Reviewerly/exHarmony huggingface dataset:
Offensive Language Detection Explained

Many online discussion platforms use a content moderation process, where human moderators check user comments for offensive language and other rule violations. It is the moderator’s decision which comments to remove from the platform because of violations and which ones to keep. Research so far focused on automating this decision process in the form of supervised machine learning for a classification task. However, even with machine-learned models achieving better classification accuracy than human experts, there is still a reason why human moderators are preferred. In contrast to black-box models, such as neural networks, humans can give explanations for their decision to remove a comment. For example, they can point out which phrase in the comment is offensive or what subtype of offensiveness applies. In this paper, we analyze and compare four explanation methods for different offensive language classifiers: an interpretable machine learning model (naive Bayes), a model-agnostic explanation method (LIME), a model-based explanation method (LRP), and a self-explanatory model (LSTM with an attention mechanism). We evaluate these approaches with regard to their explanatory power and their ability to point out which words are most relevant for a classifier’s decision. We find that the more complex models achieve better classification accuracy while also providing better explanations than the simpler models.
The Classification of Chinese Sensitive Information Based on BERT-CNN

The traditional classification method of Chinese sensitive information mainly relies on the frequency of the co-occurrence of sensitive words and keywords. However, it is difficult to detect the meaning and context relationship of some complex statements. In this paper, a new model is proposed to classify Chinese network sensitive information. The model, which is based on CNN (Convolutional Neural Network) and latest pre-trained BERT (Bidirectional Encoder Representation from Transformers), is called the BERT-CNN deep learning model. Firstly, nearly 20,000 comments of corresponding videos are collected from YouTube, after data cleaning and labeling, the texts are divided into four categories to build the SLD (sensitive language detection) dataset. Finally, the word vectors from the pre-trained BERT are used as the input of the CNN model, constructing the Chinese sensitive information classifying BERT-CNN model. The result reveals that, compared with the traditional neural network model, the BERT-CNN model proposed in this paper improves the generalization ability of word embedding, and can effectively achieve the recognition and classification of the network sensitive information in short text dataset. The experimental results perform better than the classic CNN and RNN (Recurrent Neural Network) models.
Detecting faults in inner product masking scheme

Side-channel analysis and fault injection attacks are two typical threats to cryptographic implementations, especially in modern embedded devices. Thus, there is an insistent demand for dual side-channel and fault injection protections. As we know, masking is a kind of provable countermeasure against side-channel attacks. Recently, inner product masking (IPM) was proposed as a promising higher-order masking scheme against side-channel analysis, but not for fault injection attacks. In this paper, we devise a new masking scheme named IPM-FD. It is built on IPM, which enables fault detection. This novel masking scheme has three properties: the security orders in the word-level probing model, bit-level probing model and the number of detected faults. IPM-FD is proven secure both in the word-level and in the bit-level probing models and allows for end-to-end fault detection against fault injection attacks. Furthermore, we illustrate its security order by interpreting IPM-FD as a coding problem and then linking it to one defining parameters of linear code and show its implementation cost by applying IPM-FD to AES-128.
Malaria Detection Using Convolutional Neural Network

In the tropical and subtropical countries, malaria has been a challenge, which really needs a quick and precise diagnosis to stop or control the disease. It is a serious and sometimes fatal disease caused by a parasite that commonly infects a certain type of mosquito which feeds on humans. The traditional microscopy technique has a few weaknesses which incorporate time utilization and reproducibility of the results. This paper deals with the automatic identification of malaria-infected cells using deep learning methods. Deep learning methods have the advantage of being able to automatically learn the features from the input data, thereby requiring minimal inputs from human experts for automated malaria diagnosis. An automated diagnostic system can significantly improve the efficiency of the pathologists and also reduce the need for dedicated pathologists in rural villages.
Mobile Active Authentication based on Multiple Biometric and Behavioral Patterns

In this chapter we evaluate mobile active authentication based on an ensemble of biometrics and behavior-based profiling signals. We consider seven different data channels and their combination. Touch dynamics (touch gestures and keystroking), accelerometer, gyroscope, WiFi, GPS location and app usage are all collected during human-mobile interaction to authenticate the users. We evaluate two approaches: one-time authentication and active authentication. In one-time authentication, we employ the information of all channels available during one session. For active authentication we take advantage of mobile user behavior across multiple sessions by updating a confidence value of the authentication score. Our experiments are conducted on the semi-uncontrolled UMDAA-02 database. This database comprises of smartphone sensor signals acquired during natural human-mobile interaction. Our results show that different traits can be complementary in terms of mobile user authentication and multimodal systems clearly increase the performance when compared to individual biometrics systems with accuracies ranging from 82.2% to 98.0% depending on the authentication scenario.
A Blockchain-Driven Approach to Fulfill the GDPR Recording Requirements

On 25th May 2018, the European General Data Protection Regulation (GDPR) came into effect and required transformation into national legislation in all member states of the European Union. GDPR stipulates that businesses may only process personal data on documented instructions. Furthermore, article 33 paragraph 5 states that any personal data breach and its effects have to be documented. As a conclusion, any processing (article 4 paragraph 2) has to be recorded in an appropriate manner. To this end, Blockchain technology presents a suitable approach. Blockchain technology has clear advantages in comparison to classical recording techniques, which can be manipulated and deleted more easily. The use case described in this chapter is the application of Blockchain technology to fulfill the GDPR documentation requirements for a Log Management System (LMS). The purpose is to monitor sensitive data (files or folders) that can be defined via a configuration. To facilitate recording in an appropriate manner, a private and distributed architecture of a Blockchain with a two-level hierarchy is described. In the data block the SuperBlockchain (higher level) contains a SubBlockchain (lower level) which stores the log file information. The SubBlockchain is valid for a specific time span e.g. for one day, which speeds up any search in the log files in the case of an incident.
An analysis of the professional preferences and choices of computer engineering students

Abstract The revelation of the preferences and choices of students is critical for understanding which areas of the discipline they aim. Especially for final‐year undergraduate students, job prospects are an important concern. In this study, the graduation project choices of the final‐year undergraduate students in the department of computer engineering were investigated in light of their impact on both the industry and academia. A total of 1,693 course grades of 94 final‐year undergraduate students were retrieved from the Student Information System. These course grades were utilized as the features of the employed machine learning algorithms alongside the features that were deduced from them. In addition, the popularities of the graduation project topics on both GitHub and IEEE were investigated to reveal their impact on the industry and academia. To this end, we proposed an experimental study, using 14 machine learning techniques, that predicts the topics of the graduation projects that the final‐year undergraduate students chose. According to the experimental results, the accuracy of the proposed model was calculated to be as high as when it was utilized with the Bayes Net, Kleene Star , or J48 algorithm. This experimental result confirms the efficiency of the proposed model. Finally, the insights gained from the data are discussed to shed light on the reasons for the choices of the graduation projects as well as their relationships with the courses.
A Comparative Study of the Performance of Unsupervised Text Segmentation Techniques on Dialogue Transcripts

Contact centers provide customer interaction support to numerous organizations. In 2017, the contact center industry generated $200 billion in revenue worldwide, contributing to a significant proportion of market share, and yet businesses lost $75 billion due to poor customer satisfaction. Around 48% of consumers prefer using phones as their mode of communication with contact centers. Analysis of these calls can give insights into customer views and help businesses improve their customer engagement. To understand the structure and flow of the conversation, the conversation transcript can be segmented into meaningful sections such as “greeting exchange” “problem description” and “problem resolution”, to name a few. In this paper, we present a comparative study of various unsupervised methods of dialogue segmentation. We choose three classic unsupervised text segmentation techniques: TextTiling, TopicTiling, and Content Vector Segmentation, and evaluate their performance on 50 manually labeled dialogue conversation transcripts. The transcripts used span across contact center calls, live chat, interactions with chat-bots and talk show conversations. Additionally, we build on the TextTiling algorithm by incorporating semantic word embeddings for text representation. We show that this modification outperforms the three benchmarked approaches with a mean Pk value of 0.31, indicating that 69% of the boundaries are identified accurately at an average.
Wireless Communications for Collaborative Federated Learning in the Internet of Things.

Internet of Things (IoT) services will use machine learning tools to efficiently analyze various types of data collected by IoT devices for inference, autonomy, and control purposes. However, due to resource constraints and privacy challenges, edge IoT devices may not be able to transmit their collected data to a central controller for training machine learning models. To overcome this challenge, federated learning (FL) has been proposed as a means for enabling edge devices to train a shared machine learning model without data exchanges thus reducing communication overhead and preserving data privacy. However, Google's seminal FL algorithm requires all devices to be directly connected with a central controller, which significantly limits its application scenarios. In this context, this paper introduces a novel FL framework, called collaborative FL (CFL), which enables edge devices to implement FL with less reliance on a central controller. The fundamentals of this framework are developed and then, a number of communication techniques are proposed so as to improve the performance of CFL. To this end, an overview of centralized learning, Google's seminal FL, and CFL is first presented. For each type of learning, the basic architecture as well as its advantages, drawbacks, and usage conditions are introduced. Then, three CFL performance metrics are presented and a suite of communication techniques ranging from network formation, device scheduling, mobility management, and coding is introduced to optimize the performance of CFL. For each technique, future research opportunities are also discussed. In a nutshell, this article will showcase how the proposed CFL framework can be effectively implemented at the edge of large-scale wireless systems such as the Internet of Things.
Hardware-Software Contracts for Secure Speculation

Since the discovery of Spectre, a large number of hardware mechanisms for secure speculation has been proposed. Intuitively, more defensive mechanisms are less efficient but can securely execute a larger class of programs, while more permissive mechanisms may offer more performance but require more defensive programming. Unfortunately, there are no hardware-software contracts that would turn this intuition into a basis for principled co-design. In this paper, we put forward a framework for specifying such contracts, and we demonstrate its expressiveness and flexibility. On the hardware side, we use the framework to provide the first formalization and comparison of the security guarantees provided by a representative class of mechanisms for secure speculation. On the software side, we use the framework to characterize program properties that guarantee secure co-design in two scenarios traditionally investigated in isolation: (1) ensuring that a benign program does not leak information while computing on confidential data, and (2) ensuring that a potentially malicious program cannot read outside of its designated sandbox. Finally, we show how the properties corresponding to both scenarios can be checked based on existing tools for software verification, and we use them to validate our findings on executable code.
Reconstruction of the One-Dimensional Lebesgue Measure

Summary In the Mizar system ([1], [2]), Józef Białas has already given the one-dimensional Lebesgue measure [4]. However, the measure introduced by Białas limited the outer measure to a field with finite additivity. So, although it satisfies the nature of the measure, it cannot specify the length of measurable sets and also it cannot determine what kind of set is a measurable set. From the above, the authors first determined the length of the interval by the outer measure. Specifically, we used the compactness of the real space. Next, we constructed the pre-measure by limiting the outer measure to a semialgebra of intervals. Furthermore, by repeating the extension of the previous measure, we reconstructed the one-dimensional Lebesgue measure [7], [3].
Data management plans: time wasting or time saving?

As a Research Data Librarian, one of my responsibilities is to raise awareness about the requirements for academic researchers to write data management plans for their projects. These awareness activities, often in the form of a presentation at staff meetings or departmental seminars, are frequently followed by a sceptical response from the academic staff along the lines that “This is just something else for us to try to fit into our already over-busy lives”. My answer is usually: “Yes, but it might prevent data management problems slowing up your project”. So, what are data management plans and how can they be of value to your project?
Bayes code for two-dimensional auto-regressive hidden Markov model and its application to lossless image compression

For general lossless data compression in information theory, researchers have repeated expansion of stochastic models to express target data and design of codes for the expanded models. In this paper, we apply this approach to lossless image compression. We expand an auto-regressive hidden Markov model to a 2-dimensional model to express images containing single diagonal edge. Then, we design a Bayes code with an approximative parameter estimation by variational Bayesian methods. Experimental results for synthetic images show that the proposed model is sufficiently flexible for the target images and the parameter estimation is accurate enough. We also confirm the behavior of the proposed method on real images.
Generating Future Frames with Mask-Guided Prediction

Current approaches in video prediction tend to hallucinate the future frames directly or learn global motion transformation from the entire scene. However, it is difficult for these methods without instance-aware mechanism to learn the underlying structures, dynamics and appearances of foreground and background elements simultaneously, especially when it comes to long-term prediction. In this paper, we propose an explicit instance-level prediction approach to tackle this issue and present a novel mask-guided dual network. We utilize instance masks to extract active objects from the videos, and design two LSTM branches to predict the future dynamics and appearances for objects and backgrounds individually. Superior than most recent skeleton-aided methods that only focus on single human object with two-stage procedure, our proposed network can predict instances from other categories and be trained end-to-end with a joint loss. We evaluate our approach on KTH, Penn Action and Running Horse datasets, and achieve promising results in both quality and quantity.
Salient Object Detection by Contextual Refinement

Context plays an important role in the saliency prediction task. In this work, we propose a saliency detection framework that not only extracts visual features but also models two kinds of context including object-object relationships within a single image and scene contextual information. Specifically, we develop a novel saliency detection framework with a Contextual Refinement Module (CRM) which consists of two sub-networks, Object Relation Unit (ORU) and Scene Context Unit (SCU). ORU encodes the object-object relationship based on object relative position and object co-occurrence pattern in an image, by graphical approach, while SCU incorporates the scene contextual information of an image. Object Relation Unit (ORU) and Scene Context Unit (SCU) captures complementary contextual information to give a holistic estimation of salient regions. Extensive experiments show the effectiveness of modelling object relations and scene context in boosting the performance of saliency prediction. In particular, our frame-work outperforms the state-of-the-art models on challenging benchmark datasets.
Convex Calibrated Surrogates for the Multi-Label F-Measure

The F-measure is a widely used performance measure for multi-label classification, where multiple labels can be active in an instance simultaneously (e.g. in image tagging, multiple tags can be active in any image). In particular, the F-measure explicitly balances recall (fraction of active labels predicted to be active) and precision (fraction of labels predicted to be active that are actually so), both of which are important in evaluating the overall performance of a multi-label classifier. As with most discrete prediction problems, however, directly optimizing the F-measure is computationally hard. In this paper, we explore the question of designing convex surrogate losses that are calibrated for the F-measure -- specifically, that have the property that minimizing the surrogate loss yields (in the limit of sufficient data) a Bayes optimal multi-label classifier for the F-measure. We show that the F-measure for an $s$-label problem, when viewed as a $2^s \times 2^s$ loss matrix, has rank at most $s^2+1$, and apply a result of Ramaswamy et al. (2014) to design a family of convex calibrated surrogates for the F-measure. The resulting surrogate risk minimization algorithms can be viewed as decomposing the multi-label F-measure learning problem into $s^2+1$ binary class probability estimation problems. We also provide a quantitative regret transfer bound for our surrogates, which allows any regret guarantees for the binary problems to be transferred to regret guarantees for the overall F-measure problem, and discuss a connection with the algorithm of Dembczynski et al. (2013). Our experiments confirm our theoretical findings.
An FPGA-Optimized Architecture of Real-time Farneback Optical Flow

Optical flow estimation is a fundamental tool for computer vision applications. As a classical optical flow algorithm, Farneback version was a good blend of accuracy and runtime performance for a long time. This work presents a dataflow-based architecture of Farneback optical flow with high level synthesis (HLS) tools. Multi-dimension separable convolution, Block RAM array and deep pipeline optimization techniques are applied to reduce both space and time. The system is implemented on XC7K325T FPGA with an image size of $640 \times 480.$ It supports complete Farneback algorithm for any user-defined image pyramid levels and iteration times. It can process 31 fps, 8x faster than OpenCV version with 3 pyramid levels and 1 iteration time.
A Novel Ensemble Beta-Scale Invariant Map Algorithm

This research presents a novel topology preserving map (TPM) called Weighted Voting Supervision -Beta-Scale Invariant Map (WeVoS-Beta-SIM), based on the application of the Weighted Voting Supervision (WeVoS) meta-algorithm to a novel family of learning rules called Beta-Scale Invariant Map (Beta-SIM). The aim of the novel TPM presented is to improve the original models (SIM and Beta-SIM) in terms of stability and topology preservation and at the same time to preserve their original features, especially in the case of radial datasets, where they all are designed to perform their best. These scale invariant TPM have been proved with very satisfactory results in previous researches. This is done by generating accurate topology maps in an effectively and efficiently way. WeVoS meta-algorithm is based on the training of an ensemble of networks and the combination of them to obtain a single one that includes the best features of each one of the networks in the ensemble. WeVoS-Beta-SIM is thoroughly analyzed and successfully demonstrated in this study over 14 diverse real benchmark datasets with diverse number of samples and features, using three different well-known quality measures. In order to present a complete study of its capabilities, results are compared with other topology preserving models such as Self Organizing Maps, Scale Invariant Map, Maximum Likelihood Hebbian Learning-SIM, Visualization Induced SOM, Growing Neural Gas and Beta- Scale Invariant Map. The results obtained confirm that the novel algorithm improves the quality of the single Beta-SIM algorithm in terms of topology preservation and stability without losing performance (where this algorithm has proved to overcome other well-known algorithms). This improvement is more remarkable when complexity of the datasets increases, in terms of number of features and samples and especially in the case of radial datasets improving the Topographic Error.
Graph Random Neural Features for Distance-Preserving Graph Representations

We present Graph Random Neural Features (GRNF), a novel embedding method from graph-structured data to real vectors based on a family of graph neural networks. The embedding naturally deals with graph isomorphism and preserves the metric structure of the graph domain, in probability. In addition to being an explicit embedding method, it also allows us to efficiently and effectively approximate graph metric distances (as well as complete kernel functions); a criterion to select the embedding dimension trading off the approximation accuracy with the computational cost is also provided. GRNF can be used within traditional processing methods or as a training-free input layer of a graph neural network. The theoretical guarantees that accompany GRNF ensure that the considered graph distance is metric, hence allowing to distinguish any pair of non-isomorphic graphs.