first_imgElection CommissionThe election commission is contemplating use of electronic voting machine (EVM) in the next general elections.Election commissioner Rafiqul Islam said this after a meeting of the commission at Election Bhaban on Sunday.The main opposition Bangladesh Nationalist Party has already raised its objection to any possible move to introduce RVM since, the party argued, it might be misused to manipulate the election results.The election commissioner also said the commission may amend the Representation of People Order (RPO).”We held discussions on this and the meeting has been adjourned till 30 August,” he added.RPO is the law which regulates the elections of 300 members to the Jatiya Sangsad (national assembly).Asked how an amended version of the RPO would be endorsed by parliament, the election commissioner said the commission would send the draft to the law ministry which would forward it to the parliament secretariat.Jatiya Sangsad goes to its next session on 9 September.last_img read more

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first_img Free Webinar | Sept 5: Tips and Tools for Making Progress Toward Important Goals March 11, 2014 1 min read Shoppers have the Internet in their pockets. For store owners, that’s changing the game.Almost half of shoppers admit to having participated in “showrooming,” or going into a retail store to look at a product and then buying a less expensive version online. That statistic comes from an infographic generated by MobStac, a cloud-based mobile-commerce platform that makes websites and applications for businesses.Related: 10 Questions to Ask When Optimizing Your Website for Mobile UsersAs a retail store owner, you can try to prevent consumers from using their smartphones inside your store. But that’s not much more than putting a Band-Aid on a broken leg. Mobile shopping isn’t going away.Instead, retail store owners should engage with customers through mobile technology and give them what they want. Four out of five smartphone owners say they would prefer to be able to read more about products on their devices while shopping in stores, according to the MobStac infographic.Related: Mobile Commerce Has Completely Exploded (Infographic)Check out this infographic for more trends in mobile commerce and for ways to make your retail store smartphone-friendly. center_img Register Now » Attend this free webinar and learn how you can maximize efficiency while getting the most critical things done right.last_img read more

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first_img This story originally appeared on PCMag How Success Happens Hear from Polar Explorers, ultra marathoners, authors, artists and a range of other unique personalities to better understand the traits that make excellence possible. March 19, 2019 4 min read How do you keep online trolls in check? Ban them? Require real names?Dr. Srijan Kumar, a post-doctoral research fellow in computer science at Stanford University, is developing an AI that predicts online conflict. His research uses data science and machine learning to promote healthy online interactions and curb deception, misbehavior, and disinformation.His work is currently deployed inside Indian e-commerce platform Flipkart, which uses it to spot fake reviewers. We spoke to Dr. Kumar ahead of a lecture on healthy online interactions at USC.Dr. Kumar, how do you counteract online harassment using data science and machine learning? How does your system identify the trolls? In my research, I build data science and machine learning methods to address online misbehavior, which transpires as false information and malicious users. My methods have a dual purpose: first, to characterize their behavior, and second, to detect them before they damage other users. I have been able to investigate a wide variety of online misbehavior, including fraudulent reviews, hoaxes, online trolling, and multiple account abuse, among others.How are you teaching the AI to spot these patterns? I develop statistical analysis, graph mining, embedding, and deep learning-based methods to characterize what normal behavior looks like, [and] use this to identify abnormal or malicious behavior. Oftentimes, we may also have known examples of malicious behavior, in which case I create supervised learning models where I use these examples as training data to identify similar malicious entities among the rest.Your research is currently being used in Flipkart. What problem were they trying to solve and how are they measuring results? The key problem that I helped address on Flipkart was of identifying fake reviews and fake reviewers on their platform. This is a pervasive problem in all platforms; recent surveys estimate as much as 15 percent of online reviews [are] fake. It is therefore crucial to identify and weed out fake reviews, as our decision as consumers is influenced by them.What’s the method called here? My method, which is called REV2, uses the review graph of user-review-product to identify fraudsters [who] give high scoring ratings to low-quality products or low scoring ratings to high-quality products. REV2 [compares] our recommendations to previously identified cases of fake reviewers.Is it possible for AI to keep an eye inside social networks and raise the alarm when bad behavior is about to arise? Is this purely pattern-based analysis with sentient data crunching or something entirely different? It is possible to proactively predict when something may go wrong by learning from previous such cases. For instance, in my recent research, I showed that it is possible to accurately predict when one community in the Reddit online platform will attack/harass/troll another. This phenomenon is called “brigading,” and I showed that brigades reduce the future engagement in the attacked community. This is detrimental to the users and their interactions, which calls for methods to avoid them. Thus, I created a deep learning-based model that uses the text and community structure to predict, with high accuracy, if a community is going to attack another. Such models are of practical use, as it can alert the community moderators to keep an eye out for an incoming attack.Do you see a logical extrapolation of your work used in “nudges” to prompt users to clean up their act prior to prosecution? Akin to a teacher at the front of the class keeping a wary eye on the troublemakers in the back row before they fall into criminal masterminded gangs? Absolutely! A natural and exciting follow-up work is how to discourage bad actors to do malicious acts and to encourage everyone to be benign. This will help us to create a healthy, collaborative, and more inclusive online ecosystem for everyone. There are many interesting challenges to achieve this goal, requiring new methods of interventions and better prediction models. Enabling better online conversations and nudging people to be their better self is going to be one of my key thrusts going forward.Have you have personal experience with online harassment or was this more of an interesting AI problem to solve for you? One of the major reasons for me to follow this direction of research was seeing some of my friends being harassed by social media trolls. This led to look for non-algorithmic ways to curb this problem. Being a challenging task, it piqued the interest of the scientist inside me and I eventually learned to create data science and machine learning methods to help solve these problems. Listen Nowlast_img read more

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