OpenRaiser/Intern-Atlas huggingface dataset:
Learning Federated Representations and Recommendations with Limited Negatives
Venue: arXiv cs.LG 2021
Year: 2021

Deep retrieval models are widely used for learning entity representations and recommendations. Federated learning provides a privacy-preserving way to train these models without requiring centralization of user data. However, federated deep retrieval models usually perform much worse than their centralized counterparts due to non-IID (independent and identically distributed) training data on clients, an intrinsic property of federated learning that limits negatives available for training. We demonstrate that this issue is distinct from the commonly studied client drift problem. This work proposes batch-insensitive losses as a way to alleviate the non-IID negatives issue for federated movie recommendations. We explore a variety of techniques and identify that batch-insensitive losses can effectively improve the performance of federated deep retrieval models, increasing the relative recall of the federated model by up to 93.15% and reducing the relative gap in recall between it and a centralized model from 27.22% - 43.14% to 0.53% - 2.42%. We also open-source our code framework to accelerate further research and applications of federated deep retrieval models.
Learning Implicit User Profiles for Personalized Retrieval-Based Chatbot
Venue: arXiv cs.IR 2021
Year: 2021

In this paper, we explore the problem of developing personalized chatbots. A personalized chatbot is designed as a digital chatting assistant for a user. The key characteristic of a personalized chatbot is that it should have a consistent personality with the corresponding user. It can talk the same way as the user when it is delegated to respond to others' messages. We present a retrieval-based personalized chatbot model, namely IMPChat, to learn an implicit user profile from the user's dialogue history. We argue that the implicit user profile is superior to the explicit user profile regarding accessibility and flexibility. IMPChat aims to learn an implicit user profile through modeling user's personalized language style and personalized preferences separately. To learn a user's personalized language style, we elaborately build language models from shallow to deep using the user's historical responses; To model a user's personalized preferences, we explore the conditional relations underneath each post-response pair of the user. The personalized preferences are dynamic and context-aware: we assign higher weights to those historical pairs that are topically related to the current query when aggregating the personalized preferences. We match each response candidate with the personalized language style and personalized preference, respectively, and fuse the two matching signals to determine the final ranking score. Comprehensive experiments on two large datasets show that our method outperforms all baseline models.
DeepFake MNIST+: A DeepFake Facial Animation Dataset
Venue: arXiv cs.CV 2021
Year: 2021

The DeepFakes, which are the facial manipulation techniques, is the emerging threat to digital society. Various DeepFake detection methods and datasets are proposed for detecting such data, especially for face-swapping. However, recent researches less consider facial animation, which is also important in the DeepFake attack side. It tries to animate a face image with actions provided by a driving video, which also leads to a concern about the security of recent payment systems that reply on liveness detection to authenticate real users via recognising a sequence of user facial actions. However, our experiments show that the existed datasets are not sufficient to develop reliable detection methods. While the current liveness detector cannot defend such videos as the attack. As a response, we propose a new human face animation dataset, called DeepFake MNIST+, generated by a SOTA image animation generator. It includes 10,000 facial animation videos in ten different actions, which can spoof the recent liveness detectors. A baseline detection method and a comprehensive analysis of the method is also included in this paper. In addition, we analyze the proposed dataset's properties and reveal the difficulty and importance of detecting animation datasets under different types of motion and compression quality.
De-identification of Unstructured Clinical Texts from Sequence to Sequence Perspective
Venue: arXiv cs.CL 2021
Year: 2021

In this work, we propose a novel problem formulation for de-identification of unstructured clinical text. We formulate the de-identification problem as a sequence to sequence learning problem instead of a token classification problem. Our approach is inspired by the recent state-of -the-art performance of sequence to sequence learning models for named entity recognition. Early experimentation of our proposed approach achieved 98.91% recall rate on i2b2 dataset. This performance is comparable to current state-of-the-art models for unstructured clinical text de-identification.
Stochastic Cluster Embedding
Venue: arXiv cs.LG 2021
Year: 2021

Neighbor Embedding (NE) aims to preserve pairwise similarities between data items and has been shown to yield an effective principle for data visualization. However, even the best existing NE methods such as Stochastic Neighbor Embedding (SNE) may leave large-scale patterns hidden, for example clusters, despite strong signals being present in the data. To address this, we propose a new cluster visualization method based on the Neighbor Embedding principle. We first present a family of Neighbor Embedding methods that generalizes SNE by using non-normalized Kullback-Leibler divergence with a scale parameter. In this family, much better cluster visualizations often appear with a parameter value different from the one corresponding to SNE. We also develop an efficient software that employs asynchronous stochastic block coordinate descent to optimize the new family of objective functions. Our experimental results demonstrate that the method consistently and substantially improves the visualization of data clusters compared with the state-of-the-art NE approaches.
XAI Methods for Neural Time Series Classification: A Brief Review
Venue: arXiv cs.LG 2021
Year: 2021

Deep learning models have recently demonstrated remarkable results in a variety of tasks, which is why they are being increasingly applied in high-stake domains, such as industry, medicine, and finance. Considering that automatic predictions in these domains might have a substantial impact on the well-being of a person, as well as considerable financial and legal consequences to an individual or a company, all actions and decisions that result from applying these models have to be accountable. Given that a substantial amount of data that is collected in high-stake domains are in the form of time series, in this paper we examine the current state of eXplainable AI (XAI) methods with a focus on approaches for opening up deep learning black boxes for the task of time series classification. Finally, our contribution also aims at deriving promising directions for future work, to advance XAI for deep learning on time series data.
Low-Complexity Algorithm for Outage Optimal Resource Allocation in Energy Harvesting-Based UAV Identification Networks
Venue: arXiv cs.IT 2021
Year: 2021

We study an unmanned aerial vehicle (UAV) identification network equipped with an energy harvesting (EH) technique. In the network, the UAVs harvest energy through radio frequency (RF) signals transmitted from ground control stations (GCSs) and then transmit their identification information to the ground receiver station (GRS). Specifically, we first derive a closed-form expression of the outage probability to evaluate the network performance. Then we obtain the closed-form expression of the optimal time allocation when the bandwidth is equally allocated to the UAVs. We also propose a fast-converging algorithm for time and the bandwidth allocation, which is necessary for the UAV environment with high mobility, to optimize the outage performance of EH-based UAV identification network. Simulation results show that the proposed algorithm outperforms the conventional bisection algorithm and achieves near-optimal performance.
On correctness and completeness of an n queens program
Venue: arXiv cs.LO 2021
Year: 2021

Thom Frühwirth presented a short, elegant and efficient Prolog program for the n queens problem. However the program may be seen as rather tricky and one may not be convinced about its correctness. This paper explains the program in a declarative way, and provides proofs of its correctness and completeness. The specification and the proofs are declarative, i.e. they abstract from any operational semantics. The specification is approximate, it is unnecessary to describe the program's semantics exactly. Despite the program works on non-ground terms, this work employs the standard semantics, based on logical consequence and Herbrand interpretations. Another purpose of the paper is to present an example of precise declarative reasoning about the semantics of a logic program. Under consideration in Theory and Practice of Logic Programming (TPLP).
Deep Graph Memory Networks for Forgetting-Robust Knowledge Tracing
Venue: arXiv cs.LG 2021
Year: 2021

Tracing a student's knowledge is vital for tailoring the learning experience. Recent knowledge tracing methods tend to respond to these challenges by modelling knowledge state dynamics across learning concepts. However, they still suffer from several inherent challenges including: modelling forgetting behaviours and identifying relationships among latent concepts. To address these challenges, in this paper, we propose a novel knowledge tracing model, namely \emph{Deep Graph Memory Network} (DGMN). In this model, we incorporate a forget gating mechanism into an attention memory structure in order to capture forgetting behaviours dynamically during the knowledge tracing process. Particularly, this forget gating mechanism is built upon attention forgetting features over latent concepts considering their mutual dependencies. Further, this model has the capability of learning relationships between latent concepts from a dynamic latent concept graph in light of a student's evolving knowledge states. A comprehensive experimental evaluation has been conducted using four well-established benchmark datasets. The results show that DGMN consistently outperforms the state-of-the-art KT models over all the datasets. The effectiveness of modelling forgetting behaviours and learning latent concept graphs has also been analyzed in our experiments.
Fighting Game Commentator with Pitch and Loudness Adjustment Utilizing Highlight Cues
Venue: arXiv cs.MM 2021
Year: 2021

This paper presents a commentator for providing real-time game commentary in a fighting game. The commentary takes into account highlight cues, obtained by analyzing scenes during gameplay, as input to adjust the pitch and loudness of commentary to be spoken by using a Text-to-Speech (TTS) technology. We investigate different designs for pitch and loudness adjustment. The proposed AI consists of two parts: a dynamic adjuster for controlling pitch and loudness of the TTS and a real-time game commentary generator. We conduct a pilot study on a fighting game, and our result shows that by adjusting the loudness significantly according to the level of game highlight, the entertainment of the gameplay can be enhanced.
Rendering and Tracking the Directional TSDF: Modeling Surface Orientation for Coherent Maps
Venue: arXiv cs.CV 2021
Year: 2021

Dense real-time tracking and mapping from RGB-D images is an important tool for many robotic applications, such as navigation or grasping. The recently presented Directional Truncated Signed Distance Function (DTSDF) is an augmentation of the regular TSDF and shows potential for more coherent maps and improved tracking performance. In this work, we present methods for rendering depth- and color maps from the DTSDF, making it a true drop-in replacement for the regular TSDF in established trackers. We evaluate and show, that our method increases re-usability of mapped scenes. Furthermore, we add color integration which notably improves color-correctness at adjacent surfaces.
Confidence Adaptive Regularization for Deep Learning with Noisy Labels
Venue: arXiv cs.LG 2021
Year: 2021

Recent studies on the memorization effects of deep neural networks on noisy labels show that the networks first fit the correctly-labeled training samples before memorizing the mislabeled samples. Motivated by this early-learning phenomenon, we propose a novel method to prevent memorization of the mislabeled samples. Unlike the existing approaches which use the model output to identify or ignore the mislabeled samples, we introduce an indicator branch to the original model and enable the model to produce a confidence value for each sample. The confidence values are incorporated in our loss function which is learned to assign large confidence values to correctly-labeled samples and small confidence values to mislabeled samples. We also propose an auxiliary regularization term to further improve the robustness of the model. To improve the performance, we gradually correct the noisy labels with a well-designed target estimation strategy. We provide the theoretical analysis and conduct the experiments on synthetic and real-world datasets, demonstrating that our approach achieves comparable results to the state-of-the-art methods.
Adaptive Rate NOMA for Cellular IoT Networks
Venue: arXiv cs.IT 2021
Year: 2021

Internet-of-Things (IoT) technology is envisioned to enable a variety of real-time applications by interconnecting billions of sensors/devices deployed to observe some random physical processes. These IoT devices rely on low-power wide-area wireless connectivity for transmitting, mostly fixed- but small-size, status updates of their associated random processes. The cellular networks are seen as a natural candidate for providing reliable wireless connectivity to IoT devices. However, the conventional orthogonal multiple access (OMA) to these massive number of devices is expected to degrade the spectral efficiency. As a promising alternative to OMA, the cellular base stations (BSs) can employ non-orthogonal multiple access (NOMA) for the uplink transmissions of mobile users and IoT devices. In particular, the uplink NOMA can be configured such that the mobile user can adapt transmission rate based on its channel condition while the IoT device transmits at a fixed rate. For this setting, we analyze the ergodic capacity of mobile users and the mean local delay of IoT devices using stochastic geometry. Our analysis demonstrates that the above NOMA configuration can provide better ergodic capacity for mobile users compare to OMA when IoT devices' delay constraint is strict. Furthermore, we also show that NOMA can support a larger packet size for IoT devices than OMA under the same delay constraint.
SOME/IP Intrusion Detection using Deep Learning-based Sequential Models in Automotive Ethernet Networks
Venue: arXiv cs.CR 2021
Year: 2021

Intrusion Detection Systems are widely used to detect cyberattacks, especially on protocols vulnerable to hacking attacks such as SOME/IP. In this paper, we present a deep learning-based sequential model for offline intrusion detection on SOME/IP application layer protocol. To assess our intrusion detection system, we have generated and labeled a dataset with several classes representing realistic intrusions, and a normal class - a significant contribution due to the absence of such publicly available datasets. Furthermore, we also propose a recurrent neural network (RNN), as an instance of deep learning-based sequential model, that we apply to our generated dataset. The numerical results show that RNN excel at predicting in-vehicle intrusions, with F1 Scores and AUC values greater than 0.8 depending on each intrusion type.
AIRCHITECT: Learning Custom Architecture Design and Mapping Space
Venue: arXiv cs.LG 2021
Year: 2021

Design space exploration is an important but costly step involved in the design/deployment of custom architectures to squeeze out maximum possible performance and energy efficiency. Conventionally, optimizations require iterative sampling of the design space using simulation or heuristic tools. In this paper we investigate the possibility of learning the optimization task using machine learning and hence using the learnt model to predict optimal parameters for the design and mapping space of custom architectures, bypassing any exploration step. We use three case studies involving the optimal array design, SRAM buffer sizing, mapping, and schedule determination for systolic-array-based custom architecture design and mapping space. Within the purview of these case studies, we show that it is possible to capture the design space and train a model to "generalize" prediction the optimal design and mapping parameters when queried with workload and design constraints. We perform systematic design-aware and statistical analysis of the optimization space for our case studies and highlight the patterns in the design space. We formulate the architecture design and mapping as a machine learning problem that allows us to leverage existing ML models for training and inference. We design and train a custom network architecture called AIRCHITECT, which is capable of learning the architecture design space with as high as 94.3% test accuracy and predicting optimal configurations which achieve on average (GeoMean) of 99.9% the best possible performance on a test dataset with $10^5$ GEMM workloads.
Publisher References in Bibliographic Entity Descriptions
Venue: arXiv cs.DL 2021
Year: 2021

This paper describes a method for improved access to publisher references in linked data RDF editors using data mining techniques and a large set of library metadata encoded in the MARC21 standard. The corpus is comprised of clustered sets of publishers and publisher locations from the library MARC21 records found in the POD Data Lake, an Ivy+ Library Consortium metadata sharing initiative. The POD Data Lake contains seventy million MARC21 records, forty million of which are unique. The discovery of publisher entity sets described forms the basis for the streamlined description of BIBFRAME Instance entities. This study resulted in two major outputs: 1) A prediction database and 2) sets of publisher location and name association rules. The association rules are the basis of a prototype autosuggestion feature of BIBFRAME Instance entity description properties designed specifically to support the autopopulation of publisher entities in linked data RDF editors.
Computing Steiner Trees using Graph Neural Networks
Venue: arXiv cs.LG 2021
Year: 2021

Graph neural networks have been successful in many learning problems and real-world applications. A recent line of research explores the power of graph neural networks to solve combinatorial and graph algorithmic problems such as subgraph isomorphism, detecting cliques, and the traveling salesman problem. However, many NP-complete problems are as of yet unexplored using this method. In this paper, we tackle the Steiner Tree Problem. We employ four learning frameworks to compute low cost Steiner trees: feed-forward neural networks, graph neural networks, graph convolutional networks, and a graph attention model. We use these frameworks in two fundamentally different ways: 1) to train the models to learn the actual Steiner tree nodes, 2) to train the model to learn good Steiner point candidates to be connected to the constructed tree using a shortest path in a greedy fashion. We illustrate the robustness of our heuristics on several random graph generation models as well as the SteinLib data library. Our finding suggests that the out-of-the-box application of GNN methods does worse than the classic 2-approximation method. However, when combined with a greedy shortest path construction, it even does slightly better than the 2-approximation algorithm. This result sheds light on the fundamental capabilities and limitations of graph learning techniques on classical NP-complete problems.
D3D-HOI: Dynamic 3D Human-Object Interactions from Videos
Venue: arXiv cs.CV 2021
Year: 2021

We introduce D3D-HOI: a dataset of monocular videos with ground truth annotations of 3D object pose, shape and part motion during human-object interactions. Our dataset consists of several common articulated objects captured from diverse real-world scenes and camera viewpoints. Each manipulated object (e.g., microwave oven) is represented with a matching 3D parametric model. This data allows us to evaluate the reconstruction quality of articulated objects and establish a benchmark for this challenging task. In particular, we leverage the estimated 3D human pose for more accurate inference of the object spatial layout and dynamics. We evaluate this approach on our dataset, demonstrating that human-object relations can significantly reduce the ambiguity of articulated object reconstructions from challenging real-world videos. Code and dataset are available at https://github.com/facebookresearch/d3d-hoi.
Blockchain Phishing Scam Detection via Multi-channel Graph Classification
Venue: arXiv cs.LG 2021
Year: 2021

With the popularity of blockchain technology, the financial security issues of blockchain transaction networks have become increasingly serious. Phishing scam detection methods will protect possible victims and build a healthier blockchain ecosystem. Usually, the existing works define phishing scam detection as a node classification task by learning the potential features of users through graph embedding methods such as random walk or graph neural network (GNN). However, these detection methods are suffered from high complexity due to the large scale of the blockchain transaction network, ignoring temporal information of the transaction. Addressing this problem, we defined the transaction pattern graphs for users and transformed the phishing scam detection into a graph classification task. To extract richer information from the input graph, we proposed a multi-channel graph classification model (MCGC) with multiple feature extraction channels for GNN. The transaction pattern graphs and MCGC are more able to detect potential phishing scammers by extracting the transaction pattern features of the target users. Extensive experiments on seven benchmark and Ethereum datasets demonstrate that the proposed MCGC can not only achieve state-of-the-art performance in the graph classification task but also achieve effective phishing scam detection based on the target users' transaction pattern graphs.
Learning System Parameters from Turing Patterns
Venue: arXiv cs.LG 2021
Year: 2021

The Turing mechanism describes the emergence of spatial patterns due to spontaneous symmetry breaking in reaction-diffusion processes and underlies many developmental processes. Identifying Turing mechanisms in biological systems defines a challenging problem. This paper introduces an approach to the prediction of Turing parameter values from observed Turing patterns. The parameter values correspond to a parametrized system of reaction-diffusion equations that generate Turing patterns as steady state. The Gierer-Meinhardt model with four parameters is chosen as a case study. A novel invariant pattern representation based on resistance distance histograms is employed, along with Wasserstein kernels, in order to cope with the highly variable arrangement of local pattern structure that depends on the initial conditions which are assumed to be unknown. This enables to compute physically plausible distances between patterns, to compute clusters of patterns and, above all, model parameter prediction: for small training sets, classical state-of-the-art methods including operator-valued kernels outperform neural networks that are applied to raw pattern data, whereas for large training sets the latter are more accurate. Excellent predictions are obtained for single parameter values and reasonably accurate results for jointly predicting all parameter values.