Abstract: A rack, a warehousing apparatus, a control method, an apparatus, a device and a readable storage medium, in which the rack includes: a plurality of first uprights and a plurality of second uprights arranged at intervals along a circumference of the rack, the first upright being located between two of the second uprights in a width direction of the rack; at least one temporary storage layer plate including a first cross beam arranged along a horizontal direction and a plurality of temporary storage members arranged at intervals along two sides of the first cross beam, and the temporary storage layer plate being configured to provide a plurality of temporary storage positions; and at least one storage layer plate spaced apart from the temporary storage layer plate in a vertical direction through the first uprights and the second uprights, and configured to provide a plurality of storage positions.

Abstract: Sensing circuitry of a first electronic device can be configured to sense a signal transmitted by the second, external electronic device contacting and coupling through the body of the user. The first electronic device can be configured to detect the contact while receiving data from the second device, and determine whether the signal transmitted by the external device corresponds an “intentional touch,” or corresponds to a signal coupling between a different body part and the external device or through a different signal pathway (e.g., electromagnetically, through the air, etc.).

Abstract: Disclosed is a malfunction early-warning method for production logistics delivery equipment. After a sensor obtains past signal data, performing feature extraction and dimensionality reduction so as to obtain a feature vector; using a growing neural gas (GNG) algorithm to divide normal state data into different operation situations so as to obtain several cluster centers, and calculating the Euclidean distance between the feature vector and the cluster centers obtained from current operation data, so as to obtain a similarity trend; constructing a past memory matrix, using an improved particle swarm algorithm to optimize an LS-SVM regression model parameter, and calculating the residual value of the current state. Finally, combining the residual value and the similarity trend to obtain a risk coefficient, assessing the equipment state, and issuing an early warning for an equipment malfunction.

Abstract: A warehousing apparatus including a temporary storage layer board used for providing a temporary storage station, and a plurality of shelves with each shelf including at least one storage layer board and a plurality of stand columns arranged at intervals in a horizontal direction. The storage layer board is spaced apart from the temporary storage layer board in a vertical direction by means of a stand column, and the storage layer board is used for providing a storage position, a first robot passage in which a first robot travels and a second robot passage in which a second robot travels.

Abstract: Disclosed are a shelf device, a robot and a warehousing system. The shelf device comprises: at least two shelves spaced apart, a channel being defined between two adjacent shelves; a track assembly located above the channel and detachably connected to tops of the two adjacent shelves. The track assembly is provided with a track extending in a passing direction of the channel. The track is adapted to be fitted with a robot moving along the channel to limit its position. The stability of the overall structure of the shelf device can be improved and the deformation efficiency of the shelves can be reduced, thereby improving the stability and success rate of the robot to access the material boxes.

Abstract: A warehousing apparatus including a temporary storage layer board used for providing a temporary storage station, and a plurality of shelves with each shelf including at least one storage layer board and a plurality of stand columns arranged at intervals in a horizontal direction. The storage layer board is spaced apart from the temporary storage layer board in a vertical direction by means of a stand column, and the storage layer board is used for providing a storage position, a first robot passage in which a first robot travels and a second robot passage in which a second robot travels.

Abstract: A temporary storage shelf board, a goods shelf, a control method, an apparatus and a system are provided. The temporary storage shelf board is used for providing multiple temporary storage positions. The temporary storage shelf board is provided with fork pockets, and the fork pockets is used for cooperating with a fork arm of a first robot. A goods storage and retrieval channel of the first robot is formed below the temporary storage shelf board, and when goods are stored and retrieved, the first robot is located at the goods storage and retrieval channel, and the fork pockets cooperate with the fork arm on the first robot to store and retrieve the goods. Thus, the efficiency of goods storage and retrieval can be improved.

Abstract: An object carrying assembly and a transfer robot. The object carrying assembly (100) comprises: an object carrying component, which comprises two opposite boundary portions; and clamping and bearing members (120), wherein the clamping and bearing members (120) are arranged at the two boundary portions and are rotationally connected to the object carrying component, and the clamping and bearing members (120) are provided with bearing members (121) and clamping claws (122) on two sides of a rotating shaft (130), respectively. When the object carrying component is empty, the bearing members (121) are at least partially higher than a bearing surface of the object carrying component, and when the object carrying component is loaded, the bearing members (121) are not higher than the bearing surface, and the clamping claws (122) are higher than the bearing surface.

Abstract: A temporary storage shelf board, a goods shelf, a control method, an apparatus and a system are provided. The temporary storage shelf board is used for providing multiple temporary storage positions. The temporary storage shelf board is provided with fork pockets, and the fork pockets is used for cooperating with a fork arm of a first robot. A goods storage and retrieval channel of the first robot is formed below the temporary storage shelf board, and when goods are stored and retrieved, the first robot is located at the goods storage and retrieval channel, and the fork pockets cooperate with the fork arm on the first robot to store and retrieve the goods. Thus, the efficiency of goods storage and retrieval can be improved.

Abstract: In one aspect, a warehousing apparatus comprises: a temporary storage layer board, with the temporary storage layer board being used for providing a temporary storage station; a plurality of shelves, with each shelf comprising at least one storage layer board and a plurality of stand columns arranged at intervals in the horizontal direction, the storage layer board being spaced apart from the temporary storage layer board in the vertical direction by means of the stand column, and the storage layer board being used for providing a storage position; a first robot passage for a first robot to travel in, with the first robot being used for storing goods on and taking goods to the temporary storage layer board and a second robot passage for a second robot to travel in, with the second robot being used for carrying the goods between the temporary storage layer board and the storage layer board.

Abstract: Data is received that encapsulates a spoken response to a prompt text comprising a string of words. Thereafter, the received data is transcribed into a string of words. The string of words is then compared with a prompt so that a similarity grid representation of the comparison can be generated that characterizes a level of similarity between the string of words in the spoken response and the string of words in the prompt text. The grid representation is then scored using at least one machine learning model. The score indicates a likelihood of the spoken response having been off-topic. Data providing the encapsulated score can then be provided. Related apparatus, systems, techniques and articles are also described.

Abstract: Data is received that encapsulates a spoken response to a test question. Thereafter, the received data is transcribed into a string of words. The string of words is then compared with at least one source string so that a similarity grid representation of the comparison can be generated that characterizes a level of similarity between the string of words and the at least one source string. The grid representation is then scored using at least one machine learning model. The score indicates a likelihood of the spoken response having been plagiarized. Data providing the encapsulated score can then be provided. Related apparatus, systems, techniques and articles are also described.

Abstract: Systems and methods are provided for automatic detection of plagiarized spoken responses during standardized testing of language proficiency. A first spoken response to a speaking task that elicits spontaneous speech and a second spoken response to a source-based speaking task that is assumed to be non-plagiarized are digitally recorded. A first and second sets of speaking proficiency features are calculated for the first and the second spoken response, respectively. The first spoken response is classifying as plagiarized or non-plagiarized based on the comparison between the first and the second set of speaking proficiency features. Corresponding apparatuses, systems, and methods are also disclosed.

Abstract: Disclosed is a malfunction early-warning method for production logistics delivery equipment. After a sensor obtains past signal data, performing feature extraction and dimensionality reduction so as to obtain a feature vector; using a growing neural gas (GNG) algorithm to divide normal state data into different operation situations so as to obtain several cluster centers, and calculating the Euclidean distance between the feature vector and the cluster centers obtained from current operation data, so as to obtain a similarity trend; constructing a past memory matrix, using an improved particle swarm algorithm to optimize an LS-SVM regression model parameter, and calculating the residual value of the current state. Finally, combining the residual value and the similarity trend to obtain a risk coefficient, assessing the equipment state, and issuing an early warning for an equipment malfunction.

Abstract: Computer-based systems and methods are provided for generating a narrative computer scoring model for assessing story narratives. In one embodiment, supervised machine learning is used to generate the narrative computer scoring model. For example, a collection of training story narratives with assigned scores may be used to train the model. In one embodiment, each training story narrative is processed to extract features that signify content relevance, collocation of commonly used words, coherency, detailing, and expressions of sentiment. These features, as well as others, may be selectively used to train a narrative computer scoring model. Once trained, the model can be used to automatically evaluate story narratives and assign appropriate scores.

Abstract: Systems and methods are provided for a processor-implemented method of analyzing quality of sound acquired via a microphone. An input metric is extracted from a sound recording at each of a plurality of time intervals. The input metric is provided at each of the time intervals to a neural network that includes a memory component, where the neural network provides an output metric at each of the time intervals, where the output metric at a particular time interval is based on the input metric at a plurality of time intervals other than the particular time interval using the memory component of the neural network. The output metric is aggregated from each of the time intervals to generate a score indicative of the quality of the sound acquired via the microphone.

Abstract: A liquid sampling device for use with a mobile device comprises a wired connection for connecting the liquid sampling device to the mobile device, a sample receiving and testing section capable of receiving a liquid sample and conducting electrochemical testing of the liquid sample and a sample testing circuit configured to communicate at least one liquid sample test result to the mobile device via the wired connection. Associated methods and a mobile device application for interfacing with the liquid sampling device are also disclosed.

Abstract: Computer-implemented systems and methods are provided for automatically scoring the content of moderately predictable responses. For example, a computer performing the content scoring analysis can receive a response (either in text or spoken form) to a prompt. The computer can determine the content correctness of the response by analyzing one or more content features. One of the content features is analyzed by applying one or more regular expressions, determined based on training responses associated with the prompt. Another content feature is analyzed by applying one or more context free grammars, determined based on training responses associated with the prompt. Another content feature is analyzed by applying a keyword list, determined based on the test prompt eliciting the response and/or stimulus material. Another content feature is analyzed by applying one or more probabilistic n-gram models, determined based on training responses associated with the prompt.

Abstract: Systems and methods are provided for automated detection of plagiarized spoken responses. A spoken response is processed to generate a text that is representative of the spoken response. The text is processed to remove disfluencies in the text and to identify a plurality of sentences in the text. A first numerical measure indicative of a number of words and phrases of the text that are included verbatim in a source text is determined. The source text has been designated as a source of plagiarized content. A second numerical measure indicative of an amount of the text that paraphrases portions of the source text is determined. A third numerical measure indicative of a similarity between sentences of the text and sentences of the source text is determined. A model is applied to the first, second, and third numerical measures to classify the spoken response as being plagiarized or non-plagiarized.

Abstract: Systems and methods are provided for automated detection of plagiarized spoken responses. A spoken response is processed to generate a text that is representative of the spoken response. The text is processed to remove disfluencies in the text and to identify a plurality of sentences in the text. A first numerical measure indicative of a number of words and phrases of the text that are included verbatim in a source text is determined. The source text has been designated as a source of plagiarized content. A second numerical measure indicative of an amount of the text that paraphrases portions of the source text is determined. A third numerical measure indicative of a similarity between sentences of the text and sentences of the source text is determined. A model is applied to the first, second, and third numerical measures to classify the spoken response as being plagiarized or non-plagiarized.