Seven years have passed since world leaders met in New York and agreed on 17 Sustainable Development Goals (SDGs) to resolve major challenges including poverty, hunger, inequality, climate change and health. The pandemic undoubtedly diverted attention from some of these issues in the past couple of years. But even before COVID-19, the United Nations was warning that progress to meet the SDGs was not advancing at the speed or on the scale needed. Meeting them by 2030 will be tough. The pandemic demonstrated like nothing else the power of working collaboratively, across borders, for the benefit of society.

You should be able to work in a team and show high motivation. This job requires autonomy and curiosity toward a changing environment. Flexibility: Team members can work from home, up to 4 days a week, and have the opportunity to work with colleagues around the world. Work environment: SESAMm is multicultural, with technology, sales, and management teams in the US, France, and Tunisia making important contributions to the company's growth. Career development: SESAMm is growing quickly, which means the opportunities for your own growth are continually expanding, and that you can shape the company's culture and evolution.

Deepfakes are the synthesized digital media in order to create ultra-realistic fake videos to trick the spectator. Deep generative algorithms, such as, Generative Adversarial Networks(GAN) are widely used to accomplish such tasks. This approach synthesizes pseudo-realistic contents that are very difficult to distinguish by traditional detection methods. In most cases, Convolutional Neural Network(CNN) based discriminators are being used for detecting such synthesized media. However, it emphasise primarily on the spatial attributes of individual video frames, thereby fail to learn the temporal information from their inter-frame relations. In this paper, we leveraged an optical flow based feature extraction approach to extract the temporal features, which are then fed to a hybrid model for classification. This hybrid model is based on the combination of CNN and recurrent neural network (RNN) architectures. The hybrid model provides effective performance on open source data-sets such as, DFDC, FF++ and Celeb-DF. This proposed method shows an accuracy of 66.26%, 91.21% and 79.49% in DFDC, FF++, and Celeb-DF respectively with a very reduced No of sample size of approx 100 samples(frames). This promises early detection of fake contents compared to existing modalities.

The rapid spread of COVID-19 disease has had a significant impact on the world. In this paper, we study COVID-19 data interpretation and visualization using open-data sources for 351 cities and towns in Massachusetts from December 6, 2020 to September 25, 2021. Because cities are embedded in rather complex transportation networks, we construct the spatio-temporal dynamic graph model, in which the graph attention neural network is utilized as a deep learning method to learn the pandemic transition probability among major cities in Massachusetts. Using the spectral graph wavelet transform (SGWT), we process the COVID-19 data on the dynamic graph, which enables us to design effective tools to analyze and detect spatio-temporal patterns in the pandemic spreading. We design a new node classification method, which effectively identifies the anomaly cities based on spectral graph wavelet coefficients. It can assist administrations or public health organizations in monitoring the spread of the pandemic and developing preventive measures. Unlike most work focusing on the evolution of confirmed cases over time, we focus on the spatio-temporal patterns of pandemic evolution among cities. Through the data analysis and visualization, a better understanding of the epidemiological development at the city level is obtained and can be helpful with city-specific surveillance.

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.