Reviewerly/exHarmony huggingface dataset:
The Effect of Bilateral Filtering in 3D Reconstruction Using PSP

Phase-shifting profilometry is among the most popular techniques in optical measurement due to its numerous advantages such as high resolution and low computational cost. Although, high quality 3D reconstructions can be achieved by phase-shifting profilometry, Moiré pattern noise may appear during the image acquisition stage and be carried out during the entire reconstruction process. Thus, resulting in noisy reconstructions and uncertain or unreliable height estimations. Moiré noise can be attenuated, however, or even removed in the frequency domain of the reconstructed scenes. By analyzing the spectrum of the image, we can discard the high frequencies cause by the Moiré patterns. Furthermore, a band-pass filter is applied in order to suppress such frequencies. This paper focuses of the use of a bilateral filter as a post-processing stage in phase-shifting profilometry. Moreover, we discovered that by filtering these high frequencies, Moiré pattern noise is reduced, resulting in smoother surfaces and better height estimations.
Searching for Extreme Portions in Distributions: A Comparison of Pie and Bar Charts

Aggregated data visualizations are often used by collaborative teams to gain a common understanding of a complex situations and issues. Pie and bar charts are both widely used for visualizing distributions. The study of pie versus bar charts has a long history and the results are seemingly inconclusive. Many report authors prefer pie charts while visualization theory often argues for bar graphs. Most of the studies that conclude in favor of pie charts have focused on how well they facilitate the identification of parts to the whole. This study set out to collect empirical evidence on which chart type that most rapidly and less erroneously facilitate the identification of extreme parts such as the minimum, or the maximum, when the distributions are similar, yet not identical. The results show that minimum values are identified in shorter time with bar charts compared to pie charts. Moreover, the extreme values are identified with fewer errors with bar charts compared to pie charts. One implication of this study is that bar charts are recommended in visualization situations where important decisions depend on rapidly identifying extreme values.
An Algorithm Combining the Branch-Cut Method and Rhombus Phase Unwrapping Algorithm

Abstract In order to solve the problem that the wrapped phase cannot be unwrapped effectively, this paper proposes an algorithm combining the branch-cut method and rhombus phase unwrapping algorithm for unwrap phase. The algorithm combines the advantages of the two methods, at first, the Goldstein’s branch-cut method is used to find the residual points in the wrapping phase, then the rhombus phase unwrapping algorithm is used to unwrap the non-residual area phase, finally the phase of the residual area is obtained by using the cubic spline interpolation method, so as to ensure that the phase of each pixel can be unwrapped smoothly. The simulation results verify the feasibility and effectiveness of the algorithm, the combined phase unwrapping algorithm can effectively reduce the line and island phenomenon.
Online recognition of dictionary with one gap

We formalize and examine the online Dictionary Recognition with One Gap problem (DROG) which is the following. Preprocess a dictionary D of d patterns each containing a special gap symbol that matches any string, so that given a text arriving online a character at a time, all patterns from D which are suffixes of the text that has arrived so far and have not been reported yet, are reported before the next character arrives. The gap symbols are associated with bounds determining possible lengths of matching strings. Online DROG captures the difficulty in a bottleneck procedure for cyber-security, as many digital signatures of viruses manifest themselves as patterns with a single gap. Following the work on the closely related online Dictionary Matching with One Gap problem (DMOG), we provide algorithms whose time cost depends linearly on δ(GD), where GD is a bipartite graph that captures the structure of D and δ(GD) is the degeneracy of this graph. These algorithms are of practical interest since although δ(GD) can be as large as d, and even larger if GD is a multi-graph, it is typically a small constant in practice.
ComPy-Learn: A toolbox for exploring machine learning representations for compilers

Deep Learning methods have not only shown to improve software performance in compiler heuristics, but also e.g. to improve security in vulnerability prediction or to boost developer productivity in software engineering tools. A key to the success of such methods across these use cases is the expressiveness of the representation used to abstract from the program code. Recent work has shown that different such representations have unique advantages in terms of performance. However, determining the best-performing one for a given task is often not obvious and requires empirical evaluation. Therefore, we present ComPy-Learn, a toolbox for conveniently defining, extracting, and exploring representations of program code. With syntax-level language information from the Clang compiler frontend and low-level information from the LLVM compiler backend, the tool supports the construction of linear and graph representations and enables an efficient search for the best-performing representation and model for tasks on program code.
Modeling artificial agents’ actions in context – a deontic cognitive event ontology

Although there have been efforts to integrate Semantic Web technologies and artificial agents related AI research approaches, they remain relatively isolated from each other. Herein, we introduce a new ontology framework designed to support the knowledge representation of artificial agents’ actions within the context of the actions of other autonomous agents and inspired by standard cognitive architectures. The framework consists of four parts: 1) an event ontology for information pertaining to actions and events; 2) an epistemic ontology containing facts about knowledge, beliefs, perceptions and communication; 3) an ontology concerning future intentions, desires, and aversions; and, finally, 4) a deontic ontology for modeling obligations and prohibitions which limit agents’ actions. The architecture of the ontology framework is inspired by deontic cognitive event calculus as well as epistemic and deontic logic. We also describe a case study in which the proposed DCEO ontology supports autonomous vehicle navigation.
Fine-Grained Age Estimation With Multi-Attention Network

Human age estimation from a single image is a quite challenging task due to the subtle appearance change in the slow aging process. In this article, we propose a compact multi-attention deep network for age estimation based on the idea of fine-grained learning and visual attention mechanism. Concerning the problem that age estimation is a fine-grained visual classification problem, it relies on not only the global features of the face image, but also the fine-grained feature representations from age-sensitive local regions. Therefore, accurate age estimation benefits from multi-scale features and their fusion. Therefore, in this article, a multi-attention model built on a complementary two-stream compact network is proposed for age estimation. For a given intermediate feature map from the network, spatial attentions and channel attentions can be inferred in both self-attention and mutual-attention way. To emphasize crucial features from age-sensitive regions, the multi-attention maps are then multiplied to the input feature map for adaptive feature refinement. Finally, the refined feature maps at multiple layers are aggregated as the fine-grained feature for age estimation. Compared to bulky models, our model is compact and end-to-end. However, the performance of our model is competitive compared with those state-of-the-art methods.
Research on the Development of Automatic Dialect Identification Technology Based on Language Recognition

This paper analyzes the development and evolution of phoneme-level features, especially language recognition methods based on underlying acoustic features, and Dialect identification technology at home and abroad, which Explains that the research of Dialect identification technology based on language recognition methods consists of feature extraction and model establishment, and highlights the current development of Dialect Identification technology based on i-vector features and neural network modeling methods and continuous breakthroughs in research key and difficult points.
Efficient and privacy-protected content-based image retrieval without homomorphic encryption

Images play an increasingly important role in our lives as carriers of Information. At the same time, personal devices are increasingly unable to store and compute large amounts of images, so it is necessary to outsource to cloud servers. However, this also brings privacy issues, such as personal photos, medical photos, map mapping, etc. In this paper, we present a content-based image retrieval (CBIR) scheme that can be performed on ciphertext images. In order to better represent the Lage, instead of the traditional algorithms such as SIFT and HOG, image features are extracted using fine-tuned convolutional neural networks, and then outsources the encrypted feature vector and ciphertext image to cloud server. Considering the efficiency of the search, this paper uses the k-means algorithm to and local sensitive hash function to build a secure tree index. This paper proposes a new functional encryption of the inner product to calculate the Euclidean distance between the feature vectors to obtain the similarity between the images, and finally return the ciphertext image satisfying the condition to the user. The experimental results show the efficiency of our program, in addition, the paper gives security analysis to prove that our solution can against Chosen-Plaintext Attack (CPA).
Pilot Results from University – Business Collaboration in Teaching “Thermal Management of Electronic Equipment” Course

This paper presents the pilot results from teaching “Thermal management of electronic equipment” course at the University of Plovdiv. The course curriculum is prepared together with business. The results are positive: students highly evaluated the quality and relevance of the course and showed “very good” average score from the training in DIPSEIL. During the pilot PhD students developed thermal models of a real electronic system. Business representatives highly evaluated these models which is indicative of the quality of the course.
Holistic Quality Assessment of Mediated Immersive Multisensory Social Communication

Communication through modern immersive systems that afford the representation of a wide range of multisensory (visual, auditory, haptic, olfactory) social and ambient (environmental) affective cues can provide compelling experiences that approach face-to-face communication. The quality of a mediated social communication experience (QoE) can be defined as the degree to which it matches its real-life counterpart and is typically assessed through questionnaires. However, available questionnaires are typically extensive, targeted at specific systems, and do not address all relevant aspects of social presence. Here we propose a general holistic social presence QoE questionnaire (HSPQ), that uses a single item for each of the relevant processing levels in the human brain: sensory, emotional, and cognitive, behavioral, and reasoning. The HSPQ measures social presence through the senses of spatial presence (= telepresence + agency) in the mediated environment and social interaction (= interaction + engagement) with the other persons therein. Initial validation studies confirm the content and face validity of the HSPQ. In future studies we will test the stability, sensitivity, and convergent validity of the HSPQ.
Modeling Multiple Language Learning in a Developmental Cognitive Architecture

In this work, we model multiple natural language learning in a developmental neuroscience-inspired architecture.The ANNABELL model (Artificial Neural Network with Adaptive Behaviour Exploited for Language Learning), is a large-scale neural network, however, unlike most deep learning methods that solve natural language processing (NLP) tasks, it does not represent an empirical engineering solution for specific NLP problems; rather, its organisation complies with findings from cognitive neuroscience, particularly the multi-compartment working memory models.The system is appropriately trained to understand the level of cognitive development required for language acquisition and the robustness achieved in learning simultaneously four languages, using a corpus of text-based exchanges of developmental complexity.The selected languages, Greek, Italian and Albanian, besides English, differ significantly in structure and complexity.Initially, the system was validated in each language alone and was then compared with the open-ended cumulative training, in which languages are learned jointly, prior to querying with random language at random order.We aimed to assess if the model could learn the languages together to the same degree of skill as learning each apart.Moreover, we explored the generalisation skill in multilingual context questions and the ability to elaborate a short text of preschool literature.We verified if the system could follow a dialogue coherently and cohesively, keeping track of its previous answers and recalling them in subsequent queries.The results show that the architecture developed broad language processing functionalities, with satisfactory performances in each language trained singularly, maintaining high accuracies when they are acquired cumulatively.
Designing Out-of-distribution Data Detection using Anomaly Detectors: Single Model vs. Ensemble

Image classification neural networks tend to give high probabilities to images they in fact do not recognize. This paper compares three approaches to detect such out-of-distribution data: One-Class Support Vector Machine, Isolation Forest and Local Outlier Factor. The experiments show that Isolation Forest outperforms the other two approaches. However, when combining the three algorithms using a majority voter, the results show that this ensemble is better at detecting out-of-distribution data than using the Isolation Forest algorithm solely.
Using Colorimetric Concepts for the Evaluation of Goal Models

Goal-oriented models have become important tools for the analysis of non-functional requirements (NFRs). However, the treatment of NFRs is a non-trivial task, considering that this class of requirements covers quality characteristics. This implies that when dealing with subjective requirements, we need to focus on mechanisms that can enrich the semantics of their representation. This is the case of assigning and propagating labels in the evaluation of goal-oriented models. The definition of labels on existing models has low granularity and often fails to reflect the full in-formational potential that this type of artifact could offer. This is the case of the NFR Framework. Propagation in the model is bot-tom-up and understanding about the degree of satisficing a goal becomes difficult. This paper explores a rationale to increase the informative power of the labels assigned to the goals, using the concepts of colorimetry in the SIG (Softgoal Interdependency Graph). We discuss how color may mitigate the challenge of increasing the granularity of goal models analysis, thus improving the evaluation of these models.
Neural Machine Translation Based on Prioritized Experience Replay

Reward mechanism of reinforcement learning alleviates the inconsistency between training and evaluation in neural machine translation. However, the model still incapable to learn ideal parameters when rewards are sparse or a weak sampling strategy is adopted. Therefore, we propose a reinforcement learning method based on prioritized experience replay to deal with the problems. The model experiences are obtained through reinforcement learning. Then they are stored in a experience buffer and assigned priorities according to the value of experience. The experience with higher priority in buffer will be extracted by model to optimize the parameters during training phase. To verify the robustness of our method, we not only conduct experiments on English-German and Chinese-English, but also perform on agglutinative language Mongolian-Chinese. Experimental results show that our work consistently outperforms the baselines.
Traceability Management of GRL and SysML Models

Emerging socio-cyber-physical systems integrate social concerns, often captured with goal models, with complex systems, where structure and behavior are often captured in SysML. Traceability between these two types of models is important to reason about consistency, completeness, and the impact of modifications. However, managing traceability during the co-evolution of these two views is not well supported as SysML does not provide sophisticated goal-modeling capabilities out of the box. This paper proposes an approach where the Goal-oriented Requirement Language (GRL) is used to capture and analyze social concerns as a supplement to SysML models, and where traceability is handled via a third-party requirements management system, namely IBM Rational DOORS. The approach is supported with tools automating the import in DOORS of relevant parts of the GRL and SysML models from their respective modeling environments (jUCMNav and No Magic's Cameo Systems Modeler). A traceability information model is proposed to connect elements from GRL and SysML models in a way that enables automating important completeness and consistency checks, even as the models evolve. The approach is illustrated and evaluated with a Smart Home example, with a discussion of benefits and limitations.
Designing an Assistant for the Disclosure and Management of Information about Needs and Support: the ADMINS project

In this paper, we describe accessible design considerations for the Assistants for the Disclosure and Management of Information about Needs and Support project (ADMINS). In ADMINS, artificial intelligence (AI) services are being used to create a virtual assistant (VA), which is being designed to enable students to disclose any disabilities, and to provide guidance and suggestions about appropriate accessible support. ADMINS explores the potential of a conversational user interface (CUI) to reduce administrative burden and improve outcomes, by replacing static forms with written and spoken dialogue. Students with accessibility needs often face excessive administrative burden. A CUI could be beneficial in this context if designed to be fully accessible. At the same time, we recognise the broader potential of CUIs for these types of processes, and the project aims to understand the multiple opportunities and challenges, using participatory design, iterative development and trials evaluations.
A Diverse Biases Non-negative Latent Factorization of Tensors Model for Dynamic Network Link Prediction

Dynamic networks vary over time, making it vital to capture networks temporal patterns for predicting missing links with high accuracy. A biased non-negative latent factorization of tensors (BNLFT) model is very effective in extracting such patterns from dynamic data. However, a BNLFT model only integrates single bias, which cannot adequately represents the volatility of the dynamic data. To address this issue, this paper presents a Diverse Biases Non-negative Latent Factorization of Tensors (DBNT) model for accurate prediction of missing links in dynamic networks. Meanwhile, for further prediction accuracy improvement, the preprocessing bias is integrated into the DBNT model. Empirical studies on two dynamic networks datasets from real applications show that compared with state of the art predictors, a DBNT model achieves higher prediction accuracy.
Like in the Good Old Times, but Virtual - A Case for Simulating Co-located Multiplayer Games in VR

In this paper, we present ongoing work on simulating the social setting of co-located multiplayer games in virtual reality (VR), to transfer the rich sociability associated with physical co-location to online gaming. We developed a VR application that allows two physically remote users to play games together on a virtual couch. Based on our initial experience in two small user tests, we assume that the VR version induces a social experience similar to the co-located gameplay. We will test this assumption in subsequent user studies. We also outline the theoretical and methodological relevance of VR-simulated couch-coop multiplayer games for researchers and practitioners.
Evaluation of Skill Improvement by Combining Skill and Difficulty Levels During Paper-cutting Production

In artworks such as paper-cutting, it is important to combine the skill of the artist with the difficulty of the rough sketch to improve the skill. We developed a system to measure the distances and widths of cutting lines and patterning the pictures based on the steering law. We quantitatively evaluated the difficulty level of the pictures. We developed an interactive system to support the improvement of cutting pressure control, which is one of the skills for making paper-cutting. Furthermore, we analyzed users' psychological state when making a paper cutting based on the psychological flow state. The flow state is a highly focused state that tends to be shown when the user's skills balance with the difficulty of the task. On the other hand, since it is difficult to analyze skills and difficulty quantitatively, many researchers evaluate them by qualitative analysis. In this paper, we set "skill" as the cutting ability and "challenge" as the difficulty level of the picture. We evaluated the improvement of the user's skill and the flow state through quantitative analysis. We developed a questionnaire to evaluate the psychological state of a paper-cutting process based on the existing flow state questionnaire. In this paper, we describe the changes in skill improvement due to the combination of user skill and picture difficulty, with "skill" being the cutting ability and "challenge" being the difficulty of the picture.
A Hybrid Approach for Enhancing Accuracy and Detecting Sarcasm in Sentiment Analysis

Sentiment analysis and opinion mining are gaining significant importance in the field of research. In last few years, many efforts had been made to mine the opinions and sentiments of people which they post on internet in the form of reviews, blogs, forum, discussions and micro-blogs. The important thing is to discover what people actually think. Their opinions are classified on the basis of polarity which can be positive, neutral or negative. In this study we have given a brief overview of sentiment analysis and a hybrid approach which includes different classifiers such as emoticon classification, negation and Modifier classification, Hashtag classification, Sarcasm Detection using deepmoji which is an advanced Sentiment Analysis technology, Polarity detection using SentiWordNet and Domain Specific Classifier.