Reviewerly/exHarmony huggingface dataset:
Pushing the Boundary of Multimedia Big Data: An Overview of IEEE MIPR

New forms of multimedia data (such as text, numbers, tags, networking, signals, geo-tagged information, graphs/relationships, three-dimensional/VR/AR and sensor data, etc.) have emerged in many applications in addition to traditional multimedia data (image, video, audio). Multimedia has become the biggest of big data as the foundation of today's data-driven discoveries. Almost all disciplines of science and engineering, as well as social sciences, involve multimedia data in some forms, such as recording experiments, driverless cars, unmanned aerial vehicles, smart communities, biomedical instruments, security surveillance. Some recent events demonstrate the power of real-time broadcast of unfolding events on social networks. Multimedia data is not just big in volume, but also multi-modal and mostly unstructured. Storing, indexing, searching, integrating, and recognizing from the vast amounts of data create unprecedented challenges. Even though significant progress has been made processing multimedia data, today's solutions are inadequate in handling data from millions of sources simultaneously. The IEEE International Conference on Multimedia Information Processing and Retrieval (IEEE-MIPR) aims to provide a forum for original research contributions and practical system design, implementation, and applications of multimedia information processing and retrieval for single modality or multiple modalities. The target audiences are university researchers, scientists, industry practitioners, software engineers, and graduate students who want to become acquainted with technologies for big data analytics, machine intelligence, information fusion in multimedia information processing and retrieval. Following a successful debut in Miami, Florida in 2018, the second MIPR, held in San Jose, California, attracted over 140 submissions and more than 100 attendees—a big growth from its inaugural event and an indication of its growing visibility and popularity in the multimedia research society. In addition to four keynote talks, MIPR'19 also featured six innovation multimedia forums organized by both academic and industrial researchers, with topics ranging from computer vision, self-driving, Natural Language Processing, next generation video display, to cutting edge video/audio coding techniques.
SocialDistance

Verified users on online social media (OSM) largely determine the quality of OSM services and applications, but most OSM users are unverified due to the significant effort involved in becoming a verified user. This paper presents SocialDistance, a novel technique to identify unverified users that can be considered as trustworthy as verified users. SocialDistance is motivated by the observation that online interactions initiated from verified users towards unverified users can translate into some sort of trustworthiness. It treats all verified users equally and assigns a trust score between 0 and 1 to each unverified user. The higher the trust score, the closer an unverified user to verified users. We propose various metrics to model the interactions from verified to unverified users and then derive corresponding trust scores. SocialDistance is thoroughly evaluated with large Twitter datasets containing 276,143 verified users and 19,047,202 unverified users. Our results demonstrate that SocialDistance can produce a non-trivial number of unverified users that can be regarded as verified users for OSM applications. We also show the high efficacy of SocialDistance in sybil detection, a fundamental operation performed by virtually every OSM operator.
Pedestrian-Motorcycle Binary Classification Using Data Augmentation and Convolutional Neural Networks

One common problem in vehicle and pedestrian detection algorithms is the mis-classification of motorcycle riders as pedestrians. This paper focused on a binary classification technique using convolutional neural networks for pedestrian and motorcycle riders in different road context locations. The study also includes a data augmentation technique to address the un-balanced number of training images for a machine learning algorithm. This problem in un-balanced data sets usually cause a prediction bias, in which the prediction for a learned data set usually favors the class with more image representations. Using four data sets with differing road context (DS0, DS3-1, DS4-3, and DS4-3), the binary classification between pedestrian and motorcycle riders achieved good results. In DS0, training accuracy is 96.96% while validation accuracy is 81.52%. In DS3-1, training accuracy is 93.17% while validation accuracy is 86.58%. In DS4-1, training accuracy is 94.42% while validation accuracy is 97.00%. In DS4-3, training accuracy is 95.94% while validation accuracy is 88.59%.
HEIDL: Learning Linguistic Expressions with Deep Learning and Human-in-the-Loop

While the role of humans is increasingly recognized in machine learning community, representation of and interaction with models in current human-in-the-loop machine learning (HITL-ML) approaches are too low-level and far-removed from human’s conceptual models. We demonstrate HEIDL, a prototype HITL-ML system that exposes the machine-learned model through high-level, explainable linguistic expressions formed of predicates representing semantic structure of text. In HEIDL, human’s role is elevated from simply evaluating model predictions to interpreting and even updating the model logic directly by enabling interaction with rule predicates themselves. Raising the currency of interaction to such semantic levels calls for new interaction paradigms between humans and machines that result in improved productivity for text analytics model development process. Moreover, by involving humans in the process, the human-machine co-created models generalize better to unseen data as domain experts are able to instill their expertise by extrapolating from what has been learned by automated algorithms from few labelled data.
Risk-based Decision-Support for Vulnerability Remediation in Electric Power Networks

Power grids are becoming increasingly intelligent. However, the boundaries between their operational technology (OT) environments and the (vulnerable) IT networks tend to dissolve. In such systems, several technical and economic factors can significantly affect the upgrading decisions. These factors include, just to name a few, limited time and budget as well as legal constraints. To cope with these challenges, an involved decision maker has to prudently prioritize the possible vulnerability remediation actions. The key objective of prioritization decisions is to efficiently reduce the inherent security risk to which the system in question is exposed. Due to the critical role of power systems, their decision makers tend to enhance the system resilience against extreme events. That is, they seek to avoid decision options associated with extreme (adverse) consequences. Therefore, we propose an integrated risk-based decision-support methodology for prioritizing risk remediation activities. It leverages (i) the Time-To-Compromise (TTC) metric to quantitatively assess the security risk, and (ii) a game-theoretical model to support the decision-making process. The game model considers carefully the specific risk attitude of the decision makers involved in the patch management process across electric power organizations.
Research on Fall Detection System of Wearable Devices Based on ELM Algorithm

The aging phenomenon in current society is getting more and more serious, and the health status of the elderly is facing challenges. A fracture or other disease caused by a fall, or a fall due to a sudden illness, may result in serious consequences if not treated in time. According to the difference between fall and normal activities, this paper proposes a method for detecting human fall by ELM based on inertial measurement unit. By placing the inertial measurement unit on the human body, the human acceleration and angular velocity data are sampled and the human body attitude is calculated. The ELM training is performed on the human characteristic data to obtain the ELM classification function, and the measurement data is classified by the ELM classification function to determine whether it is in a falling state. The human fall experiment is carried out, and the result shows that the ELM method can accurately detect the fall state of the human. The use and promotion of this method is of great significance for the treatment of falls and reduction of casualties.
Software Modularization by Combining Genetic and Hierarchical Algorithms

Modularization is used as one of the stages of reverse engineering to understand a software system. Due to the importance of understanding a software system for development and maintenance, software modularization is considered as one of the challenging tasks and it is important to find a suitable and understandable modularization. The aim of modularization is to partition a software system from the source code into meaningful and comprehensible subsystems (modules). In the literature, the hierarchical and search-based methods are usually used for software modularization. The major advantage of hierarchical methods is that they can show the hierarchical characteristic of a source code and therefore, make the software better and easier to understand. However, the main drawback of these methods is that they are greedy; and since software modularization problem is an NP-Hard problem, are not able to find a proper modularization. In contrast, the search-based methods are able to find a more acceptable modularization; however, the main disadvantage of these methods is that they could not represent the hierarchical characteristic of a source code. In this paper, a combined method (combining hierarchical method with the search-based method) is proposed to use the advantages of both methods. The results on a number of software systems demonstrate that in modularization with the proposed method, not only the quality of modularization is improved over hierarchical methods, but also the hierarchical characteristic make the software structure easy to understand.
Two-Stage Latent Dynamics Modeling and Filtering for Characterizing Individual Walking and Running Patterns with Smartphone Sensors

Recently, data from built-in sensors in smartphones have been readily available, and analyzing data for various types of health information from smartphone users has become a popular health care application area. Among relevant issues in the area, one of the most prominent topics is analyzing the characteristics of human movements. In this paper, we focus on characterizing the human movements of walking and running based on a novel machine learning approach. Since walking and running are human fundamental activities, analyzing their characteristics promptly and automatically during daily smartphone use is particularly valuable. In this paper, we propose a machine learning approach, referred to as ’two-stage latent dynamics modeling and filtering’ (TS-LDMF) method, where we combine a latent space modeling stage with a nonlinear filtering stage, for characterizing individual dynamic walking and running patterns by analyzing smartphone sensor data. For the task of characterizing movements, the proposed method makes use of encoding the high-dimensional sequential data from movements into random variables in a low-dimensional latent space. The use of random variables in the latent space, often called latent variables, is particularly useful, because it is capable of conveying compressed information concerning movements and efficiently handling the uncertainty originating from high-dimensional sequential observation. Our experimental results show that the proposed use of two-stage latent dynamics modeling and filtering yields promising results for characterizing individual dynamic walking and running patterns.
LC/NACO Authority File in the Library of Congress BIBFRAME Pilots

The LC/NACO Authority File is a large cooperatively-maintained authority file that also functions as the Library of Congress local name authority file. In the Library of Congress BIBFRAME Pilots, catalogers access the LC/NACO Authority File as Linked Data in MADS/RDF serialization, through the Library of Congress Linked Data Service and the BIBFRAME Editor. This article explores the steps that enable BIBFRAME Pilot participants to complete authority work in a Linked Data environment. It also addresses the limitations, opportunities, and successes encountered by BIBFRAME Pilot participants as they interact with name authority data as Linked Data.
Novelty detection improves performance of reinforcement learners in fluctuating, partially observable environments

Evolved and engineered organisms must adapt to fluctuating environments that are often only partially observed. We show that adaptation to a second environment can be significantly harder after adapting to a first, completely unrelated environment, even when using second-order learning algorithms and a constant learning rate. In effect, there is a lack of fading memory in the organism’s performance. However, organisms can adapt well to the second environment by incorporating a simple novelty detection algorithm that signals when the environment has changed and reinitializing the parameters that define their behavior if so. We propose that it may be fruitful to look for signs of this novelty detection in biological organisms, and to engineer novelty detection algorithms into artificial organisms.
Bayesian Deconditional Kernel Mean Embeddings

Conditional kernel mean embeddings form an attractive nonparametric framework for representing conditional means of functions, describing the observation processes for many complex models. However, the recovery of the original underlying function of interest whose conditional mean was observed is a challenging inference task. We formalize deconditional kernel mean embeddings as a solution to this inverse problem, and show that it can be naturally viewed as a nonparametric Bayes' rule. Critically, we introduce the notion of task transformed Gaussian processes and establish deconditional kernel means as their posterior predictive mean. This connection provides Bayesian interpretations and uncertainty estimates for deconditional kernel mean embeddings, explains their regularization hyperparameters, and reveals a marginal likelihood for kernel hyperparameter learning. These revelations further enable practical applications such as likelihood-free inference and learning sparse representations for big data.
Is lip-reading the secret to security?

Today's best user authentication systems are multi-factor: people are verified via something they know (a pin or password), something they have (like a key fob), and something they are (biometric authentication). The ‘something they are’ factor provides very reliable user authentication by identifying the individual from a unique physiological or behavioural characteristic, like their fingerprint, voice, face, lip movement or keystroke analysis. These biometric modalities are accurate, easy-to-use and should be difficult to compromise – but experience has shown that biometrics are susceptible in varying degrees to ‘spoofing’. So how can this be prevented?
Ciphertext-Delegatable CP-ABE for a Dynamic Credential: A Modular Approach

We introduce a new technique converting Ciphertext-policy Attribute-based Encryption (CP-ABE) to Ciphertext-delegatable CP-ABE (CD-CP-ABE). Ciphertext delegation is an important technique to deal with dynamic credentials, which enable users to be joined and revoked at any time while the system is operating. The delegation of CD-CP-ABE allows third parties such as cloud or proxy servers to convert a ciphertext to the other one with a more restrictive policy. Therefore, it can be used to revoke users dynamically in an access control system. Prior to our work, a delegation algorithm of CD-CP-ABE is not generic and the completeness of the delegation is shown when the size of the delegated access structure increases quadratically with the sizes of original and revocation access structures. In this paper, we provide a generic delegation algorithm to reform CP-ABE to CD-CP-ABE. We generalize properties necessary for the ciphertext delegation using the syntax of encodings for the modularity and construct a generic delegation algorithm based on those properties. In our new technique, we build the delegated access structures, which generally determines the size of the ciphertext, in a defined way. The size of delegated access structures grows only linearly with those of original and revocation access structures. Through presenting instances, we show that our technique is readily applicable to existing CP-ABE schemes including CP-ABE scheme with non-monotonic access structures.
JTML at SemEval-2019 Task 6: Offensive Tweets Identification using Convolutional Neural Networks

In this paper, we propose the use of a Convolutional Neural Network (CNN) to identify offensive tweets, as well as the type and target of the offense. We use an end-to-end model (i.e., no preprocessing) and fine-tune pre-trained embeddings (FastText) during training for learning words’ representation. We compare the proposed CNN model to a baseline model, such as Linear Regression, and several neural models. The results show that CNN outperforms other models, and stands as a simple but strong baseline in comparison to other systems submitted to the Shared Task.
Spatial Analysis of New Testament Textual Emendations Utilizing Confusion Distances

Abstract Before the interpretation of any text can start, the original wording of the text itself must be critically established. Conventionally, this is done following qualitative criteria. This article, however, explores the application of spatial analyses to New Testament textual criticism by demonstrating how the Levenshtein edit distance could be adapted to calculate confusion distances for variant readings in New Testament manuscripts, i.e. the possibility that a (combination of) letter(s) is confused by another (combination of) letter(s). Subsequently the outcomes are translated to Euclidian space using classical Multi-Dimensional Scaling, which enables visualisation and spatial analyses (in this case not related to geographical space). The article focuses on the data preparation and algorithm to make the data suitable for spatial analyses, thus providing the New Testament textual critic with new analytical tools.
UA at SemEval-2019 Task 5: Setting A Strong Linear Baseline for Hate Speech Detection

This paper describes the system developed at the University of Alicante (UA) for the Se-mEval 2019 Task 5: Multilingual detection of hate speech against immigrants and women in Twitter.The purpose of this work is to build a strong baseline for hate speech detection by means of a traditional machine learning approach with standard textual features, which could serve as a reference to compare with deep learning systems.We participated in both task A (Hate Speech Detection against Immigrants and Women) and task B (Aggressive behavior and Target Classification) for both English and Spanish.Given the text of a tweet, task A consists of detecting hate speech against women or immigrants in the text, whereas task B consists of identifying the target harassed as individual or generic, and to classify hateful tweets as aggressive or not aggressive.Despite its simplicity, our system obtained a remarkable macro-F1 score of 72.5 (sixth highest) and an accuracy of 73.6 (second highest) in Spanish (task A), outperforming more complex neural models from a total of 40 participant systems.
Wearable Wireless Devices

With the growing interest in the use of technology in daily life, the potential of using wearable wireless devices across multiple segments, e [...]
Performance Metrics and Validation Methods for Vehicle Position Estimators

Future applications of the intelligent traffic systems (ITS) will strongly rely on highly detailed traffic data. Beyond aggregated traffic parameters, such as flux, mean speed, and density, used in macroscopic traffic analysis, a continuous location estimation of individual vehicles on a microscopic scale will be required. Different data acquisition techniques based on GPS, radar, and computer vision systems have been already focused in recent years. Although the published studies in many cases include a validation of the developed methods, a more specific analysis on the performance of the vehicle position estimation is necessary. This paper presents a systematic approach to measure the performance of the position estimators based on a comparison of the automatically recorded results to manually generated ground truth of individual vehicle positions in video segments. Furthermore, the methods described in this paper enable a straight forward error handling, which is very useful in the development of position estimators. The concept is exemplified on computer vision-based vehicle tracking.
Summarization of Graph Using Question Answer Approach

From past 400 years, charts, graphs, and other visual illustration of data have become a most important medium for communicating quantitative information (Giles et al. in Proceedings of the 6th ACM/IEEE-CS Joint Conference on Digital Libraries. ACM, pp. 339–340, 2006 [1]). It contains important information and can provide essential knowledge that is not duplicated in other data formats. One important type of graph summarization is to produce small, and interactive question answer type summary based on user-based asked questions. Graphical representation, diagrams, charts, and graphs are main types of visual data in scientific domains. To detect, extract, and recognize text present in graphical are the important steps of the process. Generally textual data in charts/graphs includes axes titles, tick labels, legends, captions, notes. In general, OCR engines used, such as Tesseract OCR, ABBYY Fine Reader (Fei-Fei et al. in Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology. ACM, pp. 393–402, 2011 [2]), MODI (Microsoft Office Document Imaging) (Fomina and Vassilieva in Pattern Recogn Image Anal 23:139–144, 2013 [3]), are used to find and identify text in scanned document images. We used Tesseract OCR engine (Fei-Fei et al. in Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology. ACM, pp. 393–402, 2011 [2]) in our experiments. Particularly, the accuracy of recognition by Tesseract OCR is up to 97% when the scanned document images are used. Tesseract OCR also has some disadvantages like, it become failure in the detection of small text regions composed of one or a few words; such text regions are typical for chart images. In our proposed work, we try to resolve the failure outputs. After being cropped and deskewed, the detected text regions are passed to an OCR engine. When the proposed algorithm for text detection and localization is used as a preprocessing step, the experimental results show a significant increase in the recognition rate. For the work presented in this paper, the technical charts such as bar chart, pie chart are taken as input.
An Agent-Oriented Group Decision Architecture

Group decisions are useful and crucial across socio-technical contexts, but there is a lack of generic, systematic, and comprehensive architecture that can support decision-making practically. In this paper, we propose an agent-oriented group decision architecture. It provides separate but unified representation formalisms for both global interaction protocols and local decision rules. An accompanied runtime coordination mechanism is offered, as well as an engine for agent interpretation of global and local levels of interactions towards decision-making. The architecture is general enough for group decision-making across disciplines.