Reviewerly/exHarmony huggingface dataset:
SigGuard: Hardening Vulnerable Signal Handling in Commodity Operating Systems

Signal is a useful mechanism provided by many commodity operating systems. However, current signal handling has serious security concerns due to vulnerable design in missing integrity protections for signal handling control flow. Security weaknesses caused by vulnerable design are exploited by adversaries to mount dangerous control-flow attacks. To tackle these issues, this paper investigates root causes of signal-related attacks and proposes SigGuard to harden vulnerable signal handling mechanism. To protect unsafe signal handler execution flow, we design a customized signal handler CFI framework which supports low-cost, reentrant, online CFI analysis and enforcement. To secure signal handler return control flow, we propose an efficient, software-based, intra-process memory isolation method to ensure signal frame data integrity. We evaluate SigGuard with both security and performance experiments. In security experiments, SigGuard successfully thwarts four signal-based attacks, including two proof-of-concept exploits and two realistic attacks conducted in Nginx and Apache server programs, respectively. We also evaluate SigGuard key techniques with a series of microbenchmarks and real-world applications. Experimental results suggest that key defense techniques used in SigGuard introduce reasonable performance costs.
An Application to Automate the Google Form Submission

Google form is a free online tool that lets users construct surveys and quizzes. It's part of Google's web-based apps package, which also include Google Docs, Google sheets and Google Slides. It's a flexible tool that can be used for a variety of tasks, including gathering RSVPs for an event, producing a pop quiz, and designing stunning forms. Google forms makes it easy to create a form that looks professional. These are the most widely used form filling tool used by people these days. It has now become a daily thing to fill google forms every other day. Many people find annoying filling the same information repetitively. In this paper, an application to automate the google form submission has been proposed. In this application a user would have to enter all the information like name, age, gender, qualifications and other personal information only once and that information would be saved in a database and can be further automatically filled in any of the google forms which the user has to fill. This application will reduce the tedious task of the user consumed in filling the forms with the same information every time.
Byzantine Auditable Atomic Register with Optimal Resilience

An auditable register extends the classical register with an audit operation that returns information on the read operations performed on the register. In this paper, we study Byzantine resilient auditable registers implementations in an asynchronous message-passing system. Existing solutions implement the auditable register on top of at least <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$4\mathrm{f}+1$</tex> servers, where at most <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$f$</tex> can be Byzantine. We show that <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$4\mathrm{f}+1$</tex> servers are necessary to implement auditability without communication between servers. Then, we pursue the study by relaxing the constraint on the servers' communication, letting them interact with each other. In this setting, we prove that <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3\mathrm{f}+1$</tex> servers are sufficient. This result establishes that with communication between servers, auditability does not come with an additional cost in terms of the number of servers.
Feature Encoding For High Cardinality Categorical Variables Using Entity Embeddings: A Case Study in Customs Fraud Detection

Customs authorities nowadays are pressurized by the increasing levels of international trade and insufficient resources to perform physical check on all the trades. Government therefore aims to focus their limited resources on the highly suspicious trade with the help from data mining especially in detecting suspicious trade. In customs fraud detection system, a total of 38,173 records focusing on the Harmonized System code starting with 4011 that shared by the Royal Malaysian Customs Department was analyzed. From the analysis of the datasets provided, one of the main difficulties faced is the issue of high cardinality where there are a lot of unique values in single feature. Thus, Entity Embeddings as a method in solving this issue by providing better representation of the unique values in lower dimensions was introduced. The result of the proposed feature encoding method can achieve higher AUC-ROC and F1-Score in Logistic Regression, Support Vector Machine, and Neural Network models compared to other feature encoding techniques.
Direct Trust in Cloud Computing Based on Fuzzy Logic

Trust is a powerful aspect, particularly for services using processes in the fields of cybersecurity and information technology. Concerns concerning the dependability of the cloud platform have been raised by individuals and creativity on a number of occasions. In cloud computing, it is advantageous for the customer to select a cloud provider's service for the storing and distribution of their thoughtful content. In this research, a trust model is put forth that evaluates trust using Quality of Service (QoS) metrics. The trust-fuzzy environment has inspired us to employ fuzzy logic to estimate a beneficiary's trustworthiness in a cloudy environment, hence increasing the scheme's effectiveness. Reliability, security, accessibility, response time, and cost make up Quality of Service. All work was completed in MATLAB.
Democratizing Machine Learning: Resilient Distributed Learning with Heterogeneous Participants

The increasing prevalence of personal devices motivates the design of algorithms that can leverage their computing power, together with the data they generate, in order to build privacy-preserving and effective machine learning models. However, traditional distributed learning algorithms impose a uniform workload on all participating devices, most often discarding the weakest participants. This not only induces a suboptimal use of available computational resources, but also significantly reduces the quality of the learning process, as data held by the slowest devices is discarded from the procedure. This paper proposes HgO, a distributed learning scheme with parameterizable iteration costs that can be adjusted to the computational capabilities of different devices. HgO encourages the participation of slower devices, thereby improving the accuracy of the model when the participants do not share the same dataset. When combined with a robust aggregation rule, HgO can tolerate some level of Byzantine behavior, depending on the hardware profile of the devices (we prove, for the first time, a trade-off between Byzantine tolerance and hardware heterogeneity). We also demonstrate the convergence of HgO, theoretically and empirically, without assuming any specific partitioning of the data over the devices. We present an exhaustive set of experiments, evaluating the performance of HgO on several classification tasks and highlighting the importance of incorporating slow devices when learning in a Byzantine-prone environment with heterogeneous participants.
A Novel Recurrent Neural Network for Dynamic Process Modeling with Inertia and Delay

A novel continuous-time multi-layer Recurrent Neural Network (RNN) is presented in this paper. The proposed RNN struture has superiorities of simple structure and parameters with certain physical meanings. A four-neuron dynamic neural network was used to model the water spray desuperheating control system. The test results show that the proposed RNN sturture has good adaptability to the physical process with large inertia and large delay effects.
Achieving liveness in validators' performance efficiency consensus (VPEC) based blockchain system using the Zakat principles

The purpose of this study is to achieve the liveness property in the public blockchain system using the zakat concept. However, this property cannot be achieved without fairness in the blockchain system. Fairness is a general term and can be defined according to the context. Fairness in this study means that the user's transaction should be selected regardless of the transaction's fee, users participate as a miner without staking and purchasing high computing devices and miners receive incentives based on their performances. In this article, the authors adopt and embed the zakat concept by modifying the core components of the original validators' performance efficiency consensus (VPEC) based blockchain system. Thus, this approach not only theoretically serves as a fair incentive mechanism but also achieves fairness in users' participation as a miner which helps in achieving the liveness of the public blockchain system.
Secure Publish-Process-Subscribe System for Dispersed Computing

Publish-subscribe protocols enable real-time multi-point-to-multi-point communications for many dispersed computing systems like Internet of Things (IoT) applications. Recent interest has focused on adding processing to such publish-subscribe protocols to enable computation over real-time streams such that the protocols can provide functionalities such as sensor fusion, compression, and other statistical analysis on raw sensor data. However, unlike pure publish-subscribe protocols, which can be easily deployed with end-to-end transport layer encryption, it is challenging to ensure security in such publish-process-subscribe protocols when the processing is carried out on an untrusted third party. In this work, we present <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{XYZ}$</tex> , a secure publish-process-subscribe system that can preserve the confidentiality of computations and support multi-publisher-multi-subscriber settings. Within <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{XYZ}$</tex> , we design two distinct schemes: the first using Yao's garbled circuits (the GC-Based Scheme) and the second using homomorphic encryption with proxy re-encryption (the Proxy-HE Scheme). We build implementations of the two schemes as an integrated publish-process-subscribe system. We evaluate our system on several functions and also demonstrate real-world applications. The evaluation shows that the GC-Based Scheme can finish most tasks two orders of magnitude times faster than the Proxy-HE Scheme while Proxy-HE can still securely complete tasks within an acceptable time for most functions but with a different security assumption and a simpler system structure.
Performance Analysis of Multi-objective Data Placement Technique and Sine Optimization Algorithm to Measure Prediction in Large Volume of Data Storage

The motto of the study is to optimize the storage locations using a multi-objective data placement technique algorithm and sine optimization and comparing their accuracy. Multi-objective data placement technique algorithm (N=10) and sine optimization algorithm (N=10) was iterated 20 times to optimize the data. Multi-objective data placement technique has significantly better accuracy (99.99%) compared to sine optimization algorithm (99.94%). The statistical significance difference 0.01 (p¡ 0.05 independent sample test) value states that the results in the study are significant. With the limits of the study, a multi-objective data placement technique with credit card fraud detection data offers the best accuracy in data optimization.
Detection of Dengue Disease Empowered with Fused Machine Learning

Dengue fever is a life-threatening illness that affects both industrialized and poor nations, including Pakistan. It is necessary to forecast the illness at an early stage to avoid it. Machine Learning (ML) methods outperform other computer approaches in terms of illness prediction. The model utilized in this study to predict dengue fever is fused with machine learning. Artificial Neural Networks (ANN) and Support Vector Machine (SVM) provide the foundation of the conceptual framework. The datasets employed in these models have been collected from a government hospital in Lahore, Pakistan for diagnosing dengue fever (positive or negative). 70% of the statistics in the dataset are training data, whereas 30% are testing data. This fused model's membership functions explain whether a dengue diagnostic is positive or negative, which controls the model's output. A cloud storage system saves the fused model based on patients' real-time information for future use. The proposed model has a 96.19 % accuracy rate, which is much greater than earlier research.
Detection and Incentive: A Tampering Detection Mechanism for Object Detection in Edge Computing

The object detection tasks based on edge computing have received great attention. A common concern hasn't been addressed is that edge may be unreliable and uploads the incorrect data to cloud. Existing works focus on the consistency of the transmitted data by edge. However, in cases when the inputs and the outputs are inherently different, the authenticity of data processing has not been addressed. In this paper, we first simply model the tampering detection. Then, bases on the feature insertion and game theory, the tampering detection and economic incentives mechanism (TDEI) is proposed. In tampering detection, terminal negotiates a set of features with cloud and inserts them into the raw data, after the cloud determines whether the results from edge contain the relevant information. The honesty incentives employs game theory to instill the distrust among different edges, preventing them from colluding and thwarting the tampering detection. Meanwhile, the subjectivity of nodes is also considered. TDEI distributes the tampering detection to all edges and realizes the self-detection of edge results. Experimental results based on the KITTI dataset, show that the accuracy of detection is 95% and 80%, when terminal's additional overhead is smaller than 30% for image and 20% for video, respectively. The interference ratios of TDEI to raw data are about 16% for video and 0% for image, respectively. Finally, we discuss the advantage and scalability of TDEI.
A Detailed Study of Blockchain and dapps

With the rise of Bitcoin, blockchain technology has gained a lot of attention from the financial world. While the underlying distributed ledger technology has many potential applications, Bitcoin remains the most well-known and widely- adopted use case. As a result, Bitcoin is often seen as a bellwether for the wider blockchain industry. However, there is another side to blockchain that is often overlooked: dapps. Decentralized apps (dapps) are applications that run on a decentralized network, such as Ethereum. Unlike traditional apps, which are controlled by a central authority, dapps are open source and controlled by no one. This makes them incredibly appealing to developers who value decentralization and censorship-resistance. The readers will get an overview of blockchain and dApps research.
Voice Controlled Personal Assistant

Today technological advances are growing by the day. Previously there was a computer program where we could do only a few tasks. But now machine learning, artificial intelligence, in-depth learning, and a few more technologies have made computer systems so advanced that we can do any kind of work in a very short time. Because of rapid technological advancements, we can now do things we never thought we could before, but in order to do so, we need a platform that can smoothly perform all of our responsibilities. As a result, A Personal Assistants must be designed with more output power as well as the ability to engage with the surroundings through one among the most wonderful characteristics of human contact, the HUMAN WORD. Through a microphone, Audio is collected by a hardware device. applications and they're processed in order for the device to reply to each person with the use of a built-in microphone module. For instance, when you ask a device, “How is the weather?” ‘What's the status of the traffic?’ It checks the weather and other information using its built-in capabilities.
Towards simulating time evolution of specific quantum many-body system by lower counts of quantum gates

Abstract In the noisy intermediate-scale quantum (NISQ) era, quantum computers have become important tools to simulate the real-time evolution of strong correlated many-body systems. The general scheme of designing quantum circuits to simulate the quantum dynamics is based on the Trotter-Suzuki decomposition technique, which has its limitation because the accuracy of evolution process depends on the size of trotter steps when the evolution operator is decomposed as quantum gates. Going beyond the limitation of the Trotter-Suzuki scheme, we design a parameterized quantum circuit named α -circuit with simple determined size and the only one control parameter θ to simulate the real-time evolution of the specific XXX Heisenberg model with the specific initial state <?CDATA $|000\cdots\rangle$ ?> . The α -circuit can accurately generate the time-evolution results by tuning parameter θ , which means the circuit can also be regarded as a good state preparation machine (SPM).
Neural predictor-based automated graph classifier framework

Graph Neural Architecture Search (Graph-NAS) methods have shown great potential in finding better graph neural network designs compared to handcrafted designs. However, existing Graph-NAS frameworks are based on complex algorithms and fail to maintain low costs for high scalability with high performance. They require full training of thousands of graph neural networks to inform the search process, resulting in a prohibitive computational cost, which is not necessarily affordable for the users interested. Due to the computation cost, many researchers have limited the search space exploration ability, which may lead to a local optimum solution. In this paper, we propose a performance predictor-based graph neural architecture search (PGNAS) framework. The proposed approach consists of three conceptually much simpler and basic phases, and can broadly explore a search space with a much cheaper computation cost. We train n sampled architectures from a search space to generate n (architecture, validation accuracy) pairs used to train a performance distributions learner where the features are represented by the architecture description and the validation accuracy denotes the target. Next, we use this performance distribution learner to predict the validation accuracies of architectures in the search space. Finally, we train the top-K predicted architectures and choose the architecture with the best validation result. Although our approach seems simple, it is efficient and scalable; experiment results show that PGNAS outperforms existing both handcrafted and Graph-NAS models on four benchmark datasets.
Overlapped Speech Detection for Improved Speaker Diarization on Tamil Dataset

Speaker diarization is the task of partitioning a speech signal into homogeneous segments corresponding to speaker identities. We introduce a Tamil test dataset, considering that the existing literature on speaker diarization has experimented with English to a great extent; however, none on a Tamil dataset. An overlapped speech segment is a part of an audio clip where two or more speakers speak simultaneously. Overlapped speech regions degrade the performance of a speaker diarization system proportionally due to the complexity of identifying individual speakers. This study proposes an overlapped speech detection (OSD) model by discarding the non-speech segments and feeding speech segments into a Convolutional Recurrent Neural Network model as a binary classifier: single speaker speech and overlapped speech. The OSD model is integrated into a speaker diarizer, and the performance gain on the standard VoxConverse and our Tamil datasets in terms of Diarization Error Rate are 5.6% and 13.4%, respectively.
Privacy-Preserving Publishing of Individual-Level Pandemic Data Based on a Game Theoretic Model

Sharing individual-level pandemic data is essential for accelerating the understanding of a disease. For example, COVID-19 data have been widely collected to support public health surveillance and research. In the United States, these data need to be de-identified before being released to the public due to privacy concerns. However, current data publishing approaches for individual-level pandemic data, such as those adopted by the U.S. Centers for Disease Control and Prevention (CDC), have not flexed over time to account for the dynamic nature of infection rates. Thus, the policies generated by these strategies may either raise privacy risks or impair the data utility (or usability). To optimize the tradeoff between privacy risk and data utility, we introduce a game theoretic model that adaptively generates policies to publish individual-level COVID-19 data according to infection dynamics. We model the data publishing process as a two-player Stackelberg game between a data publisher and a data recipient and then search for the best strategy for the publisher. In this game, we consider 1) the average accuracy of predicting future case counts for all demographic groups, and 2) the mutual information between the original data and the released data. We use COVID-19 case data from Vanderbilt University Medical Center from March 2020 to December 2021 to demonstrate our model and evaluate its effectiveness. The experimental results show that our game theoretic model outperforms all baseline approaches, including those adopted by CDC, while maintaining low privacy risk.
Risk Prediction on the Elderly’ Check-out from a Continuing Care Retirement Community

In this paper, we present a Transformer based risk prediction method to infer the risk level from risk factors affecting the residents’ decisions on check-out from a continuing care retirement community (CCRC). In this Transformer based model, we multiply the length of query vector and the length of key vector with the scaling factor of the original Transformer in order to make the training process of our model more stable.
Where do countries stand in cloud computing readiness? A country-level analysis of capacity and potential

Cloud computing has become relevant due to the increasing need for computing, storage, and communication over the internet. This growth, however, is not same across the geographies. While developed economies have adopted cloud-based services, emerging economies are still lagging. As digitization has become core to policy-level strategies, a slow adoption of cloud-based Information and Communications Technology (ICT) may impact growth. The adoption and growth of cloud-based services depends on the readiness of the broader ecosystem. This study evaluates the factors influencing the cloud computing readiness of selected countries. In the process, the study extends the existing understanding of the ranking system by providing additional insights into the countries’ performances against their potentials by using technical efficiency analysis. The study identifies and categorizes countries into four groups: aspirants, initiators, performers, and achievers. Results suggest that political-regulatory-business environment, investing in research and development, tertiary education, and knowledge-worker significantly impact cloud computing readiness.