Reviewerly/exHarmony huggingface dataset:
Learning test-time augmentation for content-based image retrieval

Off-the-shelf convolutional neural network features achieve outstanding results in many image retrieval tasks. However, their invariance to target data is pre-defined by the network architecture and training data. Existing image retrieval approaches require fine-tuning or modification of pre-trained networks to adapt to variations unique to the target data. In contrast, our method enhances the invariance of off-the-shelf features by aggregating features extracted from images augmented at test-time, with augmentations guided by a policy learned through reinforcement learning. The learned policy assigns different magnitudes and weights to the selected transformations, which are selected from a list of image transformations. Policies are evaluated using a metric learning protocol to learn the optimal policy. The model converges quickly and the cost of each policy iteration is minimal as we propose an off-line caching technique to greatly reduce the computational cost of extracting features from augmented images. Experimental results on large trademark retrieval (METU trademark dataset) and landmark retrieval (ROxford5k and RParis6k scene datasets) tasks show that the learned ensemble of transformations is highly effective for improving performance, and is practical, and transferable.
How to Apply the Process Skills Approach to Improving Indonesian Language Learning Outcomes in Senior High Schools

This research is a classroom action research (Classroom Action Research) which aims to improve students' Indonesian learning outcomes through learning with a process skills approach. The subjects of this study were students of class X-3 SMA Negeri 4 Palopo with 40 students, 12 male students, and 28 female students. Data collection was carried out using learning outcomes tests and observation techniques, and journals. The author made the research instrument in collaboration with a class X Indonesian language teacher at SMA Negeri 4 Palopo. The collected data were analyzed using quantitative and qualitative analysis. The results achieved after applying the process skills approach in learning are: (1) In the first Cycle, the average score of student mastery is 14.71, (2) In the second Cycle, the average score of student mastery was 65.24; (3) There was an increase in student mastery from Cycle I to cycle II of 50.53; (4) The process skills approach can make students more active and enjoy learning Indonesian. From the results of the research above, it can be concluded that the process skills approach can improve the learning outcomes of Indonesian students in class X-3 SMA Negeri 4 Palopo.
Image Denoising Based on Correlation Adaptive Sparse Modeling

Image restoration techniques generally use intrinsic correlations of image signals to reduce the uncertainty of the unknown signal and estimate the latent ground truth. Local and non-local correlation are the two major kinds of correlations utilized. They are different sources of correlations reflecting connections between different image data, but such a difference is not taken into consideration in most of the existing schemes. Typically, sparse representation based works use the same image data to exploit both local and non-local correlation in shared regularization. This paper aims to fully exploit local and non-local correlation of image contents separately so that near-optimal sparse representations are achieved and thus the uncertainty of signals is minimized. The proposed scheme adaptively selects different image data to exploit local and non-local correlations respectively. In particular, to exploit local correlation, the image data of interest are extracted from clustered rows of patch groups that consist of similar image contents. Experimental results on image denoising show that the proposed scheme not only outperforms state-of-the-art sparsity and low rank based methods, but also surpasses successful deep learning-based approaches in terms of PSNR, SSIM, and visual quality.
Blockchain –killer of illegal wood

Abstract Distributed Ledger Technology (DTL) is a leading technologies in the current “digital revolution”. The new technology is a real boon for the national forest sector. It may bring the Russian forest sector to the forefront of competitiveness and efficiency. However, the internal shortcomings, dangers, and threats of the new technology should be timely studied and eliminated. The “digit” is as strong as nuclear power. It is able to bestowhumanity or cause a global catastrophe if mishandled andmistreated. This fully applies to the forestry sector.
Self-calibrating the look-elsewhere effect: fast evaluation of the statistical significance using peak heights

ABSTRACT In experiments where one searches a large parameter space for an anomaly, one often finds many spurious noise-induced peaks in the likelihood. This is known as the look-elsewhere effect, and must be corrected for when performing statistical analysis. This paper introduces a method to calibrate the false alarm probability (FAP), or p-value, for a given dataset by considering the heights of the highest peaks in the likelihood. Specifically, we derive an equation relating the global p-value to the rank and height of local maxima. In the simplest form of self-calibration, the look-elsewhere-corrected $\chi^2$ of a physical peak is approximated by the $\chi^2$ of the peak minus the $\chi^2$ of the highest noise-induced peak, with accuracy improved by considering lower peaks. In contrast to alternative methods, this approach has negligible computational cost as peaks in the likelihood are a byproduct of every peak-search analysis. We apply to examples from astronomy, including planet detection, periodograms, and cosmology.
Evaluation of the Difference of Human Behavior between VR and Real Environments in Searching and Manipulating Objects in a Domestic Environment

It is important to evaluate and analyze human behaviors and reactions to build robots that can interact with people. While immersive virtual reality (VR) is one of the useful tools for the evaluation, it has been concerned that the human behavior may differ between VR and real environments due to the differences in cognitive characteristics. In this study, we investigated the differences in human behavior by focusing on common situations in the home environment; for example, a change in head position and posture is required when searching for an object (e.g., peeking motion) and when high accuracy is required when manipulating an object (e.g., placing an object on a shelf). We found that object manipulation requires accuracy which in turn, requires more time in the VR environment. In addition, the amount of change in head posture during object search and manipulation behavior was larger in the VR environment. Finally, we discuss the possibility of using the findings of this study to design robot competitions to assess the quality of human-robot interaction (HRI).
The large-scale structure of the Universe

"The large-scale structure of the Universe." Contemporary Physics, ahead-of-print(ahead-of-print), pp. 1–2
Accurate Silhouette Vectorization by Affine Scale-Space

Binary shapes, or silhouettes, are essential in human communication. They include, for example, all fonts and many logos. They can be extracted from images in raster form but require a vectorization for resolution independent editing. In this paper, we propose a mathematically founded silhouette vectorization algorithm, which converts a raster 2D shape to a Scalable Vector Graphics (SVG) format whose control points are geometrically stable under affine transformations. The proposed method can also be used as a reliable feature point detector for silhouettes. Compared to state-of-the-art graphics software, our algorithm shows a superior reduction in the number of control points for an equal or better accuracy.
Generating Functional Requirements Based on Classification of Mobile Application User Reviews

User reviews are important resources for mobile developers for maintaining and evolving mobile applications that have been released. Since there can be a lot of user reviews, it is cumbersome for the mobile development team to go through all user reviews to identify which ones contain useful information for further maintenance and evolution. This paper proposes an initial attempt to facilitate the maintenance and evolution process of a mobile development team by automating the generation of functional requirements from mobile application user reviews on the App Store and Play Store. Specifically, these functional requirements serve as change requirements for maintaining existing functions of the application or enhancing the application with new functions. The proposed approach is based on machine learning and natural language processing and consists of three steps. Firstly, user reviews that identify functional requirements are classified by text classification algorithms. Secondly, since some user reviews may address the same issues, distinct user reviews are identified by clustering techniques and text similarity. Finally, relevant information is extracted from the user reviews to generate functional requirements based on user review patterns and requirement boilerplates. In an evaluation, the generated functional requirements obtained moderate to high scores in terms of readability, unambiguity, completeness, and validity. The proposed approach can help the development team identify functional requirements from direct feedback of the users which should be considered and further refined in the maintenance and evolution of the mobile application.
Deep-Learning Pipeline for Object Pose Estimation from an RGB-D Image

We propose a novel deep learning pipeline for estimating the 6D pose of an object from an RGB-D image. Our pipeline comprises two separate pipes: object detection and pose estimation pipes. By employing the You Only Look Once model, we were able to easily build the object detection pipe and achieve high accuracy for the task while decreasing the complexity of pose estimation for the next pipe. For the pose estimation pipe, we adopted the novel CNN (convolutional neural network) architecture, which can be easily configured and used in industrial sites. We generated datasets for five objects, including parts used in industrial sites, using CAD models and computer simulation in a virtual world. Our pose estimation model achieved up to 99.6% (90.9% on average) accuracy in terms of shape similarity (i.e., ) between the CAD models rotated by the ground truth and the inference result for the datasets. These results validate the usefulness of the proposed deep learning pipeline and the CNN architecture for 6D pose estimation.
Using experiential learning to teach and learn digital forensics: Educator and student perspectives

Due to its complexity, designing digital forensics curriculum can be quite challenging. This paper describes how authors used experiential learning theory to design and teach digital forensics in post-secondary education settings. Furthermore, drawing on survey data collected from students at the end of a graduate level digital forensics course and from cybersecurity educators participating in a three-day professional development workshop, this study examined educator and student perspectives. Results suggest that both students and educators had a positive learning experience. However, when asked about challenges encountered and anticipated with experiential learning in their own context, cybersecurity educators cited challenges with designing the required hands-on experiences, lack of space in curricula, and lack of instructional supports. Recommendations for teaching digital forensics are offered and discussed.
Improving Robustness of Deep Neural Networks for Aerial Navigation by Incorporating Input Uncertainty

Uncertainty quantification methods are required in autonomous systems that include deep learning (DL) components to assess the confidence of their estimations. However, to successfully deploy DL components in safety-critical autonomous systems, they should also handle uncertainty at the input rather than only at the output of the DL components. Considering a probability distribution in the input enables the propagation of uncertainty through different components to provide a representative measure of the overall system uncertainty. In this position paper, we propose a method to account for uncertainty at the input of Bayesian Deep Learning control policies for Aerial Navigation. Our early experiments show that the proposed method improves the robustness of the navigation policy in Out-of-Distribution (OoD) scenarios.
The Neural MMO Platform for Massively Multiagent Research

Neural MMO is a computationally accessible research platform that combines large agent populations, long time horizons, open-ended tasks, and modular game systems. Existing environments feature subsets of these properties, but Neural MMO is the first to combine them all. We present Neural MMO as free and open source software with active support, ongoing development, documentation, and additional training, logging, and visualization tools to help users adapt to this new setting. Initial baselines on the platform demonstrate that agents trained in large populations explore more and learn a progression of skills. We raise other more difficult problems such as many-team cooperation as open research questions which Neural MMO is well-suited to answer. Finally, we discuss current limitations of the platform, potential mitigations, and plans for continued development.
Exploring barriers and possible actions suggested in rural libraries for information society: Perspectives from library practitioners in Malaysia

This study aims to explore the barriers when developing information societies and to suggest possible actions for rural libraries to participate in information societies, tailored to the needs of Malaysian rural libraries. This study employed a three-round Delphi technique; including a face-to- face interview in the first round, followed by two rounds of close-ended survey questions presented in a Likert-type scale format distributed to a selected group of leaders with experience in rural library planning and strategies. It reveals how funding, library infrastructures, information communication technology, local content resources, and human resources adversely affected rural libraries in the development of an information society. Based on the recommendation elicited for the five categories, this study suggests possible actions that provide practical guidance on the actions to be taken by the rural libraries in developing an information society.
Digital forest pedagogy in context of distance learning

Abstract The article tells of the actual problems of the introduction of digital technologies in the higher education system. The development of digital technologies in all spheres of life is supported at the state level and by the general public. Digitalization is a new social situation.The article describes the current problems of digitalization of education. A wide range of possibilities of digital technologies in the educational process of the St. Petersburg State Forest Technical University named after S. M. Kirov (SPbFTU) is considered.It is concluded that the digitalization of education changes the content of the courses taught, as well as the presentation of information, it is not only presentations or videos, it is already direct connections to information networks, databases, forums.
Predicting the bitcoin return direction with logistic, discriminant analysis and machine learning classification techniques

This paper proposes prediction of the bitcoin return direction with logistic, discriminant analysis and machine learning classification techniques. It extends the prediction of the bitcoin return direction using exogenous macroeconomic and financial variables which have been investigated as drivers of bitcoin return. We also use google trends as proxy for investors interest on bitcoin. We consider those variables as predictors for bitcoin return direction. We conduct an in-sample and out-of-sample empirical analysis and achieve a misclassification error around 4% for in-sample evaluation and around 41% in out-of-sample empirical analysis. Ensemble learning trees based outperforms the other methods in both in-sample and out-of-sample analyses.
On nature and causes of observed MT errors.

This work describes analysis of nature and causes of MT errors observed by different evaluators under guidance of different quality criteria: adequacy and comprehension and and a not specified generic mixture of adequacy and fluency. We report results for three language pairs and two domains and eleven MT systems. Our findings indicate that and despite the fact that some of the identified phenomena depend on domain and/or language and the following set of phenomena can be considered as generally challenging for modern MT systems: rephrasing groups of words and translation of ambiguous source words and translating noun phrases and and mistranslations. Furthermore and we show that the quality criterion also has impact on error perception. Our findings indicate that comprehension and adequacy can be assessed simultaneously by different evaluators and so that comprehension and as an important quality criterion and can be included more often in human evaluations.
Querying RDF Databases with Sub-CONSTRUCTs

Graph query languages feature mainly two kinds of queries when applied to a graph database: those inspired by relational databases which return tables such as SELECT queries and those which return graphs such as CONSTRUCT queries in SPARQL.The latter are object of study in the present paper.For this purpose, a core graph query language GrAL is defined with focus on CONSTRUCT queries.Queries in GrAL form the final step of a recursive process involving so-called GrAL patterns.By evaluating a query over a graph one gets a graph, while by evaluating a pattern over a graph one gets a set of matches which involves both a graph and a table.CONSTRUCT queries are based on CONSTRUCT patterns, and sub-CONSTRUCT patterns come for free from the recursive definition of patterns.The semantics of GrAL is based on RDF graphs with a slight modification which consists in accepting isolated nodes.Such an extension of RDF graphs eases the definition of the evaluation semantics, which is mainly captured by a unique operation called Merge.Besides, we define aggregations as part of GrAL expressions, which leads to an original local processing of aggregations.
Decentralized Online Multiplayer Game Based on Blockchains

Decentralized apps offer users significant advantages. One main advantage is that the content belongs to the users. Although decentralized apps have been developed, decentralized multiplayer games have still been a challenge. This research work is therefore based on the question: Is it possible to create a turn-based real-time online multiplayer game based on decentralized applications on blockchain targeting the mobile market? This question is answered by the successful development of a decentralized online multiplayer game based on a blockchain.
Grassmannian graph-attentional landmark selection for domain adaptation

Domain adaptation aims to leverage information from the source domain to improve the classification performance in the target domain. It mainly utilizes two schemes: sample reweighting and feature matching. While the first scheme allocates different weights to individual samples, the second scheme matches the feature of two domains using global structural statistics. The two schemes are complementary with each other, which are expected to jointly work for robust domain adaptation. Several methods combine the two schemes, but the underlying relationship of samples is insufficiently analyzed due to the neglect of the hierarchy of samples and the geometric properties between samples. To better combine the advantages of the two schemes, we propose a Grassmannian graph-attentional landmark selection (GGLS) framework for domain adaptation. GGLS presents a landmark selection scheme using attention-induced neighbors of the graphical structure of samples and performs distribution adaptation and knowledge adaptation over Grassmann manifold. the former treats the landmarks of each sample differently, and the latter avoids feature distortion and achieves better geometric properties. Experimental results on different real-world cross-domain visual recognition tasks demonstrate that GGLS provides better classification accuracies compared with state-of-the-art domain adaptation methods.
Predict-Then-Decide: A Predictive Approach for Wait or Answer Task in Dialogue Systems

Different people have different habits of describing their intents in conversations. Some people tend to deliberate their intents in several successive utterances, i.e., they use several consistent messages for readability instead of a long sentence to express their question. This creates a predicament faced by the application of dialogue systems, especially in real-world industry scenarios, in which the dialogue system is unsure whether it should answer the query of user immediately or wait for further supplementary input. Motivated by such an interesting predicament, we define a novel Wait-or-Answer task for dialogue systems. We shed light on a new research topic about how the dialogue system can be more intelligent to behave in this Wait-or-Answer quandary. Further, we propose a predictive approach named Predict-then-Decide (PTD) to tackle this Wait-or-Answer task. More specifically, we take advantage of a decision model to help the dialogue system decide whether to wait or answer. The decision of decision model is made with the assistance of two ancillary prediction models: a user prediction and an agent prediction. The user prediction model tries to predict what the user would supplement and uses its prediction to persuade the decision model that the user has some information to add, so the dialogue system should wait. The agent prediction model tries to predict the answer of the dialogue system and convince the decision model that it is a superior choice to answer the query of user immediately since the input of user has come to an end. We conduct our experiments on two real-life scenarios and three public datasets. Experimental results on five datasets show our proposed PTD approach significantly outperforms the existing models in solving this Wait-or-Answer problem.
Study on Data Partition for Delimitation of Masses in Mammography

Mammography is the primary medical imaging method used for routine screening and early detection of breast cancer in women. However, the process of manually inspecting, detecting, and delimiting the tumoral massess in 2D images is a very time-consuming task, subject to human errors due to fatigue. Therefore, integrated computer-aided detection systems have been proposed, based on modern computer vision and machine learning methods. In the present work, mammogram images from the publicly available Inbreast dataset are first converted to pseudo-color and then used to train and test a Mask R-CNN deep neural network. The most common approach is to start with a dataset and split the images into train and test set randomly. However, since there are often two or more images of the same case in the dataset, the way the dataset is split may have an impact on the results. Our experiments show that random partition of the data can produce unreliable training, so the dataset must be split using case-wise partition for more stable results. In experimental results, the method achieves an average true positive rate of 0.936 with 0.063 standard deviation using random partition and 0.908 with 0.002 standard deviation using case-wise partition, showing that case-wise partition must be used for more reliable results.