Reviewerly/exHarmony huggingface dataset:
Fake News, Disinformation, and Deepfakes: Leveraging Distributed Ledger Technologies and Blockchain to Combat Digital Deception and Counterfeit Reality

The rise of ubiquitous deepfakes, misinformation, disinformation, and post-truth, often referred to as fake news, raises concerns over the role of the Internet and social media in modern democratic societies. Due to its rapid and widespread diffusion, digital deception has not only an individual or societal cost, but it can lead to significant economic losses or to risks to national security. Blockchain and other distributed ledger technologies (DLTs) guarantee the provenance and traceability of data by providing a transparent, immutable, and verifiable record of transactions while creating a peer-to-peer secure platform for storing and exchanging information. This overview aims to explore the potential of DLTs to combat digital deception, describing the most relevant applications and identifying their main open challenges. Moreover, some recommendations are enumerated to guide future researchers on issues that will have to be tackled to strengthen the resilience against cyber-threats on today's online media.
NetNCSP: Nonoverlapping closed sequential pattern mining

Sequential pattern mining (SPM) has been applied in many fields. However, traditional SPM neglects the pattern repetition in sequence. To solve this problem, gap constraint SPM was proposed and can avoid finding too many useless patterns. Nonoverlapping SPM, as a branch of gap constraint SPM, means that any two occurrences cannot use the same sequence letter in the same position as the occurrences. Nonoverlapping SPM can make a balance between efficiency and completeness. The frequent patterns discovered by existing methods normally contain redundant patterns. To reduce redundant patterns and improve the mining performance, this paper adopts the closed pattern mining strategy and proposes a complete algorithm, named Nettree for Nonoverlapping Closed Sequential Pattern (NetNCSP) based on the Nettree structure. NetNCSP is equipped with two key steps, support calculation and closeness determination. A backtracking strategy is employed to calculate the nonoverlapping support of a pattern on the corresponding Nettree, which reduces the time complexity. This paper also proposes three kinds of pruning strategies, inheriting, predicting, and determining. These pruning strategies are able to find the redundant patterns effectively since the strategies can predict the frequency and closeness of the patterns before the generation of the candidate patterns. Experimental results show that NetNCSP is not only more efficient but can also discover more closed patterns with good compressibility. Furtherly, in biological experiments NetNCSP mines the closed patterns in SARS-CoV-2 and SARS viruses. The results show that the two viruses are of similar pattern composition with different combinations.
A Survey of Deep Facial Attribute Analysis

Facial attribute analysis has received considerable attention when deep learning techniques made remarkable breakthroughs in this field over the past few years. Deep learning based facial attribute analysis consists of two basic sub-issues: facial attribute estimation (FAE), which recognizes whether facial attributes are present in given images, and facial attribute manipulation (FAM), which synthesizes or removes desired facial attributes. In this paper, we provide a comprehensive survey of deep facial attribute analysis from the perspectives of both estimation and manipulation. First, we summarize a general pipeline that deep facial attribute analysis follows, which comprises two stages: data preprocessing and model construction. Additionally, we introduce the underlying theories of this two-stage pipeline for both FAE and FAM. Second, the datasets and performance metrics commonly used in facial attribute analysis are presented. Third, we create a taxonomy of state-of-the-art methods and review deep FAE and FAM algorithms in detail. Furthermore, several additional facial attribute related issues are introduced, as well as relevant real-world applications. Finally, we discuss possible challenges and promising future research directions.
Does Syntax Need to Grow on Trees? Sources of Hierarchical Inductive Bias in Sequence-to-Sequence Networks

Learners that are exposed to the same training data might generalize differently due to differing inductive biases. In neural network models, inductive biases could in theory arise from any aspect of the model architecture. We investigate which architectural factors affect the generalization behavior of neural sequence-to-sequence models trained on two syntactic tasks, English question formation and English tense reinflection. For both tasks, the training set is consistent with a generalization based on hierarchical structure and a generalization based on linear order. All architectural factors that we investigated qualitatively affected how models generalized, including factors with no clear connection to hierarchical structure. For example, LSTMs and GRUs displayed qualitatively different inductive biases. However, the only factor that consistently contributed a hierarchical bias across tasks was the use of a tree-structured model rather than a model with sequential recurrence, suggesting that human-like syntactic generalization requires architectural syntactic structure.
Edge Computing Resources Reservation in Vehicular Networks: A Meta-Learning Approach

With the development of autonomous vehicular technologies, the execution tasks become more memory-consuming and computation-intensive. Simultaneously, a certain portion of tasks are latency-sensitive, such as collaborative perception, path planning, collaborative simultaneous localization and mapping, real-time pedestrian detection, etc. Because of the limited computation resources inside vehicles and restricted transmission bandwidth, edge computing can be an effective way to assist with the tasks execution. Considering from the perspective of business, the reservation or subscription cost is cheaper than real time requests. In order to minimize the expense of consuming edge services, the desirable situation is to reserve the resources as much as needed. However, the configuration of vehicular network is variational in practice due to the diversity of road maps, different time range like peak time and off-peak time, and the various task types, which makes it challenging to figure out a general machine learning model that is suitable for any case. Therefore, to predict the resource consumption in edge nodes accurately in different scenarios, we propose a two-stage meta-learning based approach to adaptively choose the appropriate machine learning algorithms based on the meta-features extracted on database. Besides, due to the deficiency of dataset for edge resource consumption, we program in game engine unity to generate the 3D model of Manhattan area. Meanwhile, we change the factors like different road maps and number of vehicles so as to get closer to practices. In the evaluation part, we adopt root mean square error, mean absolute percentage, and mean GEH as evaluation metrics to assess the performance of each model. Also, a quantitative analysis for the total cost and waste is also conducted. Eventually, we can find that the proposed meta-learning based method outperforms the non-meta ones.
Are We Susceptible to Rowhammer? An End-to-End Methodology for Cloud Providers

Cloud providers are concerned that Rowhammer poses a potentially critical threat to their servers, yet today they lack a systematic way to test whether the DRAM used in their servers is vulnerable to Rowhammer attacks. This paper presents an endto-end methodology to determine if cloud servers are susceptible to these attacks. With our methodology, a cloud provider can construct worst-case testing conditions for DRAM.We apply our methodology to three classes of servers from a major cloud provider. Our findings show that none of the CPU instruction sequences used in prior work to mount Rowhammer attacks create worst-case DRAM testing conditions. To address this limitation, we develop an instruction sequence that leverages microarchitectural side-effects to "hammer" DRAM at a near-optimal rate on modern Intel Skylake and Cascade Lake platforms. We also design a DDR4 fault injector that can reverse engineer row adjacency for any DDR4 DIMM. When applied to our cloud provider's DIMMs, we find that DRAM rows do not always follow a linear map.
Multi-step ahead forecasting of global solar radiation for arid zones using deep learning

Solar irradiance is fluctuating and intermittent in nature. In order to optimally harness solar energy, this variability needs to be accounted for. Forecasting solar radiation proves to be helpful in optimal design, and operation of solar-energy based systems. This paper presents a solar irradiance forecasting scheme for multi-horizon forecasting of solar radiation considering 3/6/24 hours ahead scenarios. The proposed model uses long short term memory network, considering the dependence between hours of the same day along with other variables such as: direct horizontal irradiance, direct normal irradiance, relative humidity, dew point, temperature, wind speed, and wind direction. Solar radiations for four different locations of the Thar desert region have been forecasted. The model is optimized in terms of number of neurons and is evaluated using standard statistical indicators: RMSE and MAPE. RMSE for four different locations varied in the range of 0.099 to 0.181, along with MAPE values, which range from 6.79% to 10.47%. Low values of RMSE and MAPE indicate the efficacy of the proposed model.
iWave: CNN-Based Wavelet-Like Transform for Image Compression

Wavelet transform is a powerful tool for multiresolution time-frequency analysis. It has been widely adopted in many image processing tasks, such as denoising, enhancement, fusion, and especially compression. Wavelets lead to the successful image coding standard JPEG-2000. Traditionally, wavelets were designed from the signal processing theory with certain assumption on the signal, but natural images are not as ideal as assumed by the theory. How to design content-adaptive wavelets for natural images remains a difficulty. Inspired by the recent progress of convolutional neural network (CNN), we propose iWave as a framework for deriving wavelet-like transform that is more suitable for natural image compression. iWave adopts an update-first lifting scheme, where the prediction filter is a trained CNN, to achieve wavelet-like transform. The CNN can be embedded into a deep network that is analogous to an auto-encoder, which is trained end-to-end. The trained wavelet-like transform still possesses the lifting structure, which ensures perfect reconstruction, supports multiresolution analysis, and is more interpretable than the deep networks trained as “black boxes.” We perform experiments to verify the generality as well as the speciality of iWave in comparison with JPEG-2000. When trained with a generic set of natural images and tested on the Kodak dataset, iWave achieves on average 4.4% and up to 14% BD-rate reductions. When trained and tested with a specific kind of textures, iWave provides as high as 27% BD-rate reduction.
Shielding Collaborative Learning: Mitigating Poisoning Attacks through Client-Side Detection

Collaborative learning allows multiple clients to train a joint model without sharing their data with each other. Each client performs training locally and then submits the model updates to a central server for aggregation. Since the server has no visibility into the process of generating the updates, collaborative learning is vulnerable to poisoning attacks where a malicious client can generate a poisoned update to introduce backdoor functionality to the joint model. The existing solutions for detecting poisoned updates, however, fail to defend against the recently proposed attacks, especially in the non-IID (independent and identically distributed) setting. In this article, we present a novel defense scheme to detect anomalous updates in both IID and non-IID settings. Our key idea is to realize client-side cross-validation, where each update is evaluated over other clients' local data. The server will adjust the weights of the updates based on the evaluation results when performing aggregation. To adapt to the unbalanced distribution of data in the non-IID setting, a dynamic client allocation mechanism is designed to assign detection tasks to the most suitable clients. During the detection process, we also protect the client-level privacy to prevent malicious clients from knowing the participations of other clients, by integrating differential privacy with our design without degrading the detection performance. Our experimental evaluations on three real-world datasets show that our scheme is significantly robust to two representative poisoning attacks.
A Comparative Sentiment Analysis Of Sentence Embedding Using Machine Learning Techniques

Analyzing sentiment is a process to identify the opinion of a text. It is also known as opinion mining or emotion Artificial Intelligence (AI). People post comments in social media mentioning their experience about an event and are also interested to know if the majority of other people had a positive or negative experience on the same event. This classification can be achieved using Sentiment Analysis. Sentiment analysis takes unstructured text comments about a product reviews, an event, etc., from all comments posted by different users and classifies the comments into different categories as either positive or negative or neutral opinion. This is also known as polarity classification. Sentimental analysis can be performed by Text analysis and computational linguistics. This work aims at comparing the performance of different machine learning algorithms in performing sentiment analysis of Twitter data. The proposed method uses term frequency to find the sentiment polarity of the sentence. The performance of Multinomial Naive Bayes, SVM and Logistic regression algorithms in sentence classification were compared. From the results, it is inferred that logistic regression has achieved a greatest accuracy when it is used with n-gram and bigram model.
DU-Net: Convolutional Network for the Detection of Arterial Calcifications in Mammograms

Breast arterial calcifications (BACs) are part of several benign findings present on some mammograms. Previous studies have indicated that BAC may provide evidence of general atherosclerotic vascular disease, and potentially be a useful marker of cardiovascular disease (CVD). Currently, there is no technique in use for the automatic detection of BAC in mammograms. Since a majority of women over the age of 40 already undergo breast cancer screening with mammography, detecting BAC may offer a method to screen women for CVD in a way that is effective, efficient, and broad reaching, at no additional cost or radiation. In this paper, we present a deep learning approach for detecting BACs in mammograms. Inspired by the promising results achieved using the U-Net model in many biomedical segmentation problems and the DenseNet in semantic segmentation, we extend the U-Net model with dense connectivity to automatically detect BACs in mammograms. The presented model helps to facilitate the reuse of computation and improve the flow of gradients, leading to better accuracy and easier training of the model. We evaluate the performance using a set of full-field digital mammograms collected and prepared for this task from a publicly available dataset. Experimental results demonstrate that the presented model outperforms human experts as well as the other related deep learning models. This confirms the effectiveness of our model in the BACs detection task, which is a promising step in providing a cost-effective risk assessment tool for CVD.
CAN-ADF: The controller area network attack detection framework

In recent years, there has been significant interest in developing autonomous vehicles such as self-driving cars. In-vehicle communications, due to simplicity and reliability, a Controller Area Network (CAN) bus is widely used as the de facto standard to provide serial communications between Electronic Control Units (ECUs). However, prior research reveals that several network-level attacks can be performed due to the lack of defense mechanisms in the CAN bus. In this work, we propose CAN Bus Message Attack Detection Framework (CAN-ADF) - a comprehensive anomaly generation, detection, and evaluation system for a CAN bus. In CAN-ADF, not only various anomalies and attack characteristics can be configured but also different detection methods, and visualization frameworks are provided to effectively detect those attacks and anomalies. For the detector, we employ both a rule-based approach crafted from dynamic network traffic characteristics and Recurrent Neural Networks (RNN). For evaluation, we use 7,875,791 in-vehicle CAN packets collected from real cars, KIA Soul and Hyundai Sonata. Our detection algorithm achieves accurate intrusion detection performance, with an average accuracy of 99.45% on CAN datasets, outperforming prior approach. Furthermore, we developed a visualization tool to validate the detection of anomalies by CAN-ADF and to find new patterns in the dataset.
A Hybrid Service Selection and Composition Model for Cloud-Edge Computing in the Internet of Things

Cloud-edge computing is a hybrid model of computing where resources and services provided via the Internet of Things (IoT) between large-scale and long-term data informs of the cloud layer and small-scale and short-term data as edge layer. The main challenge of the cloud service providers is to select the optimal candidate services that are doing the same work but offer different Quality of Service (QoS) values in IoT applications. Service composition in cloud-edge computing is an NP-hard problem; therefore, many meta-heuristic methods introduced to solve this issue. Also, the correctness of meta-heuristic and machine learning algorithms for evaluating service composition problem should be proven using formal methods to guarantee functional and non-functional specifications. In this paper, a hybrid Artificial Neural Network-based Particle Swarm Optimization (ANN-PSO) Algorithm presented to enhance the QoS factors in cloud-edge computing. To illustrate the correctness and improve the reachability rate of candidate composited services and QoS factors for the proposed hybrid algorithm, we present a formal verification method based on a labeled transition system to check some critical Linear Temporal Logics (LTL) formulas. The experimental results illustrated the high performance of the proposed model in terms of minimum verification time, memory consumption, and guaranteeing critical specifications rules as the Linear Temporal Logic (LTL) formulas. Also, we observed that the proposed model has optimal response time, availability, and price with maximum fitness function value than other service composition algorithms.
AlloX

Modern deep learning frameworks support a variety of hardware, including CPU, GPU, and other accelerators, to perform computation. In this paper, we study how to schedule jobs over such interchangeable resources - each with a different rate of computation - to optimize performance while providing fairness among users in a shared cluster. We demonstrate theoretically and empirically that existing solutions and their straightforward modifications perform poorly in the presence of interchangeable resources, which motivates the design and implementation of AlloX. At its core, AlloX transforms the scheduling problem into a min-cost bipartite matching problem and provides dynamic fair allocation over time. We theoretically prove its optimality in an ideal, offline setting and show empirically that it works well in the online scenario by incorporating with Kubernetes. Evaluations on a small-scale CPU-GPU hybrid cluster and large-scale simulations highlight that AlloX can reduce the average job completion time significantly (by up to 95% when the system load is high) while providing fairness and preventing starvation.
Spatial distribution dependency of soil organic carbon content to important environmental variables

Soil organic carbon (SOC) has a vital role in the physical, chemical and biological behavior of the soil, and therefore prediction of the amount and mapping spatial distribution of SOC is necessary for sustainable soil management. However, the relationship between SOC and remotely sensed and easily accessible variables have been rarely reported. The main objective of the present study is, therefore, to estimate SOC using the remote sensing of satellite images as well as some field variables for the Shazand Watershed, Iran. Towards that, 140 soil samples were taken from the top 30-cm of the soil from homogeneous units representing an area >1 km2 to measure SOC. The potential relationship between SOC and some remote sensing-based indices including Enhanced Vegetation Index (EVI), Difference vegetation index (DVI), Optimized Soil Adjusted Vegetation Index (OSAVI), Soil Adjusted Vegetation Index (SAVI), Modified Soil Adjusted Vegetation Index (MSAVI), Normalized Difference Vegetation Index (NDVI), Coloration Index (CI), Normalized Difference Moisture Index (NDMI), and some topographical and soil texture factors viz. elevation, slope, aspect, Topographical Wetness Index (TWI), clay and silt content of soil were assessed for the Shazand Watershed. In this regard, the relationships between SOC and environmental variables were analyzed using Ordinary Least Square (OLS), Geographically Weighted Regression (GWR), and Random Forest (RF) analyses. The results showed that none of the models indicated a good predictive performance due to low R2. However, CI was found as a better predictor (R2 = 0.258 for GWR and R2 = 0.040 for OLS; p-value <0.05) for SOC in comparison with other indices. Accordingly, it was concluded that the remote sensing-based and environmental indices did not have enough potential for estimating SOC to generate managerial plans for the study watershed. The low SOC content (0.63 ± 0.29%) and low variation across the Shazand Watershed were found as determinant factors on the low fitness of the study relationships. Further studies are therefore needed to find out reliable predictive models for SOC for the conditions under considerations.
A Smart Contract System for Decentralized Borda Count Voting

In this article, we propose the first self-tallying decentralized e-voting protocol for a ranked-choice voting system based on Borda count. Our protocol does not need any trusted setup or tallying authority to compute the tally. The voters interact through a publicly accessible bulletin board for executing the protocol in a way that is publicly verifiable. Our main protocol consists of two rounds. In the first round, the voters publish their public keys, and in the second round they publish their randomized ballots. All voters provide Non-interactive Zero-Knowledge (NIZK) proofs to show that they have been following the protocol specification honestly without revealing their secret votes. At the end of the election, anyone including a third-party observer will be able to compute the tally without needing any tallying authority. We provide security proofs to show that our protocol guarantees the maximum privacy for each voter. We have implemented our protocol using Ethereum's blockchain as a public bulletin board to record voting operations as publicly verifiable transactions. The experimental data obtained from our tests show the protocol's potential for the real-world deployment.