Enlisting Hardware Architecture to Thwart Malicious Code Injection
Software vulnerabilities that enable the injection and execution of malicious code in pervasive Internet-connected computing devices pose serious threats to cyber security. In a common type of attack, a hostile party induces a software buffer overflow in a susceptible computing device in order to corrupt a procedure return address and transfer control to malicious code. These buffer overflow attacks are often employed to recruit oblivious hosts into distributed denial of service (DDoS) attack networks, which ultimately launch devastating DDoS attacks against victim networks or machines. In spite of existing software countermeasures that seek to prevent buffer overflow exploits, many systems remain vulnerable.

title: Single-Cycle Bit Permutations with MOMR Execution
abstract: Secure computing paradigms impose new architectural challenges for general-purpose processors. Cryptographic processing is needed for secure communications, storage, and computations. We identify two categories of operations in symmetric-key and public-key cryptographic algorithms that are not common in previous general-purpose workloads: advanced bit operations within a word and multi-word operations. We define MOMR (Multiple Operands Multiple Results) execution or datarich execution as a unified solution to both challenges. It allows arbitrary n-bit permutations to be achieved in one or two cycles, rather than O(n) cycles as in existing RISC processors. It also enables significant acceleration of multi-word multiplications needed by public-key ciphers. We propose two implementations of MOMR: one employs only hardware changes while the other uses Instruction Set Architecture (ISA) support. We show that MOMR execution leverages available resources in typical multi-issue processors with minimal additional cost. Multi-issue processors enhanced with MOMR units provide additional speedup over standard multi-issue processors with the same datapath. MOMR is a general architectural solution for word-oriented processor architectures to incorporate datarich operations.

title: Validating Word-Oriented Processors for Bit and Multi-word Operations
abstract: We examine secure computing paradigms to identify any new architectural challenges for future general-purpose processors. Some essential security functions can be provided by different classes of cryptography algorithms. We identify two categories of operations in these algorithms that are not common in previous general-purpose workloads: bit operations within a word and multi-word operations. Both challenge the basic word orientation of processors. We show how very complex bit-level operations, namely arbitrary bit permutations within a word, can be achieved in O(1) cycles, rather than O(n) cycles as in existing RISC processors. We describe two solutions: one using only microarchitecture changes, and another with Instruction Set Architecture (ISA) support. We generalize our solutions to define datarich execution with MOMR (Multi-word Operands Multi-word Result) functional units. This can address both challenges, leveraging available resources in typical processors with minimal additional cost. Thus we validate the basic word-orientation of processor architectures, since they can also provide superior performance for both bit and multi-word operations needed by cryptographic processing.

title: Bit permutation instructions for accelerating software cryptography
abstract: Permutation is widely used in cryprographic algorithms. However, it is not well-supported in existing instruction sets. In this paper, two instructions, PPERM3R and GRP, are proposed for efficient software implementation of arbitrary permutations. The PPERM3R instruction can be used for dynamically specified permutations; the GRP instruction can be used to do arbitrary n-bit permutations with up to lg(n) instructions. In addition, a systematic method for determining the instruction sequence for performing an arbitrary permutation is described.

title: Question Answering Summarization of Multiple Biomedical Documents
abstract: In this paper we introduce a system that automatically summarizes multiple biomedical documents relevant to a question. The system extracts biomedical and general concepts by utilizing concept-level knowledge from domain-specific and domain-independent sources. Semantic role labeling, semantic subgraph-based sentence selection and automatic post-editing are involved in the process of finding the information need. Due to the absence of expert-written summaries of biomedical documents, we propose an approximate evaluation by taking MEDLINE abstracts as expert-written summaries. Evaluation results indicate that our system does help in answering questions and the automatically generated summaries are comparable to abstracts of biomedical articles, as evaluated using the ROUGE measure.

title: Perspectives on cognitive informatics
abstract: From a scientific perspective explaining how the brain thinks is a big goal. Cognitive informatics studies intelligent behavior from a computational point of view in terms of updated research efforts and processes of brain science and neuroscience. Cognitive informatics is the interdisciplinary study of cognition. Cognition includes mental states and processes, such as thinking, reasoning, remembering, language understanding and generation, visual and auditory perception, learning, consciousness, emotions, etc. In this paper we will point out basic research topics of learning, memory, thought, language, and neural computing which are active fields related to cognitive informatics.

title: Swarm Intelligence Clustering Algorithm Based on Attractor
abstract: Ant colonies behavior and their self-organizing capabilities have been popularly studied, and various swarm intelligence models and clustering algorithms also have been proposed. Unfortunately, the cluster number is often too high and convergence is also slow. We put forward a novel structure-attractor, which actively attracts and guides the ant's behavior, and implement an efficient strategy to adaptively control the clustering behavior. Our experiments show that swarm intelligence clustering algorithm based on attractor (SICABA for short) greatly improves the convergence speed and clustering quality compared with LF and also has many notable virtues such as flexibility, decentralization compared with conventional algorithms.

title: Semantics Supervised Cluster-Based Index for Video Databases
abstract: High-dimensional index is one of the most challenging tasks for content-based video retrieval (CBVR). Typically, in video database, there exist two kinds of clues for query: visual features and semantic classes. In this paper, we modeled the relationship between semantic classes and visual feature distributions of data set with the Gaussian mixture model (GMM), and proposed a semantics supervised cluster based index approach (briefly as SSCI) to integrate the advantages of both semantic classes and visual features. The entire data set is divided hierarchically by a modified clustering technique into many clusters until the objects within a cluster are not only close in the visual feature space but also within the same semantic class, and then an index entry including semantic clue and visual feature clue is built for each cluster. Especially, the visual feature vectors in a cluster are organized adjacently in disk. So the SSCI-based nearest-neighbor (NN) search can be divided into two phases: the first phase computes the distances between the query example and each cluster index and returns the clusters with the smallest distance, here namely candidate clusters; then the second phase retrieves the original feature vectors within the candidate clusters to gain the approximate nearest neighbors. Our experiments showed that for approximate searching the SSCI-based approach was faster than VA+-based approach; moreover, the quality of the result set was better than that of the sequential search in terms of semantics.