Reviewerly/exHarmony huggingface dataset:
Efficient keyword search on encrypted dynamic cloud data

Sensitive information is increasingly being outsourced to the cloud.In order to protect the privacy of such sensitive data, cloud users (clients) encrypt their data before outsourcing.However, this poses a difficulty to later perform search operations on the encrypted data.Searchable encryption schemes enable a client to search and retrieve the cloud data (based on the keywords present in the data) when the data is encrypted.Dynamic searchable encryption schemes allow the client to search over the encrypted cloud data even when new documents are added to or deleted from the encrypted data.There is a trade-off between security (that is measured in terms of information leaked to the cloud) and the efficiency of dynamic searchable encryption schemes.Stronger security guarantees often come at a cost of less efficiency.In this work, we propose a new dynamic searchable encryption scheme for cloud data that achieves better security guarantees and improved efficiency compared to popular dynamic searchable encryption schemes.Our scheme uses an efficient data structure that reduces storage, lookup (search) time, and database modification time.We build a prototype of our scheme and experiment on large real-life datasets.We show our scheme performs better than the existing schemes, which provide similar (or weaker) security.
Mining Weighted Sequential Patterns Based on Prefix-Tree and Prism Encoding

The purpose of mining sequential patterns problem with weighted constraints is to find high-valued patterns, including infrequent patterns but having items which appear in the pattern of high importance in the sequence database (SD). Therefore, weighted sequential pattern mining will collect a set of more complete patterns with items of low support but of high importance. This paper proposes a new algorithm called WSPM_PreTree to find highly weighted sequential patterns. To collect a set of complete sequential patterns with the stricter weighted constraints of sequential patterns, the proposed algorithm uses both the minimum support constraint and the actual values of items appearing in the SD. To increase the performance of the finding weighted sequential patterns process, the algorithm uses the parent–child relationship on the prefix tree structure to create candidates and combines the weighted mean of the sequential 1-patterns that is calculated from the actual value of items in the SD as conditions to find the weighted sequential patterns. Experimental results show that the proposed algorithm is more efficient than sequential patterns mining with weight constraint (SPMW) algorithm [Ref. 20 ] in the runtime.
Removal of Redundant Information via Discrete Representation for Monocular Depth Estimation

Monocular depth estimation aims at inferring three-dimensional (3D) cues from a single RGB image. Although existing methods have achieved a certain degree of success, the impact of redundant information has rarely been studied. We propose to improve estimation accuracy by implicitly eliminating redundant information. To this end, we creatively apply discrete representation to monocular depth estimation. By mapping continuous variables into the corresponding learning-based discrete latent space, a hierarchical multi-scale latent map is acquired as the decoder input. Removing redundant information can enhance prediction performance by making the depth estimator balance the local and global. Furthermore, to fully take advantage of the discrete representation, a lightweight fusion mechanism is introduced to aggregate information in multi-scale feature maps.Experiments on NYU Depth V2 dataset demonstrate that our network is competitive with the state of the arts.
Learning Spatiotemporal-Selected Representations in Videos for Action Recognition

Action recognition is a challenging task of modeling both spatial and temporal context. Numerous works focus on architectures modality and successfully make worthy progress on this task. While due to the redundancy in time and the limit of computation resources, several works focus on the efficiency study like frame sampling, some for untrimmed videos, and some for trimmed videos. With the intent of improving the effectiveness of action recognition, we propose a novel Computational Spatiotemporal Selector (CSS) to refine and reinforce the key frames with discriminative information in video. Specifically, CSS includes two modules: Temporal Adaptive Sampling (TAS) module and Spatial Frame Resolution (SFR) module. The former can refine the key frames in the temporal space for capturing the key motion information, while the latter can further zoom out some refined frames in the spatial space for eliminating the discrimination-irrelevant structural information. The proposed CSS is flexible to be embedded into most representative action recognition models. Experiments on two challenging action recognition benchmarks, i.e., ActivityNet1.3 and UCF101, show that the proposed CSS improves the performance over most existing models, not only on trimmed videos but also untrimmed videos.
The Development of Interactive English Multimedia in Tourism Vocational Education as an Adaptive Learning Strategy in the Midst of Covid-19 Pandemic

The online learning process on tourism vocational education during COVID-19 pandemic has been observed to be nonoptimal in Indonesia. Lack of educators’ knowledge and skills in creating interactive media was the obstacle, where at vocational education, more practice was required rather than theoretical materials delivery. The idea of “digital for everything” was introduced as the standard phase of work. This world has forced by Covid-19 pandemic to the digitalization. This study has two objectives, first to identify barriers in learning process, and second, to develop interactive English multimedia. This research was designed as R&amp;D method. The research subjects involved media and contents experts as alpha test subjects. Alpha test results were used as the basis for the first revision. Participation of PIB students with high, medium, and low abilities were used as beta test subjects. The final product was validated by the validator team, then applied in the learning process. The results showed there was an increase in student satisfaction from moderate (5.81) to very good (8.21) when lecturers apply interactive multimedia using videos, animations and other interactive multimedia. Developed interactive English for tourism multimedia was effective during COVID-19 pandemic and can be accessed by students anywhere and anytime.
Research on the evaluation of rectal function after LAR based on CEEMDAN‐Fast‐ICA algorithm

Abstract Rectal cancer is one of the most common lower gastrointestinal diseases worldwide. Currently, the common treatment is low anterior resection (LAR) of the rectum, which preserves the anus of the patient. However, it is easy to cause low anterior resection syndrome after surgery, which has a significant negative impact on the life of patients, and there is no unified evaluation standard for postoperative rectal function. To solve this problem, a multi‐sensor fusion rectal information acquisition system is designed in this paper, and a rectal signal processing method is proposed to theoretically evaluate the rectal function of postoperative patients. The method uses the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to decompose the one‐dimensional rectal signal to solve the underdetermined ICA problem, uses the Fast independent component analysis (Fast‐ICA) to separate the pure rectal signal, uses the wavelet packet to extract features, and uses the particle swarm optimization optimizes support vector machine (PSO‐SVM) to classify and evaluate postoperative function. According to the experimental results, the rectal signal preprocessing effect is good, the evaluation prediction rate is 99.5565%, and the algorithm classification results are accurate, which provides a certain preliminary theoretical basis and reference value for the evaluation of rectal function after LAR.
Commodious Control Apparatus for Impaired People

This paper describes the design of an eye tracking system with the main purpose of empowering patients with severe motor skills disabilities such as Amyotrophic Lateral Sclerosis (ALS), Quadriplegia, and the like, by allowing them to substantialize their thoughts into actions by means of mere and simple eye movements. The main objective is to help making the lives of thousands of people suffering from such illnesses more bearable and give them some hope and self-reliance, which will hopefully reinstate their self-confidence and somehow happiness. The idea is to conceive a system that would control some apparatus via a sensory environment devised to interpret eye movements and reify them as useful commands designated as fundamental to the most basic form of autonomy. Some implemented features includes but are not limited to, instigating letters for typing, shapes for drawing, and most importantly, crucial gestures for interacting with other devices. The system was tested on patients and the results proved indubitably promising and effective.
Decentralizing Machine Learning Operations using Web3 for IoT Platforms

Combining machine learning with IoT offers new and exciting possibilities for service innovation. A core challenge for such systems is model training and pipeline monitoring/management while not revealing sensitive data or introducing new attack points. Innovative solutions that reveal network structure and topology are often in conflict with security/privacy design principles. In this paper, we consider the system communication between sensors, edge, and the cloud that does not reveal the origin source or destination of the data. A core contribution is how to apply Machine Learning Operations (MLOps) to decentralized architectures that are not under the direct control of a service provider. The aim is to decouple the Machine Learning (ML) solution, data platform, and sensors while avoiding service level degradation. We utilize an unlinkable end-to-end encrypted asynchronous communication protocol called Whisper that is based on the Ethereum blockchain to achieve sender and receiver anonymity. The Whisper protocol provides a level of darkness that maintains anonymity in communication.
Symbolic Fault Injection for Plan-based Robotics

Autonomous robots are being used increasingly in safety-critical environments. Due to their dynamic nature and uncertainty, failures of low-level actions are common. In plan-based robotics, these failures are handled inside the higher-level plans, using a multitude of failure handling strategies. With the increasing complexity of robotic plans, failures may be accidentally left unhandled. This will usually stop the plan entirely. To avoid such a situation, the failure handling should ideally be complete, i.e. there should be no failure that can reach the top-level of the execution. In this paper, we propose to use formal methods, in particular symbolic fault injection to tackle the problem of finding unhandled failures or proving that no such failure exists. We implement symbolic fault injection for the CRAM Planning Language (CPL). We base our work on the worst-case assumption that any low-level action may fail at any time with any of its possible types of failure. Our work builds upon an existing symbolic execution engine for CPL and extends it to reason about CPL’s failure handling mechanism. We also present a way to implement the worst-case assumption directly into CPL. Our experimental evaluation suggests that symbolic fault injection is a suitable and scalable method to find unhandled failures in robotic plans.
A Survey on Cybersecurity Barrier Management in Process Control Environments

The concept of barriers is well known in the safety domain that includes traditional process control environments. However, as critical infrastructures are moving to more interconnected scenarios connected to cloud computing service providers and the internet in general, an increased focus on security is necessary. In this paper we present a survey on how cybersecurity barriers have been presented in the literature, concluding that the concept has received little attention. More specifically, the aim of the paper is to survey the state of the art in cybersecurity barrier management and the integration with safety barrier management. Like the concept of cybersecurity barrier, the integration of safety and cybersecurity barrier management has received little attention. Most of the work focus on integration of safety and cybersecurity management in general, not on barrier management specifically. We eventually aim to integrate cybersecurity barrier management into the already existing safety barrier management regime.
FVMM: Fast VM Migration for Virtualization-based Fault Tolerance Using Templates

In the era of cloud computing, virtualization based fault tolerance that utilizes the continuous virtual machine (VM) migration to synchronize a VM and its remote replica is a common technique to achieve high availability. However, traditional live VM migration, whose goal is to minimize the system downtime, has a long duration owing to the expense of the pre-copy for machine status and memory content, which increases the period of failover when failures occur. In this paper, we proposed a new VM migration method, called Fast VM Migration (FVMM), which utilizes the templating technique to accelerate the VM migration by reducing the cost of pre-copy. The templating technique that creates VMs from a master copy, called a template, is a usually used to deploy many similar VMs in a large virtual environment. FVMM employs VM templating to mitigate the cost of pre-copy. Its implementation is optimized with six new techniques: SFVMM and LFVMM, COW templating, continuous templating, asynchronous transmission buffering, VM recovery from continuous templating, and templating regularly. We also applied FVMM for virtualization based fault tolerance to accelerate the failover process. Experimental results show that FVMM is more than 56 times faster than pre-copy live migration for a VM with 16GB, and can reduce upto 32% of the system downtime and the fault tolerance resynchronization time comparing to the methods using pre-copy live migration.
Methods to prevent privacy violations on the internet on the personal level in Indonesia

In recent years, technology has advanced so much that it is so widespread and the internet is being used by 53.7% of Indonesia's population. This paper reviewed how privacy violations occur, their potential impacts, and their prevention methods personally such as individuals and organizations through literature review. Overall, it can be concluded that in general. There are two methods to prevent privacy violations, which are by using computer security software and end user awareness training.
IR/EO ship detection and tracking using SiamMask

Ship detection and tracking in maritime remains a challenging task due to sea clutters, noise, huge deformation and low contrast. In this paper, we address the previous issue using Siammask framework with sensor fusion technique. In here, infrared sensor representations integrated with electro-optical sensor features through concatenation at feature level. Experimental results show that the overall process performs segmentation and tracking with a high-margin level exploiting multi-sensor features concatenation to compensate the loss of information in case of using single-sensor technique.
Classification of Leukemia and Lymphoma using Faster R-CNN

Blood disorders are disorders that impair the ability of the blood to function normally. There are various types such as leukemia and lymphoma, and the symptoms differ accordingly. Many death cases are related to these malignant tumors as a result of the difficult therapy and its negative effects. Furthermore, patients require a variety of diagnostic tests, which are costly, time-consuming, and can be stressful and hazardous to the patients. Furthermore, only a qualified pathologist can distinguish and identify various forms of lymphomas and leukemias. In this paper, the “Faster Region-Based Convolutional Neural Network; Faster R-CNN” algorithm is implemented to classify normal patients and ill patients into four categories: Acute Lymphoblastic Leukemia (ALL), Chronic Lymphocytic Leukemia (CLL), Follicular Lymphoma (FL), and Mantle Cell Lymphoma (MCL). This is done by dealing with Region of Interest (ROI) convolutional layer, then applying Vgg-16 model & Region Proposal Network (RPN) layer and adding a classifier layer. The dataset is based on microscopic and biopsies images. The proposed model achieved an accuracy of 87,575% after training in an acceptable computational time.
How to confirm blocks in PoW blockchains

Existing PoW blockchains adopt a simple k-block confirmation rule. A block is confirmed after it is buried sufficiently deep in the blockchain. The selection of the required depth k is largely based on empirical experiences and lacks a theoretical guarantee. We ask a fundamental question in PoW blockchains: when and how should miners confirm a block in PoW blockchains? We say a block is in the consensus state if it is included in every miner’s blockchain. The issue of the current k-block rule is that it does not depend on consensus. A block buried k-block deep may not be in the consensus state. We propose that a block should not be confirmed before the miner believes it to be in the consensus state. We present a consensus-inference protocol for miners to infer whether a block is in the consensus state locally. Based on the inference protocol, we further describe a new consensus-aware confirmation rule. Compared to the existing k-block rule, we show that the new rule achieves lower confirmation reversal probability under the same confirmation latency. The reduction could be orders of magnitude when multiple blocks are mined on average per network delay. Correspondingly, we further show that the new rule could improve the scalability of current blockchains by as much as 90% in common scenarios.
Autoencoder Models Optimization for Remote Sensing Image Semantic Segmentation

With the continuous rapid advancements that take place in the computer vision world comes the hope of solving alarming problems like environmental pollution or climate change. The tools that have been created by computer vision widely contribute to the solution of such problems. Not only do those tools help solve problems caused by pollution or climate change, but they also help people like civil engineers perform land use analysis to determine whether or not to launch projects on wide areas of land. More specifically, we dive into the scope of remote-sensing, or aerial, images and how convolutional neural networks help analyze such images using techniques like semantic segmentation. We compare a few state-of-the-art auto-encoders to determine which one can optimally perform the operation of semantic segmentation using the least number of inputs and consuming the least amount of time.
Hybrid Gradient Vector Fields Based on Maneuver Space for Path-following Guidance

Guidance path-planning and following are two important functionalities of unmanned aerial vehicles (UAVs), but only a few approaches deal with both. In this study, an innovative hybrid gradient vector fields algorithm based on maneuver space (HGVFs-MS) for path-following guidance is proposed to control fixed-wing UAVs to follow a generated guidance path and oriented target curves in three-dimensional space, which can be any combination of straight lines, arcs, and helixes as motion primitives. The strategies designed in earlier studies have flaws that prevent the UAV from following arcs that make its turning angle too large. The switching strategy proposed in this study solves this problem by introducing the concept of the virtual waypoints. Finally, the performance of the HGVFs-MS algorithm is verified by three representative simulation scenarios. The simulation considers the constraints of the aircraft, and its results indicate that the algorithm performs well in following both lateral and longitudinal control, particularly for curved paths.
A Deep Learning-based Event Extraction Method in the Field of Electric Power Public Opinion

Event extraction is a sub-task of information extraction in natural language processing by extracting relevant event information from unstructured text. In order to obtain the hot events related to electric power public opinion in a timely manner and assist electric power staff to make quick decisions, this article suggests a deep learning-based event extraction model for electric power public opinion, which is mainly composed of two parts, namely, an event detection model and an argumentative meta-role extraction model. The event detection model is further extracted by using the BLSTM model to obtain the specific event categories of electrical power viewpoint text, and the argumentative role extraction model is employed to extract the features of electric power opinion text by using the BLSTM-CRF model to obtain the argumentative roles included within the text. In this paper, we solve the problem of overlapping roles by using an innovative location indexing annotation method. Finally, the events contained in the power opinion text are extracted by the joint extraction of the event category and the theoretical roles. By conducting experimental tests, this research proposes a model with superior performance in terms of event extraction outcomes and accuracy rate..
Random Swin Transformer

After deep learning appeared, the convolutional neural network (CNN) dominated various applications of image classification, object detection, and semantic segmentation. Recently, a transformer based on various attention mechanisms performed better than the CNN. But, the transformer requires a large amount of memory for full attention among tokens. Recently, a Swin transformer has been proposed to solve that memory issue. It applies the attention per sub-regions on an image. Also, it solves a problem caused by not using full attention on an image by shifting window that guarantees more tokens are involved in attention. In this paper, we investigate a method of randomly selecting tokens in Swin transformer. We randomly choose tokens within a certain range rather than using a fixed shift value in the Swin transformer. Experimental results show the feasibility of the proposed method.
Yet Another Blockchain-based Privacy-friendly Social Network

Current social media platforms suffer from the fact that ownership of personal data is transferred to the owner of the network the moment you post it. This paper demonstrates that it is possible to create and maintain a social network using Hyperledger Fabric, where personal data is protected from users that should not have it, and all operations on data is recorded in the ledger, so you can audit how your data has been used by others, even the owner of the social network.