Reviewerly/exHarmony huggingface dataset:
Hierarchical Policy Network with Multi-agent for Knowledge Graph Reasoning Based on Reinforcement Learning

Multi-hop reasoning on Knowledge Graphs (KGs) aims at inferring the triplets that not in the KGs to address the KGs incompleteness problem. Reinforcement learning (RL) methods, exploiting an agent that takes incremental steps by sampling a relation and entity (called an action) to extend its path, has yielded superior performance. Existing RL methods, however, cannot gracefully handle the large-scale action space problem in KGs, causing dimensional disaster. Hierarchical reinforcement learning is dedicated to decomposing a complex reinforcement learning problem into several sub-problems and solving them separately, which can achieve better results than directly solving the entire problem. Building on this, in this paper, we propose to divide the action selection process in each step into three stages: 1) selecting a pre-clustered relation cluster, 2) selecting a relation in the chosen relation cluster, and 3) selecting the tail entity of the relation selected by the previous stage. Each stage has an agent to determine the selection, which formulated a hierarchical policy network. Furthermore, for the environment representation of KGs, the existing methods simply concatenate the different parts (the embedding of start entity, current entity and query relation), which ignore the potential connections between different parts, so we propose a convolutional neural network structure based on inception network to better extract features of the environment and enhance the interaction across different parts of the environment. The experimental results on three datasets demonstrate the effectiveness of our proposed method.
“Men så skedde inte i Sverige” – om anklagens anatomi

Despite the fact that defense and apology have been studied thoroughly in rhetoric and communication research in recent decades, their counterpart; the accusation has received ­surprisingly limited ­attention from rhetorical criticism. This essay sets out to remedy parts of these shortcomings by suggesting to define the accusation as a situated act where somebody ascribes guilt to a group or individual. The definition ties the accusation to the representation of acts considered to be in violation of ­existing norms and the essay argues that the logic of narra­tive determines the forms and functions of an accusation. The essay tests the definition through close readings of accusations from the debate about the proper handling of the corona-virus, as this debate has played out in the Scandinavian countries during 2020. Two extreme examples of accusations – one situated in the ­formal legal system, one situated in an informal, private conversation – are briefly analyzed before the essay turns to its main case in the form of an article from Dagens Nyheter, in which Swedish scientists accuse the Swedish health authorities and the Swedish government for neglect
Equivariant Manifold Flows

Tractably modelling distributions over manifolds has long been an important goal in the natural sciences. Recent work has focused on developing general machine learning models to learn such distributions. However, for many applications these distributions must respect manifold symmetries -- a trait which most previous models disregard. In this paper, we lay the theoretical foundations for learning symmetry-invariant distributions on arbitrary manifolds via equivariant manifold flows. We demonstrate the utility of our approach by using it to learn gauge invariant densities over $SU(n)$ in the context of quantum field theory.
Parameters optimization based on neural network of practical wavelength division multiplexed decoy-state quantum key distribution

The integration of quantum key distribution (QKD) devices with the existing optical fiber networks is of great significance in reducing the deployment costs and saving fiber resources. Wavelength division multiplexing (WDM) is expected to be a desirable approach to fulfill this ultimate task. In this paper, we analyze the dominant noises in WDM-based QKD system and optimize the key parameters based on a modified model with 200 GHz channel spacing. Then, an appropriate decoy-state method is adopted to estimate the system performance considering statistical fluctuations. Finally, a three-layer artificial neural network is used to train and predict the optimal mean photon numbers within different situations. Our work provides a useful method for the parameters optimization of WDM-QKD system and accelerates the practical development of QKD that coexists with the current backbone fiber infrastructure.
Development of patient reported outcomes-based machine learning algorithm for the six-month mortality prediction in patients with advanced cancer.

273 Background: To date, studies of machine learning (ML) algorithms within oncology for mortality prediction have focused on structured electronic health record (EHR) data. Given the complex symptom burden of patients with advanced cancers, ML models may be better suited to identify patterns and interactions between symptom burden and outcomes compared to traditional statistical methods. To that end, in this study, we leverage the patient reported outcomes (PRO) data together with clinical EHR-based variables to assess the performance of ML algorithms to predict mortality in patients with advanced cancers. Methods: We randomly selected 689 patients with advanced cancer who had their first Palliative Care encounter between January 2012 and December 2017. 59 patients were lost to follow-up and were excluded from this analysis. The remaining cohort of 630 patients was split 4:1 randomly into a training and validation set to develop and test a supervised ML algorithm (Extreme Gradient Boosting [XGB] tree) to predict the 6-month mortality. Candidate variables for algorithm development included gender, age, ECOG performance status (PS), number of prior systemic therapies, and scores on the Edmonton Symptom Assessment System (ESAS)-FS, a 12-item PRO measure of physical and psychosocial symptom burden include the composite Physical Symptom Score (PHS), a sum of the physical ESAS symptoms (pain, fatigue, nausea, drowsiness, shortness of breath, appetite, wellbeing, sleep). Results: Overall, 630 patients were included in this 6-month mortality prediction; mean age 59 years, 354 (56%) female; 276 (44%) male. Variables with the most significant impact on the XGB tree mortality prediction were the ESAS symptoms of shortness of breath (1-AUC, 0.295), appetite, ESAS PHS, financial distress, age, and appetite as well as ECOG PS and number of prior systemic therapies. The XGB tree algorithm demonstrated the best overall prediction performance of 6-month mortality in the independent testing set, AUC 0.716 (95% CI 0.63 - 0.81), sensitivity 0.75 (95% CI 0.66 - 0.87), and a positive predictive value 0.67 (95% CI 0.57 - 0.79). Conclusions: Our ML model leveraged PRO-based assessment of symptom burden to correctly identify the majority of patients who died within 6 months. These models are uniquely positioned to not only automatically identify patients at high risk for short-term mortality but also the specific symptoms of concern for clinical intervention. Such models can be applied to available clinical and PRO data to facilitate clinical decision-making. Futures studies on improving model performance with the inclusion of interventions to modify symptom burden are in design.
Intelligent antiphishing framework to detect phishing scam: A hybrid classification approach

ABSTRACT In the inevitably hi‐tech universe of cybercrimes, one of the major still prevailing methods is the usage of the malicious URLs and creating a Phishing link to obtain user credentials from the people. This method is highly subtle and has more effect on people's lives as well as corporate loss. To identify the malicious URLs, the security community has listed blacklists and benign links online. Still, as the technology is being developed day by day, the attackers try to create new phishing URLs using new social engineering methods that could be easily forged into the user's account. To improve the generality of malicious URL detectors, machine‐learning techniques have been explored with increasing attention in recent years. This article addresses the detection of malicious URLs combining the intelligence of both the heuristic‐based method and the machine learning process. It has been found that there are many possibilities for detecting zero‐day attacks and spear‐phishing attacks when incorporating both lexical features and machine learning methods. Out of the six‐batch learning process analyzed, we implement a decision tree algorithm in our framework with 99.47% accuracy during evaluation. The true positive values gained in our proposed hybrid framework is 99.2% and indicate &lt;1% of the false‐positive values. The implementation shows a precision level higher than the previous model developed and by other antiphishing techniques. A high detection rate on zero‐day and spear‐phishing attacks and overall results reveal that our system outclasses the current approach to detecting phishing scams.
The Relationship between “C-Space”, “Heuristic Methods”, and “Sampling Based Planner”

Defining the collision-free C-space is crucial in robotics to find whether a robot can successfully perform a motion. However, the complexity of defining this space increases according to the robot’s degree of freedom and the number of obstacles. Heuristics techniques, such as Monte Carlo’s simulation, help developers address this problem and speed up the whole process. Many well-known motion planning algorithms, such as RRT, base their popularity on their ability to find sufficiently good representations of the collision-free C-space very quickly by exploiting heuristics methods, but this mathematical relationship is not highlighted in most textbooks and publications. Each book focuses the attention of the reader on C-space at the beginning, but this concept is left behind page after page. Moreover, even though heuristics methods are widely used to boost algorithms, they are never formalized as part of the Optimization techniques subject. The major goal of this chapter is to highlight the mathematical and intuitive relationship between C-space, heuristic methods, and sampling based planner.
Studying Squeeze-and-Excitation Used in CNN for Speaker Verification

In speaker verification, the extraction of voice representations is mainly based on the Residual Neural Network (ResNet) architecture. ResNet is built upon convolution layers which learn filters to capture local spatial patterns along all the input, then generate feature maps that jointly encode the spatial and channel information. Unfortunately, all feature maps in a convolution layer are learnt independently (the convolution layer does not exploit the dependencies between feature maps) and locally. This problem has first been tackled in image processing. A channel attention mechanism, called squeeze-and-excitation (SE), has recently been proposed in convolution layers and applied to speaker verification. This mechanism re-weights the information extracted across features maps. In this paper, we first propose an original qualitative study about the influence and the role of the SE mechanism applied to the speaker verification task at different stages of the ResNet, and then evaluate several SE architectures. We finally propose to improve the SE approach with a new pooling variant based on the concatenation of mean- and standard-deviation-pooling. Results showed that applying SE only on the first stages of the ResNet allows to better capture speaker information for the verification task, and that significant discrimination gains on Voxceleb1-E, Voxceleb1-H and SITW evaluation tasks have been noted using the proposed pooling variant.
Formation of project management skills in student joint activities in the Smartsheet service

The problem and the aim of the study. Project thinking, the ability to work in a team to solve a socially significant task, the application of the principles of self-management are among the high requirements that are made upon modern specialists applying for employment. In addition, these over-professional competencies are fundamental to the soft skills concept. The purpose of the article is to investigate the features of the formation of these popular skills with using Smartsheet tools as a digital resource that works in a mobile format and on a PC to form these skills. Research methods. Theoretical analysis and generalization of scientific literature are used to determine the importance of project activity skills for a sought-after specialist, and the need of using digital tools in project management. The technology of collaborative learning, methods of group work (comments, conversations, providing cards, project defense) are used to engineer prompt information interaction. The experiment involved 89 students of Vyatka State University studying in the direction of training 44.03.02 Psychological and pedagogical education (bachelor's degree). The software tool is the Smartsheet service (https://app.smartsheet.com/). The Pearson's chi-squared criterion (χ2) was used as a method of statistical processing of experimental data. Results. In the experimental group, students used Smartsheet tools to export data from ready-made tables, differentiate access rights and allocate resources, track comments, add files, team introspection, and defense projects. The assessment of the levels of development of project management skills was made and statistically significant differences in the qualitative changes that occurred in the pedagogical system were revealed, χ2observ. 2 &gt; χ2crit.0.05 (6.15 &gt; 5.99). In conclusion the features of organizing joint activities in the Smartsheet environment for the formation of project management skills are summarized: determining the minimum necessary range of tools for mandatory use, adding elements of mini-research, comprehending the content of the task and its development options, random dividing into teams, etc.
FPGA Implementation of Secure Block Creation Algorithm for Blockchain Technology

Blockchain technology is essential to secure storage, authenticate data, and protect information from being misused and exploitation. Traditional methods of securing data using cryptographic algorithms include hashing functions like SHA-0, SHA-1, which have limitations like excess computational time, collision attacks, scalability, backtracking to retrieve the original message, etc. Using a combination of RSA and SHA together allows us to create a block on an FPGA, which when combined with other blocks establishes an encrypted Blockchain, which overcomes such limitations. Synthesis and implementation of the encrypted block have been compared and analyzed on Virtex-4, Virtex-5, and Spartan-6 FPGA boards. Based on the resource requirement like the slice registers, LUT-FF pairs and memory, Virtex-5 was chosen. Complete security is achieved as the hashing process is irreversible and backtracking of data is not possible. Previous problems of strengthening security, backtracking, excessive memory usage, and zero collision attacks are addressed and solved.
Automatic solution for solar cell photo-current prediction using machine learning

In this paper, we discuss the prediction of future solar cell photo-current generated by the machine learning algorithm. For the selection of prediction methods, we compared and explored different prediction methods. Precision, MSE and MAE were used as models due to its adaptable and probabilistic methodology on model selection. This study uses machine learning algorithms as a research method that develops models for predicting solar cell photo-current. We create an electric current prediction model. In view of the models of machine learning algorithms for example, linear regression, Lasso regression, K Nearest Neighbors, decision tree and random forest, watch their order precision execution. In this point, we recommend a solar cell photocurrent prediction model for better information based on resistance assessment. These reviews show that the linear regression algorithm, given the precision, reliably outperforms alternative models in performing the solar cell photo-current prediction I ph
Comparative Study of Optimization Algorithm in Deep CNN-Based Model for Sign Language Recognition

The fundamental part of the neural network is the learning rate, and the strategy of adopting the learning process in a neural network is carried out using optimization algorithms or optimizers. This optimization algorithm helps us produce better results to the model by changing the parameters like bias and weights, i.e., it helps us maximize or minimize the error function and depends on the learnable parameters. In this paper, we examine how an End-to-End CNN model named ASLNET recognizes the alphabets of the American sign language using various optimizers such as Stochastic Gradient Descent (SGD), Root-Mean-Square propagation (RM-Sprop), Adaptive Gradient Algorithm (Adagrad), Adaptive Delta (Adadelta),Adaptive Moment Estimation (Adam), Adam with Nesterov Momentum (Nadam), LookAhead and Rectified Adam (RAdam). To avoid the overfitting issues, traditional data augmentation techniques are used to compare our model with data augmentation and without augmentation with these optimizers. Among these, LookAhead and RAdam are the most recently developed. The experiment is conducted on 2 NVIDIA TESLA P100 GPUs of batch size 64, and the investigation was based on benchmark ASL Finger Spelling dataset.
Transformation of higher education in the context of globalization: new formats and alternative models

Discussed is the situation of the Universities inclusion in the transformation processes of Russian higher education, which is forced to respond to global challenges and internal needs for systemic changes. The key features of the higher education development in Russia are determined and the contradictions between the traditional formats of education organization and new educational models are identified. The general trends and problem areas of the development of educational systems of modern universities are highlighted. The transformation experience of the leading Russian universities, introducing alternative formats into the educational, scientific, socio-cultural field of the university, is analyzed. The results obtained enable to comprehend in a new way the changes taking place at universities and to determine promising directions for the development of the educational system. The presented materials confirm the possibility of an effective variant of the development of the transformation processes of university education, different from those ones being realized at the leading universities sites, as well as the preliminary results of the first stage of the experiment implementation of engineering education transformation carried out at Don State Technical University (the project “School X”, the creation of the Institute of Emerging Technologies). As further prospects for the development of the experimental activities of the university, the implementation of the unique project “T-University”, built on the basis of the principle of individual educational technologies, allowing to implement a new approach to the logic, design and content of the curriculum, which includes several levels: the university minimum, the field of science and technology, “Majors” and “electives”.
Algorithms for Block Tridiagonal Systems: Stability Results for Generalized Kalman Smoothing

Abstract Block tridiagonal systems appear in classic Kalman smoothing problems, as well in generalized Kalman smoothing, where problems may have nonsmooth terms, singular covariance, constraints, nonlinear models, and unknown parameters. In this paper, first we interpret all the classic smoothing algorithms as different approaches to solve positive definite block tridiagonal linear systems. Then, we obtain new results on their numerical stability. Our outcomes apply to all systems with dynamic structure, informing both classic and modern inference for generalized Kalman smoothing.
Problem of Inconsistency and Default Consistency Rules

We investigate inconsistency problem in textual functional requirements specifications. We argue that some sources of inconsistency can be revealed during the very first steps of textual requirements analysis. In this paper, we focus on those facts and rules that domain experts find so obvious that they do not even mention them to the analysts during the discussions about the product to be constructed. However, what is very obvious for stakeholders may not be obvious for analysts. We call such rules default consistency rules. We argue that the lack of the default consistency rules leads to incompleteness in the requirements, and it causes incon- sistency with all its unpleasant consequences. In this contribution, we describe our approach to the problem of how the missing information can be both identified in the original requirements and found in external sources. We show a motivational example and explain our method.
A Brain-Inspired Approach for Collision-Free Movement Planning in the Small Operational Space

In a small operational space, e.g., mesoscale or microscale, we need to control movements carefully because of fragile objects. This article proposes a novel structure based on spiking neural networks to imitate the joint function of multiple brain regions in visual guiding in the small operational space and offers two channels to achieve collision-free movements. For the state sensation, we simulate the primary visual cortex to directly extract features from multiple input images and the high-level visual cortex to obtain the object distance, which is indirectly measurable, in the Cartesian coordinates. Our approach emulates the prefrontal cortex from two aspects: multiple liquid state machines to predict distances of the next several steps based on the preceding trajectory and a block-based excitation-inhibition feedforward network to plan movements considering the target and prediction. Responding to "too close" states needs rich temporal information, and we leverage a cerebellar network for the subconscious reaction. From the viewpoint of the inner pathway, they also form two channels. One channel starts from state extraction to attraction movement planning, both in the camera coordinates, behaving visual-servo control. The other is the collision-avoidance channel, which calculates distances, predicts trajectories, and reacts to the repulsion, all in the Cartesian coordinates. We provide appropriate supervised signals for coarse training and apply reinforcement learning to modify synapses in accordance with reality. Simulation and experiment results validate the proposed method.