Reviewerly/exHarmony huggingface dataset:
Adaptive environments with parallel reality <sup>TM</sup> displays

It is challenging to provide signage for the individual needs of each person in crowded, public spaces. Having too many signs leads to a cluttered environment, while having too few signs can fail to provide needed information to individuals. Personal display devices, such as smart phones and AR glasses can provide adaptive content, but they require each user to have a compatible device with them, turned on, and running appropriate software. PARALLEL REALITY™ displays are a new type of shared, public display that can simultaneously target personalized content to each viewer, without special glasses. In this way, adaptation becomes a feature of a venue that is available to all without encumbrances. Examples include adaptation based on language needs, visual acuity and relative location of the display.
Polarity Analysis of Customer Reviews Based on Part-of-Speech Subcategory

Abstract Nowadays, sentiment analysis is a method used to analyze the sentiment of the feedback given by a user in an online document, such as a blog, comment, and review, and classifies it as negative, positive, or neutral. The classification process relies upon the analysis of the polarity features of the natural language text given by users. Polarity analysis has been an important subtask in sentiment analysis; however, detecting correct polarity has been a major issue. Different researchers have utilized different polarity features, such as standard part-of-speech (POS) tags such as adjectives, adverbs, verbs, and nouns. However, there seems to be a lack of research focusing on the subcategories of these tags. The aim of this research was to propose a method that better recognizes the polarity of natural language text by utilizing different polarity features using the standard POS category and the subcategory combinations in order to explore the specific polarity of text. Several experiments were conducted to examine and compare the efficacies of the proposed method in terms of F-measure, recall, and precision using an Amazon dataset. The results showed that JJ + NN + VB + RB + VBP + RP, which is a POS subcategory combination, obtained better accuracy compared to the baseline approaches by 4.4% in terms of F-measure.
Microhistory and Archaeology: Some Comments and Contributions

<span style="font-size: large;">To download this paper, please click <a href="http://discovery.ucl.ac.uk/10072971/1/PIA_28_Ribeiro%20.pdf"><span><span style="text-decoration: underline;">here</span><span>.</span></span></a></span> <span style="font-size: medium;">Archaeology has always kept an inconsistent relationship with history. For decades, archaeology has either largely rejected what history could offer, such as among certain processual archaeologists, or it has cherry-picked certain elements of historical methods. The closest that archaeologists have ever come to establishing a complete historical method to be applied in archaeology was through the adoption of the idea of the <em>Annales </em>School of history.</span> <span style="font-size: medium;">Part of what made the <em>Annales </em>School so attractive to archaeologists of all backgrounds was that it tackled the past in a way that was very practical and useful for archaeology: it engaged with the past in the form of <em>total histories</em>, which could then be segmented in three separate durations and could be studied in an interdisciplinary manner. Additionally, the way the <em>Annales </em>School envisaged the past allowed for the study of the past in a very scientific way (e.g. quantitative, statistical), but also allowed the qualitative study of mentalities of the past people under analysis. </span> <span style="font-size: medium;">However, one of the greatest problems of the <em>Annales </em>School is that it suppressed the human agent. Whether they were hidden behind structural economic forces or long-term symbolic structures, the individual remained always buried under the large-scale — history, according to <em>annalistes</em>, could not be the result of individual action. This, in turn, is what eventually led to the demise of the <em>Annales </em>School, in favour of the Italian microhistory. Does this mean that the <em>Annales</em>School of History must be complete scraped? No, the aim of this paper is to demonstrate that archaeology can in fact have a fruitful historical paradigm based on some ideas of the <em>Annales </em>School, and at the same time, some ideas of Italian microhistory. This would require understanding microhistory as the reconstruction of the life of agents, small-scale case-studies that serve as exemplars of large-scale phenomena.</span>
Restart based Collective Information Powered Differential Evolution for Solving the 100-Digit Challenge on Single Objective Numerical Optimization

Different from the classic differential evolution (DE) and many of its variants, collective information powered DE (CIPDE) is characterized by the utilization of collective information of population in the mutation and crossover processes of DE. This paper proposes a restart mechanism for CIPDE, to improve its robustness for solving the 100-digit challenge on single objective numerical optimization. Restart based CIPDE (rCIPDE) restarts the population when the unsuccessful update of the population exceeds a threshold value. Simulations on the challenge show that the restart mechanism enhances the performance on five out of the total 10 benchmark functions. According to the competition rule, rCIPDE achieves a total score of 85 (10 marks on each of F1-F7 and F10, 2 marks on F8 and 3 marks on F9).
CubeVR: Digital Affordances for Architecture Undergraduate Education using Virtual Reality

CubeVR incorporates the traditional studio-based learning in architecture with virtual reality (VR) and in doing so it enables features that are not available or difficult to achieve in the real-world. CubeVR allows students and instructors to move from the existing studio to a virtual environment, allowing for a more impactful learning experience. This paper presents our current state of the CubeVR prototype, informal impressions of users, and the use of different affordances in the virtual world.
Bayesian policy selection using active inference

Learning to take actions based on observations is a core requirement for artificial agents to be able to be successful and robust at their task. Reinforcement Learning (RL) is a well-known technique for learning such policies. However, current RL algorithms often have to deal with reward shaping, have difficulties generalizing to other environments and are most often sample inefficient. In this paper, we explore active inference and the free energy principle, a normative theory from neuroscience that explains how self-organizing biological systems operate by maintaining a model of the world and casting action selection as an inference problem. We apply this concept to a typical problem known to the RL community, the mountain car problem, and show how active inference encompasses both RL and learning from demonstrations.
Augmented reality gets real

Formidable optical challenges are yielding to intensive research, development.
LSB Image Steganography Based on Blocks Matrix Determinant Method

Image steganography is one of the key types of steganography where a message to be sent is hidden inside the cover image.The most commonly used techniques for image steganography rely on LSB steganography.In this paper, a novel image steganography technique based on blocks matrix determinant method is proposed.Under this method, a cover image is divided into blocks of size 2 x 2 pixels and the determinant of each block is calculated.The comparison of the determinant values and corresponding data bits yields a delicate way for the embedment of data bits.The main aim of the proposed technique is to ensure concealment of secret data inside an image without affecting the cover image quality.When the proposed steganography method is compared with other existing LSB steganography methods, it is observed that it not only provides higher PSNR, lower MSE but also guarantees better quality of the stego image.
Invariance-inducing regularization using worst-case transformations suffices to boost accuracy and spatial robustness

This work provides theoretical and empirical evidence that invariance-inducing regularizers can increase predictive accuracy for worst-case spatial transformations (spatial robustness). Evaluated on these adversarially transformed examples, standard and adversarial training with such regularizers achieves a relative error reduction of 20% for CIFAR-10 with the same computational budget. This even surpasses handcrafted spatial-equivariant networks. Furthermore, we observe for SVHN, known to have inherent variance in orientation, that robust training also improves standard accuracy on the test set. We prove that this no-trade-off phenomenon holds for adversarial examples from transformation groups.
Improving Analogical Extrapolation Using Case Pair Competence

An analogical proportion is a quaternary relation that is to be read "a is to b as c is to d", verifying some symmetry and permutation properties. As can be seen, it involves a pair of pairs. Such a relation is at the basis of an approach to case-based reasoning called analogical extrapolation, which consists in retrieving three cases forming an analogical proportion with the target problem in the problem space and then in finding a solution to this problem by solving an analogical equation in the solution space. This paper studies how the notion of competence of pairs of source cases can be estimated and used in order to improve extrapolation. A preprocessing of the case base associates to each case pair a competence given by two scores: the support and the confidence of the case pair, computed on the basis of other case pairs forming an analogical proportion with it. An evaluation in a Boolean setting shows that using case pair competences improves significantly the result of the analogical extrapolation process.
Blockchain-Based Corporate Governance

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Digital craftsmanship in the wearable senses lab

Wearable and e-textiles as a field has tended to overlook its own documentation, as notions and overarching ideas are developed over time and across individual projects. We would like to begin addressing this by charting the development of Digital Craftsmanship through a number of projects over time. Practically, we propose to show a small selection of garments and samples alongside a simple framework for future documentation of work.
Face Recognition Approach using an Enhanced Particle Swarm Optimization and Support Vector Machine

Face recognition is one of the most promising research area in the last decades. The SVM approach is one of the famous approaches in machine learning fields because it can determine the global optimum solutions with lesser number of training samples especially, complex non-linear challenges such as in face recognition applications. Though, there is an important issue that can affects the whole classification process which is picking the optimum parameters of SVM. Recently, Particle Swarm Optimization (PSO) is used to discover the optimal parameters of SVM and many versions of are used for this purpose, like: PSO-SVM technique, opposition and SVM which called (OPSO-SVM) technique and AAPSO-SVM technique which represents adaptive acceleration and SVM. In this study, a new hybrid technique based on the combination of Accelerated PSO and is introduced for face recognition applications. The hybridization can improve the convergence speed in in order to find the optimal parameters of SVM. In the feature extraction process, the PCA algorithm is used for that purpose and the resulted features are delivered to the proposed technique in order to classify the face images. Two human face datasets are used in the experimentation stage such as, SCface dataset and CASIA face dataset in order to validate the performance of the proposed technique. The comparison process for proposed technique with the other recent technique, like: PSO-SVM, OPSO-SVM and AAPSO-SVM is done as an assessment process. The proposed technique provided high accuracy for recognition when we compared it with the other techniques and it was robust in finding the optimal parameters of SVM.
Fracturing Artefacts into 3D Printable Puzzles to Enhance Audience Engagement with Heritage Collections

Three-dimensional (3D) puzzles of heritage artefacts are typically used to engage audiences in the interpretation of archaeological objects in a museum gallery. The reason for this is that a puzzle can be seen as an enjoyable educational activity in the form of a game but also as a complex activity that archaeologists undertake when re-assembling fragments, for instance, of broken pottery. Until now the creation of this type of experiences is mostly a manual process and the artefacts used rarely reflect those in the collection due to the complex nature of the process. The contribution of this article is a novel digital worfklow for the design and fabrication of 3D puzzles that overcomes these limitations. The input to the workflow is an authentic artefact from a heritage collection, which is then digitised using technologies such as 3D scanning and 3D modelling. Thereafter, a puzzle generator system produces the puzzle pieces using a cell fracture algorithm and generates a set of puzzle pieces (female) and a single core piece (male) for fabrication. Finally, the pieces are fabricated using 3D printing technology and post-processed to facilitate the puzzle assembly. To demonstrate the feasibility of the proposed novel workflow, we deployed it to create a puzzle activity of the Saltdean urn, which is exhibited at the Archaeology Gallery of the Brighton Museum and Art Gallery. The workflow is also used with further artefacts to demonstrate its applicability to other shapes. The significance of this research is that it eases the task of creating puzzle-like activities and maintaining them in the long term within a busy public space such as a museum gallery.
Multi-subspace supervised descent method for robust face alignment

Supervised Descent Method (SDM) is one of the leading cascaded regression approaches for face alignment with state-of-the-art performance and a solid theoretical basis. However, SDM is prone to local optima and likely averages conflicting descent directions. This makes SDM ineffective in covering a complex facial shape space due to large head poses and rich non-rigid face deformations. In this paper, a novel two-step framework called multi-subspace SDM (MS-SDM) is proposed to equip SDM with a stronger capability for dealing with unconstrained faces. The optimization space is first partitioned with regard to shape variations using k-means. The generated subspaces show semantic significance which highly correlates with head poses. Faces among a certain subspace also show compatible shape-appearance relationships. Then, Naive Bayes is applied to conduct robust subspace prediction by concerning about the relative proximity of each subspace to the sample. This guarantees that each sample can be allocated to the most appropriate subspace-specific regressor. The proposed method is validated on benchmark face datasets with a mobile facial tracking implementation.
Military Object Detection Based on Optimal Gabor Filtering and Deep Feature Pyramid Network

Military object detection technology plays an important role in realizing the informatization and intelligence of military equipment, but the complex environment and scarce sample data of military objects also become the difficulties of detection. This paper proposes a detection framework of military objects based on optimal Gabor filtering and deep feature pyramid networks. At first, we combine the texture characteristics of military objects with the requirements of detection tasks and proposed the Fine Region Proposal Network (FRPN). A set of Gabor filter is designed and screened in this scheme, we construct the optimal Gabor filter Banks by analyzing the image energy after Gabor transformation of some images in the dataset, shorting the time of feature extraction and reducing the amount of calculation. Then, the Renyi threshold segmentation method is adopted to obtain the region proposals. Finally, the Highly Utilized Feature Pyramid Networks (HU-FPN) is proposed to improve the detection effect of small objects. A bottom-up and a top-down feature pyramid is constructed in the stage. Through transverse connection and integration of features at various scales, the feature expression of small objects is enriched and the detection problem of small objects is effectively solved. The experimental results show that the method proposed in this paper has prominent advantages in accuracy, effectiveness and small object detection when compared with the state-of-the-art method, which can create good conditions for the realization of rapid and accurate detection of military objects and precise strike under military background.
Communication-Efficient Distributed Optimization in Networks with Gradient Tracking.

There is growing interest in large-scale machine learning and optimization over decentralized networks, e.g. in the context of multi-agent learning and federated learning. Due to the imminent need to alleviate the communication burden, the investigation of communication-efficient distributed optimization algorithms - particularly for empirical risk minimization - has flourished in recent years. A large fraction of these algorithms have been developed for the master/slave setting, relying on a central parameter server that can communicate with all agents. This paper focuses on distributed optimization over networks, or decentralized optimization, where each agent is only allowed to aggregate information from its neighbors. By properly adjusting the global gradient estimate via local averaging in conjunction with proper correction, we develop a communication-efficient approximate Newton-type method Network-DANE, which generalizes DANE to the decentralized scenarios. Our key ideas can be applied in a systematic manner to obtain decentralized versions of other master/slave distributed algorithms. A notable development is Network-SVRG/SARAH, which employs variance reduction to further accelerate local computation. We establish linear convergence of Network-DANE and Network-SVRG for strongly convex losses, and Network-SARAH for quadratic losses, which shed light on the impacts of data homogeneity, network connectivity, and local averaging upon the rate of convergence. We further extend Network-DANE to composite optimization by allowing a nonsmooth penalty term. Numerical evidence is provided to demonstrate the appealing performance of our algorithms over competitive baselines, in terms of both communication and computation efficiency. Our work suggests that performing a certain amount of local communications and computations per iteration can substantially improve the overall efficiency.
Pretraining by Backtranslation for End-to-End ASR in Low-Resource Settings

We explore training attention-based encoder-decoder ASR in low-resource settings. These models perform poorly when trained on small amounts of transcribed speech, in part because they depend on having sufficient target-side text to train the attention and decoder networks. In this paper we address this shortcoming by pretraining our network parameters using only text-based data and transcribed speech from other languages. We analyze the relative contributions of both sources of data. Across 3 test languages, our text-based approach resulted in a 20% average relative improvement over a text-based augmentation technique without pretraining. Using transcribed speech from nearby languages gives a further 20-30% relative reduction in character error rate.
The Influence of the Viewpoint in a Self-Avatar on Body Part and Self-Localization

The goal of this study is to determine how a self-avatar in virtual reality, experienced from different viewpoints on the body (at eye- or chest-height), might influence body part localization, as well as self-localization within the body. Previous literature shows that people do not locate themselves in only one location, but rather primarily in the face and the upper torso. Therefore, we aimed to determine if manipulating the viewpoint to either the height of the eyes or to the height of the chest would influence self-location estimates towards these commonly identified locations of self. In a virtual reality (VR) headset, participants were asked to point at several of their body parts (body part localization) as well as ”directly at you” (self-localization) with a virtual pointer. Both pointing tasks were performed before and after a self-avatar adaptation phase where participants explored a co-located, scaled, gender-matched, and animated self-avatar. We hypothesized that experiencing a self-avatar might reduce inaccuracies in body part localization, and that viewpoint would influence pointing responses for both body part and self-localization. Participants overall pointed relatively accurately to some of their body parts (shoulders, chin, and eyes), but very inaccurately to others, with large undershooting for the hips, knees, and feet, and large overshooting for the top of the head. Self-localization was spread across the body (as well as above the head) with the following distribution: the upper face (25%), the upper torso (25%), above the head (15%) and below the torso (12%). We only found an influence of viewpoint (eye- vs chest-height) during the self-avatar adaptation phase for body part localization and not for self-localization. The overall change in error distance for body part localization for the viewpoint at eye-height was small (M = –2.8 cm), while the overall change in error distance for the viewpoint at chest-height was significantly larger, and in the upwards direction relative to the body parts (M = 21.1 cm). In a post-questionnaire, there was no significant difference in embodiment scores between the viewpoint conditions. Most interestingly, having a self-avatar did not change the results on the self-localization pointing task, even with a novel viewpoint (chest-height). Possibly, body-based cues, or memory, ground the self when in VR. However, the present results caution the use of altered viewpoints in applications where veridical position sense of body parts is required.
Correlation Analysis-Based Neural Network Self-Organizing Genetic Evolutionary Algorithm

Recent years, there has been an ever increasing interest and investment on Artificial Intelligence (AI), both academic and industrial.As the hotspots in AI, Artificial Neural Networks (ANNs) have already been applied to a lot of different applications.However, traditional ANNs have disadvantages, such as fixed and redundant structure, resulting in requirement of large amount of training data and training time.Biological researches have shown that the biological neural network behaves in a more flexible way, with synapses building or withering according to requirement.In this paper, we present a Correlation Analysis Based Neural Network Self-Organizing Genetic Evolutionary Algorithm.Based on correlation analysis of training process, self-organizing combined with genetic evolutionary algorithm is applied to improve the performance efficiency and structural efficiency of the built neural network.Results show that our algorithm could generate neural networks with more compact structure and reasonable classification accuracy.