SAN FRANCISCO – Eleven employees comprising engineers and managers at Alphabet Inc.'s Google published an open letter on Tuesday, demanding that the company end development of a censored search engine for Chinese users, escalating earlier protests over the secretive project. Google has described the search app, known as Project Dragonfly, as an experiment not close to launching. But as details of it have leaked since August, current and former employees, human rights activists and U.S. lawmakers have criticized Google for not taking a harder line against the Chinese government's policy that politically sensitive results be blocked. Human rights group Amnesty International also launched a public petition on Tuesday calling on Google to cancel Dragonfly. The organization said it would encourage Google workers to sign the petition by targeting them on LinkedIn and protesting outside Google offices.

CHIBA – A 75-year-old man operating a Japanese language school has been found dead at his apartment in Chiba Prefecture, prompting police to launch a murder investigation. The body of Jiro Iwai, who managed the school as well as other companies, was found around 1:30 p.m. Sunday when a female employee visited his home to find him dead and bleeding from a wound to the head. Investigative sources said Iwai, who lived alone in an apartment in Sakura, Chiba Prefecture, was likely struck multiple times in the killing. An autopsy showed he may have died of damage to his spinal cord. But despite the violent death, police said there were no signs of a struggle and that a light in the room where he was killed had been left on.

Facebook uses Artificial Intelligence for targeting users with advertisements based on the events in which they engage like sharing, liking, making comments, posts by a friend, a group creation, etcetera. Each user interacts with these events in different ways, thus receiving different recommendations curated by Facebook's intelligent systems. Facebook segregates its users into chambers, fragmenting them into communities. The technology has completely changed the marketing domain. It is however caught in a race for our finite attention with a motive to make more and more money. Facebook is not a neutral product. It is programmed to get users addicted to it with a goal of gaining added information about the users and optimizing the recommendations provided to the users according to his or her preferences. This paper delineates how Facebook's recommendation system works and presents three methods to safeguard human vulnerabilities exploited by Facebook and other corporations.

In several natural language tasks, labeled sequences are available in separate domains (say, languages), but the goal is to label sequences with mixed domain (such as code-switched text). Or, we may have available models for labeling whole passages (say, with sentiments), which we would like to exploit toward better position-specific label inference (say, target-dependent sentiment annotation). A key characteristic shared across such tasks is that different positions in a primary instance can benefit from different `experts' trained from auxiliary data, but labeled primary instances are scarce, and labeling the best expert for each position entails unacceptable cognitive burden. We propose GITNet, a unified position-sensitive multi-task recurrent neural network (RNN) architecture for such applications. Auxiliary and primary tasks need not share training instances. Auxiliary RNNs are trained over auxiliary instances. A primary instance is also submitted to each auxiliary RNN, but their state sequences are gated and merged into a novel composite state sequence tailored to the primary inference task. Our approach is in sharp contrast to recent multi-task networks like the cross-stitch and sluice network, which do not control state transfer at such fine granularity. We demonstrate the superiority of GIRNet using three applications: sentiment classification of code-switched passages, part-of-speech tagging of code-switched text, and target position-sensitive annotation of sentiment in monolingual passages. In all cases, we establish new state-of-the-art performance beyond recent competitive baselines.

ZhijiePan College of Computer Science and Technology Zhejiang University Hangzhou, China zhijie_pan@zju.edu.cn Abstract--Urban traffic systems worldwide are suffering from severe traffic safety problems. Traffic safety is affected by many complex factors, and heavily related to all drivers' behaviors involved in traffic system. Drivers with aggressive driving behaviors increase the risk of traffic accidents. In order to manage the safety level of traffic system, we propose Driving Safety Credit inspired by credit score in financial security field, and design a scoring method using driver's trajectory data and violation records. First, we extract driving habits, aggressive driving behaviors and traffic violation behaviors from driver's trajectories and traffic violation records. Next, we train a classification modelto filtered out irrelevant features. And at last, we score each driver with selected features. We verify our proposed scoring method using 40 days of traffic simulation, and proves the effectiveness of our scoring method. I. INTRODUCTION Urban traffic worldwide is facing severe traffic safety problems.

Time series forecasting gets much attention due to its impact on many practical applications. Higher-order neural network with recurrent feedback is a powerful technique which used successfully for forecasting. It maintains fast learning and the ability to learn the dynamics of the series over time. For that, in this paper, we propose a novel model which is called Ridge Polynomial Neural Network with Error-Output Feedbacks (RPNN-EOFs) that combines the properties of higher order and error-output feedbacks. The well-known Mackey-Glass time series is used to test the forecasting capability of RPNN-EOFS. Simulation results showed that the proposed RPNN-EOFs provides better understanding for the Mackey-Glass time series with root mean square error equal to 0.00416. This result is smaller than other models in the literature. Therefore, we can conclude that the RPNN-EOFs can be applied successfully for time series forecasting.

Deep learning has undoubtedly offered tremendous improvements in the performance of state-of-the-art speech emotion recognition (SER) systems. However, recent research on adversarial examples poses enormous challenges on the robustness of SER systems by showing the susceptibility of deep neural networks to adversarial examples as they rely only on small and imperceptible perturbations. In this study, we evaluate how adversarial examples can be used to attack SER systems and propose the first black-box adversarial attack on SER systems. We also explore potential defenses including adversarial training and generative adversarial network (GAN) to enhance robustness. Experimental evaluations suggest various interesting aspects of the effective utilization of adversarial examples useful for achieving robustness for SER systems opening up opportunities for researchers to further innovate in this space.

In this paper, we introduce methods of encoding propositional logic programs in vector spaces. Interpretations are represented by vectors and programs are represented by matrices. The least model of a definite program is computed by multiplying an interpretation vector and a program matrix. To optimize computation in vector spaces, we provide a method of partial evaluation of programs using linear algebra. Partial evaluation is done by unfolding rules in a program, and it is realized in a vector space by multiplying program matrices. We perform experiments using randomly generated programs and show that partial evaluation has potential for realizing efficient computation in huge scale of programs.

Hundreds of Google employees have signed an open letter published Tuesday on Medium demanding that the company cease work on Project Dragonfly, which is aimed at creating a search engine that the Chinese government would be able to control to censor certain results and surveil users. "International human rights organizations and investigative reporters have also sounded the alarm, emphasizing serious human rights concerns and repeatedly calling on Google to cancel the project," the letter reads in part. "So far, our leadership's response has been unsatisfactory." Google has kept much of Project Dragonfly under wraps, but news outlets like the Intercept have obtained documents revealing some of the details. The search engine reportedly would block websites having to do with democracy and political dissidents and also blacklist terms like "human rights." One of the prototypes also reportedly has the capability to link searches to users' phone numbers.

More than 90 Google employees have joined a petition protesting the company's plans to build a search engine that complies with China's online censorship regime. An employee-led backlash against the project has been churning for months at the company, but Tuesday's petition marks the first time workers at Google have used their names in a public document objecting to the plans. The existence of the project, code-named Dragonfly, was confirmed by chief executive Sundar Pichai last month. While China has long blocked search queries for what it has deemed politically sensitive material, Pichai said Google could still help Chinese Internet users find other information, such as health treatments, or steer them away from scams. But the project has drawn critics, who question Google's corporate values and have raised concerns about the consequences of tech companies cooperating with authoritarian governments.