Recently, recommendation systems have actively integrated Transformers to capture real-time context. However, these systems often suffer from generalization imbalance, where predictions are biased toward popular (head) items due to the sparsity and v...
Conventional recommendation methods such as collaborative filtering cannot be applied when long-term user models are not available. In this paper, we propose two session-based recommendation methods for anonymous browsing in a generic e-commerce fram...
In this article, for the first time, a new mathematical model of the schemes for organizing a session of person–system interactions between the registration center server of the information system for critical use (ISCU) and the terminal of the perso...
This study aimed to develop a performance prediction model for speed climbing based on session-RPE (sRPE) and countermovement jump (CMJ), and to determine optimal pre-competition taper strategies. Training data were collected from 12 speed climbers o...
Session-based recommendation seeks to deliver personalized suggestions by decoding transient interaction sequences generated by anonymous users. Although graph neural networks have advanced this field by modeling pairwise item transitions, they funda...
Session-based recommendation (SBR) aims to predict the next item a user may interact with based on an anonymous session, playing a crucial role in real-time recommendation scenarios. However, existing SBR models struggle to effectively capture local...
Surface electromyogram (sEMG)-based gesture recognition has emerged as a promising avenue for developing intelligent prostheses for upper limb amputees. However, the temporal variations in sEMG have rendered recognition models less efficient than ant...
Rule-based inspection of web server logs cannot detect attacks it has not already been told to look for, and most anomaly-detection studies validate their methods on a single dataset, which risks overstating how well a method generalises to new traff...
Large language models (LLMs) have shown strong performance in open-domain dialogue, yet they continue to struggle with long-term multi-session conversations (MSC), particularly in non-English languages such as Korean. In this work, we present a compr...
Data security is of the utmost importance in the domain of real-time environmental monitoring systems, particularly when employing advanced context-aware intelligent visual analytics. This paper addresses a significant deficiency in the Global System...
This study addresses the side effect of the rapid development of the Internet, positioning botnets within digital ecosystems as a very serious potential threat to the Internet users. Malicious web robot might facilitate Web/data scraping, DDoS attack...
23 October 2021
According to specifications, flexible services for traffic management should be implemented within the 5G platform in order to improve its efficiency, which is and will remain an actual task. For the first time, the article presented here proposes a...
In the session-based recommendation algorithm, a better class of methods is to model the complex interaction relationship within the session as a graph structure, and then use the graph neural network to capture the deep features of the item from it....
Session-based recommendation aims to predict anonymous user actions. Many existing session recommendation models do not fully consider the impact of similar sessions on recommendation performance. Graph neural networks can better capture the conversi...
Session-based recommendation uses past clicks and interaction sequences from anonymous users to predict the next item most likely to be clicked. Predicting the user’s subsequent behavior in online transactions becomes a problem mainly due to th...
Session-based recommendations which aim to predict subsequent user–item interactions based on historical user behaviour during anonymous sessions can be challenging to carry out. Two main challenges need to be addressed and improved: (1) how do...
High-Intensity Interval Training (HIIT) is a time-efficient training method suggested to improve health and fitness for the clinical population, healthy subjects, and athletes. Many parameters can impact the difficulty of HIIT sessions. This study ai...
There is an important application value in assessing an operator’s mental pressure (MP) level in human–computer cooperative tasks through continuous asymmetric electroencephalogram (EEG) signals, which can help predict hidden risks. Due t...
This paper presents novel intraday session models for price forecasts (ISMPF models) for hourly price forecasting in the six intraday sessions of the Iberian electricity market (MIBEL) and the analysis of mean absolute percentage errors (MAPEs) obtai...
Large-scale offline evaluations of user–project interactions in recommendation systems are often biased due to inherent feedback loops. To address this, many studies have employed propensity scoring. In this work, we extend these methods to ses...
Electric vehicle (EV) charging stations have become prominent in electricity grids in the past few years. Their increased penetration introduces both challenges and opportunities; they contribute to increased load, but also offer flexibility potentia...
High dropout rates remain a significant challenge in Massive Open Online Courses (MOOCs), making early identification of at-risk students crucial. This study introduces a novel approach called In-Session Stacked Ensemble Learning for Dropout Predicti...
With the development of deep learning, the method of large-scale dialogue generation based on deep learning has received extensive attention. The current research has aimed to solve the problem of the quality of generated dialogue content, but has fa...
The increased demand for electricity is inevitable due to transport sector electrification. A major part of this demand is from electric vehicle (EV) charging on a large scale, which is now a growing concern for the grid power distribution system. Th...
The personalized recommendation system is a useful tool adopted by e-retailers to help consumers to find items in line with their preferences. Existing methods focus on learning user preferences from a user-item matrix or online reviews after purchas...
The energy industry is a leader of introduction and development of energy supply technologies from renewable energy sources. However, there are some disadvantages of these energy systems, namely, the low density and inconsistent nature of the energy...
Cyberbullying behaviors manifest uniquely in different regions, shaped strongly by local slang, dialectal expressions, and cultural context. Code-mixed Chinese–English colloquial language (Cantonese) is commonly used in Hong Kong, Macau, and pa...
Microservice-based systems evolve continuously through API changes, service splits and merges, dependency churn, and deployment-topology drift driven by DevOps practices. This rapid evolution erodes the assumptions encoded in formal models, test suit...
5 November 2020
Background: As adolescent suicide rates continue to rise, innovation in risk identification is warranted. Machine learning can identify suicidal individuals based on their language samples. This feasibility pilot was conducted to explore this technol...
In response to the challenge of low predictive accuracy in scenarios with limited data, we propose a few adjustable parameters prediction model based on lightweight prefix-tuning (FAP-Prefix). Prefix-tuning is an efficient fine-tuning method that onl...
Background: A Special Health Care Need (SHCN) is characterized by any type of physical, mental, sensorial, cognitive, emotional, or developmental condition that requires medical treatment, specialized services, or healthcare interventions. These cond...
Background/Objectives: Emotional intelligence (EI) is a modifiable psychological competency associated with athletic performance, yet controlled interventions targeting EI in youth sport remain scarce. Pre-session video observational modeling (VOM) i...
In 2008, the African Postharvest Losses Information Systems project (APHLIS, accessed on 6 September 2022) developed an algorithm for estimating the scale of cereal postharvest losses (PHLs). The relevant scientific literature and contextual informat...
Limited intra-session information has constrained the performance of early Graph Neural Network (GNN)-based session-based recommendation (SBR) models. To address this issue, researchers have increasingly incorporated inter-session information to impr...
Knowledge graph completion (KGC) is commonly studied under static settings, whereas real-world knowledge graphs evolve continuously with newly emerging entities, relations, and facts. In continual knowledge graph completion (CKGC), the model is requi...
Session-based recommendation, which aims to match user needs with rich resources based on anonymous sessions, nowadays plays a critical role in various online platforms (e.g., media streaming sites, search and e-commerce). Existing recommendation alg...
Aiming at the problems of the incomplete recommendation and sparsity of session data in session recommendation, a new multi-granularity and multi-interest contrast-enhanced hypergraph convolutional network (MGMI-CEHCN) model for session recommendatio...
Session-based recommendation aims to model a user’s intent and predict an item that the user may interact with in the next step based on an ongoing session. Existing session-based recommender systems mainly aim to model the sequential signals based o...
This paper investigates the effects of the repetitive block-wise training process on the classification accuracy for a code-modulated visual evoked potentials (cVEP)-based brain–computer interface (BCI). The cVEP-based BCIs are popular thanks t...
28 February 2021
A patient suffering from advanced chronic renal disease undergoes several dialysis sessions on different dates. Several clinical parameters are monitored during the different hours of any of these sessions. These parameters, together with the informa...
Communication network security has become increasingly vital in an era of rapidly developing technology, and protecting against unauthorized access is essential. This paper introduces a server-aided approach for secure key distribution to users parti...
The fusion modeling of intra-session item information representation and inter-session item transition pattern for session recommendation has shown performance advantages. However, existing research still suffers from the following challenges: (1) th...
Recently, personalized news recommendation systems have been widely used, which can achieve personalized news recommendations based on people’s different preferences, optimize the reading experience, and alleviate the problem of information ove...
Electroencephalography (EEG) offers a noninvasive, high-temporal-resolution modality for estimating mental workload. However, session-to-session variability limits the generalizability of workload classifiers, and few systematic cross-session evaluat...
Cross-session variability in neural signal statistics often leads to distribution shifts that substantially degrade the generalization performance of motor decoding models. To address this challenge, this study proposes a CNN-enhanced Transformer wit...
This paper evaluates the impact of inter-speaker and inter-session variability on the development of a silent speech interface (SSI) based on electromyographic (EMG) signals from the facial muscles. The final goal of the SSI is to provide a communica...
Boosting the sales of e-commerce services is guaranteed once users find more items matching their interests in a short amount of time. Consequently, recommendation systems have become a crucial part of any successful e-commerce service. Although vari...
The Internet of Things (IoT) is rapidly transforming our lives and work, enabling a wide range of emerging services and applications. However, as the scale of the IoT expands, its security issues are becoming increasingly prominent. Malicious actors...
To address the increasing complexity of web user behavior anomaly detection and the issue of missing semantic information caused by relying solely on features like request semantics or request sequences, this study proposes a multi-angle semantic fea...
Most studies on EEG-based biometry recognition report results based on signal databases, with a limited number of recorded EEG sessions using the same single EEG recording for both training and testing a proposed model. However, the EEG signal is hig...
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