Persona and Knowledge dual context open-domain chat is a novel dialogue generation task introduced recently. While Persona and Knowledge is each interesting context of open-domain dialogue, the combination of both has not been well studied. We tackle Persona-Knowledge identification and response generation tasks in this paper. We design an informed data augmentation strategy that is compatible with neural Q&A retrieval models. With the augmented data, we perform permutative Persona-Knowledge evaluation and successive Persona search fine-tuning. Furthermore, we perform dialogue generation with various decoding techniques and illustrate crucial elements. We achieve SOTA across official metrics with 93.99% Grounding accuracy average and 23.62 SacreBLEU score.

The training of a next-best-view (NBV) planner for visual place recognition (VPR) is a fundamentally important task in autonomous robot navigation, for which a typical approach is the use of visual experiences that are collected in the target domain as training data. However, the collection of a wide variety of visual experiences in everyday navigation is costly and prohibitive for real-time robotic applications. We address this issue by employing a novel {\it domain-invariant} NBV planner. A standard VPR subsystem based on a convolutional neural network (CNN) is assumed to be available, and its domain-invariant state recognition ability is proposed to be transferred to train the domain-invariant NBV planner. Specifically, we divide the visual cues that are available from the CNN model into two types: the output layer cue (OLC) and intermediate layer cue (ILC). The OLC is available at the output layer of the CNN model and aims to estimate the state of the robot (e.g., the robot viewpoint) with respect to the world-centric view coordinate system. The ILC is available within the middle layers of the CNN model as a high-level description of the visual content (e.g., a saliency image) with respect to the ego-centric view. In our framework, the ILC and OLC are mapped to a state vector and subsequently used to train a multiview NBV planner via deep reinforcement learning. Experiments using the public NCLT dataset validate the effectiveness of the proposed method.

Experts have suggested that the knowledge of Artificial Intelligence must be domesticated in Nigeria for the nation to meet up with the world. The experts explained that government, academia, community, and private sector must come together to rejuvenate Artificial Intelligence knowledge. This was disclosed during an annual lecture by the College of Science, Engineering and Technology, Osun State University, in honour of its pioneer Provost, Prof'Diran Famurewa held at the institution's Auditorium in Osogbo on Thursday. The Vice-Chancellor of Summit University Offa, Kwara, Prof. Abiodun Musa, professor of Mechatronics and who was the guest lecturer said Nigerian universities must not only generate money, but they must generate knowledge to solve community problems and needs as students must learn to solve community problems. The Vice-Chancellor, who is also an expert in Artificial Intelligence and Robotics, explained that if Nigeria needs to go beyond user to developer, it needs to rejuvenate artificial intelligence by looking into curriculum and implementation.

In 1917, Marcel Duchamp submitted a sculpture to the Society of Independent Artists under a false name. Fountain was a urinal, bought from a toilet supplier, with the signature R. Mutt on its side in black paint. Duchamp wanted to see if the society would abide by its promise to accept submissions without censorship or favor. But Duchamp was also looking to broaden the notion of what art is, saying a ready-made object in the right context would qualify. Then, as before, the debate raged about if something mechanically produced – a urinal, or a soup can (albeit hand-painted by Warhol) – counted as art, and what that meant.

Metrical Developments, a multi-award-winning development company, has selected Yardi to enhance the end-to-end real estate process from construction and financial management to unit sales. The company will implement solutions from Yardi's Residential Suite. These solutions will help control costs, track budgets, improve forecasts for development projects and help streamline the lead-to-owner sales management process to its landlord and investor client base. AI ML in Marketing: AI and Big Data Analysis Used to Find Brands' Emotional Connection "Yardi's cloud-based solution will enable us to oversee our entire development to sales operations through a single platform," said Islam Zeyada, CEO of Metrical Real Estate Development. "By doing so, we will see further efficiencies by streamlining the end-to-end process, access better insights and provide an enhanced service to our clients."

Earlier this month, attacks that took place within minutes of each other in different parts of Nigeria, and the apparent failure of the security forces to respond to them efficiently and in a timely manner, exposed how big of a threat lawlessness and impunity currently poses to the country and its people. Late on July 5, heavily armed men on motorcycles raided the Kuje Medium Security Custodial Centre on the outskirts of Abuja and released more than 900 inmates, including more than 60 Boko Haram members in detention. The Islamic State West Africa Province (ISWAP) – an offshoot of Boko Haram now allied with the ISIL (ISIS) group – claimed responsibility for the attack. Just hours before the Kuje incident, another group of heavily armed men had attacked a convoy carrying an advance security team for President Muhammadu Buhari in his home state of Katsina. A presidential spokesperson said the convoy carrying a team of security guards, as well as protocol and media officers, was on its way to Daura, Buhari's hometown, to prepare for a visit by him when the attack took place.

The Ukrainian Army on Tuesday announced it had destroyed three more tanks belonging to the Russian army using only attack drones. The Security Service of Ukraine (SSU) said their officers successfully destroyed three T-72s, Russian battle tanks commonly used by the Soviet Army in the 1970s. The SSU did not specify when and where the attack happened. It is also unclear if there were any casualties. The SSU's report on the recent attack comes just days after it released an audio of an intercepted phone call where a Russian soldier, whose identity was not revealed, told his mother that Ukrainian soldiers who attacked their unit destroyed their tanks and killed more than a dozen comrades.

Restless multi-armed bandits (RMAB) is a framework for allocating limited resources under uncertainty. It is an extremely useful model for monitoring beneficiaries and executing timely interventions to ensure maximum benefit in public health settings (e.g., ensuring patients take medicines in tuberculosis settings, ensuring pregnant mothers listen to automated calls about good pregnancy practices). Due to the limited resources, typically certain communities or regions are starved of interventions that can have follow-on effects. To avoid starvation in the executed interventions across individuals/regions/communities, we first provide a soft fairness constraint and then provide an approach to enforce the soft fairness constraint in RMABs. The soft fairness constraint requires that an algorithm never probabilistically favor one arm over another if the long-term cumulative reward of choosing the latter arm is higher. Our approach incorporates softmax based value iteration method in the RMAB setting to design selection algorithms that manage to satisfy the proposed fairness constraint. Our method, referred to as SoftFair, also provides theoretical performance guarantees and is asymptotically optimal. Finally, we demonstrate the utility of our approaches on simulated benchmarks and show that the soft fairness constraint can be handled without a significant sacrifice on value.

This paper focuses on an important environmental challenge; namely, water quality by analyzing the potential of social media as an immediate source of feedback. The main goal of the work is to automatically analyze and retrieve social media posts relevant to water quality with particular attention to posts describing different aspects of water quality, such as watercolor, smell, taste, and related illnesses. To this aim, we propose a novel framework incorporating different preprocessing, data augmentation, and classification techniques. In total, three different Neural Networks (NNs) architectures, namely (i) Bidirectional Encoder Representations from Transformers (BERT), (ii) Robustly Optimized BERT Pre-training Approach (XLM-RoBERTa), and (iii) custom Long short-term memory (LSTM) model, are employed in a merit-based fusion scheme. For merit-based weight assignment to the models, several optimization and search techniques are compared including a Particle Swarm Optimization (PSO), a Genetic Algorithm (GA), Brute Force (BF), Nelder-Mead, and Powell's optimization methods. We also provide an evaluation of the individual models where the highest F1-score of 0.81 is obtained with the BERT model. In merit-based fusion, overall better results are obtained with BF achieving an F1-score score of 0.852. We also provide comparison against existing methods, where a significant improvement for our proposed solutions is obtained. We believe such rigorous analysis of this relatively new topic will provide a baseline for future research.

A dramatic increase in the number of outbreaks of Dengue has recently been reported, and climate change is likely to extend the geographical spread of the disease. In this context, this paper shows how a neural network approach can incorporate Dengue and COVID-19 data as well as external factors (such as social behaviour or climate variables), to develop predictive models that could improve our knowledge and provide useful tools for health policy makers. Through the use of neural networks with different social and natural parameters, in this paper we define a Correlation Model through which we show that the number of cases of COVID-19 and Dengue have very similar trends. We then illustrate the relevance of our model by extending it to a Long short-term memory model (LSTM) that incorporates both diseases, and using this to estimate Dengue infections via COVID-19 data in countries that lack sufficient Dengue data.