Reviewerly/exHarmony huggingface dataset:
Mitigating Cross-Core Cache Attacks via Suspicious Traffic Detection

Continuous Attacks are common cross-core cache side-channel attack scenarios that we observed, where adversaries frequently probe-target cache lines in a short time. Under Continuous Attacks, the attacked lines go through multiple load-evict processes between different cache (or memory) hierarchies, exhibiting Ping-Pong patterns. Identifying and obscuring these abnormal patterns effectively interfere with the attacker's probe and mitigate such attacks. Our recent proposal, Ping-Pong regulator (PPR), captures multiple Ping-Pong patterns by counting the reaccesses per cache line and blocks them with different obscuring actions (preload or lock). Although PPR mitigates Continuous Attacks, the added regulator directory (RDir) is vulnerable because it cannot record all cache lines simultaneously. Sophisticated attackers can evict the records of the attacked line from the RDir to avoid triggering defensive actions, thereby bypassing PPR. To improve robustness, we further propose PPR+, which dynamically changes the mapping of physical addresses to RDir locations by encryption and periodically changing keys. This randomness makes it difficult for attackers to evict target entries out of the RDir within a limited time. We show that PPR+ tolerates more than 100 years of attacks, induces negligible performance impacts (improves 0.13%), requires acceptable storage overhead (3.15%), and does not need any software support.
Designing CNNs for Multimodal Image Super-Resolution via the Method of Multipliers

Multimodal alias, guided, image super-resolution (SR) refers to the reconstruction of a high-resolution (HR) version of a low-resolution (LR) image with the aid of an HR image from another image modality. Common approaches for the SR problem include analytical methods which are computationally expensive. Deep learning methods are capable of learning a nonlinear mapping between LR and HR images from data, delivering high reconstruction accuracy at a low-computational cost during inference; however, these methods do not incorporate any prior knowledge about the problem, with the neural network model behaving like a black box. In this paper, we formulate multimodal image SR as a coupled convolutional sparse coding problem. To solve the corresponding minimization problem, we adopt the Method of Multipliers (MM). We then design a convolutional neural network (CNN) that unfolds the obtained MM algorithm. The proposed CNN accepts as input the LR image from the main modality and the HR image from the guidance modality to reconstruct the desired HR image. Unlike existing deep learning methods, our CNN provides an efficient and structured way to fuse information at different stages of the network and achieves high reconstruction accuracy. We evaluate the performance of the proposed model for the super-resolution of multi-spectral images guided by their high resolution RGB counterparts.
Combining Symbolic and Motion Planners for Rearranging Tasks in Daily-life Environments

Humans do rearranging tasks every day. These tasks are time-consuming and tedious. Rearranging tasks are challenging because there are many problems to solve, such as identifying the items, manipulating the items, and finding a strategy to rearrange that can be completed with the robot. In this work, we focus on how to execute rearranging tasks and avoid errors in the motion planner such as not finding a valid solution. We propose to combine the symbolic planner with the motion planner using a feasibility database to confirm if the instructions received by the symbolic planner are valid or not. The final poses of the robot end effector are previously validated with the motion planner, so we avoid sending the robot to invalid poses. The proposed method was tested in a simulation environment doing a sandwich rearranging task in a convenience store setup.
Development of a Production Structure Based on Digital Capacity Building

The article is devoted to the development of industrial enterprise production structure. According to the authors, one of the factors of influence on the production structure is the level of digital capacity formation. For calculation of enterprise digital capacity four basic components and five functional blocks of the enterprise are highlighted. The detailed approach to calculation of one of functional blocks - Production on components "IT Tools and Tools Collection" and "Technical equipment and technological base of production" is offered. For each component single indicators are given, and for the calculation of the integral indicator chosen distance method. The given approach to calculation of digital capacity will allow to define, how much blocks of the enterprise are interconnected and how flexible and adaptive the industrial structure is. The negative and positive aspects of digital transformation impact on the production structure of the enterprise are noted.
Towards Performance Evaluation of Requests Satisfaction System in the Cloud Environment

The requested Cloud services must be managed effectively according to the relevant needs of the user. For that, a performance evaluation using a formal model is necessary in order to make a judicious satisfaction of the user request, To better manage and to guarantee a good quality of the service (QoS). This paper represents a performance evaluation model using an open Jackson network for a request satisfaction system in the IaaS Cloud environment. The response time and the utilization are obtained using the rules of the open Jackson network. These performance parameters are used to quantity the Availability and the Elasticity of the IaaS Cloud. Inclusive simulations are performed to support the proposed model.
An Improved XGBoost Model Based on Spark for Credit Card Fraud Prediction

Credit card fraud causes huge economic losses for many financial institutions. Given the imbalance of dataset and the huge amount of data in the field of credit card fraud, an improved XGBoost model based on Spark is proposed. In this project, the Smote algorithm was used to to balance the training set. And the XGBoost classifier based on Spark was used as the fraud detection mechanism. Finally, the test sets were classified in parallel. In the model comparison experiment, the model proposed in this project is compared with logistic regression model, decision tree model, random forest model, and original XGBoost model. The experimental results show that in the three metrics of Recall, Fl-Score, and AUC, the model proposed in this project is the best, which is 9.1%, 1.4%, and 1.2% ahead of the model ranked second respectively. In the speedup experiment, the speedup on the dataset of 70,000, 140,000, and 280,000 samples are 2.06, 3.28, and 3.75 respectively. The experimental results of these two parts show that the proposed model can accurately and efficiently predict credit card fraud and has a good practical effect.
Cloud technologies in the education system

Abstract Distributed computing is turning into an adoptable innovation for a considerable lot of the associations with its dynamic versatility and utilization of virtualized assets as help through the Internet. It will probably significantly affect the instructive condition later on. Distributed computing is an incredible option for instructive organizations that are particularly under spending deficiency to adequately work their data frameworks without spending any more capital on the PCs and system gadgets. Universities exploit available cloud-based applications offered by specialist organizations and empower their clients/understudies to perform business and scholarly errands. In this paper, we will survey what the distributed computing foundation will give in the instructive field, particularly in the universities where the utilization of PCs is more severe and what should be possible to expand the advantages of typical applications for understudies and instructors.
Character Recognition using Tesseract enabling Multilingualism

Character recognition builds a recognizing factor for identifying the accuracy in characters. The accuracy of classifying the recognizing characters in an image is applied through deep learning methods. The character recognition is mainly focusing on the layers of text recognition through deep learning techniques. Well cleared python code assists to furnish all the levels of image by following deep learning that algorithmically analyse and recognize text from the given input image. This research work has been proposed for recognizing characters using deep learning techniques and recognize the input image with well-furnished and most efficient output. It provides a high level of accuracy-built output after the recognition of characters in the high-resolution image. This recognized character can be converted into user desired languages where the proposed model is trained to recognize some particular languages.
Bandwidth Detection of Graph Signals with a Small Sample Size

Bandwidth is the crucial knowledge to sampling, reconstruction or estimation of the graph signal (GS). However, it is typically unknown in practice. In this paper, we focus on detecting the bandwidth of bandlimited GS with a small sample size, where the number of spectral components of GS to be tested may greatly exceed the sample size. To control the significance of the result, the detection procedure is implemented by multi-stage testing. In each stage, a Bayesian score test, which introduces a prior to the spectral components, is adopted to face the high dimensional challenge. By setting different priors in each stage, we make the test more powerful against alternatives that have similar bandwidth to the null hypothesis. We prove that the Bayesian score test is locally most powerful in expectation against the alternatives following the given prior. Finally, numerical analysis shows that our method has a good performance in bandwidth detection and is robust to the noise.
Attention Guided Multi-Scale Regression for Scene Text Detection

With the development of deep learning, scene text detection has made significant progress. A large number of neural network models have been applied to this task, one of which is a fully convolutional network (FCN) model named An Efficient and Accurate Scene Text Detector (EAST). However, it usually falls short when detecting long text regions like text lines lying across the images. Therefore, we propose a new network with Attention Module and Image Pyramid Module, based on EAST but largely ameliorated, to overcome the obstacles. Experiments conducted on several public benchmarks demonstrate that our method outperforms the original EAST model.
Design and Implementation of a Drain Cleaner Robotic Vehicle

This work proposed to construct a mobile device which would remove waste materials such as leaves, bottles, polyethylene, etc. which we usually see disrupting the water flow in roadside drains. The system was developed in the context of solving one of the most common problems of our country which is drain clogging. Our system is a four-wheeled robot attached with a wiper motor for moving on the drains. The device also consists of a chain-sprocket based grabber fitted with a fixed number of claws to pick up the waste material. The claws will transfer the collected waste into a deposit box and from there it will be disposed to the desired location utilizing a conveyor system. Chain sprocket mechanism has been utilized in waste grabber and also for moving wheels. The robot can be controlled both manual and automatic dictation through Bluetooth communication.
Waste Image Segmentation Using Convolutional Neural Network Encoder-Decoder with SegNet Architecture

Sustainable waste management has become one of the great concerns in many countries to reduce the negative environmental and health impact caused by the increase of unmanaged household garbage. One of the key elements is waste recycling, that relies on the process of garbage sorting to separate the recycled garbage into different categories for further processing. The sorting process in waste recycling is usually done manually by hand-picking. Therefore, a system that can recognize waste automatically is needed so that the waste sorting process can be done more quickly and accurately. In this paper, we propose a waste segmentation method using Convolutional Neural Network based on the Encoder-Decoder approach of SegNet architecture [5]. We compare two different setups of the architecture based on the number of filters in each convolutional layer, then evaluate our model using TrashNet benchmark dataset. Our experiment shows that one of our proposed architecture managed to achieve 82.95% intersection over union (IoU) value, which is higher than the previous work by the TrashNet developer.
Icon Colorization Based On Triple Conditional Generative Adversarial Networks

Current automatic colorization systems have many defects such as "contour blur", "color overflow"and "color miscellaneous", especially when they are coloring the images with hollowed-out structure. We propose a model based on triple conditional generative adversarial networks, for generator we provide contour image, colored icon and colorization mask as inputs, our network has three discriminators, structure discriminator is trained to judge if the generated icon has similar contour to the input icon, color discriminator anticipates generated icon and the input icon has the similar color style, the function of mask discriminator is to distinguish whether the output has the similar colorization area to the input mask. For the evaluation, we compared with some existing colorization models, also we made a questionnaire to obtain the evaluation of generated icons from different models. The results showed that our colorization model obtain better results comparing to the other models both in generating hollowed-out and solid structure icons.
A new distribution version of Boneh-Goh-Nissim cryptosystem : Security and performance analysis

The aim of this paper is to provide two distributed versions of the Boneh-Goh-Nissim Cryptosystem (BGNC). We give a proof of the semantic security for the first one. This guaranties that our algorithm is semantically secure in the contest of active non-adaptive adversaries. Furthermore, we prove that the second version of our distributed scheme is computationally more efficient than the ElGamal destributed elliptic curve cryptosystem (EDECC) and secure under the Subgroup Decision problem (SDP) assumption.
Fiction Sentence Expansion and Enhancement via Focused Objective and Novelty Curve Sampling

We describe the task of sentence expansion and enhancement, in which a sentence provided by a human is expanded in some creative way. The expansion should be understandable, believably grammatical, and highly related to the original sentence. Sentence expansion and enhancement may serve as an authoring tool, or integrate in dynamic media, conversational agents, and advertising. We implement a neural sentence expander, which is trained on sentence compressions generated from a corpus of modern fiction. We modify the objective loss function to support the task by focusing on new words, and decode at test time with controlled curve-like novelty sampling. We run the sentence expander on sentences provided by human subjects and have humans evaluate these expansions. The generation methods are shown to be comparable to, and as well liked as, subjects' original input sentences, and preferred over baselines.
Dataset Selection for Transfer Learning in Information Retrieval

Information Retrieval is the task of satisfying an information need by retrieving relevant information from large collections. Recently, deep neural networks have achieved several performance breakthroughs in the field, owing to the availability of large-scale training sets. When training data is limited, however, neural retrieval systems vastly underperform. To compensate for the lack of training data, researchers have turned to transfer learning by relying on labelled data from other search domains. Despite having access to several publicly available datasets, researchers are currently unguided in selecting the best training set for their particular applications. To address this knowledge gap, we propose a rigorous method to select an optimal training set for a specific search domain. We validate this method on the TREC-COVID challenge, which was organized by the Allen Institute for Artificial Intelligence and the National Institute of Standards and Technology. Our neural model ranked first from 143 competing systems. More importantly, it was able to achieve this result by training on a dataset that was selected using our proposed method. This work highlights the performance gains that may be achieved through careful dataset selection in transfer learning.
Link prediction techniques to handle tax evasion

Circular trading of goods is a carefully designed scam ubiquitous among fraudulent business dealers all around the world. Dealers involved in this scheme create an artificial trading network by issuing doctored sales-invoices amongst themselves without any movement of goods. In practice, it is observed that almost all cases of circular trade involve two or three dealers. Here, we work towards predicting circular trade involving three dealers. For the same, we built four different classification models consisting of feature variables tailored for predicting any plausible circular trade amongst three dealers. In particular, the logistic regression model gave the best performance among all the four different models with a prediction accuracy of 80%. Interestingly, we observe that a feature variable formed by using the personalised PageRank technique significantly improves the model over the state of the art link prediction variables. Predicting a future circular trade from a huge network of sales-transactions data is of significant importance to the tax enforcement officers. In addition to automating the process of detecting circular trading, which is manually impossible, this model helps them to target on a set of plausible evaders and take appropriate preventive measures. This model have been developed for the Commercial Taxes Department, Government of Telangana, India, using their first two quarter's tax returns dataset.
Vulnerability Assessment of Ubiquitous Cities Using the Analytic Hierarchy Process

Urbanization is a challenge faced by most countries worldwide and leads to several problems. Due to rapid communication capabilities, conforming the megacities into Ubiquitous cities (U-cities) seems to be a potential solution to mitigate the problems caused by urbanization. Extensive reliance and dependencies of U-cities on information and communication technologies (ICTs) bring forth a new set of risks and vulnerabilities to these megacities. This research investigates the vulnerabilities of ICTs against man-made and natural hazards in a systematic way using the Analytic Hierarchy Process. The study identifies the vulnerabilities of different ICTs in U-cities and helps in improving the system’s resistivity against various hazards. The task is performed by evaluating the level of disruption on the different technologies and areas under the identified man-made and natural hazards. The research provides an insight into the working mechanisms of involved ICTs. It also helps to manage U-cities with more secure and sustainable services. The research identified that the new ICTs-based hazards have emerged and have become among the most influential hazards. The research has concluded that the vulnerabilities of U-cities are significantly different from that of conventional cities and need further studies to develop further understandings. The research recommends similar vulnerability studies for regional areas as well.
Enhancing Data Privacy in the Internet of Things (IoT) Using Edge Computing

The vast deployment of the Internet of Things (IoT) is improving human life standards every day. These IoT applications are producing a massive amount of data from the environment where it is deployed. The collected data are mostly including end-user private data or industrial data, which are transmitted over the internet to the cloud devices for storing, processing, and sharing with the connected applications. Recent IoT data privacy-related researches are mostly focused on data privacy within a particular location of the network or at a specific device, but as per our knowledge, none has pointed and listed all the places where the end-user or industrial data privacy risks exist. In this work, we have addressed both technical and management aspects to enhance the privacy of IoT data. We have identified and listed the places where IoT data privacy risks exist, followed by our proposed model for data privacy enhancement in the internet of things (IoT). A list of ten suggestions for avoiding data privacy leakage to enhance IoT data privacy enhancement is presented. The results of this work should be useful for both academic researchers and stakeholders from the industry while designing and implementing new IoT solutions for the enhancement of human society.
Experimental Practices for Measuring the Intuitive Comprehensibility of Modeling Constructs: An Example Design

Conceptual model comprehensibility has attracted the interest of many experimental researchers over the past decades. Several studies have employed a variety of definitions and operationalizations of the comprehensibility construct as well as procedures for measuring it on a variety of model types. Intuitive comprehensibility is a specialization of the construct, referring to model or language comprehensibility exhibited by partially trained users. We present an experimental design for measuring the intuitive comprehensibility of a proposed extension to a goal modeling language as a means for reviewing experimental practices we have followed for similar studies in the past. Through such review, we hope to demonstrate the possibility of experimental design and technique reusability and its role as a motivating factor for more experimentation within the conceptual modeling research community.
Fuzzy Encoded Markov Chains: Overview, Observer Theory, and Applications

This article provides an overview of fuzzy encoded Markov chains (FEMCs), which are finite-state Markov chains applied to transitions between fuzzy sets that encode signal or variable values. FEMCs can be used for modeling of dynamic systems, predicting/forecasting future signal values, for state estimation, and for the development of fuzzy rules for control. Under suitable assumptions, the state possibility distribution can be propagated using FEMC models in a similar manner as the state probability distribution using conventional Markov chain models. The article first discusses FEMC theory, procedures to identify FEMCs from data, and the use of FEMCs for forecasting and control. Then, we introduce, for the first time, observers for partially observable FEMCs. The observer theory is developed and computational approaches are presented. Finally, we briefly review some FEMC applications in the automotive domain.
Difference rewards policy gradients

Abstract Policy gradient methods have become one of the most popular classes of algorithms for multi-agent reinforcement learning. A key challenge, however, that is not addressed by many of these methods is multi-agent credit assignment: assessing an agent’s contribution to the overall performance, which is crucial for learning good policies. We propose a novel algorithm called Dr.Reinforce that explicitly tackles this by combining difference rewards with policy gradients to allow for learning decentralized policies when the reward function is known. By differencing the reward function directly, Dr.Reinforce avoids difficulties associated with learning the Q -function as done by counterfactual multi-agent policy gradients (COMA), a state-of-the-art difference rewards method. For applications where the reward function is unknown, we show the effectiveness of a version of Dr.Reinforce that learns an additional reward network that is used to estimate the difference rewards.