Reviewerly/exHarmony huggingface dataset:
Changing Fock matrix elements of two-mode squeezed vacuum state by employing three conditional operations in one-sided lossy channel

This paper focuses on changing Fock matrix elements of two-mode squeezed vacuum state (TMSVS) by employing three conditional operations in one-sided lossy channel. These three conditional operations include one-photon replacement (OPR), one-photon substraction (OPS) and one-photon addition (OPA). Indeed, three conditional quantum states have been generated from the original TMSVS. Using the characteristic function (CF) representation of quantum density operator, we derive the analytical expressions of their Fock matrix elements, which depend on the interaction parameters, including the squeezing parameter of the input TMSVS, the loss factor and the transmissivity of the variable beam splitter. For convenience of discussion, we only give the Fock matrices in the subspace span for these two-mode states. Obviously, the TMSVS only has the populations in and in such subspace. By comparing the generated states with the TMSVS, we find that: (1) the generated state after OPR will remain the populations in and , and add the populations in and (2) the generated state after OPS will lost the populations in and , but add the populations in and (3) the generated state after OPA will remain the population only in and add the population in .
User-Oriented Summaries Using a PSO Based Scoring Optimization Method

Automatic text summarization tools have a great impact on many fields, such as medicine, law, and scientific research in general. As information overload increases, automatic summaries allow handling the growing volume of documents, usually by assigning weights to the extracted phrases based on their significance in the expected summary. Obtaining the main contents of any given document in less time than it would take to do that manually is still an issue of interest. In~this~ article, a new method is presented that allows automatically generating extractive summaries from documents by adequately weighting sentence scoring features using \textit{Particle Swarm Optimization}. The key feature of the proposed method is the identification of those features that are closest to the criterion used by the individual when summarizing. The proposed method combines a binary representation and a continuous one, using an original variation of the technique developed by the authors of this paper. Our paper shows that using user labeled information in the training set helps to find better metrics and weights. The empirical results yield an improved accuracy compared to previous methods used in this field
Shaping Sounds

This paper presents an observational study of collaborative spatial music composition. We uncover the practical methods two experienced music producers use to coordinate their understanding of multi-modal and spatial representations of music as part of their workflow. We show embodied spatial referencing as a significant feature of the music producers' interactions. Our analysis suggests that gesture is used to understand, communicate and form action through a process of shaping sounds in space. This metaphor highlights how aesthetic assessments are collaboratively produced and developed through coordinated spatial activity. Our implications establish sensitivity to embodied action in the development of collaborative workspaces for creative, spatial-media production of music.
On the importance of cryptographic agility for industrial automation

Abstract Cryptographic primitives do not remain secure, they deteriorate over time. On the one hand increasing computing power leads to more powerful attacks on their underlying mathematical problems. On the other hand quantum computing threatens to break many widely used cryptographic primitives. The main goal of cryptographic agility is to enable an easy transition to alternative cryptographic schemes. Considering the long lifetime of products within industrial automation, we argue that vendors should strive for cryptographic agility in their products. In this work we motivate cryptographic agility by discussing the threat of quantum computers to modern cryptography. Additionally, we introduce the reader to the concept of post-quantum cryptography. Ultimately, we demonstrate that cryptographic agility requires three elements: 1) cryptographic application programming interfaces, 2) secure update mechanisms and 3) documentation of cryptographic primitives. By providing practical concepts we show how to meet these requirements in software-based systems.
A High Performance, Low Energy, Compact Masked 128-Bit AES in 22nm CMOS Technology

Advanced Encryption Standard (AES) is a specification for electronic data encryption. This standard has become one of the most widely used encryption method and has been implemented in both software and hardware. AES has excellent resistance against linear and differential cryptanalysis. Although the standard itself is algorithmically secure, based on the implementation, it can be vulnerable to attackers through side channels. For example, it has been shown that by measuring the implementation's power and performing statistical analysis on multiple traces the secret key used can be unveiled. This paper presents an efficient hardware based 128-bit AES design using a masking scheme which is resistant to a side channel attack. This masked design is implemented in TSMC 22nm technology. The resulting implementation is high in performance, low in energy and silicon area. It can run at more than 400MHz translating to a throughput of 5.12Gbps. The total area of the AES block is 0.0169mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . The energy consumption is at 9.77pJ/bit or 1.25nJ/block.
Low-Power Distributed Data Flow Anomaly-Monitoring Technology for Industrial Internet of Things

. In recent years, the industrial use of the internet of things (IoT) has been constantly growing and is now widespread. Wireless sensor networks (WSNs) are a fundamental technology that has enabled such prevalent adoption of IoT in industry. WSNs can connect IoT sensors and monitor the working conditions of such sensors and of the overall environment, as well as detect unexpected system events in a timely and accurate manner. Monitoring large amounts of unstructured data generated by IoT devices and collected by the big-data analytics systems is a challenging task. Furthermore, detecting anomalies within the vast amount of data collected in real time by a centralized monitoring system is an even bigger challenge. In the context of the industrial use of the IoT, solutions for monitoring anomalies in distributed data flow need to be explored. In this paper, a low-power distributed data flow anomaly-monitoring model (LP-DDAM) is proposed to mitigate the communication overhead problem. As the data flow monitoring system is only interested in anomalies, which are rare, and the relationship among objects in terms of the size of their attribute values remains stable within any specific period of time, LP-DDAM integrates multiple objects as a complete set for processing, makes full use of the relationship among the objects, selects only one "representative" object for continuous monitoring, establishes certain constraints to ensure correctness, and reduces communication overheads by maintaining the overheads of constraints in exchange for a reduction in the number of monitored objects. Experiments on real data sets show that LP-DDAM can reduce communication overheads by approximately 70% when compared to an equivalent method that continuously monitors all objects under the same conditions.
Automatic Detection of Online Abuse and Analysis of Problematic Users in Wikipedia

Today's digital landscape is characterized by the pervasive presence of online communities. One of the persistent challenges to the ideal of free-flowing discourse in these communities has been online abuse. Wikipedia is a case in point, as it's large community of contributors have experienced the perils of online abuse ranging from hateful speech to personal attacks to spam. Currently, Wikipedia has a human-driven process in place to identify online abuse. In this paper, we propose a framework to understand and detect such abuse in the English Wikipedia community. We analyze the publicly available data sources provided by Wikipedia. We discover that Wikipedia's XML dumps require extensive computing power to be used for temporal textual analysis, and, as an alternative, we propose a web scraping methodology to extract user-level data and perform extensive exploratory data analysis to understand the characteristics of users who have been blocked for abusive behavior in the past. With these data, we develop an abuse detection model that leverages Natural Language Processing techniques, such as character and word n-grams, sentiment analysis and topic modeling, and generates features that are used as inputs in a model based on machine learning algorithms to predict abusive behavior. Our best abuse detection model, using XGBoost Classifier, gives us an AUC of ~84%.
GestureCards

Describing gestures in detail has various advantages for project teams: communication is simplified, interaction concepts are documented, and technical decisions are supported. Common gesture notations focus on textual or graphical elements only, but we argue that hybrid approaches have various advantages, especially because some gesture traits are easier to describe with text and others with arrows or icons. We present GestureCards, a hybrid gesture notation mixing graphical and textual elements we developed to describe multi-touch gestures. To evaluate our approach, we compared how users perceive and are affected by different notations. First, we compared GestureCards with a textual notation and observed advantages in terms of speed, correctness, and confidence. Second, we asked participants to compare GestureCards, a textual, and a graphical notation and rate them. The results indicate that the participants' perception depends on the gesture, but GestureCards received consistently good ratings. Third, we monitored several participants working with GestureCards solving practical development tasks for gesture-based applications and they felt well supported by GestureCards.
A Holistic Energy-Efficient Real-Time Scheduler for Mixed Stream and Batch Processing Workloads

In recent years we have experienced a wide adoption of novel distributed processing frameworks such as Apache Spark for handling batch and stream processing big data applications. An important aspect that has not been examined in these systems yet, is the energy consumption during the applications' execution. Reducing the energy consumption of modern datacenters is a necessity, as datacenters contribute over 2 percent of the total US electric usage. However, efficiently scheduling applications in distributed processing systems can be challenging as there is a trade-off between minimizing the datacenter's energy usage and satisfying the application performance requirements. In this work we propose, ExpREsS, a scheduler for orchestrating the execution of Spark applications in a way that enables us to minimize the energy consumption while ensuring that the applications' performance requirements are met. Our approach exploits time-series segmentation for capturing the applications' energy usage and execution times, and then applies a novel DVFS technique to minimize the energy consumption. In order to tackle the limited number of application's profiling runs, we exploit regression techniques to predict the applications' execution times and power consumption. Our detailed experimental evaluation using realistic workloads on our local cluster illustrates the working and benefits of our approach.
Aligning Ground Truth Text with OCR Degraded Text

Testable data sets are a valuable commodity for testing and comparing different algorithms and experiments. An important aspect of testing OCR error correction and post processing is the ability to match an OCR generated word with the original word regardless of errors generated in the conversion process. This requires string alignment of both text files, which is often lacking in many of the available data sets. In this paper, we cover relevant research and background on the String Alignment Problem and propose an alignment algorithm that when tested with the TREC-5 data set achieves an initial alignment accuracy average of 98.547% without zoning problems and 81.07% with. We then address the Zoning Problem, and other issues, and propose solutions that could increase both accuracy and overall performance up to an alignment accuracy of 98% in both scenarios.
Modern technologies of the load rationing on large water systems

Abstract A functional model of an automated management decision support system for load rationing in the framework of water management systems is proposed. Its main blocks are: automated control systems for natural and industrial wastewater, the state register of objects that have a negative impact on the environment, mathematical models for the transfer of pollutants in water bodies, geo-information systems for visualizing geo-data and modeling results. The advantage of the proposed system is the possibility of operational control over the sources of pollution and the regulation of the load from water users in real time.
Coarse-to-Fine Deep Learning of Continuous Pedestrian Orientation Based on Spatial Co-Occurrence Feature

The continuous orientation estimation of a moving pedestrian is a crucial issue in autonomous driving that requires the detection of a pedestrian intending to cross a road. It is still a challenging task owing to several reasons, including the diversity of pedestrian appearances, the subtle pose difference between adjacent orientations, and similar poses with different orientations such as axisymmetric orientations. These problems render the task highly difficult. Recent studies involving convolutional neural networks (CNNs) have attempted to solve these problems. However, their performance is still far from satisfactory for application in intelligent vehicles. In this paper, we propose a CNN-based two-stream network for continuous orientation estimation. The network can learn representations based on the spatial co-occurrence of visual patterns among pedestrians. To boost estimation performance, we applied a coarse-to-fine learning approach that consists of two learning stages. We investigated continuous orientation performance on the TUD Multiview Pedestrian dataset and the KITTI dataset and compared them with the state-of-the-art methods. The results show that our method outperforms other existing methods.
Recognising innovative companies by using a diversified stacked generalisation method for website classification

In this paper, we propose a classification system which is able to decide whether a company is innovative or not, based only on its public website available on the internet. As innovativeness plays a crucial role in the development of myriad branches of the modern economy, an increasing number of entities are expending effort to be innovative. Thus, a new issue has appeared: how can we recognise them? Not only is grasping the idea of innovativeness challenging for humans, but also impossible for any known machine learning algorithm. Therefore, we propose a new indirect technique: a diversified stacked generalisation method, which is based on a combination of a multi-view approach and a genetic algorithm. The proposed approach achieves better performance than all other classification methods which include: (i) models trained on single datasets; or (ii) a simple voting method on these models. Furthermore, in this study, we check if unaligned feature space improves classification results. The proposed solution has been extensively evaluated on real data collected from companies’ websites. The experimental results verify that the proposed method improves the classification quality of websites which might represent innovative companies.
Linear minimum mean square error filtering with stochastic linear equality constraints

Constrained filters, through utilising the prior state constraint information, are designed to obtain more accurate state estimates in applications, and most of them deal with the estimation problem of systems with deterministic constraints. In practice, complex environmental disturbance, incomplete information or uncooperative behaviour often brings out uncertainties of the constraints. This paper tackles the filtering problem of dynamic systems subject to the stochastic linear equality constraints expressed by random weighted basis matrices. The corresponding constrained dynamic model is constructed first and the linear-minimum-mean-square-error filter is derived based on the orthogonality principle. Due to the effect of constraint randomness, the resultant filter encounters the problem of nonlinear stochastic calculation of random parameters, which is solved by the Taylor-based and the UT-based schemes, respectively, and the computational complexity as well as the tractability of both schemes are analysed. Finally, a simulation study on a road-constrained vehicle tracking demonstrates that the proposed filter has better performance than the classical estimation projection method in terms of estimation accuracy and computational complexity.
Optimization of Tilt Angle for Photovoltaic: Case Study Sabang-Indonesia

Abstract Renewable energy sources as alternative energy are needed for electrical power, reliability such as Sabang tourism area. This paper provides the most optimal angle of installation of photovoltaic (PV) to obtain the optimum PV energy with the genetic algorithm method. Daily energy that PV can accept as fitness, the tilt angle (β) as an individual evaluation with a limit of -90o to 90o. The optimal tilt angle in the area of the simulation results is equal to 6o facing south. Comparisons are made with tilt angles of 15, 30, and 60 facing south. The results show that installation with an optimal angle with the genetic algorithm method can increase the energy received by PV by 2%, 9.5%, and 38%.
Post-transitioning user performance on cross-device menu interfaces

Multi-device user-interaction behavior creates a need to design cross-device menus that support users to re-locate menu items efficiently and effectively after the transition from one device to another. We conducted two laboratory experiments to investigate the effects of specific designs of cross-device menus on post-transitioning user performance (selection time and selection error). In Experiment 1, a total of 72 participants moved between 32 desktop and mobile menu interfaces (representing 16 different designs of cross-device menus) to re-locate menu items. We investigated the effect on user performance of the cross-device menu configuration (complementary versus redundant items), menu length (short versus medium), menu item order (consistent versus inconsistent), and menu format (text-only versus text-and-icon items). The results showed significant main effects of configuration, length and item order with large partial eta-squared sizes. Users performed better with redundant menus, short menus, and menus with consistent item order than they did with complementary menus, medium menus, and menus with inconsistent item order. In Experiment 2, 20 participants moved between eight desktop and mobile menu interfaces (representing four different designs of cross-device menus) to re-locate menu items. We focused on the effect on user performance of the menu layout (horizontal-desktop and vertical-mobile versus vertical-desktop and vertical-mobile) at two different lengths (short versus medium). The results showed no significant effect of menu layout, and a significant effect of length with a small partial eta-squared size. Our study provides a foundation to researchers interested in further investigation of cross-device menu designs, and to practitioners in designing cross-device menus that allow more efficient and effective re-locating of menu items when moving between devices.
Note on Sensor Resource Allocations: Higher Rate or Better Measurements?

When tracking maneuvering targets and the need to improve the tracking arises or designing a sensor tracking system, one is often faced with the choice of increasing the measurement accuracy or rate. The answer to this question is found by assessing the impact on error in the filtered state estimates and the one-step predicted state estimates. In this paper, the tracking of maneuvering targets with a nearly constant velocity (NCV)Kalman filter is considered and the maximum mean squared error (MMSE)is utilized to study the impacts of doubling the measurement accuracy or rate. For each measurement case and the maximum acceleration of the maneuvering target, the process noise variances that minimize the MMSE in the filtered position and the one-step predicted position are used to assess the impacts of doubling the measurement accuracy or rate. The analysis shows that doubling the measurement accuracy gives the greater reduction in MMSE in filtered position, while doubling the measurement rate gives the greater reduction in the MMSE in the one-step predicted position. Selection of the process noise variance that minimizes the MMSE in the one-step predicted position estimates is also new in this paper. Monte Carlo simulation results are given for tracking a maneuvering target with an NCV Kalman filter to verify the findings and an Interacting Multiple Model (IMM)estimator tracking a maneuvering target to assess the generally applicability of the findings.
Question Answering on Structured Data using NLIDB Approach

In this paper we present our work in building Natural Language Interface to Database (NLIDB) system using Intermediate query approach. This approach is demonstrated using Movie domain chatbot and can also be extended to different domains. The need of NLIDB System has increased in this fast paced world where more number of users are accessing databases through their Smart phones and web browsers. NLIDB System maps user's Natural Language query to database query allowing user to extract information without any prior experience with databases. Results obtained are very promising and can tackle most of the user queries regarding target database.
SPEED: Accelerating Enclave Applications Via Secure Deduplication

The emerging hardware-assisted security technologies facilitate the deployment of secure and trustworthy applications in today's cloud computing infrastructure. Despite promising, the advantages appear to diminish due to limited resources of trusted execution environments and ever-increasing workload to be processed inside. Different from existing task-specific and system-level optimizations, our key observation is that those redundant computations occur commonly among several applications when handling the same input data. In light of this, we propose SPEED, a secure and generic computation deduplication system in the context of Intel SGX. It allows SGX-enabled applications to identify redundant computations and reuse computation results, while protecting the confidentiality and integrity of code, inputs, and results. To maximize the benefit of computation deduplication, we design a cross-application deduplication scheme, empowering multiple applications to securely utilize the shared results as long as they perform identical computations. To ease the use of SPEED, we implement a fully functional prototype and provide a concise and expressive API for developers to deduplicate rich computations with minimal effort, as few as 2 lines of code per function call. Extensive evaluations of four popular applications demonstrate that SPEED improves performance by up to 400 times. The source code is available on GitHub for public use.