Reviewerly/exHarmony huggingface dataset:
The influence of social interaction on the dynamics of employees’ psychological contracting in digitally transforming organizations

This paper takes a process perspective in exploring the influence of social interaction on the dynamics of psychological contracting throughout organizational change.Although social interaction is a key focus in social exchange theory, this aspect is largely overlooked in the current psychological contract literature.In this qualitative study, we adopt a retrospective design, asking change recipients to recollect events over time in the context of digital transformation in Dutch travel organizations.Our data reveal a sequence of different kinds of social interactions over the course of a change process, from collective-focused interactions (i.e., kindness and sharing) in stable contracts to transactional interactions (i.e., "what is in it for me") following psychological contract disruption, to relational interactions (i.e., vigilance about equity in social exchange) in psychological contract repair, and to a final return to resonance and alignment with others and a return to psychological contract maintenance.Our results suggest that social interactions play a more potent role in the dynamics of psychological contracting than is currently recognized in the literature.Finally, we discuss a number of implications for dynamic models of psychological contracting.
WMDO: Fluency-based Word Mover’s Distance for Machine Translation Evaluation

We propose WMD O , a metric based on distance between distributions in the semantic vector space.Matching in the semantic space has been investigated for translation evaluation, but the constraints of a translation's word order have not been fully explored.Building on the Word Mover's Distance metric and various word embeddings, we introduce a fragmentation penalty to account for fluency of a translation.This word order extension is shown to perform better than standard WMD, with promising results against other types of metrics.
The Unreasonable Effectiveness of Transformer Language Models in Grammatical Error Correction

Recent work on Grammatical Error Correction (GEC) has highlighted the importance of language modeling in that it is certainly possible to achieve good performance by comparing the probabilities of the proposed edits.At the same time, advancements in language modeling have managed to generate linguistic output, which is almost indistinguishable from that of human-generated text.In this paper, we up the ante by exploring the potential of more sophisticated language models in GEC and offer some key insights on their strengths and weaknesses.We show that, in line with recent results in other NLP tasks, Transformer architectures achieve consistently high performance and provide a competitive baseline for future machine learning models.
Aligning Visual Regions and Textual Concepts for Semantic-Grounded Image Representations

In vision-and-language grounding problems, fine-grained representations of the image are considered to be of paramount importance. Most of the current systems incorporate visual features and textual concepts as a sketch of an image. However, plainly inferred representations are usually undesirable in that they are composed of separate components, the relations of which are elusive. In this work, we aim at representing an image with a set of integrated visual regions and corresponding textual concepts, reflecting certain semantics. To this end, we build the Mutual Iterative Attention (MIA) module, which integrates correlated visual features and textual concepts, respectively, by aligning the two modalities. We evaluate the proposed approach on two representative vision-and-language grounding tasks, i.e., image captioning and visual question answering. In both tasks, the semantic-grounded image representations consistently boost the performance of the baseline models under all metrics across the board. The results demonstrate that our approach is effective and generalizes well to a wide range of models for image-related applications. (The code is available at \url{https://github.com/fenglinliu98/MIA)
An Abstraction-Refinement Approach to Formal Verification of Tree Ensembles

Recent advances in machine learning are now being considered for integration in safety-critical systems such as vehicles, medical equipment and critical infrastructure. However, organizations in these domains are currently unable to provide convincing arguments that systems integrating machine learning technologies are safe to operate in their intended environments. In this paper, we present a formal verification method for tree ensembles that leverage an abstraction-refinement approach to counteract combinatorial explosion. We implemented the method as an extension to a tool named VoTE, and demonstrate its applicability by verifying the robustness against perturbations in random forests and gradient boosting machines in two case studies. Our abstraction-refinement based extension to VoTE improves the performance by several orders of magnitude, scaling to tree ensembles with up to 50 trees with depth 10, trained on high-dimensional data.
Identifying Security Risks of Digital Transformation - An Engineering Perspective

Technological advancements continue to disrupt how organizations compete and create value in almost every industry and society. The recent digital transformation movement has expanded the reliance of companies and organizations in software technologies, such as cloud computing, big data, artificial intelligence, internet-of-things, and also increase the risk associated with software usage. This work aims at identifying security risks associated with these technologies from an engineering management perspective. We conducted two focused groups and a literature review to gather and discuss the list of security risks. The findings have implications for both practitioners to manage software security risks and future research work.
Adversarial Attack on Sentiment Classification

In this paper, we propose a white-box attack algorithm called “Global Search” method and compare it with a simple misspelling noise and a more sophisticated and common white-box attack approach called “Greedy Search”. The attack methods are evaluated on the Convolutional Neural Network (CNN) sentiment classifier trained on the IMDB movie review dataset. The attack success rate is used to evaluate the effectiveness of the attack methods and the perplexity of the sentences is used to measure the degree of distortion of the generated adversarial examples. The experiment results show that the proposed “Global Search” method generates more powerful adversarial examples with less distortion or less modification to the source text.
Low-Cost, Low-Power FPGA Implementation of ED25519 and CURVE25519 Point Multiplication

Twisted Edwards curves have been at the center of attention since their introduction by Bernstein et al. in 2007. The curve ED25519, used for Edwards-curve Digital Signature Algorithm (EdDSA), provides faster digital signatures than existing schemes without sacrificing security. The CURVE25519 is a Montgomery curve that is closely related to ED25519. It provides a simple, constant time, and fast point multiplication, which is used by the key exchange protocol X25519. Software implementations of EdDSA and X25519 are used in many web-based PC and Mobile applications. In this paper, we introduce a low-power, low-area FPGA implementation of the ED25519 and CURVE25519 scalar multiplication that is particularly relevant for Internet of Things (IoT) applications. The efficiency of the arithmetic modulo the prime number 2 255 - 19 , in particular the modular reduction and modular multiplication, are key to the efficiency of both EdDSA and X25519. To reduce the complexity of the hardware implementation, we propose a high-radix interleaved modular multiplication algorithm. One benefit of this architecture is to avoid the use of large-integer multipliers relying on FPGA DSP modules.
A Medical-History-Based Potential Disease Prediction Algorithm

As an important application of medical informatization, healthcare big data analysis has been extensively researched in the fields of intelligent consultation, disease diagnosis, intelligent question-answering doctors, and medical assistant decision support, and have made many achievements. In order to improve the comprehensiveness and pertinence of the medical examination, this paper intends to use healthcare big data analysis combined with deep learning technology to provide patients with potential diseases which is usually neglected for lacking of professional knowledge, so that patients can do targeted medical examinations to prevent health condition from getting worse. Inspired by the existing recommendation methods, this paper proposes a novel deep-learning-based hybrid recommendation algorithm, which is called medical-history-based potential disease prediction algorithm. The algorithm predicts the patient's possible disease based on the patient's medical history, providing a reference to patients and doctors to reduce the problem of delaying treatment due to unclear description of the symptom or limited professional knowledge. The experimental results show that our approach improves the accuracy of the potential diseases prediction.
How to Interact with Augmented Reality Head Mounted Devices in Care Work? A Study Comparing Handheld Touch (Hands-on) and Gesture (Hands-free) Interaction

In this paper, we investigate augmented reality (AR) to support caregivers.We implemented a system called Care Lenses that supported various care tasks on AR head-mounted devices.For its application, one question concerned how caregivers could interact with the system while providing care (i.e., while using one or both hands for care tasks).Therefore, we compared two mechanisms to interact with the Care Lenses (handheld touch similar to touchpads and touchscreens and head gestures).We found that head gestures were difficult to apply in practice, but except for that the head gesture support was as usable and useful as handheld touch interaction, although the study participants were much more familiar with the handheld touch control.We conclude that head gestures can be a good means to enable AR support in care, and we provide design considerations to make them more applicable in practice.
Charging Guiding Strategy for Electric Taxis Based on Consortium Blockchain

The issues in charging guiding for electric vehicles are meaningful studies in recent years, especially for electric taxis which need to be recharged during working hours.However, with the popularity of taxi-booking apps, how to obtain an effective charging guiding for taxis having advance orders becomes an urgent problem to be solved.To optimize various special interests while satisfying the constraints of online advance orders, a charging guiding strategy based on consortium blockchain is proposed in this paper.Firstly, a taxi charging guiding architecture based on consortium blockchain is designed, and an improved practical byzantine fault tolerance algorithm is proposed to solve the problem of charging information disconnection and trust between multiple charging station operators.Secondly, we establish the charging guiding model for electric taxis based on multi-objective optimization.The model aims to meet the constraints of online advance orders, maximize passengers' satisfaction and operators' service efficiency, and minimize the charging costs of taxis.Finally, the optimization model is solved by quantum-behaved particle swarm optimization.In order to verify the effectiveness of the proposed guiding strategy, main urban areas of a city are taken as examples for simulation.The results show that the proposed strategy has increased the passengers' satisfaction by 0.44%, and has decreased the expense cost, the time cost and distance cost by 2.38%, 5.72%, and 17.25% respectively, comparing with PSO based strategy while balancing the utilization of charging equipment.
Automated Verbal and Non-verbal Speech Analysis of Interviews of Individuals with Schizophrenia and Depression

Schizophrenia and depression are the two most common mental disorders associated with negative symptoms that contribute to poor functioning and quality of life for millions of patients globally. This study is part of a larger research project. The overall aim of the project is to develop an automated objective pipeline that aids clinical diagnosis and provides more insights into symptoms of mental illnesses. In our previous work, we have analyzed non-verbal cues and linguistic cues of individuals with schizophrenia. In this study, we extend our work to include participants with depression. Powered by natural language processing techniques, we extract verbal features, both dictionary-based and vector-based, from participants' interviews that were automatically transcribed. We also extracted conversational, phonatory, articulatory and prosodic features from the interviews to understand the conversational and acoustic characteristics of schizophrenia and depression. Combining these features, we applied ensemble learning with leave-one-out cross-validation to classify healthy controls, schizophrenic and depressive patients, achieving an accuracy of 69%-75% in paired classification. From those same features, we also predict the subjective Negative Symptoms Assessment 16 scores of patients with schizophrenia or depression, yielding an accuracy of 90.5% for NSA2 but lower accuracy for other NSA indices. Our analysis also revealed significant linguistic and non-verbal differences that are potentially symptomatic of schizophrenia and depression respectively.
Elastic Differential Evolution for Automatic Data Clustering

In many practical applications, it is crucial to perform automatic data clustering without knowing the number of clusters in advance. The evolutionary computation paradigm is good at dealing with this task, but the existing algorithms encounter several deficiencies, such as the encoding redundancy and the cross-dimension learning error. In this article, we propose a novel elastic differential evolution algorithm to solve automatic data clustering. Unlike traditional methods, the proposed algorithm considers each clustering layout as a whole and adapts the cluster number and cluster centroids inherently through the variable-length encoding and the evolution operators. The encoding scheme contains no redundancy. To enable the individuals of different lengths to exchange information properly, we develop a subspace crossover and a two-phase mutation operator. The operators employ the basic method of differential evolution and, in addition, they consider the spatial information of cluster layouts to generate offspring solutions. Particularly, each dimension of the parameter vector interacts with its correlated dimensions, which not only adapts the cluster number but also avoids the cross-dimension learning error. The experimental results show that our algorithm outperforms the state-of-the-art algorithms that it is able to identify the correct number of clusters and obtain a good cluster validation value.
Details preserved unsupervised depth estimation by fusing traditional stereo knowledge from laparoscopic images

Depth estimation plays an important role in vision-based laparoscope surgical navigation systems. Most learning-based depth estimation methods require ground truth depth or disparity images for training; however, these data are difficult to obtain in laparoscopy. The authors present an unsupervised learning depth estimation approach by fusing traditional stereo knowledge. The traditional stereo method is used to generate proxy disparity labels, in which unreliable depth measurements are removed via a confidence measure to improve stereo accuracy. The disparity images are generated by training a dual encoder–decoder convolutional neural network from rectified stereo images coupled with proxy labels generated by the traditional stereo method. A principled mask is computed to exclude the pixels, which are not seen in one of views due to parallax effects from the calculation of loss function. Moreover, the neighbourhood smoothness term is employed to constrain neighbouring pixels with similar appearances to generate a smooth depth surface. This approach can make the depth of the projected point cloud closer to the real surgical site and preserve realistic details. The authors demonstrate the performance of the method by training and evaluation with a partial nephrectomy da Vinci surgery dataset and heart phantom data from the Hamlyn Centre.
ASU at TextGraphs 2019 Shared Task: Explanation ReGeneration using Language Models and Iterative Re-Ranking

In this work we describe the system from Natural Language Processing group at Arizona State University for the TextGraphs 2019 Shared Task.The task focuses on Explanation Regeneration, an intermediate step towards general multi-hop inference on large graphs.Our approach consists of modeling the explanation regeneration task as a learning to rank problem, for which we use state-of-theart language models and explore dataset preparation techniques.We utilize an iterative reranking based approach to further improve the rankings.Our system secured 2nd rank in the task with a mean average precision (MAP) of 41.3% on the test set.
Dynamics of the measurement uncertainty in an open system and its controlling

The uncertainty principle is one of the most remarkable features of quantum mechanics, originally expressed in terms of the standard deviation of two incompatible observables. Alternatively, it can be modified and written in the form of entropy to eliminate the defect in the form of standard deviation, and can also be generalized by including a memory particle that is entangled with the particle to be measured. Herein we consider a realistic scenario where the particle A to be measured is in an open environment and B as quantum memory is under an environment typically characterized by non-Markovian regimes. Specifically, it shows that the quantum memory and the non-Markovian effect can essentially inhibit the increase of uncertainty, however, the quantum memory-assisted uncertainty will finally inflate inevitably, due to that the quantum correlation of the system will be damaged gradually by the noise from the surrounding environments. To be explicit, we study the dynamic evolution of the entropic uncertainty and correlation in open system where both particles experience the noise channels. Meanwhile, we put forward some effective operation strategies to reduce the magnitude of the measurement uncertainty under the open systems. Furthermore, we explore the applications of the uncertainty relation investigated on entanglement witness and quantum channel capacity. Thus, our investigations might offer an insight into quantum measurement estimation in open systems.
Typical Meteorological Year methodologies applied to solar spectral irradiance for PV applications

A Typical Meteorological Year (TMY) is frequently used in solar power for long-term energy yield analysis. Different approaches have been reported focusing on concentrating solar power or photovoltaic power plants that have established different relative contributions of the involved variables (mainly solar irradiance components and temperature) according to the application. For PV applications the estimation of the spectral gains and losses requires of on-site spectral measurements. Long-term analysis of the spectral influence on PV technologies has been performed for over seven years of measured spectral global tilted irradiance in Madrid. The experimental spectra were measured with an EKO spectroradiometer in the wavelength range of 300–1100 nm. The TMY methodology has been used to create a typical spectral year of global tilted irradiance that can be used for computing the spectral factor. This paper shows the different steps in applying the TMY methodology to spectral irradiance and the resulting spectral factors computed for seven different PV technologies. Thus, this approach can effectively be used to characterize the long-term spectral influence of PV technologies in a selected site.
New Approaches to Traitor Tracing with Embedded Identities

In a traitor tracing (TT) system for n users, every user has his/her own secret key. Content providers can encrypt messages using a public key, and each user can decrypt the ciphertext using his/her secret key. Suppose some of the n users collude to construct a pirate decoding box. Then the tracing scheme has a special algorithm, called $$\mathsf {Trace}$$, which can identify at least one of the secret keys used to construct the pirate decoding box. Traditionally, the trace algorithm output only the ‘index’ associated with the traitors. As a result, to use such systems, either a central master authority must map the indices to actual identities, or there should be a public mapping of indices to identities. Both these options are problematic, especially if we need public tracing with anonymity of users. Nishimaki, Wichs, and Zhandry (NWZ) [Eurocrypt 2016] addressed this problem by constructing a traitor tracing scheme where the identities of users are embedded in the secret keys, and the trace algorithm, given a decoding box D, can recover the entire identities of the traitors. We call such schemes ‘Embedded Identity Traitor Tracing’ schemes. NWZ constructed such schemes based on adaptively secure functional encryption (FE). Currently, the only known constructions of FE schemes are based on nonstandard assumptions such as multilinear maps and iO. In this work, we study the problem of embedded identities TT based on standard assumptions. We provide a range of constructions based on different assumptions such as public key encryption (PKE), bilinear maps and the Learning with Errors (LWE) assumption. The different constructions have different efficiency trade offs. In our PKE based construction, the ciphertext size grows linearly with the number of users; the bilinear maps based construction has sub-linear ($$\sqrt{n}$$) sized ciphertexts. Both these schemes have public tracing. The LWE based scheme is a private tracing scheme with optimal ciphertexts (i.e., $$\log (n)$$). Finally, we also present other notions of traitor tracing, and discuss how they can be build in a generic manner from our base embedded identity TT scheme.
Agreement is overrated: A plea for correlation to assess human evaluation reliability

Inter-Annotator Agreement (IAA) is used as a means of assessing the quality of NLG evaluation data, in particular, its reliability.According to existing scales of IAA interpretationsee, for example, Lommel et al. ( 2014), Liu et al. (2016), Sedoc et al. (2018) and Amidei et al. (2018a) -most data collected for NLG evaluation fail the reliability test.We confirmed this trend by analysing papers published over the last 10 years in NLG-specific conferences (in total 135 papers that included some sort of human evaluation study).Following Sampson and Babarczy (2008), Lommel et al. (2014), Joshi et al. (2016) and Amidei et al. ( 2018b), such phenomena can be explained in terms of irreducible human language variability.Using three case studies, we show the limits of considering IAA as the only criterion for checking evaluation reliability.Given human language variability, we propose that for human evaluation of NLG, correlation coefficients and agreement coefficients should be used together to obtain a better assessment of the evaluation data reliability.This is illustrated using the three case studies.