The coronavirus pandemic has tested public health systems globally. Few novel and infectious diseases around the world have ever posed such dramatic challenges as the novel coronavirus SARS-CoV-2, which causes COVID-19. With highly efficient human-to-human transmission and high mortality rates, COVID19 led the World Health Organization to declare a public health emergency of international concern and caused countries around the world to reassess their public health capabilities. The United Arab Emirates, like other members of the international community, faced the unprecedented challenge of ensuring public health and safety while minimizing economic fallout. These efforts by the U.A.E.'s leadership allowed the U.A.E. to be globally ranked as one of the top countries, and the highest in the Arab world, in terms of its COVID-19 response. VPS Healthcare is an integrated healthcare service provider with 22 operational hospitals, over 125 healthcare centres, 13000 employees, one of the largest pharmaceutical manufacturing plants in Dubai and medical support services spread across the Middle East, Europe and India. By providing comprehensive patient management at international quality standards across the MENA Region and beyond and to the entire strata of community, VPS Healthcare reflects a brand image of excellence in healthcare delivery system.

A miniature robot treads stealthily in a vast desert, navigating the treacherously empty terrain in pursuit of fertile areas. When it identifies one, it reports the findings and then plants seeds based on the data retrieved from its sensors and navigation system. The autonomous robot, A'seedbot, could transform desert soil into a lush landscape, reducing the percentage of the desert to a large extent in the UAE. This tiny robot farmer is the graduation project of Mazyar Etehadi from the Dubai Institute of Design and Innovation. A'seedbot was unveiled at the Global Grad Show, an event that encourages designers to unveil innovative solutions to today's pressing social and environmental problems.

Global food insecurity is expected to worsen in the coming decades with the accelerated rate of climate change and the rapidly increasing population. In this vein, it is important to remove inefficiencies at every level of food production. The recent advances in deep learning can help reduce such inefficiencies, yet their application has not yet become mainstream throughout the industry, inducing economic costs at a massive scale. To this point, modern techniques such as CNNs (Convolutional Neural Networks) have been applied to RPQD (Raw Produce Quality Detection) tasks. On the other hand, Transformer's successful debut in the vision among other modalities led us to expect a better performance with these Transformer-based models in RPQD. In this work, we exclusively investigate the recent state-of-the-art Swin (Shifted Windows) Transformer which computes self-attention in both intra- and inter-window fashion. We compare Swin Transformer against CNN models on four RPQD image datasets, each containing different kinds of raw produce: fruits and vegetables, fish, pork, and beef. We observe that Swin Transformer not only achieves better or competitive performance but also is data- and compute-efficient, making it ideal for actual deployment in real-world setting. To the best of our knowledge, this is the first large-scale empirical study on RPQD task, which we hope will gain more attention in future works.

Modern neural language models widely used in tasks across NLP risk memorizing sensitive information from their training data. As models continue to scale up in parameters, training data, and compute, understanding memorization in language models is both important from a learning-theoretical point of view, and is practically crucial in real world applications. An open question in previous studies of memorization in language models is how to filter out "common" memorization. In fact, most memorization criteria strongly correlate with the number of occurrences in the training set, capturing "common" memorization such as familiar phrases, public knowledge or templated texts. In this paper, we provide a principled perspective inspired by a taxonomy of human memory in Psychology. From this perspective, we formulate a notion of counterfactual memorization, which characterizes how a model's predictions change if a particular document is omitted during training. We identify and study counterfactually-memorized training examples in standard text datasets. We further estimate the influence of each training example on the validation set and on generated texts, and show that this can provide direct evidence of the source of memorization at test time.

Disruptive technology is the technology that affects the normal operation of a market or an industry. Digital disruption entails established companies and start-ups alike enlisting new technologies in the fight to dislodge incumbents, protect entrenched positions, or to re-invent entire industries and business activities. And to remain disruptive in the market, it is really important to keep innovating. This is crucial because, innovations occur now and then in every industry, however, to be truly disruptive, and innovation must entirely transform a product or solution that historically was so complicated only a few could access it. On a minimum level, digital transformation enables an organization to address the needs of its customers more simply and directly. But through disruptive innovation, companies can offer a far better way to users of doing things that current incumbents simply cannot compete with. Artificial intelligence (AI), E-Commerce, cloud, social networking, Internet of Things, 5G, blockchain and other emerging technologies are being leveraged to blur the lines between industries, creating new business models and converging sectors. A company that disrupts its market is in a great position to take advantage of new opportunities. Sometimes offering something different can change the whole market for the better. Most of the top disruptive companies get this label by offering highly innovative products and services and here are 100 such top disruptive companies listed below. The company provides innovative, managed cloud services to help its customers succeed. With best-in-class service and technology, 403Tech protects companies against cybercrimes while enabling greater efficiency and productivity. Some of its popular services include desktop support, server support, wired and wireless networking, virus removal, data recovery, and backup and hosted cloud services. Aegeus Technologies aims to design and develop robotic technologies and solutions.

Artificial intelligence (AI) has crossed a threshold. "In the past five years, AI has made the leap from something that mostly happens in research labs or other highly controlled settings to something that's out in society affecting people's lives," says Michael Littman, chair of the One Hundred Year Study on Artificial Intelligence, hosted at Stanford. It's easy to see what he's talking about: The technology's impact can be seen introducing automation, driving efficiency gains and enhancing productivity, creating new jobs, and reducing risks associated with cyber-threats and fraud. During the pandemic, AI enabled more effective testing for Covid-19 and faster vaccine development, and helped manage grocery supply chains and tailor lessons for individual students affected by remote schooling. As AI expands into more and more facets of our lives, there is also more scrutiny on who's developing it.

From self-driving cars to intuitive automatic vacuum cleaners, AI has taken over every industry and government organization around the world. Artificial intelligence is an emerging focus area of policy development in India. Many governments have begun to implement AI across various small-scale pilots. But many are still limited to implementation and experimentation. If implemented effectively, AI tools can generate benefits for both private and public-sector organizations.

AI and big data analytics are allowing healthcare providers in the Middle East to make faster, more cost-effective diagnostics, but security concerns are top of mind. AI and big data analytics are allowing healthcare providers in the Middle East to make faster, more cost-effective diagnostics, according to a broad cross-section of healthcare professionals. Along with the increasing use of AI and big data, though, security concerns about data privacy are also growing. AI is one of the fastest growing segments of the global healthcare market today. According to Frost & Sullivan forecasts, it will reach US$6.6 billion by the end of this year.

In the last decade, the use of Machine Learning techniques in anomaly-based intrusion detection systems has seen much success. However, recent studies have shown that Machine learning in general and deep learning specifically are vulnerable to adversarial attacks where the attacker attempts to fool models by supplying deceptive input. Research in computer vision, where this vulnerability was first discovered, has shown that adversarial images designed to fool a specific model can deceive other machine learning models. In this paper, we investigate the transferability of adversarial network traffic against multiple machine learning-based intrusion detection systems. Furthermore, we analyze the robustness of the ensemble intrusion detection system, which is notorious for its better accuracy compared to a single model, against the transferability of adversarial attacks. Finally, we examine Detect & Reject as a defensive mechanism to limit the effect of the transferability property of adversarial network traffic against machine learning-based intrusion detection systems.

We propose Selective Multiple Power Iterations (SMPI), a new algorithm to address the important Tensor PCA problem that consists in recovering a spike $\bf{v_0}^{\otimes k}$ corrupted by a Gaussian noise tensor $\bf{Z} \in (\mathbb{R}^n)^{\otimes k}$ such that $\bf{T}=\sqrt{n} \beta \bf{v_0}^{\otimes k} + \bf{Z}$ where $\beta$ is the signal-to-noise ratio (SNR). SMPI consists in generating a polynomial number of random initializations, performing a polynomial number of symmetrized tensor power iterations on each initialization, then selecting the one that maximizes $\langle \bf{T}, \bf{v}^{\otimes k} \rangle$. Various numerical simulations for $k=3$ in the conventionally considered range $n \leq 1000$ show that the experimental performances of SMPI improve drastically upon existent algorithms and becomes comparable to the theoretical optimal recovery. We show that these unexpected performances are due to a powerful mechanism in which the noise plays a key role for the signal recovery and that takes place at low $\beta$. Furthermore, this mechanism results from five essential features of SMPI that distinguish it from previous algorithms based on power iteration. These remarkable results may have strong impact on both practical and theoretical applications of Tensor PCA. (i) We provide a variant of this algorithm to tackle low-rank CP tensor decomposition. These proposed algorithms also outperforms existent methods even on real data which shows a huge potential impact for practical applications. (ii) We present new theoretical insights on the behavior of SMPI and gradient descent methods for the optimization in high-dimensional non-convex landscapes that are present in various machine learning problems. (iii) We expect that these results may help the discussion concerning the existence of the conjectured statistical-algorithmic gap.