Reviewerly/exHarmony huggingface dataset:
Generating Structurally Realistic Models With Deep Autoregressive Networks

Model generators are important tools in model-based systems engineering to automate the creation of software models for tasks like testing and benchmarking. Previous works have established four properties that a generator should satisfy: consistency, diversity, scalability, and structural realism. Although several generators have been proposed, none of them is focused on realism. As a result, automatically generated models are typically simple and appear synthetic. This work proposes a new architecture for model generators which is specifically designed to be structurally realistic. Given a dataset consisting of several models deemed as real models, this type of generators is able to produce new models which are structurally similar to the models in the dataset, but are fundamentally novel models. Our implementation, named <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ModelMime</small> (M2), is based on a deep autoregressive model which combines a Graph Neural Network with a Recurrent Neural Network. We decompose each model into a sequence of edit operations, and the neural network is trained in the task of predicting the next edit operation given a partial model. At inference time, the system produces new models by sampling edit operations and iteratively completing the model. We have evaluated M2 with respect to three state-of-the-art generators, showing that 1) our generator outperforms the others in terms of the structurally realistic property 2) the models generated by M2 are most of the time consistent, 3) the diversity of the generated models is at least the same as the real ones and, 4) the generation process is scalable once the generator is trained.
Speaker Naming in Arabic TV Programs

Automatic speaker identification is the problem of identifying speakers by their real identities. Previous approaches use textual information as a source of naming, try to associate names to neighbouring speaker segments using linguistic rules. However, these approaches have a few limitations that hinder their application on spoken text. Deep learning approaches for natural language processing have recently reached state-of-the-art results. However, deep learning requires a lot of annotated data which is difficult to obtain in the case of speaker identification task. In this paper, we present two contributions towards integrating deep learning for identifying speakers in news broadcasts: first we realise a dataset in which the names of mentioned speakers are related to the previous, next, current or other speaker turns. Moreover, we present our approach to solve the problem of speaker identification using information obtained from the transcription. We use a Long-term Recurrent Convolutional Network for name assignment and integer linear programming for name propagation into the different segments. We evaluate our model on both assignment and propagation tasks on the test part of the Arabic multi-genre broadcast dataset which consists of 17 TV programs from Aljazeera. The performance is analysed using the evaluation metrics, such as Estimated Global Error Rate (EGER) and Diarization Error Rate (DER). The outcome of the proposed method ensures better performance by achieving the lower EGER of 32.3% and DER of 8.3%.
General Cross-Architecture Distillation of Pretrained Language Models into Matrix Embeddings

Large pretrained language models (PreLMs) are rev-olutionizing natural language processing across all benchmarks. However, their sheer size is prohibitive for small laboratories or for deployment on mobile devices. Approaches like pruning and distillation reduce the model size but typically retain the same model architecture. In contrast, we explore distilling PreLMs into a different, more efficient architecture, Continual Multiplication of Words (CMOW), which embeds each word as a matrix and uses matrix multiplication to encode sequences. We extend the CMOW architecture and its CMOW/CBOW-Hybrid variant with a bidirectional component for more expressive power, per-token representations for a general (task-agnostic) distillation during pretraining, and a two-sequence encoding scheme that facilitates downstream tasks on sentence pairs, such as sentence similarity and natural language inference. Our matrix-based bidirectional CMOW/CBOW-Hybrid model is competitive to DistilBERT on question similarity and recognizing textual entailment, but uses only half of the number of parameters and is three times faster in terms of inference speed. We match or exceed the scores of ELMo for all tasks of the GLUE benchmark except for the sentiment analysis task SST-2 and the linguistic acceptability task CoLA. However, compared to previous cross-architecture distillation approaches, we demonstrate a doubling of the scores on detecting linguistic acceptability. This shows that matrix-based embeddings can be used to distill large PreLM into competitive models and motivates further research in this direction.
Teaching Robots to Span the Space of Functional Expressive Motion

Our goal is to enable robots to perform functional tasks in emotive ways, be it in response to their users' emotional states, or expressive of their confidence levels. Prior work has proposed learning independent cost functions from user feedback for each target emotion, so that the robot may optimize it alongside task and environment specific objectives for any situation it encounters. However, this approach is inefficient when modeling multiple emotions and unable to generalize to new ones. In this work, we leverage the fact that emotions are not independent of each other: they are related through a latent space of Valence-Arousal-Dominance (VAD). Our key idea is to learn a model for how trajectories map onto VAD with user labels. Considering the distance between a trajectory's mapping and a target VAD allows this single model to represent cost functions for all emotions. As a result 1) all user feedback can contribute to learning about every emotion; 2) the robot can generate trajectories for any emotion in the space instead of only a few predefined ones; and 3) the robot can respond emotively to user-generated natural language by mapping it to a target VAD. We introduce a method that interactively learns to map trajectories to this latent space and test it in simulation and in a user study. In experiments, we use a simple vacuum robot as well as the Cassie biped.
The State of Test Automation in DevOps: A Systematic Literature Review

DevOps is becoming increasingly popular in the software industry to deliver quality software in less time. However, building a DevOps pipeline is not easy. Many factors need to be considered in the process, and many decisions need to be made. Making the DevOps lifecycle successful, "Test Automation" played a pivot role in different stages of the DevOps pipeline. With automated testing, continuous testing can be achieved - a crucial driver for delivering high-quality software rapidly and a vital factor in reducing risks, increasing productivity, and lowering costs. Through automated testing tools, the entire process of testing can be automated. This paper presents a review of the use of Test Automation in a DevOps environment, it also provides glimpses of automated testing tools. The results are limited to articles published in peer-reviewed conferences, scientific journals, and books published between 2013 and 2021. The articles have been synthesized by categorizing the articles based on the research method, benefits of automated testing in DevOps, and the tools used for performing testing in an automated form. Our results shed light on different studies related to testing automation as one of the DevOps practices and its impact on software quality.
Conditional TMDTO as a MILP Instance

Conditional Time-Memory-Data Trade-off (TMDTO) attack given by Biryukov and Shamir can be reduced to the following problem: “Find the minimum number of state bits that should be fixed in order to recover the maximum number of state bits by utilizing the keystream bits and value of rest of the state bits”. As per our literature survey, existing algorithms search for state bits that should be fixed (as minimum as possible) in order to recover the maximum possible state bits directly through the keystream bits. However, those algorithms are cipher specific and require extensive manual effort in analyzing the keystream bit equations. In this manuscript, we have constructed an automated framework that is easy to implement and solves the above problem (for the case when bits are fixed to 0) for any NLFSR based stream cipher with better complexity, thereby reducing manual efforts. However, we do not claim any global optimum for fixed bits. We tried to reduce the number of fixed bits as much as possible. To show that our algorithm is applicable to a majority of NLFSR based stream ciphers, we implement it on three different stream ciphers: LIZARD, GRAIN-128a and ESPRESSO. It improves all existing TMDTO results on these ciphers. The framework involves modelling keystream bit equations into a set of linear constraints, which is then solved by using a Mixed Integer Linear Programming (MILP) solver, Gurobi. The advantages of our automated framework over other methods are that we can achieve better results with far less effort, and it can be applied to any stream cipher of a similar structure with very ease. To the best of our knowledge, our MILP model is the first work that converts the conditional TMDTO of a stream cipher into a linear optimization problem. As a consequence, for LIZARD cipher, we reduce the number of fixed bits by 20 bits from the previous best result when the number of recovered bits is 18. In the case of GRAIN-128a, the highest reduction in the number of fixed bits is by 34 bits when the number of recovered bits is 35. Lastly, for ESPRESSO cipher, the reduction is by 7 bits when the number of recovered bits is 35.
Development Kazakh-Turkish Machine Translation on the Base of Complete Set of Endings Model

This article discusses Kazakh-Turkish machine translation based on the model of a complete set of endings. The morphological model CSE (Complete Set of Endings) is used for Kazakh-Turkish machine translation. To collect the necessary materials and implement the algorithm, a lot of research and careful work was carried out during the study. The main ones include creation of a set of suffixes in the Kazakh and Turkish languages; morphological analysis of endings and creation of corresponding morphological tables of two languages; create an appropriate list of Kazakh and Turkish stem dictionaries; create an appropriate list of Kazakh and Turkish stop-word dictionaries; develop Kazakh-Turkish machine translation algorithm based on the CSE model; create software based on this algorithm. The main part of the study consists of fragments of the collected material, as well as the main algorithms and results. Scientific contribution of the article: machine translation technology for Kazakh-Turkish translation is developed based on the CSE model. Experiment results shoes possibility of proposed technology of machine translation for Turkic languages.
ASSESSMENT OF THE QUALITY OF THE APPLICATION OF ADAPTIVE TECHNOLOGIES OF THE ORGANIZATION OF THE WORKSHOP OF FIRST-YEAR STUDENTS, FUTURE CHEMISTRY TEACHERS

This article is devoted to the issues of assessing the quality of the use of adaptive technologies for organizing a workshop for first-year students, future chemistry teachers, in the conditions of university education. The authors consider the features of the use of elements of adaptive technologies for the organization of a chemical workshop in the study of chemical and methodological disciplines: «Introduction to inorganic chemistry» and «Didactic games in teaching chemistry» using digital educational resources. The basic requirements for the formed competencies of a modern chemistry teacher as a graduate of a university are given.
Smart Contract as an Enabler for the Digital Green Transition

Modern power systems are evolving towards decarbonization and digitalization phases leading to Transactive Energy Systems (TES). Application of technology like Distributed Ledger Technology (DLT) to develop a TES is not an unheard topic of research. The joint operation of TES and DLT is providing various new opportunities and business models where smart contracts can play an enabler role to amalgamate interconnected systems like power systems and digital infrastructure and services. Furthermore, the current global environmental and political climate accentuates the need for clean energy sources combined with the need for deregulation, decentralization, decarbonization, digitalization, and democratization in the energy ecosystem. This work aims to demystify the potential of smart contracts as an enabler technology for the Digital Green Transition of the energy industry from a TES perspective. The work also explores how smart contracts can be used in various TES use cases. Furthermore, some insight into how policy, legal, and legislative requirements can impact the use of a smart contract for future energy grid is also provided.
Wearable Fall Detection Based on Motion Signals Using Hybrid Deep Residual Neural Network

There have been several approaches for wearable fall detection devices during the last twenty years. The majority of technologies relied on machine learning. Although the given findings appear that the issue is practically addressed, critical problems remain about feature extraction and selection. In this research, the constraint of machine learning on feature extraction is addressed by including a hybrid convolutional operation in our proposed deep residual network, called the DeepFall model. The proposed network automatically generates high-level motion signal characteristics that can be utilized to track falls and everyday activities. FallAllD dataset, a publicly available standard dataset for fall detection that gathered motion signals of falls and other events, was utilized to analyze the proposed network. We performed investigations using a 5-fold cross-validation technique to determine overall accuracy and F-measure. The experimental outcomes show that the proposed DeepFall performs better accuracy (95.19%) and F-measure (92.79%) than the state-of-the-art baseline deep learning networks.
Formal Modeling and Verification of Convolutional Neural Networks based on MSVL

With the rapid development and wide application of neural networks, it is more and more important to use formal methods to verify and ensure their security. In this paper, we propose a comprehensive formal framework for the modeling and verification of convolutional neural networks (CNN). The framework is developed based on Modeling, Simulation and Verification Language (MSVL), a formal language with temporal-logic basis. First, the structure and basic behavior of a CNN are characterized hierarchically as MSVL specifications. On this basis, the prediction model, training model and verification module are developed. Experimental results show that the framework constructs formal models of CNNs effectively and supports the verification of various network properties.
HAIN: Multilabel Classification With Hierarchical Attention-Based Interaction Network for Multiturn Dialogue Texts

Multiturn dialogue systems with cognitive capabilities have been popularly used in many applications and generate a large volume of dialogue texts. The dialogue texts of many dialogue systems contain multiple labels, and a typical case is that the customer requirements or complaints in an online multiturn dialogue service system often need to be classified into multiple categories or forwarded to multiple departments, that is, multilabel classification on multiturn dialogue texts. Traditional methods treat the dialogue text as the plain text, which makes it difficult for classifiers to learn important features. On the other hand, regarding each utterance as the basic semantic unit may destroy the semantic information integrity of the dialogue. Furthermore, the above methods all ignore the relationship between labels. To address above issues, we propose to use the utterance pair as the basic semantic unit and design a novel multilabel classification model for multiturn dialogue texts in this article. Specifically, we propose to use the multihead attention mechanism to learn the key features of multiple labels and design a new method to learn the correlation among labels. Besides, we propose to use the penalty term to allow multihead attention to focus on different semantics. Finally, we conduct sufficient experiments on four real-world multiturn dialogue data sets and the experimental results show that our model produces superior performance.
Does the Philosophical Reflection on the Foundations of Scientific Research Follow the Empiricism Principle?

It is argued that O.E. Stoliarova’s analysis of the STS programs leads her to a conclusion that in them the principle of reflexivity (in D. Bloor’s version) is not carried out though this contradicts the orientation of these programs on a self-reflection of the scientific bases. Hence, a problem arises: whether we will apply the principle of empiricism to justification of metascientific reasonings (in particular, to sociological explanations of knowing actions of scientists). This, in turn, leads to a problem of universality of philosophy as a platform for metascientific criticism. The formulation and the solution of such a problem assume the possibility of creation of metaphilosophical systems radicalizing the principle of reflexivity before its application to philosophy. Thereby the philosophy of science appears in the field of immanent paradoxes. An exit from similar paradoxes demands new prospect of philosophical researches.
Impact of healthcare service quality on patient satisfaction and loyalty in a Jordanian private hospital

This study was aimed to investigate the effect of quality of healthcare on patient satisfaction and their loyalty towards a private hospital in Jordan. In total, 130 outpatients participated in a questionnaire survey at one of the biggest private hospitals in Jordan. These data were analysed using SPSS and applying approaches that involved frequency statistics, factor analysis, C-alpha, correlation and regressions analysis. The results indicate that the variables tangible and empathy have a direct impact on patient loyalty and an indirect impact on patient loyalty through patient satisfaction. It was also found that patient satisfaction has a significant relationship with patient loyalty in the private hospital. Practical implications are drawn from this research with the emergence of private healthcare; there has been competition by the health institutions to increase their performance and attract customers, and to achieve this, the private hospitals in Jordan need to work on enhancing quality, satisfaction and loyalty.
HSOID: Subjective Quality Evaluation on HDR Stereoscopic Omnidirectional Images

High dynamic range Stereoscopic Omnidirectional Images (HSOI) can provide users with an immersive experience closer to the real world. Compared with the existing omnidirectional images, HSOIs usually have more complex perceptual factors due to the particularities of imaging, coding and visualization, making HSOIs quality assessment more challenging. Up to now, there is no research on subjective quality assessment of HSOIs. To solve this problem, this paper establishes the first subjective HSOI quality Database (denoted as HSOID), in which a set of the distorted HSOIs with encoding distortion and visualization distortion is generated, and these distorted HSOIs are subjectively scored based on the criteria for subjective evaluation. In the HSOID, the representative HSOIs of different natural scenes (indoor, outdoor, day, night, etc.) are created as the original HSOIs, then, they are encoded with the JPEG XT standard into symmetric or asymmetric distorted HOSIs, respectively. For the visualization on the head mounted display with standard dynamic range, the encoded HSOIs undergo five tone mapping operations, respectively, which may result in different degrees of tone mapping distortion. Thus, a set the distorted HSOIs is generated, and they are subjective-scored so that the HSOID with subjective evaluation is established. Finally, by analyzing the subjective quality of different distorted images, the visual perceptual characteristics of HSOI are explored. At the same time, the HSOID is also tested with some representative objective quality metrics of visual contents. The results show that the design of the HSOID is reasonable. The database can be used to benchmark objective quality evaluation methods of the distorted HSOIs.
IRRMA: An Image Recommender Robot Meeting Assistant

The number of people who attend virtual meetings has increased as a result of COVID-19. In this paper, we present a system that consists of an expressive humanoid social robot called QTRobot, and a recommender system that employs natural language processing techniques to recommend images related to the content of the presenter’s speech to the audience in real time. This is achieved utilising the QTRobot’s platform capabilities (microphone, computation power, and Wi-Fi).
COCP: Blockchain Proof-of-Useful-Work Leveraging Real-Life Applications

We propose Combinatorial Optimization based Consensus Protocol (COCP) that considers efficient use of energy in Blockchain (BC) consensus protocol based on Proof-of-Useful-Work (PoUW) concept. Instead of classical cryptographic puzle, it involves dealing with hard real-life combinatorial optimization (CO) problems submitted by BC participants called customers. Two sources of rewards are provided for miners, one related to adding a new block and the other for solving an instance of CO problem. The main issues that arise when combining BC and CO, are security and consistency of maintaining the whole system. We discuss how to resolve them and what are the benefits of the proposed COCP for all BC participants. Our proposed doubly-rewarding scheme and efficiency in energy exploration are illustrated on a small example from the Ethereum BC network.
Eye-Gaze and Mouse-Movements on Web Search as Indicators of Cognitive Impairment

The aging population brings a drastic increase in age-related conditions that affect cognition. Early detection of cognitive changes is important but challenging. Standard paper-and-pencil neuropsychological assessments of cognition require trained personnel and are costly and time-consuming to administer. Moreover, they may not capture subtle cognitive impairments associated with aging and age-related disorders that impact activities of daily living. Eye-tracking during cognitive tasks has been demonstrated to provide sensitive metrics of cognitive changes in mild cognitive impairment (MCI) and dementia. However, the eye-tracking studies with older adults and individuals with cognitive impairment to date have utilized specialized and decontextualized cognitive tasks rather than everyday activities. We propose to examine the feasibility and utility of web page interactions, search query characteristics, eye-tracking, and mouse-movement-derived measures collected during everyday web search tasks as a metric for cognitive changes associated with healthy and pathological aging.
A Verified Implementation of B$$^+$$-Trees in Isabelle/HOL

In this paper we present the verification of an imperative implementation of the ubiquitous B $$^+$$ -tree data structure in the interactive theorem prover Isabelle/HOL. The implementation supports membership test, insertion and range queries with efficient binary search for intra-node navigation. The imperative implementation is verified in two steps: an abstract set interface is refined to an executable but inefficient purely functional implementation which is further refined to the efficient imperative implementation.