Abstract: A method of predicting one or more road segment attributes corresponding to a road segment in a geographical area, the method including: providing trajectory data and satellite image of the geographical area; calculating one or more image channels based on the trajectory data; and using at least one processor, classi-fying the road segment based on the one or more image channels and the satellite image using a trained classifier into prediction probabil-ities of the road attributes A data processing system including one or more processors configured to carry out a the method of predicting road attributes. A computer executable code including instructions for predicting one or more road segment attributes according to the method.

Abstract: A method of predicting one or more road segment attributes corresponding to a road segment in a geographical area, the method including: providing trajectory data and satellite image of the geographical area; calculating one or more image channels based on the trajectory data; and using at least one processor, classifying the road segment based on the one or more image channels and the satellite image using a trained classifier into prediction probabilities of the road attributes A data processing system including one or more processors configured to carry out a the method of predicting road attributes. A computer executable code including instructions for predicting one or more road segment attributes according to the method.

Abstract: A method for generating map data comprising training a generator neural network by acquiring training data elements and training a generative adversarial network, comprising training a generator neural network to generate, for a satellite image and a road usage image of a training data element, the map data image of the training data element and comprising generating map data for a geographical region by acquiring road usage information specifying which parts of a geographical area have been used for driving a vehicle, acquiring a satellite image of the geographical area, forming a road usage image of the geographical area which has pixels, each pixel corresponding to a respective part of the geographical area and having a value indicating whether its part of the geographical area is specified by the road usage information to have been used; and feeding the satellite image and the road usage image to the trained generator neural network.

Abstract: A method of predicting one or more road segment attributes corresponding to a road segment in a geographical area, the method including: providing trajectory data and satellite image of the geographical area; calculating one or more image channels based on the trajectory data; and using at least one processor, classifying the road segment based on the one or more image channels and the satellite image using a trained classifier into prediction probabilities of the road attributes A data processing system including one or more processors configured to carry out a the method of predicting road attributes. A computer executable code including instructions for predicting one or more road segment attributes according to the method.

Abstract: A method, data processing apparatus, and computer code for identifying road intersections includes providing location data obtained from at least one vehicle's trajectory, wherein the location data may include geographical data within a geographical perimeter. The method includes determining node vectors by applying a geographical descriptor model on a target location included in the geographical perimeter. The geographical descriptor model includes a plurality of multiscale node descriptors including a target multiscale descriptor and neighboring multiscale descriptors. Each of the plurality of multiscale node descriptors includes at least two shape descriptors of different geographical resolution. Each of the neighboring locations is at a respective geographical distance from the target location. The node vectors may be respectively determined for each of the plurality of multiscale node descriptors.

Abstract: The disclosure relates to a method of predicting one or more road attributes. The method may include providing trajectory data of a geographical area. The method may further include providing map data, wherein the map data may include image data of the geographical area. The method may further include extracting trajectory features from the trajectory data and extracting map features from the map data. The method may further include using at least one processor to predict road attributes by inputting the trajectory features and the map features in a neural network and by classifying an output of the neural network into prediction probabilities of the road attributes. The disclosure also relates to a data processing system; to a non-transitory computer-readable medium storing computer executable code; and to a method of training an automated predictor.

Abstract: A method, data processing apparatus, and computer code for identifying road intersections includes providing location data obtained from at least one vehicle's trajectory, wherein the location data may include geographical data within a geographical perimeter. The method includes determining node vectors by applying a geographical descriptor model on a target location included in the geographical perimeter. The geographical descriptor model includes a plurality of multiscale node descriptors including a target multiscale descriptor and neighboring multiscale descriptors. Each of the plurality of multiscale node descriptors includes at least two shape descriptors of different geographical resolution. Each of the neighboring locations is at a respective geographical distance from the target location. The node vectors may be respectively determined for each of the plurality of multiscale node descriptors.

Abstract: Method and device for real-time error bounded and relay bounded map matching. The method for map matching comprises of modelling each road arc as a hidden state and each location measurement as an observation emitted by the hidden state using a Hidden Markov Model, decoding each road arc and each location measurement using a Viterbi algorithm and outputting a matching road arc, wherein the outputting is delayed by a delay time in response to an optimal trade-off between selection accuracy and selection latency.

Abstract: Embodiments of methods for multimedia annotation with sensor data (referred to herein as Sensor-rich video) includes acquisition, management, storage, indexing, transmission, search, and display of video, images, or sound, that has been recorded in conjunction with additional sensor information (such as, but not limited to, global positioning system information (latitude, longitude, altitude), compass directions, WiFi fingerprints, ambient lighting conditions, etc.). The collection of sensor information is acquired on a continuous basis during recording. For example, the GPS information may be continuously acquired from a corresponding sensor at every second during the recording of a video. Therefore, the acquisition apparatus generates a continuous stream of video frames and a continuous stream of sensor meta-data values. The two streams are correlated in that every video frame is associated with a set of sensor values. Note that the sampling frequency (i.e.

Abstract: Embodiments of methods for multimedia annotation with sensor data (referred to herein as Sensor-rich video) includes acquisition, management, storage, indexing, transmission, search, and display of video, images, or sound, that has been recorded in conjunction with additional sensor information (such as, but not limited to, global positioning system information (latitude, longitude, altitude), compass directions, WiFi fingerprints, ambient lighting conditions, etc.). The collection of sensor information is acquired on a continuous basis during recording. For example, the GPS information may be continuously acquired from a corresponding sensor at every second during the recording of a video. Therefore, the acquisition apparatus generates a continuous stream of video frames and a continuous stream of sensor meta-data values. The two streams are correlated in that every video frame is associated with a set of sensor values. Note that the sampling frequency (i.e.

Abstract: A data stream recorder system, for multi-stream recording and retrieval of utilizes a number of gateways, each for sending and receiving packets containing streaming multimedia content data at real-time rates via a packet data network. A session manager communicates via the network with source client devices and receiver client devices, to establish and control recording and retrieval sessions. The manager assigns sessions to the gateways for the sending and receiving of the packets to and from client devices. Content is distributed across storage devices associated in storage nodes. Each of the gateways receives packets containing content data at real time rates during a recording session and distributes the received packets from the session across all of the storage nodes. A scheduler of each respective storage node distributes content data from packets distributed to the respective storage node, across all of the digital storage devices of the respective storage node.

Abstract: A way of making small, fragile, decorative handles on a lathe is disclosed along with tools for gluing pen-tubes into these handles is shown. Use of these tools and methods of use allows wood turners to avoid excessive rework and mess associated with the gluing process of making pen blanks while assuring better product due to more complete and uniform distribution of the adhesive layer.

Abstract: A pen assembly tool used to press-fit mating parts of a pen kit upon a lathe is disclosed. Use of the pen assembly tool allows wood turners to accurately align, assemble and press-fit mating parts of a pen kit or similar array of objects upon the same lathe used to create certain mating parts of the pen, such as a combined pen-tube and turning blank.

Abstract: Systems and techniques for transmitting data stream to a client include transmitting a data segment from one of a plurality of nodes of a continuous media server to a client according to a scheduler on the node. A system includes a plurality of data processing devices, each data processing device coupled with at least one storage device. Each data processing device includes a scheduler to schedule transmission of the data segment to a client in sequence with other data segments, and a module to transmit the data segment to the client.

Abstract: A peer to peer audio communication screen in system with adaptive bandwidth control. The peer-to-peer system can be used, for example, in classroom applications. More interactive peers are moved to a position where they will receive lower latency from the peer-to-peer system. This may be done by maintaining a score indicative of the amount of interactivity, and then using that score to move a peer either close to the source of audio packets, e.g., the core or further from that source.

Abstract: A pen assembly tool used to press-fit mating parts of a pen kit upon a lathe is disclosed. Use of the pen assembly tool allows wood turners to accurately align, assemble and press-fit mating parts of a pen kit or similar array of objects upon the same lathe used to create certain mating parts of the pen, such as a combined pen-tube and turning blank.

Abstract: A way of making small, fragile, decorative handles on a lathe is disclosed along with tools for gluing pen-tubes into these handles is shown. Use of these tools and methods of use allows wood turners to avoid excessive rework and mess associated with the gluing process of making pen blanks while assuring better product due to more complete and uniform distribution of the adhesive layer.

Abstract: A method and system for performing transactions between a client and a server. The client sends to the server a transaction request for performing a transaction, and receives from the server a transaction policy. The client displays a first representation of the transaction policy depicting transaction policy options for fulfilling the transaction policy. After the user selects a first transaction policy option, the client displays a second representation of the transaction policy, depicting at least one mechanism pertaining to the first transaction policy option for satisfying the requirements of the first transaction policy option. After the user selects a first mechanism, the client depicts evidence options for each requirement of the first transaction policy option. After the user selects an evidence option for each requirement of the first transaction policy option, the client sends to the server transaction enablement information that includes evidence dictated by the selected evidence options.

Abstract: A peer to peer audio communication screen in system with adaptive bandwidth control. The peer-to-peer system can be used, for example, in classroom applications. More interactive peers are moved to a position where they will receive lower latency from the peer-to-peer system. This may be done by maintaining a score indicative of the amount of interactivity, and then using that score to move a peer either close to the source of audio packets, e.g., the core or further from that source.

Abstract: Systems and techniques to pseudorandomly place and redistribute data blocks in a storage system. In general, in one implementation, the techniques include: distributing data blocks over multiple storage devices according to a reproducible pseudorandom sequence that provides load balancing across the storage devices, and determining current storage locations of the data blocks by reproducing the pseudorandom sequence. The techniques may also include: distributing data blocks over multiple storage devices according to a reproducible pseudorandom sequence, in response to initiation of a storage scaling operation, pseudorandomly redistributing a selected subset of the data blocks and saving information describing the storage scaling operation, determining current storage locations based on the pseudorandom sequence and the saved scaling operation information, and accessing the data blocks according to the determined current storage locations.