Reviewerly/exHarmony huggingface dataset:
Attention-Translation-Relation Network for Scalable Scene Graph Generation

We find that most Scene Graph Generation approaches suffer from two limitations as they: 1) use generic attention mechanisms and dataset-specific statistics that supersede visual features and 2) treat "no interaction" as an extra, both noisy and dominant, class and prune graph edges manually or applying simple filters. As a result, such approaches do not scale up on different settings and specifications. We propose a three-stage pipeline that employs Multi-Head Attention driven by language and spatial features, Translation Embeddings and Multi-Tasking to detect an interacting pair of objects. Our attentional scheme is able to maximize the visual features' interpretability, as well as to capture the nature of datasets of different scales, while multi-tasking robustly resolves the bias of the background class. We present an experimental overview of the related literature, unveil a multitude of evaluation inconsistencies and provide quantitative and qualitative support with experiments on a variety of datasets, where our approach performs on par or even outperforms current state-of-the-art.
Sentiment Analysis of Positive and Negative of YouTube Comments Using Naïve Bayes – Support Vector Machine (NBSVM) Classifier

Sentiment analysis on the YouTube video comments is a process of understanding, extracting, and processing textual data automatically to obtain sentiment information contained in one sentence of YouTube video comment. Text mining approach becomes the best alternative to interpret the meaning of each comment. The classification of positive and negative content becomes very important for the YouTube user to assess how meaningful the content that has been published is based on user opinion. Naïve Bayes and Support Vector Machine is extensively used as a basic line in tasks related to texts but the performance varies significantly in all variants, features, and numbers of data collection. Naïve Bayes is very good in classifying texts with the small number of data and document snippets while Support Vector is very good in classifying texts with relatively many numbers of data or full-length document. The combination of Naïve Bayes and Support Vector Machine produces better accuracy level and stronger performance with the use of a 7:3 scale of data that is 70% training data and 30% testing data. By producing the highest performance test values, namely precision of 91%, recall of 83% and flscore of 87%.
Review: Phishing Detection Approaches

Phishing is one of the most common attacks on the internet that employs social engineering techniques like deceiving user with forged websites in an attempt to gain sensitive information such as credentials and credit card details. This information can be misused, resulting in large financial losses to these users. Phishing detection algorithms can be an effective approach to safeguarding users from such attacks. This paper will review different phishing detection approaches which include: Content-Based, Heuristic-Based, and Fuzzy rule-based approaches.
A Framework for the Evaluation of Trainee Performance in Cyber Range Exercises

This paper proposes a novel approach for the evaluation of the performance achieved by trainees involved in cyber security exercises implemented in modern cyber ranges. Our main contributions include: the definition of a distributed monitoring architecture for gathering relevant information about trainees activities; an algorithm for modeling the trainee activities using directed graphs; novel scoring algorithms, based on graph operations, that evaluate different aspects (speed, precision) of a trainee during an exercise. With respect to previous work, our proposal allows to measure exactly how fast a user is progressing towards an objective and where he does wrong. We highlight that this is currently not possible in the most popular cyber ranges.
Quality of Experience of Smart-Wearables: From Fitness-Bands to Smartwatches

Of late, there has been a steep rise in the smart-wearable market. To continue this growth, it is necessary to maintain a certain level of quality of experience (QoE) as perceived by the end-users. This article attempts to create a QoE model based upon the end-user perception of using smartwatches and fitness-trackers. The QoE is evaluated from a dual perspective of the quality of data (QoD) and the quality of information (QoI) collected from these smart-wearables. QoD gives a measure of the data accuracy and precision, while QoI is representative of the information quality obtained after doing analytics on the collected data. A total of 20 participants are involved in a subjective experiment, which uses five different types of wearables for the purpose of QoE evaluation.
Universal Flying Objects: Modular Multirotor System for Flight of Rigid Objects

In this article, we introduce Universal Flying Objects-a modular aerial robotic platform for transforming a rigid object into a multirotor robot. To achieve this, we develop flight modules, in the form of a control module and propelling modules that can be affixed to an object. The object, or payload, serves as the airframe of the vehicle. The modular design produces a highly versatile platform as it is reconfigurable by the addition or removal of flight modules, adjustment of the modules' arrangement, or change of payloads. To facilitate the flight control, we propose an inertial measurement unit (IMU)-based estimation strategy for rapid computation of the robot's configuration. When combined with the adaptive geometric controller for further refinement of uncertain parameters, stable flights are accomplished with minimal manual intervention or tuning required by a user. To this end, we demonstrate hovering and trajectory tracking flights through various robot configurations with different dummy payloads, weighing ≈200-800 g, using four to eight propelling modules. The results reveal that stable flights are attainable thanks to the proposed IMU-based estimation method. The flight performance is markedly improved over time through the adaptive scheme, with position errors of a few centimeters after the parameter convergence.
Secret image sharing with separate shadow authentication ability

Secret image sharing (SIS) is attracting increasing attention due to its wide application to the distributed storage of digital images in a cloud and the importance of digital images. Shadow authentication plays an important role in the practical application of SIS. However, most SIS schemes with shadow authentication ability embed each shadow into a cover image using an existing information hiding technique, which leads to high generation and recovery (authentication) complexity and pixel expansion. In this paper, we propose an SIS scheme for a (k,n) threshold with a separate shadow authentication ability based on SIS itself rather than information hiding through applying visual secret sharing (VSS) to polynomial-based SIS by using a screening operation. The proposed scheme has low generation and recovery (authentication) complexity and no pixel expansion with a separate shadow authentication ability. In addition, it achieves lossless recovery without auxiliary encryption. We validate the proposed scheme by means of illustrations, theoretical analyses and comparisons.
A comparison of EOG baseline drift mitigation techniques

Electrooculography (EOG) is an eye movement recording technique based on the electrical activity due to the eyes, which may be used to develop human computer interfaces. The EOG signal baseline is subject to drifting and, although several baseline drift mitigation techniques have been proposed in the literature, the specific technique and the corresponding parameters are generally arbitrarily chosen. Furthermore, the literature does not establish which is the most suitable technique. Hence, this work aims to review these different techniques, and qualitatively and quantitatively compare their performance in mitigating the baseline drift using the same EOG data. This dataset is also being made publicly available to serve as a benchmark for future work. The state-of-the-art baseline drift mitigation techniques, namely, frequent DC reference resetting, signal differencing, high-pass filtering, wavelet decomposition and polynomial fitting, were implemented and statistically compared. Generally, frequent resetting and signal differencing were statistically significantly better than the other techniques. Furthermore, high-pass filtering and wavelet decomposition had statistically similar performance, while the polynomial fitting technique was never superior to the other techniques. While frequent resetting and signal differencing gave the best performance, the former disrupts the user's interaction with the system whereas the latter undesirably changes the EOG signal morphology. From the remaining techniques, high-pass filtering and wavelet decomposition would be the most suitable, but only the former would be applicable to real-time applications. This work compares five state-of-the-art EOG baseline drift mitigation techniques and provides a guideline for future work.
GradientFlow: Optimizing Network Performance for Large-Scale Distributed DNN Training

It is important to scale out deep neural network (DNN) training for reducing model training time. The high communication overhead is one of the major performance bottlenecks for distributed DNN training across multiple GPUs. Our investigations have shown that popular open-source DNN systems could only achieve 2.5 speedup ratio on 64 GPUs connected by 56 Gbps network. To address this problem, we propose a communication backend named GradientFlow for distributed DNN training, and employ a set of network optimization techniques. First, we integrate ring-based allreduce, mixed-precision training, and computation/communication overlap into GradientFlow. Second, we propose lazy allreduce to improve network throughput by fusing multiple communication operations into a single one, and design coarse-grained sparse communication to reduce network traffic by only transmitting important gradient chunks. When training AlexNet and ResNet-50 on the ImageNet dataset using 512 GPUs, our approach could achieve 410.2 and 434.1 speedup ratio, respectively.
Long-term verification of signatures based on a blockchain

Digitally signed documents must remain stored for many years. In this paper, a scheme that would allow maintaining signature validity without the necessity to use timestamps from trusted third parties is proposed. According to the scheme, after inserting data into a blockchain, a user can store a signed document in his storage without the need to perform any maintenance actions in the future. The Round-based Blockchain Time-stamping Scheme is proposed that is scalable, i.e., it requires embedding a constant number of bytes into a blockchain independent from a number of input documents. The scheme allows to prove that a document existed not only before a certain date, but after a certain date as well. Moreover, the purpose of the scheme is to meet non-repudiation requirements for digitally signed documents. The scheme allows verifying signature validity using a chain model and under some conditions using a modified shell model.
Analysis Accuracy Of Forecasting Measurement Technique On Random K-Nearest Neighbor (RKNN) Using MAPE And MSE

Abstract Forecasting is apply because of complexity and uncertainty faced by high-dimensional data available in the fields of bioinformatics, chemometrics, banking and other applications. A process for systematically estimating what is most likely to happen in the future based on past and present data requires an appropriate forecasting model, so that the difference between what happens and the estimated results can be minimized. To get the right method, a measuring technique is needed to detect the accuracy of forecasting value. In this paper we discuss the technique of measuring forecasting accuracy with Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE) using the Random K-Nearest Neighbor (RKNN) method. With the two measuring technique for the horizontal modeling above, the smallest MSE and MAPE values are chosen (the smallest error value). From the results of the analysis of the calculation of forecasting accuracy measurement values during training with RKNN, the MAPE accuracy value is 0.728427% and MSE is 0.545751, while the smallest accuracy value is achieved using MSE which is 0.545751.
Sliding Mode Control of Interval Type-2 Fuzzy Systems Under Round-Robin Scheduling Protocol

In this article, the sliding mode control (SMC) design problem is investigated for a class of discrete-time interval type-2 fuzzy systems, in which the scheduling of sensors is ruled by the round-Robin communication protocol. This means that at any time, only one sensor node can transmit its value to the controller side. To deal with this phenomenon, a compensation scheme is proposed for other sensor nodes, based on which the past measured signal stored in the corresponding buffers may be utilized by the controller. And then, a token-dependent sliding mode controller is synthesized. Sufficient conditions are derived such that the system states can reach a neighborhood of the sliding surface and the resultant closed-loop fuzzy system is input-to-state stable. Finally, simulation results verify the effectiveness of the proposed SMC method.
Automated Misspelling Detection and Correction in Persian Clinical Text

Accurate electronic health records are important for clinical care, research, and patient safety assurance. Correction of misspelled words is required to ensure the correct interpretation of medical records. In the Persian language, the lack of automated misspelling detection and correction system is evident in the medicine and health care. In this article, we describe the development of an automated misspelling detection and correction system for radiology and ultrasound’s free texts in the Persian language. To achieve our goal, we used n-gram language model and three different types of free texts related to abdominal and pelvic ultrasound, head and neck ultrasound, and breast ultrasound reports. Our system achieved the detection performance of up to 90.29% for radiology and ultrasound’s free texts with the correction accuracy of 88.56%. Results indicated that high-quality spelling correction is possible in clinical reports. The system also achieved significant savings during the documentation process and final approval of the reports in the imaging department.
An Overview of Digital Audio Steganography

Steganography deals with concealing of some messages via some media, which in digital domain is made over speech, image, or video. In the current generation, privacy-related matters are of concern to every individual that makes steganography to have an imperative role in real-world applications. Audio signals are used as covers more frequently as compared with the other cover signals; the reason being their larger size so that more information can be concealed in the cover. Moreover, digital audio signals possess higher redundancy and high data transmission rate that make these suitable for used as covers. This work focuses on audio steganography-based approaches that have effectuated attention from different researchers with its applicability to audio and speech signal processing for communicating information. In this work, different techniques, along with their algorithms and principles, useful in this regard are reviewed and a compendium to digital audio steganography is presented. Additionally, a comparative study among the existing approaches has been provided along with a critical review that can help the community to investigate particular approach according to the requirement.
Reduction of JPEG compression artifacts based on DCT coefficients prediction

The image compression-decompression process causes image quality degradations such as blocking and ringing artifacts. A convolutional neural network based on DCT domain is proposed to learn the mapping relationship between JPEG images and original images to reduce compression artifacts in this work. The convolutional neural network can exploit the prior knowledge of JPEG compression in DCT domain. The compression artifacts reduction network which is proposed in this work has three advantages. First, it can expand the receptive field and eliminate the discontinuity between each 8 × 8 block by overlapping extraction of patches. Second, it is based on the essence of distortion and can predict true DCT coefficients more accurately to reduce the loss of JPEG images. Third, it can obviously reduce the compression artifacts in JPEG images. Experiments on compressed images demonstrate that our approach achieves state-of-the-art performance in both the objective parameters and the subjective visual quality.
Improving smart interactive experiences in cultural heritage through pattern recognition techniques

New Information and Communication Technologies have a large potential to improve general public awareness of the importance of Cultural Heritage (CH) and to provide tools that can make visits to historical sites more interesting and enjoyable. The Internet of Things (IoT) technology can further contribute to these goals, by allowing visitors to museum and CH sites to manipulate smart objects by receiving information that stimulates emotions, understanding and appropriation of the contents. In our research, interaction paradigms and innovative methods are developed to allow curators and guides of cultural sites (i.e., domain experts) to manage interactive IoT-based environments, in order to create Smart Interactive Experiences, which are usage situations created by synchronizing many available smart objects to specific situations that might better satisfy the needs of the visitors. This article illustrates a system that, by means of a tangible user interface, integrated by pattern recognition and computer vision techniques, supports CH experts in creating Smart Interactive Experiences by properly tailoring the behavior of the involved smart objects. An experimental evaluation of the used techniques has been performed and it is presented and discussed.
Using Social Networks to Detect Malicious Bangla Text Content

Digital technology has accelerated social networking and brought revolutionary changes. Twitter Facebook and YouTube are the most familiar platforms for social communication, marketing and information distribution. Unhappily, these networks have been invaded by spammers who misuse these online social networking platforms with misinformation, fake news, rumors, malicious links, unsolicited messages, comments etc. Therefore, spam detection in social networks has become a novel framework for distribution of information, sentiments, and news. Our research expresses the experimental study on spam identification from text data. Extensive researches have been done on spam detection field from English texts or other languages. But detection of spam from malicious Bangla text content still needs a lot of attention. In this experimental research, we have used Multinomial Naïve Bayes (MNB) classifier, a supervised machine learning algorithm with feature extraction to detect spam from Bangla text at the sentence level. Our proposed system identifies spam based on the polarity of each sentence correlated with it. Finally, our experiment shows that the model has an accuracy of 82.44% in detecting spam Bangla text content.
Multi-Label Emotion Classification for Arabic Tweets

Emotion Analysis (EA)is a process of determining if the text has any emotion. EA spread significantly in the recent years, especially for social media applications as applied to tweets and Facebook posts. An assumption has been presented recently that each social media post has no intensity or has one emotion. Different cases for public posts have been considered in this work, it focuses on several emotions (multi-label)included in a single post. Tweeter posts (Tweets)have been employed to validate the proposed work, it is possible to have different intensities related to each tweet (multi-target). The proposed work focused on Arabic language tweets unlike previously implemented work, which focused on other languages such as English or Chinese. A multi-label multi-target data set of Arabic tweets annotated for emotion analysis has been built, and different experts participated in the annotation process and Cohens Kappa measure was employed to determine their concordance.
Obstacle detection using ultrasonic sensor for a mobile robot

Abstract There have been a number of successful attempts in designing obstacle avoiding robots. These works differ by selection of sensors, path mapping process and the algorithms applied to set the operational parameters. In this paper we present a los cost ultrasonic distance sensor for obstacle avoidance for mobile robot navigation. The system is implemented using microcontroller Arduino Uno, a Wi-Fi module, an Arduino motor shield driver which controls the robot through the geared dc motors. The system showed good performance under various lighting conditions. Experimental results with varied positions of obstacle show the flexibility of the robot to avoid it and have shown a decent performance in our laboratory. The robot is additionally ready to acknowledge victims before it, the sensing element system is extremely low-cost as a result of it solely uses one distance sensing element.
Neural Machine Translation with Universal Visual Representation

Though visual information has been introduced for enhancing neural machine translation (NMT), its effectiveness strongly relies on the availability of large amounts of bilingual parallel sentence pairs with manual image annotations. In this paper, we present a universal visual representation learned over the monolingual corpora with image annotations, which overcomes the lack of large-scale bilingual sentence-image pairs, thereby extending image applicability in NMT. In detail, a group of images with similar topics to the source sentence will be retrieved from a light topic-image lookup table learned over the existing sentence-image pairs, and then is encoded as image representations by a pre-trained ResNet. An attention layer with a gated weighting is to fuse the visual information and text information as input to the decoder for predicting target translations. In particular, the proposed method enables the visual information to be integrated into large-scale text-only NMT in addition to the multimodel NMT. Experiments on four widely used translation datasets, including the WMT'16 English-to-Romanian, WMT'14 English-to-German, WMT'14 English-to-French, and Multi30K, show that the proposed approach achieves significant improvements over strong baselines.
Evaluating the effectiveness of Adversarial Attacks against Botnet Detectors

Classifiers based on Machine Learning are vulnerable to adversarial attacks, which involve the creation of malicious samples that are not classified correctly. While this phenomenon has been extensively studied within the image processing domain, comprehensive analyses are scarce in the cybersecurity field. This is a critical problem because cyber-detectors are being increasingly integrated with machine learning methods, making them suitable targets for skilled attackers leveraging adversarial samples to evade detection. In this paper, we propose a thorough analysis of realistic adversarial attacks performed against network intrusion detection systems that focus on identifying botnet traffic through machine learning classifiers. Our large campaign of experiments involves the most recent public datasets, representing multiple realistic network scenarios. Moreover, we evaluate the impact of these attacks against state-of-the-art detectors relying on different machine learning algorithms, providing a clear overview of this problem. The results outline the fragility of these methods. Our study represent a stepping stone for devising suitable countermeasures to the menace of adversarial attacks against cyber-detectors.