OpenRaiser/Intern-Atlas huggingface dataset:
Machine learning discrimination of Parkinson's Disease stages from walker-mounted sensors data
Venue: arXiv cs.LG 2020
Year: 2020

Clinical methods that assess gait in Parkinson's Disease (PD) are mostly qualitative. Quantitative methods necessitate costly instrumentation or cumbersome wearable devices, which limits their usability. Only few of these methods can discriminate different stages in PD progression. This study applies machine learning methods to discriminate six stages of PD. The data was acquired by low cost walker-mounted sensors in an experiment at a movement disorders clinic and the PD stages were clinically labeled. A large set of features, some unique to this study are extracted and three feature selection methods are compared using a multi-class Random Forest (RF) classifier. The feature subset selected by the Analysis of Variance (ANOVA) method provided performance similar to the full feature set: 93% accuracy and had significantly shorter computation time. Compared to PCA, this method also enabled clinical interpretability of the selected features, an essential attribute to healthcare applications. All selected-feature sets are dominated by information theoretic features and statistical features and offer insights into the characteristics of gait deterioration in PD. The results indicate a feasibility of machine learning to accurately classify PD severity stages from kinematic signals acquired by low-cost, walker-mounted sensors and implies a potential to aid medical practitioners in the quantitative assessment of PD progression. The study presents a solution to the small and noisy data problem, which is common in most sensor-based healthcare assessments.
Hierarchical Inter-Message Passing for Learning on Molecular Graphs
Venue: arXiv cs.LG 2020
Year: 2020

We present a hierarchical neural message passing architecture for learning on molecular graphs. Our model takes in two complementary graph representations: the raw molecular graph representation and its associated junction tree, where nodes represent meaningful clusters in the original graph, e.g., rings or bridged compounds. We then proceed to learn a molecule's representation by passing messages inside each graph, and exchange messages between the two representations using a coarse-to-fine and fine-to-coarse information flow. Our method is able to overcome some of the restrictions known from classical GNNs, like detecting cycles, while still being very efficient to train. We validate its performance on the ZINC dataset and datasets stemming from the MoleculeNet benchmark collection.
Revisiting Loss Modelling for Unstructured Pruning
Venue: arXiv cs.LG 2020
Year: 2020

By removing parameters from deep neural networks, unstructured pruning methods aim at cutting down memory footprint and computational cost, while maintaining prediction accuracy. In order to tackle this otherwise intractable problem, many of these methods model the loss landscape using first or second order Taylor expansions to identify which parameters can be discarded. We revisit loss modelling for unstructured pruning: we show the importance of ensuring locality of the pruning steps. We systematically compare first and second order Taylor expansions and empirically show that both can reach similar levels of performance. Finally, we show that better preserving the original network function does not necessarily transfer to better performing networks after fine-tuning, suggesting that only considering the impact of pruning on the loss might not be a sufficient objective to design good pruning criteria.
Time-Variant Proof-of-Work Using Error-Correction Codes
Venue: arXiv cs.CR 2020
Year: 2020

The protocol for cryptocurrencies can be divided into three parts, namely consensus, wallet, and networking overlay. The aim of the consensus part is to bring trustless rational peer-to-peer nodes to an agreement to the current status of the blockchain. The status must be updated through valid transactions. A proof-of-work (PoW) based consensus mechanism has been proven to be secure and robust owing to its simple rule and has served as a firm foundation for cryptocurrencies such as Bitcoin and Ethereum. Specialized mining devices have emerged, as rational miners aim to maximize profit, and caused two problems: i) the re-centralization of a mining market and ii) the huge energy spending in mining. In this paper, we aim to propose a new PoW called Error-Correction Codes PoW (ECCPoW) where the error-correction codes and their decoder can be utilized for PoW. In ECCPoW, puzzles can be intentionally generated to vary from block to block, leading to a time-variant puzzle generation mechanism. This mechanism is useful in repressing the emergence of the specialized mining devices. It can serve as a solution to the two problems of recentralization and energy spending.
On the Ability of a CNN to Realize Image-to-Image Language Conversion
Venue: arXiv cs.CV 2020
Year: 2020

The purpose of this paper is to reveal the ability that Convolutional Neural Networks (CNN) have on the novel task of image-to-image language conversion. We propose a new network to tackle this task by converting images of Korean Hangul characters directly into images of the phonetic Latin character equivalent. The conversion rules between Hangul and the phonetic symbols are not explicitly provided. The results of the proposed network show that it is possible to perform image-to-image language conversion. Moreover, it shows that it can grasp the structural features of Hangul even from limited learning data. In addition, it introduces a new network to use when the input and output have significantly different features.
Siamese Meta-Learning and Algorithm Selection with 'Algorithm-Performance Personas' [Proposal]
Venue: arXiv cs.LG 2020
Year: 2020

Automated per-instance algorithm selection often outperforms single learners. Key to algorithm selection via meta-learning is often the (meta) features, which sometimes though do not provide enough information to train a meta-learner effectively. We propose a Siamese Neural Network architecture for automated algorithm selection that focuses more on 'alike performing' instances than meta-features. Our work includes a novel performance metric and method for selecting training samples. We introduce further the concept of 'Algorithm Performance Personas' that describe instances for which the single algorithms perform alike. The concept of 'alike performing algorithms' as ground truth for selecting training samples is novel and provides a huge potential as we believe. In this proposal, we outline our ideas in detail and provide the first evidence that our proposed metric is better suitable for training sample selection that standard performance metrics such as absolute errors.
Tactile Perception of Objects by the User's Palm for the Development of Multi-contact Wearable Tactile Displays
Venue: arXiv cs.HC 2020
Year: 2020

The user's palm plays an important role in object detection and manipulation. The design of a robust multi-contact tactile display must consider the sensation and perception of of the stimulated area aiming to deliver the right stimuli at the correct location. To the best of our knowledge, there is no study to obtain the human palm data for this purpose. The objective of this work is to introduce the method to investigate the user's palm sensations during the interaction with objects. An array of fifteen Force Sensitive Resistors (FSRs) was located at the user's palm to get the area of interaction, and the normal force delivered to four different convex surfaces. Experimental results showed the active areas at the palm during the interaction with each of the surfaces at different forces. The obtained results can be applied in the development of multi-contact wearable tactile and haptic displays for the palm, and in training a machine-learning algorithm to predict stimuli aiming to achieve a highly immersive experience in Virtual Reality.
Quick Lists: Enriched Playlist Embeddings for Future Playlist Recommendation
Venue: arXiv cs.IR 2020
Year: 2020

Recommending playlists to users in the context of a digital music service is a difficult task because a playlist is often more than the mere sum of its parts. We present a novel method for generating playlist embeddings that are invariant to playlist length and sensitive to local and global track ordering. The embeddings also capture information about playlist sequencing, and are enriched with side information about the playlist user. We show that these embeddings are useful for generating next-best playlist recommendations, and that side information can be used for the cold start problem.
Game Theory on the Ground: The Effect of Increased Patrols on Deterring Poachers
Venue: arXiv cs.CY 2020
Year: 2020

Applications of artificial intelligence for wildlife protection have focused on learning models of poacher behavior based on historical patterns. However, poachers' behaviors are described not only by their historical preferences, but also their reaction to ranger patrols. Past work applying machine learning and game theory to combat poaching have hypothesized that ranger patrols deter poachers, but have been unable to find evidence to identify how or even if deterrence occurs. Here for the first time, we demonstrate a measurable deterrence effect on real-world poaching data. We show that increased patrols in one region deter poaching in the next timestep, but poachers then move to neighboring regions. Our findings offer guidance on how adversaries should be modeled in realistic game-theoretic settings.
Dirichlet-Smoothed Word Embeddings for Low-Resource Settings
Venue: arXiv cs.CL 2020
Year: 2020

Nowadays, classical count-based word embeddings using positive pointwise mutual information (PPMI) weighted co-occurrence matrices have been widely superseded by machine-learning-based methods like word2vec and GloVe. But these methods are usually applied using very large amounts of text data. In many cases, however, there is not much text data available, for example for specific domains or low-resource languages. This paper revisits PPMI by adding Dirichlet smoothing to correct its bias towards rare words. We evaluate on standard word similarity data sets and compare to word2vec and the recent state of the art for low-resource settings: Positive and Unlabeled (PU) Learning for word embeddings. The proposed method outperforms PU-Learning for low-resource settings and obtains competitive results for Maltese and Luxembourgish.
Improved Bounds for Metric Capacitated Covering Problems
Venue: arXiv cs.DS 2020
Year: 2020

In the Metric Capacitated Covering (MCC) problem, given a set of balls $\mathcal{B}$ in a metric space $P$ with metric $d$ and a capacity parameter $U$, the goal is to find a minimum sized subset $\mathcal{B}'\subseteq \mathcal{B}$ and an assignment of the points in $P$ to the balls in $\mathcal{B}'$ such that each point is assigned to a ball that contains it and each ball is assigned with at most $U$ points. MCC achieves an $O(\log |P|)$-approximation using a greedy algorithm. On the other hand, it is hard to approximate within a factor of $o(\log |P|)$ even with $β< 3$ factor expansion of the balls. Bandyapadhyay~{et al.} [SoCG 2018, DCG 2019] showed that one can obtain an $O(1)$-approximation for the problem with $6.47$ factor expansion of the balls. An open question left by their work is to reduce the gap between the lower bound $3$ and the upper bound $6.47$. In this current work, we show that it is possible to obtain an $O(1)$-approximation with only $4.24$ factor expansion of the balls. We also show a similar upper bound of $5$ for a more generalized version of MCC for which the best previously known bound was $9$.
Technology Readiness Levels for AI & ML
Venue: arXiv cs.SE 2020
Year: 2020

The development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. The lack of diligence can lead to technical debt, scope creep and misaligned objectives, model misuse and failures, and expensive consequences. Engineering systems, on the other hand, follow well-defined processes and testing standards to streamline development for high-quality, reliable results. The extreme is spacecraft systems, where mission critical measures and robustness are ingrained in the development process. Drawing on experience in both spacecraft engineering and AI/ML (from research through product), we propose a proven systems engineering approach for machine learning development and deployment. Our Technology Readiness Levels for ML (TRL4ML) framework defines a principled process to ensure robust systems while being streamlined for ML research and product, including key distinctions from traditional software engineering. Even more, TRL4ML defines a common language for people across the organization to work collaboratively on ML technologies.
IXmon: Detecting and Analyzing DRDoS Attacks at Internet Exchange Points
Venue: arXiv cs.NI 2020
Year: 2020

Distributed reflective denial of service (DRDoS) attacks are a popular choice among adversaries. In fact, one of the largest DDoS attacks ever recorded, reaching a peak of 1.3Tbps against GitHub, was a memcached-based DRDoS attack. More recently, a record-breaking 2.3Tbps attack against Amazon AWS was due to a CLDAP-based DRDoS attack. Although reflective attacks have been known for years, DRDoS attacks are unfortunately still popular and largely unmitigated. In this paper, we study in-the-wild DRDoS attacks observed from a large Internet exchange point (IXP) and provide a number of security-relevant measurements and insights. To enable this study, we first developed IXmon, an open-source DRDoS detection system specifically designed for deployment at large IXP-like network connectivity providers and peering hubs. We deployed IXmon at Southern Crossroads (SoX), an IXP-like hub that provides both peering and upstream Internet connectivity services to more than 20 research and education (R&E) networks in the South-East United States. In a period of about 21 months, IXmon detected more than 900 DRDoS attacks towards 31 different victim ASes. An analysis of the real-world DRDoS attacks detected by our system shows that most DRDoS attacks are short lived, lasting only a few minutes, but that large-volume, long-lasting, and highly-distributed attacks against R&E networks are not uncommon. We then use the results of our analysis to discuss possible attack mitigation approaches that can be deployed at the IXP level, before the attack traffic overwhelms the victim's network bandwidth.
A Survey on Deep Learning for Localization and Mapping: Towards the Age of Spatial Machine Intelligence
Venue: arXiv cs.CV 2020
Year: 2020

Deep learning based localization and mapping has recently attracted significant attention. Instead of creating hand-designed algorithms through exploitation of physical models or geometric theories, deep learning based solutions provide an alternative to solve the problem in a data-driven way. Benefiting from ever-increasing volumes of data and computational power, these methods are fast evolving into a new area that offers accurate and robust systems to track motion and estimate scenes and their structure for real-world applications. In this work, we provide a comprehensive survey, and propose a new taxonomy for localization and mapping using deep learning. We also discuss the limitations of current models, and indicate possible future directions. A wide range of topics are covered, from learning odometry estimation, mapping, to global localization and simultaneous localization and mapping (SLAM). We revisit the problem of perceiving self-motion and scene understanding with on-board sensors, and show how to solve it by integrating these modules into a prospective spatial machine intelligence system (SMIS). It is our hope that this work can connect emerging works from robotics, computer vision and machine learning communities, and serve as a guide for future researchers to apply deep learning to tackle localization and mapping problems.
Multitasking Across Industry Projects: A Replication Study
Venue: arXiv cs.SE 2020
Year: 2020

Background: Multitasking is usual in software development. It is the ability to stop working on a task, switch to another, and return eventually to the first one, as needed or as scheduled. Multitasking, however, comes at a cognitive cost: frequent context-switches can lead to distraction, sub-standard work, and even greater stress. Aims: This paper reports a replication experiment where we gathered data on a group of developers from a software development company from industry on a large collection of projects stored in GitLab repositories. Method: We reused the developed models and methods from the original study for measuring the rate and breadth of a developers' context-switching behavior, and we study how context-switching affects their productivity. We applied semi-structured interviews, replacing the original survey, to some of the developers to understand the reasons for and perceptions of multitasking. Results: We found out that industry developers multitask as much as OSS developers focusing more (on fewer projects), and working more repetitively from one day to the next is associated with higher productivity, but there is no effect for higher multitasking. Some commons reasons make them multitask: dependencies, personal interests, and social relationships. Conclusions: Short context change, less than three minutes, did not impact results from industry developers; however, more than that, it brings a feeling of left the previous tasks behind. So, it is proportional to how much context is switched: as bigger the context and bigger the interruption, it is worst to come back.
SN-Engine, a Scale-free Geometric Modelling Environment
Venue: arXiv cs.GR 2020
Year: 2020

We present a new scale-free geometric modelling environment designed by the author of the paper. It allows one to consistently treat geometric objects of arbitrary size and offers extensive analytic and computational support for visualization of both real and artificial sceneries.
AFDet: Anchor Free One Stage 3D Object Detection
Venue: arXiv cs.CV 2020
Year: 2020

High-efficiency point cloud 3D object detection operated on embedded systems is important for many robotics applications including autonomous driving. Most previous works try to solve it using anchor-based detection methods which come with two drawbacks: post-processing is relatively complex and computationally expensive; tuning anchor parameters is tricky. We are the first to address these drawbacks with an anchor free and Non-Maximum Suppression free one stage detector called AFDet. The entire AFDet can be processed efficiently on a CNN accelerator or a GPU with the simplified post-processing. Without bells and whistles, our proposed AFDet performs competitively with other one stage anchor-based methods on KITTI validation set and Waymo Open Dataset validation set.
On Compression Principle and Bayesian Optimization for Neural Networks
Venue: arXiv cs.LG 2020
Year: 2020

Finding methods for making generalizable predictions is a fundamental problem of machine learning. By looking into similarities between the prediction problem for unknown data and the lossless compression we have found an approach that gives a solution. In this paper we propose a compression principle that states that an optimal predictive model is the one that minimizes a total compressed message length of all data and model definition while guarantees decodability. Following the compression principle we use Bayesian approach to build probabilistic models of data and network definitions. A method to approximate Bayesian integrals using a sequence of variational approximations is implemented as an optimizer for hyper-parameters: Bayesian Stochastic Gradient Descent (BSGD). Training with BSGD is completely defined by setting only three parameters: number of epochs, the size of the dataset and the size of the minibatch, which define a learning rate and a number of iterations. We show that dropout can be used for a continuous dimensionality reduction that allows to find optimal network dimensions as required by the compression principle.
ICE: Identify and Compare Event Sequence Sets through Multi-Scale Matrix and Unit Visualizations
Venue: arXiv cs.HC 2020
Year: 2020

Comparative analysis of event sequence data is essential in many application domains, such as website design and medical care. However, analysts often face two challenges: they may not always know which sets of event sequences in the data are useful to compare, and the comparison needs to be achieved at different granularity, due to the volume and complexity of the data. This paper presents, ICE, an interactive visualization that allows analysts to explore an event sequence dataset, and identify promising sets of event sequences to compare at both the pattern and sequence levels. More specifically, ICE incorporates a multi-level matrix-based visualization for browsing the entire dataset based on the prefixes and suffixes of sequences. To support comparison at multiple levels, ICE employs the unit visualization technique, and we further explore the design space of unit visualizations for event sequence comparison tasks. Finally, we demonstrate the effectiveness of ICE with three real-world datasets from different domains.
Helping Software Developers through Offline Repository Based API Searching in Data MiningIntegrated Environment
Venue: arXiv cs.SE 2020
Year: 2020

Software development is getting changed so rapidly. It will be highly benefited if we can accelerate software development process by guiding developers. Appropriate guidelines and accurate recommendations to developers during development process can reduce software development expenses, as well as can save valuable times of developers. There are a number of approaches to speed up the software development process. It can be done through code assistance tools that help developers by recommending relevant items from searching particular repository of Application Programming Interface (API). Some approaches are based on online searching that have some drawbacks due to request and response latency as it has to deal with the extra-large files in a server. Developers generally uses previously completed resources as well as libraries or frameworks to generate relevant snippets which are supplied by the referral repository of APIs. Developers find it hard to choose the appropriate methods as there are thousands of methods in which some are not properly documented. In this paper we have proposed a concept and its respective framework to guide developers that suggests relevant API methods from an offline mined repository. From the investigation we made, we can say that our approach works much better than some of the existing approaches.
Database Optimization to Recommend Software Developers using Canonical Order Tree
Venue: arXiv cs.DB 2020
Year: 2020

Recently frequent and sequential pattern mining algorithms have been widely used in the field of software engineering to mine various source code or specification patterns. In practice software evolves from one version to another is needed for providing extra facilities to user. This kind of task is challenging in this domain since the database is usually updated in all kinds of manners such as insertion, various modifications as well as removal of sequences. If database is optimized then this optimized information will help developer in their development process and save their valuable time as well as development expenses. Some existing algorithms which are used to optimize database but it does not work faster when database is incrementally updated. To overcome this challenges an efficient algorithm is recently introduce, called the Canonical Order Tree that captures the content of the transactions of the database and orders. In this paper we have proposed a technique based on the Canonical Order Tree that can find out frequent patterns from the incremental database with speedy and efficient way. Thus the database will be optimized as well as it gives useful information to recommend software developer.