Reviewerly/exHarmony huggingface dataset:
TDTMF: A recommendation model based on user temporal interest drift and latent review topic evolution with regularization factor

This paper constructs a novel enhanced latent semantic model based on users’ comments, and employs regularization factors to capture the temporal evolution characteristics of users’ potential topics for each commodity, so as to improve the accuracy of recommendation. The adaptive temporal weighting of multiple preference features is also improved to calculate the preferences of different users at different time periods using human forgetting features, item interest overlap, and similarity at the semantic level of the review text to improve the accuracy of sparse evaluation data. The paper conducts comparison experiments with six temporal matrix-based decomposition baseline methods in nine datasets, and the results show that the accuracy is 31.64% better than TimeSVD++, 21.08% better than BTMF, 15.51% better than TMRevCo, 13.99% better than BPTF, 9.24% better than TCMF, and 3.19% better than MUTPD ,which indicates that the model is more effective in capturing users’ temporal interest drift and better reflects the evolutionary relationship between users’ latent topics and item ratings.
A modeling environment for reinforcement learning in games

Developing Non-Player Characters, i.e., game characters that interact with the game's environment autonomously with flexibility to experiment different behavior configurations is not a trivial task. Traditionally, this has been done with techniques that limit the complexity of the behavior of Non-Player Characters (NPCs), as in the case of using Navigation Mesh (NavMesh) for navigation behaviors. For this problem, it has been shown that reinforcement learning can be more efficient and flexible than traditional techniques. However, integrating reinforcement learning into current game development tools is laborious, given that a great deal of experimentation and coding is required. For that, we have developed a modeling environment that integrates with a game development tool and allows the direct specification of reward functions and NPC agent components with maximum code reuse and automatic code generation.
Harmony

Deep neural networks (DNNs) have grown exponentially in size over the past decade, leaving only those who have massive datacenter-based resources with the ability to develop and train such models. One of the main challenges for the long tail of researchers who might have only limited resources (e.g., a single multi-GPU server) is limited GPU memory capacity compared to model size. The problem is so acute that the memory requirement of training massive DNN models can often exceed the aggregate capacity of all available GPUs on a single server; this problem only gets worse with the trend of ever-growing model sizes. Current solutions that rely on virtualizing GPU memory (by swapping to/from CPU memory) incur excessive swapping overhead. In this paper, we present a new training framework, Harmony, and advocate rethinking how DNN frameworks schedule computation and move data to push the boundaries of training massive models efficiently on a single commodity server. Across various massive DNN models, Harmony is able to reduce swap load by up to two orders of magnitude and obtain a training throughput speedup of up to 7.6x over highly optimized baselines with virtualized memory.
Cyberbullying and Cyberviolence Detection: A Triangular User-Activity-Content View

Recent years have witnessed the increasing popularity of mobile and networking devices, as well as social networking sites, where users engage in a variety of activities in the cyberspace on a daily and real-time basis. While such systems provide tremendous convenience and enjoyment for users, malicious usages, such as bullying, cruelty, extremism, and toxicity behaviors, also grow noticeably, and impose significant threats to individuals and communities. In this paper, we review computational approaches for cyberbullying and cyberviolence detection, in order to understand two major factors: 1) What are the defining features of online bullying users, and 2) How to detect cyberbullying and cyberviolence. To achieve the goal, we propose a user-activities-content (UAC) triangular view, which defines that users in the cyberspace are centered around the UAC triangle to carry out activities and generate content. Accordingly, we categorize cyberbully features into three main categories: 1) User centered features, 2) Content centered features, and 3) Activity centered features. After that, we review methods for cyberbully detection, by taking supervised, unsupervised, transfer learning, and deep learning, etc., into consideration. The UAC centered view provides a coherent and complete summary about features and characteristics of online users (their activities), approaches to detect bullying users (and malicious content), and helps defend cyberspace from bullying and toxicity.
Backup Resource Allocation Model with Two-Stage Probabilistic Protection

This paper proposes a backup resource allocation model with two-stage probabilistic protection to minimize the total required backup capacity for multiple simultaneous failures of physical machines (PMs). Probabilistic protection ensures that the probability that the PM used for backup fails to backup due to lack of computing capacity does not exceed a given survivability parameter. The proposed model protects the primary virtual machines by allocating computing capacity to backup PMs with probabilistic protection. Since it is uncertain which primary PMs fail, we apply robust optimization to the probabilistic protection. By using a table that takes into account the survivability parameter and the failure probability of PMs, this proposed model is formulated as a mixed integer linear programming problem. The proposed model extends the probabilistic protection to two stages; the VMs that fail to be protected in the first stage are protected in the second stage to achieve the probabilistic protection with the final survivability parameter. This model can reduce the total required backup capacity compared to the conventional model with one-stage probabilistic protection.
Information Theoretic Private Inference in Quantized Models

In a Private Inference scenario, a server holds a model (e.g., a neural network), a user holds data, and the user wishes to apply the model on her data. The privacy of both parties must be protected; the user’s data might contain confidential information, and the server’s model is his intellectual property.Private inference has been studied extensively in recent years, mostly from a cryptographic perspective by incorporating homo-morphic encryption and multiparty computation protocols, which incur high computational overhead and degrade the accuracy of the model. In this work we take a perpendicular approach which draws inspiration from the expansive Private Information Retrieval literature. We view private inference as the task of retrieving an inner product of a parameter vector with the data, a fundamental step in most machine learning models.By combining binary arithmetic with real-valued one, we present a scheme which enables the retrieval of the inner product for models whose weights are either binarized, or given in fixed-point representation; such models gained increased attention recently, due to their ease of implementation and increased robustness. We also present a fundamental trade-off between the privacy of the user and that of the server, and show that our scheme is optimal in this sense. Our scheme is simple, universal to a large family of models, provides clear information-theoretic guarantees to both parties with zero accuracy loss, and in addition, is compatible with continuous data distributions and allows infinite precision.
Towards a Characterization of the Covert Capacity of Bosonic Channels under Trace Distance

We characterize upper and lower bounds for the covert capacity of lossy thermal-noise bosonic channels when measuring covertness using fidelity and trace distance. Although we fall short of characterizing the exact covert capacity, we also provide bounds on the number of secret-key bits required to achieve covertness. The bounds are established by combining recent quantum information theory results in separable Hilbert spaces, including position based coding (Oskouei et al., arXiv: 1804.08144 [1]), convex splitting (Khatri et al., arXiv: 1910.03883 [2]), and perturbation theory (Grace and Guha, arXiv: 2106.05533 [3]).
Multichannel Optimal Tree-Decodable Codes are Not Always Optimal Prefix Codes

The theory of multichannel prefix codes aims to generalize the classical theory of prefix codes. Although single- two-channel prefix codes always have decoding trees, the same cannot be said when there are more than two channels. One question is of theoretical interest: Do there exist optimal codes that are not optimal prefix codes? Existing literature, focused on generalizing single-channel results, covered little about non-tree-decodable prefix codes since they have no single-channel counterparts. In this work, we study the fundamental reason behind the non-tree-decodability of prefix codes. By investigating the non-tree-decodable structure, we obtain a general sufficient condition on the channel alphabets for the existence of optimal tree-decodable codes that are not optimal prefix codes.
Pointwise Maximal Leakage

Pointwise maximal leakage (PML) is a robust and operationally meaningful privacy measure that quantifies the amount of information leaking about a secret X by disclosing a single outcome of a (randomized) function calculated on X. In this paper, we define a new privacy measure called event maximal leakage (EML), which generalizes PML by quantifying the amount of information leaking about X to arbitrary events. Then, we use our new privacy measure to define a new probabilistic privacy guarantee called (ϵ, δ)-EML. We study the data-processing and composition properties of (ϵ, δ)-EML and other privacy guarantees, where our goal is to understand whether or not they are closed under pre- and post-processing, and how they change as a result of adaptively composing privacy mechanisms.
Understanding Deep Neural Networks Using Sliced Mutual Information

Along with the practical success of deep neural networks, several theories have been proposed to explain their excellent generalization behaviour. One such theory is information bottleneck, using mutual information (MI) as a measure to understand the learning dynamics of these black-box models. However, estimating MI in high dimensions and in deterministic settings is problematic, often resulting in widely varying estimates for different estimators. This paper takes an alternative approach to analyze the behaviour of deep models by using a recently proposed information measure known as sliced mutual information (SMI), which is more computationally efficient to estimate than MI in high dimensions. We theoretically connect SMI to the classifier margin, thereby showcasing its ability to encode geometric properties of the feature distribution. We also study SMI empirically and demonstrate that the SMI between the hidden layer and the labels encodes information about the network’s ability to predict labels correctly.
A Data-driven Missing Mass Estimation Framework

Consider a finite sample from an unknown distribution over a countable alphabet. The missing mass refers to the probability of symbols that do not appear in the sample. Missing mass estimation is a fundamental problem in statistics, information theory and related fields, which dates back to the early work of Laplace, and the more recent seminal contribution of Good and Turing. Most popular missing mass estimation schemes are universal, in the sense that they preform well for every possible distribution. Interestingly, the worst-case distribution, for which these schemes perform the worst, is known to be uniform. On the other hand, real-world distributions are typically heavy-tailed. This means that current frameworks may be over-pessimistic, in many cases of interest. In this work we suggest a data-dependent estimation scheme to address this caveat. Specifically, we infer a subset of distributions from the sample, and control the worst-case performance only over that subset. Our suggested scheme demonstrates improved performance guarantees compared to alternative methods.
Joint Privacy Enhancement and Quantization in Federated Learning

Federated learning (FL) is an emerging paradigm for training machine learning models using possibly private data available at edge devices. Among the key challenges associated with FL are first the need to preserve the privacy of the local data sets, and second the communication load due to the repeated exchange of updated models; both are often tackled individually with methods whose operation distorts the updated models, e.g., local differential privacy (LDP) mechanisms and lossy compres- sion, respectively. In this work we propose a method for joint privacy enhancement and quantization (JoPEQ), unifying lossy compression and privacy enhancement for FL. JoPEQ utilizes universal vector quantization, where distortion is statistically equivalent to additive noise, and augments the compression distortion with dedicated privacy preserving noise to simultaneously achieve compression and a desired privacy level. We numerically demonstrate that JoPEQ reduces the overall distortion compared to individual LDP and compression, which is translated into improved trained models.
Aspis: Robust Detection for Distributed Learning

State-of-the-art machine learning models are routinely trained on large-scale distributed clusters. Crucially, such systems can be compromised when some of the computing devices exhibit abnormal (Byzantine) behavior and return arbitrary results to the parameter server (PS). This behavior may be attributed to a plethora of reasons, including system failures and orchestrated attacks. Existing work suggests robust aggregation and/or computational redundancy to alleviate the effect of distorted gradients. However, most of these schemes are ineffective when an adversary knows the task assignment and can choose the attacked workers judiciously to induce maximal damage. Our proposed method Aspis assigns gradient computations to workers using a subset-based assignment which allows for multiple consistency checks on the behavior of a worker. Examination of the calculated gradients and clique-finding in an appropriately constructed graph by the PS allows for efficient detection and exclusion of adversaries from the training. We prove the Byzantine resilience guarantees of Aspis under weak and strong attacks and extensively evaluate the system on various training scenarios and demonstrate an improvement of about 30% in accuracy compared to many state-of-the-art approaches on the CIFAR-10 dataset as well as reduction of the fraction of corrupted gradients ranging from 16% to 99%.
Analog Secure Distributed Matrix Multiplication over Complex Numbers

This work considers the problem of distributing matrix multiplication over the real or complex numbers to helper servers, such that the information leakage to these servers is close to being information-theoretically secure. These servers are assumed to be honest-but-curious, i.e., they work according to the protocol, but try to deduce information about the data. The problem of secure distributed matrix multiplication (SDMM) has been considered in the context of matrix multiplication over finite fields, which is not always feasible in real world applications. We present two schemes, which allow for variable degree of security based on the use case and allow for colluding and straggling servers. We analyze the security and the numerical accuracy of the schemes and observe a trade-off between accuracy and security.
Probability Distribution on Rooted Trees

The hierarchical and recursive expressive capability of rooted trees is applicable to represent statistical models in various areas, such as data compression, image processing, and machine learning. On the other hand, such hierarchical expressive capability causes a problem in tree selection to avoid overfitting. One unified approach to solve this is a Bayesian approach, on which the rooted tree is regarded as a random variable and a direct loss function can be assumed on the selected model or the predicted value for a new data point. However, all the previous studies on this approach are based on the probability distribution on full trees, to the best of our knowledge. In this paper, we propose a generalized probability distribution for any rooted trees in which only the maximum number of child nodes and the maximum depth are fixed. Furthermore, we derive recursive methods to evaluate the characteristics of the probability distribution without any approximations.
Calibration with Privacy in Peer Review

This paper is eligible for the Jack Keil Wolf ISIT Student Paper Award. Reviewers in peer review are often miscalibrated: they may be strict, lenient, extreme, moderate, etc. A number of algorithms have previously been proposed to calibrate reviews. Such attempts of calibration can however leak sensitive information about which reviewer reviewed which paper. In this paper, we identify this problem of calibration with privacy, and provide a foundational building block to address it. Specifically, we present a theoretical study of this problem under a simplified-yet-challenging model involving two reviewers, two papers, and an MAP-computing adversary. Our main results establish the Pareto frontier of the tradeoff between privacy (preventing the adversary from inferring reviewer identity) and utility (accepting better papers), and design explicit computationally-efficient algorithms that we prove are Pareto optimal.
CDMY: A Lightweight Object Detection Model Based on Coordinate Attention

In the discipline of image recognition, mobile-based traffic target detection is a hotspot of research. In a complex traffic environment, the existing technique has an issue with low detection accuracy. To achieve high-accuracy and lightweight traffic target detection, this paper proposes a lightweight object detection model based on coordinate attention (CDMY), which uses depthwise separable convolutions to optimize model computational complexity. It also introduces a coordinate attention mechanism to facilitate the model's modeling of contextual interaction information. The results of this paper's studies on the public datasets BDD100K and Pascal VOC reveal that CDMY outperforms the control model in terms of detection. The number of parameters in the model in this work is 10.86M on the data set BDD100K, accounting for just 7/25 of the original model. For common traffic target categories, the mean average precision (mAP) is 0.96% higher than the original model, which is better than the mainstream detection models SSD300 and YOLOv4-Tiny, which are 9.95% and 11.66% higher, respectively.
t-SNE Based on Sobol Sequence Initialized Exchange Market Algorithm

T-distributed stochastic neighbor embedding (t-SNE) is a nonlinear dimensionality reduction method in manifold learning in the field of artificial intelligence (AI). It is very suitable for the visualization of high-dimensional data. It has now been widely used in image processing, natural language processing, speech processing, and gene data analysis. However, t-SNE also has some problems, for example, the solution process depends on gradient information, which is prone to fall into local optimum. At present, many scholars have studied t-SNE and have tried to employ t-SNE to solve specific problems of different industries, but most of them have not done related work on the optimization of its solution process. In this paper, a Sobol sequence initialized exchange market algorithm (SSEMA) has been proposed to replace the gradient dependent solution method in the original t-SNE, which can find a more reliable global optimal solution by comparing with just considering about gradient information.
iTemporal: An Extensible Generator of Temporal Benchmarks

DatalogMTL is an extension of the fundamental rule language Datalog with metric temporal operators over rational numbers, whose adoption is soaring within an increasing number of communities (semantic web, databases, stream data processing, temporal logic, knowledge graphs, etc.), which are more and more willing to handle temporal data and deal with temporal database queries as a consequence. Despite the rising research efforts towards new extensions of DatalogMTL, such as the fundamental support for aggregations, and the uprising systems, we still lack a corpus of benchmarks for temporal reasoning. This paper contributes iTemporal, an extensible generator of temporal benchmarks. Our system is able to generate a very broad set of benchmarks, thanks to a white-box configuration mechanism to control and stimulate the theoretical underpinnings of DatalogMTL and its extensions, such as temporal operators in the presence of full recursion and aggregations. We provide a comprehensive presentation of the system as well as an empirical evaluation of the benchmarks within two reference reasoners.
Horae: A Graph Stream Summarization Structure for Efficient Temporal Range Query

Graph stream, referred to as an evolving graph with a timing sequence of updated edges through a continuous stream, is an emerging data format widely used in big data applications. Coping with a graph stream is challenging because: 1) fully storing the continuously produced and extremely large-scale datasets is difficult if not impossible; 2) supporting queries relevant to both graph topology and temporal information is nontrivial. Recently, graph stream summarization techniques have attracted much attention in providing approximate storage and query processing for a graph stream. Existing designs largely utilize hash functions to reduce the graph scale and leverage a compressive matrix to represent the graph stream. However, such designs are unable to store the time dimension information of graph streams, and thus fail to support temporal queries. In this paper, we propose Horae, a novel graph stream summarization structure for efficient temporal range query, which presents a time prefix embedded multi-layer summarization structure. Our design is based on the insight that an arbitrary temporal range of length <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L$</tex> can be decomposed to at most <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2\log L$</tex> sub-ranges, where all the time points in each sub-range have the same binary code prefix. We further design an efficient Binary Range Decomposition (BRD) algorithm, which achieves a logarithmic scale query processing time. Experimental results show that Horae significantly reduces the latency of various temporal range queries by two to three orders of magnitude compared to the state-of-the-art designs.