Reviewerly/exHarmony huggingface dataset:
Emotions Don't Lie: A Deepfake Detection Method using Audio-Visual Affective Cues

We present a learning-based method for detecting and fake deepfake multimedia content. To maximize information for learning, we extract and analyze the similarity between the two audio and visual modalities from within the same video. Additionally, we extract and compare affective cues corresponding to perceived emotion from the two modalities within a video to infer whether the input video is real or fake. We propose a deep learning network, inspired by the Siamese network architecture and the triplet loss. To validate our model, we report the AUC metric on two large-scale deepfake detection datasets, DeepFake-TIMIT Dataset and DFDC. We compare our approach with several SOTA deepfake detection methods and report per-video AUC of 84.4% on the DFDC and 96.6% on the DF-TIMIT datasets, respectively. To the best of our knowledge, ours is the first approach that simultaneously exploits audio and video modalities and also perceived emotions from the two modalities for deepfake detection.
Addressing the Target Customer Distortion Problem in Recommender Systems

Predicting the potential target customers for a product is essential. However, traditional recommender systems typically aim to optimize an engagement metric without considering the overall distribution of target customers, thereby leading to serious distortion problems. In this paper, we conduct a data-driven study to reveal several distortions that arise from conventional recommenders. Toward overcoming these issues, we propose a target customer re-ranking algorithm to adjust the population distribution and composition in the Top-k target customers of an item while maintaining recommendation quality. By applying this proposed algorithm onto a real-world dataset, we find the proposed method can effectively make the class distribution of items’ target customers close to the desired distribution, thereby mitigating distortion.
Design of Intelligent English Translation Algorithms Based on a Fuzzy Semantic Network

In order to improve the quality of intelligent English translation, an intelligent English translation algorithm based on the fuzzy semantic network is designed. By calculating the distance of fuzzy semantic network, classifying and ordering the English semantics to determine the optimal similarity and outputting the optimal translation results, the experiments show the average BLEU and NIST of the three test sets are 25.85 and 5.8925 respectively. The translation accuracy is higher than 95%. The algorithm can translate 246 Chinese sentences per second. This shows it is a high-performance intelligent translation algorithm and can be applied to practical intelligent translation software.
Graph-Dependent Implicit Regularisation for Distributed Stochastic Subgradient Descent

We propose graph-dependent implicit regularisation strategies for distributed stochastic subgradient descent (Distributed SGD) for convex problems in multi-agent learning. Under the standard assumptions of convexity, Lipschitz continuity, and smoothness, we establish statistical learning rates that retain, up to logarithmic terms, centralised statistical guarantees through implicit regularisation (step size tuning and early stopping) with appropriate dependence on the graph topology. Our approach avoids the need for explicit regularisation in decentralised learning problems, such as adding constraints to the empirical risk minimisation rule. Particularly for distributed methods, the use of implicit regularisation allows the algorithm to remain simple, without projections or dual methods. To prove our results, we establish graph-independent generalisation bounds for Distributed SGD that match the centralised setting (using algorithmic stability), and we establish graph-dependent optimisation bounds that are of independent interest. We present numerical experiments to show that the qualitative nature of the upper bounds we derive can be representative of real behaviours.
Specializing Context-Free Grammars With a (1 + 1)-EA

Context-free grammars are useful tools for modeling the solution space of problems that can be solved by optimization algorithms.For a given solution space, there exists an infinite number of grammars defining that space, and there are clues that changing the grammar may impact the effectiveness of the optimization.In this paper, we investigate theoretically and experimentally the possibility of specializing a grammar in a problem, that is, of systematically improving the quality of the grammar for the given problem.To this end, we define the quality of a grammar for a problem in terms of the average fitness of the candidate solutions generated using that grammar.Theoretically, we demonstrate the following findings: (a) that a simple mutation operator employed in a (1 + 1)-EA setting can be used to specialize a grammar in a problem without changing the solution space defined by the grammar; and (b) that three grammars of equal quality for a grammar-based version of the ONEMAX problem greatly vary in how they can be specialized with that (1 + 1)-EA, as the expected time required to obtain the same improvement in quality can vary exponentially among grammars.Then, experimentally, we validate the theoretical findings and extend them to other problems, grammars, and a more general version of the mutation operator.
When Is Moving a Cursor With a Computer Mouse Intuitive?

People have a good intuition of how to move a computer mouse to place a cursor at a desired position on a screen. This is surprising because the hand and the mouse are at different locations and they generally move in different directions and over different distances. But using a computer mouse is not always intuitive: try positioning a cursor after turning the mouse by 90° in your hand. To examine when using a computer mouse is intuitive, we asked participants to move a cursor to targets on a screen by moving a mouse along a surface. We varied the orientation of this surface in space and that of the mouse in the hand. Participants performed best when the mapping between hand and cursor motion was close to what we are accustomed to, either in space or relative to the forearm.
A Burmese (Myanmar) Treebank

A 20,000-sentence Burmese (Myanmar) treebank on news articles has been released under a CC BY-NC-SA license. Complete phrase structure annotation was developed for each sentence from the morphologically annotated data prepared in previous work of Ding et al. [1]. As the final result of the Burmese component in the Asian Language Treebank Project , this is the first large-scale, open-access treebank for the Burmese language. The annotation details and features of this treebank are presented.
Viewpoint: Human-In-The-Loop Artificial Intelligence.

Little by little, newspapers are revealing the bright future that Artificial Intelligence (AI) is building. Intelligent machines will help everywhere. However, this bright future has a dark side: a dramatic job market contraction before its unpredictable transformation. Hence, in a near future, large numbers of job seekers will need financial support while catching up with these novel unpredictable jobs. This possible job market crisis has an antidote inside. In fact, the rise of AI is sustained by the biggest knowledge theft of the recent years. Learning AI machines are extracting knowledge from unaware skilled or unskilled workers by analyzing their interactions. By passionately doing their jobs, these workers are digging their own graves. In this paper, we propose Human-in-the-loop Artificial Intelligence (HIT-AI) as a fairer paradigm for Artificial Intelligence systems. HIT-AI will reward aware and unaware knowledge producers with a different scheme: decisions of AI systems generating revenues will repay the legitimate owners of the knowledge used for taking those decisions. As modern Robin Hoods, HIT-AI researchers should fight for a fairer Artificial Intelligence that gives back what it steals.
An ethico-legal framework for social data science

Abstract This paper presents a framework for research infrastructures enabling ethically sensitive and legally compliant data science in Europe. Our goal is to describe how to design and implement an open platform for big data social science, including, in particular, personal data. To this end, we discuss a number of infrastructural, organizational and methodological principles to be developed for a concrete implementation. These include not only systematically tools and methodologies that effectively enable both the empirical evaluation of the privacy risk and data transformations by using privacy-preserving approaches, but also the development of training materials (a massive open online course) and organizational instruments based on legal and ethical principles. This paper provides, by way of example, the implementation that was adopted within the context of the SoBigData Research Infrastructure.
Review of the system model and calibration for fringe projection profilometry

Fringe projection profilometry (FPP) can well balance the system flexibility and the measurement accuracy, which is the mainstream technology of optical 3D imaging and shape measurement. When working with FPP, first an appropriate system should be established, and then the system parameters describing the system model should be determined via system calibration, finally the 3D shape is generated with 3D reconstruction by using the calibrated system model. System calibration and system model are tightly coupled, which have a direct impact on the performance of 3D imaging. The principle of FPP can be divided into two categories, i.e. the phase-3D mapping and the binocular stereo vision. This paper reviewed the system model and calibration strategy of FPP, which corresponded to the above two categories. Finally the method and basis for the accuracy evaluation of FPP were summarized.
Classifying construction and demolition waste by combining spatial and spectral features

The generation of construction and demolition waste (C&amp;DW) has increased on a yearly basis, and the damage caused to the environment is significant. To save resources, C&amp;DW needs to be managed and recycled. Currently, waste-classification methods are mainly based on wind selection, water selection, screening and manual sorting. These methods are inefficient and the classification results are not clean. To achieve efficient and intelligent recycling of C&amp;DW, it is essential to classify the wastes effectively. In this paper, four methods are reported: characteristic reflectivity and extreme learning machine (ELM); first-order derivative of characteristic reflectivity and ELM; grey level co-occurrence matrix and ELM and convolutional neural network. These methods were used to classify typical types of hard-to-distinguish waste: wood, rubber, brick and concrete. It was found that each method had inadequacies, and the correct rate was between 82·22 and 89·33%. Therefore, a weighted fusion of membership matrix, which combine all four methods, was proposed. As a result, the correct rate in repeated experiments significantly improved to 95%. The classification results can be used in further study about automatic sorting of C&amp;DW using robotics instead of people.
Differentially Private Convolutional Neural Networks with Adaptive Gradient Descent

Deep learning achieves remarkable success in the fields of target detection, computer vision, natural language processing, and speech recognition. However, traditional deep learning models may suffer the privacy risk due to some training data involve sensitive information, such as the medical histories, location information and face images. Attackers can exploit the implicit information to recover the sensitive information from the training data. In order to protecting privacy of deep learning model, we develop a novel optimization algorithm called DPAGDCNN for convolution neural network which cooperates differential privacy technique. Specifically, DPAGD-CNN allocates privacy budgets more carefully in each iteration, rather than assigning a fixed privacy budget per iteration. We theoretically prove that our approach can protect the privacy of training data and it achieves higher classification accuracy under the moderate privacy budget in the MNIST and CIFAR-10 datasets.
New Low Complexity Variance Method for Automatic Modulation Classification and Comparison with Maximum Likelihood Method

The automatic classification of modulation (ACM) scheme is divided into two classes, namely, feature-based (FB) and likelihood-based (LB) algorithms. In this paper, a new FB variance (VAR) method is proposed and its performance with the Maximum Likelihood (ML) method is compared. In ML method, at the receiver, a modulation scheme which maximizes the probability density function of matched filter samples is chosen from the set of modulation schemes under consideration. In FB method, matched filter samples are clustered into different constellation shapes using the K-means algorithm. It is followed by application of VAR criterion for identification of a modulation scheme. Simulation result of ML and VAR techniques are presented. It is observed that VAR criterion requires less number of computations compared with ML criterion, whereas, ML criterion outperforms VAR criterion in terms of a probability of correct classification.
A Knowledge-Based Approach to Enhance Provision of Location-Based Services in Wireless Environments

Location-Based Services (LBS) are attracting a great interest with the fast expansion of mobile computing nowadays. These services use the user location to customize the offered information. However, most of those services are designed for specific scenarios and goals with implicit knowledge about the application context. As a consequence, hundreds of them are available (even with the same purpose). So, it is difficult for users to choose the most suitable service as they are in charge of knowing/finding the services which will be interesting for them, and handle the information that such services need. In this paper, we present an approach to handle LBS for mobile users which relieves them from knowing and managing the knowledge related to such services. This approach consists of a proposal for the modeling of such information as ontologies, which are handled by an agent-based architecture. Also, we propose to maintain updated the knowledge each mobile device contains by leveraging the exchange of information with others. For accessing the local knowledge, we present an SPARQL-like query language which avoids the ambiguities of natural language. Finally, we propose an approach to translate the user information needs into formal requests expressed in this query language, which could be later processed against the knowledge repositories to obtain the results the user needs.
Negative Binomial Matrix Factorization

We introduce negative binomial matrix factorization (NBMF), a matrix factorization technique specially designed for analyzing over-dispersed count data. It can be viewed as an extension of Poisson factorization (PF) perturbed by a multiplicative term which models exposure. This term brings a degree of freedom for controlling the dispersion, making NBMF more robust to outliers. We describe a majorization-minimization (MM) algorithm for a maximum likelihood estimation of the parameters. We provide results on a recommendation task and demonstrate the ability of NBMF to efficiently exploit raw data.
Deep Learning Challenges in Medical Imaging

Medical imaging has taken decisive steps in diagnosing the diseases in the past few decades. Many successful diagnosis systems have been developed, and however, there is a scope for improving the accuracy of those systems. Deep learning plays a vital role in improving the performance of medical imaging. Recent developments in deep learning have brought revolution in medical diagnosis. Recent progress in deep learning offers new possibilities in medical imaging for detection of diseases, diagnosis and prediction. Use of deep learning to medical imaging has remarkable achievement in improving the performance in diagnosis. Deep learning has attracted the researchers and the academicians in developing the medical imaging tools based on historical data. However, there are many challenges while blending the deep learning in medical imaging. Some of the key areas where deep learning seems to be challenging are: massive datasets for training, overfitting of data, variations in input, quality of data, understanding of the context, high volume of data and many more. This paper focuses on the deep learning challenges with biomedical imaging. The techniques for analyzing medical images in a heterogeneous environment are also discussed.
Spiking neural network based scrambled watermark hiding in low-frequency region of digital image

Abstract The digital image watermarking system has proven its own efficiency without any doubt to establish authenticity and to prevent the misuse of digital images in today’s world. In this process, the present paper proposes a new and significant Spiking Neural Networks based image watermarking method (SNNW) with the objectives of robustness and security alongwith less time complexity. In this proposed scheme, the extraction procedure is taken as an optimization problem which is solved by implementing the spiking neural networks SNN. The proposed SNNW employs spiking neural networks with the aim of achieving more robustness against the different attacks along with less time complexity. In addition to this, security of proposed SNNW method is obtained by exploiting chaotic based scrambling on watermark. As a result, a series of experiments are conducted and carefully analyzed on a set of images and assessed in the form of peak signal to noise ratio PSNR, Normalization correlation NC, extraction rate and time complexity. The experimental results of SNNW system are carefully analyzed and found robust and secure to various attacks like histogram equalization, compression, noising and filtering attacks. Moreover, effectiveness of proposed SNNW method is exhibited through a comparison with different state of the art methods.
Understanding your buyer persona

This chapter covers the theory, application and development of a buyer persona. The buyer persona is also referred to as a marketing, user, customer or audience persona. The development of buyer personas guides decisions about product offering, value proposition, service, interaction and the customer’s journey towards purchase. Pain touchpoints were identified when the buyer persona was developed. Buyers legitimately worry that brands may misuse personal and financial information, or, indeed, that a website might be entirely fraudulent. A science fiction prototyping approach to buyer persona development is based on the idea that some services and products cannot be solely predicted by evaluation of past behaviours. The earlier example of a London-based florist uses two buyer personas: one focusing on consumers and one focusing on a business-to-business relationship. The example of Help Scout shows how a software company might use several buyer personas to target their marketing actions more effectively.
Establishing Trust in Online Advertising With Signed Transactions

Programmatic advertising operates one of the most sophisticated and efficient service platforms on the Internet. However, the complexity of this ecosystem is a direct cause of one of the most important problems in online advertising, the lack of transparency. This lack of transparency enables subsequent problems such as advertising fraud, which causes billions of dollars in losses. In this paper we propose Ads.chain, a technological solution to the lack-of-transparency problem in programmatic advertising. Ads.chain extends the current effort of the Internet Advertising Bureau (IAB) in providing traceability in online advertising through the Ads.txt and Ads.cert solutions, addressing the limitations of these techniques. Ads.chain is (to the best of the authors' knowledge) the first solution that provides end-to-end cryptographic traceability at the ad transaction level. It is a communication protocol that can be seamlessly embedded into ad-tags and the OpenRTB protocol, the de-facto standards for communications in online advertising, allowing an incremental adoption by the industry. We have implemented Ads.chain and made the code publicly available. We assess the performance of Ads.chain through a thorough analysis in a lab environment that emulates a real ad delivery process at real-life throughputs. The obtained results show that Ads.chain can be implemented with limited impact on the hardware resources and marginal delay increments at the publishers lower than 0.20 milliseconds per ad space on webpages and 2.6 milliseconds at the programmatic advertising platforms. These results confirm that Ads.chain's impact on the user experience and the overall operation of the programmatic ad delivery process can be considered negligible.
Transfer Learning from BERT to Support Insertion of New Concepts into SNOMED CT.

With advances in Machine Learning (ML), neural network-based methods, such as Convolutional/Recurrent Neural Networks, have been proposed to assist terminology curators in the development and maintenance of terminologies. Bidirectional Encoder Representations from Transformers (BERT), a new language representation model, obtains state-of-the-art results on a wide array of general English NLP tasks. We explore BERT's applicability to medical terminology-related tasks. Utilizing the "next sentence prediction" capability of BERT, we show that the Fine-tuning strategy of Transfer Learning (TL) from the BERTBASE model can address a challenging problem in automatic terminology enrichment - insertion of new concepts. Adding a pre-training strategy enhances the results. We apply our strategies to the two largest hierarchies of SNOMED CT, with one release as training data and the following release as test data. The performance of the combined two proposed TL models achieves an average F1 score of 0.85 and 0.86 for the two hierarchies, respectively.
High-resolution hyperspectral-based continuous mineralogical and total organic carbon analysis of the Eagle Ford Group and associated formations in south Texas

Hyperspectral sensing is used to generate mineral maps of a fine-grained, vertical Eagle Ford Group cored section and underlying Buda Limestone and Del Rio Formation in south Texas. This technology produces a map that shows the distribution of minerals on the core surface. Three different cameras within the hyperspectral core-imaging system were used to image a 99.1-m (325-ft) core: a (1) line scan camera, which produces a high-resolution red–green–blue (120-μm) natural-color photograph of the dry core from the visible light spectrum; (2) short-wave infrared (SWIR) spectrometer (300–500-μm resolution); and (3) long-wave infrared (LWIR) spectrometer (300–500-μm resolution). In 2016, the introduction of a new high-resolution LWIR spectrometer made it possible to identify minerals in cores that were not detected by previous SWIR systems. High-resolution hyperspectral imaging technology using both the SWIR and LWIR spectra provides a significant step toward quantifying mineralogy and total organic carbon. Hyperspectral imaging is a powerful tool for studying textural and fabric relationships because it detects and highlights the major mineralogical changes that occur between depositional beds and among depositional features. Calcite compositional variations and cyclicity appear to be closely related. The Sr-rich calcite appears to indicate a smaller allochem size dominance as well as the slower sedimentation rates that are required for nannofossils to settle from suspension. Finally, the observed diagnostic cyclicity of the lower Eagle Ford can be related to coccolithophore productivity.