Abstract: A method, computer program product, and system include a processor(s) obtains various data relevant to one or more infected individuals (e.g., location data and infection data. The processor(s) identifies relationships and physical proximity between people comprising the one or more infected individuals and additional individuals, based on the various data. The processor(s) generates, based on the various data, a geofence. The processor(s) utilizes the geofence, the relationships, the physical proximity, and the various data, to predict that a portion of the additional individuals are more likely than not to be infected with the infection. The processor(s) generates a scoring model to identify a source of the infection. The processor(s) applies the scoring model to a group consisting of the one or more individuals, the portion of the additional individuals, and the physical locations, to identify the source of the infection (i.e., a person or physical locations).

Abstract: A computer-implemented method, a computer system and a computer program product use artificial intelligence (AI) to extract information from a phisher. The method may include identifying a malicious email on a server. The malicious email comprises an attempt by the phisher to compromise a user. The method may also include generating an automated conversational agent that poses as the user. The method may further include transmitting a message to the phisher by the automated conversational agent. The message indicates that the user has been compromised. In addition, the method may include receiving a response from the phisher. Lastly, the method may include determining an intent of the phisher based on the response.

Abstract: In an approach for precipitation detection, a processor trains a first machine learning model for detecting precipitation in a region using a first training set of data including a plurality of historical data from a plurality of mobile devices collected in the region and a plurality of quantitative precipitation estimation data. A processor trains a second machine learning model for detecting a location of a mobile device in the region using a second training set of data including both historical indoor and outdoor data from the plurality of mobile devices collected. A processor receives a current data from the mobile device. A processor determines whether the mobile device is located indoor or outdoor based on the current data. A processor compares the current data against a threshold set in the first machine learning model to indicate precipitation. A processor determines whether the current data exceeds the threshold.

Abstract: Embodiments are directed to techniques for secure network connectivity. The techniques including a system having a credential server storing a Personal-Public (PP) Service Set. Identifier (SSID) profile configured according to registration information provided from a personal computing device. The system further including a Wireless Access Point (WAP) communicatively coupled to the credential server and configured to implement a PP SSID connection using the PP SSID profile to create a single-device, single-use, password-protected, unadvertised, and encrypted networking channel between the personal computing device and the Internet.

Abstract: Embodiments are directed to techniques for secure network connectivity. The techniques including a system having a credential server storing a Personal-Public (PP) Service Set Identifier (SSID) profile configured according to registration information provided from a personal computing device. The system further including a Wireless Access Point (WAP) communicatively coupled to the credential server and configured to implement a PP SSID connection using the PP SSID profile to create a single-device, single-use, password-protected, unadvertised, and encrypted networking channel between the personal computing device and the Internet.

Abstract: An approach is provided in which the approach converts a set of learning content into a teaching content vector representation. The approach determines a learning preference mode of a user based on a set of user responses responding to a set of learning type segments, and computes an environmental context vector representation of a physical environment surrounding the user. The approach conducts a conversation with the user based on the learning preference mode, the environmental context vector representation, and the teaching content vector representation.

Abstract: An approach is disclosed that ingests sets of metadata into an AI system utilizing a model trained to recognize a plurality of programming categories. Each of the sets of metadata corresponds to a computer programming language, and the ingested sets of metadata are stored in a corpus accessible by the AI system. A block of programming code is input to the AI system with the block including a number of computer instructions written in a computer programming language. Recommended programming languages are received from the AI system based on comparing the categories found in the block of programming code with the metadata corresponding to the recommended programming languages.

Abstract: Configuration and dynamic profiling of storage class memory (SCM) devices is provided. Information is retrieved that includes historical SCM device configurations, historical SCM device utilization, functional and non-functional properties of a plurality of SCM devices on a host node, current real time utilization of the plurality of SCM devices by an application workload of a customer running on the host node, and relationships between the plurality of SCM devices, needs of the customer, and resource capabilities and real time resource utilization on the host node. A configuration of each respective SCM device is determined based on retrieved information and an artificial intelligence-predicted SCM device future utilization trajectory of the customer. Each respective SCM device is dynamically configured with a set of SCM device partitions according to a corresponding SCM device profile based on the determined configuration of each respective SCM device of the plurality of SCM devices.

Abstract: A processor may run a background process to identify a first task being initiated by a first user on a device, where the first task is associated with a first application. The processor may identify the first user of the device. The processor may analyze one or more interactions of the first user associated with the first application on the device. The processor may allocate, based at least in part on identification of the first user, identification of the first task, or analysis of the one or more interactions of the first user, computing resources to one or more hardware components on the device.

Abstract: In an approach for optimally grouping containers, a processor passively monitors a set of parameters for a set of containers within a network. A processor records the set of parameters for each container of the set of containers. A processor deploys a k-nearest neighbor neural network (KNN) to determine a first set of groupings of the set of containers based on the set of parameters. A processor simulates the network having grouped containers based on the first set of groupings of the set of containers output by the KNN. A processor simulates an action on the set of containers. A processor updates a reward function based on the action. A processor determines whether a maximum value of the reward function is reached.

Abstract: A method, system, and computer program product for malicious virtual machine detection is provided. The method passes network traffic from a set of virtual machines to a set of network resources. The network traffic passes through a network monitor. The network monitor monitors a set of traffic characteristics for the network traffic. A change in a network traffic characteristic is identified. The change results in a modified traffic characteristic. The method identifies a first virtual machine of the set of virtual machines. The first virtual machine is associated with the modified traffic characteristics. An alert is then generated which identifies the first virtual machine and the modified traffic characteristic.

Abstract: A method, system and computer-usable medium are disclosed for providing a travel network infrastructure. A dedicated Li-F connection is provided for each physical space such as a seat, expected to accommodate a passenger in a travel network such as an airline. A passenger is authenticated according to a physical space occupied by the passenger. If authentication is successful, a secure Li-Fi channel is established for the passenger. The access point of the Li-Fi channel is only leveraged by the passenger and no other passengers.

Abstract: A computer-implemented method to determine which port in a container is a service port. The method includes identifying, a first container, wherein the first container comprises a plurality of ports. The method further includes, training a neural network, wherein the neural network is configured to identify at least one service port from the plurality of ports. The method further includes, monitoring, by a network monitor, a set of data sent to the first container comprising a first parameter. The method includes, identifying a first service port of the plurality of ports. The method further includes, marking the first service port.

Abstract: According to one embodiment, a method, computer system, and computer program product for detecting one or more loose or malfunctioning components within a machine is provided. The present invention may include measuring, by one or more sensors, one or more vibration signals and one or more acoustic signals of the machine; determining one or more joint signals, wherein the one or more joint signals comprise one or more relationships between the one or more vibration signals and the one or more acoustic signals; and responsive to one or more new signals deviating from the one or more vibration signals, one or more acoustic signals, and/or one or more joint signals by an amount exceeding at least one threshold, triggering one or more ameliorative actions.

Abstract: Computer software that stores information relating to a planned in-person meeting between a first user and a second user. The information includes a meeting time, a meeting location, and, for each of the first user and the second user: (i) a communications identifier associated with a device of the respective user, and (ii) a token identified by the respective user for the meeting. The computer software, in response to receiving an indication that the device of the first user is located at the meeting location at the meeting time, sends, to the device of the first user: (i) the communications identifier associated with the device of the second user, and (ii) the token identified by the second user for the meeting.

Abstract: Embodiments of the present invention provide methods, computer program products, and systems. In response to receiving a request associated with a data transfer, embodiments of the present invention dynamically determine a first device to facilitate the data transfer. Embodiments of the present invention automatically create a link between the first device that contains data to be transferred and a second device that is an intended recipient for the data.

Abstract: Embodiments of the present invention provide methods, computer program products, and systems. In response to receiving a request associated with a data transfer, embodiments of the present invention dynamically determine a first device to facilitate the data transfer. Embodiments of the present invention automatically create a link between the first device that contains data to be transferred and a second device that is an intended recipient for the data.

Abstract: A method and system for selecting and modifying a geofence is provided. The method includes registering a physical object with geofence policies of a user. The physical object is tagged with a digital tag comprising information associated with the user respect and geofence policies. Input data indicating a value, an emotional attachment, and a sentiment of the physical object with respect to the t user is received and the physical object is detected via sensors. Reactions of the user with respect to additional physical objects located within a specified geographical boundary surrounding the first user are detected and a resulting classification for the physical object is generated. A geofence policy is selected and it is detected that the physical object has been removed from a geofence for a specified time period threshold. A resulting action associated with detecting that the physical object has been removed from the geofence is executed.

Abstract: Embodiments of the present invention provide a computer system, a computer program product, and a method that comprises generating a risk score by assigning values to at least one contextual factor of a plurality of contextual factors and aggregating the assigned values using a determination engine; creating a geo-fence by establishing geographical boundaries proportional to the generated risk score; and dynamically recalculating the generated risk score based on an identified change to the at least one contextual factor of the plurality of contextual factors.

Abstract: An embodiment includes specifying an original model to be trained by a machine learning platform using training data and an algorithm, wherein the training data includes a plurality of training input values upon which to make a prediction and a plurality of training output values representing target prediction values. The embodiment generates an updated model by training the original model to algorithmically approach the training output values as provided with the training data based on the plurality of input values. The embodiment generates an updated function map representative of output values of the updated model and versions the updated model according to a classification rule for assigning a version classification based on a comparison of the updated function map to an original function map representative of output values of the original model. The embodiment deploys the updated model into a production environment. to replace the original model.