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Nowadays, research into personalization has been focusing on explainability and fairness. Several approaches proposed in recent works are able to explain individual recommendations in a post-hoc manner or by explanation paths. However, explainability techniques applied to unfairness in recommendation have been limited to finding user/item features mostly related to biased recommendations. In this paper, we devised a novel algorithm that leverages counterfactuality methods to discover user unfairness explanations in the form of user-item interactions. In our counterfactual framework, interactions are represented as edges in a bipartite graph, with users and items as nodes. Our Bipartite Graph Explainer perturbs the topological structure to find an altered version (counterfactual explanation) that minimizes the disparity in utility between the protected and unprotected demographic groups. Experiments on four real-world graphs coming from various domains showed that our method can systematically explain user unfairness on three state-of-the-art GNN-based recommendation models. Moreover, an empirical evaluation of the perturbed network uncovered relevant patterns that justify the nature of the unfairness discovered by the generated explanations. The source code and the preprocessed data sets are available at //github.com/jackmedda/RS-BGExplainer.

Smart homes are powered by numerous programmable IoT platforms. Despite tremendous innovations, these platforms often suffer from safety and security issues. One class of defense solutions dynamically enforces safety and security policies, which essentially capture the expected behavior of the IoT system. While many proposed works were built on this runtime approach, they all are under-vetted. The primary reason lies in their evaluation approach. They are mostly self-evaluated in isolation using a virtual testbed combined with manually orchestrated test scenarios that rely on user interactions with the platform's UI. Such hand-crafted and non-uniform evaluation setups are limiting not only the reproducibility but also a comparative analysis of their efficacy results. Closing this gap in the traditional way requires a huge upfront manual effort, which causes the researchers turn away from any large-scale comparative empirical evaluation. Therefore, in this paper, we propose a highly-automated uniform evaluation platform, dubbed VetIoT, to vet the defense solutions that hinge on runtime policy enforcement. Given a defense solution, VetIoT easily instantiates a virtual testbed inside which the solution is empirically evaluated. VetIoT replaces manual UI-based interactions with an automated event simulator and manual inspection of test outcomes with an automated comparator. We developed a fully-functional prototype of VetIoT and applied it on three runtime policy enforcement solutions: Expat, Patriot, and IoTguard. VetIoT reproduced their individual prior results and assessed their efficacy results via stress testing and differential testing. We believe VetIoT can foster future research/evaluation.

Federated Learning (FL) requires frequent exchange of model parameters, which leads to long communication delay, especially when the network environments of clients vary greatly. Moreover, the parameter server needs to wait for the slowest client (i.e., straggler, which may have the largest model size, lowest computing capability or worst network condition) to upload parameters, which may significantly degrade the communication efficiency. Commonly-used client selection methods such as partial client selection would lead to the waste of computing resources and weaken the generalization of the global model. To tackle this problem, along a different line, in this paper, we advocate the approach of model parameter dropout instead of client selection, and accordingly propose a novel framework of Federated learning scheme with Differential parameter Dropout (FedDD). FedDD consists of two key modules: dropout rate allocation and uploaded parameter selection, which will optimize the model parameter uploading ratios tailored to different clients' heterogeneous conditions and also select the proper set of important model parameters for uploading subject to clients' dropout rate constraints. Specifically, the dropout rate allocation is formulated as a convex optimization problem, taking system heterogeneity, data heterogeneity, and model heterogeneity among clients into consideration. The uploaded parameter selection strategy prioritizes on eliciting important parameters for uploading to speedup convergence. Furthermore, we theoretically analyze the convergence of the proposed FedDD scheme. Extensive performance evaluations demonstrate that the proposed FedDD scheme can achieve outstanding performances in both communication efficiency and model convergence, and also possesses a strong generalization capability to data of rare classes.

Point patterns are characterized by their density and correlation. While spatial variation of density is well-understood, analysis and synthesis of spatially-varying correlation is an open challenge. No tools are available to intuitively edit such point patterns, primarily due to the lack of a compact representation for spatially varying correlation. We propose a low-dimensional perceptual embedding for point correlations. This embedding can map point patterns to common three-channel raster images, enabling manipulation with off-the-shelf image editing software. To synthesize back point patterns, we propose a novel edge-aware objective that carefully handles sharp variations in density and correlation. The resulting framework allows intuitive and backward-compatible manipulation of point patterns, such as recoloring, relighting to even texture synthesis that have not been available to 2D point pattern design before. Effectiveness of our approach is tested in several user experiments.

Quantum computing promises an effective way to solve targeted problems that are classically intractable. Among them, quantum computers built with superconducting qubits are considered one of the most advanced technologies, but they suffer from short coherence times. This can get exaggerated when they are controlled directly by general-purpose host machines, which leads to the loss of quantum information. To mitigate this, we need quantum control processors (QCPs) positioned between quantum processing units and host machines to reduce latencies. However, existing QCPs are built on top of designs with no or inefficient scalability, requiring a large number of instructions when scaling to more qubits. In addition, interactions between current QCPs and host machines require frequent data transmissions and offline computations to obtain final results, which limits the performance of quantum computers. In this paper, we propose a QCP called HiSEP-Q featuring a novel quantum instruction set architecture (QISA) and its microarchitecture implementation. For efficient control, we utilize mixed-type addressing modes and mixed-length instructions in HiSEP-Q, which provides an efficient way to concurrently address more than 100 qubits. Further, for efficient read-out and analysis, we develop a novel onboard accumulation and sorting unit, which eliminates the data transmission of raw data between the QCPs and host machines and enables real-time result processing. Compared to the state-of-the-art, our proposed QISA achieves at least 62% and 28% improvements in encoding efficiency with real and synthetic quantum circuits, respectively. We also validate the microarchitecture on a field-programmable gate array, which exhibits low power and resource consumption. Both hardware and ISA evaluations demonstrate that HiSEP-Q features high scalability and efficiency toward the number of controlled qubits.

Chain-of-Thought Prompting (CoT) reinforces the reasoning capabilities of Large Language Models (LLMs) through the generation of intermediate rationales. However, these enhancements predominantly benefit large-scale models, leaving small LMs without significant performance improvements when directly applying CoT. Despite the advanced reasoning capabilities of LLMs, CoT relies primarily on their pre-trained internal knowledge. The external knowledge that is previously unknown to the model remains unexploited. This omission becomes pronounced in tasks such as stance detection, where the external background knowledge plays a pivotal role. Additionally, the large-scale architecture of LLMs inevitably present efficiency challenges during deployment. To address these challenges, we introduce the Ladder-of-Thought (LoT) for stance detection. Grounded in a dual-phase Cascaded Optimization framework, LoT directs the model to incorporate high-quality external knowledge, enhancing the intermediate rationales it generates. These bolstered rationales subsequently serve as the foundation for more precise predictions - akin to how a ladder facilitates reaching elevated goals. LoT achieves a balance between efficiency and accuracy, making it an adaptable and efficient framework for stance detection. Our empirical evaluations underscore LoT's effectiveness, marking a 16% improvement over ChatGPT and a 10% enhancement compared to ChatGPT with CoT.

A vast number of systems across the world use algorithmic decision making (ADM) to (partially) automate decisions that have previously been made by humans. When designed well, these systems promise more objective decisions while saving large amounts of resources and freeing up human time. However, when ADM systems are not designed well, they can lead to unfair decisions which discriminate against societal groups. The downstream effects of ADMs critically depend on the decisions made during the systems' design and implementation, as biases in data can be mitigated or reinforced along the modeling pipeline. Many of these design decisions are made implicitly, without knowing exactly how they will influence the final system. It is therefore important to make explicit the decisions made during the design of ADM systems and understand how these decisions affect the fairness of the resulting system. To study this issue, we draw on insights from the field of psychology and introduce the method of multiverse analysis for algorithmic fairness. In our proposed method, we turn implicit design decisions into explicit ones and demonstrate their fairness implications. By combining decisions, we create a grid of all possible "universes" of decision combinations. For each of these universes, we compute metrics of fairness and performance. Using the resulting dataset, one can see how and which decisions impact fairness. We demonstrate how multiverse analyses can be used to better understand variability and robustness of algorithmic fairness using an exemplary case study of predicting public health coverage of vulnerable populations for potential interventions. Our results illustrate how decisions during the design of a machine learning system can have surprising effects on its fairness and how to detect these effects using multiverse analysis.

Improving data systems' performance for join operations has long been an issue of great importance. More recently, a lot of focus has been devoted to multi-way join performance and especially on reducing the negative impact of producing intermediate tuples, which in the end do not make it in the final result. We contribute a new multi-way join algorithm, coined SieveJoin, which extends the well-known Bloomjoin algorithm to multi-way joins and achieves state-of-the-art performance in terms of join query execution efficiency. SieveJoin's salient novel feature is that it allows the propagation of Bloom filters in the join path, enabling the system to `stop early' and eliminate useless intermediate join results. The key design objective of SieveJoin is to efficiently `learn' the join results, based on Bloom filters, with negligible memory overheads. We discuss the bottlenecks in delaying multi-way joins, and how Bloom filters are used to remove the generation of unnecessary intermediate join results. We provide a detailed experimental evaluation using various datasets, against a state-of-the-art column-store database and a multi-way worst-case optimal join algorithm, showcasing SieveJoin's gains in terms of response time.

Generative commonsense reasoning which aims to empower machines to generate sentences with the capacity of reasoning over a set of concepts is a critical bottleneck for text generation. Even the state-of-the-art pre-trained language generation models struggle at this task and often produce implausible and anomalous sentences. One reason is that they rarely consider incorporating the knowledge graph which can provide rich relational information among the commonsense concepts. To promote the ability of commonsense reasoning for text generation, we propose a novel knowledge graph augmented pre-trained language generation model KG-BART, which encompasses the complex relations of concepts through the knowledge graph and produces more logical and natural sentences as output. Moreover, KG-BART can leverage the graph attention to aggregate the rich concept semantics that enhances the model generalization on unseen concept sets. Experiments on benchmark CommonGen dataset verify the effectiveness of our proposed approach by comparing with several strong pre-trained language generation models, particularly KG-BART outperforms BART by 5.80, 4.60, in terms of BLEU-3, 4. Moreover, we also show that the generated context by our model can work as background scenarios to benefit downstream commonsense QA tasks.

Distant supervision can effectively label data for relation extraction, but suffers from the noise labeling problem. Recent works mainly perform soft bag-level noise reduction strategies to find the relatively better samples in a sentence bag, which is suboptimal compared with making a hard decision of false positive samples in sentence level. In this paper, we introduce an adversarial learning framework, which we named DSGAN, to learn a sentence-level true-positive generator. Inspired by Generative Adversarial Networks, we regard the positive samples generated by the generator as the negative samples to train the discriminator. The optimal generator is obtained until the discrimination ability of the discriminator has the greatest decline. We adopt the generator to filter distant supervision training dataset and redistribute the false positive instances into the negative set, in which way to provide a cleaned dataset for relation classification. The experimental results show that the proposed strategy significantly improves the performance of distant supervision relation extraction comparing to state-of-the-art systems.