Reviewerly/exHarmony huggingface dataset:
AHEC: End-to-end Compiler Framework for Privacy-preserving Machine Learning Acceleration

Privacy-preserving machine learning (PPML) is driven by the emerging adoption of Machine Learning as a Service (MLaaS). In a typical MLaaS system, the end-user sends his personal data to the service provider and receives the corresponding prediction output. However, such interaction raises severe privacy concerns about both the user's proprietary data and the server's ML model. PPML integrates cryptographic primitives such as Multi-Party Computation (MPC) and/or Homomorphic Encryption (HE) into ML services to resolve the privacy issue. However, existing PPML solutions have not been widely deployed in practice since: (i) Privacy protection comes at the cost of additional computation and/or communication overhead; (ii) Adapting PPML to different front-end frameworks and back-end hardware incurs prohibitive engineering cost.We propose AHEC, the first automated, end-to-end HE compiler for efficient PPML inference. Leveraging the capability of Domain Specific Languages (DSLs), AHEC enables automated generation and optimization of HE kernels across diverse types of hardware platforms and ML frameworks. We perform extensive experiments to investigate the performance of AHEC from different abstraction levels: HE operations, HE-based ML kernels, and neural network layers. Empirical results corroborate that AHEC achieves superior runtime reduction compared to the state-of-the-art solutions built from static HE libraries.
Sentiment Detection in Economics Texts

Deriving intelligence from text is important as it can provide valuable information on how events influence public opinion. In this work, a classification task was done in order to obtain the sentiment behind the polarity of an economic text using machine learning and deep learning methods. We analyzed the text for keywords that can be categorized into positive, negative and neutral reviews and found more insights. In the final result of classifying three groups (positive, negative and neutral), the models were unable to perform up to 80% accuracy, where only one variant has the accuracy of 80% as the best on the test dataset.
Propagation Model Search for Graph Neural Networks.

This paper presents a novel neural architecture search (NAS) framework for graph neural networks (GNNs). We design an expressive search space that focuses on a common and critical component of GNNs -- propagation model. Specifically, we search for propagation matrices and the connections between propagation steps. Our search space covers various graph types, e.g., homogeneous graphs, heterogeneous graphs, and can be naturally extended to higher-dimensional recommender systems and spatial-temporal data. We propose a sampling-based one-shot NAS algorithm to search for appropriate propagation patterns efficiently. Extensive experiments in three different scenarios are used to evaluate the proposed framework. We show that the performance of the models obtained by our framework is better than state-of-the-art GNN methods. Furthermore, our framework can discover explainable meta-graphs in heterogeneous graphs.
Kinect-Based Badminton Motion Analysis Using Intelligent Adaptive Range of Movement Index

Badminton is a type of racquet sport that is played using racquets to strike a shuttlecock within a netted rectangular court, and it is one of the most popular sport in Malaysia. In this paper, the team expanded their previous adaptive novel lossless compact view invariant compression technique, named adaptive range of movement index (RoMI) by embedding an intelligent mapping algorithm. Badminton players are broadly classified into two handedness groups, which are left-handed players and right-handed players. The adaptive RoMI technique in the previous module is capable of identifying the labels of normalized RoMI and perform adaptive mapping for badminton players of different handedness. However, the mechanism requires manual judgement from humans to select the type of handedness. This technique will increase the error rate and effort of importing data properties with incorrect manual handedness input or data stream from different handedness datasets of badminton players. Thus, the proposed intelligent adaptive RoMI technique enables the analyzing system to outperform the previous module by detecting the labels of normalized RoMI automatically and by performing intelligent mapping. The motion analysis system will then become much more efficient, intelligent hence simplifying data collection and the handedness invariant benchmarking approach.
Research on Measurement Methods of Transferability between Different Domains in Transfer Learning

If transfer learning can be used in fault diagnosis of rotating machinery, the transferability between different fault domains should be measured first. A certain degree of similarity between the source domain and the target domain is the prerequisite for the effectiveness of the transfer learning method. In this paper, the transferability measurement results of mahalanobis distance, Kullback-Leibler Divergence and Maximum Mean Discrepancy (MMD) between data domains are compared and analyzed. And the stability and accuracy of mobility measurement results are compared and analyzed by using statistical analysis and anomaly detection. Finally, a case study is carried out using the gearbox data set to verify the effectiveness and superiority of the transferability measurement based on MMD method.
Data mining artificial intelligence technology for college english test framework and performance analysis system

This article first studies and designs the college English test framework and performance analysis system. The author analyzes a large number of data collected by the system in three dimensions: using data mining title association models, using machine learning to merge college English score prediction models, and finally diagnosing on the basis of the sexual evaluation model, the author designed and implemented a test paper algorithm based on the association rules of the question type, and carried out relevant verification from the three aspects of test paper time, test question recommendation and improvement according to scores. Finally, according to the needs analysis, the author uses the diagnostic evaluation model and related test paper algorithm to design and implement the diagnostic evaluation model, which is added to the college English diagnostic practice system. It can be obtained through comparative experiments that the paper-based algorithm based on the diagnostic evaluation model proposed in this paper can effectively give better practice guidance and test question recommendation to the learner’s learning status and knowledge point problem obstacles, and can effectively improve learning. The achievements of the authors have broad application prospects and research value.
Semantic segmentation with Improved Edge Detail for Autonomous Vehicles

This paper proposes a direction convolution (DC) with direction information added to solve the semantic segmentation problem. In the existing semantic segmentation, the edge of the detected object is not clear. To solve this problem, the model was trained by adding edge information. Also, coord convolution is used to add pixel location information, and DC is proposed to detect the edge accurately. DC is a method to add information about eight directions around a pixel for the model to train affinity for neighboring pixels. Using the cityscape dataset, this paper’s experimental results show the usefulness of the proposed method.
Level set segmentation with shape prior knowledge using intrinsic rotation, translation and scaling alignment

Level set segmentation has been successfully used in several image applications. However, they perform poorly when applied to severely corrupted images or when the object’s boundaries are blurred or occluded. Poor performance can be improved by introducing shape prior knowledge into the segmentation process by considering additional shape information from training examples. This can be achieved by adding a regularization term that penalizes shapes that differ from those learned from a training database. This regularizer must be invariant under translation, rotation and scaling transformations. Previous works have proposed coupling the curve evolution to a registration problem through an optimization procedure. This approach is slow and its results depend on how this optimization is implemented. An alternative approach introduced an intrinsic alignment, which normalizes each shape to be compared on a common coordinate system, avoiding the registration process. Nevertheless, the proposed intrinsic alignment considered only scaling and translation but not rotation, which is critical in several image applications. In this paper we present a new method to incorporate shape prior knowledge based on the intrinsic alignment approach, but extending it for scaling, translation and rotation invariance. Our approach uses a regularization term based on the eigenvalues and eigenvectors of the covariance matrix of each training shape, and this eigendecomposition dependency leads to a new set of evolution equations. We tested our regularizer, combined with Chan–Vese, in 2D and 3D synthetic and medical images, demonstrating the effectiveness of using shape priors with intrinsic scaling, translation and rotation alignment in different segmentation problems. • Level set performance can be improved by introducing shape prior knowledge. • Shape prior knowledge penalizes shapes that differ from the training shapes. • To avoid shape registration, we introduced intrinsic alignment for shape normalization. • Our intrinsic alignment considers scaling, translation and rotation invariance. • We tested our method in 2D and 3D synthetic and medical images.
Estratégias de ensino à distância para a educação interprofissional em Saúde frente à pandemia COVID-19

Objective: To report the experiences of remote work and distance education of a group from PET-Saude Interprofessionality in the pandemic. Method: This is an experience report of the activities developed by a group from PET-Saude, linked to the State University of Feira de Santana (UEFS) and the Diabetic and Hypertensive Care Center (CADH), from March to June of 2020. Face-to-face activities at UEFS and CADH have been replaced by remote and distance learning activities. The methodology used involved the incorporation of virtual tools in: 1) Seminars and distance education courses; 2) Establishment of innovation strategies for health activities; 3) Construction of Health Education materials. Results: The virtual educational tools allowed the realization of an innovative work, focused on the initial and continuous training of students, teachers and health professionals. Changes in the flow of patients, production of booklets and articles were also developed. Conclusion: The use of virtual platforms and distance education favored the planning of actions, providing gain of individual and collective knowledge, allowing changes in the service, guidance of patients and scientific production.
Predicting communication constructs towards determining information security policies compliance

Background: Ineffective communication using inappropriate channels and poor listening skills have resulted in poor compliance with information security (InfoSec) policies. Lack of compliance with InfoSec policies minimises employee proficiency whilst also exposing organisations to business risk. Objectives: This research addresses management’s concern regarding why employees do not comply with InfoSec policies and proposes that how policies are communicated is integral to compliance and that effective communication can serve to ameliorate compliance. Method: The research adopts communication theories from knowledge management, psychology and information systems to draw on important constructs which are then tested in order to identify those that can strongly predict InfoSec policy compliance. The research was quantitative and used a survey to elicit responses from a sample of 100 employees selected from 6 organisations. Results: Our findings suggest that of the 10 communication constructs used in the miscellany of perception and determinism (MPD) framework, half of these (five) constructs strongly predicated compliance, namely reasons for communication, media appropriateness, non-conflicting interpretations, feedback immediacy and personal focus . The rest of the constructs were weak predictors or could not predict compliance. Conclusion: The research advances InfoSec literature by adapting the MPD model as integral to the development, communication and importantly, compliance with InfoSec policies. The MPD model is pertinent as it aggregates theories of communication from a number of academic disciplines and underpinnings not considered before, thereby improving our understanding on how we communicate InfoSec policies for better compliance.
Soft-Biometrics Estimation In the Era of Facial Masks

We analyze the use of images from face parts to estimate soft-biometrics indicators. Partial face occlusion is common in unconstrained scenarios, and it has become mainstream during the COVID-19 pandemic due to the use of masks. Here, we apply existing pre-trained CNN architectures, proposed in the context of the ImageNet Large Scale Visual Recognition Challenge, to the tasks of gender, age, and ethnicity estimation. Experiments are done with 12007 images from the Labeled Faces in the Wild (LFW) database. We show that such off-the-shelf features can effectively estimate soft-biometrics indicators using only the ocular region. For completeness, we also evaluate images showing only the mouth region. In overall terms, the network providing the best accuracy only suffers accuracy drops of 2-4% when using the ocular region, in comparison to using the entire face. Our approach is also shown to outperform in several tasks two commercial off-the-shelf systems (COTS) that employ the whole face, even if we only use the eye or mouth regions.
Operationally Perfect Solar Power Forecasts: A Scalable Strategy to Lowest-Cost Firm Solar Power Generation

The SUNY solar irradiance forecast model is implemented in the SolarAnywhere platform. In this article, we evaluate its latest version and present a fully independent validation for climatically distinct individual US locations as well as one extended region. In addition to standard performance metrics such as mean absolute error or forecast skill, we apply a new operational metric that quantifies the lowest cost of operationally achieving perfect forecasts. This cost represents the amount of solar production curtailment and backup storage necessary to correct all over/under-prediction situations. This perfect forecast metric applies a recently developed algorithm to optimally transform intermittent renewable power generation into firm power generation with the optimal - least-cost - amount of curtailment and energy storage. We discuss how perfect forecast logistics can gradually evolve and scale up into firm solar power generation logistics, with the objective of cost-optimally displacing conventional [dispatchable] power generation.
Application of K-Means Clustering to Identify Similar Gene Expression Patterns during Erythroid Development

Erythropoiesis is the specific lineage in which the haematopoietic stem cells (HSC) differentiate into red blood cells.During their development, HSC undergo global gene expression changes to reflect the current developmental stage needs.A good way to identify the set of genes that have similar global expression patterns across the different developmental stages is through clustering.Unsupervised clustering aims at highlighting these co-regulated genes without prior knowledge regards their full interactions.In this study, we apply k-means clustering on a gene expression microarray data that measures the expression levels of human genes at four erythropoiesis stages.Eight clusters have been identified; one cluster, in particular, of 450 genes (C4) is more active toward the maturation stages and it is involved in cell division and DNA replication processes, which are vital during development.Another cluster of 234 genes (C7) is involved in autophagy (cells consumption/destruction), which is known to be involved in enucleation (expulsion of the nucleus from the cell).
Design Pattern Elicitation Framework for Proof of Integrity in Blockchain Applications

An emerging technology with a secure and a decentralized nature, blockchain has the potential to transform conventional practices in an efficient and dynamic manner. However, migrating to blockchain can be challenging due to the complexity of its infrastructure and processes. The complexity of building applications on blockchain has been highlighted by many studies, thus stressing the need to investigate practical solutions further. A commonly known software engineering concept, software design pattern contributes to the acceleration of software development. It offers a holistic reusable solution for commonly occurring problems in a given context. It helps to identify problems that occur repetitively and describes best practices to address them. The present study is one of the first investigations to inquire into design patterns for blockchain application. Seeking to reduce the complexity in understanding and building applications on blockchain, this paper identifies a design pattern elicitation framework from similar blockchain applications. Next, it provides a demonstration of the Proof of Integrity (PoI) pattern elicited from two different applications on the blockchain. The applicability of the pattern is evaluated by building a blockchain application to verify the integrity of the academic certificates and by explaining how this integrity has been achieved empirically.
The use of the Hello English application to improve junior high school students’ vocabulary and grammar mastery

Junior high school students are beginners in learning vocabulary and grammar for meaningful purposes. They need to understand the relationships among forms, meanings, and their uses to communicate either in spoken or written form. However, most junior high school students still do not understand how to construct a meaningful purpose. They just focus on remembering words and patterns. It is perhaps caused by their teachers’ teaching styles, which still use conventional methods. This can actually be improved by using a number of mobile applications. One of the applications that can be used in learning vocabulary and grammar in meaningful purposes is Hello English. It consists of learning materials, tests, and games. This article reviews previous studies on improving junior high school students’ vocabulary and grammar using Hello English. It aims to describe how Hello English can improve junior high school students’ vocabulary and grammar mastery.
Gaming - Hacking. When cheating leads to crime

There is a high chance that many of today's cyber criminals were introduced to hacking through modifying computer games. It's also likely that many ethical hackers, like bug bounty hunters, came into the craft the same way. Many young hackers today may have encountered a similar experience. Yet employing those coding skills against other people can get you into trouble. One post by the UK National Crime Agency warns: Booting someone offline whilst playing online games may seem like a harmless joke but is still illegal. Obviously, this gaming path doesn't always lead to life as a hacker. The impulse to start hacking other players can originate from simple annoyance and irritation.
Development of a Pseudo-Random Sequence Generation Function Based on the Cryptographic Algorithm "Kuznechik"

The paper deals with the main component of a deterministic random sequence generator - a generation function based on the algorithm "Kuznechik" (Russian encryption standard GOST R 34.12-2015). A procedure of round keys generation that uses the features of the algorithm has been developed. It allows to use the algorithm in a mode that combines the advantages of well-known OFB and CTR encryption modes. In addition, some enhancements of the algorithm that improve the generator security while maintaining its performance are implemented. The quality of the generator was evaluated by means of statistical tests (NIST SP 800-22), Pear-son's criterion, and min-entropy (NIST 800-90B tests). According to the analyzed characteristics, the enhanced generator and its ordinary version based on "Kuznechik" algorithm are comparable, but the proposed generation function has a higher security level.
Differentia: Visualizing Incremental Game Design Changes

Small changes in the code and assets of a videogame can have drastic impacts on the player experience. The primary way to discover these impacts is with extensive human playtesting. However, the time and effort expended for playtesting is costly enough that it cannot be conducted after every incremental build of a game. We propose an information visualization technique that approximately reifies a game’s space of interactivity as a static image. Considering incremental changes to the game, our visualizations can suggest what has been effectively added, preserved, or removed from the space by those changes. These visual summary reports would ideally be used in the continuous integration (CI) software development process. We demonstrate a prototype implementation of this visualization on two commercial games, considering both minor and major design changes.
Estimating a Mixing Distribution on the Sphere Using Predictive Recursion

Mixture models are commonly used when data show signs of heterogeneity and, often, it is important to estimate the distribution of the latent variable responsible for that heterogeneity. This is a common problem for data taking values in a Euclidean space, but the work on mixing distribution estimation based on directional data taking values on the unit sphere is limited. In this paper, we propose using the predictive recursion (PR) algorithm to solve for a mixture on a sphere. One key feature of PR is its computational efficiency. Moreover, compared to likelihood-based methods that only support finite mixing distribution estimates, PR is able to estimate a smooth mixing density. PR’s asymptotic consistency in spherical mixture models is established, and simulation results showcase its benefits compared to existing likelihood-based methods. Using PR we propose a method for goodness-of-fit testing and a clustering mechanism in the context of directional data with two real-data illustrations.
A New Bio-Inspired Algorithm Based on the Hunting Behavior of Cheetah

Soft computing is recognized as the fusion of methodologies mainly designed to model and formulate solutions to real-world problems that are too difficult to model mathematically. The grey wolf optimizer (GWO) algorithm is the recently proposed bio-inspired optimization algorithm that is mainly based on their foraging and hunting behavior. This GWO is proved as the recent and best in solving complex problems, but they too face some drawbacks of low solving precision, slow convergence, and bad local searching ability. In order to overcome the shortcomings of the existing algorithms, this paper is intended to propose a novel algorithm based on the foraging behavior of the cheetah. The cheetah is well known for their leadership hierarchy, decision making, and efficient communication capabilities between their teammates during group hunting. The famous benchmark functions such as unimodal and multimodal functions are being chosen as the testbed, and the experiments are performed on them. The proposed scheme outperforms in terms of computational time and optimal solution.