Reviewerly/exHarmony huggingface dataset:
Probabilistic Predictions with Federated Learning

Probabilistic predictions with machine learning are important in many applications. These are commonly done with Bayesian learning algorithms. However, Bayesian learning methods are computationally expensive in comparison with non-Bayesian methods. Furthermore, the data used to train these algorithms are often distributed over a large group of end devices. Federated learning can be applied in this setting in a communication-efficient and privacy-preserving manner but does not include predictive uncertainty. To represent predictive uncertainty in federated learning, our suggestion is to introduce uncertainty in the aggregation step of the algorithm by treating the set of local weights as a posterior distribution for the weights of the global model. We compare our approach to state-of-the-art Bayesian and non-Bayesian probabilistic learning algorithms. By applying proper scoring rules to evaluate the predictive distributions, we show that our approach can achieve similar performance as the benchmark would achieve in a non-distributed setting.
DOP: Off-Policy Multi-Agent Decomposed Policy Gradients

Multi-agent policy gradient (MAPG) methods recently witness vigorous progress. However, there is a significant performance discrepancy between MAPG methods and state-of-the-art multi-agent value-based approaches. In this paper, we investigate causes that hinder the performance of MAPG algorithms and present a multi-agent decomposed policy gradient method (DOP). This method introduces the idea of value function decomposition into the multi-agent actor-critic framework. Based on this idea, DOP supports efficient off-policy learning and addresses the issue of centralized-decentralized mismatch and credit assignment in both discrete and continuous action spaces. We formally show that DOP critics have sufficient representational capability to guarantee convergence. In addition, empirical evaluations on the StarCraft II micromanagement benchmark and multi-agent particle environments demonstrate that DOP significantly outperforms both state-of-the-art value-based and policy-based multi-agent reinforcement learning algorithms. Demonstrative videos are available at https://sites.google.com/view/dop-mapg/.
Improving Quantum Search on Simple Graphs by Pretty Good Structured Oracles

Quantum search algorithms provide a way to speed up combinatorial search, and have found several applications in modern quantum technology. In particular, spatial search on graphs, based on continuous-time quantum walks (CTQW), represents a promising platform for the implementation of quantum search in condensed matter systems. CTQW-based algorithms, however, work exactly on complete graphs, while they are known to perform poorly on realistic graphs with low connectivity. In this paper, we put forward an alternative search algorithm, based on structuring the oracle operator, which allows one to improve the localization properties of the walker by tuning only the on-site energies of the graph, i.e., without altering its topology. As such, the proposed algorithm is suitable for implementation in systems with low connectivity, e.g., rings of quantum dots or superconducting circuits. Oracle parameters are determined by Hamiltonian constraints, without the need for numerical optimization.
Multi-Class Anomaly Detection in Flight Data Using Semi-Supervised Explainable Deep Learning Model

The identification of precursors to safety incidents in aviation data is a crucial task, yet extremely challenging. The main approach in practice, leverages domain expertise to define expected tolerances in system behavior and flags exceedances from such safety margins. However, this approach is incapable of identifying unknown risks and vulnerabilities. Various machine-learning approaches have been investigated and deployed to identify anomalies, with the great challenge of procuring enough labeled data to achieve reliable and accurate performance. In this article, we present an explainable deep semi-supervised model for anomaly detection in aviation, building upon recent advancements described in the machine-learning literature. The proposed model combines feature engineering and classification in feature space, while leveraging all available data (labeled and unlabeled). Validating our approach with case studies of anomaly detection during the take-off and landing phases of commercial aircraft, we show that our model outperforms the state-of-the-art supervised anomaly-detection model and reaches significantly higher accuracy and fewer false alarms with only a small number of labeled data.
Optimization of Deep Learning Inference on Edge Devices

Concerning Artificial Intelligence (AI)-based applications, it is necessary to reduce latency in real-time inference. This paper implements and compares two separate models, Inception V3 and Mobilenet, using Intel Neural Compute Stick (NCS) 2 and Raspberry Pi 4 as the edge devices. The Model Optimizer (MO), which generates an Intermediate Representation (IR) of the network, is used for optimizing these models. Then, the IR models are inferences in the edge device. Finally, the comparison of frame per second speed (FPS) and precision is provided. The results show that the speed on Inception V3 is 9 frames per second, while that on Mobilenet is 24 frames per second. Simultaneously, the accuracy reaches 41.28% on Inception V3, but misclassifies for Nissan Altima 2014, and reaches 71.29% on Mobilenet with right classification for Toyota Camry 2014.
Weighted Gate Layer Autoencoders

A single dataset could hide a significant number of relationships among its feature set. Learning these relationships simultaneously avoids the time complexity associated with running the learning algorithm for every possible relationship, and affords the learner with an ability to recover missing data and substitute erroneous ones by using available data. In our previous research, we introduced the gate-layer autoencoders (GLAEs), which offer an architecture that enables a single model to approximate multiple relationships simultaneously. GLAE controls what an autoencoder learns in a time series by switching on and off certain input gates, thus, allowing and disallowing the data to flow through the network to increase network’s robustness. However, GLAE is limited to binary gates. In this article, we generalize the architecture to weighted gate layer autoencoders (WGLAE) through the addition of a weight layer to update the error according to which variables are more critical and to encourage the network to learn these variables. This new weight layer can also be used as an output gate and uses additional control parameters to afford the network with abilities to represent different models that can learn through gating the inputs. We compare the architecture against similar architectures in the literature and demonstrate that the proposed architecture produces more robust autoencoders with the ability to reconstruct both incomplete synthetic and real data with high accuracy.
Reform of College English Teaching Model under the Background of Artificial Intelligence

Abstract Computers have been used in teaching for more than 30 years. With the development and productization of artificial intelligence technology to promote the rapid development of education informatization, Artificial Intelligence Assisted Language Learning (AIALL) is also under continuous development. This has also promoted the reform of college English teaching model. Artificial intelligence based on big data has changed the organizational form of teaching, the form of teaching resources presentation, the form of teaching evaluation, and so on. Therefore, it is particularly important to explore the college English teaching model under the background of artificial intelligence.
QuicTor: Enhancing Tor for Real-Time Communication Using QUIC Transport Protocol

In the past decades, the internet has emerged as the fastest way to access information. However, this revolutionary information age comes with its own set of challenges. The privacy of Internet users is at increasing risk with the advances in surveillance techniques. Users' online behavior, activities, and even personal information are being tracked by ISPs and major tech companies. In response to the increasing need for preserving and protecting the privacy of online users, anonymity networks were developed. Tor anonymity network is a low-latency anonymity network that has gained quite a good reputation over the past years and is being adopted by thousands of users. With the great attention Tor's network is getting, the original design of Tor was proven to have performance limiting issues. With the motivation for addressing the performance limitation in Tor, we present QuicTor, a datagram-based design to solve Tor's transport-layer limiting issue. We evaluated the performance of QuicTor in comparison to vanilla Tor as well as other performance-enhancing proposals. QuicTor achieved significant performance improvements for interactive applications as well as streaming applications. Running Tor over a datagram-based protocol entails a careful security analysis. In this article, we assess the behavior of QuicTor under side-channel attacks aiming to de-anonymize Tor's clients. We show that the performance improvements brought by QuicTor do not reduce the anonymity of clients under the investigated types of attacks.
Using Recommender Systems for Matching Students with Suitable Specialization: An Exploratory Study at King Abdulaziz University

In Saudi Arabia, all high school graduates who want join local universities have to go through a preparatory year before selecting their specific specialization/major. One of the most concerning issues for those fresh undergraduate college students is the selection of their specialization. College specialization selection is critical for them, as their academic and career future will be affected by this decision. An un-suitable specialization selection will have unfortunate consequences, not only on the students' future but also on the university’s resources and budget. This paper sug-gests a solution to this problem by introducing a preliminary study of a recommend-er system (RS), which will recommend the appropriate specialization for the students based on various tests and grades during the preparatory year at King Abdulaziz University (KAU). The proposed system guides students through their specialization selection process based on their abilities. The collaborative filtering technique was used to build the RS and K-fold cross-validation was adopted to evaluate its accura-cy and performance. The results showed the prediction of a specialization for each student with good accuracy ratio. These promising initial results provide a feasible solution to assess this issue further in future studies.
Structural vector error correction modelling of Bitcoin price

Over the period from January 2011 to December 2019 we analyze the impact of shocks on the financial markets of emerging and developed countries on the price of Bitcoin using the structural vector error correction model. Results differ according to the duration and area selected. There is evidence of higher impact in short-term than in long-term. In the short term, bitcoin prices are positively influenced by Asian emerging countries and all countries areas, negatively by North America. In the long term, only all countries in Asia, the Pacific (negative) and Europe (positive) impact the price of bitcoin. Regardless of the duration, there is no influence of shocks in all other zones on the price of Bitcoin. The analysis of the health shock of COVID-19 and its impact on financial markets supports the results found. The results are potentially useful to academics, practitioners, and Bitcoin market participants to better facilitate risk-management-decisions.
ANOVA-SVM for Selecting Subset Features in Encrypted Internet Traffic Classification

Encryption technique is widely used in the internet network for protecting user privacy, maintaining the confidentiality of the data, avoiding firewall detection, and administrating the system.To prevent encryption techniques in malicious activities such as encrypting data that contains malware or viruses, illegal transactions like selling drugs, illegal weapons and fake documents, a company or institution uses encrypted internet traffic classification to analyze and identify the activity.A challenging problem in encrypted internet traffic classification is the massive amount of data in the dataset and the existence of many irrelevant features.In this research, we propose a technique by integrating the ANOVA algorithm with the wrapper method from LinearSVC in the SVM method to overcome this problem.The ANOVA algorithm is used to analyze the data's variance, and LinearSVC to calculate the relationship between each data to its decision boundary.A new technique is proposed by calculating the mean value of the distances to remove features, which are relatively far from the decision boundary.This technique is taken to isolate features from unused ones to be used for the next process.The experimental result shows that this proposed method can compete with the existing research method and reduce system detection time.In this case, we take some research as the baseline, including that with one-dimensional convolution neural networks, over-sampling and undersampling combination, inline and adaptive application, and FlowPic.
Effects on Time and Quality of Short Text Clustering during Real-Time Presentations

Technologies for live presentations should consider users' capabilities to manage large amounts of data in real-time, particularly, exchanges of short texts (e.g., phrases). This study examines the effects on time and quality of text clustering algorithms applied to short, medium, and long size texts, and examines whether short text clustering shows a reasonable performance for live presentations. We run several simulations in which we varied the number of phrases (from 5 to 200) contained in each text type (long, medium, and short) and the number of generated clusters (from 2 to 10). The algorithms used were snowball steamers, TF-IDF, and K-means for clustering; and the text types were Reuters, 20 NewsGroup and an experimental data set, for the long, medium, and short size texts, respectively. The first result showed that text size had a large effect on the algorithms execution time, with the shortest average time for the short texts and longer average time for the longest texts. The second result showed that the number of phrases in each text type significantly predicts execution time but not the number of clusters generated by K-means. Inertia and purity measures were used to test the quality of the clusters generated. Text size, number of phrases and number of clusters predict inertia; showing the lowest inertia for the short texts. Purity measures were like previously reported results for all text types. Thus, clustering algorithms for short texts can confidently be used in real-time presentations.
Evaluation of perceived benefits and employee satisfaction through knowledge management practices

Purpose This study aims to identify the key contributing factors of knowledge management (KM) practices with respect to an employee benefit. The research is designed to gain insight into the KM practices of Indian information technology (IT) firms and evaluate its effects on employee perceived benefit (EPB) and employee satisfaction (ES). Design/methodology/approach This study focuses on the cause and effect relationship between the variables. In this research study, a structured questionnaire is administered and accumulated responses from executives, software developers and knowledge managers from five IT firms with the help of google form. The sample size was 339 for this study. Confirmatory factor analysis and partial least square (PLS)-structural equation modeling have been used to analyze the data with the help of SMART-PLS software. Findings The well-fitted model describes the importance of KM practices with respect to increasing employee benefit and satisfaction. The hypothesized model is empirically investigated and it is also supported by the analysis. The KM practices have significant effects on EPBs. Employee benefits have a significant effect on ES. The result shows that the benefits of KM practices are more intended toward the sources of motivation where they can increase their capability with the help of KM practices. Research limitations/implications Benefit leads to the motivation and satisfaction of employees. The work itself can be a highly motivating dimension with the help of proper KM practices. The managers’ prime responsibility is to focus on collaborative work with regularizing the exchange of knowledge, where new knowledge can be created and, do better for the organization. Originality/value This research study offers a new direction of motivation to work. The employee can be highly motivated if he can be benefited from the work itself. This study gives an approach to satisfy the employee with the help of KM practices. Perceived benefit is considered as an essential element for this study.
hxtorch: PyTorch for BrainScaleS-2

We present software facilitating the usage of the BrainScaleS-2 analog neuromorphic hardware system as an inference accelerator for artificial neural networks. The hardware is transparently integrated into the PyTorch machine learning framework. In particular, we support vector-matrix multiplications and convolutions; corresponding software-based autograd functionality is provided for hardware-in-the-loop training. The software provides support for automatic partitioning and scheduling of neural networks onto one or multiple chips. We discuss the implementation including optimizations, analyze runtime overhead, measure performance and evaluate the results in terms of the hardware design limitations. As an application of the introduced framework, we present a model that classifies activities of daily living with smartphone sensor data.
Neural Structured Learning: Training Neural Networks with Structured Signals

We present Neural Structured Learning (NSL) in TensorFlow [1], a new learning paradigm to train neural networks by leveraging structured signals in addition to feature inputs. Structure can be explicit as represented by a graph, or implicit, either induced by adversarial perturbation or inferred using techniques like embedding learning. NSL is open-sourced as part of the TensorFlow [2] ecosystem and is widely used in Google across many products and services. In this tutorial, we provide an overview of the NSL framework including various libraries, tools, and APIs as well as demonstrate the practical use of NSL in different applications. The NSL website is hosted at www.tensorflow.org/neural_structured_learning, which includes details about the theoretical foundations of the technology, extensive API documentation, and hands-on tutorials.
A New Digital Signature Algorithm for Ensuring the Data Integrity in Cloud using Elliptic Curves

In this paper, we propose an Enhanced Digital Signature Algorithm (EDSA) for verifying the data integrity while storing the data in cloud database. The proposed EDSA is developed by using the Elliptic Curves that are generated by introducing an improved equation. Moreover, the proposed EDSA generates two elliptic curves by applying the upgraded equation in this work. These elliptic curve points were used as a public key which is used to perform the signing and verification processes. Moreover, a new base formula is also introduced for performing digital signature operations such as signing, verification and comparison. From the base formula, we have derived two new formulas for performing the signing process and verification process in EDSA. Finally, the proposed EDSA compares the resultant values of the signing and verification processes and it checks the document originality. The experimental results proved that the efficiency of the proposed EDSA in terms of key generation time, signing time and verification time by conducting various experiments
Investigation of Facilities for an M-learning Environment

The paper projected to study the field of m-learning focusing on investigating the facilities required to initiate an m-learning environment.Facilities and regular practices of conventional learning and e-learning was considered to find the potential facilities for m-learning environment.We used Integrated Tertiary Educational Supply Chain Model framework that stands on conventional education and illustrates the combined form of education supply chain and research supply chain model.Two surveys were conducted to collect data from students and teachers of higher education.The responses from both of the surveys have been presented and later compared with the findings from our studies of the existing learning environments.The significance of this research is in identifying the facilities for a learner and educator centric m-learning environment.
Versioning Data Is About More than Revisions: A Conceptual Framework and Proposed Principles

A dataset, small or big, is often changed to correct errors, apply new algorithms, or add new data (e.g., as part of a time series), etc. In addition, datasets might be bundled into collections, distributed in different encodings or mirrored onto different platforms. All these differences between versions of datasets need to be understood by researchers who want to cite the exact version of the dataset that was used to underpin their research. Failing to do so reduces the reproducibility of research results. Ambiguous identification of datasets also impacts researchers and data centres who are unable to gain recognition and credit for their contributions to the collection, creation, curation and publication of individual datasets. Although the means to identify datasets using persistent identifiers have been in place for more than a decade, systematic data versioning practices are currently not available. In this work, we analysed 39 use cases and current practices of data versioning across 33 organisations. We noticed that the term 'version' was used in a very general sense, extending beyond the more common understanding of 'version' to refer primarily to revisions and replacements. Using concepts developed in software versioning and the Functional Requirements for Bibliographic Records (FRBR) as a conceptual framework, we developed six foundational principles for versioning of datasets: Revision, Release, Granularity, Manifestation, Provenance and Citation. These six principles provide a high-level framework for guiding the consistent practice of data versioning and can also serve as guidance for data centres or data providers when setting up their own data revision and version protocols and procedures.
Online handwriting Arabic recognition system using k-nearest neighbors classifier and DCT features

With advances in machine learning techniques, handwriting recognition systems have gained a great deal of importance. Lately, the increasing popularity of handheld computers, digital notebooks, and smartphones give the field of online handwriting recognition more interest. In this paper, we propose an enhanced method for the recognition of Arabic handwriting words using a directions-based segmentation technique and discrete cosine transform (DCT) coefficients as structural features. The main contribution of this research was combining a total of 18 structural features which were extracted by DCT coefficients and using the k-nearest neighbors (KNN) classifier to classify the segmented characters based on the extracted features. A dataset is used to validate the proposed method consisting of 2500 words in total. The obtained average 99.10% accuracy in recognition of handwritten characters shows that the proposed approach, through its multiple phases, is efficient in separating, distinguishing, and classifying Arabic handwritten characters using the KNN classifier. The availability of an online dataset of Arabic handwriting words is the main issue in this field. However, the dataset used will be available for research via the website.