Reviewerly/exHarmony huggingface dataset:
WITHDRAWN: Factor-based structural equation modeling with WarpPLS

The Publisher regrets that this article is an accidental duplication of an article that has already been published, http://dx.doi.org/10.1016/j.ausmj.2019.02.002. The duplicate article has therefore been withdrawn. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/ourbusiness/policies/article-withdrawal.
Using Physiological Recordings for Studying User Experience: Case of Conversational Agent-Equipped TV

This study explores TV viewers’ user experience (UX) of the conversational agent (CA) assisted interactions while watching TV. In the human-computer interaction field, the user experience is primarily measured through performance and subjective self-reported data. But TV is an entertainment medium, the user experience of TV viewers should be determined based on how much the TV viewers enjoyed and immersed, rather than productivity or performance. It is also necessary to collect objective data as well as subjective self-report results of emotional experiences. Although the CA is becoming increasingly common in everyday life, the user satisfaction is still not high. However, this is mainly a result of evaluation in the performance aspect, and few studies have been carried out on the overall user experience including emotional aspects. To comparatively analyze the user experience with the CA interface and remote control unit (RCU) interface, we adopted physiological measurements for objective data as well as self-report questionnaires. Among the physiological measurement results, skin conductance (SC) differed between CA and RCU interactions. SC was high at the beginning of the CA interaction but decreased over time. In RCU sessions, SC maintained a constant value or rather increased. In the self-reporting results, the ergonomic quality of the RCU was higher than that of the CA. However, CA was evaluated more positively in terms of hedonic quality. CA had a greater effect on the overall attractiveness while having more emotional appeal. Additionally, a partial correlation between objective data and self-report results could be observed. In sum, subjective self-reports showed that CA provides a more positive TV UX with regard to emotional aspects. Physiological measurement results provide objective data for further understanding user experience by showing specific differences in cognitive involvement and emotional response changes during user interactions.
Summary of Convolutional Neural Network Compression Technology

Deep convolutional neural networks (DCNNs) obtain dramatically accuracy improvement in the area of computer vision for recent years. However, because of their large demand for memory, power, and computational ability, it is difficult for DCNNs to be employed in the light-weight devices such as mobile. Therefore, a natural idea is compressing the DCNNs, while reduce the demand for the memory and computational ability and not reduce the classification accuracy too much. In recent years, model compression gain a lot of improvement. And we summaries the compression methods in this paper. DCNNs mainly classify three types, feed-forward deep networks (FFDN), feed-back deep network (FBDN) and bi-directional deep networks (BDDN). The methods of compression and acceleration are roughly categorized into four schemes: parameter pruning and sharing, low-rank factorization, transfer /compact convolutional filters and knowledge distillation. In this paper, we introduce the performance, related applications, advantages and drawbacks of each CNNs and techniques for compacting and accelerating DCNNs model. In the last, we summarize the paper and propose the possible future work in model compression.
An enhanced probabilistic fairness-aware group recommendation by incorporating social activeness

Compared with individual recommendation, recommending services to a group of users is more complicated because of various users' preference should be considered and introduces new challenging such as fairness, which has never been well studied in current works. In this paper, we propose a novel recommendation scheme called PFGR, which combines a probabilistic model with coalition game strategy, to ensure the accuracy and fairness between groups of users. Given a group of users and a set of services, PFGR models a generative process for service selection in light of several observations: 1) each group is related with several topics; 2) users' decisions on the service selection depends on their expertise, the opinions of members they are familiar with, and group influence; 3) each group contains active users and inactive user, whose activeness contributes to the existence of group. PFGR first estimates the preference of each user on a candidate service via combining user's expertise, inherent connection, and group influence. Then, it determines a group's decision on a service by aggregating the preference of group members using adaptive weights. Finally, PFGR considers users' activeness and employs a strategy based on coalition game to produce a ranked list which is fair to each group member as much as possible. Experimental results on three real-world datasets validate that PFGR can achieve higher Hit Rate and Average Reciprocal Hit Rank than state-of-the-art approaches, which indicates that PFGR attains both the precision and fairness of recommendation.
New Approaches to the Identification of Dependencies between Requirements

There is a high demand for intelligent decision support systems which assist stakeholders in requirements engineering tasks. Examples of such tasks are the elicitation of requirements, release planning, and the identification of requirement-dependencies. In particular, the detection of dependencies between requirements is a major challenge for stakeholders. In this paper, we present two content-based recommendation approaches which automatically detect and recommend such dependencies. The first approach identifies potential dependencies between requirements which are defined on a textual level by exploiting document classification techniques (based on Linear SVM, Naive Bayes, Random Forest, and k-Nearest Neighbors). This approach uses two different feature types (TF-IDF features vs. probabilistic features). The second recommendation approach is based on Latent Semantic Analysis and defines the baseline for the evaluation with a real-world data set. The evaluation shows that the recommendation approach based on Random Forest using probabilistic features achieves the best prediction quality of all approaches (F1: 0.89).
A Scheme of Intelligent Traffic Light System Based on Distributed Security Architecture of Blockchain Technology

In recent years, under the background that the rapid development of traffic volume makes the current traffic lights far from meeting the urban traffic demand, intelligent traffic lights based on the centralized architecture began to appear.However, in the traffic network with complex structure and private data flow, there are many malicious attacks against the centralized architecture, such as Sybil and ghost car attacks, which undoubtedly brings great security risks to the traditional intelligent traffic lights.Blockchain technology is a popular security framework nowadays.Based on its outstanding characteristics in the distributed architecture and the development of Edge Intelligence (EI) technology, this paper proposes a distributed security architecture scheme based on blockchain technology for the existing intelligent traffic light system.At the same time, based on the model cutting technology proposed by EI, the smart contract is improved to achieve redundant cutting of ledger data in the process of block consensus, which greatly reduces the pressure of blockchain ledger data transmission.In the end of this paper, the superiority of this scheme compared with the traditional intelligent traffic light scheme in communication cost and time cost is demonstrated by simulation experiment.
Polar labeling: silver standard algorithm for training disease classifiers

Expert-labeled data are essential to train phenotyping algorithms for cohort identification. However expert labeling is time and labor intensive, and the costs remain prohibitive for scaling phenotyping to wider use-cases.We present an approach referred to as polar labeling (PL), to create silver standard for training machine learning (ML) for disease classification. We test the hypothesis that ML models trained on the silver standard created by applying PL on unlabeled patient records, are comparable in performance to the ML models trained on gold standard, created by clinical experts through manual review of patient records. We perform experimental validation using health records of 38 023 patients spanning six diseases. Our results demonstrate the superior performance of the proposed approach.We provide a Python implementation of the algorithm and the Python code developed for this study on Github.Supplementary data are available at Bioinformatics online.
XRD Identification of Ore Minerals during Cruises: Refinement of Extraction Procedure with Sodium Acetate Buffer

The on-board identification of ore minerals during a cruise is often postponed until long after the cruise is over. During the M127 cruise, 21 cores with deep-seafloor sediments were recovered in the Trans-Atlantic Geotraverse (TAG) field along the Mid Atlantic Ridge (MAR). Sediments were analyzed on-board for physicochemical properties such as lightness (L*), pH and Eh. Selected samples were studied for mineral composition by X-ray powder diffraction (XRD). Based on XRD data, sediment samples were separated into high-, low- and non-carbonated. Removal of carbonates is a common technique in mineralogical studies in which HCl is used as the extraction agent. In the present study, sequential extraction was performed with sodium acetate buffer (pH 5.0) to remove carbonates. The ratio between the highest calcite XRD reflection in the original samples (Iorig) vs its XRD-reflection in samples after their treatment with the buffer (Itreat) was used as a quantitative parameter of calcite removal, as well as to identify minor minerals in carbonated samples (when Iorig/Itreat &gt; 24). It was found that the lightness parameter (L*) showed a positive correlation with calcite XRD reflection in selected TAG samples, and this could be applied to the preliminary on-board determination of extraction steps with acetate buffer (pH 5.0) in carbonated sediment samples. The most abundant minerals detected in carbonated samples were quartz and Al- and Fe-rich clays. Other silicates were also detected (e.g., calcic plagioclase, montmorillonite, nontronite). In non-carbonated samples, Fe oxides and hydroxides (goethite and hematite, respectively) were detected. Pyrite was the dominant hydrothermal mineral and Cu sulfides (chalcopyrite, covellite) and hydrothermal Mn oxides (birnessite and todorokite) were mineral phases identified in few samples, whereas paratacamite was detected in the top 20 cm of the core. The present study demonstrates that portable XRD analysis makes it possible to characterize mineralogy at cored sites, in particular in both low- and high-carbonated samples, before the end of most cruises, thus enabling the quick modification of exploration strategies in light of new information as it becomes available in near-real time.
EchoWrite: An Acoustic-Based Finger Input System Without Training

Recently, wearable devices have become increasingly popular in our lives because of their neat features and stylish appearance. However, their tiny sizes bring about new challenges to human-device interaction such as texts input. Although some novel methods have been put forward, they possess different defects and are not applicable to deal with the problem. As a result, we propose an acoustic-based texts-entry system, i.e., EchoWrite, by which texts can be entered with a finger writing in the air without wearing any additional device. More importantly, different from many previous works, EchoWrite runs in a training-free style which reduces the training overhead and improves system scalability. We implement EchoWrite with commercial devices and conduct comprehensive experiments to evaluate its texts-entry performance. Experimental results show that EchoWrite enables users to enter texts at a speed of 7.5 WPM without practice, and 16.6 WPM after about 30-minute practice. This speed is better than touch screen-based method on smartwatches, and comparable with previous related works. Moreover, EchoWrite provides favorable user experience of entering texts.
Robust online model predictive control for image-based visual servoing in polar coordinates

In this paper, a novel robust online model predictive control (RMPC) method for image-based visual servoing (IBVS) in polar coordinates is proposed. First, the Jacobian matrix is transformed into a weighted combination of vertex matrices of convex polytopic by tensor product (TP) model transformation method. Then, a new IBVS control design condition for 6-DOF manipulator submitting to robot physical limitations and visibility constraints is obtained in polar coordinates by using RMPC technique. The optimal value of the control signal can be solved online when carrying out the convex optimization problem. The proposed control strategy can effectively improve the trajectory in the case that involves translation and rotation with fast response while averting the pseudo-inverse of the image Jacobian matrix. Conclusively, the effectiveness of the proposed scheme is validated by simulations and experiments on a 6-DOF manipulator.
Adaptive Exploration Strategy With Multi-Attribute Decision-Making for Reinforcement Learning

Reinforcement Learning (RL) agents often encounter the bottleneck of the performance when the dilemma of exploration and exploitation arises.In this study, an adaptive exploration strategy with multi-attribute decision-making is proposed to address the trade-off problem between exploration and exploitation.Firstly, the proposed method decomposes a complex task into several sub-tasks and trains each sub-task using the same training method individually.Then, the proposed method uses a multi-attribute decision-making method to develop an action policy integrating the training results of these trained subtasks.There are practical advantages to improve learning performance by allowing multiple learners to learn in parallel.An adaptive exploration strategy determines the probability of exploration depending on the information entropy instead of the suffocating work of empirical tuning.Finally, transfer learning extends the applicability of the proposed method.The experiment of the robot migration, the robot confrontation, and the real wheeled mobile robot are used to demonstrate the availability and practicability of the proposed method.
Superadditivity of channel capacity through quantum fields

Given that any communication is communication through quantum fields, we study here the scenario where a sender, Alice, causes information-carrying disturbances in a quantum field. We track the exact spread of these disturbances in space and time by using the technique of quantum information capsules (QIC). We find that the channel capacity between Alice and a receiver, Bob, is enhanced by Bob placing detectors not only inside but in addition also outside the causal future of Alice's encoding operation. Intuitively, this type of superadditivity arises because the field outside the causal future of Alice is entangled with the field inside Alice's causal future. Hence, the quantum noise picked up by Bob's detectors outside Alice's causal future is correlated with the noise of Bob's detectors inside Alice's causal future. In effect, this correlation allows Bob to improve the signal-to-noise ratio of those of his detectors which are in the causal future of Alice. Further, we develop the multimode generalization of the QIC technique. This allows us to extend the analysis to the case where Alice operates multiple localized and optionally entangled emitters. We apply the new techniques to the case where Alice enhances the channel capacity by operating multiple emitters that are suitably lined up and pretimed to generate a quantum shockwave in the field.
Rainfall Forecasting for the Natural Disasters Preparation Using Recurrent Neural Networks

Rainfall forecasting is still a concern for researchers considering the increasing uncertainty weather conditions in various tropical regions, including Indonesia. Therefore, a more robust computational model is needed because of uncertainty climate. Deep learning is a method that allows machines to learn time-based data patterns, such as climate data. One technique that is often used for time series data is Recurrent Neural Networks (RNN). However, the selection of climate feature, time segments, and long records of historical data, pre-processing methods, and prediction models largely determines accuracy. This paper proposed Recurrent Neural Networks for weekly rainfall forecasting. It was the rainfall, temperature, and humidity variable each week within a year. The training used LSTM to generalize climate data for the past ten years. Weighting renewal used Stochastic Gradient Descent (SGD) and Adaptive Moment Estimation (Adam). The results showed that the number of datasets and learning rates determine accuracy significantly. So that using data learning last ten years gave more than 96% accuracy of new data and more than 98% of training data.
Optical encryption and authentication scheme based on phase-shifting interferometry in a joint transform correlator

In this paper, the effectiveness of the cryptosystem based on phase-shifting technique (PST) in a joint transform correlator (JTC) is evaluated. It is shown that the cryptosystem has some inherent drawbacks, for example, since the plaintexts are required to obtain the phase-shifting intensity patterns in the first phase extraction step, which means that the information of the plaintexts should be known by the authorized users before decryption processing. It seems impossible and paradoxical in the practical case. Moreover, the cryptosystem can only achieve binary plaintexts encryption while the inputs of this scheme cannot be the gray-scale or color images. To address these issues, we propose an encryption and authentication scheme based on PST in a JTC. By introducing a pre-processing operation including an intensity modulator based on histogram equalization and photon-counting (PC) operator, a photon-limited binary distribution of the plaintexts is obtained and used as the input of the cryptosystem. Due to this design, our proposed scheme can achieve encryption for different kinds of images such as binary, gray-scale and color images using same optical setup without any changes, which can simplify the optical implementation. Furthermore, a post-processing operator to extract phase information of encoded images and authenticate the decoded images is introduced. Since no useful information will be released from the photon-limited binary decoded images obtained using correct decryption keys and the interface of the database storing the plaintexts is only given to the authorized users, an additional security layer is established. This is the first time to report the use of binary intensity pattern in a scheme based on PSI in a JTC for secured verification of different images.
Automation of Cheque Transaction using Deep Learning and Optical Character Recognition

Despite of swift advancements happening in digital technology, financial institutions like banks still rely upon conventional medium of processing the bank cheques by humans. The process is cumbersome and takes couple of days for actual transfer of money which involves verification by the intermediaries. This leads to high time and costs. In this paper, we propose an automated system which extracts relevant details on a bank cheque like Payee Name, Amount, Date, Bank Name using Optical Character Recognition and Deep Learning and verifies the signature on the cheque with the existing signature stored in the database using feature extraction and principal component analysis. The signature for a new user is stored using it's hash value for security purposes. The proposed system uses modified convolution neural network to extract the handwritten content on cheque leaf where in IAM dataset is used to train the model and get the optimized results. This system will facilitate the process and lead to reduction in time and costs. The efficiency and performance is measured on the self generated data set of bank cheques.
Audio-Visual Kinship Verification in the Wild

Kinship verification is a challenging problem, where recognition systems are trained to establish a kin relation between two individuals based on facial images or videos. However, due to variations in capture conditions (background, pose, expression, illumination and occlusion), state-of-the-art systems currently provide a low level of accuracy. As in many visual recognition and affective computing applications, kinship verification may benefit from a combination of discriminant information extracted from both video and audio signals. In this paper, we investigate for the first time the fusion audio-visual information from both face and voice modalities to improve kinship verification accuracy. First, we propose a new multi-modal kinship dataset called TALking KINship (TALKIN), that is comprised of several pairs of video sequences with subjects talking. State-of-the-art conventional and deep learning models are assessed and compared for kinship verification using this dataset. Finally, we propose a deep Siamese network for multi-modal fusion of kinship relations. Experiments with the TALKIN dataset indicate that the proposed Siamese network provides a significantly higher level of accuracy over baseline uni-modal and multi-modal fusion techniques for kinship verification. Results also indicate that audio (vocal) information is complementary and useful for kinship verification problem.
Multi Factor Two-way Hash-Based Authentication in Cloud Computing

The expansion of Cloud computing is influencing various domains such as education, the banking sector, industries, government, health informatics, and individuals. The characteristics of an easy interface, on-demand access, scalability, and low infrastructure investment have to lead to the fast adaptation of Cloud computing based upon their needs. Despite the advantages, Cloud computing is open to more security risks and attacks especially in terms of communication due to the lack of secure authentication and privacy. In this article is presented a novel hash-based multifactor secure mutual authentication scheme that includes mathematical hashing properties, certificates, nonce values, traditional user ids, and password mechanisms that resist MITM attacks, replay attacks, and forgery attacks. We implemented our proposed method in the Microsoft Azure cloud and the results are evaluated. The security analysis is done by using the Scyther tool and with a formal analysis by using GNY belief logic. The results indicate the proposed scheme is capable of providing strong secure authentication.