| Nov. 27, 2023, 2:13 p.m. |
Added
35
|
{"external_links": []}
|
|
| Nov. 20, 2023, 2:03 p.m. |
Added
35
|
{"external_links": []}
|
|
| Nov. 13, 2023, 1:34 p.m. |
Added
35
|
{"external_links": []}
|
|
| Nov. 6, 2023, 1:31 p.m. |
Added
35
|
{"external_links": []}
|
|
| Aug. 14, 2023, 1:31 p.m. |
Added
35
|
{"external_links": []}
|
|
| Aug. 7, 2023, 1:32 p.m. |
Added
35
|
{"external_links": []}
|
|
| July 31, 2023, 1:34 p.m. |
Added
35
|
{"external_links": []}
|
|
| July 24, 2023, 1:36 p.m. |
Added
35
|
{"external_links": []}
|
|
| July 17, 2023, 1:35 p.m. |
Added
35
|
{"external_links": []}
|
|
| July 10, 2023, 1:26 p.m. |
Added
35
|
{"external_links": []}
|
|
| July 3, 2023, 1:27 p.m. |
Added
35
|
{"external_links": []}
|
|
| June 26, 2023, 1:26 p.m. |
Added
35
|
{"external_links": []}
|
|
| June 19, 2023, 1:27 p.m. |
Added
35
|
{"external_links": []}
|
|
| June 12, 2023, 1:29 p.m. |
Added
35
|
{"external_links": []}
|
|
| June 5, 2023, 1:34 p.m. |
Added
35
|
{"external_links": []}
|
|
| May 29, 2023, 1:28 p.m. |
Added
35
|
{"external_links": []}
|
|
| May 22, 2023, 1:29 p.m. |
Added
35
|
{"external_links": []}
|
|
| May 15, 2023, 1:32 p.m. |
Added
35
|
{"external_links": []}
|
|
| May 8, 2023, 1:37 p.m. |
Added
35
|
{"external_links": []}
|
|
| May 1, 2023, 1:28 p.m. |
Added
35
|
{"external_links": []}
|
|
| April 24, 2023, 1:35 p.m. |
Added
35
|
{"external_links": []}
|
|
| April 17, 2023, 1:28 p.m. |
Added
35
|
{"external_links": []}
|
|
| April 10, 2023, 1:25 p.m. |
Added
35
|
{"external_links": []}
|
|
| April 3, 2023, 1:26 p.m. |
Added
35
|
{"external_links": []}
|
|
| Jan. 28, 2023, 11:09 a.m. |
Created
43
|
[{"model": "core.projectfund", "pk": 29618, "fields": {"project": 6836, "organisation": 2, "amount": 252523, "start_date": "2012-09-30", "end_date": "2013-09-29", "raw_data": 49093}}]
|
|
| Jan. 28, 2023, 10:52 a.m. |
Added
35
|
{"external_links": []}
|
|
| April 11, 2022, 3:48 a.m. |
Created
43
|
[{"model": "core.projectfund", "pk": 21751, "fields": {"project": 6836, "organisation": 2, "amount": 252523, "start_date": "2012-09-30", "end_date": "2013-09-29", "raw_data": 31575}}]
|
|
| April 11, 2022, 3:48 a.m. |
Created
41
|
[{"model": "core.projectorganisation", "pk": 82076, "fields": {"project": 6836, "organisation": 8476, "role": "COLLAB_ORG"}}]
|
|
| April 11, 2022, 3:48 a.m. |
Created
41
|
[{"model": "core.projectorganisation", "pk": 82075, "fields": {"project": 6836, "organisation": 109, "role": "LEAD_ORG"}}]
|
|
| April 11, 2022, 3:48 a.m. |
Created
40
|
[{"model": "core.projectperson", "pk": 50827, "fields": {"project": 6836, "person": 9586, "role": "COI_PER"}}]
|
|
| April 11, 2022, 3:48 a.m. |
Created
40
|
[{"model": "core.projectperson", "pk": 50826, "fields": {"project": 6836, "person": 9587, "role": "COI_PER"}}]
|
|
| April 11, 2022, 3:48 a.m. |
Created
40
|
[{"model": "core.projectperson", "pk": 50825, "fields": {"project": 6836, "person": 9588, "role": "COI_PER"}}]
|
|
| April 11, 2022, 3:48 a.m. |
Created
40
|
[{"model": "core.projectperson", "pk": 50824, "fields": {"project": 6836, "person": 9589, "role": "COI_PER"}}]
|
|
| April 11, 2022, 3:48 a.m. |
Created
40
|
[{"model": "core.projectperson", "pk": 50823, "fields": {"project": 6836, "person": 9590, "role": "COI_PER"}}]
|
|
| April 11, 2022, 3:48 a.m. |
Created
40
|
[{"model": "core.projectperson", "pk": 50822, "fields": {"project": 6836, "person": 9591, "role": "COI_PER"}}]
|
|
| April 11, 2022, 3:48 a.m. |
Created
40
|
[{"model": "core.projectperson", "pk": 50821, "fields": {"project": 6836, "person": 2502, "role": "COI_PER"}}]
|
|
| April 11, 2022, 3:48 a.m. |
Created
40
|
[{"model": "core.projectperson", "pk": 50820, "fields": {"project": 6836, "person": 2507, "role": "PI_PER"}}]
|
|
| April 11, 2022, 1:48 a.m. |
Updated
35
|
{"title": ["", "Accelerated Real-Time Information Extraction System (ARIES)"], "description": ["", "\nTechnological advances in CMOS semiconductor technology paved the way for the digital revolution. As predicted by Moore, silicon integration capability has been doubling every 18 months over the past four decades, providing the foundation for low-cost computing and memory technology.\n\nThe digitisation of information and communication technologies sparked a number of innovations revolutionising the way we compute and communicate. Ubiquitous high-bandwidth communication, enabled by WiFi and 3G/4G technologies, facilitates on-demand access to a vast amount of application and location specific information including multimedia and broadcast content, video and voice communications, email and SMS/MMS. Furthermore, it has enabled on-demand access to personalised storage and computing resources, providing the foundation for the development of cloud computing infrastructures and a wide range of online web-based services and applications. With the decreasing cost of communication and storage the Internet has also become the global communication infrastructure for a wide range of autonomous sensor technologies, referred to as the "Internet of things". Key application areas include monitoring/surveillance, smart grid, smart homes and smart cities. \n\nMonitoring internet traffic and mining meaningful information from both the online traffic and the stored information has emerged as essential for many critical applications and services. For example resource management, market intelligence, physical and cybercrime investigations and forensics, cyber space policing, situation awareness and the monitoring of malicious behaviour for criminal and terrorist intent. As the scale, diversity and distributed nature of current and emerging data assets increases and as data becomes ever more ubiquitous and critical to decision making, effective real-time mining of useful information becomes essential. \n\nConsidering the exponential increase of internet traffic and stored data, traditional software based approaches have become inadequate and unsustainable. Performance gain achieved due to Moore's law does not keep up with the required computing bandwidth of current and near future generated data assets. Internet traffic bandwidth is doubling every 12 months while the emerging content diversity is significantly increasing mining complexity. As the enterprise becomes more data centric, with a significant increase in data assets within the public and private cloud, traditional scaling by increasing the number of computing resources can no longer be sustained due to cost and power dissipation.\n\nMost data mining algorithms are derived by the software community and are optimised for data structures for platforms based upon the Von-Neumann architecture. An effective solution now requires a paradigm shift in the way we process data and also how we extract meaningful information from a large amount of distributed, constantly changing data that is partially stored or in-transit.\n\n"], "extra_text": ["", "\n\nPotential Impact:\nBeyond the immediate academic beneficiaries it is anticipated that the following groups will also benefit from the research undertaken:\n\n[1] Impact in the Defence and Security Sector\nIt is well recognised that potentially rich and valuable knowledge is often inaccessible within large and unstructured datasets. This can often come to light after a significant event has occurred and a subsequent forensic trawl of data reveals the hidden knowledge. For defence and security applications, where early visibility of emerging threat is paramount, the existing methods cannot deliver information extraction within an adequate timeframe. Situational awareness must be delivered in real-time or near real-time to inform critical decision making.\n\nPhysical Surveillance Scenario\nAdvancing surveillance and monitoring methods deployed by the military (radar, optical, infrared, audio, satellite, electronic signals) are generating vast datasets in real-time. Moreover the treatment of the inherent noise and uncertainty associated with such data, owing to operational limits of the sensing technologies, means traditional knowledge discovery methods are failing. The successful application of custom-purpose hardware offers the opportunity to exploit existing knowledge discovery models with potentially minimal modification and yet accelerate the end-to-end process bringing new capability to the situational awareness problem in live military operations.\n\nCyber Surveillance Scenario\nA worldwide consensus has emerged identifying cyberspace as a new territory warranting military protection. In 2009 the US DoD declared a new military command dedicated to cyber-security. The UK Strategic and Security Review 2011 has identified "cyber attack, including by other states, and by organised crime and terrorists" as a top priority risk. As the scope and nature of this new territory becomes better understood in the military context a natural requirement emerges to equip the UK defence and security services with new tools to detect, identify and respond to threats against UK interests in cyberspace. \nThese tools must perform a robust surveillance function in a similar manner to their physical counterparts. As such they must contend with daunting datasets deployed across a vast distributed infrastructure and with data that is frequently transient in nature. This research proposal asserts that this specialised function requires specialised hardware to deliver cyber-surveillance capabilities that are sufficiently quick, flexible and robust to address this emerging need. \n\n[2] Longer-term beneficiaries\nData mining of extremely large data sets is a pressing need expressed in multiple business sectors including medicine, security surveillance and network monitoring. This growing demand for new capabilities will drive the creation of a new and competitive commercial space to service this demand. UK based research has an opportunity to place itself at the centre of this movement and exploit future hardware advances.\n\n[3] Future Impact\nThe work programme proposed aims to look at early opportunities to exploit custom purpose hardware in data intensive systems. However it will also seek to bring clarity to future trends and new capabilities that will inevitably emerge over the coming decade to 2020. This information will be of great value to those UK bodies tasked with strategy planning both in the defence sector and in commercial sectors. A clear view of how custom purpose hardware impacts upon knowledge discovery in vast datasets can inform the extrapolation of these capabilities to longer timeframes.\n\n\n"], "status": ["", "Closed"]}
|
|
| April 11, 2022, 1:48 a.m. |
Added
35
|
{"external_links": [24892]}
|
|
| April 11, 2022, 1:48 a.m. |
Created
35
|
[{"model": "core.project", "pk": 6836, "fields": {"owner": null, "is_locked": false, "coped_id": "c039bb91-8961-43be-8b92-1d4665a01aae", "title": "", "description": "", "extra_text": "", "status": "", "start": null, "end": null, "raw_data": 31560, "created": "2022-04-11T01:44:04.970Z", "modified": "2022-04-11T01:44:04.970Z", "external_links": []}}]
|
|