Reviewerly/exHarmony huggingface dataset:
An Approximate Memory Based Defense Against Model Inversion Attacks to Neural Networks

Diverse and comprehensive training data is critical in building robust machine learning (ML) models. However, model inversion attacks (MIA) have demonstrated that an ML model can leak important information about its training dataset. This work examines the existing MIAs and proposes a hardware-oriented solution to protect the training data from such attacks. Our proposed solution – MIDAS: Model Inversion Defenses with an Approximate memory System – intentionally introduces memory faults to thwart MIA without compromising the original ML model. We use detailed SPICE simulations to build the DRAM fault model with voltage overscaling, implement the state-of-the-art MIAs, and evaluate our proposed solution. Our experiments demonstrate that MIDAS can effectively protect training data from run-time adversarial attacks. In terms of the Pearson Correlation Coefficient (PCC) similarity measure (between the original and the recovered training data), MIDAS reduces the PCC value for shallow and deep neural networks.
Expanding Rural Community Networks Through Partnerships with Key Actors

The infrastructures deployed by classical telecommunications operators are appropriate and cost-effective for urban-oriented business models, but rural areas with very low population density require alternatives both in terms of the technology used and in business models. New generation networks become more and more dense and heterogeneous, increasing the gap as operators cannot spend the required CAPEX and OPEX in sparsely populated areas where the revenues will certainly be very low. At the same time, an increasing number of communities worldwide provide themselves networking solutions, which is becoming a global alternative movement called "community networks". However, rural areas are challenging because of the technical difficulties of getting connected to the world and the scarcity of technical skills, among other reasons. Other stakeholders such as municipalities or regional governments are also relevant actors but cannot provide a solution by themselves. Partnerships between different actors for deploying rural connectivity could be the answer, taking advantage somehow of the "shared infrastructure model" proposed in IETF RFC7962. This paper analyses the relationship between different stakeholders and the main elements to consider when deploying mobile communications services in isolated and sparsely populated regions. The analysis uses a case study from the Napo River Network in Peru and compares it with other experiences such as Zenzeleni Networks in South Africa or Rizhomatica in Mexico.
Similarity Fuzzy Semantic Network for Social Media Analysis

In this paper we propose the use of the Similarity Fuzzy Semantic Networks representation model for Social Media Analysis. Similarity Fuzzy Semantic Networks are an extension of Fuzzy Semantic Networks, that incorporates reasoning by similarity through a Similarity Inference Rule. We introduce fuzzy semantic relations and similarity fuzzy semantic relations which can be used in social media analysis. To check the effectiveness we apply a similarity fuzzy semantic network in a social media complex problem, the analysis of radical discourse in Twitter, obtaining a better result than recent approaches for this problem.
The Geopolitical Repercussions of US Anti-immigrant Rhetoric on Mexican Online Speech About Migration: A Transdisciplinary Approach

This paper presents an ongoing research project that aims to propose a geopolitical analysis of anti-immigrant speech published on the Mexican twitosphere. While Mexico has long defined itself as an emigration country, the apparent growing presence of anti-immigrant discourse online, especially at the Mexican borders, invites us to question the impact of Americans’ anti-immigrant speech, bolstered by Donald Trump’s election and presidency, on Mexicans’ representations. We thus propose a transdisciplinary approach that combines a Convolutional Neuronal Network to detect anti-immigrant speech in geolocalized tweets in Mexican Spanish and a geopolitical diachronic analysis to estimate the relationship between such speeches and Americans’ anti-immigrant online representations. With an overall accuracy of 0.76, we are confident that with some improvements the CNN model will be able to detect Mexican anti-immigrant speech on Twitter. We finally discuss that the scope of the analysis would be greatly improved if paired with network and territorial analysis of Mexican anti-immigrant tweets.
Modified Correlation-Based Feature Selection for Intelligence Estimation Based on Resting State EEG Data

The effectiveness of the correlation-based method (CFS) for feature selection based on electroencephalogram (EEG) data of the resting state for the purpose of intelligence assessment is investigated. A modification of the CFS is proposed, which makes it possible to vary the cardinality of a subset of selected features using a hyperparameter. A practical example of the analysis of the relationship between the intelligence quotient (IQ), the age of subjects, the features extracted from EEG data, and the effects of their interaction is considered. A comparison of the genetic algorithm and the forward selection was made to find the optimal subset of features within the modified CFS. It was found that it is quite sufficient to use the method of forward selection. Using the nested cross-validation procedure, it was shown that the modified approach gives a lower mean absolute error compared to usual CFS, as well as building a stepwise regression by the forward selection method based on the Bayesian information criterion (BIC). In terms of the mean absolute error, the modified CFS is close to the least absolute shrinkage and selection operator (LASSO) and the improved algorithm Bolasso-S.
Bitcoin: Cryptopayments Energy Efficiency

Bitcoin introduced a cryptographic peer-to-peer version of money that allows online payments to be sent directly from one party to another without going through a financial institution. Many recent studies evaluated and criticised Bitcoinâ€™s energy consumption through its Proof of Work (PoW) consensus mechanism without evaluating its efficiency compared to classical electronic payment system. Based on physics, information science and economics, we compute and compare the energy consumption and define what is the energy efficiency of both the current monetary payment system and Bitcoin cryptopayment system. We demonstrate that Bitcoin consumes 56 times less energy than the classical system, and that even at the single transaction level, a PoW transaction proves to be 1 to 5 times more energy efficient. When Bitcoin Lightning layer is compared to Instant Payment scheme, Bitcoin gains exponentially in scalability and efficiency, proving to be up to a million times more energy efficient per transaction than Instant Payments.
Cooperative Towing by Multi-Robot System That Maintains Welding Cable in Optimized Shape

As considerable amounts of welding are required during ship construction, robotic automation is being promoted. However, current welding robots do not automate work in high or narrow places. One reason is that welding cannot be performed with sufficient accuracy because a welding robot with a torch cannot tow a heavy cable and thus may go off path. This letter proposes a multi-robot system for automating the execution of long welds in high and narrow places to address this problem. In the proposed method, towing vehicles are placed along the supply cable to reduce the load on the lead welding vehicle. In confined-space welding, the cable must not be excessively loaded, and obstacles such as walls must be avoided. Under the proposed method, autonomous movement of the towing vehicles is achieved by solving an optimization problem that considers these aims and minimizes the amount of change from the estimated current cable shape. The method was evaluated through simulation and real-world tests, in which it was applied successfully to tow the cable with a welding torch in a working area of 2.6 × 10 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> (simulation) and of 3.4 × 5.5 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> (real-world environment) without disturbing the lead vehicle's movement.
The Augmented Reality Technology. An Experimental Application in the Educational and Industrial Sector of Baja California, Mexico

The main aim of this investigation was the implementation of Augmented Reality Technology (ART) in an activity of the education sector in elementary schools, with a deficient infrastructure of Information and Communication Technologies (ICT) in the northwest’s region of the Mexican Republic in the Mexicali and San Quintin cities. Also in this analysis evaluated the possibility of implementing the ART in industrial companies of Baja California State, Mexico and as a novel way to develop distance training processes, considering the current limitations of the Covid 19 pandemic. Another limitation of the training activity is the lack of economic resources on some occasions to attend events in other cities or places of training. This scientific study was very relevant because this analysis verified that the use of the ART in the activities and the aspects of ICT mentioned made it possible to work and generate productive and representative results. As the deficiency and lack of ICT is an occurrence in some regions of Mexico and is a concern of government and industries of the persons in charge of educational and industrial operations, this investigation would be an alternative method to make educational courses at distance or personality with students. This can also apply to industrial workers with training courses at distance or personality to improve the yielding operative workers. They used in some healthy activities to understand any thematic the ART in training courses as the understanding the Covi19 in some public or private clinics or hospitals and generates the adequate tools to combat it.
Dual Secured Reversible Medical Image Watermarking for Internet of Medical Things

The Internet of Medical Things (IoMT) plays a big role in today’s healthcare industry. Smart healthcare has been a great use in sharing all kinds of medical data from Electronic Health Records (EHR) to hospital management. This advancement in healthcare made the diagnosis easy for the doctors and patients by saving their time. But in recent years there are a lot of threats in IoT and IoMT frameworks which motivated many researchers to find a way to overcome these challenges. In this paper, reversible data hiding technique using a linear and quadratic difference expansion to embed the EHR on the image is proposed. The medical image is divided into Border Region (BR) and Non Border Region (NBR). The hospital logo is watermarking in the BR of the image. Using LSB (Least Significant Bit) method hospital logo is embedded to ensure autauthentication. LSB approach is an easy and fast way to perform. To ensure confidentiality NBR is used for EPR embedding. Selecting a pair of pixels in NBR for embedding 2 bits of EPR pixels at each time with the linear and quadratic difference expansion. For ensuring dual security, EPR is encrypted using a Pseudo random key. Then encrypted EPR is partitioned into Odd and Even position pixels parts. Further, Odd and Even position pixels are watermarking in NBR using linear and quadratic difference expansion. Pseudo random key is generated adaptively from the mean of Medical image and Divide and Conquer algorithm to provide higher security. This proposed method is proved to take less computational time as there are no complex functions used in the algorithm. The test performed on this technique is proved to have high imperceptibility, robustness, security, and less computational time. All this confirms that proposed approach is a potential candidate for the security of data in the IoMT frameworks.
Delve Into Neural Activations: Toward Understanding Dying Neurons

Theoretically, a deep neuron network with nonlinear activation is able to approximate any function, while empirically the performance of the model with different activations varies widely. In this work, we investigate the expressivity of the network from an activation perspective. In particular, we introduce a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">generalized activation region/pattern</i> to describe the functional relationship of the model with an arbitrary activation function and illustrate its fundamental properties. We then propose a metric named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pattern similarity</i> to evaluate the practical expressivity of neuron networks regarding datasets based on the neuron level reaction toward the input. We find an undocumented <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dying neuron</i> issue that the postactivation value of most neurons remain in the same region for data with different labels, implying that the expressivity of the network with certain activations is greatly constrained. For instance, around 80% of postactivation values of a well-trained Sigmoid net or Tanh net are clustered in the same region given any test sample. This means most of the neurons fail to provide any useful information in distinguishing the data with different labels, suggesting that the practical expressivity of those networks is far below the theoretical. By evaluating our metrics and the test accuracy of the model, we show that the seriousness of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dying neuron</i> issue is highly related to the model performance. At last, we also discussed the cause of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dying neuron</i> issue, providing an explanation of the model performance gap caused by the choice of activation.
Random Feature-Based Collaborative Kernel Fuzzy Clustering for Distributed Peer-to-Peer Networks

Kernel clustering has the ability to get the inherent nonlinear structure of the data. But the high computational complexity and the unknown representation of the kernel space make it unavailable for the data clustering in distributed peer-to-peer (P2P) networks. To solve this issue, we propose a new series of random feature-based collaborative kernel clustering algorithms in this article. In the most basic algorithm, each node in a distributed P2P network first maps its data into a low-dimensional random feature space with the approximation of the given kernel by using the random Fourier feature mapping method. Then, each node independently searches the clusters with its local data and the collaborative knowledge from its neighbor nodes, and the distributed clustering is performed among all network nodes until reaching the global consensus result, i.e., all nodes have the same cluster centers. In addition, an improved version is designed with assignment of feature weights, which is optimized by the maximum-entropy technique to extract important features for the cluster identification. What’s more, to relief the impact of different kernel functions and related parameters on clustering results, the combination of multiple kernels rather than a single kernel is adopted for the low-dimensional approximation, and the optimized weights are assigned to provide the guidance on the choice of the kernels and their parameters and discover significant features at the same time. Experiments on synthetic and real-world datasets show that the proposed methods achieve similar and even better results than the traditional kernel clustering methods on various performance metrics, including the average classification rate, the average normalized mutual information, and the average adjusted rand index. More importantly, the low-dimensional random features approximated to kernels and the distributed clustering mechanism adopted in these methods bring the greatly lower temporal complexity.
Are AI models explainable, interpretable, and understandable?

Artificial intelligence (AI) has been evolved for the last 50 years. The recent AI techniques, machine learning (ML) and deep learning, have been successfully established in many fields such as recommendation, computer vision, machine translation, social media, and system diagnostics. The highly performing AI models, such as ensembles and neural networks, overwhelmed the prediction performance of traditional AI techniques such as symbolic and logic-based expert systems, but instead they turned to be increasingly difficult for humans to interpret and understand the model behavior. Yet, the explain ability of AI and the interpretability of ML are requisite when the AI-based systems are to be adopted for critical decision making in real world. How can you trust them for important decisions, if you cannot understand how AI works? Unfortunately, the systematic methods for the explainable AI (XAI) are not yet mature even in academia. This chapter will introduce the meaning of XAI, as well as the category of AI explanation and interpretation methods. In addition, the applications of XAI and the related important issues will be discussed.
LIVECHAIN: Lightweight Blockchain for IOT Devices and It’s Security

Blockchain requires high computation and heavy storage loads. It can be implemented in IoT devices by reducing the computational as well as space complexity of the blockchain. In this paper, a partial blockchain, called Livechain is designed for IoT type lightweight devices. Livechain is designed to hold much lesser number of blocks, i.e. few recent blocks, mining is replaced by an algorithm which is implemented using a miners’ queue. For security of the whole system, Elliptic curve cryptography (ECC) is used on the curve secp256k1 and few well known hashing function like SHA256.
The Method for Micro Expression Recognition Based on Improved Light-Weight CNN

Abstract In view of the particularity of micro expression, there are some problems, such as resource waste or parameter redundancy in micro expression training and recognition by using large convolutional neural network model alone. Therefore, a method of using lightweight model to recognize micro expression is proposed, which aims to reduce the size of model space and the number of parameters, and improve the accuracy at the same time. This method uses mini-Xception as the framework and Non-Local Net and SeNet as parallel auxiliary feature extractors to enhance feature extraction. Finally, the simulation experiments are carried out on the two public data sets of fer2013 and CK+. After a certain training cycle, the accuracy can reach 74.5% and 97.8% respectively, which slightly exceeds the commonly used classical models. It is proved that the improved lightweight model has higher accuracy, lower parameters and model size than the large convolution network model.
Evaluation of Named Entity Recognition in Handwritten Documents

Processing of handwritten documents is an important task for document analysis and cultural heritage. Among the most interesting processing tasks, Named Entity Recognition is one of the most important, since it allows to obtain relevant entities present in the handwritten documents. Many systems perform that in a decoupled way, automatically transcribing the document and, after that, applying classic Natural Language Processing techniques for Named Entity Recognition, but this approach is error-prone because of the noisy nature of the automatic transcription process. In this work we employ a single Deep Learning based model for coupled automatic transcription and Named Entity Recognition in historical handwritten texts. Such model leverages the generalization capabilities of recognition systems, combining Convolutional Recurrent Neural Networks (CRNN) and n-gram character models. The evaluation of that system is discussed and, as a consequence, two novel evaluation metrics, built upon the edit distance concept, are proposed. Additionally, we assess an alternative decoding process which includes syntactical constraints by exploring the n-best hypotheses.
Smoothing Regression and Impact Measures for Accidents of Traffic Flows

Traffic pattern prediction and measuring accidents are essential for improving traffic planning, design and management. Modeling the relationship between accidents and traffic flow would benefit decision making for road safety and traffic management. In this paper we establish classification and regression models to characterize the relationship between traffic flow and different time points and identify different patterns of the fitted traffic flows by a negative binomial model with smooth splines. It provides temporal prediction by using negative binomial smoothing splines to fit temporal patterns of traffic flows and constructing its confidence band to classify traffic flows and characterize the relationship between traffic flow and accidents. Consequently, we derive an impact measure for an accident influencing traffic volume. The proposed has been successfully applied to classify real-world traffic flows, and the results can be used for traffic administration to improve traffic management.
Predicting Argument Density from Multiple Annotations

Annotating a corpus with argument structures is a complex task, and it is even more challenging when addressing text genres where argumentative discourse markers do not abound. We explore a corpus of opinion articles annotated by multiple annotators, providing diverse perspectives of the argumentative content therein. New annotation aggregation methods are explored, diverging from the traditional ones that try to minimize presumed errors from annotator disagreement. The impact of our methods is assessed for the task of argument density prediction, seen as an initial step in the argument mining pipeline. We evaluate and compare models trained for this regression task in different generated datasets, considering their prediction error and also from a ranking perspective. Results confirm the expectation that addressing argument density from a ranking perspective is more promising than looking at the problem as a mere regression task. We also show that probabilistic aggregation, which weighs tokens by considering all annotators, is a more interesting approach, achieving encouraging results as it accommodates different annotator perspectives. The code and models are publicly available at https://github.com/DARGMINTS/argument-density .
Blockchain architecture, taxonomy, challenges, and applications

This chapter introduces the underlying technology, known as blockchain, of famous cryptocurrencies like Bitcoin. It starts by introducing the term blockchain, its analogies and characteristics. Topics like the history and benefits of blockchain over traditional technologies are discussed. It emphasizes the architecture of blockchain and various consensus mechanisms. The later part of this chapter explores types of blockchain and the blockchain technology stack. Various top blockchains used today are listed. It also explains various challenges of using blockchain such as energy consumption, scalability, and public perception, and it explores the different types of attacks upon blockchain. Various applications of blockchain followed by a number of use cases such as loans and cross-border payments using blockchain are explained. Finally, the importance of blockchain in the real world besides cryptocurrencies is detailed as well as future scope.