Reviewerly/exHarmony huggingface dataset:
C3-Sex: A Conversational Agent to Detect Online Sex Offenders

Prevention of cybercrime is one of the missions of Law Enforcement Agencies (LEA) aiming to protect and guarantee sovereignty in the cyberspace. In this regard, online sex crimes are among the principal ones to prevent, especially those where a child is abused. The paper at hand proposes C3-Sex, a smart chatbot that uses Natural Language Processing (NLP) to interact with suspects in order to profile their interest regarding online child sexual abuse. This solution is based on our Artificial Conversational Entity (ACE) that connects to different online chat services to start a conversation. The ACE is designed using generative and rule-based models in charge of generating the posts and replies that constitute the conversation from the chatbot side. The proposed solution also includes a module to analyze the conversations performed by the chatbot and calculate a set of 25 features that describes the suspect’s behavior. After 50 days of experiments, the chatbot generated a dataset with 7199 profiling vectors with the features associated to each suspect. Afterward, we applied an unsupervised method to describe the results that differentiate three groups, which we categorize as indifferent, interested, and pervert. Exhaustive analysis is conducted to validate the applicability and advantages of our solution.
Improved Embedding of QR Codes onto Surfaces to be 3D Printed

Providing additional information for parts or items usually means to enclose it next to the object or affix it on the component when that is possible. However, another solution is available by gaining the benefits of an additive manufacturing technology, 3D printing. This technology makes it possible to embed the additional information onto the surface of the items, for example, in the forms of QR codes. In the work of Kikuchi et al. (2018), the QR code is embedded into CAD models that consist of B-spline surfaces by grooving its dark regions to shadow them. The method proposed by Peng et al. (2019) optimized the modules of the QR code and the depth to carve its dark modules into any general mesh. However, embedding the QR code with these methods, in some cases, especially in case of highly curved surfaces, the QR code is deformed during the process of projection onto the surface. This deformation can highly restrict the readability of the QR code. In this paper, we propose an improved method to embed QR codes onto free-form surfaces by using a low-end consumer-level 3D printer. Our aim is to provide a robust method to project the QR code onto surfaces even with high curvature. We discuss the problematic cases for the works mentioned above, and we present a process to find an optimal position and direction of projection for the QR code to avoid deformations on highly curved surfaces. To validate our method, we compare our results with the outcomes of Kikuchi et al. (2018) and Peng et al. (2019).
ExCut: Explainable Embedding-Based Clustering over Knowledge Graphs

Clustering entities over knowledge graphs (KGs) is an asset for explorative search and knowledge discovery. KG embeddings have been intensively investigated, mostly for KG completion, and have potential also for entity clustering. However, embeddings are latent and do not convey user-interpretable labels for clusters. This work presents ExCut, a novel approach that combines KG embeddings with rule mining methods, to compute informative clusters of entities along with comprehensible explanations. The explanations are in the form of concise combinations of entity relations. ExCut jointly enhances the quality of entity clusters and their explanations, in an iterative manner that interleaves the learning of embeddings and rules. Experiments on real-world KGs demonstrate the effectiveness of ExCut for discovering high-quality clusters and their explanations.
On the Effectiveness of Discretizing Quantitative Attributes in Linear Classifiers

Linear models in machine learning are extremely computational efficient but they have high representation bias due to non-linear nature of many real-world datasets. In this article, we show that this representation bias can be greatly reduced by discretization. Discretization is a common procedure in machine learning that is used to convert a quantitative attribute into a qualitative one. It is often motivated by the limitation of some learners to handle qualitative data. Since discretization looses information (as fewer distinctions among instances are possible using discretized data relative to undiscretized data) - where discretization is not essential, it might appear desirable to avoid it, and typically, it is avoided. However, in the past, it has been shown that discretization can leads to superior performance on generative linear models, e.g., naive Bayes. This motivates a systematic study of the effects of discretizing quantitative attributes for discriminative linear models, as well. In this article, we demonstrate that, contrary to prevalent belief, discretization of quantitative attributes, for discriminative linear models, is a beneficial pre-processing step, as it leads to far superior classification performance, especially on bigger datasets, and surprisingly, much better convergence, which leads to better training time. We substantiate our claims with an empirical study on 52 benchmark datasets, using three linear models optimizing different objective functions.
Psychophysical Effects of Experiencing Burning Hands in Augmented Reality

Can interactive Augmented Reality (AR) experiences induce involuntary sensations through additional modalities? In this paper we report on our AR experience that enables users to see and hear their own hands burning while looking through a Video See-Through Head-Mounted Display (VST-HMD). In an exploratory study (n = 12, within-subject design), we investigated whether this will lead to an involuntary heat sensation based on visual and auditory stimuli. A think-aloud-protocol and an AR presence questionnaire indicated that six out of twelve participants experienced an involuntary heat sensation on their hands. Despite no significant change of perceived anxiety, we found a significant increase in skin conductance during the experiment for all participants; participants who reported an involuntary heat sensation had higher skin conductance responses than participants who did not report a heat sensation. Our results support our initial hypothesis as we found evidence of cross-modal audiovisual-to-thermal transfers. This is an example of virtual synaesthesia, a sensation occurring when single-modal (or multi-modal) stimulus sets off the simultaneous sensation over other senses—involuntarily and automatically. We believe that our results contribute to the scientific understanding of AR induced synaesthesia as well as inform practical applications.
TableView: Enabling Efficient Access to Web Data Records for Screen-Magnifier Users

People with visual impairments typically rely on screen-magnifier assistive technology to interact with webpages. As screen-magnifier users can only view a portion of the webpage content in an enlarged form at any given time, they have to endure an inconvenient and arduous process of repeatedly moving the magnifier focus back-and-forth over different portions of the webpage in order to make comparisons between data records, e.g., comparing the available flights in a travel website based on their prices, durations, etc. To address this issue, we designed and developed TableView, a browser extension that leverages a state-of-the art information extraction method to automatically identify and extract data records and their attributes in a webpage, and subsequently presents them to a user in a compactly arranged tabular format that needs significantly less screen space compared to that currently occupied by these items in the page. This way, TableView is able to pack more items within the magnifier focus, thereby reducing the overall content area for panning, and hence making it easy for screen-magnifier users to compare different items before making their selections. A user study with 16 low vision participants showed that with TableView, the time spent on panning the data records in webpages was significantly reduced by 72.9% (avg.) compared to that with just a screen magnifier, and 66.5% compared to that with a screen magnifier using a space compaction method.
Research on Application of Improved YOLO V3 Algorithm in Road Target Detection

Abstract Due to the lack of average accuracy and missed detection in the process of real road scene target detection through YOLO V3 network, the improvement scheme is put forward. The K-means clustering algorithm is used to replace the K-means clustering algorithm in the original network to analyze the anchor number and aspect ratio of the Udacity data set, in order to make the obtained parameters more suitable; in addition, in order to improve the performance of the road target detection algorithm, the existing network output is upgraded, and a 104 × 104 feature detection layer is added, and the feature map output by 8 times sampling can be output by 2 times up sampling, and 4 the feature maps of down-sampling are stitched together, and the 104 × 104 feature maps obtained can effectively reduce the disappearance of features. Through the experimental results, we can see that compared with the improved YOLOV3 algorithm, the average detection accuracy of the improved algorithm for road target detection is quite high to 3.17%, and the missing detection rate is reduced by 5.62%.
The Realization of an Earthquake Early Warning System for Schools and Its Performance during the 2019 ML 6.3 Hualien (Taiwan) Earthquake

Abstract This article discusses the earthquake early warning system (EEWS) for schools of the National Center for Research on Earthquake Engineering, Taiwan (NCREE’s EEWS, earthquake early warning system [NEEWS]) that was recently completed. The system consists of 98 seismic stations with a complete set of system capabilities and 3514 broadcast stations with only the associated alert broadcast system capabilities. The broadcast stations receive both any on-site alerts issued by the seismic stations and any regional alerts issued by the Central Weather Bureau and then broadcast whatever alert is received earliest. Shortly after the establishment of the NEEWS, the ML 6.3 Hualien earthquake, which had a maximum measured peak ground acceleration (PGA) of 515.17 Gal, struck Taiwan on 18 April 2019. During the earthquake, the performance of both the seismic stations and the broadcast stations of the system was documented. The current study analyzes and discusses the accuracy of the PGA predictions, lead times, and classification performance at both the seismic stations and the broadcast stations of the NEEWS. The results show that the NEEWS is a cost-effective and promising system of EEW.
Recognizing Very Small Face Images Using Convolution Neural Networks

Face recognition can be installed in a surveillance system so that it can be used for monitoring, tracking and access control. An excellent, intelligent surveillance system should be sensitive to the objects far away from the camera. Unfortunately, due to the long-distance, objects like human faces captured by the camera are too small to identify. As to enhance the subtle color differences in the face image, in this paper we first improve the resolution of the captured image using deep convolution neural networks (DCNNs). Then the efficient features are extracted and used to do classification. As for verifying the effectiveness of the proposed method, we used three databases including AR face database, Georgia Tech face database (GT) database, and Labelled Faces in the Wild (LFW) database, altogether, to conduct the training and testing. Compared to the existing approaches, experimental results show that the identification accuracy of the proposed method outperforms any existing approaches.
Network-accelerated distributed machine learning for multi-tenant settings

Many distributed machine learning (DML) workloads are increasingly being run in shared clusters. Training in such clusters can be impeded by unexpected compute and network contention, resulting in stragglers. We present MLfabric, a contention-aware DML system that manages the performance of a DML job running in a shared cluster. The DML application hands all network communication (gradient and model transfers) to the MLfabric communication library. MLfabric then carefully orders transfers to improve convergence, opportunistically aggregates them at idle DML workers to improve resource efficiency, and replicates them to support new notions of fault tolerance, while systematically accounting for compute stragglers and network contention. We find that MLfabric achieves up to 3x speed-up in training large deep learning models in realistic dynamic cluster settings.
Automated Machine Learning: Techniques and Frameworks

Nowadays, machine learning techniques and algorithms are employed in almost every application domain (e.g., financial applications, advertising, recommendation systems, user behavior analytics). In practice, they are playing a crucial role in harnessing the power of massive amounts of data which we are currently producing every day in our digital world. In general, the process of building a high-quality machine learning model is an iterative, complex and time-consuming process that involves trying different algorithms and techniques in addition to having a good experience with effectively tuning their hyper-parameters. In particular, conducting this process efficiently requires solid knowledge and experience with the various techniques that can be employed. With the continuous and vast increase of the amount of data in our digital world, it has been acknowledged that the number of knowledgeable data scientists can not scale to address these challenges. Thus, there was a crucial need for automating the process of building good machine learning models (AutoML). In the last few years, several techniques and frameworks have been introduced to tackle the challenge of automating the machine learning process. The main aim of these techniques is to reduce the role of humans in the loop and fill the gap for non-expert machine learning users by playing the role of the domain expert. In this chapter, we present an overview of the state-of-the-art efforts in tackling the challenges of machine learning automation. We provide a comprehensive coverage for the various tools and frameworks that have been introduced in this domain. In addition, we discuss some of the research directions and open challenges that need to be addressed in order to achieve the vision and goals of the AutoML process.
Multi-View Region of Interest Prediction for Autonomous Driving Using Semi-Supervised Labeling

Visual environment perception is one of the key elements for autonomous and manual driving. Modern fully automated vehicles are equipped with a range of different sensors and capture the surroundings with multiple cameras. The ability to predict human driver's attention is the basis for various autonomous driving functions. State-of-the-art attention prediction approaches use only a single front facing camera and rely on automatically generated training data. In this paper, we present a manually labeled multi-view region of interest dataset. We use our dataset to finetune a state-of-the-art region of interest prediction model for multiple camera views. Additionally, we show that using two separate models focusing on either front or rear view data improves the region of interest prediction. We further propose a semi-supervised annotation framework which uses the best performing finetuned models for generating pseudo labels to improve the efficiency of the labeling process. Our results show that existing region of interest prediction performs well on front view data, but finetuning improves the performance especially for rear view data. Our current dataset consists of about 16000 images and we plan to further increase the size of the dataset. The dataset and the source code of the proposed semi-supervised annotation framework will be made available on GitHub and can be used to generate custom region of interest data.
PRINCEv2

In this work, we propose tweaks to the PRINCE block cipher that help us to increase its security without changing the number of rounds or round operations. We get substantially higher security for the same complexity. From an implementation perspective, PRINCEv2 comes at an extremely low overhead compared to PRINCE in all key categories, such as area, latency and energy. We expect, as it is already the case for PRINCE, that the new cipher PRINCEv2 will be deployed in various settings.
Selective generative adversarial network for raindrop removal from a single image

The removal of raindrops from a single image is still challenging because of the diversity and density of raindrops existing in the rainy image. Moreover, the colors of raindrops are constantly changing with the background which also makes the raindrops cannot be well removed by using the current methods. In this paper, we tackle these limitations by combining the raindrops shape features with the background structure features to guide the network to accurately remove raindrops. Specifically, we propose a selective skip connection GAN (SSCGAN) combining the selective skip connection and self-attention mechanism to restoring the clean image from a raindrop degraded one. Our main idea is selectively transmitting the information of raindrops to the decoder through Gated Recurrent Units (GRU) to better generate a clean image. During the training, the selective skip connection model (SSCM) extract raindrops binary mask from the rainy image and eliminate the interference of background noise. Simultaneously, we use self-attention blocks (SABs) to make the generator network pay more attention to global structure features of the rainy image and conversely correct the raindrops binary mask. Experiments show that our method has better performance than previous methods.
Contribution Based Co-Evolutionary Algorithm for Large-Scale Optimization Problems

The solution of large-scale optimization problems is the key to many decision-making processes in practice. However, it is a challenging research topic when considered both the quality of solutions and the required computational time. One of the popular approaches for these problems is to divide the problems into a number of smaller sub-problems, that are then solved separately with an exchange of some information using the cooperative co-evolution (CC) concept. However, the characteristics of sub-components could be different, and their contributions to the overall performance can also be different while solving the problem. In the CC approach, it usually applies one optimizer and allocates equal computational budget to all sub-components. In this article, a new algorithm is proposed with the use of multiple optimizers, along with a need-based allocation of computational budget for the sub-components. In the proposed algorithm, a group of optimizers cooperate in an effective way to evolve the sub-components, depending on heuristic fuzzy rules. The performance of our proposed algorithm was evaluated by solving a number of large-scale global optimization benchmark functions. The empirical results show that the proposed algorithm outperforms equal allocation CC, a single selection characteristic, a single candidate optimizer and state-of-the-art algorithms.
Delayed Combination of Feature Embedding in Bidirectional LSTM CRF for NER

Named Entity Recognition (NER) plays a vital role in natural language processing (NLP). Currently, deep neural network models have achieved significant success in NER. Recent advances in NER systems have introduced various feature selections to identify appropriate representations and handle Out-Of-the-Vocabulary (OOV) words. After selecting the features, they are all concatenated at the embedding layer before being fed into a model to label the input sequences. However, when concatenating the features, information collisions may occur and this would cause the limitation or degradation of the performance. To overcome the information collisions, some works tried to directly connect some features to latter layers, which we call the delayed combination and show its effectiveness by comparing it to the early combination. As feature encodings for input, we selected the character-level Convolutional Neural Network (CNN) or Long Short-Term Memory (LSTM) word encoding, the pre-trained word embedding, and the contextual word embedding and additionally designed CNN-based sentence encoding using a dictionary. These feature encodings are combined at early or delayed position of the bidirectional LSTM Conditional Random Field (CRF) model according to each feature’s characteristics. We evaluated the performance of this model on the CoNLL 2003 and OntoNotes 5.0 datasets using the F1 score and compared the delayed combination model with our own implementation of the early combination as well as the previous works. This comparison convinces us that our delayed combination is more effective than the early one and also highly competitive.
Multi-language dynamic taint analysis in a polyglot virtual machine

Dynamic taint analysis is a popular program analysis technique in which sensitive data is marked as tainted and the propagation of tainted data is tracked in order to determine whether that data reaches critical program locations. This analysis technique has been successfully applied to software vulnerability detection, malware analysis, testing and debugging, and many other fields. However, existing approaches of dynamic taint analysis are either language-specific or they target native code. Neither is suitable for analyzing applications in which high-level dynamic languages such as JavaScript and low-level languages such as C interact.In these approaches, the language boundary forms an opaque barrier that prevents a sound analysis of data flow in the other language and can thus lead to the analysis being evaded.
On-Line Relaxation Versus Off-Line Spectral Algorithm in the Learning of Polynomial Neural Units

The problem of the learning of polynomial threshold units over a fixed set of polynomials is treated in the paper. We consider two approaches in the supervised learning: off-line relaxation-like algorithms and spectral off-line algorithms. Our research is focused on answering the question: what values of the learning rate provide the fast convergence of the learning algorithm? We propose and justify a new adaptive rule of the choice of learning rates, which ensures that on-line relaxation-like algorithms produce a desired weight vector of polynomial neural unit after a finite number of learning steps. Then, we deal with the spectral off-line learning algorithm and give the reasons that the extension of the range of acceptable values for the learning rate preserves the convergence and the finiteness of learning. The relaxation-like learning algorithm with margin is also considered. Its advantage is the finite learning time in the case when a constant learning rate belongs to (0, 2]. Next, we propose a new rule of the choice of initial approximation significantly accelerating the convergence both for on-line and for off-line learning. Finally, the simulation results are given for quadratic neural units, which confirm the validity and the advantage of the proposed approaches.