Abstract: The present disclosure relates to a system (100) for extracting electrode material from batteries. A shredding unit (104) configured to receive the cooled feedstock from the freezing unit (102). The shredding unit (104) is configured to shred the feedstock into powder form. A cyclone separator (110) configured with the shredding unit (104), and configured to receive air bone electrode material particles generated as a result of shredding the batteries. A separating unit (106) configured with the shredding unit (104), and configured to separate the electrode material particles. A cleaning unit (108) operatively configured with the separating unit and the cyclone separator (110). The cleaning unit (108) is configured to receive the powdered electrode particles from the shredding unit 104), and powdered electrode materials from a first output of the cyclone separator (110). A mixing agitator (110) is configured to receive the powdered electrode material from the cleaning unit (108).

Abstract: A method for detecting cloud service uptime for a cloud service on a cloud service provider, by means of a cognitive agent. The method includes receiving, by the cognitive agent, a cloud service request from a client, and determining the cloud service uptime based on a cloud service uptime model. The method further includes preventing the cloud service request from the client if it is determined that the cloud service uptime exceeds a given threshold, and transmitting, by the cognitive agent, cloud service uptime details to the client. The method further includes requesting a provisioning of a cloud service if it is determined that the cloud service uptime does not exceed the given threshold. The method further includes training, by the cognitive agent, the cloud service uptime model based on a dynamic polling of the cloud service, wherein the cloud service uptime model provides an optimal response to the client.

Abstract: Machine training for determining sentiments in social network communications. A text document is extracted from a web site and tokenized into tokens. The tokens are input to a word to vector conversion model to generate word vectors. A term frequency inverse document frequency (TF-IDF) algorithm converts the word vectors to sentence vectors. A randomly selected subset the sentence vectors are tagged and used to train a classifier. The classifier takes a sentence vector and predicts a sentiment associated with the sentence vector. Predicted sentiment associated with each of the sentence vectors may be combined to generate a sentiment associated with the text document.

Abstract: In one example, a system is described in which a storage device, communicatively coupled to a processor, includes a file system management module. This file system management module may create a hierarchical organization directory that is a device and an operating system independent under a root directory in a namespace of a file system. Further, the file system management module may create a user directory under the hierarchical organization directory. Furthermore, the file system management module may create policy and configuration data for the user directory. In addition, the file system management module may store the policy and configuration data created for the user directory in the hierarchical organization directory.

Abstract: Machine training for determining sentiments in social network communications. A text document is extracted from a web site and tokenized into tokens. The tokens are input to a word to vector conversion model to generate word vectors. A term frequency inverse document frequency (TF-IDF) algorithm converts the word vectors to sentence vectors. A randomly selected subset the sentence vectors are tagged and used to train a classifier. The classifier takes a sentence vector and predicts a sentiment associated with the sentence vector. Predicted sentiment associated with each of the sentence vectors may be combined to generate a sentiment associated with the text document.

Abstract: Machine training for determining sentiments in social network communications. A text document is extracted from a web site and tokenized into tokens. The tokens are input to a word to vector conversion model to generate word vectors. A term frequency inverse document frequency (TF-IDF) algorithm converts the word vectors to sentence vectors. A randomly selected subset the sentence vectors are tagged and used to train a classifier. The classifier takes a sentence vector and predicts a sentiment associated with the sentence vector. Predicted sentiment associated with each of the sentence vectors may be combined to generate a sentiment associated with the text document.

Abstract: In one example, a system is described in which a storage device, communicatively coupled to a processor, includes an inconsistency detection and reconstruction tool. The inconsistency detection and reconstruction tool may create a database including metadata associated with hierarchical organization directories and policy and configuration data files in a file system. Further, the inconsistency detection and reconstruction tool may perform consistency check of one or more of the hierarchical organization directories, the policy and configuration data files and the metadata to detect inconsistencies. Furthermore, the inconsistency detection and reconstruction tool may reconstruct one or more of the hierarchical organization directories, the policy and configuration data files and the metadata based on the outcome of the consistency check.

Abstract: Machine training for determining sentiments in social network communications. A text document is extracted from a web site and tokenized into tokens. The tokens are input to a word to vector conversion model to generate word vectors. A term frequency inverse document frequency (TF-IDF) algorithm converts the word vectors to sentence vectors. A randomly selected subset the sentence vectors are tagged and used to train a classifier. The classifier takes a sentence vector and predicts a sentiment associated with the sentence vector. Predicted sentiment associated with each of the sentence vectors may be combined to generate a sentiment associated with the text document.

Abstract: A method for detecting cloud service uptime for a cloud service on a cloud service provider, by means of a cognitive agent. The method includes receiving, by the cognitive agent, a cloud service request from a client, and determining the cloud service uptime based on a cloud service uptime model. The method further includes preventing the cloud service request from the client if it is determined that the cloud service uptime exceeds a given threshold, and transmitting, by the cognitive agent, cloud service uptime details to the client. The method further includes requesting a provisioning of a cloud service if it is determined that the cloud service uptime does not exceed the given threshold. The method further includes training, by the cognitive agent, the cloud service uptime model based on a dynamic polling of the cloud service, wherein the cloud service uptime model provides an optimal response to the client.

Abstract: In one example, a system is described in which a storage device, communicatively coupled to a processor, includes an inconsistency detection and reconstruction tool. The inconsistency detection and reconstruction tool may create a database including metadata associated with hierarchical organization directories and policy and configuration data files in a file system. Further, the inconsistency detection and reconstruction tool may perform consistency check of one or more of the hierarchical organization directories, the policy and configuration data files and the metadata to detect inconsistencies. Furthermore, the inconsistency detection and reconstruction tool may reconstruct one or more of the hierarchical organization directories, the policy and configuration data files and the metadata based on the outcome of the consistency check.

Abstract: In one example, a system is described in which a storage device, communicatively coupled to a processor, includes a file system management module. This file system management module may create a hierarchical organization directory that is a device and an operating system independent under a root directory in a namespace of a file system. Further, the file system management module may create a user directory under the hierarchical organization directory. Furthermore, the tile system management module may create policy and configuration data for the user directory. In addition, the file system management module may store the policy and configuration data created for the user directory in the hierarchical organization directory.