OpenRaiser/Intern-Atlas huggingface dataset:
Multi-Step Adversarial Perturbations on Recommender Systems Embeddings
Venue: arXiv cs.IR 2020
Year: 2020

Recommender systems (RSs) have attained exceptional performance in learning users' preferences and helping them in finding the most suitable products. Recent advances in adversarial machine learning (AML) in the computer vision domain have raised interests in the security of state-of-the-art model-based recommenders. Recently, worrying deterioration of recommendation accuracy has been acknowledged on several state-of-the-art model-based recommenders (e.g., BPR-MF) when machine-learned adversarial perturbations contaminate model parameters. However, while the single-step fast gradient sign method (FGSM) is the most explored perturbation strategy, multi-step (iterative) perturbation strategies, that demonstrated higher efficacy in the computer vision domain, have been highly under-researched in recommendation tasks. In this work, inspired by the basic iterative method (BIM) and the projected gradient descent (PGD) strategies proposed in the CV domain, we adapt the multi-step strategies for the item recommendation task to study the possible weaknesses of embedding-based recommender models under minimal adversarial perturbations. Letting the magnitude of the perturbation be fixed, we illustrate the highest efficacy of the multi-step perturbation compared to the single-step one with extensive empirical evaluation on two widely adopted recommender datasets. Furthermore, we study the impact of structural dataset characteristics, i.e., sparsity, density, and size, on the performance degradation issued by presented perturbations to support RS designer in interpreting recommendation performance variation due to minimal variations of model parameters. Our implementation and datasets are available at https://anonymous.4open.science/r/9f27f909-93d5-4016-b01c-8976b8c14bc5/.
A Variational Information Bottleneck Based Method to Compress Sequential Networks for Human Action Recognition
Venue: arXiv cs.CV 2020
Year: 2020

In the last few years, compression of deep neural networks has become an important strand of machine learning and computer vision research. Deep models require sizeable computational complexity and storage, when used for instance for Human Action Recognition (HAR) from videos, making them unsuitable to be deployed on edge devices. In this paper, we address this issue and propose a method to effectively compress Recurrent Neural Networks (RNNs) such as Gated Recurrent Units (GRUs) and Long-Short-Term-Memory Units (LSTMs) that are used for HAR. We use a Variational Information Bottleneck (VIB) theory-based pruning approach to limit the information flow through the sequential cells of RNNs to a small subset. Further, we combine our pruning method with a specific group-lasso regularization technique that significantly improves compression. The proposed techniques reduce model parameters and memory footprint from latent representations, with little or no reduction in the validation accuracy while increasing the inference speed several-fold. We perform experiments on the three widely used Action Recognition datasets, viz. UCF11, HMDB51, and UCF101, to validate our approach. It is shown that our method achieves over 70 times greater compression than the nearest competitor with comparable accuracy for the task of action recognition on UCF11.
Codes for Distributed Storage
Venue: arXiv cs.IT 2020
Year: 2020

This chapter deals with the topic of designing reliable and efficient codes for the storage and retrieval of large quantities of data over storage devices that are prone to failure. For long, the traditional objective has been one of ensuring reliability against data loss while minimizing storage overhead. More recently, a third concern has surfaced, namely of the need to efficiently recover from the failure of a single storage unit, corresponding to recovery from the erasure of a single code symbol. We explain here, how coding theory has evolved to tackle this fresh challenge.
Cubic Spline Smoothing Compensation for Irregularly Sampled Sequences
Venue: arXiv cs.LG 2020
Year: 2020

The marriage of recurrent neural networks and neural ordinary differential networks (ODE-RNN) is effective in modeling irregularly-observed sequences. While ODE produces the smooth hidden states between observation intervals, the RNN will trigger a hidden state jump when a new observation arrives, thus cause the interpolation discontinuity problem. To address this issue, we propose the cubic spline smoothing compensation, which is a stand-alone module upon either the output or the hidden state of ODE-RNN and can be trained end-to-end. We derive its analytical solution and provide its theoretical interpolation error bound. Extensive experiments indicate its merits over both ODE-RNN and cubic spline interpolation.
Automated Performance Tuning for Highly-Configurable Software Systems
Venue: arXiv cs.SE 2020
Year: 2020

Performance is an important non-functional aspect of the software requirement. Modern software systems are highly-configurable and misconfigurations may easily cause performance issues. A software system that suffers performance issues may exhibit low program throughput and long response time. However, the sheer size of the configuration space makes it challenging for administrators to manually select and adjust the configuration options to achieve better performance. In this paper, we propose ConfRL, an approach to tune software performance automatically. The key idea of ConfRL is to use reinforcement learning to explore the configuration space by a trial-and-error approach and to use the feedback received from the environment to tune configuration option values to achieve better performance. To reduce the cost of reinforcement learning, ConfRL employs sampling, clustering, and dynamic state reduction techniques to keep states in a large configuration space manageable. Our evaluation of four real-world highly-configurable server programs shows that ConfRL can efficiently and effectively guide software systems to achieve higher long-term performance.
Uncertainty-Aware Multi-Modal Ensembling for Severity Prediction of Alzheimer's Dementia
Venue: arXiv cs.LG 2020
Year: 2020

Reliability in Neural Networks (NNs) is crucial in safety-critical applications like healthcare, and uncertainty estimation is a widely researched method to highlight the confidence of NNs in deployment. In this work, we propose an uncertainty-aware boosting technique for multi-modal ensembling to predict Alzheimer's Dementia Severity. The propagation of uncertainty across acoustic, cognitive, and linguistic features produces an ensemble system robust to heteroscedasticity in the data. Weighing the different modalities based on the uncertainty estimates, we experiment on the benchmark ADReSS dataset, a subject-independent and balanced dataset, to show that our method outperforms the state-of-the-art methods while also reducing the overall entropy of the system. This work aims to encourage fair and aware models. The source code is available at https://github.com/wazeerzulfikar/alzheimers-dementia
3D Orientation Field Transform
Venue: arXiv cs.CV 2020
Year: 2020

The two-dimensional (2D) orientation field transform has been proved to be effective at enhancing 2D contours and curves in images by means of top-down processing. It, however, has no counterpart in three-dimensional (3D) images due to the extremely complicated orientation in 3D compared to 2D. Practically and theoretically, the demand and interest in 3D can only be increasing. In this work, we modularise the concept and generalise it to 3D curves. Different modular combinations are found to enhance curves to different extents and with different sensitivity to the packing of the 3D curves. In principle, the proposed 3D orientation field transform can naturally tackle any dimensions. As a special case, it is also ideal for 2D images, owning simpler methodology compared to the previous 2D orientation field transform. The proposed method is demonstrated with several transmission electron microscopy tomograms ranging from 2D curve enhancement to, the more important and interesting, 3D ones.
New Insights on Learning Rules for Hopfield Networks: Memory and Objective Function Minimisation
Venue: arXiv cs.NE 2020
Year: 2020

Hopfield neural networks are a possible basis for modelling associative memory in living organisms. After summarising previous studies in the field, we take a new look at learning rules, exhibiting them as descent-type algorithms for various cost functions. We also propose several new cost functions suitable for learning. We discuss the role of biases (the external inputs) in the learning process in Hopfield networks. Furthermore, we apply Newtons method for learning memories, and experimentally compare the performances of various learning rules. Finally, to add to the debate whether allowing connections of a neuron to itself enhances memory capacity, we numerically investigate the effects of self coupling. Keywords: Hopfield Networks, associative memory, content addressable memory, learning rules, gradient descent, attractor networks
Dialogue Generation on Infrequent Sentence Functions via Structured Meta-Learning
Venue: arXiv cs.CL 2020
Year: 2020

Sentence function is an important linguistic feature indicating the communicative purpose in uttering a sentence. Incorporating sentence functions into conversations has shown improvements in the quality of generated responses. However, the number of utterances for different types of fine-grained sentence functions is extremely imbalanced. Besides a small number of high-resource sentence functions, a large portion of sentence functions is infrequent. Consequently, dialogue generation conditioned on these infrequent sentence functions suffers from data deficiency. In this paper, we investigate a structured meta-learning (SML) approach for dialogue generation on infrequent sentence functions. We treat dialogue generation conditioned on different sentence functions as separate tasks, and apply model-agnostic meta-learning to high-resource sentence functions data. Furthermore, SML enhances meta-learning effectiveness by promoting knowledge customization among different sentence functions but simultaneously preserving knowledge generalization for similar sentence functions. Experimental results demonstrate that SML not only improves the informativeness and relevance of generated responses, but also can generate responses consistent with the target sentence functions.
Understanding Incentivized Mobile App Installs on Google Play Store
Venue: arXiv cs.CY 2020
Year: 2020

"Incentivized" advertising platforms allow mobile app developers to acquire new users by directly paying users to install and engage with mobile apps (e.g., create an account, make in-app purchases). Incentivized installs are banned by the Apple App Store and discouraged by the Google Play Store because they can manipulate app store metrics (e.g., install counts, appearance in top charts). Yet, many organizations still offer incentivized install services for Android apps. In this paper, we present the first study to understand the ecosystem of incentivized mobile app install campaigns in Android and its broader ramifications through a series of measurements. We identify incentivized install campaigns that require users to install an app and perform in-app tasks targeting manipulation of a wide variety of user engagement metrics (e.g., daily active users, user session lengths) and revenue. Our results suggest that these artificially inflated metrics can be effective in improving app store metrics as well as helping mobile app developers to attract funding from venture capitalists. Our study also indicates lax enforcement of the Google Play Store's existing policies to prevent these behaviors. It further motivates the need for stricter policing of incentivized install campaigns. Our proposed measurements can also be leveraged by the Google Play Store to identify potential policy violations.
NLP Service APIs and Models for Efficient Registration of New Clients
Venue: arXiv cs.LG 2020
Year: 2020

State-of-the-art NLP inference uses enormous neural architectures and models trained for GPU-months, well beyond the reach of most consumers of NLP. This has led to one-size-fits-all public API-based NLP service models by major AI companies, serving large numbers of clients. Neither (hardware deficient) clients nor (heavily subscribed) servers can afford traditional fine tuning. Many clients own little or no labeled data. We initiate a study of adaptation of centralized NLP services to clients, and present one practical and lightweight approach. Each client uses an unsupervised, corpus-based sketch to register to the service. The server uses an auxiliary network to map the sketch to an abstract vector representation, which then informs the main labeling network. When a new client registers with its sketch, it gets immediate accuracy benefits. We demonstrate the success of the proposed architecture using sentiment labeling, NER, and predictive language modeling
Problems and Prospects for Intimate Musical Control of Computers
Venue: arXiv cs.HC 2020
Year: 2020

In this paper we describe our efforts towards the development of live performance computer-based musical instrumentation. Our design criteria include initial ease of use coupled with a long term potential for virtuosity, minimal and low variance latency, and clear and simple strategies for programming the relationship between gesture and musical result. We present custom controllers and unique adaptations of standard gestural interfaces, a programmable connectivity processor, a communications protocol called Open Sound Control (OSC), and a variety of metaphors for musical control. We further describe applications of our technology to a variety of real musical performances and directions for future research.
Resonant Processing of Instrumental Sound Controlled by Spatial Position
Venue: arXiv cs.SD 2020
Year: 2020

We present an acoustic musical instrument played through a resonance model of another sound. The resonance model is controlled in real time as part of the composite instrument. Our implementation uses an electric violin, whose spatial position modifies filter parameters of the resonance model. Simplicial interpolation defines the mapping from spatial position to filter parameters. With some effort, pitch tracking can also control the filter parameters. The individual technologies -- motion tracking, pitch tracking, resonance models -- are easily adapted to other instruments.
A Fully Polynomial Time Approximation Scheme for the Replenishment Storage Problem
Venue: arXiv cs.DS 2020
Year: 2020

The Replenishment Storage problem (RSP) is to minimize the storage capacity requirement for a deterministic demand, multi-item inventory system where each item has a given reorder size and cycle length. The reorders can only take place at integer time units within the cycle. This problem was shown to be weakly NP-hard for constant joint cycle length (the least common multiple of the lengths of all individual cycles). When all items have the same constant cycle length, there exists a Fully Polynomial Time Approximation Scheme (FPTAS), but no FPTAS has been known for the case when the individual cycles are different. Here we devise the first known FPTAS for the RSP with different individual cycles and constant joint cycle length.
On the Tradeoff Between Computation and Communication Costs for Distributed Linearly Separable Computation
Venue: arXiv cs.IT 2020
Year: 2020

This paper studies the distributed linearly separable computation problem, which is a generalization of many existing distributed computing problems such as distributed gradient descent and distributed linear transform. In this problem, a master asks $N$ distributed workers to compute a linearly separable function of $K$ datasets, which is a set of $K_c$ linear combinations of $K$ messages (each message is a function of one dataset). We assign some datasets to each worker, which then computes the corresponding messages and returns some function of these messages, such that from the answers of any $N_r$ out of $N$ workers the master can recover the task function. In the literature, the specific case where $K_c = 1$ or where the computation cost is minimum has been considered. In this paper, we focus on the general case (i.e., general $K_c$ and general computation cost) and aim to find the minimum communication cost. We first propose a novel converse bound on the communication cost under the constraint of the popular cyclic assignment (widely considered in the literature), which assigns the datasets to the workers in a cyclic way. Motivated by the observation that existing strategies for distributed computing fall short of achieving the converse bound, we propose a novel distributed computing scheme for some system parameters. The proposed computing scheme is optimal for any assignment when $K_c$ is large and is optimal under cyclic assignment when the numbers of workers and datasets are equal or $K_c$ is small. In addition, it is order optimal within a factor of 2 under cyclic assignment for the remaining cases.
Mixing Strategies in Cryptocurrencies and An Alternative Implementation
Venue: arXiv cs.CR 2020
Year: 2020

Since the initial launch of Bitcoin by Satoshi Nakamoto in 2009, decentralized digital currencies have long been of major interest in both the academia and the industry. Till today, there are more than 3000 different cryptocurrencies over the internet. Each one relies on mathematical soundness and cryptographic wit to provide unique properties in addition to securing basic correctness. A common misbelief for cryptocurrencies is that they provide complete anonymity by replacing people's real-life identity with a randomly generated wallet address in payments. However, this "pseudonymity" is easily breakable under the public ledger. Many attacks demonstrate ways to deanonymize people through observing the transaction patterns or network interactions. Thus, cryptocurrency fungibility has become a popular topic in the research community. This report reviews a partial list of existing schemes and describes an alternative implementation, Eth-Tumbler. Eth-Tumbler utilizes layered encryption and multiple signatures and thus efficiently hides a user under k-anonymity.
Local Additivity Based Data Augmentation for Semi-supervised NER
Venue: arXiv cs.CL 2020
Year: 2020

Named Entity Recognition (NER) is one of the first stages in deep language understanding yet current NER models heavily rely on human-annotated data. In this work, to alleviate the dependence on labeled data, we propose a Local Additivity based Data Augmentation (LADA) method for semi-supervised NER, in which we create virtual samples by interpolating sequences close to each other. Our approach has two variations: Intra-LADA and Inter-LADA, where Intra-LADA performs interpolations among tokens within one sentence, and Inter-LADA samples different sentences to interpolate. Through linear additions between sampled training data, LADA creates an infinite amount of labeled data and improves both entity and context learning. We further extend LADA to the semi-supervised setting by designing a novel consistency loss for unlabeled data. Experiments conducted on two NER benchmarks demonstrate the effectiveness of our methods over several strong baselines. We have publicly released our code at https://github.com/GT-SALT/LADA.
Generating Gameplay-Relevant Art Assets with Transfer Learning
Venue: arXiv cs.CV 2020
Year: 2020

In game development, designing compelling visual assets that convey gameplay-relevant features requires time and experience. Recent image generation methods that create high-quality content could reduce development costs, but these approaches do not consider game mechanics. We propose a Convolutional Variational Autoencoder (CVAE) system to modify and generate new game visuals based on their gameplay relevance. We test this approach with Pokémon sprites and Pokémon type information, since types are one of the game's core mechanics and they directly impact the game's visuals. Our experimental results indicate that adopting a transfer learning approach can help to improve visual quality and stability over unseen data.
Entity Embedding as Game Representation
Venue: arXiv cs.AI 2020
Year: 2020

Procedural content generation via machine learning (PCGML) has shown success at producing new video game content with machine learning. However, the majority of the work has focused on the production of static game content, including game levels and visual elements. There has been much less work on dynamic game content, such as game mechanics. One reason for this is the lack of a consistent representation for dynamic game content, which is key for a number of statistical machine learning approaches. We present an autoencoder for deriving what we call "entity embeddings", a consistent way to represent different dynamic entities across multiple games in the same representation. In this paper we introduce the learned representation, along with some evidence towards its quality and future utility.
Mossad: Defeating Software Plagiarism Detection
Venue: arXiv cs.CR 2020
Year: 2020

Automatic software plagiarism detection tools are widely used in educational settings to ensure that submitted work was not copied. These tools have grown in use together with the rise in enrollments in computer science programs and the widespread availability of code on-line. Educators rely on the robustness of plagiarism detection tools; the working assumption is that the effort required to evade detection is as high as that required to actually do the assigned work. This paper shows this is not the case. It presents an entirely automatic program transformation approach, Mossad, that defeats popular software plagiarism detection tools. Mossad comprises a framework that couples techniques inspired by genetic programming with domain-specific knowledge to effectively undermine plagiarism detectors. Mossad is effective at defeating four plagiarism detectors, including Moss and JPlag. Mossad is both fast and effective: it can, in minutes, generate modified versions of programs that are likely to escape detection. More insidiously, because of its non-deterministic approach, Mossad can, from a single program, generate dozens of variants, which are classified as no more suspicious than legitimate assignments. A detailed study of Mossad across a corpus of real student assignments demonstrates its efficacy at evading detection. A user study shows that graduate student assistants consistently rate Mossad-generated code as just as readable as authentic student code. This work motivates the need for both research on more robust plagiarism detection tools and greater integration of naturally plagiarism-resistant methodologies like code review into computer science education.