Reviewerly/exHarmony huggingface dataset:
Multi-Type Textual Reasoning for Product-Aware Answer Generation

By reading reviews and product attributes, e-commerce question-answering task aims to automatically generate natural-sounding answers for product-related questions. Existing methods, however, typically assume that each review and each product attribute are semantically independent, ignoring the relation among all these multi-type texts. In this paper, we propose a review-attribute heterogeneous graph neural network (abbreviated as RAHGNN) to model the logical relation of all multi-type text. RAHGNN consists of four components: a review-attribute heterogeneous graph constructor, a question-aware input encoder, a heterogeneous graph relation analyzer, and a context-based answer decoder. Specifically, after constructing the heterogeneous graph with reviews and product attributes, we derive the initial representation of each review node and attribute node based on question attention network and key-value memory network respectively. RAHGNN analyzes the relation according to the subgraph structure and subgraph semantic meaning using node-level attention and semantic-level attention. Finally, the answer is generated by the recurrent neural network with the relation representation as context input. Extensive experimental results on a large-scale real-world e-commerce dataset not only show the superior performance of RAHGNN over state-of-the-art baselines, but also demonstrate its potentially good interpretability for multi-type text relation in product-aware answer generation.
A Data-Driven Dynamic Discretization Framework to Solve Combinatorial Problems Using Continuous Metaheuristics

Combinatorial optimization problems are very common in the real world but difficult to solve. Among the promising algorithms that have been successful in solving these problems are metaheuristics. The two basic search behaviors used in metaheuristics are exploration and exploitation, and the success of metaheuristic search largely depends on the balance of these two behaviors. Machine learning techniques have provided considerable support to improve data-driven optimization algorithms. One of the techniques that stands out is Q-Learning, which is a reinforcement learning technique that penalizes or rewards actions according to the consequence it entails. In this work, a general discretization framework is proposed where Q-Learning can adapt a continuous metaheuristic to work in discrete domains. In particular, we use Q-learning so that the algorithm learns an optimal binarization schemEqe selection policy. The policy is dynamically updated based on the performance of the binarization schemes in each iteration. Preliminary experiments using our framework with sine cosine algorithm show that the proposal presents promising results compared to other algorithms.
Surface Normals and Light Directions from Shading and Polarization

We introduce a method of recovering the shape of a smooth dielectric object using diffuse polarization images taken with different directional light sources. We present two constraints on shading and polarization and use both in a single optimization scheme. This integration is motivated by photometric stereo and polarization-based methods having complementary abilities. Polarization gives strong cues for the surface orientation and refractive index, which are independent of the light direction. However, employing polarization leads to ambiguities in selecting between two ambiguous choices of the surface orientation, in the relationship between the refractive index and zenith angle (observing angle). Moreover, polarization-based methods for surface points with small zenith angles perform poorly owing to the weak polarization. In contrast, the photometric stereo method with multiple light sources disambiguates the surface normals and gives a strong relationship between surface normals and light directions. However, the method has limited performance for large zenith angles and refractive index estimation and faces strong ambiguity when light directions are unknown. Taking the advantages of these methods, our proposed method recovers surface normals for small and large zenith angles, light directions, and refractive indexes of the object. The proposed method is positively evaluated in simulations and real-world experiments.
Behavioral Biometrics for Adaptive Authentication in Digital Banking - Guard Against Flawless Privacy

This study throws light on the usage of Behavioral Biometrics in Digital banking arena. In current scenario more of physiological biometric modalities are being used in Digital Banking in comparison to psychological/ behavioral modalities which are difficult to record or mimic making behavioral biometrics more secure against replay attacks. This study suggests the usage of mobile screen swipe and touch data for user verification. The experiments were performed using publicly available UMDAA02 mobile swipe data set. The results of the study present a fine-tuned feature set for a swipe-based authentication system for mobile devices used for Digital Banking. It has been observed that with the suggested feature set, K-NN outperforms Naïve bays and SVM algorithms with the best EER of 14%. With the kind of performance shown by K-NN this study strongly suggests, swipe-based authentication system that can be used as a secondary layer of security in the digital banking system.
Hard disk drive failure prediction using SMART attribute

Disk failures cause data loss and system malfunctions have various severe impacts on the performance and availability of data centers. In data center environments, failures of hard disk drive (HDD) are a very costly & rare occurrence. Therefore, to reduce the rate of failures of this occurrence, different vendors of hard disk as performed a cost saving measure. For the prediction of hard drive failure there are different attributes such as Self-Monitoring and reporting Technology (SMART) collected during normal operations by different HDD manufacturing companies. Now days by using simply threshold values prediction of disk drives failure is done by using SMART attributes. Replacement of healthy disks is very expensive for high false positive rate results. HDD health statistics can be representing by SMART attributes such as the number of command timeout, scan errors and reallocation counts of a Hard disk. The goal of this study is to apply Machine Learning techniques to accurately and proactively predict hard disk drive failures because Machine Learning techniques are best way to solve the learning and rare event-based problem. In evaluating how a method produces the highest accuracy model, we implemented Random Forest algorithms, Decision tree & Naive Bayes algorithm.
A Multi-Objective Workflow Allocation Strategyin IaaS Cloud Environment

IaaS Cloud provides infrastructural serviceon rental basis on demand by pooling of dynamically provisioned heterogeneous resources to cater the user's workflow applications. Workflows occur in business and industrial applications such as project management, supply chain management, molecular structure, blockchain, business process and require large distributed resource requirement. Workflow mapping while meeting the user's requirements in cloud environment has been proven as NP-Hard. In this work, we proposed a multi-objective workflow allocation (MOWA) strategy cloud to minimize makespan and flowtime simultaneously for IaaS. Level attributes has been taken to preserve precedence constraints. An experimental study has been conducted in MATLAB by producing a set of tradeoff solutions for MOWA and MOHEFT. Further, Pareto front performance measures of the tradeoff solutions are computed and compared. Study shows that the achieved solutions for MOWA are better Pareto-optimal solutions.
Lightweight Density Map Architecture for UAVs Safe Landing in Crowded Areas

In the context of crowd counting, most of the works have focused on improving the accuracy without regard to the performance leading to algorithms that are not suitable for embedded applications. In this paper, we propose a lightweight convolutional neural network architecture to perform crowd detection and counting using fewer computer resources without a significant loss on count accuracy. The architecture was trained using the Bayes loss function to further improve its accuracy and then pruned to further reduce the computational resources used. The proposed architecture was tested over the USF-QNRF achieving a competitive Mean Average Error of 154.07 and a superior Mean Square Error of 241.77 while maintaining a competitive number of parameters of 0.067 Million. The obtained results suggest that the Bayes loss can be used with other architectures to further improve them and also the last convolutional layer provides no significant information and even encourage over-fitting at training.
Algorithms for all‐pairs Hamming distance based similarity

Abstract All‐pairs distance computation for a collection of strings is a computation‐intensive task with important applications in bioinformatics, in particular, in distance‐based phylogenetic analysis techniques. Even if the computationally efficient Hamming distance is used for this purpose, the quadratic number of sequence pairs may be challenging. We propose a number of practical algorithms for efficient pairwise Hamming distance computation under a given distance threshold. The techniques are based on such concepts as pivot‐based similarity search in metric spaces, pigeonhole principle for approximate string matching, cache‐friendly data arrangement, bit‐parallelism, and others. We experimentally show that our solutions are often about an order of magnitude faster than the average‐case linear‐time LCP based clusters method proposed recently, both in real and synthetic benchmarks.
Self-Organizing Maps for identification of zeolitic diagenesis patterns in closed hydrologic systems on the Earth and its implications for Mars

We have conducted a survey of zeolite occurrences in saline-alkaline paleolake deposits on Earth to identify the most prominent zeolite alteration patterns and to characterize the most common authigenic minerals and their paragenetic relationships. We collected the bulk mineral assemblages (from previous and our studies) as identified by X-ray diffraction from zeolitic tuff beds and associated sedimentary beds from thirteen paleolake deposits from the USA, Mexico, Greece, and Tanzania. We applied the Kohonen Self-Organizing Maps (SOM) to look for interesting patterns in the tuff bed mineral assemblages without prescribing any specific interpretation, and for information reduction and classification. Decision Tree (DT) method was applied to characterize these clusters. We were able to define clear class boundaries between fresh glass, non-analcime zeolites, analcime, and K feldspar. The non-analcime zeolites were further grouped into several classes based on mineral type. We also discuss the potential implications for Mars, showing that the mineral assemblages of diagenetic facies identified by SOM and DT can be used to test or validate the orbital, in situ, or modeling results, while the trained SOM provides a robust generalized ability to classify the new mineral assemblage data into the most common diagenetic facies identified in saline-alkaline paleoenvironments that contain zeolites. The study concludes that generalizing the complex geochemical behaviors using unsupervised statistical learning methods can help to identify the most prominent geochemical behaviors.
LSTM vs. GRU for Arabic Machine Translation

The same Machine Translation (MT) approach may not work for European languages as for Arabic, because of its structure. MT based on Neural Networks methods has recently become an alternative approach to the statistical MT. In this paper, a case study is presented on how different sequence to sequence Deep Learning (DL) models perform in the task of Arabic MT. A comprehensive comparison between these models based mainly on: Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional LSTM (BiLSTM) and Bidirectional GRU (BiGRU) is presented. Specifically, each input sequence will be translated into English one using an Encoder-Decoder model based on the four architectures with an attention mechanism. Furthermore, we study the impact of different preprocessing techniques on Arabic MT.
CoResident Evil: Covert Communication In The Cloud With Lambdas

“Serverless” cloud services, such as AWS lambdas, are one of the fastest growing segments of the cloud services market. These services are popular in part due to their light-weight nature and flexibility in scheduling and cost, however the security issues associated with serverless computing are not well understood. In this work, we explore the feasibility of constructing a practical covert channel from lambdas. We establish that a fast co-residence detection for lambdas is key to enabling such a covert channel, and proceed to develop a reliable and scalable co-residence detector based on the memory bus hardware. Our technique enables dynamic discovery for co-resident lambdas and is incredibly fast, executing in a matter of seconds. We evaluate our approach for correctness and scalability, and use it to establish covert channels and perform data transfer on AWS lambdas. We show that we can establish hundreds of individual covert channels for every 1000 lambdas deployed, and each of those channels can send data at a rate of 00 bits per second, thus demonstrating that covert communication via lambdas is entirely feasible.
An Efficient Data Aggregation Scheme with Local Differential Privacy in Smart Grid

Smart grid achieves reliable, efficient and flexible grid data processing by integrating traditional power grid with information and communication technology. The control center can evaluate the supply and demand of the power grid through aggregated data of users, and then dynamically adjust the power supply, price of the power, etc. However, since the grid data collected from users may disclose the user's electricity using habits and daily activities, the privacy concern has become a critical issue. Most of the existing privacy-preserving data collection schemes for smart grid adopt homomorphic encryption or randomization techniques which are either impractical because of the high computation overhead or unrealistic for requiring the trusted third party. In this paper, we propose a privacy-preserving smart grid data aggregation scheme satisfying local differential privacy (LDP) based on randomized response. Our scheme can achieve efficient and practical estimation of the statistics of power supply and demand while preserving any individual participant's privacy. The performance analysis shows that our scheme is efficient in terms of computation and communication overhead.
Adapting to Context-Aware Knowledge in Natural Conversation for Multi-Turn Response Selection

Virtual assistants aim to build a human-like conversational agent. However, current human-machine conversations still cannot make users feel intelligent enough to build a continued dialog over time. Some responses from agents are usually inconsistent, uninformative, less-engaging and even memoryless. In recent years, most researchers have tried to employ conversation context and external knowledge, e.g. wiki pages and knowledge graphs, into the model which only focuses on solving some special conversation problems in local perspectives. Few researchers are dedicated to the whole capability of the conversational agent which is endowed with abilities of not only passively reacting the conversation but also proactively leading the conversation.
Where are you taking me?Understanding Abusive Traffic Distribution Systems

Illicit website owners frequently rely on traffic distribution systems (TDSs) operated by less-than-scrupulous advertising networks to acquire user traffic. While researchers have described a number of case studies on various TDSs or the businesses they serve, we still lack an understanding of how users are differentiated in these ecosystems, how different illicit activities frequently leverage the same advertisement networks and, subsequently, the same malicious advertisers. We design ODIN (Observatory of Dynamic Illicit ad Networks), the first system to study cloaking, user differentiation and business integration at the same time in four different types of traffic sources: typosquatting, copyright-infringing movie streaming, ad-based URL shortening, and illicit online pharmacy websites.
A CNN-based posture change detection for lactating sow in untrimmed depth videos

The dangerous body movements of lactating sows are mainly reflected in the frequency and duration of posture changes. To avoid the disturbances from the light environment and illumination variations caused by heat lamp and day-night cycle, depth videos were acquired for posture change detection. In this paper, a CNN-based method was developed to automatically detect sow posture change in untrimmed depth videos. Firstly, clip-level Detect and Classify Posture R-CNN (DCP R-CNN) was proposed for sow detection and posture classification. The DCP R-CNN decoupled the detection and classification task into two branches: detection branch and classification branch so that the detection branch could focus on the task of sow localization. Secondly, a method was proposed to temporally localize the possible posture changing segments from coarse to fine. Finally, a two-stream posture change classification network, which used two CNNs to extract the complementary features from DMM and the first and the last frames, was introduced to classify the possible posture changing segments into change or non-change. At the scene of commercial piggery, the videos were acquired at 5 frames per second (fps) by installing a Microsoft Kinect v2.0 sensor above the pen. The test set consisted of two 24-hour untrimmed depth videos and six 40-minute untrimmed depth videos. The test results showed that our method could detect the posture change correctly. The success rate of DCP R-CNN was 96.38% when the IoU threshold was 0.7, and the average precision and recall rate of 4 types of posture were 97.72% and 95.23%, respectively. The possible posture change was detected with a recall rate of 92.46% and the posture change was classified with an accuracy of 97.71%. The precision of posture change detection was 88.75%, the recall rate was 90.87%. The detection speed of our method was 5.71 fps, which was faster than the video frame rate (5 fps). Our study demonstrated the feasibility of 24-hour detection method for sow posture change.
Feature Selection Algorithm Based on Maximum Information Coefficient

Feature selection is the key technology to improve the performance of classification learning problem with a large feature size. In this paper, maximum information coefficient (MIC) is introduced to measure the correlation between each feature and the decision. We design a feature selection algorithm based on combining maximum information coefficient (MIC) and classification learning model. Firstly, the maximum information coefficient (MIC) is calculated to get the correlation degree between each feature and the decision. Furthermore, according to the importance, the feature ranking results are obtained. Secondly, combining with the specific classification learning model, a feature selection method based on the maximum information coefficient (MIC) is constructed. Taking the classification accuracy as the estimation basis, the optimal feature subset is selected. Then, we employ the proposed algorithm to a real-world classification problem. Experimental results show that the feature subset generated by the proposed method can effectively select sensitive features, reduce the dimension of feature space, and achieve higher classification accuracy.
A Neural Few-Shot Text Classification Reality Check

Modern classification models tend to struggle when the amount of annotated data is scarce.To overcome this issue, several neural fewshot classification models have emerged, yielding significant progress over time, both in Computer Vision and Natural Language Processing.In the latter, such models used to rely on fixed word embeddings before the advent of transformers.Additionally, some models used in Computer Vision are yet to be tested in NLP applications.In this paper, we compare all these models, first adapting those made in the field of image processing to NLP, and second providing them access to transformers.We then test these models equipped with the same transformer-based encoder on the intent detection task, known for having a large number of classes.Our results reveal that while methods perform almost equally on the ARSC dataset, this is not the case for the Intent Detection task, where the most recent and supposedly best competitors perform worse than older and simpler ones (while all are given access to transformers).We also show that a simple baseline is surprisingly strong.All the new developed models, as well as the evaluation framework, are made publicly available 1 .
A Pattern and POS Auto-Learning Method for Terminology Extraction from Scientific Text

A lot of new scientific documents are being published on various platforms every day. It is more and more imperative to quickly and efficiently discover new words and meanings from these documents. However, most of the related works rely on labeled data, and it is quite difficult to deal with unlabeled new documents efficiently. For this, we have introduced an unsupervised method based on sentence patterns and part of speech (POS) sequences. Our method just needs a few initial learnable patterns to obtain the initial terminology tokens and their POS sequences. In this process, new patterns are constructed and can match more sentences to find more POS sequences of terminology. Finally, we use obtained POS sequences and sentence patterns to extract terminology terms in new scientific text. Experiments on paper abstracts from Web of Knowledge show that this method is practical and can achieve a good performance on our test data.
On the Power of Relaxed Local Decoding Algorithms

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 18 December 2019Accepted: 05 January 2021Published online: 19 April 2021Keywordslocally decodable codes, lower bounds, local algorithmsAMS Subject Headings68Q25, 68R10, 68U05Publication DataISSN (print): 0097-5397ISSN (online): 1095-7111Publisher: Society for Industrial and Applied MathematicsCODEN: smjcat
Forged Face Detection using ELA and Deep Learning Techniques

The paper presents real and fake facial image recognition using Deep learning and CNN. Image forgery recognition becomes a difficult task to find the authenticity of an image with the naked eye. This research aims to evaluate the working of different deep learning techniques in the novel “Real and Fake Face detection” dataset by Computational Intelligence Photography Lab, Yonsei University. For the detection of forged faces, the first step of the proposed method is image normalization for real and fake image recognition. Normalized images are then preprocessed using Error Level Analysis (ELA) and train to different pre-trained deep learning models. We finetune these models for categorization of 2 classes that are forged and real to evaluate these models' performance. From all tested models, VGG models give the best training accuracy of 91.97% and 92.09% on VGG-16 and VGG-19, whereas VGG-16 shows the good test set Accuracy using a smaller number of epochs, which is competitively better than all other techniques. Results of these models were evaluated using confusion matrix evaluation measures and compared with state of the art techniques.