Product titles play a central role in connecting e-commerce buyers and sellers, concisely conveying the product information required to facilitate a transaction. Product titles are prominently presented in e-commerce search results, recommendations, and browse pages. In addition, many e-commerce tasks, such as matching, categorization, and product recommendations, heavily rely on product titles as a signal. In this paper, we report a comprehensive analysis of the linguistic characteristics of e-commerce titles. Specifically, we consider syntax, content, order of words, and attribute distribution. We compare these characteristics to those of the language used in other e-commerce and web corpora. Our analysis reveals a variety of unique properties of e-commerce title language. We consider the practical implications of our analysis with an empirical evaluation of modeling approaches on two real-world e-commerce tasks directly involving titles. Our findings suggest a number of practical approaches to applying natural language processing techniques over e-commerce titles. Based on these findings, we developed a set of clear, actionable guidelines for creating effective titles that can be adapted by e-commerce platforms.

The Electrocardiogram (ECG) is performed routinely by medical personell to identify structural, functional and electrical cardiac events. Many attempts were made to automate this task using machine learning algorithms. Numerous supervised learning algorithms were proposed, requiring manual feature extraction. Lately, deep neural networks were also proposed for this task for reaching state-of-the-art results. The ECG signal conveys the specific electrical cardiac activity of each subject thus extreme variations are observed between patients. These variations and the low amount of training data available for each arrhythmia are challenging for deep learning algorithms, and impede generalization. In this work, the use of generative adversarial networks is studied for the synthesis of ECG signals, which can then be used as additional training data to improve the classifier performance. Empirical results prove that the generated signals significantly improve ECG classification.

Dor Ringel, Gal Lavee, Ido Guy, Kira Radinsky. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Maximizing sales and revenue is an important goal of online commercial retailers. Recommender systems are designed to maximize users' click or purchase probability, but often disregard users' eventual satisfaction with purchased items. As result, such systems promote items with high appeal at the selling stage (e.g. an eyecatching presentation) over items that would yield more satisfaction to users in the long run. This work presents a novel unified model that considers both goals and can be tuned to balance between them according to the needs of the business scenario.

This paper describes an algorithm designed for Microsoft's Groove music service, which serves millions of users world wide. We consider the problem of automatically generating personalized music playlists based on queries containing a ``seed'' artist and the listener's user ID. Playlist generation may be informed by a number of information sources including: user specific listening patterns, domain knowledge encoded in a taxonomy, acoustic features of audio tracks, and overall popularity of tracks and artists. The importance assigned to each of these information sources may vary depending on the specific combination of user and seed~artist.

Collaborative filtering (CF) recommendation systems are one of the most popular and successful methods for recommending products to people. CF systems work by finding similarities between different people according to their past purchases, and using these similarities to suggest possible items of interest. In this work we show that CF systems can be enhanced using Internet browsing data and search engine query logs, both of which represent a rich profile of individuals' interests.

This paper considers the problem of recognizing activities as they occur in surveillance video. Activities are high-level nonatomic semantic concepts which may have complex temporal structure. Activities are not easily identifiable using image features, but rather by the recognition of their composing events. Unfortunately, these composing events may only be observed up to a particular certainty. This paper describes particle filter Petri Net (PFPN), an activity recognition process that combines uncertain event observations to determine the likelihood that a particular activity is taking place in a video sequence. Our paper is based on previous study in which activities are specified as Petri Nets. The stochastic PFPN framework proposed in this paper improves over existing deterministic approaches to activity recognition by enabling the certainty reasoning required for coping with inherent ambiguity in both low-level video processing and activity definition. Furthermore, the PFPN approach reduces the dependence on a duration model and enables the creation of holistic activity models. Often when activity recognition frameworks are proposed they are strongly paired with a particular methodology for low-level video processing and event recognition. Each proposed approach is then applied to a nonstandard dataset. In our experiments, we provide an empirical comparison of our approach with leading activity recognition approaches across several datasets, using a constant event recognition as input. Our results illustrate the tradeoff between deterministic and stochastic activity recognition approaches. Furthermore, our experiments suggest that the holistic PFPN approach is more robust for activity recognition in the surveillance video domain than competing approaches.

Modern search engines are expected to make documents searchable shortly after they appear on the ever changing Web. To satisfy this requirement, the Web is frequently crawled. Due to the sheer size of their indexes, search engines distribute the crawled documents among thousands of servers in a scheme called local index-partitioning, such that each server indexes only several million pages. To ensure documents from the same host (e.g., www.nytimes.com) are distributed uniformly over the servers, for load balancing purposes, random routing of documents to servers is common. To expedite the time documents become searchable after being crawled, documents may be simply appended to the existing index partitions. However, indexing by merely appending documents, results in larger index sizes since document reordering for index compactness is no longer performed. This, in turn, degrades search query processing performance which depends heavily on index sizes.

In this paper, we propose the surveillance event recognition framework using Petri Nets (SERF-PN) for recognition of event occurrences in video. The Petri Net (PN) formalism allows a robust way to express semantic knowledge about the event domain as well as efficient algorithms for recognizing events as they occur in a particular video sequence. The major novelties of this paper are extensions to both the modeling and the recognition capacities of the Object PN paradigm. The first contribution of this paper is the extension of the PN representational capacities by introducing stochastic timed transitions to allow modeling of events which have some variance in duration. These stochastic timed transitions sample the duration of the condition from a parametrized distribution. The parameters of this distribution can be specified manually or learned from available video data. A second representational novelty is the use of a single PN to represent the entire event domain, as opposed to previous approaches which have utilized several networks, one for each event of interest. A third contribution of this paper is the capacity to probabilistically predict future events by constructing a discrete time Markov chain model of transitions between states. The experiments section of the paper thoroughly evaluates the application of the SERF-PN framework in the event domains of surveillance and traffic monitoring and provides comparison to other approaches using the CAVIAR dataset , a standard dataset for video analysis applications.

Understanding video events, i.e., the translation of low-level content in video sequences into high-level semantic concepts, is a research topic that has received much interest in recent years. Important applications of this paper include smart surveillance systems, semantic video database indexing, and interactive systems. This technology can be applied to several video domains including airport terminal, parking lot, traffic, subway stations, aerial surveillance, and sign language data. In this paper, we identify the two main components of the event understanding process: abstraction and event modeling. Abstraction is the process of molding the data into informative units to be used as input to the event model. Due to space restrictions, we will limit the discussion on the topic of abstraction. See the study byLaveeetal.(Understanding video events: A survey of methods for automatic interpretation of semantic occurrences in video, Technion-Israel Inst. Technol., Haifa, Israel, Tech. Rep. CIS-2009-06, 2009) for a more complete discussion. Event modeling is devoted to describing events of interest formally and enabling recognition of these events as they occur in the video sequence. Event modeling can be further decomposed in the categories of pattern-recognition methods, state event models, and semantic event models. In this survey, we discuss this proposed taxonomy of the literature, offer a unifying terminology, and discuss popular event modeling formalisms (e.g., hidden Markov model) and their use in video event understanding using extensive examples from the literature. Finally, we consider the application domain of video event understanding in light of the proposed taxonomy, and propose future directions for research in this field.

In this paper we have addressed confidentiality and privacy for video surveillance databases. First we discussed our overall approach for suspicious event detection. Next we discussed an access control model and accedes control algorithms for confidentiality. Finally we discuss privacy preserving video surveillance. Our goal is build a comprehensive system that can detect suspicious events, ensure confidentiality as well as privacy.