Reviewerly/exHarmony huggingface dataset:
PoEx: Proof of Existence for Evil Twin Attack Prevention in Wi-Fi Personal Networks

Wi-Fi is the dominant infrastructure for last-mile internet access both in indoor and outdoor environments. The existing Wi-Fi infrastructure leverages to offer IoT services. Despite the growth and popularity of Wi-Fi, security is a significant concern associated with it. One such issue with the Wi-Fi network is Evil Twin Attack (ETA). In ETA, the adversary creates an Evil Twin AP (EAP), a copy of Legitimate Access Point (LAP), for the user to connect. Once the user connects to EAP, the adversary can perform various attacks. Enterprise networks can solve ETA with the help of certificate-based server authentication using 802.1X and Extensible Authentication Protocol. However, there is no proper solution to detect ETA in Wi-Fi personal networks. Latest Wi-Fi security algorithms, namely Wi-Fi Protected Access 3 (WPA3) and Opportunistic Wireless Encryption (OWE) also vulnerable to ETA. This paper proposes a novel Proof of Existence (PoEx) scheme, bringing network lifetime to Access Point (AP). Wi-Fi client devices will detect and prevent ETA using associated AP lifetime. We apply the proposed PoEx scheme on WPA3 and OWE and show the security improvements. Our experiment results show that lifetime forging is difficult due to an exponential number of computations (2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(N-1)</sup> ) required. The experimental result also shows negligible overhead (less than 2%) on throughput when the PoEx solution is enabled.
Obstacle Avoidance of a Point-Mass Robot using Feedforward Neural Network

Machine learning is presently acknowledged as a significant ingredient of research in many fields, including robotics. The use of robots to perform assorted tasks is evident in difficult, uncompromising, and hazardous spaces and sectors such as manufacturing, transportation, healthcare, landmines, mining, patrolling, disaster relief etc. For a robot to carry out its assigned task, it normally has to navigate safely without collisions to different locations, which also means understanding its working environment, collectively known as the robot navigation problem. This paper considers finding a solution using neural networks to the robot navigation problem, particularly the path planning problem that includes fixed obstacles. The objective of the path planning problem is to find a route to the final destination that is optimal and also collision-free. Different training algorithms and network structures are used to construct models that can predict a turning angle for the point-mass robot which will be used to avoid obstacles in the robot's path to the destination. This paper will present a comparative analysis of the performance of different feedforward neural network models. The results suggest that the feedforward neural network model with 10 neurons and Bayesian regularization performed the best. The model has been used to avoid obstacles in two different environments. The trajectories show that the robot has safely avoided obstacles in its path and reached the destination.
SwitchFlow

Accelerators, such as GPU, are a scarce resource in deep learning (DL). Effectively and efficiently sharing GPU leads to improved hardware utilization as well as user experiences, who may need to wait for hours to access GPU before a long training job is done. Spatial and temporal multitasking on GPU have been studied in the literature, but popular deep learning frameworks, such as Tensor-Flow and PyTorch, lack the support of GPU sharing among multiple DL models, which are typically represented as computation graphs, heavily optimized by underlying DL libraries, and run on a complex pipeline spanning CPU and GPU. Our study shows that GPU kernels, spawned from computation graphs, can barely execute simultaneously on a single GPU and time slicing may lead to low GPU utilization.
A Simulation System for Testing Robotic Navigation Based on CoppeliaSim and ROS

The navigation system is one of the most prominent and essential part of robotic autonomy. Along with more advanced navigation techniques are developed for autonomous robotics, it becomes more crucial to test the algorithms in simulation before applying them to real-world robotic platforms. This paper presents a combined simulation framework for testing robotic navigation algorithms, which is based on CoppeliaSim and ROS. An overview of each system is provided. Then, how the combined framework may be used for stand-alone simulated development is described in detail. Lastly, the robotic navigation experiment is conducted in a simulation scenario, where the robot is desired to navigate to a goal. The results of the experiment verify the effectiveness of the proposed simulation framework.
Bearing‐only underwater uncooperative target tracking for non‐Gaussian environment using fast particle filter

Bearing-only tracking of an underwater uncooperative target is essential to defend the sea territory. Considering the influences by uncertain underwater environment, the purpose of this work is to estimate 2-D locations and velocities of an interested underwater target for non-Gaussian environment. In this work, a fast particle filter (FPF) based on the traditional particle filter (PF) with novel jet transport (JT) technique is proposed to deal with this problem. Aiming to overcome the heavy computation burden of the traditional PF that limits most of its practical applications, the JT technique can dramatically reduce the computation time and complexity in the particle evolution process, which contributes huge computational complexities to the traditional PF. Then, the proposed FPF is tested through simulations in the 2-D underwater uncooperative target tracking scenario. Finally, the Monte Carlo simulation results demonstrate that the proposed FPF can track the underwater uncooperative target with the similar accuracies as the traditional PF but only occupies less than 20% of the computational resources.
A Mixture-of-Gaussians model for estimating the magic barrier of the recommender system

The rating data collected by the recommender system usually contains noise due to external factors such as human uncertainty and inconsistency. Such noise, usually modeled by a normal distribution, leads to a magic barrier (MGBR) to the recommender system. However, existing MGBR estimation approaches require a user-specified standard deviation of noise, or make strong assumptions about true ratings, or need additional information from experts or users. In this paper, we propose a Mixture of Gaussians (MoG) model without user intervention to handle this issue. First, the user uncertainties are modeled using MoG, which is a universal approximator for any continuous distribution. Second, we employ the expectation–maximization algorithm to determine the parameters of user uncertainty. Finally, the MGBR is computed by Bayesian formula with the parameters. Experimental results on four well-known datasets show that the MGBRs estimated by the new model are close to the results of the state-of-the-art algorithms.
Encoding Techniques for High-Cardinality Features and Ensemble Learners

This study evaluates the classification performance of five encoding techniques for high-cardinality categorical features. Encoding techniques are tested using popular bagging and boosting ensemble methods on the latest Medicare Part B fraud classification data set, where the healthcare procedure code feature includes 7,752 unique values. To provide an additional baseline, we also evaluate these encodings on the multilayer perceptron. One-hot encodings are compared to a baseline aggregated encoding that excludes the procedure code feature to determine if the categorical feature significantly affects performance. Next, LightGBM and CatBoost's built-in strategies for categorical feature handling are compared to Hcpcs2Vec embeddings, distributed representations of procedures that encode semantic similarities. Statistical tests show that the inclusion of the categorical feature significantly improves performance for all ensemble learners when a one-hot representation is not used. Results also show that the XGBoost learner with Hcpcs2Vec encodings perform best overall with an average AUC of 0.8715. Our comparison of diverse encoding techniques for the high-dimensional categorical feature makes this study a unique contribution in the areas of ensemble learning and healthcare fraud prediction.
Mid-air gestures for manipulation of multiple targets in the physical space: comparing the usability of two interaction models

The design of mid-air, accessory-free, gesture-based control of multiple targets (devices) in the physical space presents several challenges, including: (a) user control entails both addressing the device and providing input to it (either sequentially or simultaneously); (b) despite a potentially large number of devices, the gesture vocabulary must be kept small to allow for memorability; (c) the gesture set must include discrete gestures to avoid the phenomenon of Midas’ touch (accidental operations or false positive errors). We have addressed those challenges by introducing two interaction models: (a) the two-stage approach in which the user applies sequentially separate gestures for Address and then Command a device (A-t-C model), and (b) one-stage approach in which the user applies a two-handed gesture that simultaneously Addresses and Commands a device (A & C model). In this paper, we first describe the two interaction models and then we present their comparative usability evaluation. We have found that both interaction models present very high task success and very low false-positive and memorability errors. Furthermore, the A&C model is significantly more usable than A-t-C in task-time and false-negative errors (i.e. errors of no system response). These results are very encouraging for future applications of mid-air gesture control of multiple devices in the physical space.
NFRNet: A Deep Neural Network for Automatic Classification of Non-Functional Requirements

Non-functional requirements specify those qualities that software products must have in order to meet the user’s business requirements. The elicitation of these non-functional requirements requires expertise, experience, and domain knowledge, which is challenging and time-consuming for requirements engineers and developers. It would be very beneficial if the nonfunctional requirements can be automatically extracted from the requirements documentation to reduce the human efforts, time, and avoid the mental fatigue. In this paper, we present a novel deep neural network model called NFRNet to automatically extract non-functional requirements from software requirements documentation.
Real identity based access control technology under zero trust architecture

With the rapid development and application of emerging information technology, the traditional network security architecture is more and more difficult to support flexible dynamic and a wider range of business data access requirements. Zero trust technology can truly realize the aggregation of security and business by building an end-to-end dynamic new boundary based on identity, which puts forward a new direction for the upgrade and evolution of enterprise network security architecture. This paper mainly includes access control and identity authentication management functions. The goal of access control system is to ensure that legitimate and secure users can use the system normally, and then protect the security of enterprise network and server. The functions of the access control system include identifying the user&#x2019;s identity (legitimacy), evaluating the security characteristics (Security) of the user&#x2019;s machine, and taking corresponding response strategies.
dCSR: A Memory-Efficient Sparse Matrix Representation for Parallel Neural Network Inference

Reducing the memory footprint of neural networks is a crucial prerequisite for deploying them in small and low-cost embedded devices. Network parameters can often be reduced significantly through pruning. We discuss how to best represent the indexing overhead of sparse networks for the coming generation of Single Instruction, Multiple Data (SIMD)-capable microcontrollers. From this, we develop Delta-Compressed Storage Row (dCSR), a storage format for sparse matrices that allows for both low overhead storage and fast inference on embedded systems with wide SIMD units. We demonstrate our method on an ARM Cortex-M55 MCU prototype with M-Profile Vector Extension (MVE). A comparison of memory consumption and throughput shows that our method achieves competitive compression ratios and increases throughput over dense methods by up to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2.9\times$</tex> for sparse matrix-vector multiplication (SpMV)-based kernels and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1.06\times$</tex> for sparse matrix-matrix multiplication (SpMM). This is accomplished through handling the generation of index information directly in the SIMD unit, leading to an increase in effective memory bandwidth.
Data Transformation from SQL to NoSQL MongoDB Based on R Programming Language

Owing to their high availability and scalability, NoSQL databases are becoming more popular for Big data applications in web analytics and supporting large websites. Moreover, each NoSQL system has its API which does not support industry standards like SQL and JDBC, integrating these systems with other enterprise and reporting software takes more time. The main requirements of Big data and data analytics are transforming the data from SQL databases to NoSQL data structures to represent the data. In this work, we presented a method to transform the data from different types of SQL databases to the desired NoSQL database based on the R programming language. The proposed work is based on the R environment used to handle the data from the source system to the target databases and meet the data quality requirements in data transformation. The results confirmed that the development provided a good solution for the data transformation from SQL to NoSQL by taking into account the data quality requirements.
Towards a Common Testing Terminology for Software Engineering and Data Science Experts

Analytical quality assurance, especially testing, is an integral part of software-intensive system development. With the increased usage of Artificial Intelligence (AI) and Machine Learning (ML) as part of such systems, this becomes more difficult as well-understood software testing approaches cannot be applied directly to the AI-enabled parts of the system. The required adaptation of classical testing approaches and the development of new concepts for AI would benefit from a deeper understanding and exchange between AI and software engineering experts. We see the different terminologies used in the two communities as a major obstacle on this way. As we consider a mutual understanding of the testing terminology a key, this paper contributes a mapping between the most important concepts from classical software testing and AI testing. In the mapping, we highlight differences in the relevance and naming of the mapped concepts.
Image-based state tracking in Augmented Reality supported assembly operations

Visual tracking and holographic information representation techniques have become robust enough to support operators in complex tasks on the shop floor. This paper presents an approach for coupling AR-supported assembly task instructions with image-based state tracking, so as to assist the operators in product assembly operations. The developed system consists of a visualization platform for AR-supported assembly instructions, a state tracker that includes object recognition, localization and hand tracking, using deep neural networks, and a server that handles the data exchange between the two. The developed framework is applied and validated in an industrial use case.
Cross-domain Self-attentive Sequential Recommendations

Alharbi, Nawaf Caragea, DoinaSelf-attentive sequential recommendation (SR) systems have attracted significant interest, as they can model temporal information capturing users' preferences over time to produce accurate recommendations. However, data sparsity can hinder the performance of SR in single-domain tasks. Cross-domain recommendations (CDRs) have been presented to alleviate the sparsity issue by transferring knowledge across domains. A key challenge in CDR for sequential tasks is how to capture user's tastes based on previous sequential behaviors from multiple source domains and transfer knowledge into target domain(s). In this paper, we propose a cross-domain self-attentive sequential recommendation model (CD-SASRec). We apply self-attention layers over a sequence of source items to aggregate a user's representational source vector. Then, we fuse the aggregated vector into the item embedding in the target domain, while applying another self-attention layer. Thorough experiments on three large datasets extracted from Amazon dataset illustrate that CD-SASRec outperforms other state-of-the-art sequential recommendation models.
Evaluation of Machine Learning approaches for resource constrained IIoT devices

Resource-constrained devices such as sensors, industrial controllers, analyzers etc., mostly contain limited computational capacity and memory. They are largely deployed in all industries and have been generating a huge amount of data. This data is sent to the cloud servers where various Machine Learning (ML) algorithms are applied to perform the analysis or prediction as per the application. In this process, communication requires bandwidth and time. Since the data is sent into the network, the privacy of the data is not guaranteed. Cloud servers consume a huge amount of power. To reduce these cost factors, the machine learning models are compressed and optimized such that they can fit and run in small footprint devices. The Federated Learning (FL) approach at the edge device level promises to address the data privacy and bandwidth related issues. Since it is a decentralized learning method across a set of devices, the performance of the model also improves. This paper describes and evaluates the machine learning algorithms with various compression methods suitable for resource-constrained IIoT devices and federated learning approach, particularly for time series data applications. Simulation results show that FastGRNN algorithm gives the least model size compared to the traditional RNN algorithms for time series.
From Ideas to Expressed Needs: an Empirical Study on the Evolution of Requirements during Elicitation

Requirements are elicited from the customer and other stakeholders through an iterative process of interviews, prototyping, and other interactive sessions. Many communication phenomena may emerge in these early iterations, that lead initial ideas to be transformed, renegotiated, or reframed. Understanding how this process takes place can help in solving possible communication issues as well as their consequences. In this work, we perform an exploratory study of descriptive nature to understand in which way requirements get transformed from initial ideas into documented needs. To this end, we select 30 subjects that act as requirements analysts, and we perform a set of elicitation sessions with a fictional customer. The customer is required to study a sample requirements document for a system beforehand and to answer the questions of the analysts about the system. After the elicitation sessions, the analysts produce user stories for the system. These are compared with the original ones by two researchers to assess to which extent and in which way the initial requirements evolved throughout the interactive sessions. Our results show that between 30% and 38% of the produced user stories include content that can be fully traced to the initial ones, while the rest of the content is dedicated to new requirements. We also show what types of requirements are introduced through the elicitation process, and how they vary depending on the analyst. Our work contributes to theory in requirements engineering, with empirically grounded, quantitative data, concerning the impact of elicitation activities with respect to initial ideas.
Two-Branch Attention Learning for Fine-Grained Class Incremental Learning

As a long-standing research area, class incremental learning (CIL) aims to effectively learn a unified classifier along with the growth of the number of classes. Due to the small inter-class variances and large intra-class variances, fine-grained visual categorization (FGVC) as a challenging visual task has not attracted enough attention in CIL. Therefore, the localization of critical regions specialized for fine-grained object recognition plays a crucial role in FGVC. Additionally, it is important to learn fine-grained features from critical regions in fine-grained CIL for the recognition of new object classes. This paper designs a network architecture named two-branch attention learning network (TBAL-Net) for fine-grained CIL. TBAL-Net can localize critical regions and learn fine-grained feature representation by a lightweight attention module. An effective training framework is proposed for fine-grained CIL by integrating TBAL-Net into an effective CIL process. This framework is tested on three popular fine-grained object datasets, including CUB-200-2011, FGVC-Aircraft, and Stanford-Car. The comparative experimental results demonstrate that the proposed framework can achieve the state-of-the-art performance on the three fine-grained object datasets.
Research on the development of Fintech combined with AIoT

Fintech is an industry that uses a series of technological innovations such as cloud computing and big data to allow technology to serve finance and greatly improve financial efficiency. However, the Internet of Things technology connects all things to machines to realize information exchange. The effective integration of Fintech and AIoT will not only help improve finance and improve the efficiency of the financial industry, but also enable finance to better serve the real economy. This article aims to discuss the development status, prospects and development strategies of financial technology combined with Internet of Things technology. First, take "Industrial Bank, iFLYTEK and JD Finance jointly constructing AIoT finance" as an example to explore how financial technology companies use AIoT technologies to promote financial efficiency and service. Including the development status and development direction of two aspects of intelligent marketing and intelligent monitoring. Secondly, through situation analysis and political, economic, social, and technical analysis, explore the beneficial changes and difficulties faced by fintech companies after combining AIoT technology.
Gendered Artificial Intelligence in Libraries: Opportunities to Deconstruct Sexism and Gender Binarism

Historically, the majority of digital assistants, including chatbots, have been assigned names, voices, visual representations, and even “personalities” that are stereotypically feminine and reflect patriarchal ideology. This cross-sectional descriptive study of chatbots associated with large academic libraries in the United States found that there are few extant library chatbots, and in a major departure from trends, there are even fewer that are gendered. This is promising, in that it signals—whether intentionally or not—that the practices of creators and adopters are countering entrenched tendencies to typecast digital assistants as women, which may signal more feminist and gender-inclusive technology design to come.