Reviewerly/exHarmony huggingface dataset:
Real-Time Filtering Non-Intentional Bid Request on Demand-Side Platform

While real-time bidding brings a huge profit for online businesses, it also becomes a potential target for malicious purposes. In real-time bidding, the bid request traffic could be classified into two kinds: intentional and non-intentional. Intentional bid requests come from ordinal web users while non-intentional bid requests come from abnormal web users. From the perspective of a demand-side platform (DSP), the budget of advertisers should be used as effectively as possible by limiting non-intentional traffic. Therefore, it is essential to classify and predict these two kinds of bid request traffic. In this research, we propose a real-time filtering bid requests (RFBR) model to predict whether an incoming bid request is intentional or non-intentional from the DSP’s viewpoint. Our model is built on three stages. In the first stage, we analyzed all potential attributes in the bid request scheme and figured out the relations between abnormal behaviors and their attributes; in the second stage, a classification model was built to classify normal and abnormal audiences by the extracted features and self-defined thresholds; in the third stage, a RFBR model was built to classify intentional and non-intentional bid requests. The experimental result shows that our system can effectively classify incoming bid requests.
Identification of Source Camera by Amalgamation of PRNU and Noise Print Using Dimensionality Expansive Residual Network

It might be challenging in the field of image forensics to identify the source camera of a picture. This researchproposes a noise adaptable convolutional neural network-based technique for camera identification. For cameraidentification, the suggested solution combines Photo Response Non-Uniformity (PRNU) noise and Noiseprint.Three parallel dimensionality expanded residual networks with convolutional layers of kernel size 1x1 were puttogether for enhanced feature extraction. The experiment mentioned above uses pictures from the ”Vision Dataset”as its subject matter. The experimental findings demonstrate the effectiveness of the suggested methodology inidentifying the source camera at the brand, model, and device levels. When two of the three networks were fedwith PRNU and one with noiseprint, the best performance was obtained.
Equivalence, Partial Order and Lattice of Neighborhood Sequences on the Triangular Grid

In (digital) grids, neighbor relation is a crucial concept; digital distances are based on paths through neighbor points. Digital distances are significant, e.g., in digital image processing for giving an approximation of the Euclidean distance and allowing incremental algorithms on images. Neighborhood sequences (i.e., infinite sequences of the possible types of neighbors) are defining digital distances with a lower rotational dependency than the distances based only on a sole neighborhood. They allow one to change the used neighborhood condition in every step along a path. They are defined in various grids, and they can be periodic. Generalized neighborhood sequences do not need to be periodic. In this paper, the triangular grid is studied. An equivalence and two partial order relations on the set of generalized and periodic neighborhood sequences are shown on this grid. The first partial order, the “faster” relation, is based on distances defined by neighborhood sequences, and it does not provide a lattice but gives a relatively complex relation for neighborhood sequences with a short period. The other partial order, the relation “componentwise dominate”, defines a complete distributive lattice on the set of generalized neighborhood sequences. Finally, a relation of the above-mentioned relations is established. Important differences regarding the cases of the square and triangular grids are also highlighted.
Effective Model Update for Adaptive Classification of Text Streams in a Distributed Learning Environment

In this study, we propose dynamic model update methods for the adaptive classification model of text streams in a distributed learning environment. In particular, we present two model update strategies: (1) the entire model update and (2) the partial model update. The former aims to maximize the model accuracy by periodically rebuilding the model based on the accumulated datasets including recent datasets. Its learning time incrementally increases as the datasets increase, but we alleviate the learning overhead by the distributed learning of the model. The latter fine-tunes the model only with a limited number of recent datasets, noting that the data streams are dependent on a recent event. Therefore, it accelerates the learning speed while maintaining a certain level of accuracy. To verify the proposed update strategies, we extensively apply them to not only fully trainable language models based on CNN, RNN, and Bi-LSTM, but also a pre-trained embedding model based on BERT. Through extensive experiments using two real tweet streaming datasets, we show that the entire model update improves the classification accuracy of the pre-trained offline model; the partial model update also improves it, which shows comparable accuracy with the entire model update, while significantly increasing the learning speed. We also validate the scalability of the proposed distributed learning architecture by showing that the model learning and inference time decrease as the number of worker nodes increases.
Detection of Double-Compressed Videos Using Descriptors of Video Encoders

In digital forensics, video becomes important evidence in an accident or a crime. However, video editing programs are easily available in the market, and even non-experts can delete or modify a section of an evidence video that contains adverse evidence. The tampered video is compressed again and stored. Therefore, detecting a double-compressed video is one of the important methods in the field of digital video tampering detection. In this paper, we present a new approach to detecting a double-compressed video using the proposed descriptors of video encoders. The implementation of real-time video encoders is so complex that manufacturers should develop hardware video encoders considering a trade-off between complexity and performance. According to our observation, hardware video encoders practically do not use all possible encoding modes defined in the video coding standard but only a subset of the encoding modes. The proposed method defines this subset of encoding modes as the descriptor of the video encoder. If a video is double-compressed, the descriptor of the double-compressed video is changed to the descriptor of the video encoder used for double-compression. Therefore, the proposed method detects the double-compressed video by checking whether the descriptor of the test video is changed or not. In our experiments, we show descriptors of various H.264 and High-Efficiency Video Coding (HEVC) video encoders and demonstrate that our proposed method successfully detects double-compressed videos in most cases.
Soil Contamination Level in Cauvery Delta Region Through Machine Learning and GIS Techniques

The Cauvery River plays an important role and provides significant agricultural productivity in Tamil Nadu. Agricultural activities are carried out in the delta area due to the availability of water resources. In recent years, river water has been polluted in various ways through human activities and the process of industrialization. Consequently, contamination has an impact on the soil health of agricultural land. As a result, our focus has been on soil health analysis by measuring the chemical properties and heavy metals that exist in the soil. A classification model has been developed to determine the contamination level that exists in the soil through interpolation techniques. Soil samples are analysed in terms of soil chemical properties such as macronutrient, micronutrient, pH, and electric conductivity. Samples are collected at different locations at various depths where the sample size is 33. Samples are taken from the different regions that cover the category of farmland, urban areas, industrial areas, and river beds in the Cauvery delta region. A continuous surface had created for the concentration of the chemical constituents that are present in the soil through the space interpolation techniques using GIS technology which was determined from the analytical classification of the amount of the chemicals or the heavy metals that present in the soil has been determined through the fuzzy logic membership function. The soil contamination analysis based on the GIS approach provides an ongoing basis for monitoring contaminated sites. The contamination level along with to which extent it was contaminated is determined. The classification of the overall soil contamination levels of the Cauvery delta region is provided in detail, and its dispersed outcome is represented as a statistical analysis report in the form of the spatial variation maps.
Technological Vulnerability in the Fourth Industrial Revolution

Our technological vulnerability and our posthuman condition are inextricably linked in DeLillo's text. Technology Dependence and the Fourth Industrial RevolutionSince the 21st century, our reliance on technology and its seamless integration in our lives is a natural consequence of the 4th IR, which, according to Klaus Schwab, the founder of the World Economic Forum, constitutes a paradigm shift (2016, 2).Schwab argues that what distinguishes the 4th IR from the third, which developed in the latter half of the 20th century and moved from mechanical and electronic technology into digital electronics, is its exponential speed, scope, and unprecedented impact.The present 4th IR builds on the digital one and is transforming how we live, express ourselves, work, connect with others, and get information in an exponential way (2016, 2).These changes are also affecting almost every industry in every country and entire systems of production, management, and governance.Schwab and Davis argue that this revolution comes with a set of emerging technologies that include artificial intelligence (AI) and robotics, additive manufacturing (also known as 3D printing), Internet-of-things technology, Big Data, neurotechnologies, biotechnologies, virtual and augmented reality, and new materials (2018, 7).All key innovations make use of digital power and are organized around the physical, the digital and the biological.In fact, they co-evolve, fuse, and enrich each other (Schwab 2016, 8; Schwab and Davis 2018, 3).For example, digital fabrication technologies interact with the biological world through bioprinting to generate living tissue.In this context, transhumanist dreams of freeing humans from their biological/corporeal limitations to create "radically enhanced humans" (Bostrom 2005, 5) do not seem so far away.The co-evolution, fusion, and enrichment of the physical, digital, and biological spheres that Schwab and Davis describe also affect the definition of the human and fit well with the conception of the posthuman as seen by critical posthumanists who understand the human and the nonhuman (the machine, the plant, and the animal) as a continuum.Therefore, the (post)human is seen as nonfixed, mutable, and co-evolving with technology by both the theorists of the 4th IR and by critical posthumanist thinkers (Braidotti 2013;Herbrechter 2013;Nayar 2013).The 4th IR brings new forms of vulnerability such as the loss of manual and cognitive routine jobs that will be performed by computers, robots, and other digital technologies; the increase in differences in income and wealth among people; and the likely disappearance of the middle class in a more competitive, efficient, and flexible labor market (Schwab 2016, 35-38; Brynjolfsson and McAfee 2016, 125-146; Johannessen 2019, 4).However, the exponential development of information technologies and digitalization has produced an unexpected form of human vulnerability.
Practical Guide for Designing Activities that Integrate Curricular Content, Computational Thinking and Constructionist Theory

This paper presents a practical guide to encourage professionals to reflect on aspects that must be considered for the elaboration of a didactic activity that integrates: a) promotion of curricular learning; b) development of Computational Thinking; and c) a constructionist learning environment. A proof of concept shows that the guide has the potential to instigate teachers of k-12 education in the development of activities with the proposed characteristics.
Real-Time Object Detection and Recognition for the Visually Impaired: A YOLOv3 Approach

The rapid development of artificial intelligence and mobile computation facilitates the life of the blind and visually impaired people. One of the main issues, the visually impaired face, is the inability to realize the event's happenings in their surroundings. In recent years, Computer Vision and Deep Learning have received extensive coverage. Visual object tracking is one of the key areas of computer vision. The main purpose of visual object tracking from streaming video frames is to detect or connect target objects. YOLO, an approach used for object detection, follows regression to spatially separate bounding boxes and associate class probabilities. This approach has gained a lot of popularity lately. Unlike models like R-CNN, that require thousands of neural networks, for YOLO, one neural network is sufficient enough to predict all the bounding boxes and their class probabilities from the entire image in just one evaluation. Since the entire pipeline is just one network, optimization can be done easily on the detection performance. The YOLO model has various versions like YOLOv2, YOLOv3, YOLOv4 and YOLO9000. It outperforms other detection methods when generalizing natural images from other domains like artwork. YOLOv3 with proper Hyperparameter Tuning can also overcome the Speed-Accuracy Trade-off. In this paper, use of these models on a common image proves that YOLOv3 can identify 15.15% more objects with a 33% faster response time. This paper has information about the application of YOLOv3 model with hyper-parameter tuning to aid the visually impaired people by virtually visualizing events occurring in their surroundings.
StyleBin: Stylizing Video by Example in Stereo

In this paper we present StyleBin—an approach to example-based stylization of videos that can produce consistent binocular depiction of stylized content on stereoscopic displays. Given the target sequence and a set of stylized keyframes accompanied by information about depth in the scene, we formulate an optimization problem that converts the target video into a pair of stylized sequences, in which each frame consists of a set of seamlessly stitched patches taken from the original stylized keyframe. The aim of the optimization process is to align the individual patches so that they respect the semantics of the given target scene, while at the same time also following the prescribed local disparity in the corresponding viewpoints and being consistent in time. In contrast to previous depth-aware style transfer techniques, our approach is the first that can deliver semantically meaningful stylization and preserve essential visual characteristics of the given artistic media. We demonstrate the practical utility of the proposed method in various stylization use cases.
The social information needs of people with albinism (PWA): A case study of Khomas region, Namibia

This paper focuses on the social information needs of people with albinism (PWA) in Khomas region, Namibia, and the sources of information used PWA to meet their information needs. The study applies Moore's model of social information needs and Wilson's model of information behaviour to investigate and understand the information needs and sources used by PWA to find information. The study was carried out using a qualitative case design within interpretivism paradigm and semi-structured face-to-face interviews were used to collect data from participants. Sixteen participants took part in the study, eight were males, and eight were female participants. Among these participants, two were children with albinism. The following information was identified as being crucial to PWA namely: skin- and eye-related information, information on what is albinism and its causes, disability grants, educational information, and emotional and psychological support. Additionally, the study established that PWA use formal, informal, and human sources of information to find information.
A Hybrid Integer Encoding Method for Obtaining High-Quality Solutions of Quadratic Knapsack Problems on Solid-State Annealers

For formulating Quadratic Knapsack Problems (QKPs) into the form of Quadratic Unconstrained Binary Optimization (QUBO), it is necessary to introduce an integer variable, which converts and incorporates the knapsack capacity constraint into the overall energy function. In QUBO, this integer variable is encoded with auxiliary binary variables, and the encoding method used for it affects the behavior of Simulated Annealing (SA) significantly. For improving the efficiency of SA for QKP instances, this paper first visualized and analyzed their annealing processes encoded by conventional binary and unary encoding methods. Based on this analysis, we proposed a novel hybrid encoding (HE), getting the best of both worlds. The proposed HE obtained feasible solutions in the evaluation, outperforming the others in small- and medium-scale models.
A Deep Transfer Learning-Based Comparative Study for Detection of Malaria Disease

Malaria is a disease caused by a parasite. The parasite is transmitted to humans through the bite of infected mosquitoes. Thousands of people die every year due to malaria. When this disease is diagnosed early, it can be fully treated with medication. Diagnosis of malaria can be made according to the presence of parasites in the blood taken from the patient. In this study, malaria detection and diagnosis study were performed using The Malaria dataset containing a total of 27,558 cell images with samples of equally parasitized and uninfected cells from thin blood smear slide images of segmented cells. It is possible to detect malaria from microscopic blood smear images via modern deep learning techniques. In this study, 5 of the popular convolutional neural network architectures for malaria detection from cell images were retrained to find the best combination of architecture and learning algorithm. AlexNet, GoogLeNet, ResNet-50, MobileNet-v2, VGG-16 architectures from pre-trained networks were used, their hyperparameters were adjusted and their performances were compared. In this study, a maximum 96.53% accuracy rate was achieved with MobileNet-v2 architecture using the adam learning algorithm
Research on Sparse Denoising of Strong Earthquakes Early Warning Based on MEMS Accelerometers

In view of the fact that the noise in the same frequency band as the useful signal in the MEMS acceleration sensor observation data cannot be effectively removed by traditional filtering methods, a denoising method for strong earthquake signals based on the theory of sparse representation and compressive sensing is proposed in this paper. This skillfully realized the separation of strong earthquake signals from noise by adopting a fixed dictionary and utilizing sparse characteristics. Furthermore, considering the weakness of the sparse denoising method based on the fixed dictionary in the high signal-to-noise ratio, a spare denoising method based on learning an over-complete dictionary is proposed. Through the initial given seismic data, the ideal over-complete dictionary is trained to achieve seismic data denoising. In addition, for the interference waves of non-seismic events, this paper proposes an idea based on sparse representation classification to remove such non-seismic interference directly. Combining the ideas of noise reduction and non-seismic event elimination, we can obtain a standard sparse anti-interference denoising model for earthquake early warning. It's innovative that this model implements the sparse theory into the field of earthquake early warning. According to the experimental results, in the case of heavy noise, the denoising model based on sparse representation can reach average SNR of 8.73 and an average MSE of 29.53, and the denoising model based on compression perception can reach average SNR of 7.29 and an average MSE 41.34, and the denoising model based on learning dictionary can reach average SNR 11.07 and average MSE 17.32. The performance of these models is better than the traditional FIR filtering method (average SNR -0.73 and average MSE 260.37) or IIR filtering method (average SNR 4.73 and average MSE 73.95). On the other hand, the anti-interference method of the sparse classification proposed in this paper can accurately distinguish non-seismic interference events from natural earthquakes. The classification accuracy of the method based on the noise category of the selected test data set reaches 100% and achieves good results.
Differential Attack With Constants On μ2 Block Cipher

Abstract Differential attack is one of the most important methods in cryptanalysis. When finding a high-probability differential trail, the effect of constant has long been ignored. In this paper, we focus on the effect of constants on the differential attack against $\mu ^2$. $\mu ^2$ is a newly proposed block cipher based on a Type-II generalized Feistel structure. Its 16-bit F function (denoted as F-box) is an ultra-lightweight permutation equipped with different constants. The designer applied the minimum number of active S-boxes to determine $\mu ^2$’ security margin in the design document. However, the F-boxes use different round constants in different rounds; the constants may lead to incompatibility of differential trails of F-boxes. Therefore, to provide a more precise differential attack on $\mu ^2$, we construct an model based on STP (Simple Theorem Prover) constraint solver to search for the valid differential trails with a more precise probability of $\mu ^2$ for different starting rounds. Finally, the related-key differential trail covers one more round than the existing methods. Analyzing the effect of constants on the validity and the probability of the differential trail reminds the designers and the attackers to have a more comprehensive analysis of specific ciphers.
Semi‐tensor product of quaternion matrices and its application

Unlike real or complex numbers, quaternion multiplication does not satisfy the commutative law. This makes it impossible to generalize some conclusions on semi‐tensor product to quaternion directly, which is valid on real or complex number fields. In this paper, we define semi‐tensor product of quaternion matrices and study the properties of semi‐tensor product of quaternion matrices, and the conclusion is applied to the color digital image restoration model.
Deep ANFIS for Understanding Emotions in Movie Clips

Significant success of Deep learning (DL) in extracting features from big data which led to an explosion of Artificial Intelligence (AI) applications. However, most of those AI systems lack transparency and interpretability due to the black-box nature of DL architecture, which causes major trust-related problems in AI applications where validation is essential. This necessitates the need for interpretable AI systems that can explain their decisions. In this view, this paper proposes a deep adaptive neuro-fuzzy inference system (Deep ANFIS), that combines fuzzy inference with DL architecture to provide a more comprehensible explanation of the decision processes. Deep ANFIS uses ANFIS based convolution units in convolutional neural networks (CNNs) architecture that allows approximating the complex non-linear problems using fuzzy if-then rules. To verify the performance of the proposed Deep ANFIS and analyze the essential features for classification, we perform experiments with COGNIMUSE dataset for emotion recognition.