Modern Internet services generate massive volumes of observability data, yet identifying opportunities for business performance improvements remains elusive. In many cases, such insights manifest only within sub-populations defined by derived attributes that cannot be predefined, might evolve over time, and often cannot be exhaustively enumerated ahead of time. Unfortunately, existing commercial and research systems fall short in one or more aspects of generating such improvement opportunities: expressiveness, automation, and scalability. We present a vision for automatically surfacing opportunities for improvements to tackle these seemingly conflicting and intractable requirements. We highlight the early promise from a proof-of-concept system, showing evaluation on three real-world services and discuss open challenges for future work.

Recommender systems are one of the most widely used services on several online platforms to suggest potential items to the end-users. These services often use different machine learning techniques for which fairness is a concerning factor, especially when the downstream services have the ability to cause social ramifications. Thus, focusing on the non-personalised (global) recommendations in news media platforms (e.g., top-k trending topics on Twitter, top-k news on a news platform, etc.), we discuss on two specific fairness concerns together (traditionally studied separately)---user fairness and organisational fairness. While user fairness captures the idea of representing the choices of all the individual users in the case of global recommendations, organisational fairness tries to ensure politically/ideologically balanced recommendation sets. This makes user fairness a user-side requirement and organisational fairness a platform-side requirement. For user fairness, we test with methods from social choice theory, i.e., various voting rules known to better represent user choices in their results. Even in our application of voting rules to the recommendation setup, we observe high user satisfaction scores. Now for organisational fairness, we propose a bias metric which measures the aggregate ideological bias of a recommended set of items (articles). Analysing the results obtained from voting rule-based recommendation, we find that while the well-known voting rules are better from the user side, they show high bias values and clearly not suitable for organisational requirements of the platforms. Thus, there is a need to build an encompassing mechanism by cohesively bridging ideas of user fairness and organisational fairness. In this abstract paper, we intend to frame the elementary ideas along with the clear motivation behind the requirement of such a mechanism.

Building loads consume roughly 40% of the energy produced in developed countries, a significant part of which is invested towards building temperature-control infrastructure. Therein, renewable resource-based microgrids offer a greener and cheaper alternative. This communication explores the possible co-design of microgrid power dispatch and building HVAC (heating, ventilation and air conditioning system) actuations with the objective of effective temperature control under minimised operating cost. For this, we attempt control designs with various levels of abstractions based on information available about microgrid and HVAC system models using the Deep Reinforcement Learning (DRL) technique. We provide control architectures that consider model information ranging from completely determined system models to systems with fully unknown parameter settings and illustrate the advantages of DRL for the design prescriptions.

Ritam Dutt, Sayan Sinha, Rishabh Joshi, Surya Shekhar Chakraborty, Meredith Riggs, Xinru Yan, Haogang Bao, Carolyn Rose. Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. 2021.

The rapid progress in the ease of creating and spreading ultra-realistic media over social platforms calls for an urgent need to develop a generalizable deepfake detection technique. It has been observed that current deepfake generation methods leave discriminative artifacts in the frequency spectrum of fake images and videos. Inspired by this observation, in this paper, we present a novel approach, termed as MD-CSDNetwork, for combining the features in the spatial and frequency domains to extract a shared discriminative representation for classifying deepfakes. MD-CSDNetwork is a novel cross-stitched network with two parallel branches carrying spatial and frequency information, respectively. We hypothesize that these multi-domain input data streams can be considered as related supervisory signals and can ensure better performance and generalization. Further, the concept of cross-stitch connections is utilized where they are inserted between the two branches to learn an optimal combination of domain-specific and shared representations from other domains automatically. Extensive experiments are conducted on the popular benchmark datasets. We report improvements over all the manipulation types in the FaceForensics++ dataset and comparable results with state-of-the-art methods for cross-database evaluation on the Celeb-DF dataset and the Deepfake Detection Dataset.

Most of the modern processors perform out-of-order speculative executions to maximise system performance. Spectre and Meltdown exploit these optimisations and execute certain instructions leading to leakage of confidential information of the victim. All the variants of this class of attacks necessarily exploit branch prediction or speculative execution. Using this insight, we develop a two step strategy to effectively detect these attacks using performance counter statistics, correlation coefficient model, deep neural network and fast Fourier transform. Our approach is expected to provide reliable, fast and highly accurate results with no perceivable loss in system performance or system overhead.

Computing optimal routes in a road network is both space and time-consuming. This paper proposes an innovative way of finding a route, given the traffic conditions of every edge present in the map. This article aims to bring about a convergence between methods used in routing data over a network and path planning used in AI technique. It makes use of certain concepts of network routing tables in order to develop path planners suitable for urban conditions. It also has a linear space complexity.