Reviewerly/exHarmony huggingface dataset:
User Identification on Smartphones with Motion Sensors and Touching Behaviors

The usage areas of smartphones are increasing day by day and users store different private and sensitive data on these devices. Therefore, the security of these devices, user identification and authentication on them are of critical importance. Traditional methods such as PIN, password and fingerprint used in user authentication can be exposed to various attacks and create security vulnerabilities. To address these issues, motion sensors and user biometrics are widely used to improve security mechanism in smart devices. In this paper, we show the feasibility of user identification using accelerometer sensor data on smartphones. For this purpose, we use accelerometer sensor data from 120 users, extract features to analyze differences in users&#x2019; smartphone interaction and identify users. For user identification, novelty detection and two-class classification algorithms are applied and their results are compared. As a result, it is show that motion sensors and touch behaviors can be used in user identification, 0.97 AUC (area under the curve) and 3.0% ERR (equal error rate) are obtained with proposed method.
A Study of Performance and Comparison of NoSQL Databases: MongoDB, Cassandra, and Redis Using YCSB

Background/Objectives: Relational databases are a commonly utilized technology that allows for the storage, administration, and retrieval of various data schemas.However, for certain big databases, executing queries can become a time-consuming and inefficient procedure.Furthermore, storing enormous volumes of data necessitates servers with greater capacity and scalability.Relational databases have limits when it comes to dealing with scalability for big amounts of data.On the other hand, non-relational database systems, often known as NoSQL, were created to better fulfill the demands of key-value storing of enormous volumes of records.However, there are several NoSQL options, and the majority have not yet been extensively compared.The goal of this research is to examine different NoSQL databases and evaluate their performance in terms of typical data storage and retrieval.Methods: In this study, we use the YCSB tool to measure the performance of three NoSQL databases: MongoDB, Cassandra, and Redis.We test six different workloads with 100000, 250000, 500000, 750000, and 1000000 operations.Our test was designed with five different operations, i.e., 100000, 250000, 500000, 750000, and 1000000, with six different workloads to see which database is most suitable for applications which use a large amount of data to process.Findings: MongoDB is a superior performing NoSQL database among Cassandra and Redis.The numerous optimizations used by the designers of NoSQL solutions to improve performance, such as good cache memory operation, have a direct impact on the execution time.In all workloads except workload D, MongoDB has significantly reduced latency across all operation counts.Novelty: We also measure the average latency of different workload scenarios that include a mix of read, write, and update activities.
Optimal Histopathological Magnification Factors for Deep Learning-Based Breast Cancer Prediction

Pathologists use histopathology to examine tissues or cells under a microscope to compare healthy and abnormal tissue structures. Differentiating benign from malignant tumors is the most critical aspect of cancer histopathology. Pathologists use a range of magnification factors, including 40x, 100x, 200x, and 400x, to identify abnormal tissue structures. It is a painful process because specialists must spend much time sitting and gazing into the microscope lenses. Hence, pathologists are more likely to make errors due to being overworked or fatigued. Automating cancer detection in histopathology is the best way to mitigate humans’ erroneous diagnostics. Multiple approaches in the literature suggest methods to automate the detection of breast cancer based on the use of histopathological images. This work performs a comprehensive analysis to identify which magnification factors, 40x, 100x, 200x, and 400x, induce higher prediction accuracy. This study found that training Convolutional Neural Networks (CNNs) on 200x and 400x magnification factors increased the prediction accuracy compared to training on 40x and 100x. More specifically, this study finds that the CNN model performs better when trained on 200x than on 400x.
Multi-Label Code Smell Detection with Hybrid Model based on Deep Learning

smell is an indicator of potential problems in a software design that have a negative impact on readability and maintainability.Hence, it is essential for developers to make out the code smell to get tips on code maintenance in time.Fortunately, many approaches like metric-based, heuristic-based, machine-learning based and deep-learning based have been proposed to detect code smells.However, existing methods, using the simple code representation to describe different code smells unilaterally, cannot efficiently extract enough rich information from source code.What is more, one code snippet often has several code smells at the same time and there is a lack of multilabel code smell detection based on deep learning.In this paper, we propose a hybrid model with multi-level code representation to further optimize the code smell detection.First, we parse the code into the abstract syntax tree(AST) with control and data flow edges and the graph convolution network is applied to get the prediction at the syntactic and semantic level.Then we use the bidirectional long-short term memory network with attention mechanism to analyze the code tokens at the token-level in the meanwhile.Finally we get the fusion prediction result of the models.Experimental results show that our model can perform outstanding not only in single code smell detection but also in multi-label code smell detection.
The Impact of Blockchain on Supply Chains: A systematic Review

Supply chains face many challenges around coordination, information asymmetry, quality assurance, complex disruptions, and traceability. Blockchain is arguably a technology that can address these challenges and make a significant impact. To shed light on the impact of blockchain, we undertake a cross-discipline systematic literature review on blockchain and supply chains. This review focused on identifying blockchain’s current and proposed impacts on the supply chain at three levels: organisational, inter-organisational, and industry. The findings identified twelve core supply chain themes across pre-implementation, post-implementation, and emerging tensions associated with adopting blockchain. These findings extend knowledge by going beyond understanding blockchain and its application and articulating multi-levels of impacts. Based on our review, we propose future research directions. By providing an overview of the current impact of blockchain, the review also offers insights to help managers to make informed decisions around the implementation and use of blockchain in supply chains.
Towards Tight Differential Bounds of Ascon

Being one of the winners of the CAESAR competition and a finalist of the ongoing NIST lightweight cryptography competition, the authenticated encryption with associated data algorithm Ascon has withstood extensive security evaluation. Despite the substantial cryptanalysis, the tightness on Ascon’s differential bounds is still not well-understood until very recently, at ToSC 2022, Erlacher et al. have proven lower bounds (not tight) on the number of differential and linear active Sboxes for 4 and 6 rounds. However, a tight bound for the minimum number of active Sboxes for 4 − 6 rounds is still not known.In this paper, we take a step towards solving the above tightness problem by efficiently utilizing both Satisfiability Modulo Theories (SMT) and Mixed Integer Linear Programming (MILP) based automated tools. Our first major contribution (using SMT) is the set of all valid configurations of active Sboxes (for e.g., 1, 3 and 11 active Sboxes at round 0, 1 and 2, respectively) up to 22 active Sboxes and partial sets for 23 to 32 active Sboxes for 3-round differential trails. We then prove that the weight (differential probability) of any 3-round differential trail is at least 40 by finding the minimum weights (using MILP) corresponding to each configuration till 19 active Sboxes. As a second contribution, for 4 rounds, we provide several necessary conditions (by extending 3 round trails) which may result in a differential trail with at most 44 active Sboxes. We find 5 new configurations for 44 active Sboxes and show that in total there are 9289 cases to check for feasibility in order to obtain the actual lower bound for 4 rounds. We also provide an estimate of the time complexity to solve these cases. Our third main contribution is the improvement in the 7-year old upper bound on active Sboxes for 4 and 5 rounds from 44 to 43 and from 78 to 72, respectively. Moreover, as a direct application of our approach, we find new 4-round linear trails with 43 active Sboxes and also a 5-round linear trail with squared correlation 2−184 while the previous best known linear trail has squared correlation 2−186. Finally, we provide the implementations of our SMT and MILP models, and actual trails to verify the correctness of results.
An Enhanced Scheme for Reducing the Complexity of Pointwise Convolutions in CNNs for Image Classification Based on Interleaved Grouped Filters without Divisibility Constraints

In image classification with Deep Convolutional Neural Networks (DCNNs), the number of parameters in pointwise convolutions rapidly grows due to the multiplication of the number of filters by the number of input channels that come from the previous layer. Existing studies demonstrated that a subnetwork can replace pointwise convolutional layers with significantly fewer parameters and fewer floating-point computations, while maintaining the learning capacity. In this paper, we propose an improved scheme for reducing the complexity of pointwise convolutions in DCNNs for image classification based on interleaved grouped filters without divisibility constraints. The proposed scheme utilizes grouped pointwise convolutions, in which each group processes a fraction of the input channels. It requires a number of channels per group as a hyperparameter Ch. The subnetwork of the proposed scheme contains two consecutive convolutional layers K and L, connected by an interleaving layer in the middle, and summed at the end. The number of groups of filters and filters per group for layers K and L is determined by exact divisions of the original number of input channels and filters by Ch. If the divisions were not exact, the original layer could not be substituted. In this paper, we refine the previous algorithm so that input channels are replicated and groups can have different numbers of filters to cope with non exact divisibility situations. Thus, the proposed scheme further reduces the number of floating-point computations (11%) and trainable parameters (10%) achieved by the previous method. We tested our optimization on an EfficientNet-B0 as a baseline architecture and made classification tests on the CIFAR-10, Colorectal Cancer Histology, and Malaria datasets. For each dataset, our optimization achieves a saving of 76%, 89%, and 91% of the number of trainable parameters of EfficientNet-B0, while keeping its test classification accuracy.
Integrated Internet of Things with cloud developed for data integrity problems on supply chain management

It is indeed an effort to ensure integrity of data for cloudbased Internet of Things (IoT) applications because of the inherently dynamic nature of IoT data. It is challenging to discover the integrity and authenticity of various sorts of transactions in the cloud environment as the amount of supply chain transactions grows. On massive supply chain management (SCM) data, the majority of conventional supply chain management methods or methodologies have become increasingly sophisticated. Also, the conventional integrity computation methods like the SHA family, MD5, Whirlpool, linear chaotic, etc … mostly rely on fixed random parameters with constrained hash and data sizes. To optimize these problems on the cloud-based supply chain databases, a hybrid integrity verification-based encoding and decoding technique is proposed. By interactively authenticating blockchains, the proposed method reduces IoT data integrity problems and strengthens the function of intermediary media serving as gateways. Experimental results showed that, in terms of integrity bit change, runtime, and encoding average runtime, the optimized supply chain data integrity model outperformed the traditional techniques.
Public Blockchain-based Lightweight Anonymous Authentication Platform Using Zk-SNARKs for Low-power IoT Devices

Anonymous authentication is an important factor for Internet of Things (loT) applications such as Smart Grids and Vehicle Ad hoc Networks. The zero-knowledge Succinct Non-interactive Arguments of Knowledge (zk-SNARK) is one of the popular methods that has been used for transacting anonymously in public blockchains. However, a heavy computational process is involved during the generation of zk-SNARK proofs (zk-SNARKs). This intensive computation is a significant barrier that prevents resource-constrained loT devices from implementing zk-SNARKs for anonymous transactions. Therefore, this paper proposes a public blockchain-based lightweight anonymous authentication platform for low-power loT devices using zk-SNARKs. A lightweight anonymous authentication protocol is designed using the SHA256 hash function. The heavy proving processes of the zk-SNARK protocol are offloaded to a powerful loT machine. A proof of concept has been designed to verify the proposed platform is capable to provide anonymous authentication with the help of zk-SNARKs and InterPlanetary File System. The security of the proposed platform is analysed and has been proved to be resistant to tracking, replay, key disclosure, and desynchronization attacks. The performance of the proposed platform is also evaluated and has been proven to be supported by low-power loT devices.
Experimental Analysis of Training Parameters Combination of ANN Backpropagation for Climate Classification

Artificial Neural Networks are widely used in prediction activities and classification processes. However, the implementation on average only uses a network architecture with one hidden layer, while the development of architectures with two or three hidden layers has not been done much. This article discusses the process of developing ANN Backpropagation using a Matlab-based graphical user interface with three hidden layers combined with non-linear activation functions (logsig, tansig, tanh) and training functions (trainrp and trainlm) based on learning rate and momentum. Architecture was created to study climate change in the area around Lombok International Airport station by training on hydrological data (rainfall) from January 2012 to December 2021 with a data type of 10-day interval (36 data every year). The number of neurons in the first hidden layer was determined using the Hecht-Nielsen model, while the second and third hidden layers used the Lawrence-Fredrickson model. Simulation results with architecture 36-73-37-19-1, a learning rate of 0.1, and momentum of 0.9 showed that variations in the activation function logsig-logsig-logsig-purelin and trainlm function demonstrated the best result with epoch of 7, MSE of 0.00090, and RMSE of 0.03011 in the training process and epoch of 5, MSE of 0.003758, and RMSE of 0.0613 in the data testing process. Furthermore, the prediction results demonstrated that a Q-value of 0.222 based on the Schmidt-Ferguson criteria obtained higher rainfall intensity information than previous years with climate category B (wet). Therefore, the government must be careful in determining policies related to field activities especially in agriculture because of climatic conditions with high rainfall.
How Do Illiterate People Interact with an Intelligent Voice Assistant?

The use of intelligent voice assistants is enabling people who previously could not easily interact with a graphical interface to have access to digital services. However recent works have shown that voice assistants fail to attend certain types of user's profile. Our research focus on the interaction to an IVA with people with illiterate people. An investigation was made into the use of Google Assistant by literate and illiterate people. In order to verify if the assistant can understand the commands spoken to it, an experiment was conducted with 45 people. The experiment indicates that two characteristics are essential to be improved in the IVA while interacting to illiterates: the ability to understand a more diverse and specific vocabulary of this audience, and an ability to understand the grammatical structure of sentences produced in an ad hoc manner by them. As a suggestion to minimize the problem, we think that AVI should be designed to include illiterate people, specially in developing economy countries, otherwise those assistants will be a factor to increase digital exclusion for poor populations.
Fast Temporal Graph Convolutional Model for Skeleton-Based Action Recognition

Human action recognition has a wide range of applications, including Ambient Intelligence systems and user assistance. Starting from the recognized actions performed by the user, a better human–computer interaction can be achieved, and improved assistance can be provided by social robots in real-time scenarios. In this context, the performance of the prediction system is a key aspect. The purpose of this paper is to introduce a neural network approach based on various types of convolutional layers that can achieve a good performance in recognizing actions but with a high inference speed. The experimental results show that our solution, based on a combination of graph convolutional networks (GCN) and temporal convolutional networks (TCN), is a suitable approach that reaches the proposed goal. In addition to the neural network model, we design a pipeline that contains two stages for obtaining relevant geometric features, data augmentation and data preprocessing, also contributing to an increased performance.
Learning with mobile devices - insights from a university setting in Ghana

Mobile learning is the newest and emerging technology embraced by universities and this seems to be fast establishing itself as the preferred mode of learning in many countries. However, ways in which mobile devices are used to meet learning goals seem under-researched and may require more effort from researchers, especially in the context of Africa. This empirical study sought to investigate the state of mobile usage among higher education students, their experiences, and available facilitating conditions that influence mobile learning in a Ghanaian university setting. A total of 222 students (distance and vacation students) were engaged to respond to the questionnaires employed for the study. Findings revealed that mobile learning exists in a somewhat structured form as the environment showed most of the salient characteristics of mobile learning but largely remained unharnessed. To a large extent, facilitating conditions for mobile learning were available; students showed positive experiences with their mobile devices and their use in accessing learning, but largely such learning did not occur via the deployed learner management system of the institution. Implications for policy formulation for the Institution and other similar ones that are striving to incorporate mobile technology to increase access and improve the quality of instruction delivery are discussed.
Visual Concept Programming: A Visual Analytics Approach to Injecting Human Intelligence At Scale

Data-centric AI has emerged as a new research area to systematically engineer the data to land AI models for real-world applications. As a core method for data-centric AI, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">data programming</i> helps experts inject domain knowledge into data and label data at scale using carefully designed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">labeling functions (e.g., heuristic rules, logistics)</i> . Though data programming has shown great success in the NLP domain, it is challenging to program image data because of a) the challenge to describe images using visual vocabulary without human annotations and b) lacking efficient tools for data programming of images. We present Visual Concept Programming, a first-of-its-kind visual analytics approach of using visual concepts to program image data at scale while requiring a few human efforts. Our approach is built upon three unique components. It first uses a self-supervised learning approach to learn visual representation at the pixel level and extract a dictionary of visual concepts from images without using any human annotations. The visual concepts serve as building blocks of labeling functions for experts to inject their domain knowledge. We then design interactive visualizations to explore and understand visual concepts and compose labeling functions with concepts <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">without writing code</i> . Finally, with the composed labeling functions, users can label the image data at scale and use the labeled data to refine the pixel-wise visual representation and concept quality. We evaluate the learned pixel-wise visual representation for the downstream task of semantic segmentation to show the effectiveness and usefulness of our approach. In addition, we demonstrate how our approach tackles real-world problems of image retrieval for autonomous driving.
Semantic Probability Distribution Modeling for Diverse Semantic Image Synthesis

Semantic image synthesis, translating semantic layouts to photo-realistic images, is a one-to-many mapping problem. Though impressive progress has been recently made, diverse semantic synthesis that can efficiently produce semantic-level or even instance-level multimodal results, still remains a challenge. In this article, we propose a novel diverse semantic image synthesis framework from the perspective of semantic class distributions, which naturally supports diverse generation at both semantics and instance level. We achieve this by modeling class-level conditional modulation parameters as continuous probability distributions instead of discrete values, and sampling per-instance modulation parameters through instance-adaptive stochastic sampling that is consistent across the network. Moreover, we propose prior noise remapping, through linear perturbation parameters encoded from paired references, to facilitate supervised training and exemplar-based instance style control at test time. To further extend the user interaction function of the proposed method, we also introduce sketches into the network. In addition, specially designed generator modules, Progressive Growing Module and Multi-Scale Refinement Module, can be used as a general module to improve the performance of complex scene generation. Extensive experiments on multiple datasets show that our method can achieve superior diversity and comparable quality compared to state-of-the-art methods. Codes are available at https://github.com/tzt101/INADE.git.