UNIVERSITY PARK, Pa.-- Enrique Del Castillo, distinguished professor of industrial engineering and professor of statistics at Penn State, has returned from his Fulbright U.S. Scholar Program, where he conducted research at the University of Coimbra in Coimbra, Portugal. Awarded by the J. William Fulbright Foreign Scholarship Board, Del Castillo was one of approximately 800 U.S. citizens selected to take their expertise abroad for the 2019-20 academic year through the program. Recipients of Fulbright Awards are selected on the basis of academic and professional achievement, as well as record of service and demonstrated leadership in their respective fields. The research project for which he was granted the Fulbright Fellowship, titled "Optimization and control of industrial production processes by active learning methods based on'big' and complex data," sought collaborative research between Penn State's Engineering Statistics and Machine Learning Laboratory and the University of Coimbra's chemometrics group in the Department of Chemical Engineering. Del Castillo worked on the optimization of production processes via machine learning for various industries with the chemometrics group; in particular, they focused on wine, paper and pharmaceuticals.

Is your agency ready for artificial intelligence (AI)? If not, what would it take to get to a place where it can enjoy the benefits of AI? A government agency's readiness for AI is not simply a question of preparing to buy and install new technology. The transformative nature of AI typically calls for preparation across multiple critical areas. To capture AI's potential to create value, government organizations will need a plan to retool the relevant existing processes, upskill or hire key staff, refine approaches toward partnership, and develop the necessary data and technical infrastructure to deploy AI.

Bengaluru: With disruptive technologies like Artificial Intelligence (AI) driving businesses, the IT sector wants the Union Budget for fiscal 2020-21 to ensure greater use of these to spur a sluggish economy among other measures for the sector, industry experts said on Friday. "The budget should announce a fund like Singapore's Temasek that will invest only in early-stage Indian AI start-ups and lower long-term capital gain's tax for investing in AI-based firms," digital intelligence firm Germin8 founder chief executive Ranjit Nair told IANS. With the US and China racing ahead of India in AI research, AI entrepreneurship and government investment in AI, he said the budget should make it easier for start-ups to access capital, as they face an uphill task in early-stage funding. "The government bring policies that encourage AI companies. Ease of doing business means less bureaucracy so that entrepreneurs can build solutions without distractions," he said.

KYIV – A leaked recording of an exchange between an Iranian air-traffic controller and an Iranian pilot purports to show that authorities immediately knew a missile had downed a Ukrainian jetliner after takeoff from Tehran, killing all 176 people aboard, despite days of denials by the Islamic Republic. Ukraine President Volodymyr Zelenskiy acknowledged the recording's authenticity in a report aired by a Ukrainian television channel Sunday night. In Tehran on Monday, the head of the Iranian investigation team, Hassan Rezaeifar, acknowledged the recording was legitimate and said it was handed over to Ukrainian officials. After the Jan. 8 disaster, Iran's civilian government maintained for days that it didn't know the country's paramilitary Revolutionary Guard, answerable only to Supreme Leader Ayatollah Ali Khamenei, had shot down the aircraft. The downing of the jetliner came just hours after the Guard launched a ballistic missile attack on Iraqi bases housing U.S. forces in retaliation for an earlier American drone strike that killed the Guard's top general, Qassem Soleimani, in Baghdad.

Ever since the Dawn spacecraft picked up images of what look to be a vast network of bright spots in the Occator crater on Ceres--a dwarf planet in the asteroid belt--there's been conjecture over whether the whiteish spots are made up of ice, or some kind of volcanic salt deposits. Meanwhile, another controversy has been brewing over them: What exactly are those shapes seen in the bright spots, called Vinalia Faculae? Are they squares or triangles? Because the strange patterns are so strikingly geometric, researchers from the University of Cadiz in Spain have taken a closer look at the bright spots to figure out whether humans and machines look at planetary images differently. The overall goal was to figure out if artificial intelligence can help us discover and make sense of technosignatures, or potentially detectable signals from distant, advanced civilizations, according to NASA.

The deal has yet to be approved by the relevant regulators, but Google has got most of the way to buying Fitbit – the maker of wearable devices that track people's sleep, heart rates, activity levels and more. And all for a trifling $2.1bn (£1.6bn).The upshot is yet another step forward in Google's quest to break into big tech's next frontier: healthcare. Last month, in a Financial Times feature about all this, came a remarkable quote from a partner at Health Advances, a Massachusetts-based tech consulting company. Wearables, he reckoned, would be only one small part of the ensuing story: just as important were – and no guffawing at the back, please – "bedside devices, under-mattress sensors, [and] sensors integrated into toilet seats". Such inventions, it was explained, can "get even closer to you than your smartphone, and detect conditions such as depression or heart-rate variability".

Five worthy reads is a regular column on five noteworthy items we've discovered while researching trending and timeless topics. This week, we discuss the escalating significance of AI and machine learning in IT security space. One of an IT team's biggest challenges is making sense of the mammoth amount of data that corporate infrastructures produce and consume while identifying and responding to real-time cyberattacks. According to research conducted by the Ponemon Institute in its 2019 Cost of a Data Breach Report, a cyberattack has an average lifecycle of 314 days from its launch to containment. This means detection and mitigation of the risks of a cyberattack are painfully time-consuming.

The technology industry has cheered the Union Budget 2020's thrust on the technology sector applauding various announcements including the Rs. While presenting the Union Budget, finance minister Nirmala Sitharaman said that technology, will be used for better monitoring of the economic data, building a preventive regime for diseases under Ayushman Bharat, capturing and protection of intellectual property rights, improving agriculture and sea-ports and delivery of government services. A new scheme for incentivising domestic manufacturing of electronics specifically mobile phones will also be announced, she added. Debjani Ghosh, President, NASSCOM said, "Budget 2020 and the finance minister's speech has well-articulated India's vision on not just being a leading provider of digital solutions, but one where technology is the bedrock of development and growth'. Nasscom welcomed the announcements on Quantum Computing, Data Center policy, IPR portal, CoE etc, along with the removal of Dividend Distribution Tax which was a long pending demand from the industry. It, however said that the Budget lacked focus on accelerating service exports from the country. "The technology services sector has been a key contributor to India's exports and GDP, NASSCOM had recommended that new investments by services companies in SEZs should also be eligible for the lower rate of 15%.

Machine learning models have demonstrated vulnerability to adversarial attacks, more specifically misclassification of adversarial examples. In this paper, we propose a one-off and attack-agnostic Feature Manipulation (FM)-Defense to detect and purify adversarial examples in an interpretable and efficient manner. The intuition is that the classification result of a normal image is generally resistant to non-significant intrinsic feature changes, e.g., varying thickness of handwritten digits. In contrast, adversarial examples are sensitive to such changes since the perturbation lacks transferability. To enable manipulation of features, a combo-variational autoencoder is applied to learn disentangled latent codes that reveal semantic features. The resistance to classification change over the morphs, derived by varying and reconstructing latent codes, is used to detect suspicious inputs. Further, combo-VAE is enhanced to purify the adversarial examples with good quality by considering both class-shared and class-unique features. We empirically demonstrate the effectiveness of detection and the quality of purified instance. Our experiments on three datasets show that FM-Defense can detect nearly $100\%$ of adversarial examples produced by different state-of-the-art adversarial attacks. It achieves more than $99\%$ overall purification accuracy on the suspicious instances that close the manifold of normal examples.

Traditionally, researchers in automatic face recognition and biometric technologies have focused on developing accurate algorithms. With this technology being integrated into operational systems, engineers and scientists are being asked, do these systems meet societal norms? The origin of this line of inquiry is `trust' of artificial intelligence (AI) systems. In this paper, we concentrate on adapting explainable AI to face recognition and biometrics, and we present four principles of explainable AI to face recognition and biometrics. The principles are illustrated by $\it{four}$ case studies, which show the challenges and issues in developing algorithms that can produce explanations.