Reviewerly/exHarmony huggingface dataset:
Modeling Long- and Short-Term User Preferences via Self-Supervised Learning for Next POI Recommendation

With the accumulation of check-in data from location-based services, next Point-of-Interest (POI) recommendations are gaining increasing attention. It is well known that the spatio-temporal contextual information of user check-in behavior plays a crucial role in handling vital and inherent challenges in next POI recommendation, including capture of user dynamic preferences and the sparsity problem of check-in data. However, many studies either ignore or simply stack the context features with the embedding of POIs while relying only on POI recommendation loss to optimize the entire model, therefore failing to take full advantage of the potential information in contexts. Additionally, users’ interests are usually unstable and evolve over time, and accordingly recent studies have proposed various approaches to predict users’ next POIs by incorporating contextual information and modeling both their long- and short-term preferences, respectively. Yet many studies overemphasize the final POI recommendation performance, and the association between POI sequences and contextual information is not well embodied in data representations. In this article, we focus on the preceding problems and propose a unified attention framework for next POI recommendation by modeling users’ Long- and Short-term Preferences via Self-supervised Learning (LSPSL). Specifically, based on the self-attention network and two self-supervised optimization objectives, LSPSL first deeply exploits the intrinsic correlations between POI sequences and contextual information through pre-training, which strengthens data representations. Then, supported by pre-trained contextualized embeddings, LSPSL models and fuses users’ complex long- and short-term preferences in a unified way. Extensive experiments on real-world datasets demonstrate the superiority of our model compared with other state-of-the-art approaches.
DMBR-Net: deep multiple-resolution bilateral network for real-time and accurate semantic segmentation

Abstract It has been proved that the two-branch network architecture for real-time semantic segmentation is effectiveness. However, existing methods still can not obtain sufficient context information and sufficient detailed information, which limits the improvement of the accuracy of existed two-branch methods. In this paper, we proposed a real-time high-precision semantic segmentation network based on a novel multi-resolution feature fusion module, an auxiliary feature extracting module, an upsampling module and multi-ASPP(atrous spatial pyramid pooling) module. We designed a feature fusion module, which is integrated with sufficient features of different resolutions to help the network get both sufficient semantic information and sufficient detailed information. We also studied the effect of the side-branch architecture on the network, and made new discoveries that the role of the side-branch is more than regularization, it may either slow down the convergence or accelerate the convergence by influencing the gradient of different layers of the network, which is dependent on the parameters of the network and the input data. Based on the new discoveries about the side-branch architecture, we used a side-branch auxiliary feature extraction layer in the network to improve the performance of the network. We also designed an upsampling module, which can get better detailed information than the original upsampling module. In addition, we also re-considered the locations and number of atrous spatial pyramid pooling (ASPP) modules, and modified the network architecture according to the experimental results to further improve the performance of the network. We proposed a network based on the above study. We named this network Deep Multiple-resolution Bilateral Network for Real-time, referred to as DMBR-Net. The network proposed in the paper achieved 81.3% mIoU(Mean Intersection over Union) at 110FPS on the Cityscapes validation dataset, 80.7% mIoU at 104FPS on the CamVid test dataset, 32.2% mIoU at 78FPS on the COCO-Stuff test dataset.
Depth Image Enhancement Algorithm Based on Fractional Differentiation

Depth image enhancement techniques can help to improve image quality and facilitate computer vision tasks. Traditional image-enhancement methods, which are typically based on integer-order calculus, cannot exploit the textural information of an image, and their enhancement effect is limited. To solve this problem, fractional differentiation has been introduced as an innovative image-processing tool. It enables the flexible use of local and non-local information by taking into account the continuous changes between orders, thereby improving the enhancement effect. In this study, a fractional differential is applied in depth image enhancement and used to establish a novel algorithm, named the fractional differential-inverse-distance-weighted depth image enhancement method. Experiments are performed to verify the effectiveness and universality of the algorithm, revealing that it can effectively solve edge and hole interference and significantly enhance textural details. The effects of the order of fractional differentiation and number of iterations on the enhancement performance are examined, and the optimal parameters are obtained. The process data of depth image enhancement associated with the optimal number of iterations and fractional order are expected to facilitate depth image enhancement in actual scenarios.
Quantifying the Simulation–Reality Gap for Deep Learning-Based Drone Detection

The detection of drones or unmanned aerial vehicles is a crucial component in protecting safety-critical infrastructures and maintaining privacy for individuals and organizations. The widespread use of optical sensors for perimeter surveillance has made optical sensors a popular choice for data collection in the context of drone detection. However, efficiently processing the obtained sensor data poses a significant challenge. Even though deep learning-based object detection models have shown promising results, their effectiveness depends on large amounts of annotated training data, which is time consuming and resource intensive to acquire. Therefore, this work investigates the applicability of synthetically generated data obtained through physically realistic simulations based on three-dimensional environments for deep learning-based drone detection. Specifically, we introduce a novel three-dimensional simulation approach built on Unreal Engine and Microsoft AirSim for generating synthetic drone data. Furthermore, we quantify the respective simulation–reality gap and evaluate established techniques for mitigating this gap by systematically exploring different compositions of real and synthetic data. Additionally, we analyze the adaptation of the simulation setup as part of a feedback loop-based training strategy and highlight the benefits of a simulation-based training setup for image-based drone detection, compared to a training strategy relying exclusively on real-world data.
Designing accessible musical instruments by addressing musician-instrument relationships

This article explores the concept of intimacy in the relationship between a musician and their instrument, specifically in the context of designing digital and accessible musical interfaces (DMI/AMI) with disabled musicians. We argue that current DMI/AMI design frameworks are lacking in their consideration of this relationship and that this deficiency can prevent designers from understanding the specific needs and desires of disabled musicians. The paper presents an autoethnographic study of the lived experience of the first author, a disabled musician, to provide insight into the evolution of his musician-instrument relationships and his definition of “success” in this context. The authors propose that incorporating these types of lived experiences into the AMI design process, and considering cultural probes or provocations related to phenomenological experiences and characteristics that contribute to a successful musician-instrument relationship, could lead to more effective and tailored DMI/AMI designs with disabled musicians.
Everyday life information seeking behavior of transgender people in Pakistan

Purpose This study aims to investigate the everyday life information seeking behavior of transgender people in Pakistan. Design/methodology/approach A quantitative study, based on a survey design, was conducted to explore the everyday information needs of transgender people along with the types and frequency of using information sources. This study further explored the barriers to seeking everyday life information. Data were collected from 378 transgender people from Pakistan. Findings Music related information was the most important daily life information need and television appeared as one of the primary information sources frequently consulted by the transgender people. The respondents revealed a variety of challenges in accessing information including lack of education, lack of understanding about available information sources, biased treatment by the public and lack of technological skills. Furthermore, a statistically significant difference was found in everyday information needs and sources consulted on the basis of their age and education. Originality/value The findings provide a guideline to educate information providers, government agencies and other stakeholders about the information needs of this marginalized community in Pakistan. This study also suggests ways in which stakeholder may improve information systems and services to better assist transgender people.
A universal AutoScore framework to develop interpretable scoring systems for predicting common types of clinical outcomes

The AutoScore framework can automatically generate data-driven clinical scores in various clinical applications. Here, we present a protocol for developing clinical scoring systems for binary, survival, and ordinal outcomes using the open-source AutoScore package. We describe steps for package installation, detailed data processing and checking, and variable ranking. We then explain how to iterate through steps for variable selection, score generation, fine-tuning, and evaluation to generate understandable and explainable scoring systems using data-driven evidence and clinical knowledge. For complete details on the use and execution of this protocol, please refer to Xie et al. (2020),1 Xie et al. (2022)2, Saffari et al. (2022)3 and the online tutorial https://nliulab.github.io/AutoScore/.
Optimal trajectory planning of the industrial robot using hybrid S-curve-PSO approach

Purpose The paper aims to develop an efficient and compact hybrid S-curve-PSO (particle swarm optimization) controller for the optimal trajectory planning of industrial robots in the presence of obstacles, especially those used in pick-and-place operations. Design/methodology/approach The proposed methodology comprises a monotonic trajectory through bounded entropy of speed, velocity, acceleration and jerk. Thus, the robot’s trajectory planning corresponds with S-curve-PSO duality. This is achieved by dual navigation with minimal computational complexity. The matrix algebra-based computational complexity transforms the trajectory from random to compact. The linear programming problem represents the proposed robot in Euclidean space, and its optimal solution sets the corresponding optimal trajectory. Findings The proposed work ensures the efficient trajectory planning of the industrial robot in the presence of obstacles with optimized path length and time. The real-time and simulation analysis of the robot is presented for performance measurement, and their outcomes demonstrate a good correlation. Compared with the existing controller, it gives a noteworthy improvement in performance. Originality/value The novel S-curve-PSO hybrid approach is presented here, along with the LIDAR sensors, which generate the environment map and detect obstacles for autonomous trajectory planning. Based on the sensory information, the proposed approach generates the optimal trajectory by avoiding obstacles and minimizing the travel time, jerk, velocity and acceleration. The hybrid S-curve-PSO approach for optimal trajectory planning of the industrial robot in the presence of obstacles has not been presented by any researchers. This method considers the robot’s kinematics as well as its dynamics. The implementation of the PSO makes it computationally superior and faster. The selection of best-fit parameters by PSO assures the optimized trajectory in the presence of obstacles and uncertainty.
On challenges of AI to cognitive security and safety

Recent advances in deep learning have led to disruptive breakthroughs in artificial intelligence (AI), fueling the jump in ChatGPT-like large language models (LLMs). As with any emerging technology, it is a two-sided coin, bringing not only vast social impacts but also significant security concerns, especially in the socio-cognitive domain. Against this background, this work starts with an inherent mechanism analysis of cognitive domain games, from which it proceeds to explore the security concerns facing the cognitive domain as well as to analyze the formation mechanisms of a cognitive immune system. Finally, inspired by behavioral mimicry in biology, this work will elaborate on new approaches to cognitive security from three aspects: Mimicry Computing, Mimicry Defense, and Mimicry Intelligence.
Cybersecurity-Enhanced Encrypted Control System Using Keyed-Homomorphic Public Key Encryption

Encrypted control systems are secure control methods that use the cryptographic properties of a specific homomorphic encryption scheme. This study proposes a cyberattack-detectable encrypted control system and validates its effectiveness using a proportional integration derivative (PID) position-control system for an industrial motor. The proposed encrypted control system uses a keyed-homomorphic public-key encryption scheme for real-time detection of cyberattacks, such as signal and control parameter falsification. Additionally, a novel quantizer is presented to reduce the computation cost and quantization-error effects on control performance. The quantizer demonstrated a significant improvement, reducing the computation time by 47.3 % compared to using our previous quantizer, and decreasing the quantization-error effect by 30.6 % compared to a widely-used gain-multiplying quantizer. Moreover, this study establishes conditions through a theorem to avoid an overflow in the proposed control system. Experimental validation confirms that the proposed control system effectively conceals the control operation, and the presented theorem aids in designing the quantization gains to prevent overflows. Notably, the results of falsification attack tests highlight that the proposed control system enables real-time detection of attacked components within control parameters and signals, representing a significant advantage of this study.
Analyzing Resource Utilization in an HPC System: A Case Study of NERSC’s Perlmutter

Abstract Resource demands of HPC applications vary significantly. However, it is common for HPC systems to primarily assign resources on a per-node basis to prevent interference from co-located workloads. This gap between the coarse-grained resource allocation and the varying resource demands can lead to HPC resources being not fully utilized. In this study, we analyze the resource usage and application behavior of NERSC’s Perlmutter, a state-of-the-art open-science HPC system with both CPU-only and GPU-accelerated nodes. Our one-month usage analysis reveals that CPUs are commonly not fully utilized, especially for GPU-enabled jobs. Also, around 64% of both CPU and GPU-enabled jobs used 50% or less of the available host memory capacity. Additionally, about 50% of GPU-enabled jobs used up to 25% of the GPU memory, and the memory capacity was not fully utilized in some ways for all jobs. While our study comes early in Perlmutter’s lifetime thus policies and application workload may change, it provides valuable insights on performance characterization, application behavior, and motivates systems with more fine-grain resource allocation.
Diagnosing malaria with AI and image processing

This research seeks to investigate the possibility of using deep learning strategies in the process of diagnosing malaria, a virus that affects billions of people all over the world. Standard lab tests for malaria require the services of a qualified laboratory technician as well as an in-depth analysis of blood samples. This process can be expensive, time-consuming, and prone to errors caused by humans. This work attempts to enhance the accuracy of malaria diagnosis while also increasing the rate at which it can be performed by utilizing the capabilities of deep learning. We evaluate the performance of various methods for identifying the Plasmodium parasite in thin blood smear images by using deep learning models such as CNN, ResNet50, and VGG19 in accordance with noise reduction techniques and image segmentation methods. This allows us to compare the accuracy of the various methods. According to the findings of our research, the VGG19 model had the greatest overall performance. It had an accuracy of 0.9286 as well as a low false-positive and losing rate. The model is also tiny, making it easy to transport and use in a variety of contexts due to its portability. This study gives an overview of the current advancements in deep learning for malaria diagnosis. It also illustrates the potential for AI to increase both the accuracy and speed of malaria diagnosis.
Secure Credit or Debit Card Transaction Using Alert messages and OTP to prevent phishing attacks

In the digital world, the utilization of online transactions using credit and debit cards is growing every day. However, credit or debit (CD) card fraud is one of the prime challenges confronted by customers and bankers. The financial databases are stored at various cloud servers and these types of fraud make a huge loss in the financial systems as well as security concerns in the cloud. The cloud platform provides efficient and uninterrupted access to banking databases. There are distinct security initiatives that were taken to prevent fraudulent transactions, but still, hackers try different techniques to compromise the customer's account. Currently, for online Card payments, an OTP has been sent to the customer's phone number and email-id of a customer for security during the transaction. In this article, we introduce a new approach by sending an alert message before the OTP to the card owner whenever the credit or debit card number is entered by the customer or hacker for an online transaction.
Time series prediction of the COVID-19 outbreak in India using LSTM based deep learning models

The epidemic caused by COVID-19 presents a significant risk to the continuation of human civilisation and has already done irreparable damage to society. In this paper, forecasting of Coronavirus outbreak in India is performed by LSTM and CovnLSTM deep neural network techniques. COVID-19 data of confirmed cases of India is used. It was taken from John Hopkins University. The loss rate of ConvLSTM is lower than LSTM and RMSE of ConvLSTM is lower than LSTM. For training Covn-LSTM shows 0.069% and testing ConvLSTM shows 0.32% improvement over LSTM model. Therefore, ConvLSTM outperformed over LSTM model. Further wise selection of hyper-parameters could increase the accuracy of the models.
DRL-Driven Dynamic Resource Allocation for Task-Oriented Semantic Communication

Semantic communication has been regarded as a promising technology to serve upcoming intelligent applications. However, few studies have addressed the problem of resource allocation in semantic communication networks. Most resource allocation mechanisms act fairly to all original data, ignoring the meaning behind the transmitted bits. In this paper, a dynamic resource allocation scheme for the task-oriented semantic communication network (TOSCN) based on deep reinforcement learning (DRL) is proposed, which allows data with richer semantic information to preferentially occupy limited communication resources. This paper aims to design a deep deterministic policy gradient (DDPG) agent at the micro base station to maximize the long-term transmission efficiency of tasks. Firstly, the relationship between semantic information and task performance is investigated. Subsequently, a novel wireless resource allocation model for TOSCN is proposed by taking the image classification task as an example. Then, a joint optimization problem of the semantic compression ratio, transmit power, and bandwidth of each user is formulated. The agent is trained in an interactive learning environment to obtain a decent trade-off between the amount of data delivered to the receiver and the accuracy of intelligent tasks. Simulation results demonstrate that the proposed scheme achieves significant advantages in relieving communication pressure and improving task performance in resource-constrained wireless networks.
Evolutionary Algorithms With Blind Fitness Evaluation for Solving Optimization Problems With Only Fuzzy Fitness Information

Evolutionary algorithms (EAs) show strong adaptability in solving the complex optimization problems. Fitness evaluation is an important step of EA. In this step, the fitness of individuals is calculated by fitness evaluation methods. However, there are many complex optimization problems in which the existing fitness evaluation methods cannot express solutions’ fitness by crisp values but only fuzzy fitness information. Even if some fitness evaluation methods can predict crisp values to express the individuals’ fitness, these methods must “see” some crisp fitness information in advance. These shortcomings of existing fitness evaluation methods make EAs unable to work effectively in many optimization tasks. In this article, we propose the concept of blind fitness evaluation. A blind fitness evaluation method (BFEM) can output individuals’ crisp fitness information without “seeing” any crisp fitness information in advance. Based on the concept, we propose a BFEM which uses an artificial neural network, named modified fitness comparison network (MFCN) to evaluate the fitness of individuals. The MFCN can be trained by fuzzy fitness information, and the trained MFCN can predict the crisp fitness information of any individual. By applying the BFEM to EAs, the EAs with blind fitness evaluation are obtained and the algorithms can effectively solve the complex optimization problems with only fuzzy fitness information. The proposed BFEM is compared with several popular fitness evaluation methods that rely on crisp fitness information. Experimental results show that the fitness evaluation accuracy of MFCN trained based on only fuzzy fitness information is similar to or higher than that of the fitness evaluation methods relying on crisp fitness information. The EAs with MFCN also show higher performance than the EAs with the popular fitness evaluation methods in search ability and solution quality.