Reviewerly/exHarmony huggingface dataset:
Supporting Secure Dynamic Alert Zones Using Searchable Encryption and Graph Embedding

Location-based alerts have gained increasing popularity in recent years, whether in the context of healthcare (e.g., COVID-19 contact tracing), marketing (e.g., location-based advertising), or public safety. However, serious privacy concerns arise when location data are used in clear in the process. Several solutions employ Searchable Encryption (SE) to achieve secure alerts directly on encrypted locations. While doing so preserves privacy, the performance overhead incurred is high. We focus on a prominent SE technique in the public-key setting -- Hidden Vector Encryption (HVE), and propose a graph embedding technique to encode location data in a way that significantly boosts the performance of processing on ciphertexts. We show that finding the optimal encoding is NP-hard, and provide several heuristics that are fast and obtain significant performance gains. Furthermore, we investigate the more challenging case of dynamic alert zones, where the area of interest changes over time. Our extensive experimental evaluation shows that our solutions can significantly improve computational overhead compared to existing baselines.
Adaptive Depth Graph Attention Networks

As one of the most popular GNN architectures, the graph attention networks (GAT) is considered the most advanced learning architecture for graph representation and has been widely used in various graph mining tasks with impressive results. However, since GAT was proposed, none of the existing studies have provided systematic insight into the relationship between the performance of GAT and the number of layers, which is a critical issue in guiding model performance improvement. In this paper, we perform a systematic experimental evaluation and based on the experimental results, we find two important facts: (1) the main factor limiting the accuracy of the GAT model as the number of layers increases is the oversquashing phenomenon; (2) among the previous improvements applied to the GNN model, only the residual connection can significantly improve the GAT model performance. We combine these two important findings to provide a theoretical explanation that it is the residual connection that mitigates the loss of original feature information due to oversquashing and thus improves the deep GAT model performance. This provides empirical insights and guidelines for researchers to design the GAT variant model with appropriate depth and well performance. To demonstrate the effectiveness of our proposed guidelines, we propose a GAT variant model-ADGAT that adaptively selects the number of layers based on the sparsity of the graph, and experimentally demonstrate that the effectiveness of our model is significantly improved over the original GAT.
A separation logic for sequences in pointer programs and its decidability

Separation logic and its variants can describe various properties on pointer programs. However, when it comes to properties on sequences, one may find it hard to formalize. To deal with properties on variable-length sequences and multilevel data structures, we propose sequence-heap separation logic which integrates sequences into logical reasoning on heap-manipulated programs. Quantifiers over sequence variables and singleton heap storing sequence (sequence singleton heap) are new members in our logic. Further, we study the satisfiability problem of two fragments. The propositional fragment of sequence-heap separation logic is decidable, and the fragment with 2 alternations on program variables and 1 alternation on sequence variables is undecidable. In addition, we explore boundaries between decidable and undecidable fragments of the logic with prenex normal form.
PyExperimenter: Easily distribute experiments and track results

PyExperimenter is a tool to facilitate the setup, documentation, execution, and subsequent evaluation of results from an empirical study of algorithms and in particular is designed to reduce the involved manual effort significantly. It is intended to be used by researchers in the field of artificial intelligence, but is not limited to those.
Neuro-Symbolic World Models for Adapting to Open World Novelty

Open-world novelty--a sudden change in the mechanics or properties of an environment--is a common occurrence in the real world. Novelty adaptation is an agent's ability to improve its policy performance post-novelty. Most reinforcement learning (RL) methods assume that the world is a closed, fixed process. Consequentially, RL policies adapt inefficiently to novelties. To address this, we introduce WorldCloner, an end-to-end trainable neuro-symbolic world model for rapid novelty adaptation. WorldCloner learns an efficient symbolic representation of the pre-novelty environment transitions, and uses this transition model to detect novelty and efficiently adapt to novelty in a single-shot fashion. Additionally, WorldCloner augments the policy learning process using imagination-based adaptation, where the world model simulates transitions of the post-novelty environment to help the policy adapt. By blending ''imagined'' transitions with interactions in the post-novelty environment, performance can be recovered with fewer total environment interactions. Using environments designed for studying novelty in sequential decision-making problems, we show that the symbolic world model helps its neural policy adapt more efficiently than model-based and model-based neural-only reinforcement learning methods.
Mixture Modeling with Normalizing Flows for Spherical Density Estimation

Normalizing flows are objects used for modeling complicated probability density functions, and have attracted considerable interest in recent years. Many flexible families of normalizing flows have been developed. However, the focus to date has largely been on normalizing flows on Euclidean domains; while normalizing flows have been developed for spherical and other non-Euclidean domains, these are generally less flexible than their Euclidean counterparts. To address this shortcoming, in this work we introduce a mixture-of-normalizing-flows model to construct complicated probability density functions on the sphere. This model provides a flexible alternative to existing parametric, semiparametric, and nonparametric, finite mixture models. Model estimation is performed using the expectation maximization algorithm and a variant thereof. The model is applied to simulated data, where the benefit over the conventional (single component) normalizing flow is verified. The model is then applied to two real-world data sets of events occurring on the surface of Earth; the first relating to earthquakes, and the second to terrorist activity. In both cases, we see that the mixture-of-normalizing-flows model yields a good representation of the density of event occurrence.
Information and communication competence of the basic secondary education students: theoretical aspect

Nowadays, the problem of educational innovations remains the most urgent. This is due to the societal need in educated people, who quickly and effectively function in current conditions, develop their creative abilities and personal values, and are able to acquire knowledge independently. Thus, introduction of competence-based approach to teaching is becoming more and more important. It allows implementing the priorities of modern school reform and demands for a high level of students’ readiness to operate in different fields. “Information” and “communication” became important concepts of informational society that causes necessity in obtaining information and communication competences. Informatization of the society demands for new approaches in education. New standards, renewed educational programs and methodological materials, virtual platforms for distance learning, improvement and diversification of the online environment are being developed and implemented in Ukraine. These are of fundamental importance for the development of information and communication competence. Discussions are still going around information and communication competence regarding the definition of this concept, analysis and comparison of various international approaches, the features of its introduction into educational process, its ntegration with information and communication technologies. The article examines the genesis of the main concepts that form the basis of the competence approach, the normative documents related to the reform of the Ukrainian education system, the development and structure of the information and communication competence. It reveals the meaning of the main components of the information and communication competence, which depend on the level of its formation and efficiency in a whole. Keywords: competence, competence approach, information and communication competence, information and communication technologies.
Non-standard tasks in Mathematics as a means of successful learning for primary education students

The article is devoted to non-standard tasks for students of general primary education, since one of the main tasks of a primary school teacher is to remove the mechanical memorization and reproduction of educational material by students from the learning process. In fulfilling this task, teachers face certain difficulties. Primary school children, especially first-graders, often memorise the materials instead of understanding and analysing it, at least in the most accessible form. If a primary school teacher presents ready-made material, too often students are asked to memorize it; they are not guided by the systematic formation of their cognitive interests and skills. Then many students develop a strong habit of mechanical memorization, which not only leads to formal learning, but also generates dogmatism in children’s thinking and delays their development. This method of teaching contradicts the content of typical educational programs. In our opinion, the lack of its understanding is the source of many disagreements that still occur in the work of our teachers, and this is the reason for criticism of “significant shortcomings of new programs” etc. The emphasis is also placed on using non-standard tasks in mathematics lessons that makes it possible to provide a world view and moral basis for judgments as an individual. By using appropriate non-standard tasks in the mathematics lessons, it is possible to deeply comprehend the current phenomena of children’s social life, the ability to solve them, learn to respect their own opinion, because modern education is the education aimed at child development. The material should be presented by the teacher in such a way that it is understood by each student, otherwise it will not evoke a desire to work, because it will be meaningless to them. In order to maintain interest in anything new, there must be certain elements of the old, known to the children. It is in the process of solving non-standard tasks that primary school students acquire the skills to work according to a plan, economically choose the means to achieve the goal, justify and analyse their actions. It can be concluded that non-standard tasks also increase complexity, contain a condition, which helps students to identify the mathematical apparatus necessary to solve a problem in the elementary school. The teacher can control the process of consolidation of knowledge provided by the curriculum through solution of problems of increased complexity. The solution of a non-standard problem requires students to conduct research. The ultimate goal is for students to learn to find a solution to any available goal on their own. Keywords: non-standard tasks, modern education, logical and mathematical development, solutions.
Organisation of independent work at Mathematics lessons in primary school

The article is devoted to the peculiarities of the organisation of independent work with primary school students. After all, independent work in learning is the most important prerequisite for truly conscious and strong mastery of knowledge. The cognitive independence of a student in primary school is not only the purpose of education, but also a necessary means of mastering the educational material through independent work. A large portion of independent work in mathematics contributes to the development of children’s attention, the development of their ability to reason, the education of independence as a character trait. Meanwhile, the limited direct face-to-face communication between teachers and primary school students leads to the fact that their independent work loses a clear direction, and therefore is not effective enough. To save time, a teacher usually does not consider it necessary to instruct students in detail about the progress and sequence of the task. This largely explains the reproducibility of many tasks that applicants receive for independent work (to write, read, calculate, study, and repeat). Such independent tasks limit the child's independence by imitative actions that inhibit the development of their thinking. A significant disadvantage while organizing independent work in the classroom is the incorrect limit of time set to complete tasks. This finding is based either on the excessive duration of independent work in mathematics lessons, or on the discrepancy between the allotted time and the degree of complexity of the proposed task. Fuzzy statement of tasks reduces the effectiveness of independent work and is incorrect. The teacher often asks students for 10–15 minutes: “Get focused with your thoughts”, “Think about the condition of the problem”, “Prepare for oral counting” and so on. The vagueness of tasks cannot direct children’s efforts to achieve a certain result, because it distracts their attention. Such independent tasks are mostly not subject to effective teacher control, and this leads to students being inactive in mathematics lessons. Keywords: applicants, independent work, organization, degrees of complexity, efficiency, independent tasks, directions, expediency, independence.
The Socio-Cultural Context of Reading Interest of Younger Learners

This study examines the correlation between 9–10-year-old learners’ reading interest and four types of socio-cultural environments that stimulate their interest (enthusiasm) in reading as a fundamental pillar of reading literacy development. The study aimed to map and analyse the socio-cultural context of four levels of intensity of reading interest of 1195 Slovak, Czech and Polish learners. A 46-item questionnaire was used to achieve the aim of the study. Statistical testing using Spearman’s coefficient was used for analysis. The significant results of the research showed that the correlation between monitored younger learners’ reading interest and family environment is very weak, and reading as a leisure activity in the virtual environment is not attractive.
Teacher training in the Republic of Moldova: traditions and trends

The author analyses the system of teacher training in the Republic of Moldova. Teacher training is a strategic issue for the Republic of Moldova, given that education is declared a national priority, but also because there are certain difficulties in providing the education system with qualified professionals. Teacher training and the quality of training of this professional category is a priority. Teacher training is a wide-ranging process encompassing pedagogical colleges and universities. Compared to universities, colleges put a strong emphasis on learning by doing. The priority of teacher training is stated in policy documents as well. The author describes the Normative and Policy Framework. There are different Professional Training Programs in the Republic of Moldova. The study programmes provide professional training in the specialist and pedagogical fields however, the analysis of study plans and the results of surveys show that some aspects of psycho-pedagogical training are given greater weight and others are in some way neglected. The teaching profession is not among the top jobs in the Republic of Moldova, though а programme to attract the youth to teaching careers and to keep young specialists in the education system is being implemented. Concerning the motivation, teachers mention that the amount of the salary is of little importance; rather, they enjoy the field/subject they teach; they like working with children and young people; they want to change the lives of children and young people. Today, the people in the education system of the Republic of Moldova have various levels of qualification. Given the importance of teacher training in terms of quality of education, modernising training programmes, and upgrading programmes to foster the teaching profession must be a permanent concern of the competent institutions. Keywords: vocational training; psycho-pedagogical mode; career education; professional qualification.
Msanii: High Fidelity Music Synthesis on a Shoestring Budget

In this paper, we present Msanii, a novel diffusion-based model for synthesizing long-context, high-fidelity music efficiently. Our model combines the expressiveness of mel spectrograms, the generative capabilities of diffusion models, and the vocoding capabilities of neural vocoders. We demonstrate the effectiveness of Msanii by synthesizing tens of seconds (190 seconds) of stereo music at high sample rates (44.1 kHz) without the use of concatenative synthesis, cascading architectures, or compression techniques. To the best of our knowledge, this is the first work to successfully employ a diffusion-based model for synthesizing such long music samples at high sample rates. Our demo can be found https://kinyugo.github.io/msanii-demo and our code https://github.com/Kinyugo/msanii .
Using Kaldi for Automatic Speech Recognition of Conversational Austrian German

As dialogue systems are becoming more and more interactional and social, also the accurate automatic speech recognition (ASR) of conversational speech is of increasing importance. This shifts the focus from short, spontaneous, task-oriented dialogues to the much higher complexity of casual face-to-face conversations. However, the collection and annotation of such conversations is a time-consuming process and data is sparse for this specific speaking style. This paper presents ASR experiments with read and conversational Austrian German as target. In order to deal with having only limited resources available for conversational German and, at the same time, with a large variation among speakers with respect to pronunciation characteristics, we improve a Kaldi-based ASR system by incorporating a (large) knowledge-based pronunciation lexicon, while exploring different data-based methods to restrict the number of pronunciation variants for each lexical entry. We achieve best WER of 0.4% on Austrian German read speech and best average WER of 48.5% on conversational speech. We find that by using our best pronunciation lexicon a similarly high performance can be achieved than by increasing the size of the data used for the language model by approx. 360% to 760%. Our findings indicate that for low-resource scenarios -- despite the general trend in speech technology towards using data-based methods only -- knowledge-based approaches are a successful, efficient method.
Simplex Autoencoders

Synthetic data generation is increasingly important due to privacy concerns. While Autoencoder-based approaches have been widely used for this purpose, sampling from their latent spaces can be challenging. Mixture models are currently the most efficient way to sample from these spaces. In this work, we propose a new approach that models the latent space of an Autoencoder as a simplex, allowing for a novel heuristic for determining the number of components in the mixture model. This heuristic is independent of the number of classes and produces comparable results. We also introduce a sampling method based on probability mass functions, taking advantage of the compactness of the latent space. We evaluate our approaches on a synthetic dataset and demonstrate their performance on three benchmark datasets: MNIST, CIFAR-10, and Celeba. Our approach achieves an image generation FID of 4.29, 13.55, and 11.90 on the MNIST, CIFAR-10, and Celeba datasets, respectively. The best AE FID results to date on those datasets are respectively 6.3, 85.3 and 35.6 we hence substantially improve those figures (the lower is the FID the better). However, AEs are not the best performing algorithms on the concerned datasets and all FID records are currently held by GANs. While we do not perform better than GANs on CIFAR and Celeba we do manage to squeeze-out a non-negligible improvement (of 0.21) over the current GAN-held record for the MNIST dataset.
$Ae^2I$: A Double Autoencoder for Imputation of Missing Values

The most common strategy of imputing missing values in a table is to study either the column-column relationship or the row-row relationship of the data table, then use the relationship to impute the missing values based on the non-missing values from other columns of the same row, or from the other rows of the same column. This paper introduces a double autoencoder for imputation ($Ae^2I$) that simultaneously and collaboratively uses both row-row relationship and column-column relationship to impute the missing values. Empirical tests on Movielens 1M dataset demonstrated that $Ae^2I$ outperforms the current state-of-the-art models for recommender systems by a significant margin.
Quantifying and Managing Impacts of Concept Drifts on IoT Traffic Inference in Residential ISP Networks

Millions of vulnerable consumer IoT devices in home networks are the enabler for cyber crimes putting user privacy and Internet security at risk. Internet service providers (ISPs) are best poised to play key roles in mitigating risks by automatically inferring active IoT devices per household and notifying users of vulnerable ones. Developing a scalable inference method that can perform robustly across thousands of home networks is a non-trivial task. This paper focuses on the challenges of developing and applying data-driven inference models when labeled data of device behaviors is limited and the distribution of data changes (concept drift) across time and space domains. Our contributions are three-fold: (1) We collect and analyze network traffic of 24 types of consumer IoT devices from 12 real homes over six weeks to highlight the challenge of temporal and spatial concept drifts in network behavior of IoT devices; (2) We analyze the performance of two inference strategies, namely "global inference" (a model trained on a combined set of all labeled data from training homes) and "contextualized inference" (several models each trained on the labeled data from a training home) in the presence of concept drifts; and (3) To manage concept drifts, we develop a method that dynamically applies the ``closest'' model (from a set) to network traffic of unseen homes during the testing phase, yielding better performance in 20% of scenarios.
HUBO and QUBO models for Prime factorization

The security of the RSA cryptosystem is based on the difficulty of factoring a large number N into prime numbers p and q satisfying N=p*q . This paper presents a prime factoriaation method using D-Wave quantum computer that can threaten the RSA cryptosystem. The starting point for this method is very simple, representing two prime numbers as qubits. Then, set the difference between the product of two prime numbers expressed in qubits and N as a cost function, and find the solution when the cost function becomes the minimum. D-Wave's quantum annealer can find the minimum value of any quadratic problem. However, the cost function is to be a higher-order unconstrained optimiaation (HUBO) model because it contains the second or higher order terms. We used a hybrid solver and dimod package provided by -Wave Ocean software development kit (SDK) to solve the HUBO problem. We also successfully factoriaed 102,454,763 with 26 logical qubits. In addition, we factoriaed 1,000,070,001,221 using the range dependent Hamiltonian algorithm.