Reviewerly/exHarmony huggingface dataset:
Statistical Model of Total Target Registration Error in Image-Guided Surgery

In a paired-point rigid registration, target registration error (TRE) is deemed to be the most important quality metric. TRE usually cannot be directly measured, and thus many TRE estimation algorithms have been proposed. However, target localization errors (TLEs) in two spaces are not considered in the definition of TRE. In this paper, we propose a new type of evaluation metric that is referred to as total TRE (TTRE) at a given target point with TLE incorporated. Statistics including mean, root mean square (rms), and covariance matrix of TTRE are derived without making any assumption of the TLE magnitude. TTRE and fiducial registration error (FRE) are proved to be uncorrelated when an ideal weighting scheme is adopted in solving the registration problem. The proposed error model is validated through extensive experiments. In the first experiment with random fiducials and targets, in 90% of the test cases, there shows no difference between the predicted and simulated TTRE statistics when six fiducials are used. In the second experiment of deep-brain stimulation surgery, the mean value of CC(TTRE,FRE) being 8.9246 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> ± 0.0389 was observed, which indicates that TTRE and FRE are uncorrelated. In the third experiment of surgical tool-tip tracking, the mean and standard deviation of percentage differences between predicted and simulated TTRE rms values are 2.22% ± 0.77% for the planar tool and 2.62% ± 0.59% for the textral tool. In summary, our proposed algorithm can well model the TTRE metric.
A Security Model for the Enhancement of Data Privacy in Cloud Computing

As we all are aware that internet acts as a depository to store cyberspace data and provide as a service to its user. cloud computing is a technology by internet, where a large amount of data being pooled by different users is stored. The data being stored comes from various organizations, individuals, and communities etc. Thus, security and privacy of data is of utmost importance to all of its users regardless of the nature of the data being stored. In this research paper the use of multiple encryption technique outlines the importance of data security and privacy protection. Also, what nature of attacks and issues might arise that may corrupt the data; therefore, it is essential to apply effective encryption methods to increase data security.
Improved Homomorphic Discrete Fourier Transforms and FHE Bootstrapping

Homomorphic encryption (HE), which enables computation on ciphertexts without any leakage, rise as a most promising solution for privacy-preserving data processing, including secure machine learning and secure out-sourcing computation. Despite the extensive applicability of HE, the current constructions are sometimes considered as impractical due to its inefficiency. In this paper, we propose improvements on the linear transformation in bootstrapping, a technique allowing the infinite number of operation for HE, and homomorphic discrete Fourier transformation (DFT) using batch homomorphic encryption. We observe that the multiplication of a sparse diagonal matrix and ciphertext of a vector can be done within O(1) homomorphic computations. This observation induces the faster algorithm for linear transformation in bootstrapping and homomorphic DFT. To achieve this, we use Cooley-Tukey matrix factorization and construct a new recursive factorization of the linear transformation in bootstrapping. Our method with radix r only requires O(r log <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> n) constant vector multiplication and O(√r log <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> n) rotations by consuming O(log <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> n) depth when the input vector size is n. The previous method used in the library, a library that implements homomorphic encryption for approximate computation, requires O(n) and O(√n), respectively. To show the performance improvement, we implement our method on top of the library. Our implementation, along with further few techniques, of these algorithms show the significant improvements compared to the previous algorithm. New homomorphic DFT with length 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">14</sup> only takes about 8s which results 150 times faster than the previous method. Furthermore, the bootstrapping takes about 2 minutes for ℂ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">32768</sup> plaintext space with 8-bit precision, which takes 26 hours with same bit precision using the previous method.
Cyborg Perspectives on Computing Research Reform

Recent exposures of extant and potentially discriminatory impacts of technological advancement have prompted members of the computing research field to reflect on their duty to actively predict and mitigate negative consequences of their work. In 2018, Hecht et al. proposed changes to the peer-review process attending to the computing research community's responsibility for impacts on society. In requiring researchers and reviewers to expressly consider the positive and negative consequences of each study, the hope is that our community can earnestly shape more ethical innovation and inquiry. We question whether most researchers have sufficient historical context and awareness of activist movements to recognize crucial impacts to marginalized populations. Drawing from the work of feminist theorists and critical disability scholars, we present case studies in leveraging "situated knowledges" in the analysis of research ethics.
Pre-Alignment Guided Attention for Improving Training Efficiency and Model Stability in End-to-End Speech Synthesis

Recently, end-to-end (E2E) neural text-to-speech systems, such as Tacotron2, have begun to surpass the traditional multi-stage hand-engineered systems, with both simplified system building pipelines and high-quality speech.With a unique encoder-decoder neural structure, the Tacotron2 system no longer needs separately learned text analysis front-end, duration model, acoustic model, and audio synthesis module.The key of such a system lies in the attention mechanism, which learns an alignment between the encoder and the decoder, serving as an implicit duration model bridging the text sequence and the acoustic sequence.However, attention learning suffers from low training efficiency and model instability problems, which hinder the E2E approaches from wide deployment.In this paper, we address the problems and propose a novel pre-alignment guided attention learning approach.Specifically, we inject handy prior knowledge-accurate phoneme durations-in the neural network loss function to bias the attention learning to the desired direction more accurately.The explicit time alignment between an audio recording and its corresponding phoneme sequence can be achieved by forced-alignment from an automatic speech recognizer (ASR).The experiments show that the proposed pre-alignment guided (PAG) attention approach can significantly improve training efficiency and model stability.More specifically, the PAG updated version of Tacotron2 can quickly obtain the attention alignment using only 500 text, audio pairs, which is apparently not possible for the original Tacotron2.A series of subjective experiments also show that the PAG-Tacotron2 approach can synthesize more stable and natural speech.
"What does your Agent look like?"

Conversational agents (CAs) become more popular and useful at home. Creating the persona is an important part of designing the conversational user interface (CUI). Since the CUI is a voice-mediated interface, users naturally form an image of the CA's persona through the voice. Because that image affects users' interaction with CAs while using a CUI, we tried to understand users' perception via drawing method. We asked 31 users to draw an image of the CA that communicates with the user. Through a qualitative analysis of the collected drawings and interviews, we could see the various types of CA personas perceived by users and found design factors that influenced users' perception. Our findings help us understand persona perception, and that will provide designers with design implications for creating an appropriate persona.
A novel hybrid model on the prediction of time series and its application for the gold price analysis and forecasting

Gold, as a dominant ingredient in financial market, has gripped a large quantities of the financiers and scholars to research the formation mechanism of its price. Academic circles spring up plenty of methods to analyze and predict the gold price, such techniques are based on linear regression (MLR), support vector machine (SVM), artificial neural network (ANN), respectively. However, the existing methods cannot track the random and nonlinear features of the gold price well. The accurate and effective estimation models are acceptable for researching the temporal sequence, at the same time, it will be a powerful tool for governments and investors to formulate strategies. In this paper, a novel combination technique is put forward based on independent component analysis (ICA) and gate recurrent unit neural network (GRUNN) methods, which called ICA-GRUNN. In the first place, due to the ICA is multichannel mixed-signal analysis technique, variational mode decomposition (VMD) technique is utilized to decompose the original temporal series into virtual multichannel mixed-signal. Next, statistically independent components (ICs) are separated out from the time sequence via ICA, and then, the influence factors of the gold price are analyzed from the aspect of ICs. The results demonstrate that the fluctuation of the gold price will be interrupted by long-term trends, cyclic recurrent factors and random events. Thirdly, applying GRUNN on ICs to acquire the prediction series of independent components (ICPs) and the forecasting result of the gold price is the combination of the ICPs. Finally, comparison experiments indicate that ICA-GRUNN provides prediction with high accuracy and outperforms the benchmark methods, autoregressive integrated moving average (ARIMA), radial basis function neural network (RBFNN), long short term memory neural network (LSTM), GRUNN and ICA-LSTM.
Infrastructuring Public Service Transformation

HCI has a tradition of engaging in democratic practices and contributing to public service innovation. Working with complex socio-political realities presents significant challenges for HCI researchers, which are amplified by the current democratic and economic crisis. In this article, we present insights from a longitudinal study where we worked with multiple stakeholders in the context of an austerity-driven transformation of public parks service in a city in the North East of England. Over the course of 20 months, we developed a participatory socio-technical process designed to create collaborative spaces between communities and institutions to re-envision and re-shape the city's public parks service. The study contributes to HCI research concerned with developing tools and processes that aim at connecting across the boundaries between communities and institutions. Our process and the resulting analysis expose the practical complexities of transformation and co-creation processes and the troubles that come with opening spaces for wider participation within highly contested and political settings. We provide an orientation for HCI design research aspiring to contribute to social innovation and democratic practices in troubled times.
Scientific production on mobile information literacy in higher education: a bibliometric analysis (2006–2017)

This paper offers a bibliometric analysis of the scientific production on Mobile Information Literacy in Higher Education published between 2006 and 2017, taking into account papers covered by Web of Science, Scopus, Library and Information Science Abstracts, Library and Information Science and Technology Abstract, and Education Resources Information Center. Bibliometrics, as an integral part of research evaluation methodology, helps us to identify the subject's evolution over the period studied. In this research we aim to: (a) identify the most relevant journals that publish literature in this field, (b) calculate the authors' average productivity and identify the most productive authors, and (c) discover the most significant trends in this academic field, through statistical and co-occurrence word analyses of the titles and the keywords used to index papers. The bibliometric results of this research provide a snapshot of the literature on Mobile Information Literacy that highlights the most relevant journals, authors, and trending keywords.
APEx

Organizations are increasingly interested in allowing external data scientists to explore their sensitive datasets. Due to the popularity of differential privacy, data owners want the data exploration to ensure provable privacy guarantees. However, current systems for answering queries with differential privacy place an inordinate burden on the data analysts to understand differential privacy, manage their privacy budget, and even implement new algorithms for noisy query answering. Moreover, current systems do not provide any guarantees to the data analyst on the quality they care about, namely accuracy of query answers. We present APEx, a novel system that allows data analysts to pose adaptively chosen sequences of queries along with required accuracy bounds. By translating queries and accuracy bounds into differentially private algorithms with the least privacy loss, APEx returns query answers to the data analyst that meet the accuracy bounds, and proves to the data owner that the entire data exploration process is differentially private. Our comprehensive experimental study on real datasets demonstrates that APEx can answer a variety of queries accurately with moderate to small privacy loss, and can support data exploration for entity resolution with high accuracy under reasonable privacy settings.
A Case Study of Facial Emotion Classification Using Affdex

This paper focuses on the analysis of reactions captured by the face analysis system. The experiment was conducted on a sample of 50 university students. Each student was shown 100 random images and the student´s reaction to every image was recorded. The recorded reactions were subsequently compared to the reaction of the image that was expected. The results of the experiment have shown several imperfections of the face analysis system. The system has difficulties classifying expressions and cannot detect and identify inner emotions that a person may experience when shown the image. Face analysis systems can only detect emotions that are expressed externally on a face by physiological changes in certain parts of the face.
A canonical polyadic deep convolutional computation model for big data feature learning in Internet of Things

In recent years, the Internet of Things is more widely deployed with increasing amounts of data gathered. These data are of high volume, velocity, veracity and variety, posing a vast challenge on the data analysis, especially with respect to variety and velocity. To address this challenge, a canonical polyadic deep convolutional computation model is introduced to efficiently and effectively capture the hierarchical representation of the big data by employing the canonical polyadic decomposition to factorize the deep convolutional computation. In particular, to speed up the learning of local topologies hidden in the big data, a canonical polyadic convolutional kernel is devised by compacting the tensor convolutional kernel into the linear combination of the principle rank-1 tensors. Furthermore, the canonical polyadic tensor fully-connected weight is used to efficiently map the correlation in the fully-connected layer. After that, the canonical polyadic high-order back-propagation is devised to train the canonical polyadic deep convolutional computation model. At last, detailed experiments are carried out on two well-known datasets. And results illustrate that the introduced model achieves higher performance than a competing model.
Digital literacy, fake news and education / Alfabetización digital, fake news y educación

The role of digital literacy in strengthening citizens' resilience to misinformation and 'fake news' has been the subject of research projects and networking and academic and policy discourses in recent years, given prominence by an escalation of the perceived crisis following election and referendum results in the US and UK respectively. This special issue sets out to take forward critical dialogue in the field of media and digital literacy education by publishing rigorous research on the subject. The research disseminated in this collection speaks to the political and economic contexts for 'fake news', the complex issue of trust and the risks of educational solutionism; questions of definition and policy implementation; teaching about specific subgenres such as YouTube and clickbait; international comparisons of pedagogic approaches and challenges for teachers in this changing ecosystem.
Semantic knowledge LDA with topic vector for recommending hashtags: Twitter use case

Topic Modeling encompasses a set of techniques for text clustering and tag recommendation with significant advantages such as unsupervised learning. Based on Latent Dirichlet Allocation (LDA) topic modeling, every single word is related to a set of topics with different weight. The weights are furt her estimated in order to determine the semantic relation between the words and the rest of the documents. Apparently the chief drawback of topic modeling techniques, specifically LDA, lies on their incapability in clustering short texts in which semantic relation between words is neglected. This issue is deemed more severe when analyzing social networks such as Twitter wherein short texts are the case. It is assumed that semantic relation between a document and the target short text helps obtain efficient clustering of short texts via topic modeling. Hence, the current paper proposes a method of topic modeling named Semantic Knowledge LDA based on semantic relations between the words in tweets from Twitter social network based on the co-occurrence of words. Additionally, we propose a method of hashtag recommender system based on topic vector (TV) text similarity, named TV based Hashtag Recommender System (TVHRS). Accordingly, we applied our word co-occurrence LDA (SKLDAC) method together with WordNet lexical database to cluster the short texts from Twitter. The clustered topics are later used as the repository for the proposed hashtag recommender system. The proposed system of both SKLDA and TVHRS were applied to a set of 12,309,911 real tweets for testing purposes. When comparing the components of the proposed system to the existing methods, we recorded higher performance in terms of precision, recall and F-Score of 0.551, 0.682 and 0.526, respectively.
BiographySampo – Publishing and Enriching Biographies on the Semantic Web for Digital Humanities Research

This paper argues for making a paradigm shift in publishing and using biographical dictionaries on the web, based on Linked Data. The idea is to provide the user with enhanced reading experience of biographies by enriching contents with data linking and reasoning. In addition, versatile tooling for (1) biographical research of individual persons as well as for (2) prosopographical research on groups of people are provided. To demonstrate and evaluate the new possibilities, we present the semantic portal “BiographySampo – Finnish Biographies on the Semantic Web”. The system is based on a knowledge graph extracted automatically from a collection of 13 100 textual biographies, enriched with data linking to 16 external data sources, and by harvesting external collection data from libraries, museums, and archives. The portal was released in September 2018 for free public use at http://biografiasampo.fi .
Deep Reinforcement Learning for Joint Datacenter and HVAC Load Control in Distributed Mixed-Use Buildings

The majority of today's power-hungry datacenters are physically co-located with office rooms in mixed-use buildings (MUBs). The heating, ventilation, and air conditioning (HVAC) system within each MUB is often shared or partially-shared between datacenter rooms and office zones, for removing the heat generated by computing equipment and maintaining desired room temperature for building tenants. To effectively reduce the total energy cost of MUBs, it is important to leverage the scheduling flexibility in both the HVAC system and the datacenter workload. In this work, we formulate both HVAC control and datacenter workload scheduling as a Markov decision process (MDP), and propose a deep reinforcement learning (DRL) based algorithm for minimizing the total energy cost while maintaining desired room temperature and meeting datacenter workload deadline constraints. Moreover, we also develop a heuristic DRL-based algorithm to enable interactive workload allocation among geographically distributed MUBs for further energy reduction. The experiment results demonstrate that our regular DRL-based algorithm can achieve up to 26.9 percent cost reduction for a single MUB, when compared with a baseline strategy. Our heuristic DRL-based algorithm can reduce the total energy cost by an additional 5.5 percent, when intelligently allocating interactive workload for multiple geographically distributed MUBs.
Machine Learning System Architectural Pattern for Improving Operational Stability

Recently, machine learning systems with inference engines have been widely used for a variety of purposes (such as prediction and classification) in our society. While it is quite important to keep the services provided by these machine learning systems stable, maintaining stability can be difficult given the nature of machine learning systems whose behaviors can be determined by program codes and input data. Therefore, quick troubleshooting (problem localization, rollback, etc.) is necessary. However, common machine learning systems with three-layer architectural patterns complicate the troubleshooting process because of their tightly coupled functions (e.g., business logic coded from design and inference engine derived from data). To solve the problem, we propose a novel architectural pattern for machine learning systems in which components for business logic and components for machine learning are separated. This architectural pattern helps operators break down the failures into a business logic part and a ML-specific part, and they can rollback the inference engine independent of the business logic when the inference engine has some problems. Through a practical case study scenario, we will show how our architectural pattern can make troubleshooting easier than common three-layer architecture.