Reviewerly/exHarmony huggingface dataset:
Dynamic Privacy-Aware Collaborative Schemes for Average Computation: A Multi-Time Reporting Case

Collaborative computing is efficient to conduct large-scale computation tasks, especially with the surge in data volume. However, when the data contains sensitive information, privacy has to be attached significant attention during the execution of computation tasks. In this paper, based on a two-step average computation framework, we first propose three different privacy-aware schemes, where noises are carefully designed to be injected into the distributed computing process. The challenging issue is to guarantee the privacy loss in each iteration to be controllable and quantifiable, which we call the dynamic privacy-preserving collaborative computing problem. By employing Kullback-Leibler differential privacy, we obtain the privacy preserving levels in different iterations regarding the three schemes, followed by the analysis of their convergence performances. Further, we devise an approach to balance the privacy loss and the computation accuracy, whose challenge lies in how to motivate data contributors (DCs) to report more accurate data without providing them with monetized payments. This is done by allowing DCs to report data multiple times, and we obtain the optimal reporting times for each DC. Finally, extensive numerical experiments are performed to validate the obtained theoretical results.
On the Round Complexity of Black-Box Secure MPC

We consider the question of minimizing the round complexity of secure multiparty computation (MPC) protocols that make a black-box use of simple cryptographic primitives with security against any number of malicious parties. In the plain model, previous black-box protocols required a high constant number of rounds (>15). This is far from the known lower bound of 4 rounds for protocols with black-box simulators. When allowing random oblivious transfer (OT) correlations, 2-round protocols making black-box use of a pseudorandom generator were known. However, such protocols were obtained via a round-collapsing "protocol garbling" technique that has poor concrete efficiency and makes non-black-box use of an underlying maliciously secure protocol. We improve this state of affairs by presenting the following types of black-box protocols. A key technical tool for the first result is a novel combination of split-state non-malleable codes (Dziembowski, Pietrzak, and Wichs, JACM'18) with standalone secure two-party protocols to construct non-malleable two-party protocols. The second result is based on a new round-optimized variant of the "IPS compiler" (Ishai, Prabhakaran and Sahai, Crypto'08). The third result is obtained via a specialized combination of these two techniques.
AI for Social Justice: New Methodological Horizons in Technical Communication

This Methodologies and Approaches piece argues artificially intelligent machine learning systems can be used to effectively advance justice-oriented research in technical and professional communication (TPC). Using a preexisting dataset investigating patient marginalization in pharmaceuticals policy discourse, we built and tested 49 machine learning systems designed to identify and track rhetorical features of interest. Three popular and one new approach to feature engineering (text quantification) were evaluated. The results indicate that these systems have great potential for use in TPC research.
Data Set and Evaluation of Automated Construction of Financial Knowledge Graph

With the technological development of entity extraction, relationship extraction, knowledge reasoning, and entity linking, the research on knowledge graph has been carried out in full swing in recent years. To better promote the development of knowledge graph, especially in the Chinese language and in the financial industry, we built a high-quality data set, named financial research report knowledge graph (FR2KG), and organized the automated construction of financial knowledge graph evaluation at the 2020 China Knowledge Graph and Semantic Computing Conference (CCKS2020). FR2KG consists of 17,799 entities, 26,798 relationship triples, and 1,328 attribute triples covering 10 entity types, 19 relationship types, and 6 attributes. Participants are required to develop a constructor that will automatically construct a financial knowledge graph based on the FR2KG. In addition, we summarized the technologies for automatically constructing knowledge graphs, and introduced the methods used by the winners and the results of this evaluation.
Trash to Treasure: Harvesting OOD Data with Cross-Modal Matching for Open-Set Semi-Supervised Learning

Open-set semi-supervised learning (open-set SSL) investigates a challenging but practical scenario where out-of-distribution (OOD) samples are contained in the unlabeled data. While the mainstream technique seeks to completely filter out the OOD samples for semi-supervised learning (SSL), we propose a novel training mechanism that could effectively exploit the presence of OOD data for enhanced feature learning while avoiding its adverse impact on the SSL. We achieve this goal by first introducing a warm-up training that leverages all the unlabeled data, including both the in-distribution (ID) and OOD samples. Specifically, we perform a pretext task that enforces our feature extractor to obtain a high-level semantic understanding of the training images, leading to more discriminative features that can benefit the downstream tasks. Since the OOD samples are inevitably detrimental to SSL, we propose a novel cross-modal matching strategy to detect OOD samples. Instead of directly applying binary classification [39], we train the network to predict whether the data sample is matched to an assigned one-hot class label. The appeal of the proposed cross-modal matching over binary classification is the ability to generate a compatible feature space that aligns with the core classification task. Extensive experiments show that our approach substantially lifts the performance on open-set SSL and outperforms the state-of-the-art by a large margin.
Interactive Reinforcement Learning for Feature Selection with Decision Tree in the Loop

We study the problem of balancing effectiveness and efficiency in automated feature selection. Feature selection is to find an optimal feature subset from large feature space. After exploring many feature selection methods, we observe a computational dilemma: 1) traditional feature selection (e.g., mRMR) is mostly efficient, but difficult to identify the best subset; 2) the emerging reinforced feature selection automatically navigates feature space to search the best subset, but is usually inefficient. Are automation and efficiency always apart from each other? Can we bridge the gap between effectiveness and efficiency under automation? Motivated by this dilemma, we aim to develop a novel feature space navigation method. In our preliminary work, we leveraged interactive reinforcement learning to accelerate feature selection by external trainer-agent interaction. Our preliminary work can be significantly improved by modeling the structured knowledge of its downstream task (e.g., decision tree) as learning feedback. In this journal version, we propose a novel interactive and closed-loop architecture to simultaneously model interactive reinforcement learning (IRL) and decision tree feedback (DTF). Specifically, IRL is to create an interactive feature selection loop and DTF is to feed structured feature knowledge back to the loop. The DTF improves IRL from two aspects. First, the tree-structured feature hierarchy generated by decision tree is leveraged to improve state representation. In particular, we represent the selected feature subset as an undirected graph of feature-feature correlations and a directed tree of decision features. We propose a new embedding method capable of empowering Graph Convolutional Network (GCN) to jointly learn state representation from both the graph and the tree. Second, the tree-structured feature hierarchy is exploited to develop a new reward scheme. In particular, we personalize reward assignment of agents based on decision tree feature importance. In addition, observing agents’ actions can also be a feedback, we devise another new reward scheme, to weigh and assign reward based on the selected frequency ratio of each agent in historical action records. Finally, we present extensive experiments with real-world datasets to demonstrate the improved performances of our method.
A Neural Network Technique for the Derivation of Runge–Kutta Pairs Adjusted for Scalar Autonomous Problems

We consider the scalar autonomous initial value problem as solved by an explicit Runge–Kutta pair of orders 6 and 5. We focus on an efficient family of such pairs, which were studied extensively in previous decades. This family comes with 5 coefficients that one is able to select arbitrarily. We set, as a fitness function, a certain measure, which is evaluated after running the pair in a couple of relevant problems. Thus, we may adjust the coefficients of the pair, minimizing this fitness function using the differential evolution technique. We conclude with a method (i.e. a Runge–Kutta pair) which outperforms other pairs of the same two orders in a variety of scalar autonomous problems.
Motif-aware Sequential Recommendation

Sequential recommendation is intended to model the dynamic behavior regularity through users' behavior sequences. Recently, various deep learning techniques are applied to model the relation of items in the sequences. Despite their effectiveness, we argue that the aforementioned methods only consider the macro-structure of the behavior sequence, but neglect the micro-structure in the sequence which is important to sequential recommendation. To address the above limitation, we propose a novel model called Motif-aware Sequential Recommendation (MoSeR), which captures the motifs hidden in behavior sequences to model the micro-structure features. MoSeR extracts the motifs that contain both the last behavior and the target item. These motifs reflect the topological relations among local items in the form of directed graphs. Thus our method can make a more accurate prediction with the awareness of the inherent patterns between local items. Extensive experiments on three benchmark datasets demonstrate that our model outperforms the state-of-the-art sequential recommendation models.
Relationships between software architecture and source code in practice: An exploratory survey and interview

Software Architecture (SA) and Source Code (SC) are two intertwined artefacts that represent the interdependent design decisions made at different levels of abstractions - High-Level (HL) and Low-Level (LL). An understanding of the relationships between SA and SC is expected to bridge the gap between SA and SC for supporting maintenance and evolution of software systems. We aimed at exploring practitioners’ understanding about the relationships between SA and SC. We used a mixed-method that combines an online survey with 87 respondents and an interview with 8 participants to collect the views of practitioners from 37 countries about the relationships between SA and SC. Our results reveal that: practitioners mainly discuss five features of relationships between SA and SC; a few practitioners have adopted dedicated approaches and tools in the literature for identifying and analyzing the relationships between SA and SC despite recognizing the importance of such information for improving a system's quality attributes, especially maintainability and reliability. It is felt that cost and effort are the major impediments that prevent practitioners from identifying, analyzing, and using the relationships between SA and SC. The results have empirically identified five features of relationships between SA and SC reported in the literature from the perspective of practitioners and a systematic framework to manage the five features of relationships should be developed with dedicated approaches and tools considering the cost and benefit of maintaining the relationships.
Collective Variational Inference for Personalized and Generative Physiological Modeling: A Case Study on Hemorrhage Resuscitation

Individual physiological experiments typically provide useful but incomplete information about a studied physiological process. As a result, inferring the unknown parameters of a physiological model from experimental data is often challenging. The objective of this paper is to propose and illustrate the efficacy of a collective variational inference (C-VI) method, intended to reconcile low-information and heterogeneous data from a collection of experiments to produce robust personalized and generative physiological models.To derive the C-VI method, we utilize a probabilistic graphical model to impose structure on the available physiological data, and algorithmically characterize the graphical model using variational Bayesian inference techniques. To illustrate the efficacy of the C-VI method, we apply it to a case study on the mathematical modeling of hemorrhage resuscitation.In the context of hemorrhage resuscitation modeling, the C-VI method could reconcile heterogeneous combinations of hematocrit, cardiac output, and blood pressure data across multiple experiments to obtain (i) robust personalized models along with associated measures of uncertainty and signal quality, and (ii) a generative model capable of reproducing the physiological behavior of the population.The C-VI method facilitates the personalized and generative modeling of physiological processes in the presence of low-information and heterogeneous data.The resulting models provide a solid basis for the development and testing of interpretable physiological monitoring, decision-support, and closed-loop control algorithms.
DNN-based Detectors for Massive MIMO Systems with Low-Resolution ADCs

Low-resolution analog-to-digital converters (ADCs) have been considered as a practical and promising solution for reducing cost and power consumption in massive Multiple-Input-Multiple-Output (MIMO) systems. Unfortunately, low-resolution ADCs significantly distort the received signals, and thus make data detection much more challenging. In this paper, we develop a new deep neural network (DNN) framework for efficient and low-complexity data detection in low-resolution massive MIMO systems. Based on reformulated maximum likelihood detection problems, we propose two model-driven DNN-based detectors, namely OBMNet and FBMNet, for one-bit and few-bit massive MIMO systems, respectively. The proposed OBMNet and FBMNet detectors have unique and simple structures designed for low-resolution MIMO receivers and thus can be efficiently trained and implemented. Numerical results also show that OBMNet and FBMNet significantly outperform existing detection methods.
Federated Learning over Noisy Channels

Does Federated Learning (FL) work when both uplink and downlink communications have errors? How much communication noise can FL handle and what is its impact to the learning performance? This work is devoted to answering these practically important questions by explicitly incorporating both uplink and downlink noisy channels in the FL pipeline. We present a rigorous convergence analysis of FL over simultaneous uplink and downlink noisy communication channels, and characterize the sufficient conditions for FL to maintain the same convergence rate scaling as the ideal case of no communication error. The analysis reveals that, in order to maintain the $\mathcal{O}\left( {1/T} \right)$ convergence rate of FedAvg with perfect communications, the uplink and downlink signal-to-noise-ratio (SNR) should be controlled such that they scale as $\mathcal{O}\left( {{t^2}} \right)$ where t is the index of communication rounds. This key result leads to a transmit power control policy for analog aggregation, whose performance is shown to be superior over the standard method via extensive numerical experiments using real-world FL tasks.
Generating and Validating Certificates Using Blockchain

Every year a lot of students graduate from the universities due to which a huge number of certificates are generated. The problem arises when it comes to authenticate these certificates. At present there is no such mechanism to validate the certificates. Hence, a lot of certificates are forged every year and are being used to fulfill their job requirements. To overcome this problem a decentralized web application is built that registers the certificate on ethereum blockchain and a smart contract to validate the certificates on blockchain. Once the data is entered by the user the details of certificates are pushed on ethereum blockchain using hashing. Using this generated hash we can verify the authenticity of the user.
Fractional Differential Equation-Based Instantaneous Frequency Estimation for Signal Reconstruction

In this paper, we propose a fractional differential equation (FDE)-based approach for the estimation of instantaneous frequencies for windowed signals as a part of signal reconstruction. This approach is based on modeling bandpass filter results around the peaks of a windowed signal as fractional differential equations and linking differ-integrator parameters, thereby determining the long-range dependence on estimated instantaneous frequencies. We investigated the performance of the proposed approach with two evaluation measures and compared it to a benchmark noniterative signal reconstruction method (SPSI). The comparison was provided with different overlap parameters to investigate the performance of the proposed model concerning resolution. An additional comparison was provided by applying the proposed method and benchmark method outputs to iterative signal reconstruction algorithms. The proposed FDE method received better evaluation results in high resolution for the noniterative case and comparable results with SPSI with an increasing iteration number of iterative methods, regardless of the overlap parameter.
Exploiting Long-Distance Interactions and Tolerating Atom Loss in Neutral Atom Quantum Architectures

Quantum technologies currently struggle to scale beyond moderate scale prototypes and are unable to execute even reasonably sized programs due to prohibitive gate error rates or coherence times. Many software approaches rely on heavy compiler optimization to squeeze extra value from noisy machines but are fundamentally limited by hardware. Alone, these software approaches help to maximize the use of available hardware but cannot overcome the inherent limitations posed by the underlying technology.An alternative approach is to explore the use of new, though potentially less developed, technology as a path towards scalability. In this work we evaluate the advantages and disadvantages of a Neutral Atom (NA) architecture. NA systems offer several promising advantages such as long range interactions and native multiqubit gates which reduce communication overhead, overall gate count, and depth for compiled programs. Long range interactions, however, impede parallelism with restriction zones surrounding interacting qubit pairs. We extend current compiler methods to maximize the benefit of these advantages and minimize the cost.Furthermore, atoms in an NA device have the possibility to randomly be lost over the course of program execution which is extremely detrimental to total program execution time as atom arrays are slow to load. When the compiled program is no longer compatible with the underlying topology, we need a fast and efficient coping mechanism. We propose hardware and compiler methods to increase system resilience to atom loss dramatically reducing total computation time by circumventing complete reloads or full recompilation every cycle.
Impossible (Food) Experiences in Extended Reality

We introduce a model to think about impossible experiences in mixed and virtual reality, while emphasizing the role of said experiences in the context of food. This reality-impossibility model includes two continua, namely, the reality-fantasy character of objects and environments, and the extent to which they follow the laws of physics-other laws. We present a series of examples in each of the quadrants of the model and discuss both the research possibilities and implications of impossible experiences.
Memorize, Factorize, or be Naive: Learning Optimal Feature Interaction Methods for CTR Prediction

Click-through rate prediction is one of the core tasks in commercial recommender systems. It aims to predict the prob-ability of a user clicking a particular item given user and item features. As feature interactions bring in non-linearity, they are widely adopted to improve the performance of CTR prediction models. Therefore, effectively modelling feature interactions has attracted much attention in both the research and industry field. The current approaches can generally be categorized into three classes: (i) naïve methods, which do not model feature interactions and only use original features; (ii) memorized methods, which memorize feature interactions by explicitly viewing them as new features and assigning trainable embeddings; (iii) factorized meth-ods, which learn latent vectors for original features and implicitly model feature interactions through factorization functions. Studies have shown that modelling feature interactions by one of these methods alone are suboptimal due to the unique characteristics of different feature interactions. To address this issue, we first propose a general framework called OptInter which finds the most suitable modelling method for each feature interaction. Different state-of-the-art deep CTR models can be viewed as instances of OptInter. To realize the functionality of OptInter, we also introduce a learning algorithm that automatically searches for the optimal modelling method. We conduct extensive experiments on four large datasets, including three public and one private. Experimental results demonstrate the effectiveness of OptInter. Because our OptInter finds the optimal modelling method for each feature interaction, our experiments show that OptInter improves the best performed state-of-the-art baseline deep CTR models by up to 2.21%. Compared to the memorized method, which also outperforms baselines, we reduce up to 91% parameters. In addition, we conduct several ablation studies to investigate the influence of different components of OptInter. Finally, we provide interpretable discussions on the results of OptInter.
Understanding the Computational Demands Underlying Visual Reasoning

Visual understanding requires comprehending complex visual relations between objects within a scene. Here, we seek to characterize the computational demands for abstract visual reasoning. We do this by systematically assessing the ability of modern deep convolutional neural networks (CNNs) to learn to solve the synthetic visual reasoning test (SVRT) challenge, a collection of 23 visual reasoning problems. Our analysis reveals a novel taxonomy of visual reasoning tasks, which can be primarily explained by both the type of relations (same-different versus spatial-relation judgments) and the number of relations used to compose the underlying rules. Prior cognitive neuroscience work suggests that attention plays a key role in humans' visual reasoning ability. To test this hypothesis, we extended the CNNs with spatial and feature-based attention mechanisms. In a second series of experiments, we evaluated the ability of these attention networks to learn to solve the SVRT challenge and found the resulting architectures to be much more efficient at solving the hardest of these visual reasoning tasks. Most important, the corresponding improvements on individual tasks partially explained our novel taxonomy. Overall, this work provides a granular computational account of visual reasoning and yields testable neuroscience predictions regarding the differential need for feature-based versus spatial attention depending on the type of visual reasoning problem.