OpenRaiser/Intern-Atlas huggingface dataset:
Data Preprocessing for Evaluation of Recommendation Models in E-Commerce
Venue: arXiv cs.CY 2019
Year: 2019

E-commerce businesses employ recommender models to assist in identifying a personalized set of products for each visitor. To accurately assess the recommendations' influence on customer clicks and buys, three target areas -- customer behavior, data collection, user-interface -- will be explored for possible sources of erroneous data. Varied customer behavior misrepresents the recommendations' true influence on a customer due to the presence of B2B interactions and outlier customers. Non-parametric statistical procedures for outlier removal are delineated and other strategies are investigated to account for the effect of a large percentage of new customers or high bounce rates. Subsequently, in data collection we identify probable misleading interactions in the raw data, propose a robust method of tracking unique visitors, and accurately attributing the buy influence for combo products. Lastly, user-interface issues discuss the possible problems caused due to the recommendation widget's positioning on the e-commerce website and the stringent conditions that should be imposed when utilizing data from the product listing page. This collective methodology results in an exact and valid estimation of the customer's interactions influenced by the recommendation model in the context of standard industry metrics, such as Click-through rates, Buy-through rates, and Conversion revenue.
Penerapan Knowledge Management System Sales and Customer Care pada PT Satria Medikantara Palembang
Venue: arXiv cs.CY 2019
Year: 2019

PT Satria Medikantara Palembang has a great desire to apply knowledge management system, therefore the documentation of knowledge and its utilization needs to be well managed in the context of performance improvement. The implementation of knowledge management in PT Satria Medikantara Palembang is considered very good and can have a positive impact for the quality of employees. Where every employee can store and document and share knowledge owned, so that other employees can access, even learn and discuss with other employees based on the knowledge posted. Then when needed a knowledge, it is very easy to find in the database searching feature in the web based knowledge management system at PT Satria Medikantara Palembang. The methodology used in this study refers to the methodology of knowledge management system life cycle developed by Awad and Ghaziri (2010), and this system will be based on web using PHP programming language.
Automated Smart Wick System-Based Microfarm Using Internet of Things
Venue: arXiv cs.CY 2019
Year: 2019

This paper presents a study conducted to allow urban farmers to remotely monitor their farm through the design and development of an Internet of Things-based (IoT) microfarm prototype which utilized wick system as planting method. The system involves the detection of three environmental parameters namely, light intensity, soil moisture and temperature through the use of respective sensors which were connected to the Arduino microcontroller, the sensor node of the system. Irregularities in the aforementioned parameters were neutralized through the use of parameter regulators such as LED growlight strips, water pump and air cooler. The data collected by these sensors were gathered by the Arduino microcontroller and were sent to the Web database through the IoT gateway which was the Raspberry Pi computer chip. These data were also sent to an Android unit installed with the Microfarm Companion application which was capable of monitoring and controlling the environmental parameters observed in the microfarm. The application allows the user to view the current value of the parameter involved and to choose whether to control the parameter regulators automatically or manually. The microfarm system runs autonomously which reduces the labor required to produce healthy plants and crops. Mustard greens samples were used in testing the system. After a month of monitoring the height of the samples, it was observed that the average height of the samples is about 0.23 cm taller than the standard height. The proponents has also tested the system functionality by evaluating the sensor data log that provides the values gathered by the sensors and the turn-on times of the parameter regulators. From these data, it can be observed that whenever the values obtained by the sensors fall outside the threshold range, the parameter regulators turns on, indicating that the system is working properly.
Reactive Failure Mitigation through Seamless Migration in Telecom Infrastructure Networks
Venue: arXiv cs.NI 2019
Year: 2019

Various methods are proposed in the literature to mitigate the failures in an infrastructure network. Failure mitigation can be carried out in an active or passive manner. In active manner, live backups provide the required reliability. Due to the high cost of active backups, failures can be mitigated in a reactive manner where mitigation starts when a function fails. In this paper, network functions are divided into two classes of essential (i.e., core) functions and additive (i.e., service) functions. Core functions need active backups and cannot tolerate any failure. However, additive functions do not require backups and their absence is tolerable in short periods. Maximum tolerable (function) absence time (TAT) is a measure used for their reliability level. In our model and within the mitigation process, first, the backup host to migrate the failed function is selected. Then, the state of the function is migrated. The process should be carried out in less than TAT. An optimization problem is formulated investigating cost-effective failure mitigation of additive functions. The problem is of mixed integer non convex form. To tackle the computational complexity, it is bisected into two back-to-back leader-follower parts. The leader selects the migration destinations and the follower divides network resources among functions for migration. These problems are both NP-hard. For the leader problem, a central heuristic based on the Viterbi algorithm suggested. The follower problem is converted into two convex sub-problems. The validity of the proposed algorithms is proved via extensive numerical results.
Robust low-delay Streaming PIR using convolutional codes
Venue: arXiv cs.IT 2019
Year: 2019

In this paper we investigate the design of a low-delay robust streaming PIR scheme on coded data that is resilient to unresponsive or slow servers and can privately retrieve streaming data in a sequential fashion subject to a fixed decoding delay. We present a scheme based on convolutional codes and the star product and assume no collusion between servers. In particular we propose the use of convolutional codes that have the maximum distance increase, called Maximum Distance Profile (MDP). We show that the proposed scheme can deal with many different erasure patterns.
A Failure of Aspect Sentiment Classifiers and an Adaptive Re-weighting Solution
Venue: arXiv cs.CL 2019
Year: 2019

Aspect-based sentiment classification (ASC) is an important task in fine-grained sentiment analysis.~Deep supervised ASC approaches typically model this task as a pair-wise classification task that takes an aspect and a sentence containing the aspect and outputs the polarity of the aspect in that sentence. However, we discovered that many existing approaches fail to learn an effective ASC classifier but more like a sentence-level sentiment classifier because they have difficulty to handle sentences with different polarities for different aspects.~This paper first demonstrates this problem using several state-of-the-art ASC models. It then proposes a novel and general adaptive re-weighting (ARW) scheme to adjust the training to dramatically improve ASC for such complex sentences. Experimental results show that the proposed framework is effective \footnote{The dataset and code are available at \url{https://github.com/howardhsu/ASC_failure}.}.
Combinatorial list-decoding of Reed-Solomon codes beyond the Johnson radius
Venue: arXiv cs.IT 2019
Year: 2019

List-decoding of Reed-Solomon (RS) codes beyond the so called Johnson radius has been one of the main open questions since the work of Guruswami and Sudan. It is now known by the work of Rudra and Wootters, using techniques from high dimensional probability, that over large enough alphabets most RS codes are indeed list-decodable beyond this radius. In this paper we take a more combinatorial approach which allows us to determine the precise relation (up to the exact constant) between the decoding radius and the list size. We prove a generalized Singleton bound for a given list size, and conjecture that the bound is tight for most RS codes over large enough finite fields. We also show that the conjecture holds true for list sizes $2 \text{ and }3$, and as a by product show that most RS codes with a rate of at least $1/9$ are list-decodable beyond the Johnson radius. Lastly, we give the first explicit construction of such RS codes. The main tools used in the proof are a new type of linear dependency between codewords of a code that are contained in a small Hamming ball, and the notion of cycle space from Graph Theory. Both of them have not been used before in the context of list-decoding.
Discrete Argument Representation Learning for Interactive Argument Pair Identification
Venue: arXiv cs.CL 2019
Year: 2019

In this paper, we focus on extracting interactive argument pairs from two posts with opposite stances to a certain topic. Considering opinions are exchanged from different perspectives of the discussing topic, we study the discrete representations for arguments to capture varying aspects in argumentation languages (e.g., the debate focus and the participant behavior). Moreover, we utilize hierarchical structure to model post-wise information incorporating contextual knowledge. Experimental results on the large-scale dataset collected from CMV show that our proposed framework can significantly outperform the competitive baselines. Further analyses reveal why our model yields superior performance and prove the usefulness of our learned representations.
Sparse Lifting of Dense Vectors: Unifying Word and Sentence Representations
Venue: arXiv cs.CL 2019
Year: 2019

As the first step in automated natural language processing, representing words and sentences is of central importance and has attracted significant research attention. Different approaches, from the early one-hot and bag-of-words representation to more recent distributional dense and sparse representations, were proposed. Despite the successful results that have been achieved, such vectors tend to consist of uninterpretable components and face nontrivial challenge in both memory and computational requirement in practical applications. In this paper, we designed a novel representation model that projects dense word vectors into a higher dimensional space and favors a highly sparse and binary representation of word vectors with potentially interpretable components, while trying to maintain pairwise inner products between original vectors as much as possible. Computationally, our model is relaxed as a symmetric non-negative matrix factorization problem which admits a fast yet effective solution. In a series of empirical evaluations, the proposed model exhibited consistent improvement and high potential in practical applications.
A Deep Gradient Boosting Network for Optic Disc and Cup Segmentation
Venue: arXiv cs.CV 2019
Year: 2019

Segmentation of optic disc (OD) and optic cup (OC) is critical in automated fundus image analysis system. Existing state-of-the-arts focus on designing deep neural networks with one or multiple dense prediction branches. Such kind of designs ignore connections among prediction branches and their learning capacity is limited. To build connections among prediction branches, this paper introduces gradient boosting framework to deep classification model and proposes a gradient boosting network called BoostNet. Specifically, deformable side-output unit and aggregation unit with deep supervisions are proposed to learn base functions and expansion coefficients in gradient boosting framework. By stacking aggregation units in a deep-to-shallow manner, models' performances are gradually boosted along deep to shallow stages. BoostNet achieves superior results to existing deep OD and OC segmentation networks on the public dataset ORIGA.
Study of Constrained Network Structures for WGANs on Numeric Data Generation
Venue: arXiv cs.LG 2019
Year: 2019

Some recent studies have suggested using GANs for numeric data generation such as to generate data for completing the imbalanced numeric data. Considering the significant difference between the dimensions of the numeric data and images, as well as the strong correlations between features of numeric data, the conventional GANs normally face an overfitting problem, consequently leads to an ill-conditioning problem in generating numeric and structured data. This paper studies the constrained network structures between generator G and discriminator D in WGAN, designs several structures including isomorphic, mirror and self-symmetric structures. We evaluates the performances of the constrained WGANs in data augmentations, taking the non-constrained GANs and WGANs as the baselines. Experiments prove the constrained structures have been improved in 17/20 groups of experiments. In twenty experiments on four UCI Machine Learning Repository datasets, Australian Credit Approval data, German Credit data, Pima Indians Diabetes data and SPECT heart data facing five conventional classifiers. Especially, Isomorphic WGAN is the best in 15/20 experiments. Finally, we theoretically proves that the effectiveness of constrained structures by the directed graphic model (DGM) analysis.
Detecting Point Outliers Using Prune-based Outlier Factor (PLOF)
Venue: arXiv cs.LG 2019
Year: 2019

Outlier detection (also known as anomaly detection or deviation detection) is a process of detecting data points in which their patterns deviate significantly from others. It is common to have outliers in industry applications, which could be generated by different causes such as human error, fraudulent activities, or system failure. Recently, density-based methods have shown promising results, particularly among which Local Outlier Factor (LOF) is arguably dominating. However, one of the major drawbacks of LOF is that it is computationally expensive. Motivated by the mentioned problem, this research presents a novel pruning-based procedure in which the execution time of LOF is reduced while the performance is maintained. A novel Prune-based Local Outlier Factor (PLOF) approach is proposed, in which prior to employing LOF, outlierness of each data instance is measured. Next, based on a threshold, data instances that require further investigation are separated and LOF score is only computed for these points. Extensive experiments have been conducted and results are promising. Comparison experiments with the original LOF and two state-of-the-art variants of LOF have shown that PLOF produces higher accuracy and precision while reducing execution time.
Joint Ranking SVM and Binary Relevance with Robust Low-Rank Learning for Multi-Label Classification
Venue: arXiv cs.LG 2019
Year: 2019

Multi-label classification studies the task where each example belongs to multiple labels simultaneously. As a representative method, Ranking Support Vector Machine (Rank-SVM) aims to minimize the Ranking Loss and can also mitigate the negative influence of the class-imbalance issue. However, due to its stacking-style way for thresholding, it may suffer error accumulation and thus reduces the final classification performance. Binary Relevance (BR) is another typical method, which aims to minimize the Hamming Loss and only needs one-step learning. Nevertheless, it might have the class-imbalance issue and does not take into account label correlations. To address the above issues, we propose a novel multi-label classification model, which joints Ranking support vector machine and Binary Relevance with robust Low-rank learning (RBRL). RBRL inherits the ranking loss minimization advantages of Rank-SVM, and thus overcomes the disadvantages of BR suffering the class-imbalance issue and ignoring the label correlations. Meanwhile, it utilizes the hamming loss minimization and one-step learning advantages of BR, and thus tackles the disadvantages of Rank-SVM including another thresholding learning step. Besides, a low-rank constraint is utilized to further exploit high-order label correlations under the assumption of low dimensional label space. Furthermore, to achieve nonlinear multi-label classifiers, we derive the kernelization RBRL. Two accelerated proximal gradient methods (APG) are used to solve the optimization problems efficiently. Extensive comparative experiments with several state-of-the-art methods illustrate a highly competitive or superior performance of our method RBRL.
One Network to Segment Them All: A General, Lightweight System for Accurate 3D Medical Image Segmentation
Venue: arXiv cs.LG 2019
Year: 2019

Many recent medical segmentation systems rely on powerful deep learning models to solve highly specific tasks. To maximize performance, it is standard practice to evaluate numerous pipelines with varying model topologies, optimization parameters, pre- & postprocessing steps, and even model cascades. It is often not clear how the resulting pipeline transfers to different tasks. We propose a simple and thoroughly evaluated deep learning framework for segmentation of arbitrary medical image volumes. The system requires no task-specific information, no human interaction and is based on a fixed model topology and a fixed hyperparameter set, eliminating the process of model selection and its inherent tendency to cause method-level over-fitting. The system is available in open source and does not require deep learning expertise to use. Without task-specific modifications, the system performed better than or similar to highly specialized deep learning methods across 3 separate segmentation tasks. In addition, it ranked 5-th and 6-th in the first and second round of the 2018 Medical Segmentation Decathlon comprising another 10 tasks. The system relies on multi-planar data augmentation which facilitates the application of a single 2D architecture based on the familiar U-Net. Multi-planar training combines the parameter efficiency of a 2D fully convolutional neural network with a systematic train- and test-time augmentation scheme, which allows the 2D model to learn a representation of the 3D image volume that fosters generalization.
Efficient Multi-robot Exploration via Multi-head Attention-based Cooperation Strategy
Venue: arXiv cs.AI 2019
Year: 2019

The goal of coordinated multi-robot exploration tasks is to employ a team of autonomous robots to explore an unknown environment as quickly as possible. Compared with human-designed methods, which began with heuristic and rule-based approaches, learning-based methods enable individual robots to learn sophisticated and hard-to-design cooperation strategies through deep reinforcement learning technologies. However, in decentralized multi-robot exploration tasks, learning-based algorithms are still far from being universally applicable to the continuous space due to the difficulties associated with area calculation and reward function designing; moreover, existing learning-based methods encounter problems when attempting to balance the historical trajectory issue and target area conflict problem. Furthermore, the scalability of these methods to a large number of agents is poor because of the exponential explosion problem of state space. Accordingly, this paper proposes a novel approach - Multi-head Attention-based Multi-robot Exploration in Continuous Space (MAMECS) - aimed at reducing the state space and automatically learning the cooperation strategies required for decentralized multi-robot exploration tasks in continuous space. Computational geometry knowledge is applied to describe the environment in continuous space and to design an improved reward function to ensure a superior exploration rate. Moreover, the multi-head attention mechanism employed helps to solve the historical trajectory issue in the decentralized multi-robot exploration task, as well as to reduce the quadratic increase of action space.
Boost Your CotS IEEE 802.15.4 Network with Inter-Slot Interference Cancellation for Industrial IoT
Venue: arXiv cs.NI 2019
Year: 2019

The current cellular standardization for 5G is working towards wireless advances to enable further productivity for industrial automation. However, this development will take several years. Meanwhile, the capabilities of the currently available standards should be pushed to their limits. To this end, in this work, we present results from the first inter-slot successive interference cancellation testbed using commercial off the shelf IEEE 802.15.4 sensors. Through our implementation, we have measured a throughput of $0.72$ packets per slot which doubles the currently used contention-based access, Slotted ALOHA, with a limit of $0.36$ packets per slot. The hardware effects of the boards, which degrade the successive interference cancellation performance from the theoretical limit of $1$ packet per slot, are modeled and validated through measurements. We also propose a model that can be used to calculate the expected successive interference cancellation throughput with the specific hardware available in a factory. Furthermore, our proposed model should replace the perfect physical layer assumptions for researchers to design new MAC algorithms taking practical limitations into account.
Lyndon words versus inverse Lyndon words: queries on suffixes and bordered words
Venue: arXiv cs.FL 2019
Year: 2019

Lyndon words have been largely investigated and showned to be a useful tool to prove interesting combinatorial properties of words. In this paper we state new properties of both Lyndon and inverse Lyndon factorizations of a word $w$, with the aim of exploring their use in some classical queries on $w$. The main property we prove is related to a classical query on words. We prove that there are relations between the length of the longest common extension (or longest common prefix) $lcp(x,y)$ of two different suffixes $x,y$ of a word $w$ and the maximum length $\mathcal{M}$ of two consecutive factors of the inverse Lyndon factorization of $w$. More precisely, $\mathcal{M}$ is an upper bound on the length of $lcp(x,y)$. This result is in some sense stronger than the compatibility property, proved by Mantaci, Restivo, Rosone and Sciortino for the Lyndon factorization and here for the inverse Lyndon factorization. Roughly, the compatibility property allows us to extend the mutual order between local suffixes of (inverse) Lyndon factors to the suffixes of the whole word. A main tool used in the proof of the above results is a property that we state for factors $m_i$ with nonempty borders in an inverse Lyndon factorization: a nonempty border of $m_i$ cannot be a prefix of the next factor $m_{i+1}$. The last property we prove shows that if two words share a common overlap, then their Lyndon factorizations can be used to capture the common overlap of the two words. The above results open to the study of new applications of Lyndon words and inverse Lyndon words in the field of string comparison.
On Online Learning in Kernelized Markov Decision Processes
Venue: arXiv cs.LG 2019
Year: 2019

We develop algorithms with low regret for learning episodic Markov decision processes based on kernel approximation techniques. The algorithms are based on both the Upper Confidence Bound (UCB) as well as Posterior or Thompson Sampling (PSRL) philosophies, and work in the general setting of continuous state and action spaces when the true unknown transition dynamics are assumed to have smoothness induced by an appropriate Reproducing Kernel Hilbert Space (RKHS).