<strong>This research explores the use of mobile application in the leading transportation industry service in Indonesia. The purpose of study is to understand how the company delivers innovation with the mobile application to meet the needs of an ever-evolving market. According to James March, who launched the innovation theory, an organization can innovate through events that ultimately result in driven. This research was conducted qualitatively by interviewing parties who in charge in development in the taxi company being researched and 15 customers. The findings of this study are the theory of March and digital master applicable in the transportation business become one of the company’s drives from conventional to the technology mindset. The conclusion stated the reach event of the March was displayed in the organization studied through the right decision in technology disruption</strong>
This study focuses on the vulnerabilities and attack vectors connected with ransomware in Elastic Sky X integrated (ESXi) settings. We offer a novel technique to address these concerns by mimicking an ESXi environment, focusing on honeypot deployment and monitoring behaviours. Our strategy is creating a controlled emulation of ESXi in which we place honeypots to lure and capture ransomware activity. Furthermore, we use sophisticated monitoring methods to watch and evaluate ransomware behaviours in this simulated environment. Our approach’s effectiveness is tested using the simulated ESXi environment’s detection and response capabilities. The findings show that using honeypots in conjunction with careful behavioural monitoring can considerably improve the identification and mitigation of ransomware threats in virtualized environments.
Task-oriented dialogue (TOD) is a system that helps users achieve their goals. While the task is reviewed and improved regularly, a formal system for industrial standards has not yet been established. Dialogue state tracking is a sub-task that involves predicting current dialogue slot values given the conversation and in some cases, the slots that are being required or informed. Based on a well-documented schema with instructions for possible slots and intents along with their descriptions, schema-guided TOD exploits a concrete set of guidelines to add extra context and perform general zero-shot ability on state tracking. Despite having contextual schema descriptions, language models hardly keep up with a full TOD dialogue flow. The TOD system as a whole lacks the mechanics to detect out-of-scope events, decide when to query the database, and is hardly extensible for further processing. To address these issues, we propose a full TOD system designed to overcome the listed weaknesses. Additionally, we experiment with dialogue state tracking, the system’s first stage, and measure out-of-scope detection effectiveness via user-undefined actions.
With the rapid growth of online transactions and interactions, the threat landscape of scams and fraud has evolved, necessitating sophisticated detection mechanisms. This paper provides an extensive review of the latest advances in detecting online scams and fraud, covering technological solutions, machine learning techniques, and emerging trends in the field. Key methods discussed include advanced machine learning algorithms for anomaly detection, user behavior analytics, and the integration of threat intelligence. Additionally, the study highlights the role of public awareness and education in preventing scams, as well as the importance of international collaboration in law enforcement. By examining current trends and emerging technologies, this study provides strategies for organizations and individuals to enhance their digital security posture, effectively mitigating the risks associated with online scams and frauds.
In this paper, we have considered the coverage problem in wireless sensor network (WSN) on a convex subset of $R^2$. Sensors are dropped from the air randomly on some pre-fixed points, which is known as vertices, of Region of Interest (ROI). We use optimal partition of the ROI, which is actually partition in several regular hexagons. Since sensors are distributed randomly, a sensor may not be placed on the target vertex. For this reason, ROI will not be completely covered by a set of sensors. In practice, few more sensors are deployed on few (randomly chosen) vertices or used actuator (it can carry sensors to the proper vertex) to reduce the uncovered region or area. In one of our previous works, we have developed a strategy as follows: reduce the distance among two adjacent vertices and deployed one sensor on a vertex so that total number of sensors will be same as in existing old method (drop two sensors on some vertices and one sensor on the rest). We have compared the proportion of uncovered region using the commonly used old strategy with our previous one. We have simulated for several values of percentage of extra sensors and observed that our previous strategy is better for low standard deviation (s.d.), but not better for higher s.d. in both two and three-dimension. Inspiring from the above fact, in this paper, we combined above two strategies to find a general one, for deploying sensors in two-dimension. The excess sensors are divides in two parts. One part is used for decrease the side of the regular hexagon and other part is used for using one more sensor on some selected points. We simulate uncovered area and results indicate the optimal choice of these two parts, which change with the standard deviation of randomness.
<p><strong><em> The developments of artificial intelligence (AI) are growing along with its applications. This growth is so quick that it often surprises even researchers who had hypothised different times. Within the field of criminal profiling this is interesting because it can help to recognize errors and biases which are typical of humans [1]. Even though training AI to recognize emotions based on biometric parameters is becoming easier, the subsequent analyses are problematic. In fact, it is difficult to interpret biometric data which are also influenced by cultural and social factors. In terrorism analysis, for instance, the behaviors that are analyzed are different among the different groups or tribes. Therefore, the influence of social factors goes beyond the analysis of the complex neural responses [2-4]. Another element that plays a role is in the interpretation of emotions for the judicial system is represented by ethical and moral factors [5]. Artificial Intelligence cannot be used for reconstructing the origine of a crime [6] and only an expert’s opinion can be considered reliable [7]. Only an analysis based on the individual and aspects, and only the knowledge of the psychopathology, together with the scientific analysis of the non-verbal language can help reconstructing the origin and the dynamics of the crime [10-12]. In conclusion, even though AI offers an important support, since it can speed up some processes of the analysis, it currently cannot replace humans when it comes to profiling [13-14].</em></strong><em> </em><strong><em>In light of the chosen method, the analyses are ongoing, and the initial results indicate a trend toward greater reliability for profiling conducted by a human compared to that performed by AI. This is not due to the AI's capacity for emotional recognition but rather to the methodology employed by the AI. Humans respond to any sensory stimulation with an emotion, making any inference, reasoning, or behavioral choice closely dependent on the emotion experienced. In contrast, AI recognizes emotions through a process of analysis comparable to purely cognitive processes. Consequently, the capacity for emotional recognition through empathy is lacking. To guarantee the best possible analysis and limit the possibility of moral and ethical issues, it is extremely important for a human to oversee this process. AI can be used to recognize emotions based on biometric alterations, but it should not go further than that. Relying solely on its conclusions would be sterile and incomplete, and from a legal standpoint, could impact the admissibility of the analysis in court. </em></strong><br /> </p>
Phishing attacks are the most common cyber attacks nowadays. Phishing attacks rely on social engineering concepts. However, URLs are a fulcrum for phishing attacks. A web application is proposed to classify URLs based on the Random Forest model, and results with an accuracy of 98.2% are achieved.
The response time of Artificial Neural Network (ANN)-inference is of utmost importance in embedded applications, particularly continual stream-processing. Predictive maintenance applications require timely predictions of state changes. This study serves to enable the reader to estimate the response time of a given model based on the underlying platform, and emphasises the relevance of benchmarking generic ANN applications on edge devices. We analyse the influence of net parameters, activation functions as well as single- and multi-threading on execution times. Potential side effects such as tact rate variances or other hardware-related influences are being outlined and accounted for. The results underline the complexity of task-partitioning and scheduling strategies while emphasising the necessity of precise concertation of the parameters to achieve optimal performance on any platform. This study shows that cutting-edge frameworks don't necessarily perform the required concertations automatically for all configurations, which may negatively impact performance.
<p>Many engineering optimization problems may be rephrased in terms of equivalent binary problems, and these can be effectively tackled with Evolutionary Algorithms. Unfortunately, when dealing with antenna designs, the fitness function computation may be extremely time consuming and therefore it is of paramount importance to speed up the convergency and to improve the performances of this kind of algorithms. The recent introduction and the increasing availability of quantum computing may be very effective to accelerate the design process, even though new approaches and new algorithms are needed in order to exploit the specificity of these instruments. In this paper, a new version of a novel quantum crossover operator for binary Genetic Algorithm (bGA) has been introduced and compared with its previous version. It has been successfully tested on different mathematical benchmark functions and on a preliminary thinned array design.</p>
Scale up the large language models to store vast amounts of knowledge within their parameters incur higher costs and training times. Thus, in this study, we aim to examine the effects of language models enhancing external knowledge and compare the performance of extractive and abstractive generation tasks in building the question-answering system. To ensure consistency in our evaluations, we modified the MS MARCO and MASH-QA datasets by filtering irrelevant support documents and enhancing contextual relevance by mapping the input question to the closest supported documents in our database setup. Finally, we materiality assess the performance in the health domain, our experience presents a promising result not only with information retrieval but also with retrieval augmentation tasks aimed at improving performance for future work.
The Internet of Things (IoT) is a network that connects a vast number of objects, enabling them to communicate and interact each other with human intervention. The IoT is seeing rapid growth in the field of computing. However, it is important to acknowledge that IoT is very susceptible to many forms of assaults due to the hostile nature of the internet. In order to address this problem, it is necessary to implement practical steps to ensure the security of IoT networks, such as the implementation of network anomaly detection. While it is impossible to completely prevent assaults indefinitely, timely discovery of an attack is essential for effective defence. Because IoT devices have limited storage and processing power, standard high-end security solutions cannot protect them. In addition, IoT devices are now autonomously linked for extended durations. Consequently, it is necessary to create advanced network-based security solutions such as deep neural network solutions. While several research have focused on the use of neural network methods for attack detection, there has been less emphasis on detecting assaults especially in IoT networks. The objective of this research is to develop a Gradient Clipping Long Short-Term Memory network (GC-LSTM) that can efficiently and promptly identify IoT network assaults. The Bot-IoT dataset is employed for evaluating various detection methodologies. The incorporation of additional features resulted in improved results. The GC-LSTM model, as proposed, achieves a remarkable accuracy of 99.98%.
Delicate cloth simulations have long been desired in computer graphics. Various methods were proposed to improve engaged force interactions, collision handling, and numerical integrations. Deep learning has the potential to achieve fast and real-time simulation, but common neural network (NN) structures often demand many parameters to capture cloth dynamics. This paper proposes a physics-embedded learning framework that directly encodes physical features of cloth simulation. The convolutional neural network is used to represent spatial correlations of the mass-spring system, after which three branches are designed to learn linear, nonlinear, and time derivate features of cloth physics. The framework can also integrate with other external forces and collision handling through either traditional simulators or sub neural networks. The model is tested across different cloth animation cases, without training with new data. Agreement with baselines and predictive realism successfully validate its generalization ability. Inference efficiency of the proposed model also defeats traditional physics simulation. This framework is also designed to easily integrate with other visual refinement techniques like wrinkle carving, which leaves significant chances to incorporate prevailing macing learning techniques in 3D cloth amination.
