In the current digital age, the volume of data generated by various cyber activities has become enormous and is constantly increasing. The data may contain valuable insights that can be harnessed to improve cyber security measures. However, much of this data is unclassified and qualitative, which poses significant challenges to traditional analysis methods. Clustering facilitates the identification of hidden patterns and structures in data through grouping similar data points, which makes it simpler to identify and address threats. Clustering can be defined as a data mining (DM) approach, which uses similarity calculations for dividing a data set into several categories. Hierarchical, density-based, along with partitioning clustering algorithms are typical. The presented work use K-means algorithm, which is a popular clustering technique. Utilizing K-means algorithm, we worked with two different types of data: first, we gathered data with the use of XG-boost algorithm following completing the aggregation with K-means algorithm. Data was gathered utilizing Kali Linux environment, cicflowmeter traffic, and Putty Software tools with the use of diverse and simple attacks. The concept could assist in identifying new attack types, which are distinct from the known attacks, and labeling them based on the characteristics they will exhibit, as the dynamic nature regarding cyber threats means that new attack types often emerge, for which labeled data might not yet exist. The model counted the attacks and assigned numbers to each one of them. Secondly, We tried the same work on the ready data inside the Kaggle repository called (Intrusion Detection in Internet of Things Network), and the clustering model worked well and detected the number of attacks correctly as shown in the results section.

Robotic arms are key components in fruit-harvesting robots. In agricultural settings, conventional serial or parallel robotic arms often fall short in meeting the demands for a large workspace, rapid movement, enhanced capability of obstacle avoidance and affordability. This study proposes a novel hybrid six-degree-of-freedom (DoF) robotic arm that combines the advantages of parallel and serial mechanisms. Inspired by yoga, we designed two sliders capable of moving independently along a single rail, acting as two feet. These sliders are interconnected with linkages and a meshed-gear set, allowing the parallel mechanism to lower itself and perform a split to pass under obstacles. This unique feature allows the arm to avoid obstacles such as pipes, tables and beams typically found in greenhouses. Integrated with serially mounted joints, the patented hybrid arm is able to maintain the end's pose even when it moves with a mobile platform, facilitating fruit picking with the optimal pose in dynamic conditions. Moreover, the hybrid arm's workspace is substantially larger, being almost three times the volume of UR3 serial arms and fourteen times that of the ABB IRB parallel arms. Experiments show that the repeatability errors are 0.017 mm, 0.03 mm and 0.109 mm for the two sliders and the arm's end, respectively, providing sufficient precision for agricultural robots.

Large Language Models (LLMs) are increasingly integrated with external tools. While these integrations can significantly improve the functionality of LLMs, they also create a new attack surface where confidential data may be disclosed between different components. Specifically, malicious tools can exploit vulnerabilities in the LLM itself to manipulate the model and compromise the data of other services, raising the question of how private data can be protected in the context of LLM integrations. In this work, we provide a systematic way of evaluating confidentiality in LLM-integrated systems. For this, we formalize a "secret key" game that can capture the ability of a model to conceal private information. This enables us to compare the vulnerability of a model against confidentiality attacks and also the effectiveness of different defense strategies. In this framework, we evaluate eight previously published attacks and four defenses. We find that current defenses lack generalization across attack strategies. Building on this analysis, we propose a method for robustness fine-tuning, inspired by adversarial training. This approach is effective in lowering the success rate of attackers and in improving the system's resilience against unknown attacks.

Rough graph is the graphical structure of information system with imprecise knowledge. Tong He designed the properties of rough graph in 2006[6] and following that He and Shi introduced the notion of edge rough graph[7]. He et al developed the concept of weighted rough graph with weighted attributes[6]. In this paper, we introduce a new type of labeling called Even vertex {\zeta}- graceful labeling as weight value for edges. We investigate this labeling for some special graphs like rough path graph, rough cycle graph, rough comb graph, rough ladder graph and rough star graph.

This paper describes the design and development of a prototype technique for artificial intelligence based on the fusion of genetic algorithm, neural network and fuzzy logic. It starts by establishing a relationship between the neural network and fuzzy logic. Then, it combines the genetic algorithm with them. Information fusions are at the confidence level, where matching scores can be reported and discussed. The technique is called the Genetic Neuro-Fuzzy (GNF). It can be used for high accuracy real-time environments.

Terror attacks have been linked in part to online extremist content. Although tens of thousands of Islamist extremism supporters consume such content, they are a small fraction relative to peaceful Muslims. The efforts to contain the ever-evolving extremism on social media platforms have remained inadequate and mostly ineffective. Divergent extremist and mainstream contexts challenge machine interpretation, with a particular threat to the precision of classification algorithms. Our context-aware computational approach to the analysis of extremist content on Twitter breaks down this persuasion process into building blocks that acknowledge inherent ambiguity and sparsity that likely challenge both manual and automated classification. We model this process using a combination of three contextual dimensions -- religion, ideology, and hate -- each elucidating a degree of radicalization and highlighting independent features to render them computationally accessible. We utilize domain-specific knowledge resources for each of these contextual dimensions such as Qur'an for religion, the books of extremist ideologues and preachers for political ideology and a social media hate speech corpus for hate. Our study makes three contributions to reliable analysis: (i) Development of a computational approach rooted in the contextual dimensions of religion, ideology, and hate that reflects strategies employed by online Islamist extremist groups, (ii) An in-depth analysis of relevant tweet datasets with respect to these dimensions to exclude likely mislabeled users, and (iii) A framework for understanding online radicalization as a process to assist counter-programming. Given the potentially significant social impact, we evaluate the performance of our algorithms to minimize mislabeling, where our approach outperforms a competitive baseline by 10.2% in precision.

Content-dense news report important factual information about an event in direct, succinct manner. Information seeking applications such as information extraction, question answering and summarization normally assume all text they deal with is content-dense. Here we empirically test this assumption on news articles from the business, U.S. international relations, sports and science journalism domains. Our findings clearly indicate that about half of the news texts in our study are in fact not content-dense and motivate the development of a supervised content-density detector. We heuristically label a large training corpus for the task and train a two-layer classifying model based on lexical and unlexicalized syntactic features. On manually annotated data, we compare the performance of domain-specific classifiers, trained on data only from a given news domain and a general classifier in which data from all four domains is pooled together. Our annotation and prediction experiments demonstrate that the concept of content density varies depending on the domain and that naive annotators provide judgement biased toward the stereotypical domain label. Domain-specific classifiers are more accurate for domains in which content-dense texts are typically fewer. Domain independent classifiers reproduce better naive crowdsourced judgements. Classification prediction is high across all conditions, around 80%.

The face of homebrew, remote-controlled military robotics in Iraq is a man named Ali Hashem al-Daraji, better known by the nickname Abu Ali. In 2014 he was a policeman for Iraq's interior ministry, but in June of that year, when the Iraqi Security Forces collapsed as ISIS took over Mosul, Abu Ali hooked up with the Hashd al Shaabi, or "Popular Mobilization Units," an umbrella organization of anti-ISIS militias, some of which had also fought against US forces during the Iraq War. Before eventually returning to the Iraqi Federal Police last November, Abu Ali fought with a couple of militia organizations across Iraq, was injured by an improvised explosive device in Fallujah, and took a selfie with Qasem Soleimani, the head of Iran's covert-action Qods Force, in charge of Tehran's wars in Iraq and Syria and a sworn enemy of the US. "My purpose was to help the Hashd with minimal casualties," he says. Abu Ali produces little wheeled robots designed to allow troops to fire from behind cover.

MOSUL, IRAQ/SALAHIYAH IRAQ – Iraq's special forces worked Sunday to clear neighborhoods on the eastern edge of Islamic State-held Mosul as bombings launched by the extremist group elsewhere in the country killed at least 20 people. The Mosul offensive has slowed in recent days as Iraqi forces have pushed into more densely populated areas, where they cannot rely as much on airstrikes and shelling because of the risk posed to civilians, who have been told to stay in their homes. "There are a lot of civilians and we are trying to protect them," said Lt. Col. Muhanad al-Timimi. "This is one of the hardest battles that we've faced till now." Some civilians are fleeing the combat zone, while IS militants are holding others back for use as human shields, making it harder for Iraqi commanders on the ground to get approval for requested U.S.-led coalition airstrikes.

Chilling aerial footage of Ramadi, a once bustling city in central Iraq, has captured the extent of destruction caused by war. In late December, Iraqi forces, backed by US air strikes, announced the recapturing of Ramadi, which had been lost to the Islamic State of Iraq and the Levant (ISIL, also known as ISIS) group in May 2015. The US-led coalition carried out more than 600 air strikes in the area from July to December last year. A new six-minute clip, released by the International Red Committee of The Red Cross (ICRC) shows homes in Ramadi turned to rubble, along with flattened school, destroyed hospitals and damaged ambulances. READ MORE: Dramatic video'shows destruction of huge ISIL convoy' "Rare aerial footage gathered by ICRC shows the once prosperous Ramadi in central Iraq now in tatters - a ghost town," the ICRC said on Monday.