Demo Data from SIGIR 2026 / reviewers in the wild / expert
Moloud Shahbazi
dblp:60/11514
· DBLP profile ↗
6ranked-venue papers
1as first author
1since 2021 · last 2021
0000-0002-8031-0169ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 4 · 1 first-author · 1 since 2021Computer networks · 2Human-computer interaction and ubiquitous computing · 1Applied, interdisciplinary, general and emerging computing · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer networks
1 paper |
Physical-layer communications · 77% Wireless networking · 23% | |
| Databases, data mining, and information retrieval
1 paper |
Recommender systems · 44% Information retrieval · 44% Web and social media mining · 13% |
Topics — the 7 heaviest of 8, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Information retrieval › ranking
query-dependent ranking |
0.3 | 1 | 2017 | IRanker: Query-Specific Ranking of Reviewed Items · ICDE 2017 |
Physical-layer communications › modulation
adaptive modulation and coding |
0.2 | 1 | 2015 | Adaptive Sub-Carrier Level Power Allocation in OFDMA Networks · IEEE Trans. Mob. Comput. 2015 |
Physical-layer communications › multiple access › multicarrier multiple access
OFDMA networks |
0.2 | 1 | 2015 | Adaptive Sub-Carrier Level Power Allocation in OFDMA Networks · IEEE Trans. Mob. Comput. 2015 |
Physical-layer communications
power allocation |
0.2 | 1 | 2015 | Adaptive Sub-Carrier Level Power Allocation in OFDMA Networks · IEEE Trans. Mob. Comput. 2015 |
Wireless networking › link adaptation
rate adaptation |
0.2 | 1 | 2015 | Adaptive Sub-Carrier Level Power Allocation in OFDMA Networks · IEEE Trans. Mob. Comput. 2015 |
Web and social media mining
user-generated content |
0.1 | 1 | 2017 | IRanker: Query-Specific Ranking of Reviewed Items · ICDE 2017 |
Physical-layer communications › fading channels
frequency-selective fading |
0.1 | 1 | 2015 | Adaptive Sub-Carrier Level Power Allocation in OFDMA Networks · IEEE Trans. Mob. Comput. 2015 |
Methods — techniques the papers use, named apart from their topics
testbed experiments · 0.2simulation · 0.2power redistribution · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | Query by documents on top of a search interface
Nhat X. T. Le, Moloud Shahbazi, Abdulaziz Almaslukh, Vagelis Hristidis |
Inf. Syst. | 2 |
| 2017 | IRanker: Query-Specific Ranking of Reviewed ItemsabstractItem (e.g., product) reviews are one of the most popular types of user-generated content in Web 2.0. Reviews have been effectively used in collaborative filtering to recommend products to users based on similar users, and also to compute a product's star rating. However, little work has studied how reviews can be used to perform query-specific ranking of items. In this paper, we present efficient top-k algorithms to rank items, by weighing each review's rating by its relevance to the user query. We propose a non-random access algorithm and perform a comprehensive evaluation of our method on multiple datasets. We show that our solution significantly outperforms the baseline approach in terms of query response time. Moloud Shahbazi, Matthew T. Wiley, Vagelis Hristidis |
ICDE | 1 |
| 2017 | Querying Documents Annotated by Interconnected Entities
Shouq Sadah, Moloud Shahbazi, Vagelis Hristidis |
ICWSM | 2 |
| 2016 | OSNI: Searching for Needles in a Haystack of Social Network DataabstractThis paper presents the Online Social Network Investigator (OSNI), a scalable distributed system to search social net- work data, based on a spatiotemporal window and a list of keywords. Given that only 2% of tweets are geolocated, we have implemented and compared various state-of-art loca- tion estimation techniques. Further, to enrich the context of posts, associations of images to terms are estimated through various classication techniques. The accuracies of these es- timations are evaluated on large real datasets. OSNI's query interface is available on the Web. Shiwen Cheng, James Fang, Vagelis Hristidis, Harsha V. Madhyastha, Niluthpol Chowdhury Mithun, Dorian Jean Perkins, Amit K. Roy-Chowdhury, Moloud Shahbazi, Vassilis J. Tsotras |
EDBT | 8 |
| 2015 | Adaptive Sub-Carrier Level Power Allocation in OFDMA NetworksabstractIn today's OFDMA networks, the transmission power is typically fixed and the same for all the sub-carriers that compose a channel. The sub-carriers though, experience different degrees of fading and thus, the received power is different for different sub-carriers; while some frequencies experience deep fades, others are relatively unaffected. In this paper, we make a case for redistributing the power across the sub-carriers (subject to a fixed power budget constraint) to better cope with this frequency selectivity. Specifically, we design a joint power and rate adaptation scheme (called JPRA for short) wherein power redistribution is combined with sub-carrier level rate adaptation to yield significant throughput benefits. We further consider three variants of JPRA: (a) JPRA-Basic where, the power is redistributed across sub-carriers so as to support a maximum common rate across all the sub-carriers (b) JPRA-Intermediate where, the power is redistributed across sub-carriers so as to support a maximum common rate across a “subset” of sub-carriers such that the aggregate rate is maximized. (c) JPRA-Adaptive where, the goal is to redistribute power such that the transmission time of a packet is minimized. While the first two variants decrease transceiver complexity and are simpler, the third is geared towards achieving the maximum throughput possible. We implement all three variants of JPRA on our WARP radio testbed. Our extensive experiments demonstrate that JPRA can provide a 35 percent improvement in total network throughput in testbed experiments compared to FARA, a scheme where only sub-carrier level rate adaptation is used. We also perform simulations to demonstrate the efficacy of JPRA in larger scale networks. Shailendra Singh 0004, Moloud Shahbazi, Konstantinos Pelechrinis, Karthikeyan Sundaresan, Srikanth V. Krishnamurthy, Sateesh Addepalli |
IEEE Trans. Mob. Comput. | 2 |
| 2012 | A case for adaptive sub-carrier level power allocation in OFDMA networksabstractIn today's OFDMA networks, the transmission power is typically fixed and the same for all the sub-carriers that compose a channel. The sub-carriers though, experience different degrees of fading and thus, the received power is different for different sub-carriers; while some frequencies experience deep fades, others are relatively unaffected. In this paper, we make a case of redistributing the power across the sub-carriers (subject to a fixed power budget constraint) to better cope with this frequency selectivity. Specifically, we design a joint power and rate adaptation scheme (called JPRA for short) wherein power redistribution is combined with sub-carrier level rate adaptation to yield significant throughput benefits. We further consider two variants of JPRA: (a) JPRA-CR where, the power is redistributed across sub-carriers so as to support a maximum common rate (CR) across sub-carriers and (b) JPRA-MT where, the goal is to redistribute power such that the transmission time of a packet is minimized. While the first variant decreases transceiver complexity and is simpler, the second is geared towards achieving the maximum throughput possible. We implement both variants of JPRA on our WARP radio testbed. Our extensive experiments demonstrate that our scheme provides a 35% improvement in total network throughput in testbed experiments compared to FARA, a scheme where only sub-carrier level rate adaptation is used. We also perform simulations to demonstrate the efficacy of JPRA in larger scale networks. Shailendra Singh 0004, Moloud Shahbazi, Konstantinos Pelechrinis, Karthikeyan Sundaresan, Srikanth V. Krishnamurthy, Sateesh Addepalli |
MobiHoc | 2 |