Reviewerly/exHarmony huggingface dataset:
An Experience Report on Building a Big Data Analytics Framework Using Cloudera CDH and RapidMiner Radoop with a Cluster of Commodity Computers

Many real-world data are not only large in volume but also heterogeneous and fast generated. This type of data, known as big data, typically cannot be analyzed by using traditional software tools and techniques. Although an open-source software project, Apache Hadoop, has been successfully developed and used for handling big data, its setup and configuration complexity including its requirement to learn other additional related tools have hindered non-technical researchers and educators from actually entering the area of big data analytics. To support big-data community, this paper describes procedures and experiences gained from building a big data analytics framework, and demonstrates its usage on a popular case study, Twitter sentiment analysis. The framework comprises a cluster of four commodity computers run by Cloudera CDH 6.0.1 and RapidMiner Studio 9.3 with Text Processing, Hive Connector, and Radoop extensions. According to the study results, setting up a big data analytics framework on a cluster of computers does not require advanced computer knowledge but needs meticulous system configurations to satisfy system installation and software integration requirements. Once all setup and configurations are correctly done, data analysis can be readily performed using visual workflow designers provided by RapidMiner. Finally, the framework is further evaluated on a large data set of 185 million records, “TalkingData AdTracking Fraud Detection” data set. The outcome is very satisfied and proves that the framework is easy to use and can practically be deployed for big data analytics.
The EmojiGrid as an Immersive Self-report Tool for the Affective Assessment of 360 VR Videos

Immersive 360° VR systems are increasingly used in entertainment, marketing and design and development processes. Many of these applications involve emotional experiences. Since momentary emotions significantly determine a user’s response and decisions, it is essential to understand the influence of media content and technological factors on the user’s emotional response. To measure the emotional responses to immersive VR experiences, efficient and validated instruments are required. Most tools currently used to measure the emotional response of users compromise the immersive experience since they are cognitively demanding, time consuming, and their application requires the user to leave the VR. We investigated the validity of an immersive, efficient and intuitive EmojiGrid graphical self-report tool for the assessment of emotions evoked by 360º VR videos. Using the EmojiGrid, 40 participants rated 62 360º VR videos from a validated public database. The resulting mean valence and arousal values agree with the corresponding values provided with the database (obtained with an alternative validated rating tool). We conclude that the EmojiGrid is a valid self-report tool for the assessment of VR-evoked emotions.
Teaching Blockchain for Business

Blockchain is a disruptive new opportunity for businesses to unlock value through trusted and “smart” peer-to-peer transactions, wherein smart means business people can custom design processes for verification and transfer of assets. This paper presents a 7-scenario design for teaching blockchain technology to non-technical students. The design identifies key learning objectives as well as administrative and infrastructure component solutions that can be used by others seeking to teach such a course.
Adjustable Adaptation for Spatial Augmented Reality Workspaces

Many cases in which augmented reality would be useful in everyday life requires the ability to access to information on the go. This means that interfaces should support user movement and also adjust to different physical environments. Prior research has showed that spatial adaptation can reduce the effort required to manage windows when walking and moving to different spaces. We designed and implemented an unified interaction system for AR windows that allow users to quickly switch and fine tune spatial adaptation. Our study indicates that a small number of adaptive behaviors is sufficient to facilitate information access in variety of conditions.
Emotion Mining in Social Media Data

Emotions are known to influence the perception of human beings along with their memory, thinking and imagination. Human perception is important in today's world in a wide range of factors including but not limited to business, education, art, and music. Microblogging and Social networking sites like Twitter, Facebook are challenging sources of information that allow people to share their feelings and thoughts on a daily basis. In this paper we propose an approach to automatically detect emotions on Twitter messages that explores characteristics of the tweets and the writer's emotion using Support Vector Machine LibLinear model and achieve 98% accuracy. Emotion mining gained attraction in the field of computer science due to the vast variety of systems that can be developed and promising applications like remote health care system, customer care services, smart phones that react based on users's emotion, vehicles that sense emotion of the driver. These emotions help understand the current state of user. In order to perform suitable actions or provide suggestions on how user's can enhance their feeling for a better healthy life-style we use actionable recommendations. In this work we extract action rules with respect to the user emotions that help provide suggestions for user's.
Regional attention‐based single shot detector for SAR ship detection

Automatic ship detection in SAR imagery has been playing a significant role in the field of marine monitoring but great challenges still exist in real-time application. Despite the exciting progresses made by deep-learning techniques, most detectors failed to yield locations of fairly high quality. Moreover, the ships with variant sizes and aspects are easily omitted especially for small objects under complicated background. To alleviate the above problem, the authors propose an elaborately designed single shot detection framework combined with attention mechanism, which roughly locates the regions of interest via an automatically learned attentional map. This lay the foundation of accurate positioning of extremely small objects since the background interference can be effectively suppressed. Furthermore, a multi-level feature fusion module integrated in top-down and bottom-up manner is adopted to adequately aggregate features from not only adjacent but also distant layers. This strengthens local details and merge strong semantic information, enabling the generation of higher qualified anchors for the efficient detection of multi-scale and multi-orientated objects. Experiments on SAR ship dataset have achieved a promising result, surpassing current state-of-the-art methods.
RGB-D Object Classification for Autonomous Driving Perception

Autonomous driving systems (ADS) comprise, essentially, sensory perception (including AI-ML-based techniques), localization, decision-making, and control. The cornerstone of an ADS is the sensory perception part, which is involved in most of the essential and necessary tasks for safe driving such as sensor-fusion, environment representation, scene understanding, semantic segmentation, object detection/recognition, and tracking. Multimodal sensor-fusion is an established strategy to enhance safety and robustness of perception systems in autonomous driving. In this work, a fusion of data from color-camera (RGB) and 3D-LIDAR (D-distance), henceforth designated RGB-D, will be particularly addressed, highlighting use-cases on road-users classification using deep learning. 3D-LIDAR data, in the form of point-cloud, can be processed directly by using the PointNet network or, alternatively, by using depth-maps, known as range-view representation, which is a suitable representation to train state-of-the-art Convolutional Neural Network (CNN) models and to make the combination with RGB-images more practical. Experiments are carried out using the KITTI dataset on object classification, i.e., vehicles, pedestrians, cyclists. We report extensive results in terms of classification performance of deep-learning models using RGB, 3D, and RGB-D representations. The results show that RGB-D models have better performance in comparison with 3D and range-view models but, in some circumstances RGB-only achieved superior performance.
DomSent: Domain-Specific Aspect Term Extraction in Aspect-Based Sentiment Analysis

In recent research aspect-based sentiment analysis has played a vital role in identifying user’s opinions from the unstructured natural text. One of the most critical subtasks in aspect-based sentiment analysis is to extract the most prominent aspect terms. In this paper, we have studied previous research done on aspect term extraction and proposed an approach DomSent to identify aspects using domain-specific information while applying frequency and similarity pruning. Our experimental results show that the aspect extraction using domain-specific information contributes better as compared to the recent aspect term extraction approaches.
Annotating evaluative sentences for sentiment analysis: a dataset for Norwegian

This paper documents the creation of a large-scale dataset of evaluative sentences – i.e. both subjective and objective sentences that are found to be sentiment-bearing – based on mixed-domain professional reviews from various news-sources. We present both the annotation scheme and first results for classification experiments. The effort represents a step toward creating a Norwegian dataset for fine-grained sentiment analysis.
Ultrasmall design of a universal linear circuit based on microring resonators

We designed a universal linear circuit by using microring resonators instead of conventional Mach-Zehnder interferometers and phase shifters. We illustrated that the footprint of the universal linear circuit can be drastically reduced (∼ 1/10). In addition, power consumption can also be reduced by using the sensitivity of the phase change in the vicinity of the resonant peak. Furthermore, as an important example of the application for optical communication, MIMO compensation operation is numerically demonstrated by our proposed universal linear circuit. The proposed design can be adapted to other experimentally reported devices, which will accelerate the integration of the universal linear circuit.
Deep fuzzy model for non-linear effective connectivity estimation in the intuition of consciousness correlates

The brain connectivity, as a promising technique to explore brain networks during resting-states or cognitive tasks, has been employed remarkably in recent years. The aim of this study is to propose a new approach to improve the Granger causality as one of the fundamental methods for calculation of brain effective connectivity. To this end, we utilized a deep fuzzy structure to model the multivariate autoregressive used in the Granger causality. The proposed model benefits from the hierarchical stacked structure where first – order TSK fuzzy rules are the cores of the network. In the first layer of the stacked structure, the antecedents of the fuzzy rules are extracted from the fuzzy clustering of the input space. For subsequent layers, due to the input perturbation which is caused by the previous layer output, a shuffling approach is adopted. To assess our proposed model, we applied it to two nonlinear synthetic time series and compared it with linear Granger. Results revealed that our model is superior in the detection of effective connectivity. We additionally exploit the pioneer model for one of the controversial concepts in cognitive neuroscience in recent years: the neural correlates of visual consciousness. We applied our method to detect connectivity networks of EEG in consciousness states. Our results demonstrated that the proposed nonlinear connectivity estimator was capable of detecting novel correlates: significant differences have been observed among different states of consciousness, not only in presence of attention, as the linear method detected it, but also in absence of attention.
Newcomers’ socialization: the proactive behaviors, satisfaction and social integration

When newcomers enter into organizations they often feel stressed and find difficulties to adjust to the new organizational context. Through the socialization process they learn how to adjust to the new context. Previous studies investigated the role of organizations in the process of newcomer adjustment. However, the individual role innewcomer adjustment has not been sufficiently discussed in the literature; particularly there is no such study in the Sri Lankan context. This study examines the influence of newcomer proactive behaviors (positive framing, relationship building, information seeking and feedback seeking) on adjustment (job satisfaction and social integration). This study was conducted with 114 newcomers who had less than one year of work experience from banking sector in Northern Province of Sri Lanka. The study employed PLS-SEM with Smart PLS to test the proposed hypotheses. The findings revealed that among the four proactive behaviors, positive framing positively influences both satisfaction and social integration. Information seeking, feedback seeking and relationship building also positively influence either job satisfaction or social integration. These findings suggest that newcomers need to engage in proactive behaviors to achieve satisfaction at the workplace and to become acceptable members of the organization. Further, this study contributes to the literature by investigating the influence of proactive behavior in the socialization process among new employees in the banking sector in the Sri Lankan cultural context.
Why pay? An empirical study of paid-for SQA sites in China

Purpose The once failed paid-for social question and answer (SQA) service is in full swing in China. The traditional profit model, which typically relies on advertising, is abandoned in this case. Instead, users have to pay for content and service. The purpose of this paper is to explore why users are willing to pay in paid-for SQA sites. Design/methodology/approach This study carried out 14 valid semi-structured interviews to investigate “why did they pay.” The interviewees are users of three popular paid-for SQA sites. The qualitative data were obtained from valid interviews and processed through thematic analysis. Findings The analysis revealed five overarching themes: paying for the answerer’s heterogeneous resource, paying for more credible answer, the cognition of the question, the price is affordable and expecting potential revenue. The five themes and their sub-themes constitute the motivation for why users would pay in paid-for SQA. Practical implications As a new business model for online information services, paid-for SQA sites are facing fierce competition from traditional ones. The findings not only indicate the importance of establishing a reciprocal network relationship among users, but also provide a better understanding of users’ needs and demands for paid-for SQA services. The results are helpful for paid-for SQA sites to conduct a differential competitive strategy according to the user’s paying motivation. Originality/value To authors’ knowledge, this is the first study, which provides primary-source data and valuable insights into users’ paying motivation in the context of new paid-for SQA sites in China.
Efficient Private PEZ Protocols for Symmetric Functions

A private PEZ protocol is a variant of secure multi-party computation performed using a (long) PEZ dispenser. The original paper by Balogh et al. presented a private PEZ protocol for computing an arbitrary function with n inputs. This result is interesting, but no follow-up work has been presented since then, to the best of our knowledge. We show herein that it is possible to shorten the initial string (the sequence of candies filled in a PEZ dispenser) and the number of moves (a player pops out a specified number of candies in each move) drastically if the function is symmetric. Concretely, it turns out that the length of the initial string is reduced from $$\mathcal {O}(2^n!)$$ for general functions in Balogh et al.’s results to $$\mathcal {O}(n\cdot n!)$$ for symmetric functions, and $$2^n$$ moves for general functions are reduced to $$n^2$$ moves for symmetric functions. Our main idea is to utilize the recursive structure of symmetric functions to construct the protocol recursively. This idea originates from a new initial string we found for a private PEZ protocol for the three-input majority function, which is different from the one with the same length given by Balogh et al. without describing how they derived it.
On Fully Secure MPC with Solitary Output

We study the possibility of achieving full security, with guaranteed output delivery, for secure multiparty computation of functionalities where only one party receives output, to which we refer as solitary functionalities. In the standard setting where all parties receive an output, full security typically requires an honest majority; otherwise even just achieving fairness is impossible. However, for solitary functionalities, fairness is clearly not an issue. This raises the following question: Is full security with no honest majority possible for all solitary functionalities? We give a negative answer to this question, by showing the existence of solitary functionalities that cannot be computed with full security. While such a result cannot be proved using fairness-based arguments, our proof builds on the classical proof technique of Cleve (STOC 1986) for ruling out fair coin-tossing and extends it in a nontrivial way. On the positive side, we show that full security against any number of malicious parties is achievable for many natural and useful solitary functionalities, including ones for which the multi-output version cannot be realized with full security.
Forensic Analysis with Anti-Forensic Case Studies on Amazon Alexa and Google Assistant Build-In Smart Home Speakers

People's communication with machines is evolving. The process that started with the buttons has evolved to the touchscreen and now people can command to machines just talking with them. The use of smart home assistants, which allows people to control their smart homes, access mail accounts and even order, is becoming increasingly popular. For this reason, it is possible that they will be found at crime scenes soon and carry the value of digital evidence. In this study, the bestselling products Alexa Echo and Google Home were examined in terms of forensic evidence and the data containing digital evidence were found. Then fake activities were created by changing device name, creating fake routine creation and custom skill development. As a result of the investigations, for the cyber security experts or academics working in this field the information was provided about which kind of digital evidence could be found in smart home assistant's activities. Also, difference between real activity and fake activity were elicited against anti-forensic.
Exploiting Spatial Invariance for Scalable Unsupervised Object Tracking.

The ability to detect and track objects in the visual world is a crucial skill for any intelligent agent, as it is a necessary precursor to any object-level reasoning process. Moreover, it is important that agents learn to track objects without supervision (i.e. without access to annotated training videos) since this will allow agents to begin operating in new environments with minimal human assistance. The task of learning to discover and track objects in videos, which we call \textit{unsupervised object tracking}, has grown in prominence in recent years; however, most architectures that address it still struggle to deal with large scenes containing many objects. In the current work, we propose an architecture that scales well to the large-scene, many-object setting by employing spatially invariant computations (convolutions and spatial attention) and representations (a spatially local object specification scheme). In a series of experiments, we demonstrate a number of attractive features of our architecture; most notably, that it outperforms competing methods at tracking objects in cluttered scenes with many objects, and that it can generalize well to videos that are larger and/or contain more objects than videos encountered during training.
PathFlowAI: A High-Throughput Workflow for Preprocessing, Deep Learning and Interpretation in Digital Pathology

The diagnosis of disease often requires analysis of a biopsy. Many diagnoses depend not only on the presence of certain features but on their location within the tissue. Recently, a number of deep learning diagnostic aids have been developed to classify digitized biopsy slides. Clinical workflows often involve processing of more than 500 slides per day. But, clinical use of deep learning diagnostic aids would require a preprocessing workflow that is cost-effective, flexible, scalable, rapid, interpretable, and transparent. Here, we present such a workflow, optimized using Dask and mixed precision training via APEX, capable of handling any patch-level or slide level classification and prediction problem. The workflow uses a flexible and fast preprocessing and deep learning analytics pipeline, incorporates model interpretation and has a highly storage-efficient audit trail. We demonstrate the utility of this package on the analysis of a prototypical anatomic pathology specimen, liver biopsies for evaluation of hepatitis from a prospective cohort. The preliminary data indicate that PathFlowAI may become a cost-effective and time-efficient tool for clinical use of Artificial Intelligence (AI) algorithms.
Improved dynamic multiparty quantum direct secret sharing protocol based on generalized GHZ states to prevent collusion attack

In 2018, Song et al. [Quantum Inf. Process 17, 244 (2018)] proposed a dynamic quantum direct secret sharing (DQDSS) protocol based on generalized GHZ states. However, this protocol is vulnerable to a security problem, known as the collusion attack, using which the dishonest agents can collude to reveal the master key without collaborating with other agents. This security problem, if unaddressed, can prevent the protocol from providing the secret sharing function. Therefore, this study proposes an improved DQDSS protocol to address the security problem in Song et al.’s DQDSS protocol. The proposed DQDSS protocol is resistant to the collusion attack and various other common attacks. Furthermore, the advantages of Song et al.’s DQDSS protocol are retained in the proposed scheme.