Reviewerly/exHarmony huggingface dataset:
Spatiotemporal Models of Human Activity for Robotic Patrolling

We present a method that allows autonomous systems to detect anomalous events in human-populated environments through understating of their structure and how they change over time. We represent the environment by temporary warped space-hypertime continuous models derived from patterns of changes driven by human activities within the observed space. The ability of the method to detect anomalies is evaluated on real-world datasets gathered by robots over the course of several weeks. An earlier version of this approach was already applied to robots that patrolled offices of a global security company (G4S).
Sound Shifting

Sound Shifting is an artistic research project that focuses on the physical representation of sound - this means the visualization and materialization of invisible phenomena that significantly shape our perception. We present a system that allows the transformation from sound into form in real-time by using a newly developed machine, the Audio Foam Cutter. This machine converts sound into polystyrene stripes that are arranged into sculptural objects. The resulting sound sculptures provide information about the represented sounds by their shape and aesthetic features and expand the range of our auditory perception to the tangible domain. The sound sculptures are snapshots of our soundscape and form a physical archive of sound representations. The Sound Shifting project aims to create an awareness of the materiality of sonic movements and affects.
Distributional Semantics of Clinical Words

Word embeddings are the distributed representation of the words in numerical form. Recent research in word embeddings shows the importance of using them in deep learning algorithms. Word embeddings are commonly leveraged as feature inputs to many deep learning models. A lot of research in word embeddings helped in various open source releases of embeddings to the larger deep learning community. However, these embeddings are trained on generic corpus which limits their use for domain specific tasks. In our paper, we propose a transfer learning based approach to train the skip gram model for medical terms. In addition to pre-trained embeddings, we also added customized clinical knowledge for each term in our training data. We have compared our word embeddings with Google pre-trained word embeddings. Both the pre-trained embeddings and embeddings from our approach are used for training a Named entity recognition classifier and follow-up detection use case on radiology reports. We are able to achieve improvement in the Fl-score and also observed faster convergence in respective NLP classification tasks.
Selection and Analysis of Objects in Multi-Threshold Image Processing

Selection and classification of objects according to certain objective criteria is one of the immense tasks for automated image analysis in computer vision systems. Here we revisit a recently suggested approach to the object selection problem based on choosing the optimal threshold to select a certain subclass of objects determined by objective criteria such as size, shape etc. While our earlier investigations mainly focused on the analysis of microscopic images, here we concentrate more on remote sensing applications. Besides revisiting the previously suggested method based on the size vs threshold histogram analysis, we also suggest several additional quantities that can be effectively used to find the best threshold once the empirical histograms exhibit several characteristic peaks. Moreover, similar approach could be useful for an automatic distinction between subclasses of objects with different morphology, thus resolving not only the selection but also the classification problem.
Lectures and Discussions in Semi-Immersive Virtual Reality Learning Environments

This chapter describes a study that was conducted in a semi-immersive desktop virtual reality environment. The study investigated teacher trainees' perceptions of their mental effort in Second Life, their satisfaction with the communication modalities, and their perceived social behavioral changes. In the first event, only the instructor (host) used voice to communicate while all participants as well as the in-text facilitator (co-host) used text chat only. In the second event, not only did both hosts use voice, but the participants also had the option to use voice rather than text. The majority of teacher trainees appreciated the freedom to choose either modality. The integration of voice was perceived as humanizing the discussion, increasing the flow, and making the conversation more engaging. However, the addition of multiple voices was believed to increase their mental effort. While some teacher trainees felt more relaxed and more open in a virtual discussion, others reported a lack of attention and honesty as well as a tendency to ignore social conventions.
Matrix-Based Method for Inferring Elements in Data Attributes Using a Vector Space Model

This article addresses the task of inferring elements in the attributes of data. Extracting data related to our interests is a challenging task. Although data on the web can be accessed through free text queries, it is difficult to obtain results that accurately correspond to user intentions because users might not express their objects of interest using exact terms (variables, outlines of data, etc.) found in the data. In other words, users do not always have sufficient knowledge of the data to formulate an effective query. Hence, we propose a method that enables the type, format, and variable elements to be inferred as attributes of data when a natural language summary of the data is provided as a free text query. To evaluate the proposed method, we used the Data Jacket’s datasets whose metadata is written in natural language. The experimental results indicate that our method outperforms those obtained from string matching and word embedding. Applications based on this study can support users who wish to retrieve or acquire new data.
A Measurable SocialToTech Software Trust Framework

With the rapid development of Internet, software trustworthiness has been increasingly important. How to measure this property has become a real challenge. Based on Sociology Trust system, this paper presents a framework which could be measurable, called as SocialToTech Software Trust Framework (SSTF). The SSTF includes three parts of software trustworthiness and this paper proposes a whole metric solution through a generalized indicator Cost-Loss.
Hierarchical Scenarios for Behavior Planning in Autonomous Robots

Task scheduling and behavior planning are important components for enabling robot autonomy in a number of applied problems. This paper provides a methodology for straightforward description and automatic formation of scenarios that lead a robot or a multi-robot system to a successful completion of a given complex task. We employ a finite-automata approach to definition of individual tasks within the scenario, which as a whole is represented by an oriented graph. A multilevel scenario formation is described for simplification of the traveling salesman problem and for scene analysis in the problem of demolition debris removal. The input of the proposed algorithm is a weakly structured sensory data. The produced output contains the hierarchical scenario in the form of a recursively nested graph.
Multi-space-enabled deep learning of breast tumors improves prediction of distant recurrence risk

In this study, we proposed a multi-space-enabled deep learning modeling method for predicting Oncotype DX recurrence risk categories from digital mammogram images on breast cancer patients. Our study included 189 estrogen receptor-positive (ER+) and node-negative invasive breast cancer patients, who all have Oncotype DX recurrence risk score available. Breast tumors were segmented manually by an expert radiologist. We built a 3- channel convolutional neural network (CNN) model that accepts three-space tumor data: the spatial intensity information and the phase and amplitude components in the frequency domain. We compared this multi-space model to a baseline model that is based on sorely the intensity information. Classification accuracy is based on 5- fold cross-validation and average area-under the receiver operating characteristics curve (AUC). Our results showed that the 3-channel multi-space CNN model achieved a statistically significant improvement than the baseline model.
An Efficient Consensus Protocol for Real-Time Permissioned Blockchains Under Non-Byzantine Conditions

Blockchains are increasingly used in the collaboration between business as a trusted distributed ledger. Coping with massive data transactions raises the requirement of real-time safety of blockchains. The celebrated Raft protocol has limitations of being a consensus protocol for permissioned blockchains where a strong consistency is needed between clients and servers. In this work, we propose a new consensus protocol called Dynasty which ensures the real-time safety and the liveness under all non-Byzantine conditions. We design and implement a three-layer permissioned blockchain framework which tolerates f failures with 2f + 1 correct servers based on Dynasty. We demonstrate the blockchain as a service in an application of used-vehicle trading management and evaluate the performance of the blockchain framework in terms of throughput and latency. Experimental results show that while the latency in different scales of the system increases as expected, the number of committed transactions per second stabilizes at a point within less than 8% difference after a warming-up period.
Determining underlying presence in the learning of grammars that allow insertion and deletion

The simultaneous learning of a phonological map from inputs to outputs and a lexicon of phonological underlying forms has been a focus of several research efforts (Jarosz 2006; Apoussidou 2007; Merchant 2008; Merchant &amp;amp; Tesar 2008; Tesar 2014). One of the numerous challenges is that of computational efficiency, which led to the investigation of learning with output-driven maps (Tesar 2014). Prior work on learning with output-driven maps has focused on systems in which the only disparities between inputs and outputs were segmental identity disparities (differences in the value of a feature). Inclusion of segmental insertion and deletion disparities exacerbates computational concerns, as it increases the number of possible correspondence relations between an input and an output, and makes the space of possible inputs for a word infinite due to the possible presence of an unbounded number of deleted segments. We propose an extension of that earlier work to handle phonologies that permit insertion and deletion, and evaluate the proposal by applying it to cases in Basic CV Syllable Theory (Jakobson 1962; Clements &amp;amp; Keyser 1983; Prince &amp;amp; Smolensky 2004). First, we propose that a learner represent information about the possible presence/absence of a segment in an underlying form via a presence feature. The presence feature can be set using the same inconsistency detection method that has previously been used to set other segmental features. This allows the learner to combine evidence from paradigmatically related words in a single compact representation. Second, we propose that the learner only consider for underlying forms segments that surface in at least one surface realization of the morpheme. This approach is justified by the structure of output-driven maps, and avoids the potential for an unbounded number of possibly deleted segments in an underlying form. A proof is given for the validity of the method for avoiding unbounded deletion. The resulting learner is able to learn some grammatical regularities about segmental insertion and deletion; this is shown via two manual step-by-step applications of the algorithm. Verificatory simulations for learning the entire typology of Basic CV Syllable Theory are left to work in the near future.
Transference

Transference is a hybrid computational system for improvised violin performance. With hardware sensors and digital signal processing (DSP), the system shapes live acoustic input and computer-generated sound. An electromyographic (EMG) sensor unobtrusively monitors movements of the left hand, while a custom glove controller tracks bowing gestures of the right arm. Through continuous musical gesture the performer is able to actuate and perturb streams of computationally transmuted audio. No additional layers of windowing or semantically-inflected processes of machine learning mediate this process. Remaining at the level of signal processing, the lack of windowed and/or statistical mediation creates a sense of fine-grain tactility and physical transduction for the performer. The strategies employed are sufficiently generalizable to apply to situations beyond those imagined and implemented here within the scope of augmented violin performance.
Multimodal Tweet Sentiment Classification Algorithm Based on Attention Mechanism

With the rapid development of Internet, multimodal sentiment classification has become an important task in natural language processing research. In this paper, we focus on the sentiment classification of tweets that contains both text and image, a multimodal sentiment classification method for tweets is proposed. In this method Bidirectional-LSTM model is used to extract text modality features and VGG-16 model is used to extract image modality features. Where all features are extracted, a new multimodal feature fusion algorithm based on attention mechanism is used to finish the fusion of text and image features. This fusion method proposed in this paper can give different weights to modalities according to their importance. We evaluated the proposed method on the Chinese Weibo dataset and SentiBank Twitter dataset. The experimental results show method proposed in this paper is better than models that only use single modality feature, and attention based fusion method is more efficient than directly summing or concatenating features from different modalities.
Adversarial Attacks and Defense on Deep Learning Models for Big Data and IoT

Intelligent systems are capable of doing tasks on their own with minimal or no human intervention. With the advent of big data and IoT, these intelligence systems have made their ways into most industries and homes. With its recent advancements, deep learning has created a niche in the technology space and is being actively used in big data and IoT systems globally. With the wider adoption, deep learning models unfortunately have become susceptible to attacks. Research has shown that many state-of-the-art accurate models can be vulnerable to attacks by well-crafted adversarial examples. This chapter aims to provide concise, in-depth understanding of attacks and defense of deep learning models. The chapter first presents the key architectures and application domains of deep learning and their vulnerabilities. Next, it illustrates the prominent adversarial examples, including the algorithms and techniques used to generate these attacks. Finally, it describes challenges and mechanisms to counter these attacks, and suggests future research directions.
A Context-Aware Approach for Generating User Interfaces Based on Usability Requirements

The advent of mobile devices has raised new challenges. One main challenge concerns the quality of the dialogue between human beings and interactive systems. This dialogue pertains principally to the user interface since it is the visible part of the systems to humans. Developing a usable interface is crucial for the success or failure in the actual use of mobile applications. One of the important research issues for the effective use of user interfaces is regarding how to conduct usability requirements in ubiquitous environments. The present article addresses this issue taking into account context-awareness. The authors propose a methodology in this direction that considers the human factors while designing user interfaces. Particularly, the approach focuses on how to select consistent usability requirements and how to incorporate them into user interface development process considering the context of use. An illustrative case study is presented. Finally, an experimental platform for interface evaluation is proposed.
Correction to “Characterizing and Predicting Early Reviewers for Effective Product Marketing on E-Commerce Websites”

Presents corrections to author information from the paper, “Characterizing and predicting early reviewers for effective product marketing on e-commerce websites,” (Bai, T., et al), IEEE Trans. Knowl. Data Eng., vol. 30, no. 12, pp. 2271–2284, Dec. 2018.
Fast Side-Channel Security Evaluation of ECC Implementations

Horizontal attacks are a suitable tool to evaluate the (nearly) worst-case side-channel security level of ECC implementations, due to the fact that they allow extracting a large amount of information from physical observations. Motivated by the difficulty of mounting such attacks and inspired by evaluation strategies for the security of symmetric cryptography implementations, we derive shortcut formulas to estimate the success rate of horizontal differential power analysis attacks against ECSM implementations, for efficient side-channel security evaluations. We then discuss the additional leakage assumptions that we exploit for this purpose, and provide experimental confirmation that the proposed tools lead to good predictions of the attacks' success.
A personalized context-aware soft keyboard adapted by random forest trained with additional data of same cluster

Soft keyboard has been popular since the smartphone made an appearance. As the smartphone is a personal device used for user's preference, we need to personalize the soft keyboard. In order to make a personal context-aware keyboard, a large amount of data is required from each individual, but it is difficult to collect the data enough. In this paper, we propose a novel method to construct random forests with additional data of the same clusters, which copes with small amount of the individual data by adding similar data through K-means clustering algorithm. In addition, for the personalization of the soft keyboard, the preferred GUIs are recommended according to the activities and input hand postures of the user recognized by the random forest models trained with the clustered data. To train the proposed system, we have collected the data from 200 people. Each person can use the most necessary keyboard by selecting the appropriate GUI for each situation depending on the smartphone usage activity and input hand postures. The proposed system showed better performance than a model using all common data and a model using individual data only.
Gaussian Markov Random Fields-Based Features for Volumetric Texture Segmentation

A new method based on three dimensional Gaussian Markov Random fields (3D-GMRF) is proposed in this paper for volumetric texture segmentation (VTS). A feature vector is extracted for each voxel in a given volumetric texture image. These feature vectors that consist of the estimated parameters of the GMRF and form the parameter volume are employed to segment volumetric textures. To overcome the issues related to boundaries and isolated voxels, a solution is proposed by sliding an averaging volume inside the parameter volume to assign each voxel a new feature vector derived as the mean of the surrounding voxels that are collected by the averaging volume. Our proposed method is evaluated on a synthetic volumetric texture and compared with another method demonstrating good segmentation performance. A further evaluation is carried out to examine the performance of the method proposed here in the presence of noise to show robustness to noise.
Centralized cloud information accountability integrity with Firefly Key Generation Algorithm (CCIAI‐FKGA) for cloud environment

Summary Cloud Computing (CC) is fundamentally a current and quick evolving domain of improvement in applications. In this research work, with the objective of dealing with security issue, a new and hugely Centralized Cloud Information Accountability Integrity with Firefly Key Generation Algorithm (CCIAI ‐FKGA) framework is introduced for keeping track of the real utilization of the healthcare data of the users in the CC. Particularly, the programmable capability of Java Archives (JAR) files is leveraged so as to include the logging methodology in conjunction with healthcare data of the users along with the policies. Third, every customer produces a pair of public as well as private keys dependent upon CP‐ABE with key generation making use of FKGA and trapdoor generator. Fourth, by trapdoor generator, data integrity in user level as well as cloud server level is carried out. Developing a working principle of the CCIAI‐FKGA framework as well as assess its performance dependent upon the time taken for the period of numerous operations strictly confirm the functioning of CCIAI‐FKGA framework with the help of cloud simulator. The outcomes confirmed that CCIAI‐FKGA framework contains the prospective to be effective in protected data sharing in the CC environment.
A watermarking scheme based on DCT, SVD and BCM

Many techniques are used to solve problems of security in the Internet such as cryptography or watermarking. In this context watermarking is a way for protecting copyright and proving authenticity of a digital data. In this paper, a non blind digital watermark scheme is proposed. It is based on Discrete Cosine transformation (DCT), singular Values Decomposition (SVD) and Beta Chaotic Map (BCM). The experimental results show that this scheme is robust against several attacks compared to other algorithms.
Diversity and Inclusion

The academic library profession has discussed the importance of diversity and inclusion, but has yet to explore their intersection with social media marketing. Given changing demographics and an increase in activism on college campuses, libraries must produce social media content that resonates with underrepresented groups. This chapter introduces strategies for effectively incorporating diversity and inclusion into social media and advice on avoiding mistakes. The author examines social media use at her library and lessons learned from a campaign that received criticism. The chapter concludes with challenges to practicing diversity in social media, including recruiting diverse staff and discrimination that emerges from new social media technologies.
Named Entity Recognition Based on BiRHN and CRF

Named entity recognition is one of the basic work in the field of natural language processing. By utilizing bidirectional LSTM, Lample achieved the best results in the field of named entity recognition in 2016. In this paper, we propose a new neural network structure based on Recurrent Highway Networks (BiRHN for short) and Conditional Random Field (CRF for short). RHN is a good solution to the problem caused by gradients, which extends the LSTM architecture to allow step-to-step transition depths larger than one. Experiments on several datasets show that our model achieves better results (F1 values) than Lample.