The rapid advancement of software solutions in the industry has brought significant ethical concerns, ranging from data privacy issues to algorithmic bias and cybersecurity threats. Addressing these concerns requires ...
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In this panel session, the relationship between computerscience programs and information technology programs at universities that house both will be explored. People outside the computing disciplines often find the d...
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ISBN:
(纸本)9781450338356
In this panel session, the relationship between computerscience programs and information technology programs at universities that house both will be explored. People outside the computing disciplines often find the distinction between these programs confusing. The panelists, who have experience with both types of program, will discuss strategies for differentiating the programs in the eyes of administrators, for advising students into the correct program, and for maintaining focus and excellence in both computerscience and information technology programs.
In the very beginning,the computer Laboratory of the University of Cambridge was founded to provide computing service for different disciplines across the *** computerscience developed as a discipline in its own righ...
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In the very beginning,the computer Laboratory of the University of Cambridge was founded to provide computing service for different disciplines across the *** computerscience developed as a discipline in its own right,boundaries necessarily arose between it and other disciplines,in a way that is now often detrimental to ***,it is necessary to reinvigorate the relationship between computerscience and other academic disciplines and celebrate exploration and creativity in *** do this,the structures of the academic department have to act as supporting scaffolding rather than *** examples are given that show the efforts being made at the University of Cambridge to approach this problem.
The rapid advancement of software solutions in the industry has brought significant ethical concerns, ranging from data privacy issues to algorithmic bias and cybersecurity threats. Addressing these concerns requires ...
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ISBN:
(数字)9798331527792
ISBN:
(纸本)9798331527808
The rapid advancement of software solutions in the industry has brought significant ethical concerns, ranging from data privacy issues to algorithmic bias and cybersecurity threats. Addressing these concerns requires software developers who are technically proficient and ethically aware. To foster such ethical awareness, embedding ethical training within the computerscience (CS) curriculum has emerged as a preferred approach. This study presents a methodology for integrating ethics topics into various CS units, informed by the IMPACTCS project’s guidelines and implemented through a structured algorithm. After an initial formative assessment to gauge students’ perceptions of responsible computing, the curriculum was adapted to include relevant ethical concepts. Following the instructional period, a summative assessment was conducted to evaluate changes in students’ understanding and attitudes towards ethical issues in computing. The results indicated a significant improvement in students’ ethical awareness and their ability to apply ethical principles to real-world scenarios. These findings underscore the effectiveness of incorporating ethics education into the CS curriculum and highlight the importance of preparing future software developers to navigate the ethical challenges in the tech industry. This study contributes to the ongoing efforts to enhance responsible computing education and promote ethical practices in software development.
The virtual private cloud service currently lacks a real-time end-to-end consistency validation mechanism, which prevents tenants from receiving immediate feedback on their requests. Existing solutions consume excessi...
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The virtual private cloud service currently lacks a real-time end-to-end consistency validation mechanism, which prevents tenants from receiving immediate feedback on their requests. Existing solutions consume excessive communication and computational resources in such large-scale cloud environments, and suffer from poor timeliness. To address these issues, we propose a lightweight consistency validation mechanism that includes real-time incremental validation and periodic full-scale validation. The former leverages message layer aggregation to enable tenants to swiftly determine the success of their requests on hosts with minimal communication overhead. The latter utilizes lightweight validation checksums to compare the expected and actual states of hosts locally, while efficiently managing the checksums of various host entries using inverted indexing. This approach enables us to efficiently validate the complete local configurations within the limited memory of hosts. In summary, our proposed mechanism achieves closed-loop implementation for new requests and ensures their long-term effectiveness.
Recently, redactable blockchain has been proposed and leveraged in a wide range of real systems for its unique properties of decentralization, traceability, and transparency while ensuring controllable on-chain data r...
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Recently, redactable blockchain has been proposed and leveraged in a wide range of real systems for its unique properties of decentralization, traceability, and transparency while ensuring controllable on-chain data redaction. However, the development of redactable blockchain is now obstructed by three limitations, which are data privacy breaches, high communication overhead, and low searching efficiency, respectively. In this paper, we propose PriChain, the first efficient privacy-preserving fine-grained redactable blockchain in decentralized settings. PriChain provides data owners with rights to control who can read and redact on-chain data while maintaining downward compatibility, ensuring the one who can redact will be able to read. Specifically, inspired by the concept of multi-authority attribute-based encryption, we utilize the isomorphism of the access control tree, realizing fine-grained redaction mechanism, downward compatibility, and collusion resistance. With the newly designed structure, PriChain can realize O（n） communication and storage overhead compared to prior O（n2） schemes. Furthermore, we integrate multiple access trees into a tree-based dictionary, optimizing searching efficiency. Theoretical analysis proves that PriChain is secure against the chosen-plaintext attack and has competitive complexity. The experimental evaluations show that PriChain realizes 10× efficiency improvement of searching and 100× lower communication and storage overhead on average compared with existing schemes.
Anomaly detection(AD) has been extensively studied and applied across various scenarios in recent years. However, gaps remain between the current performance and the desired recognition accuracy required for practical...
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Anomaly detection(AD) has been extensively studied and applied across various scenarios in recent years. However, gaps remain between the current performance and the desired recognition accuracy required for practical *** paper analyzes two fundamental failure cases in the baseline AD model and identifies key reasons that limit the recognition accuracy of existing approaches. Specifically, by Case-1, we found that the main reason detrimental to current AD methods is that the inputs to the recovery model contain a large number of detailed features to be recovered, which leads to the normal/abnormal area has not/has been recovered into its original state. By Case-2, we surprisingly found that the abnormal area that cannot be recognized in image-level representations can be easily recognized in the feature-level representation. Based on the above observations, we propose a novel recover-then-discriminate(ReDi) framework for *** takes a self-generated feature map(e.g., histogram of oriented gradients) and a selected prompted image as explicit input information to address the identified in Case-1. Additionally, a feature-level discriminative network is introduced to amplify abnormal differences between the recovered and input representations. Extensive experiments on two widely used yet challenging AD datasets demonstrate that ReDi achieves state-of-the-art recognition accuracy.
Witness encryption(WE) is a novel type of cryptographic primitive that enables a message to be encrypted via an NP instance. Anyone who possesses a solution to this instance(i.e., a witness) can then recover the messa...
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Witness encryption(WE) is a novel type of cryptographic primitive that enables a message to be encrypted via an NP instance. Anyone who possesses a solution to this instance(i.e., a witness) can then recover the message from the *** introduce a variant of WE that allows ciphertext updates, referred to as ciphertext updateable WE(CUWE). With CUWE,a user can encrypt a message using an instance x and a tag t, and those who possess a valid witness w for x and match the access policy defined by tag t can decrypt the message. Furthermore, CUWE allows for the use of an update token to change the tag t of ciphertext to a different tag. This feature enables fine-grained access control, even after the ciphertext has been created, thereby significantly increasing the usefulness of the WE scheme. We demonstrate that such a WE framework with an updatable ciphertext scheme can be constructed using our puncturable instance-based deterministic encryption(PIDE) and indistinguishability obfuscation(iO). We also propose an instantiation of PIDE utilizing puncturable pseudorandom functions(PRFs) that provide(selectively) indistinguishable security. Finally, we expand our CUWE to ciphertext-updatable functional WE(CUFWE), which offers enhanced data access control.
Cross-platform binary code similarity detection aims at detecting whether two or more pieces of binary code are similar or not. Existing approaches that combine control flow graphs(CFGs)-based function representation ...
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Cross-platform binary code similarity detection aims at detecting whether two or more pieces of binary code are similar or not. Existing approaches that combine control flow graphs(CFGs)-based function representation and graph convolutional network(GCN)-based similarity analysis are the best-performing ones. Due to a large amount of convolutional computation and the loss of structural information, the use of convolution networks will inevitably bring problems such as high overhead and sometimes inaccuracy. To address these issues, we propose a fast cross-platform binary code similarity detection framework that takes advantage of natural language processing(NLP)and inductive graph neural network(GNN) for basic blocks embedding and function representation respectively by simulating extracting structural features and temporal features. GNN's node-centric and small batch is a suitable training way for large CFGs, it can greatly reduce computational overhead. Various NLP basic block embedding models and GNNs are evaluated. Experimental results show that the scheme with long short term memory(LSTM)for basic blocks embedding and inductive learning-based Graph SAGE(GAE) for function representation outperforms the state-of-the-art works. In our framework, we can take only 45% overhead. Improve efficiency significantly with a small performance trade-off.
Therapeutic peptides contribute significantly to human health and have the potential for personalized medicine. The prediction for the therapeutic peptides is beneficial and emerging for the discovery of drugs. Althou...
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Therapeutic peptides contribute significantly to human health and have the potential for personalized medicine. The prediction for the therapeutic peptides is beneficial and emerging for the discovery of drugs. Although several computational approaches have emerged to discern the functions of therapeutic peptides, predicting multi-functional therapeutic peptide types is challenging. In this research, a novel approach termed TPpred-SC has been introduced. This method leverages a pretrained protein language model alongside multi-label supervised contrastive learning to predict multi-functional therapeutic *** framework incorporates sequential semantic information directly from large-scale protein sequences in TAPE. Then, TPpred-SC exploits multi-label supervised contrastive learning to enhance the representation of peptide sequences for imbalanced multi-label therapeutic peptide prediction. The experimental findings demonstrate that TPpred-SC achieves superior performance compared to existing related methods. To serve our work more efficiently, the web server of TPpred-SC can be accessed at http://***/TPpred-SC.
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