Reviewerly/exHarmony huggingface dataset:
Improving customer experience with artificial intelligence by adhering to ethical principles

The intensive development and application of artificial intelligence technologies in organizing interaction with clients is accompanied by such difficulties as: the client’s unwillingness to communicate with the robot, distrust, fear, negative experience of the clients. Such problems can be solved by adhering to ethical principles of using artificial intelligence. In scientific and practical research on this topic, there are many general recommendations that are difficult to apply in practice, or, on the contrary, that describe the methods for solving a highly specialized technical or management problem. The purpose of this article is to determine the ethical principles and methods, the observance and implementation of which would increase confidence in artificial intelligence systems among client of a particular organization. As a result of the analysis and synthesis of the scientific and practical investigations, as well as the empirical experience of Russian and foreign companies, the main areas of application of artificial intelligence technologies affecting the customer experience were identified. The ethical principles recommended to be followed by business have been formulated and systematized. The main methods have been also identified to enable implementation of these principles in practice, and so to reduce the negative effects of customer interaction with artificial intelligence and increase their confidence in the company.
Decentralized Refinement Planning and Acting

We describe Dec-RPAE, a system for decentralized multi-agent acting and planning in partially observable and non-deterministic environments. The system includes both an acting component and an online planning component. The acting component is similar to RAE, a well-known acting engine, but incorporates changes that enable it to be used by multiple autonomous agents working independently in a collaborative setting. Each agent runs a local copy of Dec-RPAE, with a set of hierarchical refinement methods using operational models that specify various ways to accomplish its designated tasks. To perform actions, the agent uses Dec-RPAE’s acting component to execute the methods in the agent’s environment. To advise the acting component on which method to execute, the planning component repeatedly does Monte Carlo simulations of the methods to estimate their potential outcomes. Agents can communicate with each other to exchange information about their states, tasks, goals, and plans in order to cooperatively succeed in their respective missions. Our experimental results demonstrate that Dec-RPAE is useful for improving the agents’ performances.
Named Entity Recognition through Deep Representation Learning and Weak Supervision

Weakly supervised methods estimate the labels for a dataset using the predictions of several noisy supervision sources.Many machine learning practitioners have begun using weak supervision to more quickly and cheaply annotate data compared to traditional manual labeling.In this paper, we focus on the specific problem of weakly supervised named entity recognition (NER) and propose an endto-end model to learn optimal assignments of latent NER tags using observed tokens and weak labels provided by labeling functions.To capture the sequential dependencies between the latent and observed variables, we propose a sequential graphical model where the components are approximated using neural networks.State-of-the-art contextual embeddings are used to further discriminate the quality of noisy weak labels in various contexts.Results of experiments on four public weakly supervised named entity recognition datasets show a significant improvement in F1 score over recent approaches.
Multilayer Graph Association Rules for Link Prediction

Multiplex networks allow us to study a variety of complex systems where nodes connect to each other in multiple ways, for example friend, family, and co-worker relations in social networks. Link prediction is the branch of network analysis allowing us to forecast the future status of a network: which new connections are the most likely to appear in the future? In multilayer link prediction we also ask: in which layer? Because this last question is unanswerable with classical link prediction, here we investigate the use of graph association rules to inform multilayer link prediction. We derive such rules by identifying all frequent patterns in a network via multilayer graph mining, and then score each unobserved link's likelihood by finding the occurrences of each rule in the original network. Association rules add new abilities to multilayer link prediction: to predict new node arrivals, to consider higher order structures with four or more nodes, and to be memory efficient. In our experiments, we show that, exploiting graph association rules, we are able to achieve a prediction performance close to an ideal ensemble classifier. Further, we perform a case study on a signed multilayer network, showing how graph association rules can provide valuable insights to extend social balance theory.
Grammar-Constrained Neural Semantic Parsing with LR Parsers

Target meaning representations for semantic parsing tasks are often based on programming or query languages, such as SQL, and can be formalized by a context-free grammar.Assuming a priori knowledge of the target domain, such grammars can be exploited to enforce syntactical constraints when predicting logical forms.To that end, we assess how syntactical parsers can be integrated into modern encoder-decoder frameworks.Specifically, we implement an attentional SEQ2SEQ model that uses an LR parser to maintain syntactically valid sequences throughout the decoding procedure.Compared to other approaches to grammar-guided decoding that modify the underlying neural network architecture or attempt to derive full parse trees, our approach is conceptually simpler, adds less computational overhead during inference and integrates seamlessly with current SEQ2SEQ frameworks.We present preliminary evaluation results against a recurrent SEQ2SEQ baseline on GEOQUERY and ATIS and demonstrate improved performance while enforcing grammatical constraints.
Neural Entity Recognition with Gazetteer based Fusion

Incorporating external knowledge into Named Entity Recognition (NER) systems has been widely studied in the generic domain. In this paper, we focus on clinical domain where only limited data is accessible and interpretability is important. Recent advancement in technology and the acceleration of clinical trials has resulted in the discovery of new drugs, procedures as well as medical conditions. These factors motivate towards building robust zero-shot NER systems which can quickly adapt to new medical terminology. We propose an auxiliary gazetteer model and fuse it with an NER system, which results in better robustness and interpretability across different clinical datasets. Our gazetteer based fusion model is data efficient, achieving +1.7 micro-F1 gains on the i2b2 dataset using 20% training data, and brings + 4.7 micro-F1 gains on novel entity mentions never presented during training. Moreover, our fusion model is able to quickly adapt to new mentions in gazetteers without re-training and the gains from the proposed fusion model are transferable to related datasets.
Using novelty search to explicitly create diversity in ensembles of classifiers

The diversity between individual learners in an ensemble is known to influence its performance. However, there is no standard agreement on how diversity should be defined, and thus how to exploit it to construct a high-performing classifier. We propose two new behavioural diversity metrics based on the divergence of errors between models. Following a neuroevolution approach, these metrics are then used to guide a novelty search algorithm to search a space of neural architectures and discover behaviourally diverse classifiers, iteratively adding the models with high diversity score to an ensemble. The parameters of each ANN are tuned individually with a standard gradient descent procedure. We test our approach on three benchmark datasets from Computer Vision --- CIFAR-10, CIFAR-100, and SVHN --- and find that the ensembles generated significantly outperform ensembles created without explicitly searching for diversity and that the error diversity metrics we propose lead to better results than others in the literature. We conclude that our empirical results signpost an improved approach to promoting diversity in ensemble learning, identifying what sort of diversity is most relevant and proposing an algorithm that explicitly searches for it without selecting for accuracy.
Quasi-experiment in the educational reality

Aim. The main aim of the study is a presentation of the results of a quasi-experiment related to using a serious game in the preparation phase of supervised teaching practice as well as during its completion. Methods. The main method of the study is a quasi-experiment with the factor rotation technique. The method comprised two phases with two unequal groups of surveyed university students. In the first phase, the experimental influence was investigated in the first group and the second group served as the control group (to compare the results). In the second phase, the groups were switched around in the quasi-experiment; the control group became the experimental group and vice versa. Results. The presented study has a theoretical-empirical character. In the theoretical part, the characteristics of quasi-experiment and its use are briefly discussed. In the empirical part, the quasi-experiment is applied to the educational reality of future teachers. Supervised practice teaching is an important part of the university education of future teachers. Using a serious game therein is a positive element also when it comes to the development of students’ approach towards supervised teaching practice. Conclusion. Quasi-experiment is used as an experimental method for verification of examined reality. In the educational reality, its realisation is more difficult because it is a more demanding environment than a laboratory one. By realising the quasi-experiment, the theoretical supposition about the positive influence of serious games on the development of a university student’s personality can be confirmed.
Fast and Memory Efficient Graph Optimization via ICM for Visual Place Recognition

Visual place recognition is the task of finding same places in a set of database images for a given set of query images.This task becomes particularly challenging if the environmental condition changes between database and query, for example from day to night.In this paper, we build upon our recent work on graph optimization for place recognition, where a graph was used to model additional structural knowledge like sequences.A subsequent non-linear least squares optimization (NLSQ) improved the place recognition performance.While this approach achieves very high performance, it is quite slow and memory inefficient.This paper addresses the long runtime and the high memory usage in order to obtain the same or better place recognition performance faster on larger problems.We propose a novel graph optimization procedure that is based on Iterated Conditional Modes (ICM).In addition, we investigate a new cost function for an edge in the graph.Our novel ICMbased approach achieves 9.1msec maximum runtime per query, which is 260× faster than the minimum runtime with NLSQ.Moreover, with ICM we can optimize problems that are not feasible with NLSQ on a full graph due to memory limitations.To demonstrate the superior performance of our ICM-based method, we provide extensive experimental evaluations with the essence of 987 precision-recall curves: Our proposed ICM-based method is compared to the NLSQ-based method as well as to six sequence-based approaches from the literature on 21 sequence combinations from five datasets with four different image descriptors.Our experiments show that our ICM-based method with sequence-exploitation not only improves the NLSQbased performance by 10% on average while being 385× faster and using more than 60× less memory.It also significantly outperforms all six sequence-based methods from the literature by at least 32% on average with the NetVLAD descriptor while using comparable runtime and memory.Code is available online 1 .
What is Stablecoin?: A Survey on Its Mechanism and Potential as Decentralized Payment Systems

Our study provides a survey on how existing stablecoins—cryptocurrencies aiming at price stabilization—peg their value to other assets, from the perspective of Decentralized Payment Systems (DPSs). This attempt is important because there has been no preceding surveys focusing on the stablecoin as DPSs, i.e., the one aiming at not only price stabilization but also decentralization. For clarity, we first classified existing stablecoins into four types according to their collaterals (fiat, commodity, crypto, and non-collateralized) and pointed out the high potential of non-collateralized stablecoins as DPSs; then, we further classified existing non-collateralized stablecoins into two types according to their intervention layers (protocol, application) and confirmed details of their representative m echanisms. Utilizing concepts such as Quantity Theory of Money (QTM), Tobin tax, and speculative attack, our survey revealed the status quo where, despite the high potential of non-collateralized stablecoins, they have no standard mechanism to achieve the stablecoin for practical DPSs.
ArtCoder: An End-to-end Method for Generating Scanning-robust Stylized QR Codes

Quick Response (QR) code is one of the most worldwide used two-dimensional codes. Traditional QR codes appear as random collections of black-and-white modules that lack visual semantics and aesthetic elements, which inspires the recent works to beautify the appearances of QR codes. However, these works adopt fixed generation algorithms and therefore can only generate QR codes with a pre-defined style. In this paper, combining the Neural Style Transfer technique, we propose a novel end-to-end method, named ArtCoder, to generate the stylized QR codes that are personalized, diverse, attractive, and scanning-robust. To guarantee that the generated stylized QR codes are still scanning-robust, we propose a Sampling-Simulation layer, a module-based code loss, and a competition mechanism. The experimental results show that our stylized QR codes have high-quality in both the visual effect and the scanning-robustness, and they are able to support the real-world application.
Geological characteristics of the Republic of North Macedonia

Detailed description of the geological characteristics of the Republic of North Macedonia is given.The characteristics of the six major tectonic units are described: the Vardar zone (VZ) in Central Region, the Pelagonian massif (PM), the West-Macedonian zone (WMZ) and a small part of the Cukali-Krasta zone (CKZ) in the west, and the Serbo-Macedonian massif (SMM) and the Kraishtide zone (KZ) in the east.The geological map with these units is also shown, including the most presented lithological units: Quaternary alluvial and deluvial/proluvial sediments, Neogene, Paleogene and Mesozoic clastic rocks, Mesozoic, Paleozoic and Proterozoic carbonate rocks, Paleozoic and Proterozoic metamorphic rocks from Pelagonian and Serbo-Macedonian massif and Neogene, Paleogene, Mesozoic and Paleozoic magmatic rocks.Additionally, special attention is given to the Neogene and the Quaternary in North Macedonia.
IoT systems in the process of multidisciplinary training of personnel for the digital economy and their design

In the context of digitalization of the most knowledge-intensive sectors of the domestic economy, the development of an industrial training system in the field of electronic instrumentation is of great importance. The key areas of its development with the use of information and communication technologies include the development and improvement of the technological basis for training and retraining of personnel in engineering educational programs. One of the elements of this basis is the service of multi-user remote access via the internet to a high-tech experimental equipment laboratory as a service based on internet of things (IoT) systems. Within the framework of this service, an urgent problem is to increase the functional saturation of automated stands/installations, which is currently characterized by a paucity of scientific research. The purpose of the research is to expand the areas of experimental research carried out in the mode of multiuser remote access based on specialized IoT systems. As a result, a method of multidisciplinary application of specialized IoT systems was developed. This consists of the technical implementation of possibilities for additional research: research into technologies underlying both multi-user distributed measuring and control systems and IoT systems in general; research into technologies used in their end-to-end computer-aided design; research into joint interaction of several geographically distributed automated stands/installations, implemented on the basis of a four level IoT reference architecture. A methodology for the design of multi-user distributed measuring and control systems as specialized IoT systems has also been developed, focused on solving multidisciplinary research problems in an interactive dialogue mode based on single sample of experimental equipment. The methodology mobilizes organizational, technical and methodological support for the process of creating such systems with specified target characteristics. In general, the method and methodology developed open up opportunities for systematically implementing the basic principles of the “Education 4.0” concept in the preparation and retraining of engineering personnel in the field of electronic instrumentation.
Deep social force network for anomaly event detection

Anomaly event detection is vital in surveillance video analysis. However, how to learn the discriminative motion in the crowd scene is still not tackled. Here, a deep social force network by exploiting both social force extracting and deep motion coding is proposed. Given a grid of particles with velocity provided by the optical flow, the interaction force in the crowd scene is investigated and a social force module is embedded in a deep network. A deep motion convolution was further designed with a 3D (DMC-3D) module. The DMC-3D not only eliminates the noise motion in the crowd scene with a spatial encoder–decoder but also learns the 3D feature with a spatio-temporal encoder. The deep social force coding is modelled with multiple features, in which each feature can describe specific anomaly motion. The experiments on UCF-Crime and ShanghaiTech datasets demonstrate that our method can predict the temporal localization of anomaly events and outperform the state-of-the-art methods.
Supporting Cognitive and Emotional Empathic Writing of Students

We present an annotation approach to capturing emotional and cognitive empathy in student-written peer reviews on business models in German.We propose an annotation scheme that allows us to model emotional and cognitive empathy scores based on three types of review components.Also, we conducted an annotation study with three annotators based on 92 student essays to evaluate our annotation scheme.The obtained inter-rater agreement of α=0.79 for the components and the multi-π=0.41for the empathy scores indicate that the proposed annotation scheme successfully guides annotators to a substantial to moderate agreement.Moreover, we trained predictive models to detect the annotated empathy structures and embedded them in an adaptive writing support system for students to receive individual empathy feedback independent of an instructor, time, and location.We evaluated our tool in a peer learning exercise with 58 students and found promising results for perceived empathy skill learning, perceived feedback accuracy, and intention to use.Finally, we present our freely available corpus of 500 empathy-annotated, student-written peer reviews on business models and our annotation guidelines to encourage future research on the design and development of empathy support systems.
What Would a Teacher Do? Predicting Future Talk Moves

Recent advances in natural language processing (NLP) have the ability to transform how classroom learning takes place.Combined with the increasing integration of technology in today's classrooms, NLP systems leveraging question answering and dialog processing techniques can serve as private tutors or participants in classroom discussions to increase student engagement and learning.To progress towards this goal, we use the classroom discourse framework of academically productive talk (APT) to learn strategies that make for the best learning experience.In this paper, we introduce a new task, called future talk move prediction (FTMP): it consists of predicting the next talk move -an utterance strategy from APT -given a conversation history with its corresponding talk moves.We further introduce a neural network model for this task, which outperforms multiple baselines by a large margin.Finally, we compare our model's performance on FTMP to human performance and show several similarities between the two.
An Empirical Study on Adversarial Attack on NMT: Languages and Positions Matter

In this paper, we empirically investigate adversarial attack on NMT from two aspects: languages (the source vs. the target language) and positions (front vs. back).For autoregressive NMT models that generate target words from left to right, we observe that adversarial attack on the source language is more effective than on the target language, and that attacking front positions of target sentences or positions of source sentences aligned to the front positions of corresponding target sentences is more effective than attacking other positions.We further exploit the attention distribution of the victim model to attack source sentences at positions that have a strong association with front target words.Experiment results demonstrate that our attention-based adversarial attack is more effective than adversarial attacks by sampling positions randomly or according to gradients.
A New Multi-stage Secret Sharing Scheme for Hierarchical Access Structure with Existential Quantifier

Multi-stage secret sharing scheme is practical in the case that there is a security system with m ordered checkpoints.It is natural to divide the m checkpoints into m different levels. There are m different secrets, and eachof them with a different importance corresponds to a checkpoint/level. The participants are also divided intom disjoint levels as they do in the hierarchical threshold access structure. Hierarchical threshold access structurewith the existential quantifier ( HTAS∃ ) does not cover the common practice that at least a few numbersof high-ranking participants are required to be involved in any recovery of the secret. The popular schemeswith hierarchical access structure were needed to check many matrices for non-singularity. We propose amulti-stage secret sharing scheme for HTAS∃ , and the tools are based on the linear homogeneous recurrencerelations (LHRRs) and one-way functions. We give the HTAS∃ a modification, so that this hierarchical accessstructure can satisfy the common practice. In our scheme, if the participants are divided into m levels, thereusually has m secrets. But before the (j − 1)-th secret is recovered, the j-th secret cannot be recovered. Ourscheme is a computational secure. The proposed scheme requires a share for each participant and the shareis as long as each secret. Our scheme has high efficiency by comparing with the state-of-the-art hierarchicalsecret sharing schemes.