United Arab Emirates, February 6th, 2025/Chainwire/–The Middle East Blockchain Awards (MEBA) returns for its second year after the success of its inaugural edition, with Dubai selected as the host city. The ceremony will take place at the iconic Jumeirah Burj Al Arab on April 29, coinciding with the TOKEN2049 conference. The event will unite industry … Read more
Singularity Finance Launches $SFI Token for Decentralized AI Ecosystem Singularity Finance announced the launch of its $SFI token on February 6, 2025. The token results from a merger between SingularityDAO and Cogito Finance and will support a network that builds and uses artificial intelligence products. The token is set to serve multiple roles within the … Read more
Discovering the next wave of game-changing startups is one of the most exciting parts of working in VC. Every week, I come across founders who are tackling massive problems with fresh ideas and relentless ambition. This time, I found three early-stage startups on Space Slice platform that are worth watching. From AI-powered sales prospecting to … Read more
This piece on the Futures of Ethereum explores its ongoing fight for censorship resistance, examining the impact of regulatory pressure, the role of Proposer-Builder Separation (PBS), and potential solutions like encrypted mempools to keep the network neutral and decentralized. Ethereum has adopted the PBS structure to mitigate the centralization risks of MEV profits. In this … Read more
:::info Authors: (1) Anonymous authors Paper under double-blind review Jarrod Haas, SARlab, Department of Engineering Science Simon Fraser University; Digitalist Group Canada and jhaas@sfu.ca; (2) William Yolland, MetaOptima and yollandw@gmail.com; (3) Bernhard Rabus, SARlab, Department of Engineering Science, Simon Fraser University and bernhardtrabus@sfu.ca. ::: Abstract and 1 Introduction 2 Background 2.1 Problem Definition 2.2 Related … Read more
:::info Authors: (1) Anonymous authors Paper under double-blind review Jarrod Haas, SARlab, Department of Engineering Science Simon Fraser University; Digitalist Group Canada and jhaas@sfu.ca; (2) William Yolland, MetaOptima and yollandw@gmail.com; (3) Bernhard Rabus, SARlab, Department of Engineering Science, Simon Fraser University and bernhardtrabus@sfu.ca. ::: Abstract and 1 Introduction 2 Background 2.1 Problem Definition 2.2 Related … Read more
:::info Authors: (1) Anonymous authors Paper under double-blind review Jarrod Haas, SARlab, Department of Engineering Science Simon Fraser University; Digitalist Group Canada and jhaas@sfu.ca; (2) William Yolland, MetaOptima and yollandw@gmail.com; (3) Bernhard Rabus, SARlab, Department of Engineering Science, Simon Fraser University and bernhardtrabus@sfu.ca. ::: Abstract and 1 Introduction 2 Background 2.1 Problem Definition 2.2 Related … Read more
:::info Authors: (1) Anonymous authors Paper under double-blind review Jarrod Haas, SARlab, Department of Engineering Science Simon Fraser University; Digitalist Group Canada and jhaas@sfu.ca; (2) William Yolland, MetaOptima and yollandw@gmail.com; (3) Bernhard Rabus, SARlab, Department of Engineering Science, Simon Fraser University and bernhardtrabus@sfu.ca. ::: Abstract and 1 Introduction 2 Background 2.1 Problem Definition 2.2 Related … Read more
:::info Authors: (1) Anonymous authors Paper under double-blind review Jarrod Haas, SARlab, Department of Engineering Science Simon Fraser University; Digitalist Group Canada and jhaas@sfu.ca; (2) William Yolland, MetaOptima and yollandw@gmail.com; (3) Bernhard Rabus, SARlab, Department of Engineering Science, Simon Fraser University and bernhardtrabus@sfu.ca. ::: Abstract and 1 Introduction 2 Background 2.1 Problem Definition 2.2 Related … Read more
How are you, hacker? 🪐Want to know what’s trending right now?: The Techbeat by HackerNoon has got you covered with fresh content from our trending stories of the day! Set email preference here. ## Intergalactic Aliens: No Little Green Men—Just Cosmic USB Drives on a Mission By @thebojda [ 9 Min read ] Explore the … Read more
Table of Links Abstract and 1 Introduction 2. Data 3. Measuring Media Slant and 3.1. Text pre-processing and featurization 3.2. Classifying transcripts by TV source 3.3. Text similarity between newspapers and TV stations and 3.4. Topic model 4. Econometric Framework 4.1. Instrumental variables specification 4.2. Instrument first stage and validity 5. Results 5.1. Main results … Read more
Table of Links Abstract and 1 Introduction 2. Data 3. Measuring Media Slant and 3.1. Text pre-processing and featurization 3.2. Classifying transcripts by TV source 3.3. Text similarity between newspapers and TV stations and 3.4. Topic model 4. Econometric Framework 4.1. Instrumental variables specification 4.2. Instrument first stage and validity 5. Results 5.1. Main results … Read more
Table of Links Abstract and 1 Introduction 2. Data 3. Measuring Media Slant and 3.1. Text pre-processing and featurization 3.2. Classifying transcripts by TV source 3.3. Text similarity between newspapers and TV stations and 3.4. Topic model 4. Econometric Framework 4.1. Instrumental variables specification 4.2. Instrument first stage and validity 5. Results 5.1. Main results … Read more
Table of Links Abstract and 1 Introduction 2. Data 3. Measuring Media Slant and 3.1. Text pre-processing and featurization 3.2. Classifying transcripts by TV source 3.3. Text similarity between newspapers and TV stations and 3.4. Topic model 4. Econometric Framework 4.1. Instrumental variables specification 4.2. Instrument first stage and validity 5. Results 5.1. Main results … Read more
Table of Links Abstract and 1 Introduction 2. Data 3. Measuring Media Slant and 3.1. Text pre-processing and featurization 3.2. Classifying transcripts by TV source 3.3. Text similarity between newspapers and TV stations and 3.4. Topic model 4. Econometric Framework 4.1. Instrumental variables specification 4.2. Instrument first stage and validity 5. Results 5.1. Main results … Read more
Table of Links Abstract and 1 Introduction 2. Data 3. Measuring Media Slant and 3.1. Text pre-processing and featurization 3.2. Classifying transcripts by TV source 3.3. Text similarity between newspapers and TV stations and 3.4. Topic model 4. Econometric Framework 4.1. Instrumental variables specification 4.2. Instrument first stage and validity 5. Results 5.1. Main results … Read more
Table of Links Abstract and 1 Introduction 2. Data 3. Measuring Media Slant and 3.1. Text pre-processing and featurization 3.2. Classifying transcripts by TV source 3.3. Text similarity between newspapers and TV stations and 3.4. Topic model 4. Econometric Framework 4.1. Instrumental variables specification 4.2. Instrument first stage and validity 5. Results 5.1. Main results … Read more
In an era where almost anything can be shared and go viral, no business wants to be embroiled as the subject of a frustrated customer’s post, especially one that sparks a chain reaction of similar complaints. Bad publicity is still publicity, yes, but it’s definitely not the kind you want for your business. That’s why … Read more
APT handles software installation and dependency management on Debian-based Linux distributions, ensuring compatibility and ease of use. Nix, a declarative package manager, introduces functional package management with atomic upgrades, rollbacks, and reproducibility, making it appealing for developers requiring isolated environments and precise dependency control. This article examines Nix’s approach to package management, comparing it with … Read more
One thing is clear: We are witnessing the slow digital decay of the internet and people are reticent to admit it – if they recognize it is happening in the first place. Remember that time, not too long ago, when the internet felt like an endless playground of information, creativity, connections, and discovery? It was … Read more
Throughout history, many hackers have stood firm and challenged corporate capitalism and government surveillance. The corporations and the governments, including the mainstream media, funded by the corporate donors, worked together countless times to fool the masses, manufacturing consent to several policies that are indeed harmful to the masses but beneficial to the elite ruling class. … Read more
Sentient has completed a large-scale ownership mint with 650,000 participants, setting a new benchmark in crypto and AI history. The campaign distributes NFTs tied to Dobby, the first decentralized Loyal AI model. Holders can later claim ownership and unique model fingerprints. The scale of participation reflects growing interest in decentralized AI. Sentient’s mint follows the … Read more
Table of Links Part 1: Abstract & Introduction Part 2: Background Part 3: Attacks & Countermeasures Part 4: Experimental Setup Part 5: Datasets & Evaluation Part 6: Attack, Countermeasure Parameters, & Baseline: Random Perturbations Part 7: Results & Discussion Part 8: Transfer Attacks & Countermeasures Part 9: Conclusion, Limitations, & Ethics Statement Part 10: Appendix: … Read more
Table of Links Part 1: Abstract & Introduction Part 2: Background Part 3: Attacks & Countermeasures Part 4: Experimental Setup Part 5: Datasets & Evaluation Part 6: Attack, Countermeasure Parameters, & Baseline: Random Perturbations Part 7: Results & Discussion Part 8: Transfer Attacks & Countermeasures Part 9: Conclusion, Limitations, & Ethics Statement Part 10: Appendix: … Read more
Table of Links Part 1: Abstract & Introduction Part 2: Background Part 3: Attacks & Countermeasures Part 4: Experimental Setup Part 5: Datasets & Evaluation Part 6: Attack, Countermeasure Parameters, & Baseline: Random Perturbations Part 7: Results & Discussion Part 8: Transfer Attacks & Countermeasures Part 9: Conclusion, Limitations, & Ethics Statement Part 10: Appendix: … Read more
Table of Links Part 1: Abstract & Introduction Part 2: Background Part 3: Attacks & Countermeasures Part 4: Experimental Setup Part 5: Datasets & Evaluation Part 6: Attack, Countermeasure Parameters, & Baseline: Random Perturbations Part 7: Results & Discussion Part 8: Transfer Attacks & Countermeasures Part 9: Conclusion, Limitations, & Ethics Statement Part 10: Appendix: … Read more
Table of Links Part 1: Abstract & Introduction Part 2: Background Part 3: Attacks & Countermeasures Part 4: Experimental Setup Part 5: Datasets & Evaluation Part 6: Attack, Countermeasure Parameters, & Baseline: Random Perturbations Part 7: Results & Discussion Part 8: Transfer Attacks & Countermeasures Part 9: Conclusion, Limitations, & Ethics Statement Part 10: Appendix: … Read more
Table of Links Part 1: Abstract & Introduction Part 2: Background Part 3: Attacks & Countermeasures Part 4: Experimental Setup Part 5: Datasets & Evaluation Part 6: Attack, Countermeasure Parameters, & Baseline: Random Perturbations Part 7: Results & Discussion Part 8: Transfer Attacks & Countermeasures Part 9: Conclusion, Limitations, & Ethics Statement Part 10: Appendix: … Read more
Table of Links Part 1: Abstract & Introduction Part 2: Background Part 3: Attacks & Countermeasures Part 4: Experimental Setup Part 5: Datasets & Evaluation Part 6: Attack, Countermeasure Parameters, & Baseline: Random Perturbations Part 7: Results & Discussion Part 8: Transfer Attacks & Countermeasures Part 9: Conclusion, Limitations, & Ethics Statement Part 10: Appendix: … Read more
:::info Authors: (1) Raghuveer Peri, AWS AI Labs, Amazon and with Equal Contributions (raghperi@amazon.com); (2) Sai Muralidhar Jayanthi, AWS AI Labs, Amazon and with Equal Contributions; (3) Srikanth Ronanki, AWS AI Labs, Amazon; (4) Anshu Bhatia, AWS AI Labs, Amazon; (5) Karel Mundnich, AWS AI Labs, Amazon; (6) Saket Dingliwal, AWS AI Labs, Amazon; (7) … Read more