Abstract: In an example, a method for fine-tuning a Large Visual Language Model (LVLM) includes providing visual queries, each of the visual queries comprises at least an image and a textual query related to the image; processing, by the LVLM, the visual queries to extract visual embeddings from the visual queries, wherein the LVLM comprises a Visual Language Model (VLM), a first Large Language Model (LLM), and a linear projection layer interconnecting the VLM and the LLM; for visual queries: i) generating, by the LVLM, a response to the corresponding visual query based on the corresponding visual embedding; ii) evaluating, by a second LLM, the generated response to verify that the generated response satisfies predefined criteria; and iii) providing, by the second LLM, a feedback to the LVLM, in response to the evaluating the generated response; and fine-tuning the LVLM using aggregated feedback provided by the second LLM for the visual queries.

Abstract: In an example, a method for fine-tuning a Large Visual Language Model (LVLM) includes providing visual queries, each of the visual queries comprises at least an image and a textual query related to the image; processing, by the LVLM, the visual queries to extract visual embeddings from the visual queries, wherein the LVLM comprises a Visual Language Model (VLM), a first Large Language Model (LLM), and a linear projection layer interconnecting the VLM and the LLM; for visual queries: i) generating, by the LVLM, a response to the corresponding visual query based on the corresponding visual embedding; ii) evaluating, by a second LLM, the generated response to verify that the generated response satisfies predefined criteria; and iii) providing, by the second LLM, a feedback to the LVLM, in response to the evaluating the generated response; and fine-tuning the LVLM using aggregated feedback provided by the second LLM for the visual queries.

Abstract: A method, apparatus, and system for training a language model for enhanced consistency include selecting at least a portion of the content data of the language model, generating reasoning statements in the form of natural language relevant to the selected portion of the content data, and training the language model using the generated reasoning statements such that a logical inference of the trained language model in response to a prompt directed to the selected portion of the content data is increased as compared with the logical inference of the language model in response to the same or similar prompt before the training of the language model to enhance the consistency of the language model with respect to the selected portion of the content data. The trained language model can be used to generate a logical inference having enhanced consistency for at least a portion of content data.

Abstract: A method includes collecting system calls and call parameters invoked by monitored applications for target computer systems. The system calls and call parameters are received from operating system kernels on the plurality of target computer systems. Sequences of systems calls and call parameters of the monitored applications are correlated among different target computer systems to deduce malicious activities. Remedial action(s) are performed in response to malicious activities being deduced as being malicious by the correlating. Another method includes determining that network activity at a specific time is deemed to be suspicious. Using IP addresses involved in the suspicious network activity, computer system(s) are determined that are sources of the suspicious network activity. Based on the specific time and the determined computer system(s), application(s) are determined that are executing on the determined computer system(s) that are causing the suspicious network activity.

Abstract: System and methods are provided for performing privacy-preserving, high-performance, and scalable DNA read mapping on hybrid clouds including a public cloud and a private cloud. The systems and methods offer strong privacy protection and have the capacity to process millions of reads and allocate most of the workload to the public cloud at a small overall cost. The systems and methods perform seeding on the public cloud using keyed hash values of individual sequencing reads' seeds and then extend matched seeds on the private cloud. The systems and methods are designed to move the workload of read mapping from the extension stage to the seeding stage, thereby ensuring that the dominant portion of the overhead is shouldered by the public cloud.

Abstract: A method includes collecting system calls and call parameters invoked by monitored applications for target computer systems. The system calls and call parameters are received from operating system kernels on the plurality of target computer systems. Sequences of systems calls and call parameters of the monitored applications are correlated among different target computer systems to deduce malicious activities. Remedial action(s) are performed in response to malicious activities being deduced as being malicious by the correlating. Another method includes determining that network activity at a specific time is deemed to be suspicious. Using IP addresses involved in the suspicious network activity, computer system(s) are determined that are sources of the suspicious network activity. Based on the specific time and the determined computer system(s), application(s) are determined that are executing on the determined computer system(s) that are causing the suspicious network activity.

Abstract: A method includes collecting system calls and call parameters invoked by monitored applications for target computer systems. The system calls and call parameters are received from operating system kernels on the plurality of target computer systems. Sequences of systems calls and call parameters of the monitored applications are correlated among different target computer systems to deduce malicious activities. Remedial action(s) are performed in response to malicious activities being deduced as being malicious by the correlating. Another method includes determining that network activity at a specific time is deemed to be suspicious. Using IP addresses involved in the suspicious network activity, computer system(s) are determined that are sources of the suspicious network activity. Based on the specific time and the determined computer system(s), application(s) are determined that are executing on the determined computer system(s) that are causing the suspicious network activity.

Abstract: System and methods are provided for performing privacy-preserving, high-performance, and scalable DNA read mapping on hybrid clouds including a public cloud and a private cloud. The systems and methods offer strong privacy protection and have the capacity to process millions of reads and allocate most of the workload to the public cloud at a small overall cost. The systems and methods perform seeding on the public cloud using keyed hash values of individual sequencing reads' seeds and then extend matched seeds on the private cloud. The systems and methods are designed to move the workload of read mapping from the extension stage to the seeding stage, thereby ensuring that the dominant portion of the overhead is shouldered by the public cloud.

Abstract: System and methods are provided for performing privacy-preserving, high-performance, and scalable DNA read mapping on hybrid clouds including a public cloud and a private cloud. The systems and methods offer strong privacy protection and have the capacity to process millions of reads and allocate most of the workload to the public cloud at a small overall cost. The systems and methods perform seeding on the public cloud using keyed hash values of individual sequencing reads' seeds and then extend matched seeds on the private cloud. The systems and methods are designed to move the workload of read mapping from the extension stage to the seeding stage, thereby ensuring that the dominant portion of the overhead is shouldered by the public cloud.

Abstract: A method includes collecting system calls and call parameters invoked by monitored applications for target computer systems. The system calls and call parameters are received from operating system kernels on the plurality of target computer systems. Sequences of systems calls and call parameters of the monitored applications are correlated among different target computer systems to deduce malicious activities. Remedial action(s) are performed in response to malicious activities being deduced as being malicious by the correlating. Another method includes determining that network activity at a specific time is deemed to be suspicious. Using IP addresses involved in the suspicious network activity, computer system(s) are determined that are sources of the suspicious network activity. Based on the specific time and the determined computer system(s), application(s) are determined that are executing on the determined computer system(s) that are causing the suspicious network activity.

Abstract: System and methods are provided for performing privacy-preserving, high-performance, and scalable DNA read mapping on hybrid clouds including a public cloud and a private cloud. The systems and methods offer strong privacy protection and have the capacity to process millions of reads and allocate most of the workload to the public cloud at a small overall cost. The systems and methods perform seeding on the public cloud using keyed hash values of individual sequencing reads' seeds and then extend matched seeds on the private cloud. The systems and methods are designed to move the workload of read mapping from the extension stage to the seeding stage, thereby ensuring that the dominant portion of the overhead is shouldered by the public cloud.