Reviewerly/exHarmony huggingface dataset:
Implementation of optimized dynamic trajectory modification algorithm to avoid obstacles for secure navigation of UAV

To develop escape manoeuvre from obstacles and to find new waypoints for dynamic trajectory modification of UAV, a novel Particle Swarm Optimization based Collision Avoidance algorithm (PSO-CA) is presented in this paper. The proposed “obstacle sense and avoid” algorithm and the logical decision-making system aids the Unmanned Aerial Vehicle (UAV) to re-route its current path to a safer flight course when an obstacle pops up along its intended path. A radar with 10 km range identifies obstacles and the UAV manoeuvres based on radar data, making it suitable for any unknown environment. The proposed system would manoeuvre the UAV autonomously along optimized alternate path to avoid the conflicting traffic. New waypoints are identified and the waypoint list is modified dynamically to avoid collision with stationary threats like hill, tree and moving intruders like other UAVs. The proposed algorithm steers the vehicle safely along alternate path with less change in intended trajectory while avoiding all potential threats. As the PSO-CA algorithm detects obstacles and identifies alternate path well in advance for unknown pop up threats, the UAV is safe and is suitable for real time environment. The proposed algorithm has considered obstacles with different positions, different sizes and random motion. Experimental results conducted on 6-DOF model UAV with different obstacles clearly justify the efficiency of the proposed method in comparison with other planners.
ОБҐРУНТУВАННЯ СИСТЕМИ ПІДГОТОВКИ МАЙБУТНІХ УЧИТЕЛІВ ДО ФАСИЛІТАЦІЙНОЇ ВЗАЄМОДІЇ З УЧНЯМИ ЗАКЛАДІВ ЗАГАЛЬНОЇ СЕРЕДНЬОЇ ОСВІТИ

The relevance of the research is in: modern Ukrainian school students need the teachers who will implement in educational process of the institutions of general secondary education an interaction based on facilitating principles. According to the NUS Concept the educational process in schools should be filled with an atmosphere of cooperation, adoption, support. For this reason future teachers should be ready for facilitation interaction. Therefore, the article makes a theoretical generalization and presents a new solution of the problem of training the future teachers for facilitation interaction with students of institutions of general secondary education. The purpose of the article is to develop and theoretically substantiate the system of training the future teachers for facilitation interaction with school students. To reach this goal we used common scientific methods such as: analysis, synthesis, systematization of scientific sources and conceptualization of theoretical knowledge to form the main positions of the work. The developed system represents an educational, self-educational, research and practical units, built on the basis of systemic, personal, competence, resource and activity approaches. The proposed system is a combination of purpose (the formed willingness of future teachers to interact in facilitating way with students), conceptual (basic provisions of the system), content-technological (describes and formulates the components of future teachers' readiness for facilitation interaction: value-motivational, cognitive, activity-operational, reflective; determines the means, didactic and methodological provision; reveals the pedagogical conditions of its effectiveness, control and evaluation (criteria, indicators, levels of formations future teachers' readiness for facilitation interaction with school students and the result of the study) blocs.
Solving Challenges at the Interface of Simulation and Big Data Using Machine Learning

Modeling & Simulation (M&S) and Machine Learning (ML) have been used separately for decades. They can also straightforwardly be employed in the same study by contrasting the results of a theory-driven M&S model with the most accurate data-driven ML model. In this paper, we propose a paradigm shift from seeing ML and M&S as two independent activities to identifying how their integration can solve challenges that emerge in a big data context. Since several works have already examined this interaction for conceptual modeling or model building (e.g., creating components with ML and embedding them in the M&S model), our analysis is devoted on three relatively under-studied stages: calibrating a simulation model using ML, dealing with the issues of large search space by employing ML for experimentation, and identifying the right visualizations of model output by applying ML to characteristics of the output or actions of the users.
Verifying OpenJDK’s LinkedList using KeY

Abstract As a particular case study of the formal verification of state-of-the-art, real software, we discuss the specification and verification of a corrected version of the implementation of a linked list as provided by the Java Collection framework.
CLUECorpus2020: A Large-scale Chinese Corpus for Pre-training Language Model

In this paper, we introduce the Chinese corpus from CLUE organization, CLUECorpus2020, a large-scale corpus that can be used directly for self-supervised learning such as pre-training of a language model, or language generation. It has 100G raw corpus with 35 billion Chinese characters, which is retrieved from Common Crawl. To better understand this corpus, we conduct language understanding experiments on both small and large scale, and results show that the models trained on this corpus can achieve excellent performance on Chinese. We release a new Chinese vocabulary with a size of 8K, which is only one-third of the vocabulary size used in Chinese Bert released by Google. It saves computational cost and memory while works as good as original vocabulary. We also release both large and tiny versions of the pre-trained model on this corpus. The former achieves the state-of-the-art result, and the latter retains most precision while accelerating training and prediction speed for eight times compared to Bert-base. To facilitate future work on self-supervised learning on Chinese, we release our dataset, new vocabulary, codes, and pre-trained models on Github.
Speckle reduction of medical ultrasound images using deep learning with fully convolutional network

Smoothing filters are frequently used for speckle reduction of medical ultrasound images. However, such filters may cause loss of the detailed structures of tissues in terms of image contrast. To improve image contrast in speckle reduction, we investigated a filter for medical ultrasound images using deep learning with a fully convolutional network, which was trained with pairs of input and target data generated by computer simulation. The proposed method achieved higher contrast-to-noise ratio and contrast values than the conventional methods with about 300 times faster processing speed than the NL-means filter.
Age Estimation Using Aging/Rejuvenation Features With Device-Edge Synergy

Estimating human age is a challenging task in computer vision and most researchers are trying to make age estimation via a static facial image. However, it ignores the fact that the age of a person is the specific representation of aging. In this paper, we attempt to explore the aging/rejuvenation (AR) characteristics of faces for age estimation and we called the whole network as AR-Net. Firstly, we use GAN model for learning a manifold of the aging/rejuvenation process to a face dataset with preserving personalized face features ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.,</i> gender, race). Secondly, we seek the correlated aging/rejuvenation characteristics from a narrow age interval, ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.,</i> ((0-100) <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rightarrow $ </tex-math></inline-formula> (0-5), (5-10),…, (90-100)). Thirdly, the fine-tuned GAN is used to generate aging/rejuvenation features of all age groups and these features are applied to train corresponding ELM regressors. AR-Net is deployed on every edge server, and all AR-Nets are trained offline. Afterwards, our AR-Net is constantly updated based on the face dataset collected by the edge sensors. Finally, enormous experiments on Morph-II, CACD, and captured facial dataset have been conducted to verify the performance of our fine-tuned AR-Net and the experimental results show that the approach enhanced than the current state of the art methods.
ConAML: Constrained Adversarial Machine Learning for Cyber-Physical Systems

Recent research demonstrated that the superficially well-trained machine learning (ML) models are highly vulnerable to adversarial examples. As ML techniques are becoming a popular solution for cyber-physical systems (CPSs) applications in research literatures, the security of these applications is of concern. However, current studies on adversarial machine learning (AML) mainly focus on pure cyberspace domains. The risks the adversarial examples can bring to the CPS applications have not been well investigated. In particular, due to the distributed property of data sources and the inherent physical constraints imposed by CPSs, the widely-used threat models and the state-of-the-art AML algorithms in previous cyberspace research become infeasible. We study the potential vulnerabilities of ML applied in CPSs by proposing Constrained Adversarial Machine Learning (ConAML), which generates adversarial examples that satisfy the intrinsic constraints of the physical systems. We first summarize the difference between AML in CPSs and AML in existing cyberspace systems and propose a general threat model for ConAML. We then design a best-effort search algorithm to iteratively generate adversarial examples with linear physical constraints. We evaluate our algorithms with simulations of two typical CPSs, the power grids and the water treatment system. The results show that our ConAML algorithms can effectively generate adversarial examples which significantly decrease the performance of the ML models even under practical constraints.
A Graph to Graphs Framework for Retrosynthesis Prediction

A fundamental problem in computational chemistry is to find a set of reactants to synthesize a target molecule, a.k.a. retrosynthesis prediction. Existing state-of-the-art methods rely on matching the target molecule with a large set of reaction templates, which are very computationally expensive and also suffer from the problem of coverage. In this paper, we propose a novel template-free approach called G2Gs by transforming a target molecular graph into a set of reactant molecular graphs. G2Gs first splits the target molecular graph into a set of synthons by identifying the reaction centers, and then translates the synthons to the final reactant graphs via a variational graph translation framework. Experimental results show that G2Gs significantly outperforms existing template-free approaches by up to 63% in terms of the top-1 accuracy and achieves a performance close to that of state-of-the-art template based approaches, but does not require domain knowledge and is much more scalable.
Assisted concentration of Gaussian resources

In spite of their outstanding experimental relevance, Gaussian operations in continuous-variable quantum systems are subjected to fundamental limitations, as it is known that general resources cannot be distilled within the Gaussian paradigm. We show that these limitations can be overcome by considering a collaborative setting where one party increases the amount of local resource with the aid of another party, whose operations are assumed to be Gaussian but are otherwise unrestricted; the two parties can only communicate classically. We show that in single-shot scenarios, unlike in the well-known case of entanglement theory, two-way classical communication does not lead to any improvement over one-way classical communication from the aiding party to the aided party. We then provide a concise general expression for the Gaussian resource of assistance, i.e., the maximum amount of resource that can be obtained when the aiding party holds a purification of the aided party's state, as measured by a general monotone. To demonstrate its usefulness, we apply our result to two important kinds of resources, squeezing and entanglement, and find some simple analytic solutions. In the case of entanglement theory, we are able to find general upper bounds on the regularized Gaussian entanglement of assistance, and to establish additivity for tensor powers of thermal states. This allows us to draw a quantitative and enlightening comparison with the performance of assisted entanglement distillation in the non-Gaussian setting. On the technical side, we develop some variational expressions to handle functions of symplectic eigenvalues that may be of independent interest. Our results suggest further potential for Gaussian operations to play a major role in practical quantum information processing protocols.
A Novel Steganography Approach for Audio Files

Abstract We present a novel robust and secure steganography technique to hide images into audio files aiming at increasing the carrier medium capacity. The audio files are in the standard WAV format, which is based on the LSB algorithm, while images are compressed by the GMPR technique which is based on the Discrete Cosine Transform and high-frequency minimization encoding algorithm. The method involves compression–encryption of an image file by the GMPR technique followed by hiding it into audio data by appropriate bit substitution. The maximum number of bits without significant effect on audio signal for LSB audio steganography is 6 LSBs. The encrypted image bits are hidden into variable and multiple LSB layers in the proposed method. Experimental results from observed listening tests show that there is no significant difference between the stego-audio reconstructed from the novel technique and the original signal. A performance evaluation has been carried out according to quality measurement criteria of signal-to-noise ratio and peak signal-to-noise ratio.
A Joint Neural Network for Session-Aware Recommendation

Session-aware recommendation is a special form of sequential recommendation, where users' previous interactions before current session are available. Recently, Recurrent Neural Network (RNN) based models are widely used in sequential recommendation tasks with great success. Previous works mainly focus on the interaction sequences of the current session without analyzing a user's long-term preferences. In this paper, we propose a joint neural network (JNN) for session-aware recommendation, which employs a Convolutional Neural Network(CNN) and a RNN to process the long-term historical interactions and the short-term sequential interactions respectively. Then, we apply a fully-connected neural network to study the complex relationship between these two types of features, which aims to generate a unified representation of the current session. Finally, a recommendation score for given items is generated by a bi-linear scheme upon the session representation. We conduct our experiments on three public datasets, showing that JNN outperforms the state-of-the-art baselines on all datasets in terms of Recall and Mean Reciprocal Rank (MRR). The outperforming results indicate that proper handling of historical interactions can improve the effectiveness of recommendation. The experimental results show that JNN is more prominent in samples with short current session or long historical interactions.
Realities and prospects of distance learning at higher education institutions of Ukraine

Introduction. The paper deals with the issue of realities and prospects of distance learning at higher education institutions of Ukraine. Distance learning in Ukraine has been implementing for more than twenty years and distant learning technologies are used at most Ukrainian higher education institutions, but the necessity and feasibility of the introduction and development of remote technology is questionable both to faculty and students. The main purpose of this paper is to synthesize the real state of distance learning in Ukraine and outline its development prospects at higher education institutions of Ukraine.&#x0D; Materials and Methods. The paper represents the analysis of the survey results of student answers of four classical, pedagogical, maritime and agrotechnological higher education institutions regarding the practical implementation of distance learning in their institutions. The study of the realities of distance learning in the practice of higher education institutions in Ukraine has been conducted among 102 students. The questions concerned student attitude to distance learning, distance learning organisation, advantages and disadvantages of distance learning.&#x0D; Results. The survey results show all four higher education institutions where the survey has been conducted have the information technology support of distance learning (websites, access to the Internet), students of all four higher education institutions are familiar with distance education, use this technology, but most of them prefer mixed learning. It is partially uncomfortable for students to work remotely with theoretical training materials and practical tasks, but they prefer automated remote testing. The most preferable aspects of the distance learning technology students are reading the theoretical material and viewing video lectures remotely and sending completed assignments over the Internet. According to the opinion of most students, the great advantage of distance learning is the ability to combine work and study and self-determination of the study time and place. As the most significant disadvantage of distance learning, students have indicated that it requires self-motivating learners.&#x0D; Discussion and Conclusion. The research results allow researchers to identify the realities of distance learning in Ukraine consisting in provision students with the access to distance learning at most higher education institutions. Certain prospects of distance learning development in Ukraine such as updating the software, hardware and material resources of higher education institutions, providing the broadband access to the Internet to higher education institutions of Ukraine are highlighted. It has been noted that ensuring the process of obtaining an educational degree (Bachelor’s, Master’s degrees) at higher education institutions of Ukraine is possible by taking massive open distance courses.
Reinforced Rewards Framework for Text Style Transfer

Style transfer deals with the algorithms to transfer the stylistic properties of a piece of text into that of another while ensuring that the core content is preserved. There has been a lot of interest in the field of text style transfer due to its wide application to tailored text generation. Existing works evaluate the style transfer models based on content preservation and transfer strength. In this work, we propose a reinforcement learning based framework that directly rewards the framework on these target metrics yielding a better transfer of the target style. We show the improved performance of our proposed framework based on automatic and human evaluation on three independent tasks: wherein we transfer the style of text from formal to informal, high excitement to low excitement, modern English to Shakespearean English, and vice-versa in all the three cases. Improved performance of the proposed framework over existing state-of-the-art frameworks indicates the viability of the approach.
Joint Word and Entity Embeddings for Entity Retrieval from a Knowledge Graph

Recent years have witnessed the emergence of novel models for ad-hoc entity search in knowledge graphs of varying complexity. Since these models are based on direct term matching, their accuracy can suffer from a mismatch between vocabularies used in queries and entity descriptions. Although successful applications of word embeddings and knowledge graph entity embeddings to address the issues of vocabulary mismatch in ad-hoc document retrieval and knowledge graph noisiness and incompleteness, respectively, have been reported in recent literature, the utility of joint word and entity embeddings for entity search in knowledge graphs has been relatively unexplored. In this paper, we propose Knowledge graph Entity and Word Embedding for Retrieval (KEWER), a novel method to embed entities and words into the same low-dimensional vector space, which takes into account a knowledge graph's local structure and structural components, such as entities, attributes, and categories, and is designed specifically for entity search. KEWER is based on random walks over the knowledge graph and can be considered as a hybrid of word and network embedding methods. Similar to word embedding methods, KEWER utilizes contextual co-occurrences as training data, however, it treats words and entities as different objects. Similar to network embedding methods, KEWER takes into account knowledge graph's local structure, however, it also differentiates between structural components. Experiments on publicly available entity search benchmarks and state-of-the-art word and joint word and entity embedding methods indicate that a combination of KEWER and BM25F results in a consistent improvement in retrieval accuracy over BM25F alone.
Prototypical Networks for Small Footprint Text-Independent Speaker Verification

Speaker verification aims to recognize target speakers with very few enrollment utterances. Conventional approaches learn a representation model to extract the speaker embeddings for verification. Recently, there are several new approaches in meta-learning which try to learn a shared metric space. Among these approaches, prototypical networks aim at learning a non-linear mapping from the input space to an embedding space with a predefined distance metric. In this paper, we investigate the use of prototypical networks in a small footprint text-independent speaker verification task. Our work is evaluated on SRE10 evaluation set. Experiments show that prototypical networks outperform the conventional method when the amount of data per training speaker is limited.
Towards Faithfully Interpretable NLP Systems: How should we define and evaluate faithfulness?

With the growing popularity of deep-learning based NLP models, comes a need for interpretable systems. But what is interpretability, and what constitutes a high-quality interpretation? In this opinion piece we reflect on the current state of interpretability evaluation research. We call for more clearly differentiating between different desired criteria an interpretation should satisfy, and focus on the faithfulness criteria. We survey the literature with respect to faithfulness evaluation, and arrange the current approaches around three assumptions, providing an explicit form to how faithfulness is defined by the community. We provide concrete guidelines on how evaluation of interpretation methods should and should not be conducted. Finally, we claim that the current binary definition for faithfulness sets a potentially unrealistic bar for being considered faithful. We call for discarding the binary notion of faithfulness in favor of a more graded one, which we believe will be of greater practical utility.
On Network Science and Mutual Information for Explaining Deep Neural Networks

In this paper, we present a new approach to interpret deep learning models. By coupling mutual information with network science, we explore how information flows through feedforward networks. We show that efficiently approximating mutual information allows us to create an information measure that quantifies how much information flows between any two neurons of a deep learning model. To that end, we propose NIF, Neural Information Flow, a technique for codifying information flow that exposes deep learning model internals and provides feature attributions.