Séminaire en ligne Franco-Africain du LIRIMA

Inaugural lecture by Esther Pacitti
Graduate Program in Computer Science , CEFET/RJ, Rio de Janeiro
12 August 2019, 10:00– Auditorium 5,  Maracanã campus

Data Processing: an evolutionary and multidisciplinary perspective
E. Pacitti
Inria and LIRMM, Montpellier, France

The inaugural lecture will address the context of the growth of the amount and variety of data (images, audio, matrixes, text, etc.), produced in various areas (social networks, agronomy, botany, medicine and others), which has also increased the technological and research challenges in the processing  of this large volume of data, termed by the term Big Data.

In the lecture, Professor Esther Pacitti will present a vision of the evolution of data processing  methods from relational databases, distributed databases, and big data to data science. It will also expose some specific applications in Agronomy, Botany and Seismology, and share research experiences in France and Brazil.

Seminar by Patrick Valduriez (Inria) at Inria, Lille
17 May, 10:30 – Amphi B – Inria – Bat B

The Case for Hybrid Transaction Analytical Processing
P. Valduriez
Inria and LIRMM, Montpellier, France

Abstract. Hybrid Transaction Analytical Processing (HTAP) is poised to revolutionize data management. By providing online analytics over operational data, HTAP systems open up new opportunities in many application domains where real-time decision is critical. Important use cases are proximity marketing, real-time pricing, risk monitoring, real-time fraud detection, etc. HTAP also simplifies data management, by removing the traditional separation between operational database and data warehouse/ data lake (no more ETLs!). However, a hard problem is scaling out transactions in mixed operational and analytical workloads over big data, possibly coming from different data stores (HDFS, SQL, NoSQL, …).
In this talk, I will introduce HTAP systems and illustrate with LeanXcale, a new generation HTAP DBMS that provides ultra-scalable transactions, big data analytics, SQL/JSON support and polystore capabilities

Zenith seminar : 13/05/2019, 10h30

Campus Saint Priest, BAT5-02.124

Neural speaker diarization

Hervé Bredin (CNRS, LIMSI)

Speaker diarization is the task of determining “who speaks when” in an audio stream. It is an enabling technology for multiple downstream applications such as meeting transcription or indexing of ever-growing audio-visual archives.

Speaker diarization workflows usually consist of four consecutive tasks: speech activity detection, speaker change detection, speech turn clustering, and re-segmentation.

Recent advances in deep learning led to major improvements in multiple domains such as computer vision or natural language processing, and speaker diarization is no exception to the rule. In this talk, I will discuss our recent progress towards end-to-end neural speaker diarization (including speech and overlap detection with recurrent neural networks, and triplet loss for speaker embedding).

# References

“Tristounet: Triplet Loss for Speaker Turn Embedding.” Bredin 2017. ICASSP.

“Speaker Change Detection in Broadcast TV Using Bidirectional Long Short- Term Memory Networks.”

Yin 2017. Interspeech.

“Neural Speech Turn Segmentation and Affinity Propagation for Speaker Diarization.”

Yin 2018. Interspeech.

# Code

pyannote.audio: Neural building blocks for speaker diarization: speech activity detection, speaker change detection, speaker embedding github.com/pyannote/pyannote-audio

Zenith seminar : 27/05/2019, 15h
Campus Saint Priest, BAT5-01.124

Bounce Blockchain: a secure, energy-efficient permissionless blockchain
Dennis Shasha
New York University & Inria International Chair in the Zenith team at LIRMM

Performing proof-of-work for the Bitcoin blockchain currently requires as much electricity as consumed by the country of Denmark. This enormous energy expenditure translates into higher costs for users (on the order of $US 6.00 or more per transaction) and is frankly ecologically irresponsible. As of this writing, it can be subverted (i.e. the blockchain can be forked) by a collusion attack of just a handful of data centers. This paper proposes the design of a cheap (less than $US 0.01 per transaction), essentially energy-free public blockchain called a Bounce Blockchain which cannot be forked in any reasonable failure scenario, even most Byzantine failure scenarios.
The basic idea is to send one or  more cubesats  into orbit, each equipped with a hardware security module. Users would send their transaction to the cubesats which would collect them into blocks, sign them, and send (bounce) them back to earth (and to one another).  Bounce Blockchain provides scalability through sharding (transactions will be partitioned over cubesats).
Because modern hardware security modules are tamper-resistant (become inoperable if tampered with) or tamper-responsive (erase their keys if tampered with), take their keys from physical processes, and have been validated, socio-technical protocols can ensure that it is infeasible to forge the identity of a hardware security module in a cubesat with another cubesat. If, however, some cubesats are destroyed, the blockchain will continue to execute correctly though some transactions will be lost. New cubesats can be sent up in short order as they are quite cheap to launch. If, in spite of these assurances, some cubesats fail traitorously, the blockchain can survive through algorithms similar to Practical Byzantine Fault Tolerance techniques.

Biography
Dennis Shasha is a Julius Silver Professor of computer science at the Courant Institute of New York University and an Associate Director of NYU Wireless. He works on meta-algorithms for machine learning to achieve guaranteed correctness rates, with biologists on pattern discovery for network inference; with physicists and financial people on algorithms for time series; on computational reproducibility; and on energy-efficient blockchains. Other areas of interest include database tuning as well as tree and graph matching. Because he likes to type, he has written six books of puzzles about a mathematical detective named Dr. Ecco, a biography about great computer scientists, and a book about the future of computing. He has also written five technical books about database tuning, biological pattern recognition, time series, DNA computing, resampling statistics, and causal inference in molecular networks. He has written the puzzle column for various publications including Scientific American, Dr. Dobb’s Journal, and currently the Communications of the ACM. He is a fellow of the ACM and an Inria International Chair in the Zenith team at LIRMM.

Seminar by Patrick Valduriez (Inria) at IBM Research, Rio de Janeiro, Brazil
Chair: Renan Francisco Santos Souza (IBM Brazil)
Time: Thu, Apr 25, 2019 11:00 AM – 12:30 PM
Location: Corcovado

The Case for Hybrid Transaction Analytical Processing
P. Valduriez
Inria and LIRMM, Montpellier, France

Abstract. Hybrid Transaction Analytical Processing (HTAP) is poised to revolutionize data management. By providing online analytics over operational data, HTAP systems open up new opportunities in many application domains where real-time decision is critical. Important use cases are proximity marketing, real-time pricing, risk monitoring, real-time fraud detection, etc. HTAP also simplifies data management, by removing the traditional separation between operational database and data warehouse/ data lake (no more ETLs!). However, a hard problem is scaling out transactions in mixed operational and analytical workloads over big data, possibly coming from different data stores (HDFS, SQL, NoSQL, …).
In this talk, I will introduce HTAP systems and illustrate with LeanXcale, a new generation HTAP DBMS that provides ultra-scalable transactions, big data analytics, SQL/JSON support and polystore capabilities.