Reviewerly/exHarmony huggingface dataset:
Fine-grained modeling of user interests for sequential recommendation

In recent years, the sequential recommendation method has attracted extensive attention owing to its practicability and high accuracy. Different from general recommendations, explicitly capturing short-term interests based on users' recent actions lies at the heart of a sequential recommender. Existing methods have either modeled successive items step by step without considering the many-to-one relationship or neglected the local-order information, considering the recent items as a set. This neither fully mines complex item transitions nor depicts the evolution of user interests. Therefore, we propose the gating coupled capsule network (GCC), a fine-grained analysis method to model individual-level, union-level, and local-order relationships of short-term user interests. Specifically, we designed a user-specific capsule module to capture high-level temporal interaction sequences, which model union-level patterns and perceive local-order relations. Moreover, we present a personalized gating module to focus on the pairwise relationships among items to capture the influence of individual-level information on short-term user interests. Extensive experiments under four real-world recommendation scenarios demonstrate that GCC outperforms the state-of-the-art methods on different ranking metrics.
Multi-target ranging using an optical reservoir computing approach in the laterally coupled semiconductor lasers with self-feedback

We utilize three parallel reservoir computers using semiconductor lasers with optical feedback and light injection to model radar probe signals with delays. Three radar probe signals are generated by driving lasers constructed by a three-element laser array with self-feedback. The response lasers are implemented also by a three-element lase array with both delay-time feedback and optical injection, which are utilized as nonlinear nodes to realize the reservoirs. We show that each delayed radar probe signal can be predicted well and to synchronize with its corresponding trained reservoir, even when parameter mismatches exist between the response laser array and the driving laser array. Based on this, the three synchronous probe signals are utilized for ranging to three targets, respectively, using Hilbert transform. It is demonstrated that the relative errors for ranging can be very small and less than 0.6%. Our findings show that optical reservoir computing provides an effective way for applications of target ranging.
Optimized setting of privacy budget in a recommendation system with local differential privacy

Recommendation system can help users find the data they need from the massive amounts of data. At the same time, uploading original user data to the server may reveal user privacy. We utilize local differential privacy techniques to provide privacy protection for users in the recommendation system. In the local differential privacy model, the degree of privacy protection is measured by the privacy budget, and a high privacy budget usually means high analysis accuracy. To help users minimize privacy loss and maximize recommendation accuracy, we model the privacy budget setting problem as a multiarmed bandit problem and propose the upper confidence bound learning policy to help each user choose the privacy budget. Considering that users have different sensitivity levels to different data, we modify the above policy. Experimental results reveal that the proposed policy can help users choose an appropriate privacy budget, which can effectively increase the total user payoff.
Test-Time Detection of Backdoor Triggers for Poisoned Deep Neural Networks

Backdoor (Trojan) attacks are emerging threats against deep neural networks (DNN). A DNN being attacked will predict to an attacker-desired target class whenever a test sample from any source class is embedded with a backdoor pattern; while correctly classifying clean (attack-free) test samples. Existing backdoor defenses have shown success in detecting whether a DNN is attacked and in reverse-engineering the backdoor pattern in a "post-training" regime: the defender has access to the DNN to be inspected and a small, clean dataset collected independently, but has no access to the (possibly poisoned) training set of the DNN. However, these defenses neither catch culprits in the act of triggering the backdoor mapping, nor mitigate the backdoor attack at test-time. In this paper, we propose an "in-flight" defense against backdoor attacks on image classification that 1) detects use of a backdoor trigger at test-time; and 2) infers the class of origin (source class) for a detected trigger example. The effectiveness of our defense is demonstrated experimentally against different strong backdoor attacks.
Over-the-Air Computation with DFT-spread OFDM for Federated Edge Learning

In this study, we propose an over-the-air computation (AirComp) scheme for federated edge learning (FEEL) without channel state information (CSI) at the edge devices (EDs) or the edge server (ES). The proposed scheme relies on non-coherent communication techniques for achieving distributed training by majority vote (MV). In this work, the votes, i.e., the signs of the local gradients, from the EDs are represented with the pulse-position modulation (PPM) symbols constructed with discrete Fourier transform (DFT)-spread orthogonal frequency division multiplexing (OFDM) (DFT-s-OFDM). By taking the delay spread and time-synchronization errors into account, the MV at the ES is obtained with an energy detector. Hence, the proposed scheme does not require CSI at the EDs and ES. We also prove the convergence of the distributed training when the MV is obtained with the proposed scheme under fading channel. Through simulations, we show that the proposed scheme provides a high test accuracy in fading channels while resulting in lower peak-to-mean envelope power ratio (PMEPR) symbols.
Application of Deep Reinforcement Learning to Payment Fraud

The large variety of digital payment choices available to consumers today has been a key driver of e-commerce transactions in the past decade. Unfortunately, this has also given rise to cybercriminals and fraudsters who are constantly looking for vulnerabilities in these systems by deploying increasingly sophisticated fraud attacks. A typical fraud detection system employs standard supervised learning methods where the focus is on maximizing the fraud recall rate. However, we argue that such a formulation can lead to sub-optimal solutions. The design requirements for these fraud models requires that they are robust to the high-class imbalance in the data, adaptive to changes in fraud patterns, maintain a balance between the fraud rate and the decline rate to maximize revenue, and be amenable to asynchronous feedback since usually there is a significant lag between the transaction and the fraud realization. To achieve this, we formulate fraud detection as a sequential decision-making problem by including the utility maximization within the model in the form of the reward function. The historical decline rate and fraud rate define the state of the system with a binary action space composed of approving or declining the transaction. In this study, we primarily focus on utility maximization and explore different reward functions to this end. The performance of the proposed Reinforcement Learning system has been evaluated for two publicly available fraud datasets using Deep Q-learning and compared with different classifiers. We aim to address the rest of the issues in future work.
Training Lipschitz continuous operators using reproducing kernels

This paper proposes that Lipschitz continuity is a natural outcome of regularized least squares in kernel-based learning. Lipschitz continuity is an important proxy for robustness of input-output operators. It is also instrumental for guaranteeing closed-loop stability of kernel-based controlllers through small incremental gain arguments. We introduce a new class of nonexpansive kernels that are shown to induce Hilbert spaces consisting of only Lipschitz continuous operators. The Lipschitz constant of estimated operators within such Hilbert spaces can be tuned by suitable selection of a regularization parameter. As is typical for kernel-based models, input-output operators are estimated from data by solving tractable systems of linear equations. The approach thus constitutes a promising alternative to Lipschitz-bounded neural networks, that have recently been investigated but are computationally expensive to train.
An Augmented Reality Method Based on MobileNetV2_SSD

Aiming at the problem that the registration area of augmented reality systems can not be registered normally due to partial occlusion and scale change, this paper proposes a lightweight object detection network augmented reality method based on deep learning for mobile embedded devices. In the process of target detection, the improved MobileNetV2_SSD lightweight target detection network detects the target image to obtain the location and category of the region to be registered. The SURF algorithm is used to extract and match the feature points in the registration area, and the RANSAC algorithm is used to purify the matching feature points and eliminate the similar matching and wrong matching. Finally, according to the purified feature points, the registration matrix is calculated to complete the registration of augmented reality system. The experimental results show that, compared with traditional methods, this method can effectively solve the registration inaccuracy caused by the scale change of the area to be registered, partial occlusion and other scenes, and provides a new augmented reality implementation method for mobile embedded devices.
INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE ON SCADA DATA

As AI algorithms thrive on data, SCADA would be considered a natural ground for Artificial Intelligence (AI) applications to be developed, translating that avalanche of information into meaningful and fast insights to human operators. However, presently, the high complexity of the events, the data semantics, the large variety of equipment and technologies translate into very few AI applications developed in SCADA. Aware of the enormous potential yet to be explored, E-REDES partnered with INESC TEC to experiment on the development of two novel AI applications based on SCADA data. The first tool, called Alarm2Insights, identifies anomalous behaviours regarding the performance of the protection functions associated with HV and MV line panels. The second tool, called EventProfiler, uses unsupervised learning to identify similar events (i.e., with similar log messages) in HV line panels, and supervised learning to classify new events into previously defined clusters and detect unique or rare events. Aspects associated to data handling and pre-processing are also discussed. The project&apos;s results show a very promising potential of applying AI to SCADA data, enhancing the role of the operator and support him in doing better and more informed decisions.
Sequencing Through a Global Decision Instance Based on a Neural Network

An existing concept for sequence planning in production planning and control was extended by a global decision instance based on neural networks. Therefore, information regarding the state of the production and available orders were normalized and analyzed by one agent. In contrast to a partially observable Markow Decision Problem one single agent was allowed and used to process all available information. Feasibility and problems were examined and compared with a concept for decentralized decisions. The implementation consists of two parts, which continuously interact with each other. One part is a simulation of a job shop, including multiple machines. The other parts tackle the Markow Decision Problem with the use of double Q reinforcement learning in order to estimate the best sequence at any given time. Later, problems due to scaling and comparisons to the usage of multiple agents are given.
AutoDIME: Automatic Design of Interesting Multi-Agent Environments

Designing a distribution of environments in which RL agents can learn interesting and useful skills is a challenging and poorly understood task, for multi-agent environments the difficulties are only exacerbated. One approach is to train a second RL agent, called a teacher, who samples environments that are conducive for the learning of student agents. However, most previous proposals for teacher rewards do not generalize straightforwardly to the multi-agent setting. We examine a set of intrinsic teacher rewards derived from prediction problems that can be applied in multi-agent settings and evaluate them in Mujoco tasks such as multi-agent Hide and Seek as well as a diagnostic single-agent maze task. Of the intrinsic rewards considered we found value disagreement to be most consistent across tasks, leading to faster and more reliable emergence of advanced skills in Hide and Seek and the maze task. Another candidate intrinsic reward considered, value prediction error, also worked well in Hide and Seek but was susceptible to noisy-TV style distractions in stochastic environments. Policy disagreement performed well in the maze task but did not speed up learning in Hide and Seek. Our results suggest that intrinsic teacher rewards, and in particular value disagreement, are a promising approach for automating both single and multi-agent environment design.
BARCOR: Towards A Unified Framework for Conversational Recommendation Systems

Recommendation systems focus on helping users find items of interest in the situations of information overload, where users' preferences are typically estimated by the past observed behaviors. In contrast, conversational recommendation systems (CRS) aim to understand users' preferences via interactions in conversation flows. CRS is a complex problem that consists of two main tasks: (1) recommendation and (2) response generation. Previous work often tried to solve the problem in a modular manner, where recommenders and response generators are separate neural models. Such modular architectures often come with a complicated and unintuitive connection between the modules, leading to inefficient learning and other issues. In this work, we propose a unified framework based on BART for conversational recommendation, which tackles two tasks in a single model. Furthermore, we also design and collect a lightweight knowledge graph for CRS in the movie domain. The experimental results show that the proposed methods achieve the state-of-the-art performance in terms of both automatic and human evaluation.
CNNs and Transformers Perceive Hybrid Images Similar to Humans

Hybrid images is a technique to generate images with two interpretations that change as a function of viewing distance. It has been utilized to study multiscale processing of images by the human visual system. Using 63,000 hybrid images across 10 fruit categories, here we show that predictions of deep learning vision models qualitatively matches with the human perception of these images. Our results provide yet another evidence in support of the hypothesis that Convolutional Neural Networks (CNNs) and Transformers are good at modeling the feedforward sweep of information in the ventral stream of visual cortex. Code and data is available at https://github.com/aliborji/hybrid_images.git.
Deep neural networks for fine-grained surveillance of overdose mortality

Surveillance of drug overdose deaths relies on death certificates for identification of the substances that caused death. Drugs and drug classes can be identified through the International Classification of Diseases, 10th Revision (ICD-10) codes present on death certificates. However, ICD-10 codes do not always provide high levels of specificity in drug identification. To achieve more fine-grained identification of substances on a death certificate, the free-text cause of death section, completed by the medical certifier, must be analyzed. Current methods for analyzing free-text death certificates rely solely on look-up tables for identifying specific substances, which must be frequently updated and maintained. To improve identification of drugs on death certificates, a deep learning named-entity recognition model was developed, which achieved an F1-score of 99.13%. This model can identify new drug misspellings and novel substances that are not present on current surveillance look-up tables, enhancing the surveillance of drug overdose deaths.
Discovering Human-Object Interaction Concepts via Self-Compositional Learning

A comprehensive understanding of human-object interaction (HOI) requires detecting not only a small portion of predefined HOI concepts (or categories) but also other reasonable HOI concepts, while current approaches usually fail to explore a huge portion of unknown HOI concepts (i.e., unknown but reasonable combinations of verbs and objects). In this paper, 1) we introduce a novel and challenging task for a comprehensive HOI understanding, which is termed as HOI Concept Discovery; and 2) we devise a self-compositional learning framework (or SCL) for HOI concept discovery. Specifically, we maintain an online updated concept confidence matrix during training: 1) we assign pseudo-labels for all composite HOI instances according to the concept confidence matrix for self-training; and 2) we update the concept confidence matrix using the predictions of all composite HOI instances. Therefore, the proposed method enables the learning on both known and unknown HOI concepts. We perform extensive experiments on several popular HOI datasets to demonstrate the effectiveness of the proposed method for HOI concept discovery, object affordance recognition and HOI detection. For example, the proposed self-compositional learning framework significantly improves the performance of 1) HOI concept discovery by over 10% on HICO-DET and over 3% on V-COCO, respectively; 2) object affordance recognition by over 9% mAP on MS-COCO and HICO-DET; and 3) rare-first and non-rare-first unknown HOI detection relatively over 30% and 20%, respectively. Code is publicly available at https://github.com/zhihou7/HOI-CL.
Supervising the Multi-Fidelity Race of Hyperparameter Configurations

Multi-fidelity (gray-box) hyperparameter optimization techniques (HPO) have recently emerged as a promising direction for tuning Deep Learning methods. However, existing methods suffer from a sub-optimal allocation of the HPO budget to the hyperparameter configurations. In this work, we introduce DyHPO, a Bayesian Optimization method that learns to decide which hyperparameter configuration to train further in a dynamic race among all feasible configurations. We propose a new deep kernel for Gaussian Processes that embeds the learning curve dynamics, and an acquisition function that incorporates multi-budget information. We demonstrate the significant superiority of DyHPO against state-of-the-art hyperparameter optimization methods through large-scale experiments comprising 50 datasets (Tabular, Image, NLP) and diverse architectures (MLP, CNN/NAS, RNN).
Equilibrium Aggregation: Encoding Sets via Optimization

Processing sets or other unordered, potentially variable-sized inputs in neural networks is usually handled by aggregating a number of input tensors into a single representation. While a number of aggregation methods already exist from simple sum pooling to multi-head attention, they are limited in their representational power both from theoretical and empirical perspectives. On the search of a principally more powerful aggregation strategy, we propose an optimization-based method called Equilibrium Aggregation. We show that many existing aggregation methods can be recovered as special cases of Equilibrium Aggregation and that it is provably more efficient in some important cases. Equilibrium Aggregation can be used as a drop-in replacement in many existing architectures and applications. We validate its efficiency on three different tasks: median estimation, class counting, and molecular property prediction. In all experiments, Equilibrium Aggregation achieves higher performance than the other aggregation techniques we test.
Federated Learning with Erroneous Communication Links

In this paper, we consider the federated learning (FL) problem in the presence of communication errors. We model the link between the devices and the central node (CN) by a packet erasure channel, where the local parameters from devices are either erased or received correctly by CN with probability $\epsilon$ and $1-\epsilon$, respectively. We proved that the FL algorithm in the presence of communication errors, where the CN uses the past local update if the fresh one is not received from a device, converges to the same global parameter as that the FL algorithm converges to without any communication error. We provide several simulation results to validate our theoretical analysis. We also show that when the dataset is uniformly distributed among devices, the FL algorithm that only uses fresh updates and discards missing updates might converge faster than the FL algorithm that uses past local updates.
Less is More: Summary of Long Instructions is Better for Program Synthesis

Despite the success of large pre-trained language models (LMs) such as Codex, they show below-par performance on the larger and more complicated programming related questions. We show that LMs benefit from the summarized version of complicated questions. Our findings show that superfluous information often present in problem description such as human characters, background stories, and names (which are included to help humans in understanding a task) does not help models in understanding a task. To this extent, we create a meta-dataset from the frequently used APPS dataset and the newly created CodeContests dataset for the program synthesis task. Our meta-dataset consists of human and synthesized summaries of the long and complicated programming questions. Experimental results on Codex show that our proposed approach outperforms baseline by 8.13% on the APPS dataset and 11.88% on the CodeContests dataset on average in terms of strict accuracy. Our analysis shows that summaries significantly improve performance for introductory (9.86%) and interview (11.48%) programming questions. However, it shows improvement by a small margin (~ 2%) for competitive programming questions, implying scope for future research in this direction.
List Decoding of Quaternary Codes in the Lee Metric

We present a list decoding algorithm for quaternary negacyclic codes over the Lee metric. To achieve this result, we use a Sudan-Guruswami type list decoding algorithm for Reed-Solomon codes over certain ring alphabets. Our decoding strategy for negacyclic codes over the ring $\mathbb Z_4$ combines the list decoding algorithm by Wu with the Gr\"obner basis approach for solving a key equation due to Byrne and Fitzpatrick.
Machine Learning for Multi-Output Regression: When should a holistic multivariate approach be preferred over separate univariate ones?

Tree-based ensembles such as the Random Forest are modern classics among statistical learning methods. In particular, they are used for predicting univariate responses. In case of multiple outputs the question arises whether we separately fit univariate models or directly follow a multivariate approach. For the latter, several possibilities exist that are, e.g. based on modified splitting or stopping rules for multi-output regression. In this work we compare these methods in extensive simulations to help in answering the primary question when to use multivariate ensemble techniques.