Reviewerly/exHarmony huggingface dataset:
A semantic approach for document classification using deep neural networks and multimedia knowledge graph

The amount of available multimedia data in different formats and from different sources increases everyday. From an information retrieval point of view, this high volume and heterogeneity of data involves several issues to be addressed related to information overload and lacks of well structured information. Even if modern information retrieval systems offer to the user manifold search options, it is still hard to find systems with optimal performances in the document seeking process starting from a given topic. In recent years, several frameworks have been proposed and developed to support this task based on different models and techniques. In this paper we propose a semantic approach to document classification using both textual and visual topic detection techniques based on deep neural networks and multimedia knowledge graph. A semantic multimedia knowledge base has been exploited and several experimental results show the effectiveness of our proposed approach.
Open-Set Adversarial Defense

Open-set recognition and adversarial defense study two key aspects of deep learning that are vital for real-world deployment. The objective of open-set recognition is to identify samples from open-set classes during testing, while adversarial defense aims to defend the network against images with imperceptible adversarial perturbations. In this paper, we show that open-set recognition systems are vulnerable to adversarial attacks. Furthermore, we show that adversarial defense mechanisms trained on known classes do not generalize well to open-set samples. Motivated by this observation, we emphasize the need of an Open-Set Adversarial Defense (OSAD) mechanism. This paper proposes an Open-Set Defense Network (OSDN) as a solution to the OSAD problem. The proposed network uses an encoder with feature-denoising layers coupled with a classifier to learn a noise-free latent feature representation. Two techniques are employed to obtain an informative latent feature space with the objective of improving open-set performance. First, a decoder is used to ensure that clean images can be reconstructed from the obtained latent features. Then, self-supervision is used to ensure that the latent features are informative enough to carry out an auxiliary task. We introduce a testing protocol to evaluate OSAD performance and show the effectiveness of the proposed method in multiple object classification datasets. The implementation code of the proposed method is available at: https://github.com/rshaojimmy/ECCV2020-OSAD .
On the Potential of Numerical Association Rule Mining

In association rule mining, both the classical algorithms and today's available tools either use binary data items or discretized data. However, in real-world scenarios, data are available in many different forms (numerical, text) and these types of data items are not supported in the classical association rule mining algorithms. There are some association rule mining algorithms that have been proposed for numerical data items but unfortunately, for working data scientists and decision makers, it is challenging to find concrete algorithms that fit their purposes best. Therefore, it is highly desired to have a study on the different existing numerical association rule mining algorithms (NARM). In this paper, we provide such a detailed study by thoroughly reviewing 24 NARM algorithms from different categories (optimization, discretization, distribution).
Recursive Proof Composition from Accumulation Schemes

Recursive proof composition has been shown to lead to powerful primitives such as incrementally-verifiable computation (IVC) and proof-carrying data (PCD). All existing approaches to recursive composition take a succinct non-interactive argument of knowledge (SNARK) and use it to prove a statement about its own verifier. This technique requires that the verifier run in time sublinear in the size of the statement it is checking, a strong requirement that restricts the class of SNARKs from which PCD can be built. This in turn restricts the efficiency and security properties of the resulting scheme. Bowe, Grigg, and Hopwood (ePrint 2019/1021) outlined a novel approach to recursive composition, and applied it to a particular SNARK construction which does not have a sublinear-time verifier. However, they omit details about this approach and do not prove that it satisfies any security property. Nonetheless, schemes based on their ideas have already been implemented in software. In this work we present a collection of results that establish the theoretical foundations for a generalization of the above approach. We define an accumulation scheme for a non-interactive argument, and show that this suffices to construct PCD, even if the argument itself does not have a sublinear-time verifier. Moreover we give constructions of accumulation schemes for SNARKs, which yield PCD schemes with novel efficiency and security features.
Detection of Tor Traffic using Deep Learning

Tor, originally known as The Onion Router, is a free software that allows users to communicate anonymously on the Internet. This makes Tor attractive to cyber criminals, and the anonymity provided can be misused by hackers to enable remote control of victim systems. Indeed, a large volume of Tor traffic is used for malicious purposes such as fast port scans, hacking attempts, ex-filtration of stolen credentials, etc. This makes Tor traffic detection an important component of intrusion detection and prevention systems. Hence, in this paper we present a deep neural network (DNN) based system for the detection and classification of encrypted Tor traffic. The system achieved 99.89% accuracy in the classification of Tor and non-Tor traffic on the UNB-CIC Tor network dataset. Experiments conducted for classifying Tor traffic types demonstrated an accuracy of 95.6%, which is 6.2% higher than previous work on the same dataset. Additionally, the robustness of the proposed DNN classifier is evaluated using adversarial samples generated from a Generative Adversarial Network (GAN). We observed that 100% of the adversarial examples were unidentified by the DNN classifiers. Further retraining of the DNN classifiers with adversarial examples eventually improved their robustness against the adversarial attack.
Deep Learning based Optimal Multimodal Fusion Framework for Intrusion Detection Systems for Healthcare Data

Data fusion is a multidisciplinary research area that involves different domains.It is used to attain minimum detection error probability and maximum reliability with the help of data retrieved from multiple healthcare sources.The generation of huge quantity of data from medical devices resulted in the formation of big data during which data fusion techniques become essential.Securing medical data is a crucial issue of exponentially-pacing computing world and can be achieved by Intrusion Detection Systems (IDS).In this regard, since singularmodality is not adequate to attain high detection rate, there is a need exists to merge diverse techniques using decision-based multimodal fusion process.In this view, this research article presents a new multimodal fusion-based IDS to secure the healthcare data using Spark.The proposed model involves decision-based fusion model which has different processes such as initialization, pre-processing, Feature Selection (FS) and multimodal classification for effective detection of intrusions.In FS process, a chaotic Butterfly Optimization (BO) algorithm called CBOA is introduced.Though the classic BO algorithm offers effective exploration, it fails in achieving faster convergence.In order to overcome this, i.e., to improve the convergence rate, this research work modifies the required parameters of BO algorithm using chaos theory.Finally, to detect intrusions, multimodal classifier is applied by incorporating three Deep Learning (DL)-based classification models.Besides, the concepts like Hadoop MapReduce and Spark were also utilized in this study to achieve faster computation of big data in parallel computation platform.To validate the outcome of the presented model, a series of experimentations was performed using the benchmark NSLKDDCup99 Dataset repository.The proposed model demonstrated its effective results on the applied dataset by offering the maximum accuracy of 99.21%, precision of 98.93% and detection rate of 99.59%.The results assured the betterment of the proposed model.
Bio-signal data sharing security through watermarking: a technical survey

Due to smart healthcare systems highly connected information and communications technologies, sensitive medical information and records are easily transmitted over the networks. However, stealing of healthcare data is increasing crime every day to greatly impact on financial loss. In order to this, researchers are developing various cost-effective bio-signal based data hiding techniques for smart healthcare applications. In this paper, we first introduce various aspects of data hiding along with major properties, generic embedding and extraction process, and recent applications. This survey provides a comprehensive survey on data hiding techniques, and their new trends for solving new challenges in real-world applications. Then, we survey the various notable bio-signal based data hiding techniques. The summary of some notable techniques in terms of their objective, type of data hiding, methodology and database used, performance metrics, important features, and limitations are also presented in tabular form. At the end, we discuss the major issues and research directions to explore the promising areas for future research.
Turkish Sentiment Analysis Using BERT

While sentiment analysis is a popular research area, most of the research has been conducted for English and the number of studies for Turkish are rather limited. Limited resources for Turkish natural language processing (NLP) is one of the major challenges for Turkish NLP research. In order to overcome these limitations, we propose two approaches for Turkish sentiment analysis: 1) fine tuning multilingual model of BERT 2) using main model of BERT after machine translation of Turkish texts into English. We conducted experiments on Turkish movie and hotel review datasets where each review is labeled either positive or negative. Our methods achieve high accuracy scores such that in the movie dataset, our BERT models outperform existing methods.
Quasi-Periodic Parallel WaveGAN: A Non-Autoregressive Raw Waveform Generative Model With Pitch-Dependent Dilated Convolution Neural Network

In this paper, we propose a quasi-periodic parallel WaveGAN (QPPWG) waveform generative model, which applies a quasi-periodic (QP) structure to a parallel WaveGAN (PWG) model using pitch-dependent dilated convolution networks (PDCNNs). PWG is a small-footprint GAN-based raw waveform generative model, whose generation time is much faster than real time because of its compact model and non-autoregressive (non-AR) and non-causal mechanisms. Although PWG achieves high-fidelity speech generation, the generic and simple network architecture lacks pitch controllability for an unseen auxiliary fundamental frequency (F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> ) feature such as a scaled F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> . To improve the pitch controllability and speech modeling capability, we apply a QP structure with PDCNNs to PWG, which introduces pitch information to the network by dynamically changing the network architecture corresponding to the auxiliary F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> feature. Both objective and subjective experimental results show that QPPWG outperforms PWG when the auxiliary F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> feature is scaled. Moreover, analyses of the intermediate outputs of QPPWG also show better tractability and interpretability of QPPWG, which respectively models spectral and excitation-like signals using the cascaded fixed and adaptive blocks of the QP structure.
Deep Learning-Based Natural Language Processing for Screening Psychiatric Patients

The introduction of pre-trained language models in natural language processing (NLP) based on deep learning and the availability of electronic health records (EHRs) presents a great opportunity to transfer the “knowledge” learned from data in the general domain to enable the analysis of unstructured textual data in clinical domains. This study explored the feasibility of applying NLP to a small EHR dataset to investigate the power of transfer learning to facilitate the process of patient screening in psychiatry. A total of 500 patients were randomly selected from a medical center database. Three annotators with clinical experience reviewed the notes to make diagnoses for major/minor depression, bipolar disorder, schizophrenia, and dementia to form a small and highly imbalanced corpus. Several state-of-the-art NLP methods based on deep learning along with pre-trained models based on shallow or deep transfer learning were adapted to develop models to classify the aforementioned diseases. We hypothesized that the models that rely on transferred knowledge would be expected to outperform the models learned from scratch. The experimental results demonstrated that the models with the pre-trained techniques outperformed the models without transferred knowledge by micro-avg. and macro-avg. F-scores of 0.11 and 0.28, respectively. Our results also suggested that the use of the feature dependency strategy to build multi-labeling models instead of problem transformation is superior considering its higher performance and simplicity in the training process.
Cybersecurity awareness training programs: a cost–benefit analysis framework

Purpose Employees must receive proper cybersecurity training so that they can recognize the threats to their organizations and take the appropriate actions to reduce cyber risks. However, many cybersecurity awareness training (CSAT) programs fall short due to their misaligned training focuses. Design/methodology/approach To help organizations develop effective CSAT programs, we have developed a theoretical framework for conducting a cost–benefit analysis of those CSAT programs. We differentiate them into three types of CSAT programs (constant, complementary and compensatory) by their costs and into four types of CSAT programs (negligible, consistent, increasing and diminishing) by their benefits. Also, we investigate the impact of CSAT programs with different costs and the benefits on a company's optimal degree of security. Findings Our findings indicate that the benefit of a CSAT program with different types of cost plays a disparate role in keeping, upgrading or lowering a company's existing security level. Ideally, a CSAT program should spend more of its expenses on training employees to deal with the security threats at a lower security level and to reduce more losses at a higher security level. Originality/value Our model serves as a benchmark that will help organizations allocate resources toward the development of successful CSAT programs.
Model Patching: Closing the Subgroup Performance Gap with Data Augmentation

Classifiers in machine learning are often brittle when deployed. Particularly concerning are models with inconsistent performance on specific subgroups of a class, e.g., exhibiting disparities in skin cancer classification in the presence or absence of a spurious bandage. To mitigate these performance differences, we introduce model patching, a two-stage framework for improving robustness that encourages the model to be invariant to subgroup differences, and focus on class information shared by subgroups. Model patching first models subgroup features within a class and learns semantic transformations between them, and then trains a classifier with data augmentations that deliberately manipulate subgroup features. We instantiate model patching with CAMEL, which (1) uses a CycleGAN to learn the intra-class, inter-subgroup augmentations, and (2) balances subgroup performance using a theoretically-motivated subgroup consistency regularizer, accompanied by a new robust objective. We demonstrate CAMEL’s effectiveness on 3 benchmark datasets, with reductions in robust error of up to 33% relative to the best baseline. Lastly, CAMEL successfully patches a model that fails due to spurious features on a real-world skin cancer dataset.
FASHE: A FrActal Based Strategy for Head Pose Estimation

Head pose estimation (HPE) represents a topic central to many relevant research fields and characterized by a wide application range. In particular, HPE performed using a singular RGB frame is particular suitable to be applied at best-frame-selection problems. This explains a growing interest witnessed by a large number of contributions, most of which exploit deep learning architectures and require extensive training sessions to achieve accuracy and robustness in estimating head rotations on three axes. However, methods alternative to machine learning approaches could be capable of similar if not better performance. To this regard, we present FASHE, an approach based on partitioned iterated function systems (PIFS) to represent auto-similarities within face image through a contractive affine function transforming the domain blocks extracted only once by a single frontal reference image, in a good approximation of the range blocks which the target image has been partitioned into. Pose estimation is achieved by finding the closest match between fractal code of target image and a reference array by means of Hamming distance. The results of experiments conducted exceed the state of the art on both Biwi and Ponting'04 datasets as well as approaching those of the best performing methods on the challenging AFLW2000 database. In addition, the applications to GOTCHA Video Dataset demonstrate that FASHE successfully operates in-the-wild.
Knowledge management and firm innovative performance with the moderating role of transformational leadership

Purpose This study aims to examine the effect of knowledge management on firm innovative performance and the moderating effect of transformational leadership in the relationship between knowledge management and firm innovative performance. Design/methodology/approach In total, 200 managers of participating Malaysian public listed service companies responded to a self-report set of the survey questionnaire. Partial least squares-structural equation modelling technique is used to estimate the main effects of knowledge management, particularly its infrastructures and processes, on firm innovative performance and the moderating effects of transformational leadership on the relationship. Findings Knowledge management infrastructures and knowledge management processes both have statistically significant and positive effects on firm innovative performance. In addition, transformational leadership significantly and negatively moderates the relationships. Practical implications The findings of this study can be a reference for the Malaysian public listed service companies to understand how and why managing well knowledge management infrastructures and processes can improve firm innovative performance. Moreover, this study highlights the role of transformational leaders in the context of knowledge management. Originality/value This study brings about managerial viewpoints of the relationship between knowledge management and firm innovative performance, with the moderating role of transformational leadership.
10 Tense variation in Ptolemaic papyri: Towards a grammar of epistolary dialogue

This study investigates, through a qualitative approach, the distribution of finite verb forms expressing absolute time within a small corpus of Ptolemaic private papyrus letters, particularly focusing on the use of the present, perfect and aorist indicatives. Since sender and recipient do not share the same chrono-topic coordinates, compared to other forms of discourse, the epistolary communication, which is time-delayed and mediated by letters, offers an interesting perspective on the use of those tenses that are anchored to the moment of utterance. As a result, the sender’s point of view may shift from his/her own present (i.e. the moment of the encoding) to the receiver’s (i.e. the moment of the decoding), alternatively assumed as reference points for the statement. Scholars report similar cases in letters only for the past tenses (accordingly labelled as epistolary), through which - unexpectedly - sometimes the senders describe events occurring at the moment of writing. However, our survey highlights a number of inconsistencies in the anchoring of the verb not only in the past tenses, but also in the primary stems here under examination. In conclusion, the notion of epistolary tense - traditionally restricted to past tenses - may therefore represent a heuristic tool enabling a better understanding of the category of grammatical tense in letters.
Adaptive Knowledge Transfer in Multifactorial Evolutionary Algorithm for the Clustered Minimum Routing Cost Problem

Having tremendous promising impact on society, the Internet of Things refers to the connection of millions of worldwide devices to communicate and exchange data, which requires to aggregate similar devices into clusters for maximizing the network efficiency. Minimum Routing Cost Clustered Tree Problem (CluMRCT) is a lately investigated topic that has significant application towards enhancing the interconnectivity among various devices. Belonging to NP-Hard class, the CluMRCT problem can be solved effectively by a meta-heuristic approach such as Multifactorial Evolutionary Algorithm (MFEA), which can facilitate to find better solutions for multiple problems simultaneously. Recently, an improved framework (called MFEA-II) has been introduced to overcome the weakness of algorithm performance governed by the degree of underlying inter-task synergies in the previous version. Therefore, this paper proposes to apply MFEA-II for solving multiple CluMRCT problems concurrently with the population representation under a probabilistic distribution model adept at online learning. In the proposed algorithm, evolutionary operators are applied in two levels: the first level is to construct a spanning tree in each cluster, while the second one builds a spanning tree for connecting all clusters. To reduce consuming resources, this paper also introduces a new method to calculate the cost of CluMRCT solution in linear time complexity. Numerous types of test instances are implemented to demonstrate the effectiveness of the proposed algorithm when its performance surpassed other state-of-the-art algorithms in most of the datasets.