Reviewerly/exHarmony huggingface dataset:
Neuro-Symbolic Approaches for Text-Based Policy Learning

Text-Based Games (TBGs) have emerged as important testbeds for reinforcement learning (RL) in the natural language domain.Previous methods using LSTM-based action policies are uninterpretable and often overfit the training games showing poor performance to unseen test games.We present SymboLic Action policy for Textual Environments (SLATE), that learns interpretable action policy rules from symbolic abstractions of textual observations for improved generalization.We outline a method for end-to-end differentiable symbolic rule learning and show that such symbolic policies outperform previous stateof-the-art methods in text-based RL for the coin collector environment from 5 -10x fewer training games.Additionally, our method provides human-understandable policy rules that can be readily verified for their logical consistency and can be easily debugged.
Federated Learning Using Sparse-Adaptive Model Selection for Embedded Edge Computing

Federated learning (FL) is a distributed machine learning paradigm in which multiple clients collaboratively learn a global model without sharing local training data.This study presents an adaptive model selection algorithm to enhance existing aggregation for FL.The proposed method uses sparsely learned local models and adaptively selects the best models based on the entropy-inspired information using the neural nodes of fully connected layers.This enables substantial nodes to be included in federated averaging (FedAvg), unlike standard FedAvg.In the case of a client model (at an embedded edge cluster), we exploited sparse learning to absorb more features associated with new classes by retraining weak neural nodes.For global model averaging, we used the proposed model selection technique to enhance the performance of traditional FedAvg by up to 20% in terms of global accuracy in the same number of rounds, which, in turn, has increased the convergence in federated rounds.For local sparse learning models, we simulated clients at edge clusters using the Docker container on embedded boards (NVIDIA Jetson Series).To ensure the efficacy of our proposed technique, we rigorously tested our algorithm on various datasets (CIFAR 10, MNIST, OCS lab(driving), UCI-HAR) using different models for image classification (EfficientNet, MobileNetv2, ResNet18) and time-series classification (FCN-LSTM, DepthConv-LSTM), and compared our algorithm with several state-of-the-art methods, such as FedAvg and FedFS.The proposed method outperformed the others in terms of generalizability and assists in converging earlier than existing algorithms in federated rounds.
Text-to-Speech Recognition using Google API

Speech is the most natural mode of human communication.To enable machines to understand human speech, computers can act as an intermediary for human experts, allowing them to respond accurately and reliably to human voices.This can be accomplished by a text-to-speech recognition device, which allows a data processor to accurately interpret the language in which a message was written and translate it to an audio file that can be heard through a sound medium such as a speaker.This paper uses Python programming language to introduce a text-to-speech model to see whether the messages written are read.Using Google API, text-to-speech conversion was successful.
An Efficient Ciphertext Retrieval Scheme Based on Homomorphic Encryption for Multiple Data Owners in Hybrid Cloud

More and more individuals and enterprises outsource data and applications to cloud servers in recent years.Since the public cloud servers are not completely trusted, users usually encrypt important data before sending it to cloud servers.As a result, ciphertext retrieval technology has gradually become a research hotspot.In the existing related schemes, there are some defects such as not supporting "multiple owners" mode and multi-keyword retrieval, having low retrieval efficiency, accuracy and security, and difficult data updating.Hence, we propose an efficient Ciphertext Retrieval scheme based on Homomorphic encryption for Multiple data owners in hybrid cloud (CRHM), in which the public cloud server and the private cloud server cooperate to perform the ciphertext retrieval.In CRHM, an encrypted balanced binary index tree structure and a homomorphic encryption method based on large integer operations are designed to support "multiple owners" mode and multi-keyword ranked retrieval.The security analysis shows that CRHM can effectively guarantee the privacy and security of user file and retrieval, and the performance evaluation demonstrates that compared with the existing related schemes, CRHM has high efficiency in the index generation and retrieval processes, while keeps relatively high retrieval accuracy.
Scalable Font Reconstruction with Dual Latent Manifolds

We propose a deep generative model that performs typography analysis and font reconstruction by learning disentangled manifolds of both font style and character shape.Our approach enables us to massively scale up the number of character types we can effectively model compared to previous methods.Specifically, we infer separate latent variables representing character and font via a pair of inference networks which take as input sets of glyphs that either all share a character type, or belong to the same font.This design allows our model to generalize to characters that were not observed during training time, an important task in light of the relative sparsity of most fonts.We also put forward a new loss, adapted from prior work that measures likelihood using an adaptive distribution in a projected space, resulting in more natural images without requiring a discriminator.We evaluate on the task of font reconstruction over various datasets representing character types of many languages, and compare favorably to modern style transfer systems according to both automatic and manually-evaluated metrics.
Towards An Extension Of RBAC Model

SystemsRole-based access control (RBAC) has been widely used in information systems including so-called critical systems.Access control models describe the frameworks that dictate permissions.The RBAC model is generally static, ie., access control decisions are: grant or deny.This model is effective in normal situations.In other situations, such as exceptions or emergencies, flexible access control is required.In order to increase the flexibility of access control, the concept of obligation has been proposed.Obligations are requirements to be fulfilled in order to execute permission decisions.The purpose of this article is to produce a flexible model which uses obligations to manage exception situations.Our model increases the flexibility of the RBAC model.It allows to assign permissions dynamically.For illustration, Anderson's clinical information system is used.Finally, Alloy is used to analyze the validity of the proposed model.
Reducing Energy Consumption in Green Cloud Computing

Wide-ranging cloud data centres are required for the provision of numerous software solutions across a wide range of industry verticals.Power usage is expected to rise as cloud data centres ramp up operations and expand their footprints.Here, a single-objective optimization is accomplished through the application of a Simple Genetic Algorithm (SGA).The Artificial Neural Network (ANN) uses the genetic optimization strategy to estimate the existing resources based on their features and role characteristics, and Dynamic voltage frequency scaling (DVFS) has also been utilised to reduce energy usage.The multi-objective system is empowered to reduce energy consumption because of the overall recommended method.OpenStack simulation tools were used in this context to arrive at an accurate conclusion prior to implementing on the cloud.Open Stack provides a single computer node with the ability to model and simulate a data centre.Energy usage and virtual machine migration were used to evaluate the results of the project.
discopy: A Neural System for Shallow Discourse Parsing

This paper demonstrates discopy, a novel framework that makes it easy to design components for end-to-end shallow discourse parsing.For the purpose of demonstration, we implement recent neural approaches and integrate contextualized word embeddings to predict explicit and non-explicit discourse relations.Our proposed neural feature-free system performs competitively to systems presented at the latest Shared Task on Shallow Discourse Parsing.Finally, a web front end is shown that simplifies the inspection of annotated documents.The source code, documentation, and pretrained models are publicly accessible.
Conditional Poisson Stochastic Beams

Beam search is the default decoding strategy for many sequence generation tasks in NLP.The set of approximate K-best items returned by the algorithm is a useful summary of the distribution for many applications; however, the candidates typically exhibit high overlap and may give a highly biased estimate for expectations under our model.These problems can be addressed by instead using stochastic decoding strategies.In this work, we propose a new method for turning beam search into a stochastic process: Conditional Poisson stochastic beam search.Rather than taking the maximizing set at each iteration, we sample K candidates without replacement according to the conditional Poisson sampling design.We view this as a more natural alternative to Kool et al. ( 2019)'s stochastic beam search (SBS).Furthermore, we show how samples generated under the CPSBS design can be used to build consistent estimators and sample diverse sets from sequence models.In our experiments, we observe CPSBS produces lower variance and more efficient estimators than SBS, even showing improvements in high entropy settings. 1
Classification, metallogenesis and exploration of silver deposits in Daxing’anling of Inner Mongolia and its adjacent areas

By the end of 2020, 83 silver deposits (or ore occurrences), including four super-large-scale deposits, nine large large-scale deposits, 33 medium-scale deposits and 37 small-scale deposits or ore occurrences, have been proved. The amount of silver metal exceeds 86000 t with average grade of 100 g/t, which makes Daxing’anling region one of the the most important silver ore belt in China. However, the metallogenic characteristics and metallogenesis need to be clarified. The silver deposits in the study area are classified into three main types, which are magmatic hydrothermal vein type, continental volcano-subvolcanic type and skarn type, respectively. The super large deposits include the Shuangjianzishan deposit (silver metal amount of 15214 t with average grade of 138 g/t), the Baiyinchagandongshan deposit (silver metal amount of 9446 t with average grade of 187 g/t), the Huaobaote deposit (silver metal amount of 6852 t with average grade of 170 g/t), and the Fuxingtun deposit (silver metal amount of 5240 t with average grade of 196 g/t). The silver deposits are mainly distributed in the central and south of the Daxing’anling area, and mainly formed in the Yanshanian period. The silver polymetallic deposits in the Daxinganling area are significantly controlled by regional faults and the junction zone of volcanic rock basins and their margins. The north-east trending deep faults are the most important ore-controlling structures in this area. The distribution of silver polymetallic deposits along the main faults is obvious, and the intersection area of multiple groups of faults often form important mine catchments. The Permian is the most important ore-bearing formation in this area, but some important silver polymetallic deposits occur in Mesozoic volcanic basins or pre-Mesozoic strata. The magmatic rocks related to mineralization are mainly intermediate acidic or acidic intrusions, intermediate acidic lavas, pyroclastic rocks, and small intrusions of ultra-shallow or shallow facies of the Yanshanian Period. The mineralization element combination is mainly determined by the elemental geochemical background of surrounding rocks or source layers. In addition, the type of deposit, the distance from the mineralization center, and the degree of differentiation of ore-forming rock mass are also important influence factors. The article analyzes the prospecting prospects of each silver deposit type in the study area, discusses the relationship between mineralization center and deep prospecting, and proposes that porphyry silver deposits should be paid attention to. In the prospecting and exploration of silver deposits, comprehensive evaluation and multi-target prospecting need to be strengthened because silver can coexist or be associated with a variety of metals.
Join Classifier of Type and Index Mutation on Lung Cancer DNA Using Sequential Labeling Model

The sequential labeling model is commonly used for time series or sequence data where each instance label is classified using previous instance label. In this work, a sequential labeling model is proposed as a new approach to detect the type and index mutations simultaneously, using DNA sequences from lung cancer study cases. The methods used are One Dimensional Convolutional Neural Network (1D-CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Bidirectional Gated Recurrent Unit (Bi-GRU). Each nucleotide in the patient’s DNA sequence is classified as either normal or with a certain type of mutation in which case, its index mutation is predicted. The mutation types detected are either substitution, insertion, deletion, or delins (deletion insertion) mutations. Based on the experiments that were conducted using <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EGFR</i> gene, BiLSTM and Bi-GRU displayed better performance and were more stable than 1D-CNN. Further tests were carried out on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TP53</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">KRAS</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CTNNB1</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SMARCA4</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CDKN2A</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PTPRD</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BRAF</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ERBB2</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PTPRT</i> gene. The proposed model reports F1-scores of 0.9596, and 0.9612 using Bi-GRU and BiLSTM, respectively. Based on the results the model can successfully detect the type and index mutations in the DNA sequence more accurately and faster without the need for other supporting data and tools, and does not require re-alignment to reference sequences. This will greatly facilitate the user in detecting type and index mutations faster by entering only the DNA sequence.
Special Issue Editorial:

The global pandemic has brought about fundamental changes in education. The abrupt closing of schools has disrupted the teaching and learning processes and presented challenges for schools worldwide. This Special Issue explores “digital inclusion” through the use of technology-facilitated learning platforms and modalities within the multicultural environment of schooling. It especially gives attention to cases that highlight the responses of parents, teachers, administrators, and students in countries that have the digital infrastructure and technological advancement and in those that do not in order to question the “digital divide” and the challenges and implications that this disparity brings to education.
Analysis onto the Evolving Cyber-Attack Trends during COVID-19 Pandemic

This report aims to study today's different types of cyber threats faced during COVID-19 Pandemic.Search terms such as "face masks", "corona virus" information sky rocketed during the last few months.This proof the target of Coronavirus related anxiety among users due to the pandemic which could be taken advantage by hackers in order to exploit their machines.It could also be seen 70% of organizations increased their cyber security spending, in an attempt to prevent, narrow down their vulnerabilities, shows the significance of this issue to be studied.Cyber threats during pandemic placed organizations in a financial stress, to their already affected cash flows.DDoS, Ransomware, Malware, Malicious domains were the most common attacks seen during this pandemic.Challenges faced by organizations due to cyber threats and factors (new technological applications such as video conferencing) contributing to increased vulnerability are critical issues.Importance of Cyber security consultations when implementing new applications to support WFH are essential.
Toolbox for Distance Estimation and Cluster Validation on Data With Missing Values

Missing data are unavoidable in the real-world application of unsupervised machine learning, and their nonoptimal processing may decrease the quality of data-driven models.Imputation is a common remedy for missing values, but directly estimating expected distances have also emerged.Because treatment of missing values is rarely considered in clustering related tasks and distance metrics have a central role both in clustering and cluster validation, we developed a new toolbox that provides a wide range of algorithms for data preprocessing, distance estimation, clustering, and cluster validation in the presence of missing values.All these are core elements in any comprehensive cluster analysis methodology.We describe the methodological background of the implemented algorithms and present multiple illustrations of their use.The experiments include validating distance estimation methods against selected reference methods and demonstrating the performance of internal cluster validation indices.The experimental results demonstrate the general usability of the toolbox for the straightforward realization of alternate data processing pipelines.Source code, data sets, results, and example macros are available on GitHub.https://github.com/markoniem/nanclustering_toolbox
Constructive Decision via Redundancy-Free Proof-Search

We give a constructive account of Kripke–Curry’s method which was used to establish the decidability of implicational relevance logic ( $$\mathbf{R}_{{\rightarrow }}$$ ). To sustain our approach, we mechanize this method in axiom-free Coq, abstracting away from the specific features of $$\mathbf{R}_{{\rightarrow }}$$ to keep only the essential ingredients of the technique. In particular we show how to replace Kripke/Dickson’s lemma by a constructive form of Ramsey’s theorem based on the notion of almost full relation. We also explain how to replace König’s lemma with an inductive form of Brouwer’s Fan theorem. We instantiate our abstract proof to get a constructive decision procedure for $$\mathbf{R}_{{\rightarrow }}$$ and discuss potential applications to other logical decidability problems.
Private Blockchain for Visitor Authentication and Access Control

This study applies the high data integrity that comes with blockchain technology towards authentication and access control for visitors of a physical facility. The use of smart contracts on an Ethereum based implementation of the blockchain allows for smart contract code to handle both access control and visitor authentication at scale. Javascript code executed off the blockchain enables the system to interact with and parse through the blockchain data. The proposed system is scalable, applies to multiple use cases, and mitigates issues a centralized approach faces.
K-Means and Fuzzy C-Means Optimization using Genetic Algorithm for Clustering Questions

The grouping of data can be used in the development strategy of an educational game application. The process of grouping data that initially behaved differently into several groups that now behaved more uniformly. As well as grouping the data on the difficulty level of the questions on the educational game question board. This grouping of questions is needed to get the dominant values ​​that will be the characteristics of each group of questions that exist. The clustering method is quite widely used to overcome problems related to data grouping. This clustering is a method of grouping based on the size of the proximity, the more accurate the cluster formed, the clearer the similarity of the difficulty level of the questions. Thus, educational game developers can determine the strategy for placing the existing questions more precisely. Many clustering methods can be used to group the data on this question, including K-Means and Fuzzy C-Means (FCM) which are then optimized using the Algorithm Genetics. From the results of the research conducted, optimization gives better results for clustering questions.
Ciphertext-Policy Attribute Based Encryption with Selectively-Hidden Access Policy

In conventional Ciphertext-Policy Attribute-Based Encryption (CP-ABE), the access policy appears in plaintext form that might reveal confidential user information and violate user privacy. CP-ABE with hidden access policies hides all attributes, but the computational burden increases due to the attribute hiding. In this paper, we present a Linear Secret Sharing Scheme (LSSS) access structure CP-ABE scheme that hides only sensitive attributes, rather than all attributes, in the access policy. We also provide an attribute selection method to choose these sensitive attributes and use an Attribute Bloom Filter (ABF) to hide them. Compared with the existing major CP-ABE schemes with hidden access policies, our proposed scheme is flexible in selecting attributes to hide. This scheme enhances the efficiency of policy hiding while still protecting policy privacy. Test results show that our approach is reasonable and feasible.
Breast Cancer Prediction Using Classification Techniques of Machine Learning

Abstract: Data analytics play vital roles in diagnosis and treatment in the health care sector. To enable practitioner decisionmaking, huge volumes of data should be processed with machine learning techniques to produce tools for prediction and classification Breast Cancer reports 1 million cases per year. We have proposed a prediction model, which is specifically designed for prediction of Breast Cancer using Machine learning algorithms Decision tree classifier, Naïve Bayes, SVM and KNearest Neighbour algorithms. The model predicts the type of tumour, the tumour can be benign (noncancerous) or malignant (cancerous) . The model uses supervised learning which is a machine learning concept where we provide dependent and independent columns to machine. It uses classification technique which predicts the type of tumour. Keywords: Cancer, Machine learning, Prediction, Data Visualization, SVM, Naïve Bayes, Classification.
Generalised Synchronisation for Continuous Time Reservoir Computers

In this paper we establish conditions under which a continuous time reservoir computer (for example a leaky integrator echo state network) admits a smooth generalised synchronisation f between between the source dynamics and reservoir dynamics. We discuss the connections between generalised synchronisation and the echo state property (ESP), and in particular discuss the relationship between multi-ESP and the existence of multiple synchronisation manifolds. In the special case of a linear reservoir computer, we derive a closed form expression for the generalised synchronisation f.