Reviewerly/exHarmony huggingface dataset:
Pindex: Private multi-linked index for encrypted document retrieval

Cryptographic cloud storage is used to make optimal use of the cloud storage infrastructure to outsource sensitive and mission-critical data. The continuous growth of encrypted data outsourced to cloud storage requires continuous updating. Attacks like file-injection are reported to compromise confidentiality of the user as a consequence of information leakage during update. It is required that dynamic schemes provide forward privacy guarantees. Updates should not leak information to the untrusted server regarding the previously issued queries. Therefore, the challenge is to design an efficient searchable encryption scheme with dynamic updates and forward privacy guarantees. In this paper, a novel private multi-linked dynamic index for encrypted document retrieval namely P index is proposed. The multi-linked dynamic index is constructed using probabilistic homomorphic encryption mechanism and secret orthogonal vectors. Full security proofs for correctness and forward privacy in the random oracle model is provided. Experiments on real world Enron dataset demonstrates that our construction is practical and efficient. The security and performance analysis of P index shows that the dynamic multi-linked index guarantees forward privacy without significant loss of efficiency.
Methods and procedure of referenced in situ control of lateral contour displacements in additive manufacturing

Abstract. Additive manufacturing technologies are further developing from prototype to serial production. This trend requires rising challenges to the process-accompanying quality assurance. Optical in situ quality control approaches show great potential to generate accurate measurement data, which are essential for feedback control. If a reliable referencing concept for the layer-by-layer measured data is guaranteed, contour information can be used during the manufacturing to correct occurring geometrical deviations. Within this scientific study, two methods of optical, referenced in situ control of lateral displacements of additive manufactured contours are presented. In the first approach the 2-D contour of the melting pool is analysed in relation to a position-stable reference system implemented in the powder bed. The second approach uses the translucent contour of deeper layers covered with powder as a reference. Within the image evaluation several pre-processing steps like calibration, undistortion, rectification, illumination correction and low-pass filtering are essential for reliable and correct geometric measurements. The following adapted contour detection and position determination of the referenced melting pool contours are based on an extended edge detection algorithm according to Canny (1986). With the evaluation of further manufacturing layers of already lowered powder bed levels, it is possible to specify the influence of powder application on geometrical displacements separately. This is done by a comparison of the position of the detected powder-covered melting pool contours with the previously applied melted region. Consequently a better understanding of lateral contour displacements within the additive manufacturing process is the goal, which is important for a process-accompanying correction of geometrical deviations.
A Boundary Determined Neural Model for Relation Extraction

Existing models extract entity relations only after two entity spans have been precisely extracted that influenced the performance of relation extraction. Compared with recognizing entity spans, because the boundary has a small granularity and a less ambiguity, it can be detected precisely and incorporated to learn better representation. Motivated by the strengths of boundary, we propose a boundary determined neural (BDN) model, which leverages boundaries as task-related cues to predict the relation labels. Our model can predict high-quality relation instance via the pairs of boundaries, which can relieve error propagation problem. Moreover, our model fuses with boundary-relevant information encoding to represent distributed representation to improve the ability of capturing semantic and dependency information, which can increase the discriminability of neural network. Experiments show that our model achieves state-of-the-art performances on ACE05 corpus.
Robot Coalition Coordination in Precision Agriculture by Smart Contracts in Blockchain

Precision farming is becoming a leading area of agricultural development. The driver of this development is global digitalization and robotization of the main functions of agriculture. At the same time, robots (combines, transporters, tractors, quadcopters) are equipped with algorithms that provide a high degree of autonomy in decision making. In this regard, it is required to ensure the joint work of robots of various types in a single coalition to achieve the maximum effect from the processing of the field, that is, growing and collecting the maximum possible yield per hectare. This paper proposes a mechanism for the formation of a coalition of robots based on socio-inspired self-organization. The proposed mechanism was tested in a virtual environment based on Gazebo/ROS to assess the quality of the coalition being formed. Test results show appropriateness for any coalition type creating. The type of coalition is defined in dynamic way by the environmental state, task type, and robots’ types. Smart contracts in a distributed ledger based on the Hyperledger Fabric blockchain platform are used to provide trusted interaction between robots during and after coalition formation.
Pemanfaatan Scale Invariant Feature Transform Berbasis Saliency untuk Klasifikasi Sel Darah Putih

White blood cells are cells that makeup blood components that function to fight various diseases from the body (immune system). White blood cells are divided into five types, namely basophils, eosinophils, neutrophils, lymphocytes, and monocytes. Detection of white blood cell types is done in a laboratory which requires more effort and time. One solution that can be done is to use machine learning such as Support Vector Machine (SVM) with Scale Invariant Feature Transform (SIFT) feature extraction. This study uses a dataset of white blood cell images that previously carried out a pre-processing stage consisting of cropping, resizing, and saliency. The saliency method can take a significant part in image data and. The SIFT feature extraction method can provide the location of the keypoint points that SVM can use in studying and recognizing white blood cell objects. The use of region-contrast saliency with kernel radial basis function (RBF) yields the best accuracy, precision, and recall results. Based on the test results obtained in this study, saliency can improve the accuracy, precision, and recall of SVM on the white blood cell image dataset compared to without saliency.
Factors Affecting Learners’ Academic Success in Online Liberal Arts Courses Offered by a Traditional Korean University

This study aimed to empirically examine the factors affecting full-time undergraduate students’ satisfaction and academic performance measured by grades using an existing large administrative dataset. The sample consisted of 21,662 undergraduate students who took online liberal arts courses offered by a large traditional Korean university in the spring semester of 2020. The theoretical framework of this study was formulated by selectively adopting and slightly modifying some of the factors from Choi’s conceptual model for adult dropout from online degree programs. The findings indicated that gender, previous GPA, campus, type of online course, the relevance of the course, adequacy of assignments and assessments, learner-instructor interaction, and learner-content interaction significantly affect students’ degree of satisfaction with online liberal arts courses. This study also found that students who considered the course less relevant to their goals or interests, had a low previous GPA, had frequent learner-instructor interactions, few learner-content interactions, and a low level of course satisfaction are more likely to earn a grade of B, C, or lower than to receive an A in online liberal arts courses.
Integrating Unsupervised Data Generation into Self-Supervised Neural Machine Translation for Low-Resource Languages

For most language combinations and parallel data is either scarce or simply unavailable. To address this and unsupervised machine translation (UMT) exploits large amounts of monolingual data by using synthetic data generation techniques such as back-translation and noising and while self-supervised NMT (SSNMT) identifies parallel sentences in smaller comparable data and trains on them. To this date and the inclusion of UMT data generation techniques in SSNMT has not been investigated. We show that including UMT techniques into SSNMT significantly outperforms SSNMT (up to +4.3 BLEU and af2en) as well as statistical (+50.8 BLEU) and hybrid UMT (+51.5 BLEU) baselines on related and distantly-related and unrelated language pairs.
Accessing spatial knowledge networks with maps

Currently, knowledge networks develop to establish common data spaces. A common data-space offers mutual exchange and reusability for data sources and their derived information and provides access to structured knowledge and even creates wisdom. The geospatial domain becomes included in those knowledge networks and, therefore, creates spatial knowledge networks. ‘Geospatial’ is moving from a special expert domain to a ‘normal’ common data source that is processed for specific data science use cases. Maps with their different levels of abstraction according to its transmission task may offer (1) strategies to enhance processing performance, due to its abstraction, (2) persistent references of map features throughout different scales (abstractions) and (3) improvement of the transmission of spatial information, which includes the transmission interfaces as well as geo-communication. This paper tries to identify new functions for maps in new developing application areas. For example, a ‘universal semantic structure of topographic content’ could help to establish relations/links across domains that only have their own feature keys. We try to set the scene of cartography in a common data-space and highlight some requirements in the world of spatial knowledge networks, which are needed for automatization, machine learning and AI. According to Gordon and de Souza location matters: ‘Mapping is not simply a mode of visualisation, but a “central organizational device for networked communications”, an adaptive interface through which users can access, alter and deploy an expansive database of information, and a platform to socialize spatial information through collective editing, annotations, discussion, etc.’ [Gordon, E., & de Souza e Silva, A. (2011). Net locality: Why location matters in a networked world. John Wiley & Sons, p. 28].
Improving personalised query reformulation with embeddings

As a mechanism to guide users towards a better representation of their information needs, the query reformulation method generates new queries based on users’ historical queries. To preserve the original search intent, query reformulations should be context-aware and should attempt to meet users’ personal information needs. The mainstream method aims to generate candidate queries first, according to their past frequencies, and then score (re-rank) these candidates based on the semantic consistency of terms, dependency among latent semantic topics and user preferences. We exploit embeddings (i.e. term, user and topic embeddings) to use contextual information and individual preferences more effectively to improve personalised query reformulation. Our work involves two major tasks. In the first task, candidate queries are generated from an original query by substituting or adding one term, and the contextual similarities between the terms are calculated based on the term embeddings and augmented with user personalisation. In the second task, the candidate queries generated in the first task are evaluated and scored (re-ranked) according to the consistency of the semantic meaning of the candidate query and the user preferences based on a graphical model with the term, user and topic embeddings. Experiments show that our proposed model yields significant improvements compared with the current state-of-the-art methods.
Seeing the Invisible: A VR Approach to Radiation Attenuation Visualization for Nuclear Engineering Laboratory Practices

The principle of “As Low As Reasonably Achievable” or ALARA is taught through laboratory practices composed of lectures and simulations to maintain the radiation exposure at a minimum. Hands-on practices are limited due to the health risks associated with radioactive exposure, leading to the development of virtual, augmented, and mixed reality simulations that pose no harm to trainees. This article presents the development of a virtual reality (VR) model for attenuating radiation visualization during runtime, employing numerical simulation with VR. Our attenuation model responds dynamically to the environment and does not rely on precalculated radiation fields as other works in the literature. Our approach also includes game elements to enhance the laboratory experience. Our goal is to understand the effects of the virtual environments on usability, engagement, completion time, and radiation dose exposure. Preliminary results indicate that the gamified version was found more engaging as participants felt more competent, less frustrated, and more immersed; it was also perceived as more usable with a SUS score of 81.87/100 in comparison to the nongamified with a SUS score of 58.12/100. Participants were faster when completing the nongamified version with an average of 103.28 ± SD 41.26 s in comparison to 175.31 ± SD 91.16 s with the gamified version. Finally, participants received 2.11 mSv less dose exposure with the nongamified version. We believe that practicing the ALARA principle in VR can offer insights on how trainees approach and work around radiation sources, as not necessarily the faster completion results in less exposure.
Virtual and Augmented Reality in school context: A literature review

Virtual and Augmented reality has established itself in many sectors including education. The virtual is progressively entering the classrooms. MOOCs (Massive Open Online Course), those online interactive lessons available for all, are useful tools to complete the traditional school system. There are also more and more e-learning platforms that propose online lessons and follow-up for any student. Immersive contents appear as playful alternatives to traditional lectures. However, the integration of VR/AR equipment in classrooms raises questions. What place takes the augmented (AR) and virtual reality (VR) in the modern educational and teaching system? In order to be able to answer this important question, our article takes place as the result of a literature review on the field of virtual and augmented reality in education. Our objective, first of all, to deepen our knowledge domain in order to provide the necessary answers to « the main question », and, secondly, to identify principles that can guide the design of educational artifacts in virtual and augmented reality. The methodology of the literature review is based on the EPPI method (Evidence for Policy and Practice Information and Co-ordinating). Results will be presented by themes such as motivation, immersion, collaboration, and design.
A Multi-resolution Mechanism with Multiple Decoders for Empathetic Dialogue Generation

To build an interactive human-like conversational agent, one of the key challenges is to learn to generate responses in a more empathic way. When a conversation has a tone of empathy to it, the person conversing with the conversational agent will feel a sense of contentment. Building an empathetic dialogue system results in increased user interaction and thereby more effective communication. To improve the perception and expression of emotional states, the proposed model uses a multi-resolution mechanism that helps to capture the nuances of user emotions effectively. Moreover, it uses separate decoders, which are optimized to respond to specific emotions, and combines the output response representations to generate an empathetic response. Experimental results show that the proposed approach excels the state-of-the-art baselines in empathy, relevance, and fluency.
Emotion Carrier Recognition from Personal Narratives

Personal Narratives (PN) - recollections of facts, events, and thoughts from one's own experience - are often used in everyday conversations. So far, PNs have mainly been explored for tasks such as valence prediction or emotion classification (i.e. happy, sad). However, these tasks might overlook more fine-grained information that could nevertheless prove relevant for understanding PNs. In this work, we propose a novel task for Narrative Understanding: Emotion Carrier Recognition (ECR). We argue that automatic recognition of emotion carriers, the text fragments that carry the emotions of the narrator (i.e. 'loss of a grandpa', 'high school reunion'), from PNs, provides a deeper level of emotion analysis needed, for instance, in the mental healthcare domain. In this work, we explore the task of ECR using a corpus of PNs manually annotated with emotion carriers and investigate different baseline models for the task. Furthermore, we propose several evaluation strategies for the task. Based on the inter-annotator agreement, the task in itself was found to be complex and subjective for humans. Nevertheless, we discuss evaluation metrics that could be suitable for applications based on ECR.
Named Entity Recognition Based on Reinforcement Learning and Adversarial Training

In this paper, we propose a new model that combines reinforcement learning and adversarial training to exploit the data generated by distant supervision for named entity recognition. Our model can not only reduce the influence of noise in generated data, but also find more informative instances for training. In the pre-training stage of the model, in order to make full use of the data generated by distant supervision, we use reinforcement learning to select reliable instances to pre-train a classifier. In the training stage of the model, we introduce the adversarial training mechanism, which can not only find more reliable instances to enhance the ability of the classifier, but also use noise data to improve the ability of the model to resist noise. To evaluate the performance of the model, we conduct experiments on two public datasets, Species800 dataset in biology and EC dataset in e-commerce domain. The experimental results show that in Species800 dataset, the F1 score of our model is 1.68% higher than that of baseline, and in EC dataset, the F1 score of our model is 6.32% higher than that of baseline. Compared to the state of art models, our model can achieve comparable performance just using word2vec embedding.
SolDetector: Detect Defects Based on Knowledge Graph of Solidity Smart Contract

Smart contract security is one of core security issues in the application of blockchain.In recent years, attacks on smart contracts occur frequently, there are a lot of researches concerning on smart contract security issues.However, almost all solutions proposed in these researches are low precision and high False Negative Rate(FNR).In this paper, we propose a defect detection method for checking security of Solidity smart contract based on knowledge graph.Therefore, we first construct knowledge graph of smart contracts by fully integrating syntax and semantic information of Solidity source code; then, we define defect patterns by analyzing defect characteristics; furthermore, we define inference rules for defects based on knowledge graph and defect patterns; finally, we detect defects by SPARQL query.We also implement a tool named SolDetector and perform experiment on three different datasets, which shows that SolDetector is effective and efficient.
Revisiting some results on APN and algebraic immune functions

<p style='text-indent:20px;'>We push a little further the study of two recent characterizations of almost perfect nonlinear (APN) functions. We state open problems about them, and we revisit in their perspective a well-known result from Dobbertin on APN exponents. This leads us to a new result about APN power functions and more general APN polynomials with coefficients in a subfield <inline-formula><tex-math id="M1">\begin{document}$ \mathbb{F}_{2^k} $\end{document}</tex-math></inline-formula>, which eases the research of such functions. It also allows to construct automatically many differentially uniform functions from them (this avoids calculations for proving their differential uniformity as done in a recent paper, which are tedious and specific to each APN function). In a second part, we give simple proofs of two important results on Boolean functions, one of which deserves to be better known but needed clarification, while the other needed correction.
Comparison of threshold measurements in laboratory and online studies using a Quest+ algorithm

Online experiments have become popular and it is useful to test how data collected online compare to data measured in the laboratory. Here we compared perceptual thresholds of the perceived quality of computer-generated images (CGI) in a large-sample (N=174) measured online, and a smaller-sample (N=71) obtained in a laboratory-controlled study. Stimuli were three sets of CGIs picturing different scenes of the interior of an apartment. The algorithm used for generating the images reduces the amount of visual noise when the computation time increases so that each successive image within a set has less noise than the previous one. The last generated image of each set (i.e. with the less visual noise) was used as a reference image (RI) and compared to other images of the set. Each comparison image (CI), was cut randomly and the missing part was replaced by its corresponding part from the RI. On each trial observers had unlimited time to report whether they see a single or a composite image. We used a QUEST+ Bayesian adaptive method, which minimizes the expected Shannon entropy, to choose the next CI after each trial. Perceptual thresholds were expressed relatively to the size of the image set. Although online participants reached a stable threshold in fewer trials than laboratory participants (90 vs 156 trials on average, respectively), the equivalence test (TOST) revealed a significant similarity (p<0.05) between the online and laboratory perceptual thresholds. Online measurements also replicated the effects of the scene (THRESHOLDscene1=0.38, MADscene1=0.12; THRESHOLDscene2=0.35, MADscene2=0.09; THRESHOLDscene3=0.42, MADscene3 =0.08) we observed in the laboratory (THRESHOLDscene1 = 0.39, MADscene1 =0.11; THRESHOLDscene2 = 0.38, MADscene2 =0.09; THRESHOLDscene3 = 0.41, MADscene3 = 0.11). Overall, this study shows consistent data collected online and in the laboratory. Despite strong differences in experimental conditions, online measurement of perceived image quality could accurately substitute the laboratory measurements.