Reviewerly/exHarmony huggingface dataset:
Forensic analysis of instant messengers: Decrypt Signal, Wickr, and Threema

As organized criminals use instant messengers, it becomes increasingly important to obtain digital evidence from instant messengers. Recently, instant messengers apply end-to-end encryption, so all digital evidence can only be obtained from your mobile device. However, some instant messengers encrypt and store database and multimedia files, making forensic analysis of mobile devices difficult. In this paper, we present a methodology for analyzing the decryption algorithm of the messenger, and apply this methodology to Signal, Wickr, and Threema. We extracted data from both unrooted and rooted devices and performed static and dynamic analysis. As a result, we succeeded in decrypting all the encrypted database, multimedia, log, and preferences files of three messengers. We describe the decryption algorithms and disclose all decryption scripts.
Effect of Hyper-Threading in Latency-Critical Multithreaded Cloud Applications and Utilization Analysis of the Major System Resources

Multithreaded latency-critical applications represent an important subset of workloads running on public cloud systems. Most of these systems deploy powerful computing servers including Intel Hyper-Threading processors. Understanding how performance is affected by the consumption of the main system resources is a major concern for cloud providers in order to devise virtualization strategies that improve the system efficiency. With this aim, this paper first characterizes the impact of QPS on tail latency, analyzing different scenarios varying the number of threads and the thread-to-core allocation (single-task and multi-task execution) policy. The characterization study reveals that the performance of some applications does not scale with the number of threads, and the performance of some others is insensitive to the Hyper-Threading technology, so they can be allocated in less physical cores and improve system utilization. Identifying these applications, however, at run-time is challenging. Despite identifying these applications at run-time is challenging, this paper shows that they can be successfully detected at run-time by analyzing the utilization trend of the major system resources. In addition to CPU, we have also studied how assigning the share of each application of other major shared system resources impacts on performance. We outline considerations cloud providers should take into account to improve performance and resource utilization.
System Neural Network: Evolution and Change Based Structure Learning

System evolution analytics with artificial neural networks is a challenging and path-breaking direction, which could ease intelligent processes for systems that evolve over time. In this article, we contribute an approach to do <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Evolution and Change Learning</i> (ECL), which uses an evolution representor and forms a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">System Neural Network</i> (SysNN). We proposed an algorithm <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">System Structure Learning</i> , which is divided in two steps. First step uses the evolution representor as an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Evolving Design Structure Matrix</i> (EDSM) for intelligent design learning. Second step uses a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Deep Evolution Learner</i> that learns from evolution and changes patterns of an EDSM to generate Deep SysNN. The result demonstrates application of the proposed approach to analyze four real-world system domains: software, natural-language, retail market, and movie genre. We achieved significant learning over highly imbalanced datasets. The learning from previous states formed SysNN as a feed-forward neural network, and then memorized information as an output matrix to recommend entity-connections.
Application of the Case Models Restriction for Modeling Argumentation Reasoning

Within the framework of this article, for the formal presentation of case based and argumentative reasoning the apparatus of Case and Fuzzy Models is used. Knowledge about the Cases of the object domain is formalized in the form of algebraic systems. Then they are combined into a Case Model, which gives us a qualitative description of certain events in the object domain. By fuzzification of the Case Model, we get a Fuzzy Model of the object domain. The Fuzzy Model allows us to work with the quantitative characteristics of various events in the object domain, as well as construct samples of argumentation in the form of domain signature formulas. Using the Restrictions of Case Models allows us to model the influence of examples of argumentation on the quantitative characteristics of domain events.
Preliminary Indicators of EFL Essay Writing for Teachers’ Feedback Using Automatic Text Analysis

&lt;p style="text-align:justify"&gt;During the pandemic of Coronavirus disease 2019 (COVID-19), English as a foreign language (EFL) students have to study and submit their assignments and quizzes through online systems using electronic files instead of hardcopies. This has created an opportunity for teachers to use computer tools to conduct preliminary assessment of the students’ writing performance and then give advice to them timely. Hence, this paper proposed some indicators which were essay readability scored by Flesch Reading Ease (FRE), length of essays, errors in writing and a method to assist the teachers in providing writing feedback to the students. The results showed a large difference in FRE, the number of words, sentences, paragraphs and errors. The K-means clustering findings were applied to classify groups of students based on writing proficiency indicators. The findings also revealed that the number of words and sentences in the essays indicated some deficiencies. The concept of paragraph should be reinforced while some specific errors such as misspelling, grammatical and typographical errors found need to be eliminated. This study showcased that the computer tools should be integrated to process the students’ essays so that the teachers can pinpoint the problems and make suggestions to their students in appropriate time. Lastly, the results can be served as the guidelines for the teachers to develop and adjust teaching materials pertinent to writing to enhance the writing performance of EFL learners.&lt;/p&gt;
An Asian perspective on the governance of cyber civilization

Abstract This paper proposes potential benefits of adopting Asian philosophies in considering the design of “cyber civilization” governance. The West is currently struggling to balance the use of data for commercial purposes with the social good stemming from the protection of personal privacy and dignity. It is also grappling with the idea of machines having autonomous capabilities that human minds alone were supposed to monopolize. East Asia, with its tradition of Confucianism, Buddhism, and animism, has long emphasized the virtue of mutual benevolence as well as conviviality with nature, both of which may be contrasted with the Western emphasis on the autonomy of individuals and the supremacy of humans over nature. We need to revisit such foundational schools of thought in view of such factors as network externalities of data, extremely low marginal cost of digital services, and high level of traceability, all of which are altering the historical prerequisites of the modern market economy. In the face of the emergence of a new civilization, we need to find a guiding philosophy which allows us to develop policies that adequately benefit all people. There are notions such as integrity (fiduciary responsibility) that East and West share and that may be adopted to build broadly acceptable governance principles.
Client Selection with Bandwidth Allocation in Federated Learning

Federated learning (FL) is emerging as a promising paradigm for achieving distributed machine learning while protecting users' privacy. The accuracy and convergence speed of the global model benefit from involving as many clients as possible during the model training. On the other hand, the scarcity of wireless spectrum restricts the number of clients involved at each round. In this paper, we aim to maximize the number of participating clients in each round with fixed wireless bandwidth. Instead of assuming that the prior information about wireless channel state is available, we consider a more practical scenario under the absence of prior information. We first reformulate the client selection problem with limited bandwidth as a combinatorial multi-armed bandit (CMAB) problem and then propose an online learning algorithm with elegant bandwidth allocation based on the framework of combinatorial upper confidence bound. The proposed algorithm can make full use of the scarce bandwidth to increase the number of involved clients in each round and minimize the training latency for a given training accuracy. Numerical results validate the efficiency of the proposed algorithm.
RNN Encoder And Decoder With Teacher Forcing Attention Mechanism for Abstractive Summarization

Automatic text summarization is utilized to address the sustained increasing proportion of text data available in online, to address such numerous data to short data summarization is preferred. In this work, abstractive text summarization is reviewed with Recurrent Neural Network based Long Short Term Memory with encoder and attention decoder. In recent years, this type of summarization has carried out with Recurrent Neural Network which learns from the previous time steps. In this paper, we propose Teacher Forcing Technique to improve the slow convergence and poor performance of Recurrent Neural Network. Our technique improves the performance of text summarization by using Attention Decoder which learns from ground truth instead of learning from previous time steps and minimizes the error rate with Stochastic Gradient Descent optimizer with more accurate results on Wikihow dataset when measured with metric of ROUGE and performed well when compared our result with the state of art results.
Clustering-Guided Pairwise Metric Triplet Loss for Person Reidentification

Most of the loss functions proposed for person reidentification (Re-ID) are expected to be easy to deploy, efficiently improve network performance, and will not introduce redundant parameters. This study proposes a no-parameter and generic clustering-guided pairwise metric triplet (CPM-Triplet) loss based on the hard sample mining triplet loss for the metric learning loss. CPM-Triplet loss deploys two metrics: 1) the Euclidean metric and 2) the cosine metric, to complementarily improve the metric learning of the model. Paralleled to the Euclidean metric, the cosine metric quantifies the sample similarity in a different way to the Euclidean metric, which takes a different perspective to explore the distribution of samples. But the pairwise metric mainly improves the precision between dissimilar samples of the same label and could not solve the problem of excessive outliers. Therefore, the clustering-guided correction term was deployed to apply to all samples with the same label to mine the similarity in the samples, while weakening the influence of outliers in CPM-Triplet loss. Experiments conducted on four benchmark data sets show that the combination of the CPM-Triplet loss and the widely used Bag-of-Tricks baseline generally outperforms the baseline and numerous state-of-the-art methods studied in this article. The source code would be available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/weiyu-zeng/CPM-Triplet-loss</uri> .
A Privacy-Preserving Incentive Mechanism for Federated Cloud-Edge Learning

The federated learning scheme enhances the privacy preservation through avoiding the private data uploading in cloud-edge computing. However, the attacks against the uploaded model updates still cause private data leakage which demotivates the privacy-sensitive participating edge devices. Facing this issue, we aim to design a privacy-preserving incentive mechanism for the federated cloud-edge learning (PFCEL) system such that 1) the edge devices are motivated to actively contribute to the updated model uploading, 2) a trade-off between the private data leakage and the model accuracy is achieved. We formulate the incentive design problem as a three-layer Stackelberg game, where the server-device interaction is further formulated as a contract design problem. Extensive numerical evaluations demonstrate the effectiveness of our designed mechanism in terms of privacy preservation and system utility.
Applications of blockchain in automated heavy vehicles: Yesterday, today, and tomorrow

Autonomous vehicles will play a bigger role in the future. In a real-life application, an autonomous vehicle can be used by a passenger to reach a destination autonomously with necessary maneuverings. The autonomous vehicle includes multiple computing devices that can be easily operated by users using input buttons. Thus communicating requests from human to machine and initiating a trip to the destination would be convenient, secure, and easy. The human-to-machine instructions provide multiple facilities to the users including emergency stop, start, speed control, emergency message conveyer, and timed operations. However, the user inputs and control of steering, acceleration, and deacceleration are very limited. In this chapter, the importance of blockchain technology for autonomous vehicles is explored. This study analyzes the data received from system perception, planning, movement, and decision-making for autonomous vehicles. Blockchain technology provides immutability, transparency and security measurements that can guarantee performance and safety under all driving circumstances. For example, data collection and storage in a blockchain network can provide a planned method that can further ensure safe and system-compliant performance in complex and dynamic environments. This work emphasizes the recent blockchain-based approaches for integrating perception and planning for end-to-end communication, verification, and safety processes and managing fleets of autonomous vehicles. Further, blockchain integrated autonomous vehicle’s movement-based use cases are presented for multiple applications. These real-time applications realize the importance of autonomous vehicles in-vehicle networks. These use cases present the requirements of advancements in many aspects of vehicle autonomy: for example, the vehicle’s design to control the operations, plan the movement, and coordinate with other vehicles; the human-to-machine instructions and interactions; human perceptions in real-time implementation; and vehicle state driving. Finally, future research directions and open challenges are highlighted for future readers.
Reduce the medical burden: An automatic medical triage system using text classification BERT based on Transformer structure

To reduce the pressure of medical triage in the hospitals, this paper proposes a medical triage system that could classify patients' questions or texts about their symptoms into several given categories to give suggestions on which kind of consulting room patients could choose. First, we have done extensive research on the medical care situation and the hospitals' problems in China and conclude that reducing the triage pressure is of great importance for hospitals. We then collect the medical Question Answering datasets, including questions and answers with symptom tags. According to the form of our data, we use BERT, a mainstream model in Natural Language Processing, as the base of our system and modify it with additional components specified to our task. We developed two models based on different datasets. One is trained by data from the five most frequent symptom tags. And for the other one, we use the whole dataset by identifying the appearance of special words, measuring the overlap of all the tags, and merging them into 20 categories. Both of them utilize several training techniques and result in relatively high accuracy: top1 85% accuracy and top2 accuracy 96% on the smaller dataset, top1 accuracy 66.2%, and top2 accuracy 78.3% on the other one. Then we analyze the results and build up our web system for medical use. If given real-world data with similar data distribution, our system could help patients judge diseases and alleviate the triage problem in medical treatment to a certain extent. Moreover, a similar strategy of our model could also be adapted for use in different fields like book searching in libraries. Therefore, our system has a broad application prospect.
Adversarial Attacks to Solar Power Forecast

With development of the photovoltaic industry, solar power generation forecasting using weather data has become an important problem. Various machine learning (ML) algorithms have been proposed to handle the random and massive weather data, with considerable recent interest on deep neural networks (DNN). Recent studies show that DNNs are vulnerable to adversarial examples, but most prior work has focused on their impact on the classification problem. In this paper, we investigate the problem of adversarial attacks on solar power generation forecasting, which is a regression problem. We examine the impact of adversarial attacks on both the DNN model and a LASSO-based statistical model proposed in our prior work. Both white-box attack and black-box attack are examined, along with the effect of adversarial training.
This should help with that: A behavioral investigation into self‐derivation of knowledge about prescription medications

Self-derivation of new factual knowledge is crucial for building a knowledge base. In three experiments, we investigated self-derivation about prescription medications. In Experiment 1, adults self-derived new knowledge across textual materials on 40% of trials. Participants in Experiment 2 performed similarly (42%), even when half the information was presented in videos. It was crucial that participants received both learning episodes to successfully self-derive: control condition participants received half the necessary information and performed significantly lower. When a delay was imposed between related facts in Experiment 3, participants self-derived on only 33% of trials and performance did not differ from the control condition. The present research expanded our understanding of adults' learning and self-derivation across media about medications. It revealed room for improvement in adults' learning and self-derivation about health information. This work suggests the need to identify factors that alter performance, including better understanding of the properties of information sources.
SMPL-Based 3D Pedestrian Pose Prediction

Modeling human motion is a long-standing problem in computer vision.The rapid development of deep learning technologies for computer vision problems resulted in increased attention in the area of pose prediction due to its vital role in a multitude of applications, for example, behavior analysis, autonomous vehicles, and visual surveillance.In 3D pedestrian pose prediction, joint-rotation-based pose representation is extensively used due to the unconstrained degree of freedom for each joint and its ability to regress the 3D statistical wireframe.However, all the existing joint-rotation-based pose prediction approaches ignore the centrality of the distinct pose parameter components and are consequently prone to suffer from error accumulation along the kinematic chain, which results in unnatural human poses.In joint-rotationbased pose prediction, Skinned Multi-Person Linear (SMPL) parameters are widely used to represent pedestrian pose.In this work, a novel SMPL-based pose prediction network is proposed to address the centrality of each SMPL component by distributing the network weights among them.Furthermore, to constrain the network to generate only plausible human poses, an adversarial training approach is employed.The effectiveness of the proposed network is evaluated using the PedX and BEHAVE datasets.The proposed approach significantly outperforms state-of-the-art methods with improved prediction accuracy and generates plausible human pose predictions.
Color-Dense Illumination Adjustment Network for Removing Haze and Smoke from Fire Scenario Images

The atmospheric particles and aerosols from burning usually cause visual artifacts in single images captured from fire scenarios. Most existing haze removal methods exploit the atmospheric scattering model (ASM) for visual enhancement, which inevitably leads to inaccurate estimation of the atmosphere light and transmission matrix of the smoky and hazy inputs. To solve these problems, we present a novel color-dense illumination adjustment network (CIANet) for joint recovery of transmission matrix, illumination intensity, and the dominant color of aerosols from a single image. Meanwhile, to improve the visual effects of the recovered images, the proposed CIANet jointly optimizes the transmission map, atmospheric optical value, the color of aerosol, and a preliminary recovered scene. Furthermore, we designed a reformulated ASM, called the aerosol scattering model (ESM), to smooth out the enhancement results while keeping the visual effects and the semantic information of different objects. Experimental results on both the proposed RFSIE and NTIRE'20 demonstrate our superior performance favorably against state-of-the-art dehazing methods regarding PSNR, SSIM and subjective visual quality. Furthermore, when concatenating CIANet with Faster R-CNN, we witness an improvement of the objection performance with a large margin.
RockSL: an integrated rock spectral library for better global shared services

Spectral data of different rocks and minerals usually show different waveforms and absorption characteristics in visible and infrared wavelengths, which allow identification of mineral species and composition. However, massive spectra of rock/mineral on earth surface were scattered across a variety of spectral libraries worldwide, exhibiting inconsistent data structures and measurement conditions. To advance the data interoperability and the data usability, we collected data and information from six shared libraries with different format and measured field specimen in laboratory to establish an integrated rock spectral library (RockSL). Both the data quality of spectral curves and the integrity of descriptive metadata are considered in the integrated RockSL to be published in GitHub open-source repository. RockSL contains not only the big spectral dataset of rocks and minerals for data service (i.e. data sharing and retrieval) and geological discrimination, but also the characteristics dataset of key parameters/metadata (e.g. particle size, mineral composition and full-band signature, etc.) for exploration of data mining and knowledge discovery. We hope that more researchers will join to improve the availability and practical value of RockSL for remote sensing community. This article introduces the database structure and data processing workflow, and demonstrates a matching service and several examples of characteristic datasets of RockSL.