Reviewerly/exHarmony huggingface dataset:
Accumulating automata and cascaded equations automata for communicationless information theoretically secure multi-party computation

Information theoretically secure multi-party computation implies severe communication overhead among the computing participants, as there is a need to reduce the polynomial degree after each multiplication. In particular, when the input is (practically) unbounded, the number of multiplications and therefore the communication bandwidth among the participants may be practically unbounded. In some scenarios the communication among the participants should better be avoided altogether, avoiding linkage among the secret share holders. For example, when processes in clouds operate over streaming secret shares without communicating with each other, they can actually hide their linkage and activity in the crowd. An adversary that is able to compromise processes in the cloud may need to capture and analyze a very large number of possible shares. Consider a dealer that wants to repeatedly compute functions on a long file with the assistance of m servers. The dealer does not wish to leak either the input file or the result of the computation to any of the servers. We investigate this setting given two constraints. The dealer is allowed to share each symbol of the input file among the servers and is allowed to halt the computation at any point. However, the dealer is otherwise stateless. Furthermore, each server is not allowed any communication beyond the shares of the inputs that it receives and the information it provides to the dealer during reconstruction. We present a protocol in this setting for generalized string matching, including wildcards. We also present solutions for identifying other regular languages, as well as particular context free and context sensitive languages. The results can be described by a newly defined accumulating automata (AA) and cascaded equations automata (CEA) which may be of an independent interest. As an application of accumulating automata and cascaded equations automata, secure and private repeated computations on a secret shared file among communicationless clouds are presented.
Investigating the learning performances between sequence‐ and context‐based teaching designs for virtual reality (VR)‐based machine tool operation training

Abstract Background Training workers in machine tool operations is indispensable. Because training course at school with real machine tools is costly and dangerous, most researchers have concentrated on developing and adopting virtual reality (VR)‐based applications. However, to improve students’ learning performance levels, teaching design is imperative. Purpose This study hopes to investigate the performance differences between sequence‐ (traditional) and context‐based teaching designs along with a self‐developed VR computer numerical control (VRCNC) training environment. Method Experimental design, sequence‐ and context‐based teaching designs were developed for this study. Forty male senior students were recruited from the department of mechanical engineering at an industrial high school. Then, corresponding pretests, teaching activities and posttests were implemented based on the proposed teaching and experimental designs. Finally, experiment‐, questionnaire‐, and interview‐based performance evaluations were utilized to conduct the final comparison and conclusion. Results Averagely, the context‐based teaching design exhibited superior results for mostly the learning performances. The survey results also revealed that the students obtain more satisfaction and self‐confidence after the context‐based teaching. Conclusions According to the results, the context‐based teaching design has obvious improvement on the performance of machine tool operation training. Three types of evaluation results provides a comprehensive support for the proposed context‐based teaching design along with the VRCNC in machine tool education.
Research on Blockchain Application for E-Commerce, Finance and Energy

Research and apply blockchain technologies for e-commerce, finance and energy. The blockchain system includes blockchain technology components, blockchain application programming interfaces, and applications. The blockchain-based applications cover supply chain finance, e-commerce transactions, product traceability, user credits, financial services, trust systems, new energy, etc. Based on blockchain technology, it builds a creative and security trading system, payment system and trust system for e-commerce, financial services and new energy business.
Detecting nonclassicality of light via Lieb's concavity

Nonclassical light states are important for both conceptual and practical reasons: they are basic ingredients in testing and exploring quantum foundations, and are crucial resources in quantum technologies. Various useful criteria have been developed to detect nonclassicality in the literature, and several meaningful measures of nonclassicality have been introduced and measured experimentally. In this work, by use of a non-Hermitian generalization of the Wigner-Yanase-Dyson skew information and playing with operator ordering in evaluating average photon number, we develop a novel family of criteria for detecting nonclassicality of light based on Lieb's concavity, which is a deep and powerful result concerning interaction between quantum states and observables. We elucidate the information-theoretic as well as the physical meaning of the criteria, and illustrate their effectiveness in capturing and quantifying nonclassicality of various important light states.
Efficient k-anonymous microaggregation of multivariate numerical data via principal component analysis

k-Anonymous microaggregation is a widespread technique to address the problem of protecting the privacy of the respondents involved beyond the mere suppression of their identifiers, in applications where preserving the utility of the information disclosed is critical. Unfortunately, microaggregation methods with high data utility may impose stringent computational demands when dealing with datasets containing a large number of records and attributes. This work proposes and analyzes various anonymization methods which draw upon the algebraic-statistical technique of principal component analysis (PCA), in order to effective reduce the number of attributes processed, that is, the dimension of the multivariate microaggregation problem at hand. By preserving to a high degree the energy of the numerical dataset and carefully choosing the number of dominant components to process, we manage to achieve remarkable reductions in running time and memory usage with negligible impact in information utility. Our methods are readily applicable to high-utility SDC of large-scale datasets with numerical demographic attributes. © 2019 The Authors. Preprint submitted to Elsevier, Inc.
A system for gender recognition on mobile robots

The combination of artificial intelligence and robotics opens the way to disruptive future developments in the industrial and collaborative robotics. The recent advances of the deep learning technologies materialize the possibility to provide a robot perceptive and reasoning skills and, consequently, the capability to autonomously interact with a human. In this paper we ride the wave of intelligent robotics by designing an autonomous robot able to recognize the gender of the customers in a shopping center and to interact with them proposing customized advertising and promotional material. We train two well-known Convolutional Neural Network architectures to recognize gender from face images. In order to run them in real time we extend the computational capabilities of a social robotics platform with an embedded parallel computation accelerator. The experimental analysis, carried out on video sequences acquired in real scenarios, demonstrate the suitability of the proposed platform for the considered social robotics application in terms of both latency and accuracy.
Proof-of-Play

Data storage in peer-to-peer (P2P) games in a perfect applications scenario for blockchain. However, suffering from high transaction cost and latency, proof-of-work (PoW) becomes the bottleneck of blockchain games. Many attempts have been made to overcome various limitations of blockchain for P2P games, but many of them require modifying the game itself to be compatible with a blockchain solution. These overheads often bring new undesirable results to deal with. In this paper, we propose Proof-of-Play, a novel consensus model, to address these issues with a blockchain naturally integrated with P2P games, with minimum intervene to the game. The ultimate goal is to create a secure and fully decentralized architecture to transform a game being community-sustainable.
Hand Segmentation from Complex Background for Gesture Recognition

Hand gesture recognition has become very popular in the field of human–computer interface in recent years. Hand region identification is a fundamental task in most vision-based gesture recognition systems, since the subsequent detection and then segmentation depends on the quality of segmentation. If the background is complex and the illumination is varying, then the segmentation can be a difficult task. Different physical controllers like data gloves, color bands, mouse, and joysticks are used for human–computer interaction, which hinders natural interface as there is a strong barrier between the user and the computer. In such environments, most hand detection techniques fail to obtain the exact region of the hand shape, especially in cases of dynamic gestures. Meeting these requirements becomes very difficult, due to real-life scenarios. To overcome these problems, in this paper, we propose a new method for static hand detection and contour extraction from a complex background. We employ a new technique, histogram thresholding which gives better result over depth thresholding to improve hand region extraction.
A Comparative Study of Machine Learning Classifiers for Network Intrusion Detection

The network intrusion detection system (NIDS) has become an essential tool for detecting attacks in computer networks and protecting the critical information and systems. The effectiveness of an NIDS is usually measured by the high number of detected attacks and the low number of false alarms. Machine learning techniques are widely used for building robust intrusion detection systems, which adapt with the continuous changes in the network attacks. However, a comparison of such machine learning techniques needs more investigation to show their efficiency and appropriateness for detecting sophisticated malicious attacks. This study compares the most popular machine learning methods for intrusion detection in terms of accuracy, precision, recall, and training time cost. This comparison can provide a guideline for developers to choose the appropriate method when developing an effective NIDS. The evaluation of the adopted baseline machine learning classifiers is conducted on two public datasets, i.e., KDD99 and UNSW-NB15. The time taken to build a model for each classifier is also evaluated to measure their efficiency. The experimental results show that the Decision Tree (DT), Random Forests (RF), Hoeffding Tree (HT), and K-Nearest Neighbors (KNN) classifiers show higher accuracy with reasonable training time in the 10-fold cross validation test mode compared to other machine learning classifiers examined in this study.
The Artificial Facilitator: Guiding Participants in Developing Causal Maps Using Voice-Activated Technologies

Complex problems often require coordinated actions from stakeholders. Agreeing on a course of action can be challenging as stakeholders have different views or 'mental models' of how a problem is shaped by many interacting causes. Participatory modeling allows to externalize mental models in forms such as causal maps. Participants can be guided by a trained facilitator (with limitations of costs and availability) or use a free software (with limited guidance). Neither solution easily copes with large causal maps, for instance by preventing redundant concepts. In this paper, we leveraged voice-activated virtual assistants to create causal models at any time, without costs, and by avoiding redundant concepts. Our three case studies demonstrated that our artificial facilitator could create causal maps similar to previous studies. However, it is limited by current technologies to identify concepts when the user speaks (i.e. entities), and its design had to follow pre-specified rules in the absence of sufficient data to generate rules by discriminative machine-learned methods.
A validation study of whole slide imaging for primary diagnosis of lymphoma

Whole slide imaging (WSI) is being increasingly used worldwide. Although previous studies have asserted the validity of WSI diagnosis, they have primarily targeted only small specimens and excluded cases requiring immunohistochemistry or special staining, such as lymphoma. The purpose of this study was to evaluate the accuracy of WSI diagnosis of lymphoma, for which 240 biopsies and resections of lymphoma cases were selected from the study set of lymphomas. All slides including H&E, immunohistochemical and special staining were digitized using a WSI image scanner. An experienced pathologist performed the WSI diagnoses, which were compared with original diagnoses based on light microscopic examinations. Discrepancy between the two interpretations were classified into three categories: concordance, minor discrepancy (no clinical significance), and major discrepancy (with clinical significance). Overall concordance between the light microscopic and WSI diagnosis was found in 223 cases (92.92%; 95%CI = 88.90-95.82), minor discrepancy in fifteen (6.25%; 95%CI = 3.54-10.10), and major discrepancy in two (0.83%; 95%CI = 0.10-2.98). Diagnosis of lymphoma using WSI appeared to be mostly accurate, suggesting that WSI may be a reliable technology for the diagnosis of lymphoma.
Block Delivery Time in Bitcoin Distribution Network

In this work we provide comprehensive analytical model for Bitcoin distribution network. We apply Jackson network model on the whole Bitcoin network where individual nodes operate as priority M/G/1 queuing systems. Data arrival process to the nodes is modeled as a non-homogeneous Poisson process in which the data arrival rates to the nodes are derived from the analytical model of gossip data delivery protocol. This model considers random probability distribution of node connectivity. Performance results include network distribution time for blocks, node response time for blocks, and populations of data distribution algorithms as functions on network size. Usefulness of this model is demonstrated by efficiently computing the forking probability for the Bitcoin blockchain.
TPNE: Topology preserving network embedding

Network embedding aims at mapping nodes into a vectorial feature space while maximally preserving the topological relations of nodes in a network so as to facilitate complex network analysis. The existing works in network embedding are focused on preserving local or global topological information within limited step-sizes, which could be insufficient in many applications, even the underlying network is undirected and unweighted. The complex and rich topological information in networks exerts paramount impacts on the formation of networks and can reveal the high-order relevance among different nodes. In this paper, we propose a novel network embedding framework based on deep neural networks, named Topology Preserving Network Embedding (TPNE), which is suitable for arbitrary types of information networks: directed or undirected, weighted or unweighted. In this framework, we devise a closeness matrix to capture more comprehensive global topology of the network, and combine both global closeness reconstruction and local neighborhood preserving into a single loss function. To justify our model, we conduct extensive experiments on node classification, link prediction and node visualization tasks, employing the learned embedding vectors. It demonstrates that the proposed approach has promising performance and it outperforms the existing state-of-the-art approaches on several networks in such tasks.
An improved artificial bee colony algorithm with fast strategy, and its application

In recent years, artificial bee colonies (ABC) have yielded favorable results from among other evolutionary algorithms. In spite of its strong global presence, how to accelerate ABC's convergence is an important factor that contributes toward improving its performance. In this paper, the proposed updating equation of onlooker bees employs two alternatives which are selected based upon whether the achievement of the updated bee is better than that of the generated individual. A Cauchy operator is employed to balance the global and local search capabilities of individuals. Various common benchmark functions and a real world problem are employed to verify the effectiveness of our algorithm by comparing these with some modern ABC variants.
Osteoarthritis Detection and Classification from Knee X-Ray Images Based on Artificial Neural Network

Osteoarthritis is a chronic and degenerative disorder of multifactorial etiology. Knee, hip and spine joints are majorly affected by the Osteoarthritis due to loss of articular cartilage, bone remodeling (due to accidents, for instance) and heavy weight-bearing on joints. In orthopedics, it is one of the most occurring disorder nowadays. Hence an effective computer-aided diagnosis (CAD) system is required to diagnose the Osteoarthritis. This paper presents a simple artificial neural network (ANN) based classification system to differentiate between healthy and affected knee X-ray images. In the proposed system, guided filter and adaptive histogram equalization techniques are respectively used for noise removal and image enhancement. Global thresholding-based segmentation technique is adapted to extract synovial cavity region from an image and curvature values (like mean, standard deviation, range, and skewness) are computed. To draw a fair conclusion, the experiments are conducted on real patient-specific images collected from local hospitals in India. By confirming the results, the proposed method accurately classifies the inputted image to their respective classes.
The Effects of Immersion in a Virtual Reality Game: Presence and Physical Activity

This study explored the effects of immersion in virtual reality games on physical exercise performance. Traditional exercising was compared with a VR exercising set up. In the experiment, subjects were asked to play a cycling game with a stationary bike on different immersion levels. The non-VR group played the game with traditional PC set-up on a flat screen while VR-group played the game in a full VR setting using the Head-Mounted Display. The input remained the same to eliminate other possible effects on the result. The travel distance within the game was recorded and analyzed. The results showed that subjects within the VR set up showed a longer travel distance as well as a higher level of presence and psychological arousal. That is to say, this study provided the evidence of VR exercising games can increase user’s physical exercising performance.
RIP-RH

Run-time attacks pose a continuous threat to the security of computer systems. These attacks aim at hijacking the operation of a computer program by subverting its execution at run time. While conventional run-time attacks usually require memory-corruption vulnerabilities in the program, hardware bugs represent an increasingly popular attack vector. Rowhammer represents a vulnerability in the design of DRAM modules that allows an adversary to modify memory locations in physical proximity to attacker-controlled memory on the module without accessing them. This is a serious threat to real-world systems, since DRAM is used as main memory on virtually all platforms. Recent research proposed defenses against rowhammer, such by patching the memory controller in hardware, or statically partitioning physical memory to protect the operating system kernel from a user space adversary. However, sharing DRAM memory securely between a number of different entities currently remains as an open problem. In this paper, we present RIP-RH, a DRAM-aware memory allocator that allows for dynamic management of multiple user-space processes. RIP-RH ensures that the memory partitions belonging to individual processes are physically isolated. In our detailed evaluation we demonstrate that our prototype implementation of RIP-RH incurs a modest run-time overhead of 3.17% for standard benchmarks and offers practical performance in a number of real-world scenarios.
Deep Image Captioning: An Overview

Image captioning is a process of automatically describing an image with one or more natural language sentences. In recent years, image captioning has witnessed rapid progress, from initial template-based models to the current ones, based on deep neural networks. This paper gives an overview of issues and recent image captioning research, with a particular emphasis on models that use the deep encoder-decoder architecture. We discuss the advantages and disadvantages of different approaches, along with reviewing some of the most commonly used evaluation metrics and datasets.
Prostate Cancer Detection and Segmentation in Multi-parametric MRI via CNN and Conditional Random Field

Multi-parametric MRI (mp-MRI) is a powerful diagnostic tool for prostate cancer (PCa). However, interpreting prostate mp-MRI requires high-level expertise, causing significant inter-reader variations. Convolutional neural networks (CNNs) have recently shown great promise for various tasks. In this study, we propose an improved CNN to jointly detect PCa lesions and segment for accurate lesions contours. Specifically, we adapt focal loss to overcome the imbalance between cancerous and non-cancerous areas for improved lesion detection and design selective dense conditional random field (SD-CRF), a post-processing step to refine the CNN prediction into the lesion segmentation based on a specific imaging component of mp-MRI. We trained and validated the proposed CNN in 5-fold cross-validation using 397 preoperative mp-MRI exams with whole-mount histopathology-confirmed lesion annotations. In the free-response receiver operating characteristics (FROC) analysis, the proposed CNN achieved 75.1% lesion detection sensitivity at the cost of 1 false positive per patient. In the evaluation for lesion segmentation, the proposed CNN improved the Dice coefficient by 20.6% from the baseline CNN.
VIAN: A Visual Annotation Tool for Film Analysis

Abstract While color plays a fundamental role in film design and production, existing solutions for film analysis in the digital humanities address perceptual and spatial color information only tangentially. We introduce VIAN, a visual film annotation system centered on the semantic aspects of film color analysis. The tool enables expert‐assessed labeling, curation, visualization and Classification of color features based on their perceived context and aesthetic quality. It is the first of its kind that incorporates foreground‐background information made possible by modern deep learning segmentation methods. The proposed tool seamlessly integrates a multimedia data management system, so that films can undergo a full color‐oriented analysis pipeline.