Abstract: A folding pallet truck comprises a frame module, a lifting module installed on the frame module and comprising a mount, and a handle module comprising a tube in rotating fit with the mount, wherein a first constraint part is formed between an end, rotatably connected to the mount, of the tube and the mount, and the handle module is folded by detaching the first constraint part or is unfolded by reassembling the first constraint part. The handle module can be folded to be stored or be unfolded and locked to be used by detaching or reassembling the first constraint part formed between the tube of the handle module and the mount of the lifting module, operation is easy, and the pallet truck has a small size when stored.

Abstract: Embodiments of the present invention provide a system that can be used to classify a feedback image in a user review into a semantically meaningful class. During operation, the system analyzes the captions of feedback images in a set of user reviews and determines a set of training labels from the captions. The system then trains an image classifier with the set of training labels and the feedback images. Subsequently, the system generates a signature for a respective feedback image in a new set of user reviews using the image classifier. The signature indicates a likelihood of the image matching a respective label in the set of training labels. Based on the signature, the system can allocate the image to an image cluster.

Abstract: Embodiments of the present invention provide a system that that can be used to determine whether a sentiment analysis model is portable between two data sets. During operation, the system analyzes the text of a respective review in a data set (e.g., a set of reviews) using the sentiment analysis model to determine a sentiment expressed in the review. The system then computes a confidence score, which indicates the accuracy of a respective sentiment. The system subsequently determines a confidence score distribution for various sentiments, as determined by the sentiment analysis model. The system determines the significance of changes between the confidence score distribution and a benchmark confidence score distribution, which is associated with a benchmark data set for which the sentiment analysis model yields a high accuracy. The system can then determine whether the sentiment analysis model is portable to the data set based on the significance of changes.

Abstract: Embodiments of the present invention provide a system that that can be used to determine whether a sentiment analysis model is portable between two data sets. During operation, the system analyzes the text of a respective review in a data set (e.g., a set of reviews) using the sentiment analysis model to determine a sentiment expressed in the review. The system then computes a confidence score, which indicates the accuracy of a respective sentiment. The system subsequently determines a confidence score distribution for various sentiments, as determined by the sentiment analysis model. The system determines the significance of changes between the confidence score distribution and a benchmark confidence score distribution, which is associated with a benchmark data set for which the sentiment analysis model yields a high accuracy. The system can then determine whether the sentiment analysis model is portable to the data set based on the significance of changes.

Abstract: One embodiment provides a system that obtains, for a business entity, review and revenue information for a period of time from network packets, and extracts rankings and revenues from the review and revenue information. The system determines respective correspondence between the rankings and revenues for a plurality of points of time. The system determines first and second normalized rankings for first and second points of time, respectively, in the plurality of points of time. The system determines first and second normalized revenues for the first and second points of time, respectively. The system calculates a first correlation strength between the changes in the first and second normalized rankings, and the changes in the first and second normalized revenues. The system computes a correlation between the rankings and corresponding revenues based on a plurality of correlation strengths, which includes the first correlation strength, over the period of time.

Abstract: Embodiments of the present invention provide a system that can be used to classify a feedback image in a user review into a semantically meaningful class. During operation, the system analyzes the captions of feedback images in a set of user reviews and determines a set of training labels from the captions. The system then trains an image classifier with the set of training labels and the feedback images. Subsequently, the system generates a signature for a respective feedback image in a new set of user reviews using the image classifier. The signature indicates a likelihood of the image matching a respective label in the set of training labels. Based on the signature, the system can allocate the image to an image cluster.

Abstract: Embodiments of the present invention provide a system that that can be used to determine whether a sentiment analysis model is portable between two data sets. During operation, the system analyzes the text of a respective review in a data set (e.g., a set of reviews) using the sentiment analysis model to determine a sentiment expressed in the review. The system then computes a confidence score, which indicates the accuracy of a respective sentiment. The system subsequently determines a confidence score distribution for various sentiments, as determined by the sentiment analysis model. The system determines the significance of changes between the confidence score distribution and a benchmark confidence score distribution, which is associated with a benchmark data set for which the sentiment analysis model yields a high accuracy. The system can then determine whether the sentiment analysis model is portable to the data set based on the significance of changes.

Abstract: Embodiments of the present invention provide a system that can be used to classify a feedback image in a user review into a semantically meaningful class. During operation, the system analyzes the captions of feedback images in a set of user reviews and determines a set of training labels from the captions. The system then trains an image classifier with the set of training labels and the feedback images. Subsequently, the system generates a signature for a respective feedback image in a new set of user reviews using the image classifier. The signature indicates a likelihood of the image matching a respective label in the set of training labels. Based on the signature, the system can allocate the image to an image cluster.

Abstract: Embodiments of the present invention provide a system that that can be used to determine whether a sentiment analysis model is portable between two data sets. During operation, the system analyzes the text of a respective review in a data set (e.g., a set of reviews) using the sentiment analysis model to determine a sentiment expressed in the review. The system then computes a confidence score, which indicates the accuracy of a respective sentiment. The system subsequently determines a confidence score distribution for various sentiments, as determined by the sentiment analysis model. The system determines the significance of changes between the confidence score distribution and a benchmark confidence score distribution, which is associated with a benchmark data set for which the sentiment analysis model yields a high accuracy. The system can then determine whether the sentiment analysis model is portable to the data set based on the significance of changes.

Abstract: Embodiments of the present invention provide a system that can be used to classify a feedback image in a user review into a semantically meaningful class. During operation, the system analyzes the captions of feedback images in a set of user reviews and determines a set of training labels from the captions. The system then trains an image classifier with the set of training labels and the feedback images. Subsequently, the system generates a signature for a respective feedback image in a new set of user reviews using the image classifier. The signature indicates a likelihood of the image matching a respective label in the set of training labels. Based on the signature, the system can allocate the image to an image cluster.

Abstract: A frame is provided, for applying potting material to a printed circuit board (PCB). The frame includes a plurality of substantially vertically oriented walls arranged to isolate a surface of the PCB into a plurality of zones. This allows the frame to be aligned over the surface of the PCB to isolate the surface of the PCB into a plurality of zones, each zone comprising at least one PCB component; and allows the potting material to be applied into the zones to achieve coverage of the PCB components in that zone.

Abstract: Embodiments of the present invention provide a system that can be used to classify a feedback image in a user review into a semantically meaningful class. During operation, the system analyzes the captions of feedback images in a set of user reviews and determines a set of training labels from the captions. The system then trains an image classifier with the set of training labels and the feedback images. Subsequently, the system generates a signature for a respective feedback image in a new set of user reviews using the image classifier. The signature indicates a likelihood of the image matching a respective label in the set of training labels. Based on the signature, the system can allocate the image to an image cluster.

Abstract: Embodiments of the present invention provide a system that that can be used to determine whether a sentiment analysis model is portable between two data sets. During operation, the system analyzes the text of a respective review in a data set (e.g., a set of reviews) using the sentiment analysis model to determine a sentiment expressed in the review. The system then computes a confidence score, which indicates the accuracy of a respective sentiment. The system subsequently determines a confidence score distribution for various sentiments, as determined by the sentiment analysis model. The system determines the significance of changes between the confidence score distribution and a benchmark confidence score distribution, which is associated with a benchmark data set for which the sentiment analysis model yields a high accuracy. The system can then determine whether the sentiment analysis model is portable to the data set based on the significance of changes.

Abstract: Text clustering includes: identifying, for a set of non-stop words in a text, a corresponding set of related topic clusters relating to the set of non-stop words, the identification being based at least in part on a plurality of topic clusters each comprising a corresponding plurality of topically related words and a corresponding cluster identifier; for non-stop words in the set of non-stop words that are identified to have corresponding related topic clusters, replacing the non-stop words with corresponding cluster identifiers of the corresponding related topic clusters to generate a clustered version of the text; and providing the clustered version of the text to be further analyzed.

Abstract: One embodiment provides a system that obtains, for a business entity, review and revenue information for a period of time from network packets, and extracts rankings and revenues from the review and revenue information. The system determines respective correspondence between the rankings and revenues for a plurality of points of time. The system determines first and second normalized rankings for first and second points of time, respectively, in the plurality of points of time. The system determines first and second normalized revenues for the first and second points of time, respectively. The system calculates a first correlation strength between the changes in the first and second normalized rankings, and the changes in the first and second normalized revenues. The system computes a correlation between the rankings and corresponding revenues based on a plurality of correlation strengths, which includes the first correlation strength, over the period of time.

Abstract: Context-based sentiment analysis includes: determining whether a piece of comment data included in a comment is context-sensitive, the determination being made with reference to a set of comment features; determining a context sentiment type of an associated context related to the piece of comment data, the associated context being distinct from the comment, in the event that the piece of comment data is determined to be context-sensitive, and the determination being made with reference to at least a set of context features; and classifying the comment data in the event that the piece of comment data is determined to be context-sensitive, the classification being based at least in part on the comment data and the context sentiment type.

Abstract: One embodiment provides a system that facilitates detects and analyzes surprises in user reviews. During operation, the system stores, in a storage device, a plurality of user reviews. A user review includes a recommend score indicating a likelihood of recommending, and one or more feature values indicating user opinions about features in the user review. The system determines a first user review from the plurality of user reviews to be a first surprise in response to detecting a discrepancy between a recommend score and feature values of the first user review. The system then performs a text analysis on the first surprise to discover impactful features in the surprise.

Abstract: An automatic paraphrase acquisition technique is provided. A common theme of the various embodiments described herein resides in careful design of simple tasks that can elicit the necessary information for the automated process. These tasks are performed quickly and inexpensively. By gathering the results produced, paraphrases can be generated automatically using the method and/or system.

Abstract: A fine-grained channel access system and method to facilitate fine-grained channel access in a high-data rate wide-band wireless local-area network (WLAN). Embodiments of the system and method divide an entire wireless channel into proper size subchannels commensurate with the physical layer data rate and typical frame size. Once the subchannels are defined, each node on the WLAN contends independently for each of the fine-grained subchannels. A first orthogonal frequency-division multiplexing (OFDM) technique is used to signal an access point on the WLAN that the node desires one or more of the subchannels. A second OFDM technique (which is different from the first OFDM technique) is used for data transmission. Sometimes there is contention between nodes that want the same subchannel. The access point resolves any contention between the nodes using a frequency domain contention technique that includes a frequency domain backoff technique.

Abstract: Techniques and systems that improve throughput between a pair of nodes by using two multi-hop paths of one-way flows regardless of the one-way flows interfering with each other are described herein. These techniques enable nearly full-rate data flow through frame transmissions, even though these frame transmissions can interfere with substantially concurrent relay transmissions. In some implementations, relays on the two paths forward mixed frame signals to the next hop without trying to decode the mixed frame signals of interfered frames. The destination successfully recovers the useful information from the mixed frame signals by canceling out interference based on previously received frames.