Miracle-dz/UniFAR-FTUs huggingface dataset:
Contextual Price Features for e-Commerce Search Ranking

Motivation The price of an item is one of the most useful attributes for e-commerce search. A prominent feature used by the machine learned ranker at eBay search measures, for each query, the deviance of the price of a candidate item to be ranked from the typical price distribution of the clicked items for that query. Like most e-commerce websites, in eBay search, users can restrict their search queries to certain specific categories, where category refers to nodes in the product catalog structure. As an example, a user could search for mens shirts and restrict results to either T-Shirts or Dress Shirts. In its most simple form, the price deviance feature is calculated in a category context agnostic fashion. However, the distribution from which the query price statistics (e.g. median, variance) are drawn varies significantly for different categories for the same query. For example, the query iphone x will have very different price distribution among clicked items in the Cell Phones Smartphones category as opposed to the Cell Phone Accessories category. The motivation of this work stems from accounting for category context for user queries while deriving and leveraging such distributional statistics. The core idea is extendable to other features used in the current search ranker besides item price, such as query-title similarity measure, and also to other contexts besides category, such as structured key value aspects.Problem Statement Given a query and a set of matching items, the machine learned search ranking model uses an array of diverse features to rank items. The problem addressed in this work is to enhance one such feature capturing item price deviance from query level price statistics to be sensitive to the category context of queries. Concretely, the ranker should be able to use different price distributions for the same query depending on the category context in which the query was issued. This problem has intriguing challenges in two major areas: first, since we have a hierarchical category tree structure, we need to Figure out an effective way to propagate the price distribution across the tree; second, selecting the right statistic to measure the price deviation of candidate items from the typical price distribution for the query.
Optimizing NFV Chain Deployment in Software-Defined Cellular Core

Today’s cellular core relies on a few expensive and dedicated hardware racks to connect the radio access network and the egress point to the Internet, which are geographically placed at fixed locations and use the specific routing policies. This inelastic architecture fundamentally leads to increased capital and operating expenses, poor application performance and slow evolution. The emerging paradigm of Network Function Virtualization (NFV) and Software Defined Networking (SDN) bring new opportunities for cellular networks, which makes it possible to flexibly deploy service chains on commodity servers and fine-grained control the routing policies in a centralized way. We present a two-stage optimization framework Plutus. The network-level optimization aims to minimize the service chain deployment cost, while the server-level optimization requires to determine which Virtualized Network Function (VNF) should be deployed onto which CPU core to balance the CPU processing capability. We formulate these two problems as two optimization programs and prove their hardness. Based on parallel multi-block ADMM, we propose a $(\delta, 2)$ -bicriteria approximation algorithm and a learning-based algorithm to address two cases whether the flow information and the resource consumption can be known as a priori, respectively. Large-scale simulations and DPDK-based OpenNetVM platform show that Plutus can reduce the capital cost by 84% and increase the throughput by 36% on average.
Study of Dynamic Tracking Algorithms for Apples Under the Influence of Oscillation

Influenced by the force of the wind and agricultural operations, fruits often undergo oscillation, which makes it difficult to automatically monitor their growing status. It is very important to realize dynamic tracking of these oscillating fruits in order to improve automatic monitoring systems and the efficiency of picking robots. In order to investigate the accuracy of the tracking of oscillating fruits, three classic tracking algorithms were adopted and compared: the kernelized correlation filter algorithm (KCF), the compressive tracking algorithm (CT), and the multi-task tracking algorithm (MTT). The effectiveness of these algorithms was verified by testing six video sequences acquired in different environments, and three indices (the average central error, frame loss rate, and time efficiency) were used to verify their performance. The results showed that the KCF algorithm was most appropriate for the tracking of oscillating fruit objects, as it has a lower centering error and a much higher frame rate.
Optimization-Based Calibration Process for Position-Sensitive Detector Systems

In this paper, a calibration process for position-sensitive detector (PSD) systems is proposed. In a PSD system using a quadrature detector (QD), various error sources including the QD, circuit components, and mechanical misalignment that appear in actual implementation introduce inaccuracies in the beam position estimation. The error pattern can be non-linear in the beam power, and non-uniform in the beam position. To calibrate the PSD and enhance the estimation accuracy, this work proposes an optimization-based calibration process. The proposed calibration process is comprehensive, correcting all errors at once in the final assembly state, which can reduce the calibration time and cost. Moreover, the result of the calibration is stable with respect to variations in the beam power, gain stage, and beam position. The impressive enhancement in accuracy and stability of beam position estimation is demonstrated experimentally.
Segmented Spherical Projection-Based Blind Omnidirectional Image Quality Assessment

In contrast with traditional images, omnidirectional image (OI) has a higher resolution and provides the user with an interactive wide field of view. OI with equirectangular projection (ERP) format, as the default for encoding and transmitting omnidirectional visual contents, is not suitable for quality assessment of OI because of serious geometric distortion in the bipolar regions, especially for blind image quality assessment. In this paper, a segmented spherical projection (SSP) based blind omnidirectional image quality assessment (SSP-BOIQA) method is proposed. The OI with ERP format is first converted into that with SSP format, so as to solve the problem of stretching distortion in the bipolar regions of ERP format, but retain the equatorial region of ERP format. On the one hand, considering that the bipolar regions of the SSP format are circular, a local/global perceptual features extraction scheme with fan-shaped window is proposed for estimating the distortion in the bipolar regions of OI. On the other hand, the perceptual features of the equatorial region are extracted with heat map as weighting factor to reflect users’ visual behavior. Then, the features extracted from the OI’s bipolar and equatorial regions are pooled to predict the quality of distorted OIs. The experiments on two databases, namely CVIQD2018 and MVAQD databases, demonstrate that the proposed SSP-BOIQA method outperforms the state-of-the-art blind quality assessment methods, and is more consistent with human visual perception.
The impact of preprocessing in natural language for open source intelligence and criminal investigation

Underground forums serves as gathering place for like-minded cyber criminals and are an continued threat to law and order. Law enforcement agencies can use Open-Source Intelligence (OSINT) to gather valuable information to proactively counter existing and new threats. For example, by shifting criminal investigation’s focus onto certain cyber criminals with large impact in underground forums and related criminal business models. This paper presents our study on text preprocessing requirements and document construction for the topic model algorithm Latent Dirichlet Allocation (LDA). We identify a set of preprocessing requirements based on literature review and demonstrate them on a real-world forum, similar to those used by cyber criminals. Our result show that topic modelling processes needs to follow a very strict procedure to provide significant result that can be useful in OSINT. Additionally, more reliable results are produced by tuning the hyper-parameters and the number of topics for LDA. We demonstrate improved results by iterative preprocessing to continuously improve the model, which provide more coherent and focused topics.
Malicious URL Linkage Analysis and Common Pattern Discovery

Malicious domain names are consistently changing. It is challenging to keep blacklists of malicious domain names up-to-date because of the time lag between its creation and detection. Even if a website is clean itself, it does not necessarily mean that it won’t be used as a pivot point to redirect users to malicious destinations. To address this issue, this paper demonstrates how to use linkage analysis and open-source threat intelligence to visualize the relationship of malicious domain names whilst verifying their categories, i.e., drive-by download, unwanted software etc. Featured by a graph-based model that could present the inter-connectivity of malicious domain names in a dynamic fashion, the proposed approach proved to be helpful for revealing the group patterns of different kinds of malicious domain names. When applied to analyze a blacklisted set of URLs in a real enterprise network, it showed better effectiveness than traditional methods and yielded a clearer view of the common patterns in the data.
Spectral Analysis of User Interests for Experimental Verification of the Oscillation Model for OSNs

Recent development of online social networks (OSNs) has accelerated communication between individuals and has led to efficient activities in the real world. However, activities on OSNs does not necessarily have positive impact on the real world. For example, explosive user dynamics such as flaming on OSNs seriously impacts not only on online communities but also on social activities in the real world. Therefore, to prevent the flaming, we need to understand the cause of explosive user dynamics. We have proposed to describe user dynamics in OSNs by the wave equation on networks which called the oscillation model of OSNs [1]. Although this model was motivated from a purely theoretical framework, it gives natural extension of the conventional node centrality measures [2], [3] and enables us to understand the cause of explosive user dynamics [1], [4]. However, its experimental verification is not sufficiently conducted. This paper discuss the validity of the oscillation model by analyzing real data.
Demo Paper: Large Scale Spatial Data Processing With User Defined Filters In BBoxDB

BBoxDB is a distributed key-bounding-box-value store which is capable of handling large scale $n-$dimensional data. Unlike existing key-value stores, each value is stored together with a bounding box which describes the location of the value in an $n-$dimensional space. BBoxDB splits large datasets automatically and spreads them across a cluster of nodes. The software works primarily on the bounding boxes of the stored data; operations like range queries or joins take only the bounding boxes of the data into consideration. In version 0.9. 1, we implemented support for user defined filters (UDF) in BBoxDB; UDFs operate on the real values of the data. In this paper, we describe this novel feature for the first time. As an example, a UDF is developed in this paper which is capable of handling GeoJSON encoded data. The UDF consists only of a few lines of code and turns the generic distributed datastore BBoxDB into a specialized system that is capable of processing large scale GeoJSON encoded datasets. Operations like spatial joins or range queries on the real geometries of the data become possible. During our demonstration, we show the implementation of the UDF and perform range queries and spatial joins. The used dataset contains spatial data of the whole world and is obtained from the Open Street Map project. For visualization, the GUI of BBoxDB was extended in a way that spatial queries can easily be executed. Using the GUI, query results can be interactively explored; they are shown as an overlay on a map dynamically fetched from the Open Street Map project.
Normalisation of SWIFT Message Counterparties with Feature Extraction and Clustering

Short text clustering is a known use case in the text analytics community. When the structure and content falls in the natural language domain e.g. Twitter posts or instant messages, then natural language techniques can be used, provided texts are of sufficient length to allow for use of (pre)trained models to extract meaningful information, such as part-of-speech or topic annotations. However, natural language models are not suitable for clustering transaction counterparties, as they are found in bank payment messaging systems, such as SWIFT. The manually typed tags are typically physical or legal entity details, which lack sentence structure, while containing all the variations and noise that manual entry introduces. This leaves a gap in an investigator or counter-fraud professional’s toolset when looking to augment their knowledge of payment flow originator and beneficiary entities and trace funds and assets. A gap that vendors traditionally try to close with fuzzy matching tools. With these considerations in mind, we are proposing a hybrid string similarity, topic modelling, hierarchical clustering and rule-based pipeline to facilitate clustering of transaction counterparties, also catering for unknown number of expected clusters. We are also devising metrics to supplement the evaluation of the approach, based on the well-known measures of precision and recall. Testing on a real-life labelled dataset demonstrates significantly improved performance over a baseline rule-based (“keyword”) approach. At the same time our approach retains most of the interpretability found in rule-based systems, as the former adds an additional level of cluster refinement to the latter. The result is a workflow for standardising populations of entities, with reduced need for manual review. In cases where only a subset of the population needs to be investigated, such as in sanctions investigations, the approach allows for better control of the risks of missing entity variations.
A Comparison of Recognition and Sensitivity in the Upper Arm and Lower Arm to Mechanotactile Stimulation

Sensory feedback is a highly researched area for upper limb prosthetics, for which the stimulation is applied to either the upper arm or lower arm with minimal justification for the chosen site. In this study, we compare the recognition of three stimulation sites and their sensitivities to the stimulation applied to the upper and lower arm sites from a mechanotactile stimulation device. Our stimulation device is a crank-based mechanotactile feedback system, which applies a combination of a pressure applied in the normal direction and tangential direction to the skin at the stimulation sites on the lower arm and upper arm. The recognition rate for six grip patterns was found to be not statistically different between the upper and lower sites. Further, through Just Noticeable Difference (JND) measurements, there was no statistical difference between the sensitivities of the upper arm and lower arm at four stimulation sites. This study contributes to the literature from the point of view of identifying the upper arm as the alternative site of mechanotactile feedback for transradial prosthetic hand users in comparison to the lower arm, which is primarily used for EMG electrodes to identify the user’s intention to control their robotic prosthetic hand.
Extracting Explainable Deep Representation for Machine Tutoring

In this work, we propose a machine tutoring framework through a modified Adversarial AutoEncoder(AAE) for Interactive Machine Learning(IML). In IML, masters can teach machines for valuable knowledge and skills and machines can transfer such knowledge and skills to novices. Generally in IML, we can discuss transferring knowledge, something to be known and skills, something needs to know and to practice to learn it well from masters to machines and from machines to novices. In this work, we focus on one part of IML techniques, transferring knowledge and skills from machines to novices, called machine tutoring and realize it, in a Taiko-drum playing game platform. In Taiko-drum playing, skill learning is considered more important than knowledge learning. In particular, we adopt Internal Measurement Unit (IMU), attached on novice’s forearms to collect the necessary data to understand novice’s motions. We assume that the IMU data can help us understand how well novices learn on some particular motions from machines and from masters eventually. On the side, we capture game scores in each novice’s play for the evaluation purpose. Four songs with different levels from easy to hard are used in the experiments. When a novice plays the game, we extract the explainable values from latent features in a deep learner for the representation of the novice’s play. The machine tutoring is operated as we show the visualization that is built given the latent representation to the novice to suggest on how we may perform a task better and better as we play more. We demonstrate the effectiveness of the proposed machine tutoring framework by using three different deep neural network structures stacked on the Autoencoder included in the framework including MLP, CNN, and LSTM. The results show that AAE-regression CNN (AAER-CNN) performs the best on latent information representation when the song level is from easy to normal, and AAER-LSTM also performs the best when the song level is difficult on two tasks namely score prediction subject classification. Afterwards, we illustrate how the representation can help on machine tutoring.
PVC Recognition for Wearable ECGs Using Modified Frequency Slice Wavelet Transform and Convolutional Neural Network

Progress in wearable techniques makes the long-term daily electrocardiogram (ECG) monitoring possible. Premature ventricular contraction (PVC) is one of the most common cardiac arrhythmias. This study proposed a method by combining the modified frequency slice wavelet transform (MFSWT) and convolutional neural network (CNN). Training data are from the 2018 China physiological signal challenge (934 PVC and 906 non-PVC recordings). The first 10-s ECG waveforms in each recording were transformed into 2-D time-frequency images (frequency range of 0-50 Hz and size of 300 × 100) using MFSWT. A 25-layer CNN structure was constructed, which includes five convolution layers with kernel size of 3×3, five dropout layers, five ReLU layers, five maximum pooling layers with kernel size of 2 × 2, a flatten layer, two fully connected layers, as well as the input and output layers. Test data were recorded from 12-lead Smart ECG vests, including 775 PVC and 742 non-PVC recordings. Results showed that, the proposed method achieved a high accuracy of 97.89% for PVC/non-PVC episodes classification, indicating that the combination of MFSWT and CNN provides new insight to accurately identify PVC from the wearable ECG recordings.
Study of real time face recognition using few shot recognition techniques

This paper aims at studying the problem of face recognition using very few examples. Face recognition is a tough problem yet is important for several practical applications. Contemporary techniques require thousands of samples to recognize them effectively. However, constructing such a dataset is not always feasible for practical applications. Therefore, we analyze the effectiveness of such techniques with few shot learning paradigms on benchmark datasets with standard evaluation metrics.
Combining Integrated Sampling with Nonlinear Manifold Detection Techniques for Software Defect Prediction

In present days, quality software has become a necessity in almost all line of work. But on the other hand, defective software system gives undesired products resulting in user dissatisfaction as well as huge hike in development and maintenance cost. That’s why, an accurate prediction of defect prone areas is needed for reducing development and maintenance cost. From various research work already undertaken in this field, it has been observed that basically due to two problems such as imbalance in class distribution and irrelevant attributes present in high dimensional datasets, the prediction performance of classification techniques are below the desired level and inaccurate. For solving these problems, a model has been proposed by integrating Sampling Methods (SM) such as Random Under Sampling method, Synthetic Minority Over Sampling Technique for random over sampling with Nonlinear Manifold Detection Techniques (Nonlinear MDTs) like ISOMAP, FASTMVU and Diffusion Maps for evaluating the impact of this integrated model on prediction performance of classifiers. The statistical results obtained after conducting Friedman test validated that integrated model SMOTE with Nonlinear MDTs used on Bayesian Belief Network classifier showed significantly different and accurate prediction performance than other methods for all high dimensional software datasets.
An Orthogonal Algorithm for Key Management in Hardware Obfuscation

The globalization of supply chain makes semiconductor chips susceptible to various security threats. Design obfuscation techniques have been widely investigated to thwart intellectual property (IP) piracy attacks. Key distribution among IP providers, system integration team, and end users remains as a challenging problem. This work proposes an orthogonal obfuscation method, which utilizes an orthogonal matrix to authenticate obfuscation keys, rather than directly examining each activation key. The proposed method hides the keys by using an orthogonal obfuscation algorithm to increasing the key retrieval time, such that the primary keys for IP cores will not be leaked. The simulation results show that the proposed method reduces the key retrieval time by 36.3% over the baseline. The proposed obfuscation methods have been successfully applied to ISCAS'89 benchmark circuits. Experimental results indicate that the orthogonal obfuscation only increases the area by 3.4% and consumes 4.7% more power than the baseline.1