Reviewerly/exHarmony huggingface dataset:
Investigating Multilingual Abusive Language Detection: A Cautionary Tale

Abusive language detection has received much attention in the last years, and recent approaches perform the task in a number of different languages.We investigate which factors have an effect on multilingual settings, focusing on the compatibility of data and annotations.In the current paper, we focus on English and German.Our findings show large differences in performance between the two languages.We find that the best performance is achieved by different classification algorithms.Sampling to address class imbalance issues is detrimental for German and beneficial for English.The only similarity that we find is that neither data set shows clear topics when we compare the results of topic modeling to the gold standard.Based on our findings, we can conclude that a multilingual optimization of classifiers is not possible even in settings where comparable data sets are used.
Exploiting stack-based buffer overflow using modern day techniques

One of the most commonly known vulnerabilities that can affect a binary executable is the stack-based buffer overflow. The buffer overflow occurs when a program, while writing data to a buffer, overruns the buffer's boundary and overwrites adjacent memory locations. Nowadays, due to multiple protection mechanisms enforced by the operating system and on the executable level, the buffer overflow has become harder to exploit. Multiple bypassing techniques are often required to be used in order to successfully exploit the vulnerability and control the execution flow of the studied executable. One of the security features designed as protection mechanisms is Data Execution Prevention (DEP) which helps prevent code execution from the stack, heap or memory pool pages by marking all memory locations in a process as non-executable unless the location explicitly contains executable code. Another protection mechanism targeted is the Address Space Layout Randomization (ASLR), which is often used in conjunction with DEP. This security feature randomizes the location where the system executables are loaded into memory. By default, modern day operating systems have these security features implemented. However, on the executable level, they have to be explicitly enabled. Most of the protection mechanisms, like the ones mentioned above, require certain techniques in order to bypass them and many of these techniques are using some form of address memory leakage in order to leverage an exploit. By successfully exploiting a buffer overflow, the adversary can potentially obtain code execution on the affected operating system which runs the vulnerable executable. The level of privilege granted to the adversary is highly depended on the level of privilege that the binary is executed with. As such, an adversary may gain elevated privileges inside the system. Most of the times, this type of vulnerability is used for privilege escalation attacks or for gaining remote code execution on the system.
Stochastic AUC Maximization with Deep Neural Networks

Stochastic AUC maximization has garnered an increasing interest due to better fit to imbalanced data classification. However, existing works are limited to stochastic AUC maximization with a linear predictive model, which restricts its predictive power when dealing with extremely complex data. In this paper, we consider stochastic AUC maximization problem with a deep neural network as the predictive model. Building on the saddle point reformulation of a surrogated loss of AUC, the problem can be cast into a {\it non-convex concave} min-max problem. The main contribution made in this paper is to make stochastic AUC maximization more practical for deep neural networks and big data with theoretical insights as well. In particular, we propose to explore Polyak-\L{}ojasiewicz (PL) condition that has been proved and observed in deep learning, which enables us to develop new stochastic algorithms with even faster convergence rate and more practical step size scheme. An AdaGrad-style algorithm is also analyzed under the PL condition with adaptive convergence rate. Our experimental results demonstrate the effectiveness of the proposed algorithms.
Hashtag Recommendation Using LSTM Networks with Self-Attention

On Twitter, people often use hashtags to mark the subject of a tweet. Tweets have specific themes or content that are easy for people to manage. With the increase in the number of tweets, how to automatically recommend hashtags for tweets has received wide attention. The previous hashtag recommendation methods were to convert the task into a multi-class classification problem. However, these methods can only recommend hashtags that appeared in historical information, and cannot recommend the new ones. In this work, we extend the self-attention mechanism to turn the hashtag recommendation task into a sequence labeling task. To train and evaluate the proposed method, we used the real tweet data which is collected from Twitter. Experimental results show that the proposed method can be significantly better than the most advanced method. Compared with the state-of-the-art methods, the accuracy of our method has been increased 4%.
VIRET at Video Browser Showdown 2020

During the last three years, the most successful systems at the Video Browser Showdown employed effective retrieval models where raw video data are automatically preprocessed in advance to extract semantic or low-level features of selected frames or shots. This enables users to express their search intents in the form of keywords, sketch, query example, or their combination. In this paper, we present new extensions to our interactive video retrieval system VIRET that won Video Browser Showdown in 2018 and achieved the second place at Video Browser Showdown 2019 and Lifelog Search Challenge 2019. The new features of the system focus both on updates of retrieval models and interface modifications to help users with query specification by means of informative visualizations.
A Topic Representation Model for Online Social Networks Based on Hybrid Human–Artificial Intelligence

With the widespread use of online social networks, billions of pieces of information are generated every day. How to detect new topics quickly and accurately at such data scale plays a vital role in information recommendation and public opinion control. One of the basic research tasks of topic detection is how to represent a topic. The existing topic representation models do not focus on how to select better differentiated words to represent topics, are still computer-centered, and do not effectively combine human intelligence and artificial intelligence (AI). To solve these problems, this article proposes a word-distributed sensitive topic representation model (WDS-LDA) based on hybrid human-AI (H-AI). The basic idea is that the distribution of words within a topic or among different topics has a great influence on the selection of topic expression words. If a word is evenly distributed among all documents of a certain topic, it indicates that the word is the common word of all documents in the topic, and it is more suitable to represent this topic. If a word is more evenly distributed among various topics, it indicates that the word is a common word of all topics, and cannot be used for the purpose of distinguishing among topics, becoming less suitable to represent any topic. At the same time, the human cognitive ability and cognitive models are introduced into topic representation based on H-AI. We introduce the user's modification of topic expression words into the topic model representation so that the topic model can learn human wisdom and become more and more accurate. Therefore, three different weights are introduced: inside weight; outside weight; and manual adjustment weight. The inside weight describes the uniform distribution of a word in the given topic, the outside weight describes the uniform distribution of a word in all topics, and the manual adjustment weight reflects whether a word is suitable as a representative vocabulary in the past manual adjustment. Tests using Sina microblog's actual data sets show that the WDS-LDA algorithm makes the representative words more important, the distinction among different topic words higher, and effectively improves the precision of subsequent algorithms, such as topic detection and topic evolutionary analysis using the topic model.
Towards Certificated Model Robustness Against Weight Perturbations

This work studies the sensitivity of neural networks to weight perturbations, firstly corresponding to a newly developed threat model that perturbs the neural network parameters. We propose an efficient approach to compute a certified robustness bound of weight perturbations, within which neural networks will not make erroneous outputs as desired by the adversary. In addition, we identify a useful connection between our developed certification method and the problem of weight quantization, a popular model compression technique in deep neural networks (DNNs) and a ‘must-try’ step in the design of DNN inference engines on resource constrained computing platforms, such as mobiles, FPGA, and ASIC. Specifically, we study the problem of weight quantization – weight perturbations in the non-adversarial setting – through the lens of certificated robustness, and we demonstrate significant improvements on the generalization ability of quantized networks through our robustness-aware quantization scheme.
CSI: A Coarse Sense Inventory for 85% Word Sense Disambiguation

Word Sense Disambiguation (WSD) is the task of associating a word in context with one of its meanings. While many works in the past have focused on raising the state of the art, none has even come close to achieving an F-score in the 80% ballpark when using WordNet as its sense inventory. We contend that one of the main reasons for this failure is the excessively fine granularity of this inventory, resulting in senses that are hard to differentiate between, even for an experienced human annotator. In this paper we cope with this long-standing problem by introducing Coarse Sense Inventory (CSI), obtained by linking WordNet concepts to a new set of 45 labels. The results show that the coarse granularity of CSI leads a WSD model to achieve 85.9% F1, while maintaining a high expressive power. Our set of labels also exhibits ease of use in tagging and a descriptiveness that other coarse inventories lack, as demonstrated in two annotation tasks which we performed. Moreover, a few-shot evaluation proves that the class-based nature of CSI allows the model to generalise over unseen or under-represented words.
A Big Data Analysis Method Based on Modified Collaborative Filtering Recommendation Algorithms

Abstract With the rapid development of e-commerce, collaborative filtering recommendation system has been widely used in various network platforms. Using recommendation system to accurately predict customers’ preferences for goods can solve the problem of information overload faced by users and improve users’ dependence on the network platform. Because the recommendation system based on collaborative filtering technology has the ability to recommend more abstract or difficult to describe goods in words, the research related to collaborative filtering technology has attracted more and more attention. According to the past research, in collaborative filtering algorithm, if Pearson correlation coefficient is used, errors will occur under special circumstances. In this study, the normal recovery similarity measure is used to modify the similarity value to correct the error value of a collaborative filtering recommendation algorithm. Based on this, a big data analysis method based on a modified collaborative filtering recommendation algorithm is proposed. This research implemented it in the cloud Hadoop environment, and measure the execution time with 2, 5 and 8 nodes. Then the research compared it with the execution time of a single machine, and analyze its speedup ratio and efficiency. The experimental results show that the execution time increases with the number of neighbors. When the number of nodes is 5 and 8, the execution time is greatly improved, which improves the efficiency of collaborative filtering algorithm and can cope with massive data in the future.
A Novel Hardware Architecture for Human Detection using HOG-SVM Co-Optimization

Histogram of Oriented Gradient (HOG) in combination with Supported Vector Machine (SVM) has been used as an efficient method for object detection in general and human detection in particular. Human detection using HOG-SVM in hardware shows high classification rate at higher throughput when compared with deep learning methods. However, data dependencies and complicated arithmetic in HOG feature generation and SVM classification limit the maximum throughput of these applications. In this paper, we propose a novel high-throughput hardware architecture for human detection by co-optimizing HOG feature generation and SVM classification. The throughput is improved by using a fast, highly-parallel and low-cost HOG feature generation in combination with a modified datapath for parallel computation of SVM and HOG feature normalization. The proposed architecture has been implemented in TSMC 65nm technology with a maximum operating frequency of 500MHz and throughput of 139fps for Full-HD resolution. The hardware area cost is about 145kGEs along with 242kb SRAMs.
Public-Key Encryption Secure Against Related Randomness Attacks for Improved End-to-End Security of Cloud/Edge Computing

Public-key encryption is often used to protect data security/privacy and secure communication in scenarios of cloud computing and edge computing. Related randomness attacks model (RRA) for public-key encryption was motivated by randomness failures. This paper proposes some methods of constructing secure public-key encryption scheme against related randomness attacks, i.e. RRA-CPA secure public-key encryption scheme with efficient decryption algorithm and short ciphertexts size obtained from one-way function with weak RKA-security and indistinguishability obfuscation, RRA-CPA secure public-key encryption scheme against arbitrarily function from any publicly deniable encryption and RRA-CCA secure public-key encryption scheme against arbitrarily function from standard IND-CCA public-key encryption scheme with a hardcore function for arbitrarily correlated inputs.
Predicting webpage aesthetics with heatmap entropy

This paper introduces a descriptive global index for eye-tracking data called heatmap entropy, or visual attention entropy (VAE), and discerns its predictive value for webpage aesthetics. VAE represents the chaos, or uncertainty, in the allocation of visual attention. In the experiment, we tracked and recorded 30 observers' initial landings on 40 web pages displayed for 3 seconds each. The results show that the VAE and aesthetic ratings of the web pages are negatively correlated (r=−0.54, P<0.001). A calibrated form of VAE, known as relative VAE (rVAE), has a more significant correlation with the aesthetic ratings (r=−0.65, P<0.00001). On its own, the rVAE can differentiate between good- and bad-looking pages to a certain degree of accuracy (two-class ANOVA with F=26.84, P<0.00001). Further investigation reveals that the performances of both VAE and rVAE improve steadily after the first second, and could be better, if the tracking duration was longer than 3 seconds or if more observers were recruited.
Agnostic Feature Selection

Unsupervised feature selection is mostly assessed along a supervised learning setting, depending on whether the selected features efficiently permit to predict the (unknown) target variable. Another setting is proposed in this paper: the selected features aim to efficiently recover the whole dataset. The proposed algorithm, called AgnoS, combines an AutoEncoder with structural regularizations to sidestep the combinatorial optimization problem at the core of feature selection. The extensive experimental validation of AgnoS on the scikit-feature benchmark suite demonstrates its ability compared to the state of the art, both in terms of supervised learning and data compression.
Consumption Analysis of Smartphone based Fall Detection Systems with Multiple External Wireless Sensors

Fall Detection Systems (FDSs) based on wearable technologies have gained much research attention in recent years. Due to the networking and computing capabilities of smartphones, these widespread personal devices have been proposed to deploy cost-effective wearable systems intended for automatic fall detection. In spite of the fact that smartphones are natively provided with inertial sensors (accelerometers and gyroscopes), the effectiveness of a smartphone-based FDS can be improved if it also exploits the measurements collected by small low-power wireless sensors, which can be firmly attached to the user’s body without causing discomfort. For these architectures with multiple sensing points, the smartphone transported by the user can act as the core of the FDS architecture by processing and analyzing the data measured by the external sensors and transmitting the corresponding alarm whenever a fall is detected. In this context, the wireless communications with the sensors and with the remote monitoring point may impact on the general performance of the smartphone and, in particular, on the battery lifetime. In contrast with most works in the literature (which disregard the real feasibility of implementing an FDS on a smartphone), this paper explores the actual potential of current commercial smartphones to put into operation an FDS that incorporates several external sensors. This study analyzes diverse operational aspects that may influence the consumption (as the use of a GPS sensor, the coexistence with other apps, the retransmission of the measurements to an external server, etc.) and identifies practical scenarios in which the deployment of a smartphone-based FDS is viable.
Effect of Body Representation Level of an Avatar on Quality of AR-Based Remote Instruction

In manufacturing, augmented reality (AR)-based remote instruction systems, which enable workers to receive instructions from an avatar, are widely used. In this study, we developed such a system and investigated the effect of the body representation level of the avatar on the quality of AR-based remote instruction. Drawing on the avatar designs of previous works, three different avatar designs (“Hand only”, “Hand + Arm”, and “Body”), representing three body representation levels, were created. In the experiment with a within-participant design, the avatar pointed at blocks sequentially and participants touched each block as soon as they identified it. The results of the experiment indicate that an AR-based remote instruction system with a “Body” avatar exhibits higher usability and can enable the participants to have a lower workload and higher efficiency.
Prediction of Endpoint Sulfur Content in KR Desulfurization Based on the Hybrid Algorithm Combining Artificial Neural Network With SAPSO

In the present work, the endpoint sulfur content prediction model of Kambara Reactor (KR) desulfurization in the steelmaking process is investigated.For Artificial Neural Network (ANN), the effects of different structure parameters, including the number of hidden layer neurons, activation functions and training functions, on the performance of desulfurization model are studied.The initial weights and biases of the neural network is optimized to further elevated the prediction accuracy of the model.Three models established by using Multiple Linear Regression (MLR), ANN and a hybrid algorithm (artificial neural network optimized by SAPSO, named SAPSO-ANN) are compared by the Correlation Coefficient (R), Root Mean Square Error (RMSE) and Mean Absolute Relative Error (MARE).The results show that in the process of KR desulfurization, the nonlinear model of ANN and SAPSO-ANN has a higher accuracy than the linear model of MLR.Among the three models, the SAPSO-ANN model achieves the highest accuracy with R value of 0.54, RMSE of 2.61×10 -4 % and MAER of 0.47, which is selected to analyze the effect of process parameters on the desulfurization rate and design the amount of desulfurization flux in the KR desulfurization process.Experimental results show good agreements with the calculation results, indicating the practicability of the model.
Detection and Localisation of Life Signs from the Air Using Image Registration and Spatio-Temporal Filtering

In search and rescue operations, it is crucial to rapidly identify those people who are alive from those who are not. If this information is known, emergency teams can prioritize their operations to save more lives. However, in some natural disasters the people may be lying on the ground covered with dust, debris, or ashes making them difficult to detect by video analysis that is tuned to human shapes. We present a novel method to estimate the locations of people from aerial video using image and signal processing designed to detect breathing movements. We have shown that this method can successfully detect clearly visible people and people who are fully occluded by debris. First, the aerial videos were stabilized using the key points of adjacent image frames. Next, the stabilized video was decomposed into tile videos and the temporal frequency bands of interest were motion magnified while the other frequencies were suppressed. Image differencing and temporal filtering were performed on each tile video to detect potential breathing signals. Finally, the detected frequencies were remapped to the image frame creating a life signs map that indicates possible human locations. The proposed method was validated with both aerial and ground recorded videos in a controlled environment. Based on the dataset, the results showed good reliability for aerial videos and no errors for ground recorded videos where the average precision measures for aerial videos and ground recorded videos were 0.913 and 1 respectively.
Conceptual Model of Visual Analytics for Hands-on Cybersecurity Training

Hands-on training is an effective way to practice theoretical cybersecurity concepts and increase participants' skills. In this article, we discuss the application of visual analytics principles to the design, execution, and evaluation of training sessions. We propose a conceptual model employing visual analytics that supports the sensemaking activities of users involved in various phases of the training life cycle. The model emerged from our long-term experience in designing and organizing diverse hands-on cybersecurity training sessions. It provides a classification of visualizations and can be used as a framework for developing novel visualization tools supporting phases of the training life-cycle. We demonstrate the model application on examples covering two types of cybersecurity training programs.