Inter-Cell Interference Coordination (ICIC) is a promising way to improve energy efficiency in wireless networks, especially where small base stations are densely deployed. However, traditional optimization based ICIC schemes suffer from severe performance degradation with complex interference pattern. To address this issue, we propose a Deep Reinforcement Learning with Deterministic Policy and Target (DRL-DPT) framework for ICIC in wireless networks. DRL-DPT overcomes the main obstacles in applying reinforcement learning and deep learning in wireless networks, i.e. continuous state space, continuous action space and convergence. Firstly, a Deep Neural Network (DNN) is involved as the actor to obtain deterministic power control actions in continuous space. Then, to guarantee the convergence, an online training process is presented, which makes use of a dedicated reward function as the target rule and a policy gradient descent algorithm to adjust DNN weights. Experimental results show that the proposed DRL-DPT framework consistently outperforms existing schemes in terms of energy efficiency and throughput under different wireless interference scenarios. More specifically, it improves up to 15% of energy efficiency with faster convergence rate.

As humans we are driven by a strong desire for seeking novelty in our world. Also upon observing a novel pattern we are capable of refining our understanding of the world based on the new information---humans can discover their world. The outstanding ability of the human mind for discovery has led to many breakthroughs in science, art and technology. Here we investigate the possibility of building an agent capable of discovering its world using the modern AI technology. In particular we introduce NDIGO, Neural Differential Information Gain Optimisation, a self-supervised discovery model that aims at seeking new information to construct a global view of its world from partial and noisy observations. Our experiments on some controlled 2-D navigation tasks show that NDIGO outperforms state-of-the-art information-seeking methods in terms of the quality of the learned representation. The improvement in performance is particularly significant in the presence of white or structured noise where other information-seeking methods follow the noise instead of discovering their world.

As much as data science is playing a pivotal role everywhere, healthcare also finds it prominent application. Breast Cancer is the top rated type of cancer amongst women; which took away 627,000 lives alone. This high mortality rate due to breast cancer does need attention, for early detection so that prevention can be done in time. As a potential contributor to state-of-art technology development, data mining finds a multi-fold application in predicting Brest cancer. This work focuses on different classification techniques implementation for data mining in predicting malignant and benign breast cancer. Breast Cancer Wisconsin data set from the UCI repository has been used as experimental dataset while attribute clump thickness being used as an evaluation class. The performances of these twelve algorithms: Ada Boost M 1, Decision Table, J Rip, Lazy IBK, Logistics Regression, Multiclass Classifier, Multilayer Perceptron, Naive Bayes, Random forest and Random Tree are analyzed on this data set. Keywords- Data Mining, Classification Techniques, UCI repository, Breast Cancer, Classification Algorithms

Constraining linear layers in neural networks to respect symmetry transformations from a group $G$ is a common design principle for invariant networks that has found many applications in machine learning. In this paper, we consider a fundamental question that has received little attention to date: Can these networks approximate any (continuous) invariant function? We tackle the rather general case where $G\leq S_n$ (an arbitrary subgroup of the symmetric group) that acts on $\mathbb{R}^n$ by permuting coordinates. This setting includes several recent popular invariant networks. We present two main results: First, $G$-invariant networks are universal if high-order tensors are allowed. Second, there are groups $G$ for which higher-order tensors are unavoidable for obtaining universality. $G$-invariant networks consisting of only first-order tensors are of special interest due to their practical value. We conclude the paper by proving a necessary condition for the universality of $G$-invariant networks that incorporate only first-order tensors. Lastly, we propose a conjecture stating that this condition is also sufficient.

Terrorism has become one of the most tedious problems to deal with and a prominent threat to mankind. To enhance counter-terrorism, several research works are developing efficient and precise systems, data mining is not an exception. Immense data is floating in our lives, though the scarce availability of authentic terrorist attack data in the public domain makes it complicated to fight terrorism. This manuscript focuses on data mining classification techniques and discusses the role of United Nations in counter-terrorism. It analyzes the performance of classifiers such as Lazy Tree, Multilayer Perceptron, Multiclass and Na\"ive Bayes classifiers for observing the trends for terrorist attacks around the world. The database for experiment purpose is created from different public and open access sources for years 1970-2015 comprising of 156,772 reported attacks causing massive losses of lives and property. This work enumerates the losses occurred, trends in attack frequency and places more prone to it, by considering the attack responsibilities taken as evaluation class.

A web browser should not be only for browsing web pages but also help users to find out their target websites and recommend similar type websites based on their behavior. Throughout this paper, we propose two methods to make a web browser more intelligent about link prediction which works during typing on address-bar and recommendation of websites according to several categories. Our proposed link prediction system is actually frecency prediction which is predicted based on the first visit, last visit and URL counts. But recommend system is the most challenging as it is needed to classify web URLs according to names without visiting web pages. So we use existing model for URL classification. The only existing approach gives unsatisfactory results and low accuracy. So we add hyperparameter optimization with an existing approach that finds the best parameters for existing URL classification model and gives better accuracy. In this paper, we propose a category wise recommendation system using frecency value and the total visit of individual URL category.

China steps up plans for using artificial intelligence to strengthen its military; Bill Hemmer reports. The President and the Pentagon are signaling that artificial intelligence (AI) is now a major priority for U.S. national security, and competition from China and Russia may be a key motivator. President Trump issued an executive order on Feb. 11 titled "Maintaining American Leadership in Artificial Intelligence." It's a directive that he says "will affect the missions of nearly all executive departments and agencies," and he didn't mince words on the significance of this quest. "Continued American leadership in AI is of paramount importance to maintaining the economic and national security of the United States," the executive order reads.

Samsung has unveiled a whole range of new smartphones, including the Galaxy S10, Galaxy S10e and Galaxy S10 . The official release date is on 8 March but UK customers can already pre-order the phone from EE, Sky Mobile, O2, BT Mobile and other local networks. Depending on which network they choose. The full price of the S10 without a network plan is £799, while the S10e is £699 and the S10 is £899. Anyone who pre-orders the S10 or S10 before the official release date will receive a free pair of Samsung Galaxy Buds headphones.

Samsung unveiled a collection of new devices today in San Francisco, from S10 flagship phones to the Galaxy Fold, a smartphone that can fold out into a tablet. Underlying each of these powerful pieces of hardware is artificial intelligence and access to voice assistant Bixby. To improve Bixby's smarts, Samsung today also introduced Bixby Routines, a service that functions alongside Bixby Vision and Bixby Home. As shown in a tweet from Samsung Mobile, in the evening Routines may mute your phone, turn on Night Mode, and shut off mobile data, and in the car Routines can switch on hands-free voice commands and your favorite music provider. Support for Italian, Spanish, and British English was also made available with Bixby today.

Samsung has announced a range of new state-of-the-art smartphones, 10 years after its first ever flagship Galaxy S-series smartphone. The Galaxy S10 comes in three variants - the Galaxy S10e, Galaxy S10 and Galaxy S10 - and features a number of new features, including an ultrasonic in-screen fingerprint sensor and ground-breaking camera. The S10 includes a triple rear camera system, which includes an ulta-wide and telephoto lens to take pictures ranging from landscapes to "incredible" close-ups. "With this camera, what you see is what you get," said Suzanne De Silva, director of product marketing at Samsung. Samsung partnered with Instagram to allow Galaxy S10 users to upload images to the photo-sharing app directly from the camera.