Reviewerly/exHarmony huggingface dataset:
Automatic Forgery Localization via Artifacts Analysis of JPEG and Resampling

With the availability of highly sophisticated editing tools, the authenticity of digital images has now become questionable. The level of image tampering is getting higher and higher, and the tampering procedures become more and more complicated. To recognize the tampering area of the original image, the tampered image is usually executed a series of post-processing. This behavior has greatly increased the difficulty of forgery detection. In this paper, a blind JPEG image forgery detection and localization technique based on JPEG and resampling artifacts analysis is proposed. The process of tampering is to first tamper with JPEG images by bitmaps. Then original JPEG image and tampered area are manipulated by a series of operations, that is, the image is enlarged and then saved as JPEG. A novel tampering localization method is presented based on resampling and JPEG blockness artifacts. Theoretical analysis and experimental results show that the proposed method can effectively identify and locate the tampered region of a spliced image with a JPEG-resampling-JPEG operation chain.
Scheme of a hydrogen-molecule quantum simulator based on two ultracold rubidium atoms

Abstract A scheme is proposed for implementing a hydrogen-molecule quantum simulator based on two ultracold rubidium atoms trapped into spatially separated optical dipole traps. The scheme includes the adiabatic preparation of the initial quantum state of two atoms and the iterative quantum phase estimation. The accuracy of measuring the ground state energy of a molecule is numerically calculated as a function of the number of iterations. The simulation is performed using two-qubit gates based on the dipole blockade effect under short-term excitation of atoms into the Rydberg states with allowance for the finite lifetime of Rydberg states and the finite energies of the van der Waals interaction.
Research on the Integration Demonstration System of Space Information Network Based on TDRSS

Aiming at demonstration of space information network, this paper proposed the integration demonstration system based on TDRSS, which can make up for the difference of transport protocols, operation mode and data distribution between space information network and TDRSS. The system expanded the topology structure of TDRSS in order to be compatible with space information network service and supply the theoretical and technical base for the improvement of space information network.
Improving the Tracking of Subatomic Particles Using the Unscented Kalman Filter With Measurement Redundancy in High Energy Physics Experiments

In this paper, the unscented Kalman filter (UKF) is applied to a particle tracking problem in the high energy physics (HEP) experiments, where the determination of position and momentum associated to a particle with a high level of the desired accuracy, in the presence of multiple scattering and ionization energy loss, is crucial. The main issue that naturally arises due to such particle and detection dynamics is relative (marginal) observability which causes the existing track reconstruction algorithm, the Extended Kalman filter (EKF), to underperform or even diverge which has been elaborated through a comparison with the UKF. We present this challenging problem and investigate the issue of relative observability in terms of its relationship with measurement redundancy in order to improve the performance of the UKF. We also present an interesting issue where no duplicate measurements are available due to the measurement setup used at the HEP system under consideration and how in that particular scenario redundancy can be introduced.
Networked Society and its New Power Actors: Critical Analysis of Hardware, Software, and Communicative Level of Digital Space

The paper analyses the networked society with the use of critical Internet studies approach. The authors consider the expansion of digital space as a transformational factor of post-modern individual work. In particular, there are negative trends: the decline in productivity, the decline in leisure quality and the problem of digital exploitation. The latest trend is considered using such concepts as “digital prosumerism”, “digital labor product” and “digital capital”. The authors note that capital in the digital space consists of two components: market and communicative ones. Both of these components play an important role in the spread of digital branding and in the formation of digital power actors at three levels of the digital space: communicative (digital profi-sumers), software (platforms as traffic oligopolies) and hardware (digital architects). The paper deals with the problems that may arise due to the strengthening of these actors. The authors propose approaches and possible tools (including Big Data analyses and math modelling) for empirical verification of the concepts applied.
Multi-stage mixed rule learning approach for advancing performance of rule-based classification

Rule learning is a special type of machine learning approaches, and its key advantage is the generation of interpretable models, which provides a transparent process of showing how an input is mapped to an output. Traditional rule learning algorithms are typically based on Boolean logic for inducing rule antecedents, which are very effective for training models on data sets that involve discrete attributes only. When continuous attributes are present in a data set, traditional rule learning approaches need to employ crisp intervals. However, in reality, problems usually show shades of grey, which motivated the development of fuzzy rule learning approaches by employing fuzzy intervals for handling continuous attributes. While a data set contains a large portion of discrete attributes or even no continuous attributes, fuzzy approaches cannot be used to learn rules effectively, leading to a drop in the performance. In this paper, a multi-stage approach of mixed rule learning is proposed, which involves strategic combination of both traditional and fuzzy approaches to handle effectively various types of attributes. We compare our proposed approach with existing algorithms of rule learning. Our experimental results show that our proposed approach leads to significant advances in the performance compared with the existing algorithms.
A Comparative Study of the Algorithms for Path finding to Determine the Adversary Path in Physical Protection System of Nuclear Facilities

The shortest path algorithm is the process of finding a path on a weighted graph that minimizes the number of the weights of the path-forming side. Thus, the resulting path is the distance that has the least weight or distance. In physical protection systems (PPS), the implementation of the shortest path algorithm is determining the most vulnerable adversary path to penetrate the physical protection system of a nuclear facility. The paths between points at a facility passed by the adversary will form a directed and weighted graph. The weights on the graph represent the ability of a protective device (e.g. CCTV, sensor, alarm etc.). The formed graph will be processed using algorithms to determine the most vulnerable path. In this paper, we study and examine the performance of two types of algorithms namely Dijkstra and A*. The purpose of this study is to compare the two algorithms which we will use it to determine the most vulnerable path related to the physical protection system of nuclear facilities. Our results show that the A* algorithm provides more efficient path results than the Dijkstra algorithm.
Causal Inference from Noise

Abstract Correlation is not causation is one of the mantras of the sciences—a cautionary warning especially to fields like epidemiology and pharmacology where the seduction of compelling correlations naturally leads to causal hypotheses. The standard view from the epistemology of causation is that to tell whether one correlated variable is causing the other, one needs to intervene on the system—the best sort of intervention being a trial that is both randomized and controlled. In this paper, we argue that some purely correlational data contains information that allows us to draw causal inferences: statistical noise. Methods for extracting causal knowledge from noise provide us with an alternative to randomized controlled trials that allows us to reach causal conclusions from purely correlational data.
Boosting Recommendation Systems through an Offline Machine Learning Evaluation Approach

Nowadays, recommendation systems are widely deployed to suggest a variety of products and services for target users. Practical examples of recommendation systems that we daily encounter include social, educational, and political services such as academic courses, movies, travel, music, and news feed on the web. Current recommendation systems can be categorized into threefold: knowledge-based, collaborative filtering, and content-based systems. Knowledge-based recommendation systems are based on domain knowledge without user-specific data. Collaborative filtering provides recommendations based on existing user's historical record, which can be a problem when the user is new to the platform. Content-based recommendations build models based on a number of records on product transactions. In this paper, we introduce the three recommendation systems, then suggest a new strategy that integrates domain knowledge for new users into a machine learning based recommendation system for existing users. We also propose an offline/online evaluation strategy to reduce customer churn, which enables the deployment of accurate recommendations for both new and existing users. Using the movie Tweetings dataset, we implement each recommendation system and generate a boosted system which addresses the issues caused by current recommendation systems.
End-to-end Speech Synthesis for Tibetan Lhasa Dialect

Speech synthesis for Tibetan Lhasa dialect is implemented on the basis of an end-to-end novel speech synthesis framework, Tacotron. The training transcript has used the phoneme list transcribed from Tibetan characters, and feature parameters were extracted from the mel-spectrogram. Then the model is trained by the mapping of character to spectrum. Tibetan language is an important minority language of the Chinese nation, but there is little research on Tibetan language at present. The experimental results were compared with traditional speech synthesis methods, with the audio quality significantly better than that of the traditional GMM-HMM in both naturalness and rhythm. It provides a crucial reference for the later research methods of Tibetan language and promotes the development of Tibetan language research.
A Visual Leader-Following Approach With a T-D-R Framework for Quadruped Robots

The quadruped robot imitates the motions of four-legged animals with a superior flexibility and adaptability to complex terrains, compared with the wheeled and tracked robots. Its leader-following ability is unique to help a human to accomplish complex tasks in a more convenient way. However, long-term following is severely obstructed due to the high-frequency vibration of the quadruped robot and the unevenness of terrains. To solve this problem, a visual approach under a novel T-D-R framework is proposed. The proposed T-D-R framework is composed of a visual tracker based on correlation filter, a person detector with deep learning, and a person re-identification (re-ID) module. The result of the tracker is verified by the detector to improve tracking performance. Especially, the re-ID module is introduced to handle distractions and occlusion caused by other persons, where the convolutional correlation filter (CCF) is employed to discriminate the leader among multiple persons through recording the appearance information in the long run. By comparing the results of the tracker and the detector as well as their similarity scores with the leader identified by the re-ID module, a stable and real-time tracking of the leader can be guaranteed. Experiments reveal that our approach is effective in handling distractions, appearance changes, and illumination variations. A long-distance experiment on a quadruped robot indicates the validity of the proposed approach.
Adjustable round-pulse time delayer for round-robin differential phase-shift quantum key distribution

Round-robin differential phase-shift (RRDPS) is one of the most promising quantum key distribution (QKD) protocol. Because cutting off the link between phase error and bit error and needing not of monitor the signal disturbance, the RRDPS protocol is in principle highly robust against channel disturbance for a large pulse packet size L and provides a new route towards QKD over long distances. However, once the experimental system is set up, the value of L is fixed. It cannot choose an optimal L to maximize the final key rate. In this paper, we propose a round-pulse time delayer, which can effectively realize the adjustable and a wide range of delay selection. This round-pulse time delayer has the advantages of simple structure and stable performance, which ensures the wide application for RRDPS-QKD protocol.
Detecting Suspicious Transactions in IoT Blockchains for Smart Living Spaces

The idea of connecting physical things and cyber components to enable new and richer interactions is a key component in any smart space concept. One of the central challenges in these new smart spaces is the access control of data, services and things. In recent years, Distributed Ledger technology (DLT) like Blockchain Technology (BCT), emerged as the most promising solution for decentralized access management. Using capability-based access control, access to data/services/things is achieved by transferring tokens between the accounts of a distributed ledger. Managing how the access tokens are transferred is, of course, a major challenge. Within the IoT space, smart contracts are at the center of most of the proposals for DLT/BCT networks targeting access control. The main problem in using smart contracts as a means for checking if and what access token can be transferred from one account to another is their immutability and accessibility. Smart contracts and chain code are by design meant to be immutable since they represent a binding contract between parties. In addition, they need to be accessible since they are to be executed on many nodes. This allows an attacker to study them and design the attack in a manner that passes the rules of the smart contract/chain code. This paper focuses on the use of metadata as a more effective means to prevent attackers from gaining access to data/services/things in a smart living space.
Looking Into Online Gaming From Measurement Perspective

Owing to the development of computing hardware and networking related technologies, online gaming has become an important entertainment format for people of all ages. Understanding the system architecture and game players' behavior can benefit the users' Quality of Experience (QoE) optimization, the game competitiveness enhancement, and even a new game deployment. To this end, many attentions have been paid to the online game measurements. In this paper, we investigate the development of online games and look into the online gaming from the perspective of measurements, including the system architecture, users' QoE, and the user behaviour. We hope the work can benefit the game system design and optimization in the future.
A Secure Incentive Mechanism for Competitive Organization Data Sharing: A Contract Theoretic Approach

With the development of information technology, various data-driven intelligent services have changed our daily life greatly. As the data fuels the development of these services, data sharing is desirable for data availability issues. Many mechanisms and technologies have been proposed to support data sharing. However, some organizations, especially with potential competitive relationships are still reluctant to share their data. On one hand, no data holder wish to see the competitiveness improvement of competitors by data sharing. On the other hand, data sharing suffers from huge privacy security risks. To encourage data sharing, in this paper, a competitiveness-driven and secure incentive mechanism is proposed. By introducing the concept of data competitiveness as incentive motivation, competitiveness worry of data holders is eliminated. Privacy is protected by adopting differential privacy. As the privacy protection levels of data holders are unknown to the data demander, a contract theoretic approach is proposed to formulate the incentive mechanism. With the design of optimal contracts, the data demander can make the best decisions to pay data holders and data holders can optimize their utilities by choosing proper contracts. The numerical results demonstrate the effectiveness of the proposed incentive scheme.
CacheGuard

Modern processor cores share the last-level cache and directory to improve resource utilization. Unfortunately, such sharing makes the cache vulnerable to cross-core cache side channel attacks. Recent studies show that information leakage through cross-core cache side channel attacks is a serious threat in different computing domains ranging from cloud servers and mobile phones to embedded devices. However, previous solutions have limitations of losing performance, lacking golden standards, requiring software support, or being easily bypassed.
Models of QOE ensuring for OTT services

This article discusses the main models that contribute to improving the quality of OTT services. The main parameter for quality assessment was chosenQoE-Quality of Experience. An analysis was made of a number of factors that directly affect the assessment of QoE. The second part of the article deals with models that can provide the necessary level of quality for OTT services. These models were divided into three groups: traffic-based models, application-based models, and speed-based models. The main task of the study is to find optimal solutions to ensure the quality of OTT services.
Crowd analysis using Bayesian Risk Kernel Density Estimation

Crowd behavior analysis is one of the significant issues in video surveillance. In this paper while having a video sequence of a public place, the goal is to extract the regions of interest in the input video that can be the regions where more people commute. To identify these regions, first a non-parametric kernel density estimation method is applied to create a crowd density map. Then, we propose to use a kernel density estimation based on the Bayesian risk. By defining a new loss function for the Bayesian risk, we can decrease the effects of outliers in order to extract the distribution’s peaks of the resulted map more accurately. To evaluate the proposed method, we utilize 9 different real-world datasets on various scenarios where among them PETS2009 and Mall dataset are popularly used. We also make use of multiple synthetic datasets as our evaluation benchmark. Experimental results present that the proposed method has higher accuracy to find different centers of attention in compared with other similar state-of-the-art methods on both real-world and synthetic datasets.
A Concise Temporal Data Representation Model for Prediction in Biomedical Wearable Devices

Predictive analytics and event forecasting using deep learning techniques require processing long-term historical data which is infeasible in low-power wearable devices. Such devices are constrained in memory and computational power, and are pushed to their limits by resource hungry deep neural networks. Current techniques either ignore historical data, or convert temporal sequences to pattern sequences, eliminating valuable properties such as time and/or recency. The proposed model maps arbitrary-length multivariate discrete time series to a concise sequence, called mapped interval sequence (MIS). MIS retains original data properties such as time, recency, and scale, without being susceptible to missing values. Life model for time series (LMts) mapping, is capable of mapping billions of data elements with sampling rate of several kHz or higher into a sequence of 32 elements or fewer. Furthermore, a new loss function called as tolerance error is introduced to improve long-term forecasting events using LMts. In a smart health Internet of Things environment, LMts enables real-time health predictions depending on both recent and historical data. In addition, the LMts model can predict the approximate time of events, with granularity of seconds and up to years. Experimental results show that, compared to previous studies in fall prediction, LMts achieves the same 100% accuracy with a single long short-term memory layer, while covering 16× longer time period and using 80× less weight parameters. LMts is also used to forecast human fall up to 14 s in advance even with 50% missing values.
Feedback Learning: Automating the Process of Correcting and Completing the Extracted Information

In recent years, with the increasing usage of digital media and advancements in deep learning architectures, most of the paper-based documents have been revolutionized into digital versions. These advancements have helped state-of-the-art information extraction and digital mailroom technologies become progressively efficient. Even though many efficient post-Information Extraction (IE) error rectification methods have been introduced in the recent past to improve the quality of digitized documents. They are still imperfect and they demand improvements in the area of context-based error correction, specifically when we are dealing with the documents involving sensitive information such as invoices. This paper describes the self-correction approach based on the sequence to sequence Neural Machine Translation (NMT) as applied to rectify the incorrectness in the results of any information extraction approach such as Optical Character Recognition (OCR). We accomplished this approach by exploiting the concepts of sequence learning with the help of feedback provided during each cycle of training. Finally, we have compared state-of-the-art post-OCR error correction methods with our feedback learning approach. Our empirical results have outperformed state-of-the-art post-OCR error correction methods.
Configuration-Space Flipper Planning for Rescue Robots

For rescue robots, flippers endow the robot with additional ability to pass through various terrain. Also, autonomous capabilities become more important, especially with respect to autonomous motion including flippers. In recent work, autonomy for flippers is done by either planning with several special states or based on collected data. We are considering if it is possible to find a way to build continuous states without collecting old trail data. In this paper, we first model the possible states as a global planning path with parameter configuration of the scene. Then, we follow the path to achieve the autonomous run. We plot the morphology of each path points to show the correctness of the path and implement a simple path following on real robot to demonstrate the performance of our algorithm.
A Novel SQLite-Based Bare PC Email Server

We describe a SQLite-based mail server that runs on a bare PC with no operating system. The mail server application is integrated with a server-based adaptation of the popular SQLite client database engine. The SQLite database is used for storing mail messages, and mail clients can send/receive email and share files using any Web browser as in a conventional system. The unique features of the bare PC SQLite-based email server include (1) no OS vulnerabilities; (2) the inability for attackers to run any other software including scripts; (3) no support for dynamic linking and execution of external code; (4) a small code footprint making it easy to analyze the code for security flaws; and (5) performance benefits due to eliminating OS overhead. We describe system design and implementation, and give details of the bare machine mail server application. This work serves as a foundation to build future bare machine servers with integrated databases that can support Internet-based collaboration in high-security environments.