Reviewerly/exHarmony huggingface dataset:
A random walk sampling on knowledge graphs for semantic-oriented statistical tasks

A knowledge graph (KG) manages large-scale and real-world facts as a big graph in a schema-flexible manner, which has recently attracted considerable attention . It is very common that users deploy some statistical tasks on a KG to achieve the latent information of interest. There are two types of the statistical tasks, that are, topology-oriented and semantic-oriented statistical tasks. Many efforts have been made for the former one (e.g., finding the average degree of a KG). The basic idea is concluded as: estimating an approximate statistical result based on a random sample collected through a topology-aware KG sampling approach. Unfortunately, this method cannot be directly deployed to support semantic-oriented statistical tasks (e.g., achieving the average fuel economy of cars produced in Germany), because the topology-aware sampling does not consider the semantics of a KG (or we say the sample is collected only based on the topological information of a KG, while excluding the semantics of a KG), hence leading to a low-quality random sample and would significantly affect the accuracy. In this paper, we propose a semantic-aware random walk sampling on KGs to quickly and accurately collect samples that match the semantic constraint of the semantic-oriented statistical task, and obtain an approximate statistical result by well-designed unbiased estimators. Moreover, we propose an optimization on our semantic-aware sampling to improve the sampling efficiency. Finally, extensive experiments were conducted on our method, which confirmed the effectiveness and efficiency of our approach.
A privacy-preserving target pattern matching scheme for digital health system

The traditional disease detection method is that a doctor judges if a user has a specific disease through the doctor's treatment based on his inference and medical detection reports provided by the medical equipment. In recent years, with the continuous development of digital healthcare, more and more users choose to conduct disease detection through a digital health system. To provide accurate disease detection service, the system needs to collect mass patients' medical information such as symptoms and signs. However, patients' medical information and users' query requests will reveal their privacy information, such as age, identification, address, physical condition. For the above issues, this paper proposes a privacy preserving target pattern matching scheme (PP-TPMS). Our scheme utilizes bloom filters and secret sharing technology to realize secure pattern matching between given query requests and mass medical information. The auxiliary diagnosis results are returned to users. The experimental results show that this scheme has efficient computation performance and communication performance.
WebID + biometrics with permuted disposable features

For networked communications, cyber security and authentication are critical components. This work deals with the issue of security and authentication as it relates to features of the Semantic Web. The phrase "Semantic Web" alludes to the World Wide Web Consortium's idea of standards to make internet data machine-readable and reusable. WebID, for example, is a technique for managing profile data connected with people and services at self-defined locations. While the WebID protocol alone allows users more control over their connections to online services, biometric authentication is an additional process that can add security and convenience for individuals. The WebID protocol with biometric authentication allows more control for the individual user, but networked connections are still vulnerable to replay attacks (an attack in which the network is compromised and authenticating information is captured and replayed to allow unauthorized access).
Comparing Generalised Linear Mixed-Effects Models, Generalised Linear Mixed-Effects Model Trees and Random Forests

In a comparison of generalised linear mixed-effects models, generalised linear mixed-effects model trees and random forests, the author applies the three methodologies to a binary variable from the field of interactional pragmatics, the choice between filled and unfilled pauses across varieties of English represented by components of the International Corpus of English. Based on a large number of examples annotated for linguistic and extralinguistic factors the steps and decisions involved in the analyses are demonstrated. Though different in essence, the three resulting models share central trends. A more fine-grained evaluation of results and interpretations shows, however, that the three approaches differ in their systematicity of handling multiple observations from the same source, in that only the mixed-effects models explicitly account for and systematically partial out the relatedness of data points contributed by the same speaker. As to the way the approaches balance researcher involvement and control of the outcome, the approaches also differ substantially. A modelling choice can thus lead to notably different perspectives on an identical set of data and variables.
Perceptual Mapping-Based Image Tamper Detection and Recovery Algorithm

The importance of digital image authentication has grown in the last decade particularly with the widespread availability of digital media and image manipulation tools. As a result, different techniques were developed to detect fraudulent alterations in digital images and restore the original data. In this paper, a new algorithm is proposed to authenticate images by hiding a copy of the approximation band in the original image. The approximation band is hidden by embedding it inside the image pixels. The intensity of the hiding was decided using a perceptual map that simulates the human vision system and adds more intensity in areas where the human eye cannot recognize changes. The perceptual map consists of three parts, luminance mask, texture mask, and edge detection mask. Results show a high ability to blindly recover images after different attacks such as removing and blocking attacks. At the same time, the structure similarity index of resultant images was higher than 0.99 for all tested images.
Internet of vehicles data security risks and protection

The internet of vehicles industry is a new industrial form of automobiles, electronics, information and communications, and road transportation. It is a hot spot for global innovation and a future development trend. As the country attaches importance to data security, the internet of Vehicles, as the direction of future auto industry transformation and upgrading, is also facing huge security challenges. Among them, data security is the focus of current internet of vehicles security research and management. This article first focuses on the threats of data leakage, data theft, and data tampering faced by internet of vehicles data, describes the data security risks. Analyze the disadvantages of the current solutions for data security in the internet of vehicles. Then proposed the use of blockchain technology to protect data. Finally, relevant suggestions were put forward in terms of building a firm bottom line of data security, advancing the construction of a data security system for the internet of vehicles, strengthening basic technology research and blockchain-based big data protection security mechanisms.
SPPGKM: A secure polynomial function powered group key management scheme for dynamic user environments in cloud

Abstract In this modern era of smart cities supported by digital locker facilities, e‐governance, e‐healthcare, e‐banking, e‐learning systems, and other digital systems, People use mobile phones to upload and share sensitive documents with others. Previous group key management schemes suffer from problems such as expensive double encryptions, the need for re‐encryption during every user leave operation and becoming vulnerable to attacks. Hence, to address these three issues, this manuscript introduces a pairing based cryptography based computationally efficient protocol powered by polynomial equations. First, to reduce computational overhead during group communication, the document is encrypted only once with minimal complexity before uploaded to cloud. Second, during a user leave or join operation, the re‐encryption of documents in cloud is avoided in the group manager side by tactically relieving the group key parameters from the polynomial parameters. Finally, the security analysis shows that the careful handling of secure parameters of users averts the collusion attack, man‐in‐the‐middle attack, message modification attacks, and all other possible attacks. The implementation results portray the capacity of this work to handle secure group communications in cloud storage.
Lower Face Inpainting Aiming at Face Recognition under Occlusions

Although both face recognition and object inpainting have become promising approaches through the use of deep learning, the COVID-19 pandemic has created a tremendous challenge to their further development. Masks, which people have become accustomed to as an effective sanitary measure to prevent infection of COVID-19, have also become an undeniable physical barrier between devices applying face recognition authentication and the faces to be recognized. Therefore, methods that can overcome this dilemma are urgently needed. This study proposes a method that applies a generative model to recognize masked faces based on face inpainting. We introduced a newly proposed identity loss term to conform to the identity information. The reconstructed face will be fed into a face recognition network to extract the feature embeddings for a distance comparison. Taking a naive generative model without an identity loss term introduced as the baseline, the model with an identity loss term improves the recognition accuracy by more than 4%.
Artifact: Resource-Aware Classification via Model Management Enabled Data Stream Optimization

This is the artifact documentation of the paper "Resource-Aware Classification via Model Management Enabled Data Stream Optimization" [1]. In Sec. I the prerequisites are defined. In Sec. II all software modules are introduced, and their functionality is briefly described. In Sec. III a step-by-step tutorial is provided to execute the software modules and to perform the evaluation cases successfully. In addition, we explain how to inspect the artifact during execution and how to access the evaluation measurements.
FTCNet: a lightweight model for large-pose face alignment

Face alignment and face reconstruction are hotspots in computer vision and artificial intelligence. Aiming at the problem of large model and low accuracy of large pose face alignment algorithm, a new lightweight network model FTCNet is designed and implemented. First, deep separable convolution is used to build a lightweight deep neural network model, which directly inputs images in an end-to-end manner for face alignment. Secondly, the network model classifies and predicts pose parameters, shape parameters and expression parameters, and improves the performance of the model through the feature transfer mechanism. Tested on multiple datasets, the experimental results show that the FTCNet gets small, fast, and high-quality face alignment and reconstruction results for unconstrained face images.
Ultra-fast seam carving for real time object detection and removal

This paper outlines a method used to successfully modify the Seam Carving algorithm so that it can be run to remove objects from videos in real time. The successful approach was a combination of multiprocessing speedups and what we called the "Spiral Model". Resultant removal took about 35 milliseconds. The performant object recognition technique was not novel, but resulted in a total processing delay of less than .1 seconds and reasonably continuous video streaming while detecting and removing an object of interest. This paper will discuss the methods used and display the visual results of the approach.
Open research problem for effective IoT authentication

IoT is collection of different “things” which are associated with open web. As all the things are connected to the Internet, it offers convenience to end users for accessing the resources from “Any Where, Any Time” throughout the globe. At the same time, open nature of IoT provides a fertile ground to an intruder for launching different security related threats. If we can not apply proper security safeguards to the IoT System, then it will be not useful to society. Authentication, Encryption, Trust Management and Secure Routing are different domains to offer security in IoT system. Among them, Authentication is very much important security service as it validates device identity before granting access to system services/resources. Existing IoT Authentication algorithms fail to verify device identity in unambiguous way. Existing authentication methods are based on Identity, Key, Password or token attribute for validating device identity. They are vulnerable to different security threats such as Key Stolen threat, MITM threat and Location Spoofing threat. These kinds of approaches are not suitable in the applications related to target tracking, smart city or defense. They provide device identification in ambiguous way and because of that intruder can perform malicious activities in a system, which can decrease system performance. So, it is a demand of time to design an efficient and secure Multi-factor Context aware Multi Key based IoT algorithm which can offer better security from well-known security threats and validate device identity in unambiguous way.
PSYCHOSOCIAL IMPACT OF VIRTUAL NETWORKS ON UNIVERSITY STUDENTS

"Technology has modified all aspects of the lives of individuals, making it more comfortable, this is a great achievement of humanity, but it has also made us dependent beings. The use of electronic platforms, social networks, applications (App), artificial intelligence, virtual assistants, among others, represent unimaginable achievements just 50 years ago. Daily life requires a smartphone, since we are not able to orient ourselves geographically, remember a phone number, or any information that is needed since it is easy to take a photo, carry the schedule of activities, financial services through banking online, have the Covid-19 vaccination record, or check food options at a food service, because a QR reader is required. In addition to the daily technological dependence, we find the attitudes and emotional dependence in which preferences, tastes, photographs, music, virtual approach to family and friends, and much more, become an essential support, especially in the young generations. With this framework, the aim is to evaluate the psychosocial impact of virtual networks on university students in the Republic of Panama. For this, a measurement instrument was designed that was applied remotely through Google form ©. The Likert-type format allowed inferential statistical analyzes to guarantee the discrimination index of the items, the reliability of the instrument through Cronbach's Alpha, as well as the construct validity with the factorial analysis using the SPSS 24 © software. The results reveal that university students have a positive attitude towards virtual networks, which represent psychological and social support, which allows users an emotional protection that they use as a defense against the uncertainty of the day to day and about the near future. Thus, the psychosocial impact of virtual networks encourages emotional containment, in addition, they become social support when links are established with small close groups, regardless of their membership in extended groups. Another finding shows that not all the impact of virtual networks is positive, for example, it was discovered that sleep patterns are not healthy, in addition, the information they receive at night can generate anxiety, anguish and stress."
Fair and Efficient Robust Secret Sharing Scheme against Rushing Adversaries

Robust secret sharing (RSS) is an extension of secret sharing, which can reconstruct a secret correctly even if any <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>t</mi> </math> shares are incorrect. The existing scheme would not effectively achieve fairness. Moreover, even for an optimal scheme, RSS still has the problem that one party is verified by multiple parties, leading to expensive communication costs. In this work, we construct a blockchain-aided RSS scheme that can ensure decentralization and fairness. The central building block for our scheme to handle a rushing adversary is a bulletin board we implement on the InterPlanetary File System. Furthermore, we design a monetary penalty mechanism to impose real penalties on corrupt parties. Each participant either uploads his share correctly or loses his deposit. In addition, our scheme eliminates considerable communication between participants. Compared with the previous schemes, our scheme has a lower communication complexity, which is close to <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mover accent="true"> <mi>O</mi> <mo>˜</mo> </mover> <mfenced open="(" close=")" separators="|"> <mrow> <mi>n</mi> </mrow> </mfenced> </math> . We conduct experiments to show the performance of our scheme. To our knowledge, this is the first implementation of a fair RSS scheme. For shares with a length of 128 bits, the time for each participant to execute the verification phase is 470 ms.
Indexer++

With the increasing workload complexity in modern databases, the manual process of index selection is a challenging task. There is a growing need for a database with an ability to learn and adapt to evolving workloads. This paper proposes Indexer++, an autonomous, workload-aware, online index tuner. Unlike existing approaches, Indexer++ imposes low overhead on the DBMS, is responsive to changes in query workloads and swiftly selects indexes. Our approach uses a combination of text analytic techniques and reinforcement learning. Indexer++ consist of two phases: Phase (i) learns workload trends using a novel trend detection technique based on a pre-trained transformer model. Phase (ii) performs online, i.e., continuous or while the DBMS is processing workloads, index selection using a novel online deep reinforcement learning technique using our proposed priority experience sweeping. This paper provides an experimental evaluation of Indexer++ in multiple scenarios using benchmark (TPC-H) and real-world datasets (IMDB). In our experiments, Indexer++ effectively identifies changes in workload trends and selects the set of optimal indexes.
Random projection and hashing based privacy preserving for image retrieval paradigm using invariant and clustered feature

In recent years, exploration of local characteristics have been an effective feature extraction technique for managing multimedia repository for image retrieval. These class of approaches accelerate the image retrieval. However, with the increase of online intrusion, it is also essential to provide confidentiality for the private online data with effective functionality and significant retrieval accuracy. In this paper, we have solved the protuberant bottleneck problem of privacy-preserving in CBIR using random projection technique for feature encryption with effective retrieval. For this, we formulated highly discriminative interest points based features that are rotation and translation invariant and are constructed using k-means clustering and silhouette concept. Moreover, we have devised locally likely arrangement hashing technique that can incorporate fast, accurate, and secure matching over encrypted features. Based on the experiment, it substantially outperforms the recent state-of-the-art techniques in terms of retrieval accuracy and time overhead on various standard benchmarks, namely, Corel, Olivia, ALOI, and MPEG-7. The proposed method on average provided retrieval accuracy of 83.10%, 81.30%, 81.62%, 73.01% for Corel, Olivia, ALOI and MPEG-7 plaintexted feature, respectively while retrieval accuracy of 79.90% for Corel, 79.10% for Olivia, 79.97% for ALOI, and 67.34% for MPEG-7 using the encrypted feature.
An architecture proposal for checkpoint/restore on stateful containers

Containers have been widely adopted for the development of microservices and cloud-native applications. In parts, this popularization stems from the container orchestration platforms support, facilitating the development and continuously integrating large-scale applications. However, the established techniques offered by orchestration tools to replicate containers are insufficient to provide high availability and strong consistency for stateful containers. Thus, this paper proposes a Checkpoint/Restore (C/R) service to achieve fault-tolerance on stateful containers. Our service aims to eliminate the checkpoint management burden by adding a transparent C/R service into container orchestration platforms. The checkpointing service, implemented as an interceptor, is responsible for taking snapshots of application containers and coordinating the checkpoint execution with the delivery of requests. In case of faults, a new application container is automatically built from a recent snapshot, and the interceptor resumes the delivery of client requests since that checkpoint. A Proof of Concept using a Kubernetes cluster is implemented and corroborates for the feasibility of the C/R. Some challenges and future directions are discussed.
Predicting the category of customers' next product to buy in web shops

Recommender systems are widely used by online retailers to entice customers into making new purchases. Understanding and predicting customer behavior is thus of utmost importance to retailers. In this paper our main goal is to predict the next product category that a certain customer will buy given his/her purchase history. We propose a Sequential Event Prediction model that captures both general and customer-specific consumption behavior through confidence rules. We use anonymized purchasing data from a Web shop in the Netherlands to show empirically that our approach outperforms several models proposed in the literature.
Some results on the Z2Z4[u]-additive codes

Algebraic coding theory has so many important applications in information sciences, cryptogram and communication technology, etc. In this paper, we study Z<sub>2</sub>Z<sub>4</sub>[u]-additive codes in three aspects: Gray map, MacWilliams identity and one-weight codes. We first defined a Gray map and then prove that it is a weight preserving and a distance preserving map. A MacWilliams identity which connects the weight enumerator of an additive code and its dual code is deduced. Finally, we discuss one-weight Z<sub>2</sub>Z<sub>4</sub>[u]-additive codes.
MIRST-DM: Multi-instance RST with Drop-Max Layer for Robust Classification of Breast Cancer

Robust self-training (RST) can augment the adversarial robustness of image classification models without significantly sacrificing models' generalizability. However, RST and other state-of-the-art defense approaches failed to preserve the generalizability and reproduce their good adversarial robustness on small medical image sets. In this work, we propose the Multi-instance RST with drop-max layer, namely MIRST-DM, which involves a sequence of iteratively generated adversarial instances during training to learn smoother decision boundaries on small datasets. The proposed drop-max layer eliminates unstable features and helps learn representations that are robust to image perturbations. The proposed approach was validated using a small breast ultrasound dataset with 1,190 images. The results demonstrate that the proposed approach achieves state-of-the-art adversarial robustness against three prevalent attacks.