Reviewerly/exHarmony huggingface dataset:
QuantifyML: How Good is my Machine Learning Model?

The efficacy of machine learning models is typically determined by computing their accuracy on test data sets.However, this may often be misleading, since the test data may not be representative of the problem that is being studied.With QuantifyML we aim to precisely quantify the extent to which machine learning models have learned and generalized from the given data.Given a trained model, QuantifyML translates it into a C program and feeds it to the CBMC model checker to produce a formula in Conjunctive Normal Form (CNF).The formula is analyzed with off-the-shelf model counters to obtain precise counts with respect to different model behavior.QuantifyML enables i) evaluating learnability by comparing the counts for the outputs to ground truth, expressed as logical predicates, ii) comparing the performance of models built with different machine learning algorithms (decision-trees vs. neural networks), and iii) quantifying the safety and robustness of models.
CNN-based no-reference video quality assessment method using a spatiotemporal saliency patch selection procedure

We propose a yet lightweight no-reference (NR) video quality assessment (VQA) method, which uses a convolution neural network (CNN) architecture. The proposed method implements a spatiotemporal saliency patch selection procedure that crops the frame into small nonoverlapping blocks of images (patches) and selects the most perceptually relevant ones. The selected patches are then forwarded to the CNN. To determine which patches are the most relevant, spatial and temporal saliency features are computed for each frame. The proposed method does not require subjective scores to train the CNN. It uses objective quality scores as target quality scores for each video frame, which are computed using an NR image quality assessment method. Given the lack of large annotated video quality databases, this is an advantage of the proposed method. Finally, although it has much smaller cost of data-processing, compared with other state-of-the-art methods, the proposed NR-VQA obtains robust and competitive results.
Immersive technologies in project activities as an innovative vector of biology and ecology teaching methods

The key goal of modern biological and ecological education is the process of forming a natural scientific worldview based on the biological picture of the world and, in particular, the formation of biological (ecological) thinking based on a systematic approach. This contributes to the formation of a personality capable of independently building a trajectory of personal development, subject to mastering key competencies. An innovative approach to the formation of a biological picture of the world, from the authors’ point of view, is the use of immersive technologies in the educational process in biology and ecology. The article discusses the implementation of new teaching aids and technologies, especially virtual and augmented reality, 3D images in the biological and ecological education of school students, and the problems of a comprehensive study of the influence of VR and AR technologies on students in the context of project activities. It proposes several approaches to the training of teachers and instructors who can effectively organize an immersive educational process, build flexible scenarios for the inclusion of these technologies in project activities. The examples of students’ project work on the basics of genetics in virtual laboratories using virtual and augmented reality technologies are examined. In particular, an innovative approach to teaching the foundations of heredity is revealed in accordance with the challenges of our time, taking into account social demand, allowing to provide a personality-oriented approach to learning, variability of the process, increase the effectiveness of the material being studied, and adapt participants to the conditions of passing the mandatory standard. At the diagnostic stage of the study, it was revealed that the heredity basics module in the school biology course is a complex topic. This served as the basis for starting the development of the author’s methodological construct, which is based on cultural, historical and problem approaches. When designing a construct, the material on the basics of heredity is divided into semantic modules – didactic units, the structure of which assumes the presence of a motivating task, a problem situation and work in a virtual training ground. The approbation of the innovative module for the study of the molecular foundations of genetics by school students shows that for its implementation into the practice of general and additional education, it is necessary to update the material base, to conduct both real and virtual genetic experiments, as well as to improve the qualifications of teaching staff.
Anomaly Detection in Traffic Trajectories Using a Combination of Fuzzy, Deep Convolutional and Autoencoder Networks

Owing to the increasing deployment of vehicles in human societies and the necessity for smart traffic control, anomaly detection is among the various tasks widely employed in traffic monitoring. As the issue of urban traffic and their relative smart monitoring systems have gained popularity among researchers in recent years, there exist several studies in this regard in the literature. In most of these studies, classifications are based on the behavior of the drivers, where a set of default trajectories are used in order to learn the system and classify the related data. However, two less-studied challenges are the lack of access to sufficient data to provide an efficient model, along with the lack of access to anomaly data that covers all possible abnormal trajectories. While the former challenge can be fulfilled through long-term data recording, the latter requires appropriate considerations. To this aim, we have utilized a combination of optimized convolutional neural network and fuzzy neural network classifiers, along with autoencoding neural networks. The final combination occurs at the decision level. First, the CNN-ANFIS classifier assigns the input trajectory to one of the predefined categories. Then, the trained Autoencoder networks examine the result in order to find whether the trajectory is normal or abnormal. Obtaining 87.5% accuracy on QMUL and 99.5% on the T15 datasets confirms the superior performance of the proposed method.
Autoencoder Assist: An Efficient Profiling Attack on High-Dimensional Datasets

Deep learning (DL)-based profiled attack has been proved to be a powerful tool in side-channel analysis. However, most attacks merely focus on small datasets, in which their points of interest are well-trimmed for attacks. Countermeasures applied in embedded systems always result in high-dimensional side-channel traces, i.e., the high-dimension of each input trace. These traces inevitably require complicated designs of neural networks and large sizes of trainable parameters for exploiting the correct keys. Therefore, performing profiled attacks (directly) on high-dimensional datasets is difficult. To bridge this gap, we propose a dimension reduction tool for high-dimensional traces by combining signal-to-noise ratio (SNR) analysis and autoencoder. With the designed asymmetric undercomplete autoencoder (UAE) architecture, we extract a small group of critical features from numerous time samples. The compression rate by using our UAE method reaches 40x on synchronized datasets and 30x on desynchronized datasets. This preprocessing step facilitates the profiled attacks by extracting potential leakage features. To demonstrate its effectiveness, we evaluate our proposed method on the raw ASCAD dataset with 100,000 samples in each trace. We also derive desynchronized datasets from the raw ASCAD dataset and validate our method under random delay effect. We further propose a $$2^n$$ -structure MLP network as the attack model. By applying UAE and attack model on these traces, experimental results show all correct subkeys on synchronized datasets and desynchronized datasets are successfully revealed within hundreds of seconds. This indicates that our autoencoder can significantly facilitate DL-based profiled attacks on high-dimensional datasets.
Explainable, interactive c<scp>ontent‐based</scp> image retrieval

Abstract Quantifying the value of explanations in a human‐in‐the‐loop (HITL) system is difficult. Previous methods either measure explanation‐specific values that do not correspond to user tasks and needs or poll users on how useful they find the explanations to be. In this work, we quantify how much explanations help the user through a utility‐based paradigm that measures change in task performance when using explanations vs not. Our chosen task is content‐based image retrieval (CBIR), which has well‐established baselines and performance metrics independent of explainability. We extend an existing HITL image retrieval system that incorporates user feedback with similarity‐based saliency maps (SBSM) that indicate to the user which parts of the retrieved images are most similar to the query image. The system helps the user understand what it is paying attention to through saliency maps, and the user helps the system understand their goal through saliency‐guided relevance feedback. Using the MS‐COCO dataset, a standard object detection and segmentation dataset, we conducted extensive, crowd‐sourced experiments validating that SBSM improves interactive image retrieval. Although the performance increase is modest in the general case, in more difficult cases such as cluttered scenes, using explanations yields an 6.5% increase in accuracy. To the best of our knowledge, this is the first large‐scale user study showing that visual saliency map explanations improve performance on a real‐world, interactive task. Our utility‐based evaluation paradigm is general and potentially applicable to any task for which explainability can be incorporated.
Hotel Employees’ Burnout and Turnover Intentions

This study was conducted to explore the employees’ burnout issues and the employees’ intention to leave in a selected hotel. A descriptive case study design was used to provide the framework for the research investigation and used as a guide in the data collection process, the development of instruments and scale, and the analysis of data or information. The findings indicated that there was a close association between employee burnout variable with the employee turnover intentions variable. Where appropriate, findings from previous research had been included to support or refute the results from this study. However, since the purpose of the study was to explore the employees’ situation at one hotel, the results had to be taken cautiously. Regardless, however, the study did highlight aspects that should be of concern to the hotel management since the condition of work, work scheduling and the matching of job with the employees’ capabilities had to be considered to minimize the burnout problems.
Precipitation and Temperature trend analysis by Mann Kendall test: The case of  Addis Ababa methodological station, Addis Ababa, Ethiopia

The impact of climate change on annual and monthly air precipitation and temperature has received a great deal of attention by scholars worldwide. This study focuses on detecting trends in annual precipitation and temperature for Addis Ababa methodological station located in Addis Ababa. SPSS in combination with the excel spreadsheet used for analyzing the statistics. We were also used the Mann-Kendall trend test and Sen’s slope method to detect trends and the magnitude of change. According to our analysis, the mean minimum temperature ranges between 8.45°C and 12.5°C, while, the mean maximum temperature varies between 21.1°C and 25.5°C From the analysis the monthly coefficient of variation (CV%) for both maximum and minimum temperature ranging from (5.4% in April) to (14.08% in December). According to the Mann Kendal test and Sen’s slope estimate, the annual maximum temperature shows a statistically significant increasing trend but no significant positive or negative trend was detected in annual perception as well minimum temperature.
Improved Binary JAYA algorithm-based scheduling dynamic cloud requests for Cloud-based computing

Since the cloud users are expanding, their voluminous amount of data, along with their demands, are also growing alarmingly. To tackle these rising demands, Cloud computing has been evolved as cutting-edge technology. Cloud computing felicitates an efficient task scheduling mechanism that can schedule the cloud requests on the most compatible heterogeneous virtual machines. Since the involvement of various scheduling parameters, task scheduling algorithms fall under an NP-hard problem. Therefore, a metaheuristic approach is implemented in this work to mitigate the aforementioned challenges. In this regard, this work proposes a nature-inspired improved variant of the JAYA (IJAYA) optimization algorithm for scheduling tasks on heterogeneous resources. A binary IJAYA is adopted in order to convert the continuous values into discrete values. The CloudSim is used as a simulation tool to conduct the experiments. To assess the effectiveness of the algorithm, a real-world dataset is taken into consideration. The proposed algorithm is compared over Bird Swarm Optimization (BSO), Particle Swarm Optimization (PSO), Salp Swarm Algorithm (SSA), and the standard JAYA for gauging the performance. The simulation results depict notable improvements for makespan, degree of imbalance (DOI), response time and resource utilization over other compared algorithms.
DynGraphTrans: Dynamic Graph Embedding via Modified Universal Transformer Networks for Financial Transaction Data

Dynamic graphs representation learning has gradually become a research trend, especially for unsupervised graph embedding learning for numerous graph analytic tasks such as node classification, graph mining and visualization, etc. In this paper, we propose a dynamic embedding method, DynGraphTrans, which leverage powerful modelling capability of universal transformer for temporal evolutionary patterns of financial transaction graph. Real-world transaction graphs are dynamic and continuously evolving over time. According to the characteristics of transaction data, DynGraphTrans computes account node representations by employing smoothed attention layer and time interval-aware relative position embedding mechanism. We demonstrate the advantages of the proposed dynamic network embedding by conducting empirical evaluations on a large synthetic transaction graph dataset for Anti-Money Laundering (AML) task.
MicroNets: A multi-phase pruning pipeline to deep ensemble learning in IoT devices

With the proliferation of Internet of Things (IoT) devices and the launch of 5G networks, there are more than 8 billion IoT devices worldwide generating sheer amounts of data causing a throughput burden on the global network. In such an environment, Deep Neural Networks (DNNs) are pushed towards the network’s edge to unleash the power of the data on edge devices. However, there are two main challenges: (1) DNNs are computationally expensive to run on resource-constrained IoT devices, and (2) IoT typically generate noisy data due to the surrounding environment causing DNN overfitting as it learns the noise along with the underlying patterns in data. To tackle this, we propose MicroNets, a multi-phase pruning framework to enable deep ensemble learning on edge devices. The experiments on Raspberry PIs show that MicroNets generates lightweight models, and utilise ensemble learning to outperform the predictability levels of a ResNet, CIFAR10CNN baseline models (up to 7%).
Organizing Classification of Application Logic Attacks in Component-based E-Commerce Systems

This research paper addresses the topic of application logic attack taxonomy that is due to unclear and incorrect implementation in component-based applications.The issue addresses the detection and classification of two separate types of vulnerabilities in component-based applications.The paper completes this aim through organising the classification of each attack and then proposes the classification of logical vulnerabilities and discusses the two distinct forms of weakness and coding faults in the application software found in the mid-level of the framework.The most important argument is to desegregate awareness of attack patterns with boundary profile status relevant to an application logic vulnerability and possible threats.Having review of two different types of attack taxonomies, a logical vulnerability classification based taxonomy is proposed.
Deep Supervised Descent Method With Multiple Seeds Generation for 3-D Tracking in Point Cloud

Three-dimensional (3-D) tracking in point cloud is a core competence of autonomous robots to perceive and forecast the environment. How to initialize bounding box seeds and optimize their position and orientation are very crucial for 3-D object tracking in point clouds. Nevertheless, existing methods mainly resort to developing a powerful classifier based on the initial bounding box seeds. In this article, we propose an end-to-end deep supervised descent method (SDM), which seamlessly integrates multiple seeds generation for the initialization of seeds and sequential updates for the estimation of accurate result. Specifically, we start with transforming the SDM iterative process into a trainable recurrent module. It explicitly learns a series of descent directions in the parameter space, to gradually optimize the initial seeds. Moreover, to alleviate drifting of this process, we initialize multiple seeds based on aggregated point sets generated by the deep Hough voting. Besides, a discrimination module is introduced to determine the bounding box with the highest score as the final result. Importantly, a specific multitask loss is proposed to train our model in an end-to-end way. Experiments on KITTI, PandaSet, and Waymo datasets show that our method could achieve significant improvements (up to 11.2% in success ratio) as compared to state-of-the-art trackers.
Retrieval Augmentation Reduces Hallucination in Conversation

Despite showing increasingly human-like conversational abilities, state-of-the-art dialogue models often suffer from factual incorrectness and hallucination of knowledge (Roller et al., 2020). In this work we explore the use of neural-retrieval-in-the-loop architectures - recently shown to be effective in open-domain QA (Lewis et al., 2020b; Izacard and Grave, 2020) - for knowledge-grounded dialogue, a task that is arguably more challenging as it requires querying based on complex multi-turn dialogue context and generating conversationally coherent responses. We study various types of architectures with multiple components - retrievers, rankers, and encoder-decoders - with the goal of maximizing knowledgeability while retaining conversational ability. We demonstrate that our best models obtain state-of-the-art performance on two knowledge-grounded conversational tasks. The models exhibit open-domain conversational capabilities, generalize effectively to scenarios not within the training data, and, as verified by human evaluations, substantially reduce the well-known problem of knowledge hallucination in state-of-the-art chatbots.
A Systematic Literature Review of Educational Apps: What Are They Up To?

As the use of Educational Apps rises every day and the population begins its use at an increasingly early age, it becomes relevant to understand the positive and negative aspects of this technological tool. However, the information may seem overwhelming, especially for those starting investigations on this topic. For this reason, a Systematic Literature Review was conducted on 119 published scientific papers, in order to create a work that synthesizes all the recent data about most used keywords, funding aid, authors and publishing journals, just to name a few. Additional data also reveals the need, expressed by authors and backed by their research, to evaluate the effectiveness of the Educational Apps. As an additional point, some solutions are offered to deal with the aforementioned problem, as well as some recommendations for the correct development of applications.
Past, Present, and Future of GMDH

Cybernetics was originally conceived by Norbert Wiener as the science of “control and communication in animals and machine” [1]. Rapid growth of cybernetics resulted in the development of many allied sciences. But in recent years, beginning in the late sixties, its outward development slowed and turned inward. Mathematical modeling was the new focus necessary for the solution of a great variety of society’s problems, including the optimal design of computers and computer networks. Cybernetics, indeed, had become the general and unifying science of the modeling of communications and control of complex systems.
Machine-in-the-Loop Rewriting for Creative Image Captioning

Machine-in-the-loop writing aims to build models that assist humans to accomplish their writing tasks more effectively.Prior work has found that providing users a machine-written draft or sentence-level continuations has limited success since the generated text tends to deviate from users' intention.To allow the user to retain control over the content, we train a rewriting model that, when prompted, modifies specified spans of text within the user's original draft to introduce descriptive and figurative elements in the text.We evaluate the model on its ability to collaborate with humans on the task of creative image captioning.On a user study through Amazon Mechanical Turk, our model is rated to be more helpful by users than a baseline infilling language model.In addition, third-party evaluation shows that users write more descriptive and figurative captions when collaborating with our model compared to completing the task alone.However, the improvement is not uniform across user groups: the model is more helpful to skilled users, which risks widening the gap between skilled and novice users, highlighting a need for careful, user-centric evaluation of interactive systems. 1
Mind the Gap!: Learning Missing Constraints from Annotated Conceptual Model Simulations

Conceptual modeling plays a fundamental role to capture information about complex business domains (e.g., finance, healthcare) and enables semantic interoperability. To fulfill their role, conceptual models must contain the exact set of constraints that represent the worldview of the relevant domain stakeholders. However, as empirical results show, modelers are subject to cognitive limitations and biases and, hence, in practice, they produce models that fall short in that respect. Moreover, the process of formally designing conceptual models is notoriously hard and requires expertise that modelers do not always have. This paper falls in the general area concerned with the development of artificial intelligence techniques for the enterprise. In particular, we propose an approach that leverages model finding and inductive logic programming (ILP) techniques. We aim to move towards supporting modelers in identifying domain constraints that are missing from their models, and thus improving their precision w.r.t. their intended worldviews. Firstly, we describe how to use the results produced by the application of model finding as input to an inductive learning process. Secondly, we test the approach with the goal of demonstrating its feasibility and illustrating some key design issues to be considered while using these techniques.