Reviewerly/exHarmony huggingface dataset:
Examining translation behaviour of Turkish student translators in scientific text translation with think-aloud protocols

The process of translation has been dramatically influenced by the latest developments in technology. Students’ behaviours during the translation process have also changed as they try to seek information and use different resources. This study aims to investigate the translation behaviour of students in an English translation department. For this purpose, 11 students were recruited. The students were asked to translate a scientific text from English into Turkish. For the analysis of their translation behaviour, Think-Aloud Protocols (TAPs) and their translated texts were used. Monologue Protocol was used to see what goes on in a prospective translator’s mind. The students were audio-recorded while translating. Then, the translations were scored and the transcriptions of the recordings were coded. The results were presented under three main themes: (i) Recruiting translation tools: when and how, (ii) Following a pattern of translation process, and (iii) Challenges: language(s), context and more. All in all, the present study highlights the importance of guiding students in the use of the appropriate tools for the translation of specialised texts, and also suggests that student translators should be more critical of Machine Translation outputs and should practice post-editing procedures in their courses.
Tax-Scheduler: An interactive visualization system for staff shifting and scheduling at tax authorities

Given a large number of applications and complex processing procedures, how to efficiently shift and schedule tax officers to provide good services to taxpayers is now receiving more attention from tax authorities. The availability of historical application data makes it possible for tax managers to shift and schedule staff with data support, but it is unclear how to properly leverage the historical data. To investigate the problem, this study adopts a user-centered design approach. We first collect user requirements by conducting interviews with tax managers and characterize their requirements of shifting and scheduling into time series prediction and resource scheduling problems. Then, we propose Tax-Scheduler, an interactive visualization system with a time-series prediction algorithm and genetic algorithm to support staff shifting and scheduling in the tax scenarios. To evaluate the effectiveness of the system and understand how non-technical tax managers react to the system with advanced algorithms and visualizations, we conduct user interviews with tax managers and distill several implications for future system design.
Discrete mixture representations of spherical distributions

We obtain discrete mixture representations for parametric families of probability distributions on Euclidean spheres, such as the von Mises--Fisher, the Watson and the angular Gaussian families. In addition to several special results we present a general approach to isotropic distribution families that is based on density expansions in terms of special surface harmonics. We discuss the connections to stochastic processes on spheres, in particular random walks, discrete mixture representations derived from spherical diffusions, and the use of Markov representations for the mixing base to obtain representations for families of spherical distributions.
Understanding the Role of Socio-Technical Infrastructures on the Organization of Access for the Mixed-Ability Collaborators

TPositioning disabled people as deficient, dysfunctional, and locating the 'problem' of disability within the individual, the over-medicalized, individualistic, and not equity-oriented perspectives of disability have led to oppression, discrimination, and exclusion of disabled people from important parts of public life. The global politics of disability rights and disability movements have brought thorny questions regarding the nature of dominant explanations. Equity-oriented perspectives and collaborative approaches regarding organization and distribution of access started to gain visibility. HCI research has a vital potential to contribute to this by providing related tools and technologies for integrating equal access in the collaborative organization of access. Considering the existing literature, the question of how access is collaboratively organized, negotiated, distributed and scaled through socio-technical mechanisms especially at an institutional level, as well as how mixed-ability groups reorganize access by interacting with institutional socio-technical structures remains open. In this research, I aim to extend the body of literature in collaborative access by presenting the importance of socio-technical perspectives for designing collaborative technologies to support equal distribution of access. My research is about the signifcance of equity perspectives in access and interaction. Specifcally, this research focuses on understanding the role of socio-technical infrastructures for the organization and distribution of access by mixed-ability collaborators and developing design insights for socio-technical mechanisms to support equal distribution of access for people with disabilities.
A rod-mass method of soft tissue modeling and three dimensional force vector estimation for tele-surgery training

In a tele-surgery training system, the transparency is extremely important so as to ensure the success of the operation and the safety of soft objects. Due to current technique limits, it is difficult to mount force sensors at the end of the slave manipulator. In this paper, we propose a novel rod-mass algorithm and construct the model of soft objects. Through the modeling process, the accurate three dimensional contact force vector between the end of the manipulator and the soft object can be estimated in real time. A virtual spring using Hooke s law is introduced to the novel mass–spring method. Applying an impedance model, the three dimensional contact force estimates can be calculated from the deformation of masses’ positions and velocities. In order to verify our methods, a virtual reality interaction platform is constructed including the Omni master manipulator, a four joints manipulators, a virtual reality display, and the soft object’s model. Numerical simulations and experiments are performed to verify the accuracy and the feasibility of soft objects grasping. Results show the high effectiveness and efficiencies of our methods.
Biomedical Text Classification Using Augmented Word Representation Based on Distributional and Relational Contexts

Due to the increasing use of information technologies by biomedical experts, researchers, public health agencies, and healthcare professionals, a large number of scientific literatures, clinical notes, and other structured and unstructured text resources are rapidly increasing and being stored in various data sources like PubMed. These massive text resources can be leveraged to extract valuable knowledge and insights using machine learning techniques. Recent advancement in neural network-based classification models has gained popularity which takes numeric vectors (aka word representation) of training data as the input to train classification models. Better the input vectors, more accurate would be the classification. Word representations are learned as the distribution of words in an embedding space, wherein each word has its vector and the semantically similar words based on the contexts appear nearby each other. However, such distributional word representations are incapable of encapsulating relational semantics between distant words. In the biomedical domain, relation mining is a well-studied problem which aims to extract relational words, which associates distant entities generally representing the subject and object of a sentence. Our goal is to capture the relational semantics information between distant words from a large corpus to learn enhanced word representation and employ the learned word representation for various natural language processing tasks such as text classification. In this article, we have proposed an application of biomedical relation triplets to learn word representation through incorporating relational semantic information within the distributional representation of words. In other words, the proposed approach aims to capture both distributional and relational contexts of the words to learn their numeric vectors from text corpus. We have also proposed an application of the learned word representations for text classification. The proposed approach is evaluated over multiple benchmark datasets, and the efficacy of the learned word representations is tested in terms of word similarity and concept categorization tasks. Our proposed approach provides better performance in comparison to the state-of-the-art GloVe model. Furthermore, we have applied the learned word representations to classify biomedical texts using four neural network-based classification models, and the classification accuracy further confirms the effectiveness of the learned word representations by our proposed approach.
COMMUNICATIVE MOVES IN THE DISCUSSION SECTIONS OF EFL INDONESIAN VOCATIONAL COLLEGE STUDENTS

Studies on the move analysis in the various sections of research articles have developed rapidly. However, the studies on the communicative moves in the discussion section particularly written by vocational college students are still limited. In fact, those students categorized as the novice writers inevitably need illustrations of how to write the proper discussion related to their findings. Therefore, this study was aimed at exploring the communicative moves and the move cycle series in the discussion sections of the final project reports written by EFL vocational college students in one of the state polytechnics in Indonesia. The method used is a discourse analysis on 15 discussion sections. The results showed that Move II, Move IV, and Move I were used frequently in all students’ discussion sections. Pertaining to the move cycle series, students fulfilled the rhetorical functions of introduction (1) and evaluation (4+2 or 2+4) but the cycle for showing the conclusion was commonly not used. The results provide references and the real applications pertinent to communication moves in the discussion section by EFL students.
Perfect splitting of a two-photon pulse

We employ a cascaded system approach to numerically simulate the interaction of photon pulses with a two-level scatterer in a chiral waveguide QED setup and show that the scattering of a two-photon pulse may lead to the predominant population of only two output wave packet modes in an entangled state, $|2,0\ensuremath{\rangle}--|0,2\ensuremath{\rangle}$. In a complementary wave-packet basis, this is a product state of two orthogonal single-photon wave packets. Operating this process in reverse allows a perfect merging of distinguishable single-photon wave packets into a single-mode pulse carrying two identical photons.
Mobile health‐empowered traditional ethnic sports: AI‐based data analysis improving security

Traditional ethnic sports shape the Chinese nation's solid national spirit, and mobile health development has been extended to various fields. In this study, we empower mobile health to traditional ethnic sports. Sensors used for collecting health data are worn on athletes and communicated with sink nodes through the network to provide better training guidance for traditional ethnic sports athletes through data analysis. However, the devices used to collect health data may come from many companies, and aggregating the data inevitably involves data security. As a new basic artificial intelligence technology, federated learning can use the health data of athletes to train the data analysis model in the case of original data localization, to solve the security and privacy problems in health data sharing to a certain extent. To this end, a differentially private‐dynamic federated learning framework for dynamic aggregation weights under an untrusted central server is proposed, which sets a dynamic model aggregation weight, and this method directly learns federated learning from the data of different participants. The learning model aggregates the weights so that it is suitable for non‐independent data environments. Experimental results show that the proposed framework not only provides local privacy guarantees, but also achieves higher accuracy and improves the security of mobile health data of traditional ethnic sports athletes in federated learning.
Quantum superposition of causal structures as a universal resource for local implementation of nonlocal quantum operations

Spatial separation restricts the set of locally implementable quantum operations on distributed multipartite quantum systems. We propose that indefinite causal structure arising due to quantum superposition of different space-time geometries can be used as an independent universal resource for local implementation of any quantum operation on spatially distributed quantum systems. Consequently, all such quantum tasks that are not accomplishable by local operations and classical communication (LOCC) only also become locally accomplishable. We show that exploiting indefinite causal structure as the sole resource, it is possible to perfectly teleport the state of one agent's subsystem to the other distant laboratory in such a way that the agent at the distant laboratory can have access to the whole initially shared state in his or her laboratory and can perform any global quantum operation on the joint state locally. We further find that, after the teleportation process, the resource -- indefinite causal structure of the space-time does not get consumed. Hence, after implementing the desired quantum operation the state of the first agent's subsystem can be teleported back to its previous laboratory using the same resource. We show that this two-way teleportation is not always necessary for locally executing all nonlocal quantum tasks that are not realisable by LOCC only. Without invoking any kind of teleportation, we present a protocol for perfect local discrimination of the set of four Bell states that exploits indefinite causal structure as the sole resource. As immediate upshots, we present some more examples of such nonlocal tasks as local discrimination of the set of states exhibiting ``quantum nonlocality without entanglement" and activation of bound entangled states that are also achievable by our proposed protocol incorporating indefinite causal structure as a resource.
Decentralized decision-making and scaled autonomy at Spotify

While modern software companies strive to increase team autonomy to enable them to successfully operate the piece of software they develop and deploy, efficient ways to orchestrate the work of multiple autonomous teams working in parallel are still poorly understood. In this paper, we report how team autonomy is maintained at Spotify at scale, based on team retrospectives, interviews with team managers and archival analysis of corporate databases and work procedures. In particular, we describe how managerial authority is decentralized through various workgroups with collective authority, what compromises are made to team autonomy to ensure alignment and which team-related factors can further hinder autonomy. Our findings show that scaled autonomy at Spotify does not mean anarchy, or unlimited permissiveness. Instead, squads are expected to take responsibility for their work and coordinate, communicate and align their actions with others, and comply with a few enabling constraints. Further, squads take many decisions independently without management control or due to collective efforts that bypass formal boundary structures. Mechanisms and strategies that enable self-organization at Spotify are related to effective sharing of the codebase, achieving alignment, networking and knowledge sharing, and are described to guide other companies in their efforts to scale autonomy.
TSI-based hierarchical clustering method and regular-hypersphere model for product quality detection

In modern industry, the production process is always accompanied by a large amount of high-dimensional data, which is always too sparse to provide sufficient information for quality detection. Moreover, traditional quality detection methods show the ambiguity and uncertainty in the clustering process. To overcome these difficulties, an improved hierarchical clustering method and a hypersphere model are proposed respectively in this paper. First, the weights of the minimum spanning tree in each cluster are used to calculate the intra-cluster dispersion and the inter-cluster expansion. Next, to enhance the effectiveness of the clustering process, a threshold-based scheduling index is designed to determine whether the merging process for different clusters continues or not, by which the optimal clustering results can be obtained eventually. Then, the obtained clustering results can be used to calculate the centroid of each cluster, thereby to train the judgment boundary to form a hypersphere. Finally, the hypersphere can be used to detect the product quality. The simulation and experimental results show that, the clustering accuracy is close to 100%, and the accuracy of detection model is superior to 91.83%. Those results are competitive with existing state-of-the-art detection methods in terms of clustering effectiveness and the real-time performance.
Multi‐task multi‐scale attention learning‐based facial age estimation

Face-based age estimation strongly depends on deep residual networks (ResNets), used as the backbone in the relevant research. However, ResNet-based methods ignore the importance of some large-scale facial information and other facial age attributes. Inspired by the attention mechanism, a multi-task learning framework for face-based age estimation called multi-task multi-scale attention is proposed. First, the authors embed the alternative strategy structure of dilated convolution into ResNet34 to construct a multi-scale attention module (MSA) to improve the network's receptive field, which extracts local age-sensitive information while obtaining multi-scale features. The MSA can have a larger receptive field to extract both large-scale and local detailed feature information. Second, multi-task learning network structures are built to predict gender and race, which can share rigid network parameters to improve age estimation and improve the accuracy of age estimation by other age-related parameters. Finally, the Kullback-Leibler divergence loss is adopted between a Dirac delta label and a Gaussian prediction to guide the training. The numerical tests on the MORPH Album II and Adience datasets prove the superiority of the proposed method over other state-of-the-art ones.
Hierarchical Policies of Subgoals for Safe Deep Reinforcement Learning

Reinforcement learning is a machine learning method that relies on the agent to learn by trial and error to solve decision optimization problems. It is well known that an agent based on deep reinforcement learning in complex environments is difficult to train. Moreover, the agent will generate unsafe and strange actions due to the lack of sufficient reward feedback from the environment. To make the agent converge to a better policy and make its behavior safer and more controllable under sparse rewards, we propose a subgoal embedding method based on prior knowledge and hierarchical strategy that can make the training process converge faster. The subgoal embedding method can be combined with existing reinforcement learning methods. In this paper, we combine the subgoal embedding method with REINFORCE algorithm and PPO(Proximal Policy Optimization) algorithm to test the method in the MiniGrid-DoorKey game environment of the gym platform. The experiments demonstrate the effectiveness of the subgoal embedding method.
Situational support and information security behavioural intention: a comparative study using conservation of resources theory

The formation of information security behavioural intention (ISBI) can be complex and dynamic in different contexts.This paper aims to examine and compare different users' ISBI formalization mechanisms when dealing with their personal affairs (non-work users) and organizational affairs (work users).Drawing on two principles of Conservation of Resources (COR) theory (i.e., resource loss principle, and resource gain principle), we developed two models to examine how situational support affects ISBI formation.The results of a study of 432 non-worker users and 261 work users indicate a curvilinear relationship between situational support and ISBI through subjective norms and risk perception for non-worker users, whilst a linear relationship via subjective norms is found for worker users.This is the first time that COR has been applied to explain the formation of ISBI.The findings broaden the research scope of individuals' ISBI by revealing how situational support affects the formalization mechanism for different users in crosscontexts.The theoretical and practical implications of the findings and the future study are discussed.
FedTA: Locally-Differential Federated Learning with Top-k Mechanism and Adam Optimization

With the explosive development of fields including big data and cloud computing, it has become a global trend for the public to place a premium on data security and privacy. A federated learning (FL) system is a useful weapon for utilizing decentralized information and files, since it enables several parties to construct a unified, robust model without disclosing their raw data, hence resolving critical issues such as data leakage and data silos. Nonetheless, an increasing number of research indicates that the federated learning algorithm cannot withstand sophisticated privacy threats. Additionally, federated learning does not guarantee privacy in a formal sense. In order to offer adequate protection for individuals’ personal information, we include local differential privacy within the user uploaded settings. In addition, we proposed a framework to improve the performance of federated machine learning by employing several technologies including Top-k mechanism and Adam optimization. Detailed experimental results are provided to demonstrate the applicability and effectiveness of our approach.
An Improved Cuckoo Search Algorithm and Its Application in Function Optimization

An improved cuckoo search (CS) algorithm focusing on optimizing the updating of the bird’s nest position is proposed to overcome the shortcomings of CS algorithm, such as low search accuracy, easy premature convergence, and weak local search ability in the late stage. The proposed algorithm employs three main strategies. First, in the early and middle stage, aiming to expand the search domain, increase the population diversity, and prevent the premature convergence of the algorithm, the global optimization strategy combining opposition-based learning and Levy flight is used to update the nest position. Second, to avoid the algorithm from falling into a local optimum, a dynamic inertia weight is employed to reduce the effect of the current nest position when the nest position is updated during the random migration. Third, in the late stage, to improve the local search ability and search accuracy, the nest position is updated using the local exploitation strategy of the Aquila optimization (AO) algorithm to replace the Levy flight mechanism. The results of comparative experiments with CS algorithm and its four variants on function optimization show that the proposed algorithm has a better global search ability, better local development ability, faster convergence speed and better search accuracy than other selected algorithms.