Abstract: There is described a method of producing a flexible structure and sensor devices incorporating the former, such as wireless LC sensors, that comprises a plurality of thin-film layers of elastomeric material and at least one layer of micro-wrinkled electrically conductive material. The method includes steps leading to 2D wrinkled metallised polydimethylsiloxane (PDMS) layers enabling considerable flexibility with negligible bending failure for angles up to 180 degrees.

Abstract: A vehicle component includes a body, a first beam, and a second beam. The body has a first fixture region and a second fixture region. The first beam is formed of a first composite material and has a first beam shape. The first beam is attached to the body and extends between the first fixture region and the second fixture region of the body. The second beam is formed of a second composite material and has a second beam shape that is simple compared to the first beam shape. The second beam is attached to the body and extends between the first fixture region and the second fixture region of the body.

Abstract: The electronic architecture carries out the management of the functions of a vehicle, the functions being implemented via a set of sensors and actuators, the architecture comprising at least: a central computer; a real-time communication network; a set of interface modules, each module: aggregating signals from at least one of the sensors and sending the signals to the central computer via the communication network; and/or distributing control signals to at least one of the actuators; the central computer driving the actuators according to the signals from the sensors, the control signals for the actuators being sent to the interface modules via the communication network.

Abstract: A method, apparatus and computer program product to protect a deep neural network (DNN) having a plurality of layers including one or more intermediate layers. In this approach, a training data set is received. During training of the DNN using the received training data set, a representation of activations associated with an intermediate layer is recorded. For at least one or more of the representations, a separate classifier (model) is trained. The classifiers, collectively, are used to train an outlier detection model. Following training, the outliner detection model is used to detect an adversarial input on the deep neural network. The outlier detection model generates a prediction, and an indicator whether a given input is the adversarial input. According to a further aspect, an action is taken to protect a deployed system associated with the DNN in response to detection of the adversary input.

Abstract: A process is provided for thermal molding an article with at least one layer of thermoplastic fibers that are non-woven and uni-directionally oriented in combination with at least one layer of reinforcing fibers. The reinforcing fibers including glass, carbon, nature based, and combinations thereof; alone or mixed with chopped thermoplastic fibers. Upon subjecting the layers to sufficient heat to thermally bond in the presence of non-oriented filler fibers, thermoplastic fiber fusion encapsulates the filler fibers. The filler fibers impart physical properties to the resulting article and the residual unidirectional orientation of the thermoplastic melt imparts physical properties in the fiber direction to the article. By combining layers with varying orientations of uni-directional fibers relative to one another, the physical properties of the resulting article may be controlled and extended relative to conventional thermoplastic moldings.

Abstract: A process is provided for thermal molding an article with at least one layer of thermoplastic fibers that are non-woven and uni-directionally oriented in combination with at least one layer of reinforcing fibers. The reinforcing fibers including glass, carbon, nature based, and combinations thereof; alone or mixed with chopped thermoplastic fibers. Upon subjecting the layers to sufficient heat to thermally bond in the presence of non-oriented filler fibers, thermoplastic fiber fusion encapsulates the filler fibers. The filler fibers impart physical properties to the resulting article and the residual unidirectional orientation of the thermoplastic melt imparts physical properties in the fiber direction to the article. By combining layers with varying orientations of uni-directional fibers relative to one another, the physical properties of the resulting article may be controlled and extended relative to conventional thermoplastic moldings.

Abstract: A method, system and apparatus for software intelligence as-a-service, including decomposing software into functional blocks to provide a software genome, building a representation of the software genome in a knowledge graph linking granularities of the functional blocks, and identifying issues with a target software based on the knowledge graph.

Abstract: A technique for storage-efficient cyber incident reasoning by graph matching. The method begins with a graph pattern that comprises a set of elements with constraints and connections among them. A graph of constraint relations (GoC) in the graph pattern is derived. An activity graph representing activity data captured in association with a host machine is then obtained. In response to a query, one or more subgraphs of the activity graph that satisfy the graph pattern are then located and, in particular, by iteratively solving constraints in the graph pattern. In particular, a single element constraint is solved to generate a result, and that result is propagated to connected constraints in the graph of constraint relations. This process continues until all single element constraints have been evaluated, and all propagations have been performed. The subgraphs of the activity graph that result are then returned in response to a database query.

Abstract: A cloud based implemented method (and apparatus) includes receiving input data including bipartite graph data in a format of source MAC (Media Access Control) address data versus destination IP (Internet Protocol) data and timestamp information, and providing the input bipartite graph data into a first processing to detect malicious beaconing activities using a lockstep detection module on the input bipartite graph data, as executed in a cloud environment, to detect possible synchronized attacks against a targeted infrastructure.

Abstract: A process and system are provided for introducing chopped and dispersed carbon fibers on an automated production line amenable for inclusion in molding compositions, including the debundling of many carbon fibers collectively forming a tow into dispersed chopped carbon fibers that form a filler that undergoes plasma treatment prior to introducing coating silanes to uniformly increase bonding sites for coupling to a thermoset matrix. By exposing carbon tow to a plasma discharge, the carbon tow debundles and is used to form sheets of molding compositions with chopped dispersed fibers added to the composition, as the sheets move along a conveyor belt on the automated production line and at least one plasma generator mounted above the conveyor belt ionizes the carbon fibers. With resort to deionized air to mix plasma-treated chopped fibers, still further dispersion results.

Abstract: Decoy data is generated from regular data. A deep neural network, which has been trained with the regular data, is trained with the decoy data. The trained deep neural network, responsive to a client request comprising input data, is operated on the input data. Post-processing is performed using at least an output of the operated trained deep neural network to determine whether the input data is regular data or decoy data. One or more actions are performed based on a result of the performed post-processing.

Abstract: An automated technique for security monitoring leverages a labeled semi-directed temporal graph derived from system-generated events. The temporal graph is mined to derive process-centric subgraphs, with each subgraph consisting of events related to a process. The subgraphs are then processed to identify atomic operations shared by the processes, wherein an atomic operation comprises a sequence of system-generated events that provide an objective context of interest. The temporal graph is then reconstructed by substituting the identified atomic operations derived from the subgraphs for the edges in the original temporal graph, thereby generating a reconstructed temporal graph. Using graph embedding, the reconstructed graph is converted into a representation suitable for further machine learning, e.g., using a deep neural network. The network is then trained to learn the intention underlying the temporal graph.

Abstract: An automated method for processing security events. It begins by building an initial version of a knowledge graph based on security information received from structured data sources. Using entities identified in the initial version, additional security information is then received. The additional information is extracted from one or more unstructured data sources. The additional information includes text in which the entities (from the structured data sources) appear. The text is processed to extract relationships involving the entities (from the structured data sources) to generate entities and relationships extracted from the unstructured data sources. The initial version of the knowledge graph is then augmented with the entities and relationships extracted from the unstructured data sources to build a new version of the knowledge graph that consolidates the intelligence received from the structured data sources and the unstructured data sources. The new version is then used to process security event data.

Abstract: A stackable filesystem that transparently tracks process file writes for forensic analysis. The filesystem comprises a base filesystem, and an overlay filesystem. Processes see the union of the upper and lower filesystems, but process writes are only reflected in the overlay. By providing per-process views of the filesystem using this stackable approach, a forensic analyzer can record a process's file-based activity—i.e., file creation, deletion, modification. These activities are then analyzed to identify indicators of compromise (IoCs). These indicators are then fed into a forensics analysis engine, which then quickly decides whether a subject (e.g., process, user) is malicious. If so, the system takes some proactive action to alert a proper authority, to quarantine the potential attack, or to provide other remediation. The approach enables forensic analysis without requiring file access mediation, or conducting system event-level collection and analysis, making it a lightweight, and non-intrusive solution.

Abstract: A process and system are provided for introducing a blend of chopped and dispersed fibers on an automated production line amenable for inclusion in molding compositions as a blended fiber mat for structural applications. The blend of fibers are simultaneously supplied to an automated cutting machine illustratively including a rotary blade chopper disposed above a vortex supporting chamber. The blend of chopped fibers and binder form a chopped mat. The chopped mat has a veil mat placed on either side, and is consolidated with the veil mat using heated rollers maintained at the softening temperature of thermoplastic binder, with consolidated mats being amenable to being stored in rolls or as flat sheets. A charge pattern is made using the consolidated mat, and the charge pattern can be compression molded in a mold maintained at a temperature lower than the melting point of the thermoplastic fibers.

Abstract: A process for forming a mat containing a fiber filler including providing one or more sources of extended length fiber; feeding the one or more sources of extended length fiber simultaneously to an automated cutting machine to produce chopped tow fibers; separating the chopped fiber tow into individual chopped fibers that form a fiber filler; coating the fiber filler with a binder; depositing the fiber filler on a first sheet of thermoplastic; covering the fiber filler with a second sheet of thermoplastic to form a stack; and moving the stack to a treatment chamber to form a fiber mat.

Abstract: Malware is detected and mitigated by differentiating HTTP error generation patterns between errors generated by malware, and errors generated by benign users/software. In one embodiment, a malware detector system receives traffic that includes HTTP errors and successful HTTP requests. Error traffic and the successful request traffic are segmented for further analysis. The error traffic is supplied to a clustering component, which groups the errors, e.g., based on their URI pages and parameters. During clustering, various statistical features are extracted (as feature vectors) from one or more perspectives, namely, error provenance, error generation, and error recovery. The feature vectors are supplied to a classifier component, which is trained to distinguish malware-generated errors from benign errors. Once trained, the classifier takes an error cluster and its surrounding successful HTTP requests as inputs, and it produces a verdict on whether a particular cluster is malicious.

Abstract: There is described a method of producing a flexible structure and sensor devices incorporating the former, such as wireless LC sensors, that comprises a plurality of thin-film layers of elastomeric material and at least one layer of micro-wrinkled electrically conductive material. The method includes steps leading to 2D wrinkled metallised polydimethylsiloxane (PDMS) layers enabling considerable flexibility with negligible bending failure for angles up to 180 degrees.

Abstract: Decoy data is generated from regular data. A deep neural network, which has been trained with the regular data, is trained with the decoy data. The trained deep neural network, responsive to a client request comprising input data, is operated on the input data. Post-processing is performed using at least an output of the operated trained deep neural network to determine whether the input data is regular data or decoy data. One or more actions are performed based on a result of the performed post-processing.

Abstract: A lightweight and simplified vehicle door assembly is provided with four main components, an upper frame positioned about the window, an inner frame structure, an outer body panel, and a composite reinforcement component. The composite reinforcement component provides an all-in-one solution for a side impact cross beam, as well as an upper door reinforcement, a front door pillar reinforcement, and a rear door pillar reinforcement. The composite reinforcement component acts to reinforce the overall door structure, offers resistance to a side impact that limits the intrusion into the passenger compartment, and offers resistance to a frontal impact by maintaining the spacing between the vehicle A and B pillars (for a front door) and pillar C and D for a back door. A coupe vehicle door in which there is no window frame may be reinforced with the composite reinforcement component. The composite reinforcement component may have continuous or chopped fibers.