Relation extraction (RE) aims to identify the semantic relations between named entities in text. Recent years have witnessed it raised to the document level, which requires complex reasoning with entities and mentions throughout an entire document. In this paper, we propose a novel model to document-level RE, by encoding the document information in terms of entity global and local representations as well as context relation representations. Entity global representations model the semantic information of all entities in the document, entity local representations aggregate the contextual information of multiple mentions of specific entities, and context relation representations encode the topic information of other relations. Experimental results demonstrate that our model achieves superior performance on two public datasets for document-level RE. It is particularly effective in extracting relations between entities of long distance and having multiple mentions.

Sign language visual recognition from continuous multi-modal streams is still one of the most challenging fields. Recent advances in human actions recognition are exploiting the ascension of GPU-based learning from massive data, and are getting closer to human-like performances. They are then prone to creating interactive services for the deaf and hearing-impaired communities. A population that is expected to grow considerably in the years to come. This paper aims at reviewing the human actions recognition literature with the sign-language visual understanding as a scope. The methods analyzed will be mainly organized according to the different types of unimodal inputs exploited, their relative multi-modal combinations and pipeline steps. In each section, we will detail and compare the related datasets, approaches then distinguish the still open contribution paths suitable for the creation of sign language related services. Special attention will be paid to the approaches and commercial solutions handling facial expressions and continuous signing.

Botnet is one of the major threats to computer security. In previous botnet command and control (C&C) scenarios using online social networks (OSNs), methods for finding botmasters (e.g. ids, links, DGAs, etc.) are hardcoded into bots. Once a bot is reverse engineered, botmaster is exposed. Meanwhile, abnormal contents from explicit commands may expose botmaster and raise anomalies on OSNs. To overcome these deficiencies, we propose an AI-powered covert C&C channel. On leverage of neural networks, bots can find botmasters by avatars, which are converted into feature vectors. Commands are embedded into normal contents (e.g. tweets, comments, etc.) using text data augmentation and hash collision. Experiment on Twitter shows that the command-embedded contents can be generated efficiently, and bots can find botmaster and obtain commands accurately. By demonstrating how AI may help promote a covert communication on OSNs, this work provides a new perspective on botnet detection and confrontation.

However, the performance of AL heuristics depends on the structure of the underlying classifier model and the data. We propose an imitation learning scheme that imitates the selection of the best expert heuristic at each stage of the AL cycle in a batch-mode pool-based setting. With multiple AL heuristics as experts, the policy is able to reflect the choices of the best AL heuristics given the current state of the AL process. Our experiment on well-known datasets show that we both outperform state of the art imitation learners and heuristics. The high performance of deep learning on various tasks from computer vision (Voulodimos et al., 2018) to natural language processing (NLP) (Barrault et al., 2019) also comes with disadvantages. One of their main drawbacks is the large amount of labeled training data they require. Obtaining such data is expensive and time-consuming and often requires domain expertise (Löffler et al., 2020). Active Learning (AL) is an iterative process where during every iteration an oracle (e.g. a human) is asked to label the most informative unlabeled data sample(s). In pool-based AL all data samples are available (while most of them are unlabeled). In batch-mode pool-based AL, we select unlabeled data samples from the pool in acquisition batches greater than 1. Batch-mode AL decreases the number of AL iterations required and makes it easier for an oracle to label the data samples (Settles, 2009).

Recently, a hybrid Deep Neural Network (DNN) algorithm, TreNet was proposed for predicting trends in time series data. While TreNet was shown to have superior performance for trend prediction to other DNN and traditional ML approaches, the validation method used did not take into account the sequential nature of time series data sets and did not deal with model update. In this research we replicated the TreNet experiments on the same data sets using a walk-forward validation method and tested our optimal model over multiple independent runs to evaluate model stability. We compared the performance of the hybrid TreNet algorithm, on four data sets to vanilla DNN algorithms that take in point data, and also to traditional ML algorithms. We found that in general TreNet still performs better than the vanilla DNN models, but not on all data sets as reported in the original TreNet study. This study highlights the importance of using an appropriate validation method and evaluating model stability for evaluating and developing machine learning models for trend prediction in time series data.

The original example of a deepfake (by reddit user /u/deepfake) swapped the face of an actress onto the body of a porn performer in a video – which was, of course, completely unethical, although not initially illegal. Other deepfakes have changed what famous people were saying, or the language they were speaking. Deepfakes extend the idea of video (or movie) compositing, which has been done for decades. Significant video skills, time, and equipment go into video compositing; video deepfakes require much less skill, time (assuming you have GPUs), and equipment, although they are often unconvincing to careful observers. Originally, deepfakes relied on autoencoders, a type of unsupervised neural network, and many still do.

A new AI system could be used to protect the lives of both humans and elephants. The environmental organization RESOLVE has recently collaborated with the AI developer CVEDIA to engineer an AI that can help prevent encounters between elephants and humans, which sometimes end in the death of one or both parties. For people who live in close proximity to elephants, it isn't uncommon to hear of someone who has died due to an encounter with an elephant or hear of an elephant killed by a human. Although people around the globe are fascinated by elephants, they often raid farm fields and scour small villages for sources of food. This problem has only exacerbated in recent years as traditional elephant food supplies have dwindled and they have been forced to search out alternative food sources.

Scholarship on underresourced languages bring with them a variety of challenges which make access to the full spectrum of source materials and their evaluation difficult. For Coptic in particular, large scale analyses and any kind of quantitative work become difficult due to the fragmentation of manuscripts, the highly fusional nature of an incorporational morphology, and the complications of dealing with influences from Hellenistic era Greek, among other concerns. Many of these challenges, however, can be addressed using Digital Humanities tools and standards. In this paper, we outline some of the latest developments in Coptic Scriptorium, a DH project dedicated to bringing Coptic resources online in uniform, machine readable, and openly available formats. Collaborative web-based tools create online 'virtual departments' in which scholars dispersed sparsely across the globe can collaborate, and natural language processing tools counterbalance the scarcity of trained editors by enabling machine processing of Coptic text to produce searchable, annotated corpora.

The E.U. supports the Iranian nuclear deal as the Trump administration announces new sanctions. Iran's Revolutionary Guard on Saturday threatened to avenge the killing of its top general, saying it would go after everyone responsible for the January U.S. drone strike in Iraq. The guard's website quoted Gen. Hossein Salami as saying, "Mr. Our revenge for martyrdom of our great general is obvious, serious and real." FILE: Chief of Iran's Revolutionary Guard Gen. Hossein Salami speaks at a pro-government rally, in Tehran, Iran.

All over the world today, businesses and brands are employing automation to make their operations more efficient and effective while reducing redundancy as much as possible. While this might bring to mind a picture of robots take over jobs, that is not entirely the case as Artificial Intelligence is, in fact, a business asset, enhancing and promoting human capabilities and efforts. Before we can talk about AI and its numerous advantages, we need a clear definition of what it is. In simple terms, AI is all about systems mimicking human intelligence when carrying out tasks, and improving on this intelligence using information garnered from observation and interaction. AI involves data analysis and super thinking to predict patterns using past events.