Reviewerly/exHarmony huggingface dataset:
Supervised Paragraph Vector: Distributed Representations of Words, Documents and Class Labels

While the traditional method of deriving representations for documents was bag-of-words, they suffered from high dimensionality and sparsity.Recently, many methods to obtain lower dimensional and densely distributed representations were proposed.Paragraph Vector is one of such algorithms, which extends the word2vec algorithm by considering the paragraph as an additional word.However, it generates a single representation for all tasks, while different tasks may require different representations.In this paper, we propose a Supervised Paragraph Vector, a task-specific variant of Paragraph Vector for situations where class labels exist.Essentially, Supervised Paragraph Vector uses class labels along with words and documents and obtains corresponding representations with respect to the particular classification task.In order to prove the benefits of the proposed algorithm, three performance criteria are used: interpretability, discriminative power, and computational efficiency.To test interpretability, we find words that are close and far to class vectors and demonstrate that such words are closely related to the corresponding class.We also use principal component analysis to visualize all words, documents, and class labels at the same time and show that our method effectively displays the related words and documents for each class label.To evaluate discriminative power and computational efficiency, we perform document classification on four commonly used datasets with various classifiers and achieve comparable classification accuracies to bag-of-words and Paragraph Vector.
Interaction Topologies in Mobile-Based Situated Networked Music Systems

In this paper, we present a complete framework, both technical and conceptual, aimed at developing and analysing Networked Music Systems. After a short description of our technical framework called soundworks , a JavaScript library especially designed for collective music interaction using web browser of mobile phones, we introduce a new conceptual framework, we named interaction topologies , that aims at providing a generic tool for the description of interaction in such systems. Our proposition differs from the theoretical approaches generally proposed in the literature by decoupling the description of interaction topologies from the low level technical implementation of the network. We then report on a set of scenarios and prototypes, illustrating and assessing our framework, which were successfully deployed in several public installations and performances. We particularly show that our concept of interaction topologies succeeds at describing and analysing global aspects of interaction from multiple point of views (e.g., social, human-computer) by allowing for composing simple abstract figures. We finally introduce a discussion on agencies and perception of users engaged in such systems that could later complete our framework to conceive and analyse Networked Music Systems.
Data Stewardship for Open Science: Implementing FAIR Principles

This is a review of book titled  Data Stewardship for Open Science: Implementing FAIR Principles , written by Barend Mons, and published by CRC Press, London, 2018.
Capability Construction of C4ISR Based on AI Planning

This paper considers a capability construction problem of the C4ISR system under serviceoriented architecture.A capability construction model is first established and described in the planning domain definition language as an artificial intelligence (AI) planning problem.To adapt the complex requirements of a C4ISR system and large scale of required services, an incremental macro-operation learning method based on n-gram analysis is proposed, and an enhanced domain is generated using a relaxation scheme.To improve the efficiency of the search algorithm, an ordered-hill-climbing (OHC) method is designed based on the length of the operations.With the above procedures, the AI planner, using macro-operation and the OHC, is presented for capability construction problems.The simulation results show that this method can effectively shorten the search time of capability construction.
Enhancing students’ active learning and self-efficacy using mobile technology in medical English classes

Purpose This study investigated student experiences of mobile technology in a medical English course using a free mobile classroom application, Socrative and its impact on student learning to help them achieve the competency expected in the course. Methods Questionnaires were administered of year 2 medical students enrolled in a medical English course between 2016 and 2017 to investigate their experience in using Socrative. Learning outcomes were assessed by comparing studentsâ academic performance with that of the 2015 cohort, who had taken the same course without using mobile technology, and also by measuring changes in their self-efficacy in English over time during the course. Results This study found studentsâ positive perceptions of and overall satisfaction with their experience in using mobile technology in the medical English course. There were no differences in the academic performance between the study cohort and the comparison group. The ratio of students who were in the remediation program after the mid-term examination was 11%, whereas the remediation ratio of the 2015 cohort was 21%. Studentsâ self-efficacy in English improved significantly in the writing and reading domains (p<0.05). Conclusion This study found several benefits of using Socrative in the medical English course. Socrative fostered the classroom interaction by encouraging input from every student and sharing it instantly with the whole class. There was also learning improvement with the use of Socrative. Furthermore, it has the potential to enhance assessment by offering more various forms of assessment than that of conventional audience response systems. Keywords: Educational technology, Mobile applications, Teaching methods, Medical English
Modified honey encryption scheme for encoding natural language message

Conventional encryption schemes are susceptible to brute-force attacks. This is because bytes encode utf8 (or ASCII) characters. Consequently, an adversary that intercepts a ciphertext and tries to decrypt the message by brute-forcing with an incorrect key can filter out some of the combinations of the decrypted message by observing that some of the sequences are a combination of characters which are distributed non-uniformly and form no plausible meaning. Honey encryption (HE) scheme was proposed to curtail this vulnerability of conventional encryption by producing ciphertexts yielding valid-looking, uniformly distributed but fake plaintexts upon decryption with incorrect keys. However, the scheme works for only passwords and PINS. Its adaptation to support encoding natural language messages (e-mails, human-generated documents) has remained an open problem. Existing proposals to extend the scheme to support encoding natural language messages reveals fragments of the plaintext in the ciphertext, hence, its susceptibility to chosen ciphertext attacks (CCA). In this paper, we modify the HE schemes to support the encoding of natural language messages using Natural Language Processing techniques. Our main contribution was creating a structure that allowed a message to be encoded entirely in binary. As a result of this strategy, most binary string produces syntactically correct messages which will be generated to deceive an attacker who attempts to decrypt a ciphertext using incorrect keys. We evaluate the security of our proposed scheme.
Editorial for Special Issue “Structural Control of Mineral Deposits: Theory and Reality”

“Structural Control” remains a crucial point that is frequently absent in scientific and/or economic analyses of ore deposits, whatever their type and class, although a selection of references illustrates its importance [...]
Document Summarization using Word and Part-of-speech based on Attention Mechanism

Automatic text summarization is one of the research hotspots in natural language processing (NLP). Recently, there has been a considerable research interest in summarization. While most of the current researches about automatic text summarization are only based on word embedding features, which has the problem of too few text features and can't effectively utilize some other features of the text. In this paper, we propose a novel deep learning model called Document Summarization using Word and Part-of-speech based on Attention Mechanism (WPABS). We employ the word embedding and part-of-speech embedding to make full use of the features of the text and evaluate our model on English datasets Gigaword and DUC-2004. The experimental results show that our model is better than most methods. Also, compared with WABS(Word Attention-Based Summarization), our proposed model performs better. It is proved that it is useful to combine the word and part-of-speech.
HYBRID ENCRYPTION ALGORITHMS FOR MEDICAL DATA STORAGE SECURITY IN CLOUD DATABASE

Cloud database are derivatives of Cloud computing.At present, most medical institutions store data in cloud database.Although the cloud database improves the efficiency of use, it also poses a huge impact and challenge to the secure storage of data.The article proposes a hybrid algorithm to solve the data security problem in the hospital cloud database.First, the AES algorithm is improved.The improved algorithm is called P-AES algorithm.The P-AES algorithm is then combined with the RSA algorithm, called a hybrid algorithm.The experimental results show that the hybrid encryption algorithm has the advantages of fast encryption and decryption speed, high security, good processing ability for longer data, and can solve the data security problem in cloud database to a certain extent.The algorithm has been successfully applied to hospital information management system.
Can Video Surveillance Systems Promote the Perception of Safety? Evidence from Surveys on Residents in Beijing, China

Ubiquitous utilization of video surveillance systems is supposed to promote safety, yet whether these systems improve the residents’ perception of safety is unknown. Moreover, the factors that affect the perception of safety are also unclear. We seek to fill these knowledge gaps via a survey-based study, which aims at evaluating the impact of video surveillance systems on perceived safety from crimes and accidents and on behavioral preferences, as well as the attitudes towards such systems. A total of 1080 residents in Beijing, China are validly surveyed, and the surveys returned are exploited for univariate analysis and regression analysis. The analysis results indicate that female respondents are more accustomed to adopt video surveillance systems, as they feel safer and more comfortable with the existence of surveillance systems. Higher levels of education, greater length of residency, and richer knowledge of surveillance systems tend to increase the perception of safety, while age and monthly income act negatively. The effects of these factors should be considered during the implementation of video surveillance systems and recommendations are given to facilitate the adoption of such systems.
Do You Care for Robots That Care? Exploring the Opinions of Vocational Care Students on the Use of Healthcare Robots

Background: There has been a rapid increase in the population of senior citizens in many countries. The shortage of caregivers is becoming a pressing concern. Robots are being deployed in an attempt to fill this gap and reduce the workload of caregivers. This study explores how healthcare robots are perceived by trainee care professionals. Methods: A total of 2365 students at different vocational levels completed a questionnaire, rating ethical statements regarding beneficence, maleficence, justice, autonomy, utility, and use intentions with regard to three different types of robots (assistive, monitoring, and companion) along with six control variables: gender, age, school year, technical skills, interest in technology, and enjoying working with computers. The scores were analyzed by MANOVA statistics. Results: In relation to our research questions: All students viewed companion robots as more beneficent than monitoring and assistive robots. Level of education did not lead to any differences in appraisal. Participants rated maleficence lowest and the highest scores were given to autonomy and utility, meaning a positive evaluation of the use of healthcare robots. Surprisingly, all students rated use intentions low, indicating a poor motivation to actually use a robot in the future, although participants stated a firmer intention for using monitoring devices. Conclusion: Care students find robots useful and expect clients to benefit from them, but still are hesitant to use robots in their future practice. This study suggests that it would be wise to enrich the curriculum of intermediate care education with practical classes on the use and ethical implications of care robots, to ensure that this group of trainee care professionals fully understand the possibilities and potential downside of this emerging kind of healthcare technology.
Vibration Detection and Motion Compensation for Multi-Frequency Phase-Shifting-Based 3D Sensors

Phase-shifting profilometry, especially employing the multi-frequency phase-shifting method, is increasingly used for in situ 3D metrology and for the inspection of industrial parts. However, environmental vibrations cause fatal measurement errors and are inevitable in such applications. To this end, an effective and fast vibration detection and motion compensation method for multi-frequency phase-shifting-based 3D sensors is presented. The proposed method quantitatively indicates the strength of the vibration and compensates for the motion error by revising the wrapped phase without accessing neighboring pixels. Different vibration intensities were simulated using an industrial robot moving to test the feasibility of the method. According to experiments, this method is valid and capable for 3D inspection systems affected by inevitable vibrations.
FaceDetectNet: face detection via fully-convolutional network

Face detection is one of the most popular computer vision tasks. There are a lot of face detection approaches proposed including different CNN-based techniques, but the problem of optimal balancing between detection quality and computational speed is still relevant. In this paper we propose new CNN-based solution for face detection called FaceDetectNet. Our CNN architecture is based on ideas of YOLO/DetectNet and GoogleNet architecture supported with some new tools and implementation details created especially for our face detection application. We propose: original iterative proposal clustering (IPC) algorithm for aggregation of output face proposals formed by CNN and the 2-level “weak pyramid” providing better detection quality on the testing sets containing both small and huge images. Our face detection approach is close to previously proposed SSD-based face detection, but the principal difference is that we use the deep features of top hidden CNN layer for forming the face proposals of any size. Thus we utilize the global semantic and context information for improving the detection quality for small faces. Our FaceDetectNet is trained and tested on the most challenging WIDER FACE detection benchmark. Our algorithm achieves the average precision (AP) 0.69 on the WIDER FACE hard level, and thus outperforms all competitive detectors on the Hard level besides the HR state-of-the-art solution. Note that HR solution is based on essentially deeper and slower CNN, while our FaceDetectNet can work in real-time on the NVIDIA GeForce 1080 GPU. On the other hand, SSD-based face detector with comparable CNN parameters provides AP 0.625 only on the WIDER FACE hard level. So, our approach provides the best quality with reasonable computational speed.
A Game-Theoretic Framework to Preserve Location Information Privacy in Location-based Service Applications

Recently, the growing ubiquity of location-based service (LBS) technology has increased the likelihood of users' privacy breaches due to the exposure of their real-life information to untrusted third parties. Extensive use of such LBS applications allows untrusted third-party adversarial entities to collect large quantities of information regarding users' locations over time, along with their identities. Due to the high risk of private information leakage using resource-constrained smart mobile devices, most LBS users may not be adequately encouraged to access all LBS applications. In this paper, we study the use of game theory to protect users against private information leakage in LBSs due to malicious or selfish behavior of third-party observers. In this study, we model a scenario of privacy protection gameplay between a privacy protector and an outside visitor and then derive the situation of the prisoner's dilemma game to analyze the traditional privacy protection problems. Based on the analysis, we determine the corresponding benefits to both players using a point of view that allows the visitor to access a certain amount of information and denies further access to the user's private information when exposure of privacy is forthcoming. Our proposed model uses the collection of private information about historical access data and current LBS access scenario to effectively determine the probability that the visitor's access is an honest one. Moreover, we present the procedures involved in the privacy protection model and framework design, using game theory for decision-making. Finally, by employing a comparison analysis, we perform some experiments to assess the effectiveness and superiority of the proposed game-theoretic model over the traditional solutions.
Filter-Wise Pruning Approach to FPGA Implementation of Fully Convolutional Network for Semantic Segmentation

This paper presents a hardware-aware sparse fully convolutional network (SFCN) for semantic segmentation on an FPGA. Semantic segmentation attracts interest since for self-driving car it is important to recognize road and obstacles in pixel level. However, it is hard to implement the system on embedded systems since the number of weights for the SFCN is so large that embedded systems cannot store them using limited on-chip memory. To realize good a trade-off between speed and accuracy, we construct an AlexNet-based SFCN which has no skip connections and deconvolution layers to reduce the computation costs and the latency. Furthermore, we propose a filter-wise pruning technique that sorts the weights of each filter by their absolute values and prunes them by a preset percent filter-by-filter from a small order. It is more suitable for the hardware implementation since the number of computation of each filter becomes equal. We trained the AlexNet-based SFCN by using Camvid image dataset and implemented on Xilinx zcu102 evaluation board. The results show that the FPGA system is 10.14 times faster than a mobile GPU one, and its performance per power consumption is 24.49 times higher than the GPU counterpart.
Ranking Network Embedding via Adversarial Learning

Network Embedding is an effective and widely used method for extracting graph features automatically in recent years. To handle the widely existed large-scale networks, most of the existing scalable methods, e.g., DeepWalk, LINE and node2vec, resort to the negative sampling objective so as to alleviate the expensive computation. Though effective at large, this strategy can easily generate false, thus low-quality, negative samples due to the trivial noise generation process which is usually a simple variant of the unigram distribution. In this paper, we propose a Ranking Network Embedding (RNE) framework to leverage the ranking strategy to achieve scalability and quality simultaneously. RNE can explicitly encode node similarity ranking information into the embedding vectors, of which we provide two ranking strategies, vanilla and adversarial, respectively. The vanilla strategy modifies the uniform negative sampling method with a consideration of edge existance. The adversarial strategy unifies the triplet sampling phase and the learning phase of the model with the framework of Generative Adversarial Networks. Through adversarial training, the triplet sampling quality can be improved thanks to a softmax generator which constructs hard negatives for a given target. The effectiveness of our RNE framework is empirically evaluated on a variety of real-world networks with multiple network analysis tasks.
Spam image email filtering using K-NN and SVM

&lt;p&gt;The developing utilization of web has advanced a simple and quick method for e-correspondence. The outstanding case for this is e-mail. Presently days sending and accepting email as a method for correspondence is prominently utilized. Be that as it may, at that point there stand up an issue in particular, Spam mails. Spam sends are the messages send by some obscure sender just to hamper the improvement of Internet e.g. Advertisement and many more. Spammers introduced the new technique of embedding the spam mails in the attached image in the mail. In this paper, we proposed a method based on combination of SVM and KNN. SVM tend to set aside a long opportunity to prepare with an expansive information set. On the off chance that "excess" examples are recognized and erased in pre-handling, the preparation time could be diminished fundamentally. We propose a k-nearest neighbor (k-NN) based example determination strategy. The strategy tries to select the examples that are close to the choice limit and that are effectively named. The fundamental thought is to discover close neighbors to a question test and prepare a nearby SVM that jelly the separation work on the gathering of neighbors. Our experimental studies based on a public available dataset (Dredze) show that results are improved to approximately 98%.&lt;/p&gt;
Let’s Get in Touch - Decision Making about Enterprise Architecture Using 3D Visualization in Augmented Reality

Making informed decisions about historically grown and often complex business and Information Technology (IT) landscapes can be particularly difficult. Enterprise Architecture Management (EAM) addresses this issue by enabling stakeholders to base their decisions on relevant information about the organization's current and future Enterprise Architectures (EAs). However, visualization of EA is often confronted with low usefulness perceptions. Informed by the cognitive fit theory (CFT), we argue that decision-makers benefit from interacting with EA visualizations using Augmented Reality (AR), because it enables a consistent task-related mental representation based on the natural use of decision-makers' visualspatial abilities. The goal of this paper is to demonstrateARs suitability for EA-related decision-making.We follow the design science research (DSR) approach to develop and evaluate an AR head-mounted display (HMD) prototype, using the Microsoft HoloLens.Our results suggest that EA-related decision-making can profit from applying AR, but users find the handling of the HMD device cumbersome.