fast R-CNN and Faster R-CNN methods to automatically detect and recognise the predators in underwater videos. We compare the results of these methods on real data and discuss their strengths and weaknesses. We build a dataset using footage captured from representative environment of the wild and devise a data model with three classes (seal, dolphin, background). Following this, we train R-CNN, Fast R-CNN and Faster R-CNN, then evaluate them on a test dataset compose of challenging objects that had not been seen during training. We perform evaluation on GPU, acquiring information about the AP and IOU for each model and network based on various proposal numbers as well as runtime speeds. Based on the results, we found that the best model of predator detection using visual deep learning models is Faster R-CNN with 2000 proposals.
Fake News Detection in Microblogging Through Quantifier-Guided Aggregation

Nowadays, big volumes of User-Generated Content (UGC) spread across various kinds of social media. In microblogging, UCG can be generated in the form of 'newsworthy' posts, i.e., related to information that has a public utility for the people. In this context, being the UGC diffused without almost any traditional form of trusted external control, the possibility of incurring in possible fake news is far from remote. For this reason, several approaches for fake news detection in microblogging have been proposed up to now, mostly based on machine learning techniques. In this paper, an ongoing work based on the use of the Multi-Criteria Decision Making (MCDM) paradigm to detect fake news is proposed. The aim is to reduce data dependency in building the model, and to have flexible control over the choices behind the fake news detection process.
Quantitative perspectives in Cognitive Linguistics

Abstract As a usage-based approach to the study language, cognitive linguistics is theoretically well poised to apply quantitative methods to the analysis of corpus and experimental data. In this article, I review the historical circumstances that led to the quantitative turn in cognitive linguistics and give an overview of statistical models used by cognitive linguists, including chi-square test, Fisher test, Binomial test, t-test, ANOVA, correlation, regression, classification and regression trees, naïve discriminative learning, cluster analysis, multi-dimensional scaling, and correspondence analysis. I stress the essential role of introspection in the design and interpretation of linguistic studies, and assess the pros and cons of the quantitative turn. I also make a case for open access science and appropriate archiving of linguistic data.
Coding with Asymmetric Numeral Systems

Asymmetric Numeral Systems (ANS) are an entropy-based encoding method introduced by Jarek Duda, combining the Shannon-optimal compression effectiveness of arithmetic coding with the execution efficiency of Huffman coding. Existing presentations of the ANS encoding and decoding algorithms are somewhat obscured by the lack of suitable presentation techniques; we present here an equational derivation, calculational where it can be, and highlighting the creative leaps where it cannot.
Skew information correlations beyond entanglement in dissipative two Su(2)-systems

We investigate the dynamics of quantum correlations [including skew information correlations beyond entanglement] of two isolated Su(2)-systems that are initially in a maximum Bell state or in a separable state. Each Su(2)-system is interacting with two-mode Su(1,1) coherent states. The time evolution of the density matrix of the two Su(2)-systems with the effect of intrinsic decoherence is analytically explored. The robustness of the initial quantum correlations for several initial coherence intensities, detunings, and intrinsic decoherences, is investigated. The robustness analysis of various correlation functions is based on the concurrence and quantum skew information. It is shown that the phenomena of sudden death and birth entanglement, and the sudden changes of local quantum uncertainty can be controlled. The correlations of the skew information present good robustness against the intrinsic decoherence and the detuning.
Who Tweets in Italian? Demographic Characteristics of Twitter Users

In this paper we try for the first time to shed light on the use of Twitter by the Italian speaking users quantifying the total audience and some relevant characteristics: in particular, gender and location. The attempt is based on publicly available APIs data referring both to profile documents and tweets. Through real-time calculation is possible to infer the gender mainly using the name field of the users' profile, while the geo-location is deduced using the location field and the geotagged tweets.
Real-valued syntactic word vectors

We introduce a word embedding method that generates a set of real-valued word vectors from a distributional semantic space. The semantic space is built with a set of context units (words) which are selected by an entropy-based feature selection approach with respect to the certainty involved in their contextual environments. We show that the most predictive context of a target word is its preceding word. An adaptive transformation function is also introduced that reshapes the data distribution to make it suitable for dimensionality reduction techniques. The final low-dimensional word vectors are formed by the singular vectors of a matrix of transformed data. We show that the resulting word vectors are as good as other sets of word vectors generated with popular word embedding methods.
Generative Adversarial Networks in AI-Enabled Safety-Critical Systems: Friend or Foe?

Generative adversarial networks can be exploited to launch attacks against detection systems that rely on artificial intelligence (AI). To build effective cyberphysical systems that are operationally robust and socially accepted, we must expend significant effort to develop novel AI-based safety-critical systems.
Answer-Focused and Position-Aware Neural Network for Transfer Learning in Question Generation

Question generation aims to generate natural questions conditioned on answer and the corresponding context pairs, which has the potential value of developing annotated data sets for natural language processing (NLP) researches in education, reading comprehension and question answering. In this paper, focusing on the problem that scarce labeled data of different domains may constrain researchers training their models using supervised or semi-supervised approaches to obtain more semantically similar questions in those areas, we present two transfer learning techniques to further improve a well-performed hybrid question generation model trained on one domain and adapted to another domain. The hybrid question generation model uses an answer-focused and position-aware neural network to improve the model’s ability on source domain data, while the utilization of transfer learning approaches can further enhance the performance of the hybrid model on target domain data. We conduct experiments on two data sets and a significant improvement in the model performance is observed which confirms our hypothesis that a preferable neural network model trained on one domain, attached with transfer learning approaches can further promote the performance on another domain.
An ethnic costumes classification model with optimized learning rate

In the convolutional neural network model, the learning rate represents the magnitude of the neural network parameters that change at each iteration. When the learning rate is too high, the loss function will oscillate without converging. When the learning rate is too low, the loss function converges slowly. How to set the appropriate value of the learning rate becomes an important issue. Based on the learning rate annealing algorithm, this paper sets the segmentation attenuation and adds periodic pulse perturbation. The learning rate gradually declines and rises at the end of the cycle, and then continues to fall. This prevents the network from oscillating at a local minimum or saddle point in the late training period due to the low learning rate [1]. Finally, this paper verifies the method in the application of ethnic clothing classification using the transfer learning VGG-16 model.
Word Embeddings for Unsupervised Named Entity Linking

The huge amount of textual user-generated content on the Web has incredibly grown in the last decade, creating new relevant opportunities for different real-world applications and domains. In particular, microblogging platforms enables the collection of continuously and instantly updated information. The organization and extraction of valuable knowledge from these contents are fundamental for ensuring profitability and efficiency to companies and institutions. This paper presents an unsupervised model for the task of Named Entity Linking in microblogging environments. The aim is to link the named entity mentions in a text with their corresponding knowledge-base entries exploiting a novel heterogeneous representation space characterized by more meaningful similarity measures between words and named entities, obtained by Word Embeddings. The proposed model has been evaluated on different benchmark datasets proposed for Named Entity Linking challenges for English and Italian language. It obtains very promising performance given the highly challenging environment of user-generated content over microblogging platforms.
Context for language understanding by intelligent agents

This paper describes the layers of context leveraged by language-endowed intelligent agents (LEIAs) during incremental natural language understanding (NLU). Context is defined as a combination of (a) the perceptual stimuli available to the agent at the given point in time, and (b) the knowledge ele ments and reasoning activated at the given stage of the agent’s interpretation of those stimuli. This approach to NLU addresses the treatment of a large number of difficult linguistic phenomena that are essential for high-quality NLU but are not being tackled by the knowledge-lean approaches that are typical of modern-day natural language processing. Although LEIAs are being developed as components of prototype application systems, this paper is not about implementations or evaluations – its contribution is conceptual, with everything described applicable to any artificial intelligent agent environment.
Development of a classifier system for a continuous environment

Abstract A learning classifier system is an adaptive system that obtains a set of appropriate action rules that adapts to multistep problems by training action rules defined in if‐then form by trial and error process, in a similar framework as reinforcement learning. Because of that the input signals of the classifier system are encoded into binary values, bit strings are often lengthened when dealing with such a problem that the state of the environment continuously changes. A neural network can treat with real values as input signal; however, it cannot be applied to multistep problems. This paper proposes a system that responds to problems such that the state of the environment continuously changes by combining a neural network and a classifier system, and actions are selected from multiple options, so that output can be defined as discrete values. In order to verify the effectiveness of the proposed system, this paper conducts several numerical experiments using benchmarks corresponding to multistep problems defined by continuous values.
SPLX-Perm: A Novel Permutation-Based Representation for Approximate Metric Search

Many approaches for approximate metric search rely on a permutation-based representation of the original data objects. The main advantage of transforming metric objects into permutations is that the latter can be efficiently indexed and searched using data structures such as inverted-files and prefix trees. Typically, the permutation is obtained by ordering the identifiers of a set of pivots according to their distances to the object to be represented. In this paper, we present a novel approach to transform metric objects into permutations. It uses the object-pivot distances in combination with a metric transformation, called n-Simplex projection. The resulting permutation-based representation, named SPLX-Perm, is suitable only for the large class of metric space satisfying the n-point property. We tested the proposed approach on two benchmarks for similarity search. Our preliminary results are encouraging and open new perspectives for further investigations on the use of the n-Simplex projection for supporting permutation-based indexing.
Taylor's Q1 “Visceral” level of information need: What is it?

Taylor (1968) dramatically stated that information seekers/searchers do not use their real Q1-level of information need when formulating their query to the system. Instead, they use a compromised Q4-level form of their need. The article directly confronts what Taylor's (1968) Q1-level information need is–the "actual" or "real" information need of the searcher. The article conceptually and operationally defines what Taylor's Q1-level of information need is using Belkin's (1980) ASK concept as a basis for designing a system intervention that shifts the searcher from representing the Q4-level compromised form of the need in her query to representing instead her Q1-level real information need. The article describes the Q1 Actualizing Intervention Model, which can be built into a system capable of actualizing the uncertainty distribution of the searcher's belief ASK so that information search is directed by the searcher's real Q1-level information need. The objective of the Q1 Actualizing Intervention Model is to enable in our Knowledge Age the introduction of intervention IR systems that are organic and human-centric, designed to initiate organic knowledge production processes in the searcher.
Age Differences in Menu Item Selection for Smartphone

Previous research found that icon background colors and symbols influenced menu selection time. However, the previous research did not consider non-generic users; especially, children and older adult users that have gathered momentum. Literature highlights the age-related differences in various dimensions, and how they are related to technology use. The purpose of the present study is to extend the previous research on the influence of icon background colors and symbols [1], by examining the age-related difference in smartphone menu item selection task, with the manipulation of icon background colors and icon symbols. A three-way ANOVA indicated that there are significant main effects of age group, background colors, and icon symbols, as well as high-order effects of the three variables.
An Improved Method for Named Entity Recognition and Its Application to CEMR

Named Entity Recognition (NER) on Clinical Electronic Medical Records (CEMR) is a fundamental step in extracting disease knowledge by identifying specific entity terms such as diseases, symptoms, etc. However, the state-of-the-art NER methods based on Long Short-Term Memory (LSTM) fail to exploit GPU parallelism fully under the massive medical records. Although a novel NER method based on Iterated Dilated CNNs (ID-CNNs) can accelerate network computing, it tends to ignore the word-order feature and semantic information of the current word. In order to enhance the performance of ID-CNNs-based models on NER tasks, an attention-based ID-CNNs-CRF model, which combines the word-order feature and local context, is proposed. Firstly, position embedding is utilized to fuse word-order information. Secondly, the ID-CNNs architecture is used to extract global semantic information rapidly. Simultaneously, the attention mechanism is employed to pay attention to the local context. Finally, we apply the CRF to obtain the optimal tag sequence. Experiments conducted on two CEMR datasets show that our model outperforms traditional ones. The F1-scores of 94.55% and 91.17% are obtained respectively on these two datasets, and both are better than LSTM-based models.
New extremal binary self-dual codes from a Baumert–Hall array

In this work, we introduce new construction methods for self-dual codes using a Baumert–Hall array. We apply the constructions over the alphabets F2 and F4+uF4 and combine them with extension theorems and neighboring constructions. As a result, we construct 46 new extremal binary self-dual codes of length 68, 26 new best known Type II codes of length 72 and 8 new extremal Type II codes of length 80 that lead to new 3−(80,16,665) designs. Among the new codes of length 68 are the examples of codes with the rare γ=5 parameter in W68,2. All these new codes are tabulated in the paper.
CapDRL: A Deep Capsule Reinforcement Learning for Movie Recommendation

Recommender systems provide users with a personalized list based on individual interests. There are three main challenges in traditional movie recommendation models: (1) considering recommendation procedure as a static one; (2) not taking user’s feedback into consideration; (3) it’s hard to extract similar features of items rated by users effectively. To address these, we propose a Deep Reinforcement Learning method based on the Capsule Network for the movie recommendation, called CapDRL. Roughly speaking, to solve the first two problems, we formulate the task of sequential interactions between users and recommender systems as a Markov Decision Process and automatically learn the optimal strategies by deep reinforcement learning. For the third problem, we leverage Capsule Network to dynamically decide what and how much similar information need be transferred from each item, which can capture the user’s preference. Experiments on real datasets indicate that CapDRL outperforms state-of-the-art methods, validating the effectiveness of our approach on the recommender system. In addition, we explore the effects of different features on the proposed model.