Reviewerly/exHarmony huggingface dataset:
A novel hotel recommendation method based on personalized preferences and implicit relationships

On tourism websites, hotel recommendations have drawn growing attention from researchers, as they can help customers select a satisfactory hotel from many options with massive information. However, some inherent challenges exist in conventional hotel recommendations, specifically the extent to which there is considerable room for improvement in user preference models and neighbour recognition. Therefore, we propose a two-stage hotel recommendation approach that employs hotel feature information to support preference analysis. First, in the filling stage, association rules between features are considered to accurately capture users’ personalized preferences, which can be incorporated with public preferences to estimate potential ratings of users for unvisited hotels. Then, in the recommendation stage, we combine rating similarities between users with their closeness relationships to identify more reliable neighbours. Finally, a hotel recommendation case on Ctrip.com is performed to evaluate the model. Experimental results confirm that our method outperforms the other five benchmark methods.
Inference speed and quantisation of neural networks with TensorFlow Lite for Microcontrollers framework

In the last few years, microcontrollers became more and more powerful, and many authors have started to use them for different machine learning projects. One of the most popular frameworks for machine learning is TensorFlow, and their authors began to develop this framework for microcontrollers. The goal of this paper is to analyses the full connected neural networks inference speed depending on the number of neurons of one popular microcontroller (Arduino Nano 33 BLE Sense) with simple neural networks implementation, as well as the impact of neural network weights quantisation. We expected a reduction in the size of the model with the selected quantization by four times, which was achieved, but with a large number of neurons in the neural network. TensorFlow Lite for Microcontrollers is used with the Arduino Integrated Development Environment. Neural networks with two hidden layers are used with a different number of neurons.
Addressing the New Item problem in video recommender systems by incorporation of visual features with restricted Boltzmann machines

Abstract Over the past years, the research of video recommender systems (RSs) has been mainly focussed on the development of novel algorithms. Although beneficial, still any algorithm may fail to recommend video items that the system has no form of data associated to them (New Item Cold Start). This problem occurs when a new item is added to the catalogue of the system and no data are available for that item. In content‐based RSs, the video items are typically represented by semantic attributes, when generating recommendations. These attributes require a group of experts or users for annotation, and still, the generated recommendations might not capture a complete picture of the users' preferences, for example, the visual tastes of users on video style. This article addresses this problem by proposing recommendation based on novel visual features that do not require human annotation and can represent visual aspects of video items. We have designed a novel evaluation methodology considering three realistic scenarios, that is, (a) extreme cold start, (b) moderate cold start and (c) warm‐start scenario. We have conducted a set of comprehensive experiments, and our results have shown the superior performance of recommendations based on visual features, in all of the evaluation scenarios.
Don’t Forget the Human: a Crowdsourced Approach to Automate Response and Containment Against Spear Phishing Attacks

Organizations are increasingly facing sophisticated social engineering attacks that exploit human vulnerabilities and overcome commonly available countermeasures. Spear-phishing campaigns are becoming the most prevalent attack and source of compromise for most organizations. We argue that existing prevention and detection countermeasures are fundamentally ineffective against this class of attacks. In this work, we propose a novel approach to address the limitations of existing countermeasures. Our proposition is a new course of action to exploit human detection capabilities as a basis of automated response strategies. Preliminary results unveil users' mental models for phishing detection and reporting as a way to improve the phishing reporting process altogether. A real word case study is provided to promote the feasibility of our proposal.
Exemplar-based Layout Fine-tuning for Node-link Diagrams

We design and evaluate a novel layout fine-tuning technique for node-link diagrams that facilitates exemplar-based adjustment of a group of substructures in batching mode. The key idea is to transfer user modifications on a local substructure to other substructures in the entire graph that are topologically similar to the exemplar. We first precompute a canonical representation for each substructure with node embedding techniques and then use it for on-the-fly substructure retrieval. We design and develop a light-weight interactive system to enable intuitive adjustment, modification transfer, and visual graph exploration. We also report some results of quantitative comparisons, three case studies, and a within-participant user study.
Semi-Supervised Clustering with Neural Networks

Clustering using neural networks has recently demonstrated promising performance in machine learning and computer vision applications. However, the performance of current approaches is limited either by unsupervised learning or their dependence on large set of labeled data samples. In this paper, we propose ClusterNet that uses pairwise semantic constraints from very few labeled data samples (<; 5% of total data) and exploits the abundant unlabeled data to drive the clustering approach. We define a new loss function that uses pairwise semantic similarity between objects combined with constrained k-means clustering to efficiently utilize both labeled and unlabeled data in the same framework. The proposed network uses convolution autoencoder to learn a latent representation that groups data into k specified clusters, while also learning the cluster centers simultaneously. We evaluate and compare the performance of ClusterNet on several datasets and other clustering approaches.
Deep Learning based approach to detect Customer Age, Gender and Expression in Surveillance Video

In the current information era, customer analytics play a key role in the success of any business. Since customer demographics primarily dictate their preferences, identification and utilization of age & gender information of customers in sales forecasting, may maximize retail sales. In this work, we propose a computer vision based approach to age and gender prediction in surveillance video. The proposed approach leverage the effectiveness of Wide Residual Networks and Xception deep learning models to predict age and gender demographics of the consumers. The proposed approach is designed to work with raw video captured in a typical CCTV video surveillance system. The effectiveness of the proposed approach is evaluated on real-life garment store surveillance video, which is captured by low resolution camera, under non-uniform illumination, with occlusions due to crowding, and environmental noise. The system can also detect customer facial expressions during purchase in addition to demographics, that can be utilized to devise effective marketing strategies for their customer base, to maximize sales.
Efficient Malware Originated Traffic Classification by Using Generative Adversarial Networks

With the booming of malware-based cyber-security incidents and the sophistication of attacks, previous detections based on malware sample analysis appear powerless due to time-consuming and labor-intensive analysis process. The existing detection methods based on traffic analysis rely heavily on the available traffic patterns, which hinder detecting the zero-day attacks caused by malware variants. In this paper, we propose an approach based on deep learning referred to as TrafficGAN, which analyzes (HTTP) traffic sessions to distinguish between malware-related and normal traffic. We first try to explore traffic patterns of malware variants by adding noise and category condition to the Generative Adversarial Networks (GAN), thus generating various similar but slightly different traffic. And then, we use discriminative model to seek the deviation between abnormal traffic and normal traffic by extracting the essential difference. Notablely, we increase the diversity of data by generating samples adversarially, which enhances the robustness of the system to detect zero-day attacks and highlights the lack of sensitive data in the security community. We conduct extensive experiments on the public dataset and our data collected for specific targets. The results demonstrate that our method achieves superior performance to other methods and protects specific targets from the susceptibility of malware.
Trends in Software Engineering Processes using Deep Learning: A Systematic Literature Review

In recent years, several researchers have applied machine learning techniques to several knowledge areas achieving acceptable results. Thus, a considerable number of deep learning models are focused on a wide range of software processes. This systematic review investigates the software processes supported by deep learning models, determining relevant results for the software community. This research identified that the most extensively investigated sub-processes are software testing and maintenance. In such sub-processes, deep learning models such as CNN, RNN, and LSTM are widely used to process bug reports, malware classification, libraries and commits recommendations generation. Some solutions are oriented to effort estimation, classify software requirements, identify GUI visual elements, identification of code authors, the similarity of source codes, predict and classify defects, and analyze bug reports in testing and maintenance processes.
A geometric probability randomized Kaczmarz method for large scale linear systems

For solving large scale linear systems, a fast convergent randomized Kaczmarz-type method was constructed in Bai and Wu (2018) [4]. In this paper, we propose a geometric probability randomized Kaczmarz (GPRK) method by introducing a new index set Jk and three supervised probability criteria defined on Jk from a geometric point of view. Linear convergence of GPRK is proved, and the way of argument for the analysis of GPRK also leads to new sharper upper bounds for the randomized Kaczmarz (RK) method and the greedy randomized Kaczmarz (GRK) method. In practice, GPRK is implemented with a simple geometric probability criterion, i.e., the most efficient one of the aforementioned three supervised probability criteria defined on Jk. The numerical results demonstrate that GPRK is robust and efficient, and it is faster than GRK in most of the tests in the sense of computing time.
Go&amp;Grow

Caregivers of persons with dementia (PWD) experience higher rates of stress, social isolation, and poor mental and physical health compared to non-caregiving populations. There is a vital need for engaging, sustainable, and scalable resources to support social, physical, and emotional wellbeing amongst caregivers of PWD. To explore this open design space, we designed and conducted a 6-week mixed-method evaluation of Go&amp;Grow, a pervasive social exergame in which flowers grow as users increase physical activity and interact with other caregivers of PWD. Our findings showed that using Go&amp;Grow helped participants relieve stress, increase physical activity, and develop empathy for and patience towards the loved one with dementia that they cared for. At the same time, tension arose as some caregivers desired to learn about the life challenges that Go&amp;Grow users faced, while others hesitated to share such content. We discuss our findings and recommendations for future technology that promotes caregivers? time for themselves, understanding of PWD, and connections with other caregivers.
Exploring Missing Interactions: A Convolutional Generative Adversarial Network for Collaborative Filtering

Adversarial examples can be detrimental to a recommender,leading to a surging enthusiasm for applying adversarial learning to improve recommendation performance, e.g. raising model robustness, alleviating data sparsity, generating initial profiles for cold-start users or items, etc. Most existing adversarial example generation methods fall within three categories: attacking the user-item interactions or auxiliary contents, adding perturbations in latent space, sampling the latent space according to certain distribution. In this work, we focus on the semantic-rich user-item interactions in a recommender system and propose a novel generative adversarial network (GAN) named Convolutional Generative Collaborative Filtering (Conv-GCF). We develop an effective perturbation mechanism (adversarial noise layer) for convolutional neural networks (CNN), based on which we design a generator with residual blocks to synthesize user-item interactions. We empirically demonstrate that on Conv-GCF, the adversarial noise layer is superior to the conventional noise-adding approach. Moreover, we propose two types of discriminators: one using Bayes Personalized Ranking (BPR) and the other with binary classification. On four public datasets, we show that our approach achieves the state-of-the-art top-n recommendation performance among competitive baselines.
High-Performance Low-Memory Lowering: GEMM-based Algorithms for DNN Convolution

Deep Neural Network Convolution is often implemented with general matrix multiplication ( GEMM ) using the well-known im2col algorithm. This algorithm constructs a Toeplitz matrix from the input feature maps, and multiplies them by the convolutional kernel. With input feature map dimensions C × H × W and kernel dimensions M × C × K^2, im2col requires O(K^2CHW ) additional space. Although this approach is very popular, there has been little study of the associated design space. We show that the im2col algorithm is just one point in a regular design space of algorithms which translate convolution to GEMM. We enumerate this design space, and experimentally evaluate each algorithmic variant. Our evaluation yields several novel low-memory algorithms which match the performance of the best known approaches despite requiring only a small fraction of the additional memory.
2ch-TCN: A Website Fingerprinting Attack over Tor Using 2-channel Temporal Convolutional Networks

In a website fingerprinting attack, an eavesdropper analyses the traffic between the Tor user and entry node of the Tor network to infer which websites the user has visited. Some recent work apply deep learning algorithms, however, most of them do not fully exploit the packet timing information. In this work, we propose a novel website fingerprinting attack based on a two-channel Temporal Convolutional Networks model that extracts features from both the packet sequences and packet timing information. Our attack is proved to perform better compared to the state-of-the-art attacks. Experiment results also show that the timing information is very useful for classification. Furthermore, we collect our own traffic traces between client and entry node, and transform them into three extraction layers: TCP, TLS and Tor cell layer, and meanwhile record Tor’s cell log at the entry node. The experimental results show that the data of the cell layer is the most divisible among the three layers. Based on the experimental results, we conclude that the adversary at the entry node has an advantage over the one who just listens to traffic between client and entry node.
Learning to Distract: A Hierarchical Multi-Decoder Network for Automated Generation of Long Distractors for Multiple-Choice Questions for Reading Comprehension

The task of generating incorrect options for multiple-choice questions is termed as distractor generation problem. The task requires high cognitive skills and is extremely challenging to automate. Existing neural approaches for the task leverage encoder-decoder architecture to generate long distractors. However, in this process two critical points are ignored - firstly, many methods use Jaccard similarity over a pool of candidate distractors to sample the distractors. This often makes the generated distractors too obvious or not relevant to the question context. Secondly, some approaches did not consider the answer in the model, which caused the generated distractors to be either answer-revealing or semantically equivalent to the answer.
Hierarchical Group-Level Emotion Recognition

Group-level emotion recognition is a technique for estimating the emotion of a group of people. In this paper, we propose a novel method for group-level emotion recognition. Our method lies in the two-fold contributions: (1) recognition of group-level emotion using a hierarchical classification approach; (2) incorporation of novel features to contribute to the description of the group-level emotion. We consider that the use of facial expressions of people will only be effective in differentiating images labeled as “Positive” because those labeled as “Neutral” or “Negative” are likely to include similar facial expressions. Therefore, we first perform binary classification based on facial expression recognition to distinguish “Positive” labels that include discriminative facial expressions (e.g., smile) from the others. We evaluate outcomes that are not classified as “Positive” during the first classification by exploiting scene features that describe what type of events (e.g., demonstration or funeral) are shown in the image. The other novelty of our method lies in two-fold. The first is the exploitation of visual attention for the first classification. It allows us to estimate which faces are the main subjects in the target image, thereby suppressing the influences of faces in the background that contribute less to group-level emotion. The second is the exploitation of object-wise semantic information (labels) for the second classification. This allows a more detailed description of the scene context in the image and enables performance enhancement in the second classification. We demonstrate the effectiveness of our method through experiments using public datasets.
Personalized Flight Itinerary Ranking at Fliggy

Flight itinerary ranking is critical for Online Travel Agencies (OTAs) since more and more customers book flights online. Currently, most OTAs still adopt rule-based strategies. However, rule-based methods are not able to model context-aware information and user preferences. To this end, a novel Personalized Flight itinerary Ranking Network (PFRN) is proposed in this paper. In PFRN, a Listwise Feature Encoding (LFE) structure is proposed to capture global context-aware information and mutual influences among inputs. Besides, we utilize behaviors of both individual user and group users sharing the same intention to express user preferences. Then a User Attention Mechanism is proposed to rank flight itineraries based on the user preferences effectively and efficiently. Offline experiments on real-world datasets from Amadeus and Fliggy show the superior performance of the proposed PFRN. Moreover, PFRN has been successfully deployed on online system for searching itineraries at Fliggy and achieved significant improvements.
Memory based fusion for multi-modal deep learning

The use of multi-modal data for deep machine learning has shown promise when compared to uni-modal approaches with fusion of multi-modal features resulting in improved performance in several applications. However, most state-of-the-art methods use naive fusion which processes feature streams independently, ignoring possible long-term dependencies within the data during fusion. In this paper, we present a novel Memory based Attentive Fusion layer, which fuses modes by incorporating both the current features and long-term dependencies in the data, thus allowing the model to understand the relative importance of modes over time. We introduce an explicit memory block within the fusion layer which stores features containing long-term dependencies of the fused data. The feature inputs from uni-modal encoders are fused through attentive composition and transformation followed by naive fusion of the resultant memory derived features with layer inputs. Following state-of-the-art methods, we have evaluated the performance and the generalizability of the proposed fusion approach on two different datasets with different modalities. In our experiments, we replace the naive fusion layer in benchmark networks with our proposed layer to enable a fair comparison. Experimental results indicate that the MBAF layer can generalize across different modalities and networks to enhance fusion and improve performance.
Pkg2Vec: Hierarchical package embedding for code authorship attribution

Authorship attribution of software is the task of identifying the author of a given piece of code. Code attribution is of importance in multiple scenarios, ranging from software plagiarism to cybersecurity. In this paper, we introduce authorship attribution of software packages that better reflect real-world scenarios in which code is organized in packages and written by teams. We present a novel approach for software package authorship attribution called Pkg2Vec, based on a hierarchical deep neural network (DNN) architecture, corresponding to the hierarchical nature of software (code) packages. The hierarchical neural network model consists of a token level encoder and an attention mechanism for a function level encoder, together producing package embedding. Beyond package embedding, we use keywords and API calls as resilient features, which reflect the programmer's intention and style. Pkg2Vec is evaluated on a large dataset of public packages and compared to a number of other source code authorship attribution state-of-the-art algorithms. We find that Pkg2Vec significantly outperforms other approaches, achieving a 13% improvement in accuracy
Artificial Intelligence and Critical Systems: From Hype to Reality

Artificial intelligence will be deployed increasingly in more systems that affect public health, safety, and welfare. These systems will better utilize scarce resources; prevent disasters; and increase safety, reliability, comfort, and convenience. Despite the technological challenges and public fears, these systems will improve the quality of life of millions of people worldwide.
The Impact of Virtual Reality in the Social Presence of a Virtual Agent

In this work we test the hypothesis that interacting with an intelligent virtual character in Virtual Reality (VR) has a stronger impact compared to the same interaction in a traditional non-immersive platform, both in terms of presence and believability.
Motion Pattern Recognition in 4D Point Clouds

We address an actively discussed problem in signal processing, recognizing patterns from spatial data in motion. In particular, we suggest a neural network architecture to recognize motion patterns from 4D point clouds. We demonstrate the feasibility of our approach with point cloud datasets of hand gestures. The architecture, PointGest, directly feeds on unprocessed timelines of point cloud data without any need for voxelization or projection. The model is resilient to noise in the input point cloud through abstraction to lower-density representations, especially for regions of high density. We evaluate the architecture on a benchmark dataset with ten gestures. PointGest achieves an accuracy of 98.8%, outperforming five state-of-the-art point cloud classification models.