Reviewerly/exHarmony huggingface dataset:
Foraging-Theoretic Tool Composition: An Empirical Study on Vulnerability Discovery

Discovering vulnerabilities is an information-intensive task that requires a developer to locate the defects in the code that have security implications. The task is difficult due to the growing code complexity and some developer's lack of security expertise. Although tools have been created to ease the difficulty, no single one is sufficient. In practice, developers often use a combination of tools to uncover vulnerabilities. Yet, the basis on which different tools are composed is under explored. In this paper, we examine the composition base by taking advantage of the tool design patterns informed by foraging theory. We follow a design science methodology and carry out a three-step empirical study: mapping 34 foraging-theoretic patterns in a specific vulnerability discovery tool, formulating hypotheses about the value and cost of foraging when considering two composition scenarios, and performing a human-subject study to test the hypotheses. Our work offers insights into guiding developers' tool usage in detecting software vulnerabilities.
Combustion Optimization of Pulverized Coal-Fired Boiler in Thermal Power Plant Using Artificial Neural Network and Genetic Algorithm

Paiton’s power plant performs coal switching from medium rank to low rank to compete compared to power plants with new technology. It has an impact on decreasing efficiency due to delayed combustion. Optimizing the boiler efficiency and combustion was necessary to reduce production costs and make the plant greener. Three steps were required to optimize the Boiler operating parameters. The first step was problem formulation, consisting of objective functions, problem boundaries or constraints, and optimized variables determination. The second step was a valid model representing the characteristics of a Boiler installed in Paiton’s power plant 2x400MW. Moreover, the final step was the optimization technique suitable for the optimization problem solved. The objective function of the paper is to provide a model and optimization of fuel and air mixing by manipulating relevant fuel and air injection points to improve PC-boiler fuel efficiency. The model was developed using Artificial Neural Network (ANN), and the Genetic Algorithm (GA) was selected as the stochastic optimization algorithm to solve the problem. The prediction of boiler efficiency shows a good agreement with the plant data, with a Root Mean Squared Error of 0.127. The results indicate that the use of data can help generalize the ANN models. From the stochastic optimization (GA) simulation results, the potential reduction of coal consumption was around 33668 kg/hours with manipulating other operating parameters and increasing the efficiency up to 90.14%.
Foundations For Temporal Reasoning Using Lower Previsions Without A Possibility Space

We introduce a new formal mathematical framework for probability theory, taking random quantities to be the fundamental objects of interest, without reference to a possibility space, in spirit of de Finetti’s treatment of probability, Goldstein’s Bayes linear analysis, and Williams’s treatment of lower and upper previsions. The aim of our framework is to formalize temporal reasoning, where we treat future beliefs as random quantities themselves. We do this by taking random quantities to form a linear space of expressions, which we endow with structure through a linear projection operator. We then use a version of the temporal sure preference principle as a basis for inference over time. We formulate the principle in terms of desirability, and explore its implications for lower previsions. We derive an explicit expression for the natural extension of a lower prevision under the temporal sure preference principle. We establish consistency of the temporal sure preference principle with any given collection of assessments. We also derive various bounds on the natural extension. Finally, we show how we can recover standard Bayes linear calculus from our framework.
Multimode Free Space Optical Link Enabled by SiP Integrated Meshes

A silicon photonic mesh of tuneable Mach-Zehnder Interferometers (MZIs) is employed to receive two spatially-overlapped Hermite-Gaussian beams modulated at 10 Gbit/s, sharing the same wavelength and state of polarization. The mesh automatically self-configures, separating and sorting the two beams out without any excess loss.
Secure Sharing of Text Based Data Using Hybrid Encryption Algorithms in a Client-Server Model

The current internet ecosystem has a variety of applications growing at a very fast pace. There exists a requirement to secure these applications to preserve the privacy of the users that employ its usage. This paper proposes a system to secure text-based data sharing between users using a hybrid encryption methodology. On the other hand, encryption algorithms consume a significant amount of time for computation. This paper provides an evaluation of 6 different algorithmic combinations: AES+RSA, DES+RSA, 3DES+RSA, RC4+RSA, Blowfish+RSA and Rabbit+RSA. A comparison study has been conducted for the different combinations by using files with different file sizes to determine which algorithmic combination is best for the proposed system. The simulation results have been published to demonstrate the effectiveness of the algorithmic combinations and suitability for the proposed system.
Multi-UAVs Cooperative Target Tracking

Compared with single UAV, multiple UAVs collaborated in target tracking is much more robust and have better performance in tracking. The key of multiple UAVs collaborated in target tracking is ground target prediction and track planning. As for target prediction, this paper gives a method of rolling forecast based on gray Verhulst model, which could update the history data continuously and predict the value of the forecast gradually, make it to become more precise and cost less time. As for track planning, firstly, this paper let the predicted position as the next target position, then allocate the targets and generate the track. Hungary algorithm allocate the best position to every UAVs, CAPT (Concurrent Assignment and Planning of Trajectories) algorithm could give the safest trail to the target, ensuring the security and efficiency of reforming the UAVs formation. As for flight control, this paper used PX4, for it is reliable and classic. Besides, this paper also builds a simulation environment in Gazebo-ROS, simulating six drones simultaneously, which verified the reliability of the algorithms and could reform the formation of the UAVs with higher timeliness.
On Generating a Probability Distribution Amenable to NISQ Using Modifications to Grover’s Algorithm

An efficient generation of certain form of probability distributions is considered using both quantum and classical computing through suitable modifications to Grovers Algorithm. The suggested combination of the Oracle and the low-depth Local Diffuser is applied only once per circuit (irrespective of the problem size), followed by measurements for a number of shots and classical post-processing. This can facilitate Noisy Intermediate Scale Quantum (NISQ) implementation.
An Analytical Framework for Video Quality and Excess Data Distribution in Multiple-Quality Video Under Dynamic Channel Conditions

We present an analytical framework to obtain the distribution of frozen, low quality, and high quality video in a setting with two video quality levels where the channel is dynamic and the data rate the user can achieve varies with time. The presented model, which is based on multi-regime Markov fluid queues, is also capable of producing the distribution of the excess data present in the playout buffer at the end of the video session duration, which will be wasted. The playout control is assumed to be hysteretic, and the effects of the values of thresholds selected for starting playout, switching to low/high quality levels, and pausing/resuming download on the distribution of video quality and excess data is investigated. The presented model can be extended to quality levels more than two.
25 Years on: progress in computer-based learning

1996 saw several papers addressing and informing readers about developments in computer-based learning and their effective use in teaching also the impact of technological developments on services and personnel.
Effects of User’s Gaze on the Unintended Positional Drift in Walk-in-Place

Walk-In-Place (WIP) is a technique in which users perform walking or jogging-like movements in a stationary place to move around in virtual environments (VEs). However, unintended positional drift (UPD) while performing WIP often occurs, thus weakening its benefits of keeping users in a fixed position in a physical space. In this paper, we present our preliminary study exploring whether users’ gaze while WIP affects the direction of the UPD. Participants of the study jogged in a VE five times. Each time, we manipulated their gaze direction by displaying visual information in 5 different locations in their view. Although a correlation between the gaze and UPD direction was not found, we report the results from this study, including the amount of observed drift and preferred location of visual information, and discuss future research directions.
Mitigation of Insulator Ultraviolet Emission Measurement Errors Using Facular Area

The use of an ultraviolet (UV) imaging camera is very useful in evaluating insulator conditions since it can detect pre-breakdown phenomena and inform their location. However, the accuracy of UV count measurement, as a quantitative indicator that can be read directly on the camera screen, is greatly influenced by how the operator conducts the data recording on the observed insulator. This paper reported the presence of human error in UV count measurement. Furthermore, the facular area was applied to be compared with the UV count. The discharge phenomena were generated through various experiments. The results showed that the application of facular area was able to evaluate the insulator condition more accurately if it was compared to the UV count when the data recording was conducted incorrectly. In this case, there was no doubt when the quantitative results were related to the qualitative results.
Automatic Defect Review of a Patterned Wafer using Hybrid Metrology

The scaling trend toward under 5nm in semiconductor design requires tighter control over defects on wafers. To measure defects on wafer surfaces, several kinds of inspection and review tools can be employed—i.e., Surface Scanning Inspection Systems (SSIS), Scanning Electron Microscopy (SEM), or Transmission Electron Microscopy (TEM). Among the options, Atomic Force Microscopy (AFM) has proven particularly advantageous because it directly measures the dimensions (height and width) and physical properties of defects of interest and gives reference information for other characterization tools. At the same time, since the AFM measurement speed is relatively slow, it is unrealistic to find all defects on the wafer surface by using AFM alone. Therefore, we have introduced the hybrid metrological instrument by combining White Light Interferometer (WLI) to an industrial automated AFM platform. By utilizing the vibration-isolated platform and the low noise z scanner of AFM, the performance of WLI has significantly been improved, achieving unprecedented high z resolution. We introduce a method to identify the differences between wafers to locate the hot spots quickly and investigate the problems, thereby measuring many minor defects on the patterned wafer with higher throughput. The hybridization of WLI and AFM provides us new possibilities in semiconductor and other industrial applications.
Research on Fast Forensic Analysis Method of Fraud cases Based on Social Software

With the rapid development of network technology, people's social methods have changed a lot, while online fraud has also emerged. The backup tool for entire APP data or entire mobile phone data are usually used to extract the fraud data in victim's social software in the past. However, the methods are inefficient in extracting evidence and cannot guarantee the privacy of the victims. Therefore, this article proposes a method for rapid forensic analysis of fraud information based on social software. This method focuses on overcoming the difficulties of data acquisition, mining useful information, and data analysis, which can extract data efficiently, and guarantee the victims' personal privacy. At the same time, social software communication protocol, decryption of encrypted files, and data analysis methods were researched. On the basis of the above research, a method and system for quickly extracting fraud information based on social software was formed. It is concluded that this method has achieved a substantial efficiency improvement compared with the commonly used mobile phone backup methods, meanwhile, Apriori algorithm was used to conduct case association analysis and trajectory analysis through experiments. This method is highly efficient and reliable in actual case investigation and evidence collection.
Multi-objective evolutionary based feature selection supported by distributed multi-label classification and deep learning on image/video data

We live in an era in which a myriad of computer systems produce immense amounts of (raw) data every day. This big data must be processed efficiently to gain valuable and hidden knowledge. Complex processing pipelines need to be designed for filtering out irrelevant data, also for efficient data mining and machine learning methods must be used to discover useful correlations in the big data. The purpose of this PhD research is the implementation of multi-objective evolutionary-based dimensionality reduction on a high volume of image/video data with the support of distributed multi-label classification algorithms.
Experiments on Portuguese Clinical Question Answering

Question answering (QA) systems aim to answer human questions made in natural language. This type of functionality can be very useful in the most diverse application domains, such as the biomedical and clinical. Considering the clinical context, where we have a growing volume of information stored in electronic health records, answering questions about the patient status can improve the decision-making and optimize the patient care. In this work, we carried out the first experiments to develop a QA model for clinical texts in Portuguese. To overcome the lack of corpora for the required language and context, we used a transfer learning approach supported by pre-trained attention-based models from the Transformers library. We fine-tuned the BioBERTpt model with a translated version of the SQuAD dataset. The evaluation showed promising results when evaluated in different clinical scenarios, even without the application of a clinical QA corpus to support a training process. The developed model is publicly available to the scientific community.
Machine Learning for Demand Prediction of Seasonal Second-hand Fashion Items Based on Prior and Fine-tuning Prediction Models

With the rapid development of information technology in recent years, it has become common for consumers to purchase various products via electric commerce (EC) sites. As a case study, this study focuses on ZOZOUSED, which is engaged in the business of buying used clothes from users, and reselling them as second-hand goods. From the perspective of inventory and management costs, it is desirable for items to be sold as soon as possible after they are listed on EC sites, and the number of listed items has been conventionally controlled, depending on the experience of item managers. However, owing to the subjective assessment of item managers, unnecessary price reductions for sales promotion of items, or opportunity losses triggered by an excessive number of listed items, may occur. For reasonable item management, the demand prediction for items by customers is a crucial task required to develop the optimal listing plan that balances supply and demand. Therefore, this study proposes a forecasting method of sales figures for the actual operation of listing second-hand goods, which comprises two-stage models: the first model is a prior seasonal long-term prediction of sales figures for each item group based on seasonal similarity, and the second model is a short-term fine-tuning prediction for daily operation via residual predictions with recent data. Furthermore, we apply the proposed model to the actual data of past sales figures accumulated in ZOZOUSED, and analyze the obtained results to demonstrate the usefulness of the proposed method. In addition, we empirically demonstrate the effectiveness of the proposed method by designing and performing an empirical experiment on an actual business by applying the output of the proposed method as a new index for determining the number of new items to be listed.
New hybrid signature schemes with increasing level of resistance

Abstract Post-quantum cryptography is becoming an increasingly popular topic for research around the world. The global cryptographic community is on the verge of standardizing new post-quantum algorithms. The world’s largest organizations conduct their own research in this direction. In this article, two hybrid schemes are proposed. They are constructed on generalized methods of increasing resistance of authentication schemes. Hybrid schemes consist of a combination of two independent signature schemes, one of which is the well-known classical asymmetric electronic signature scheme and another one is post-quantum scheme. Thus, this paper suggests the combining Crystals-Dilithium scheme with Rabin scheme and Elgamal scheme respectively. The paper also provides estimates of public key and signature lengths. Conclusions are drawn about the expediency of using generalized methods of combining with such kind of schemes.
An Innovative Marketing Strategy: -Adopting AR &amp; VR in Jewelry Sales

Jewelry industry, as a conventional industry, urgently needs an innovative transformation by combining itself with big data so as to advance with the time and to gradually realize industrial informatisation. Namely, the industry needs new technological means besides traditional ones to achieve industrial transformation and upgrading. A new jewelry sales mode is needed to strengthen experiential consumption. Adopting technology such as augmented reality (AR) and virtual reality (VR) in jewelry sales seems a good way to improve consumers' experience and meets their needs. In this way, consumers are capable of trying on jewelry at home with virtual try-on system, which overturns the traditional jewelry sales mode. Consumers can easily, conveniently and quickly try on any jewelry they like, and fully enjoy the virtual experience in the experiential consumption. In addition, the platform will deeply analyze consumers' purchasing habits and preferences with big data, and feed the results back in real time so that jewelry companies can timely adjust sales direction and their marketing strategies. The article will briefly explain the definition of AR & VR technology, present some examples of adopting AR & VR in jewelry sales, and how the technologies boost the industrial transformation and upgrading.
Multi-threaded System Design of A Multi-Precision Deep Learning Accelerator on FPGA with Optimized Memory Usage

A complete deep learning acceleration system usually consists of bus-connected micro-controllers, hardware accelerators, and external memory in order to support all the operations in various deep neural network (DNN) applications. In this paper, we present a hardware/software integration flow that facilitates the FPGA deployment of a DNN hardware accelerator which supports different bitwidths of activation data and weights. The complete DNN acceleration system mapped on a Linux-based System-on-Chip (SoC) FPGA with customized DNN instruction set includes multi-threading for streaming processing, optimized DRAM allocation for small external memory footprint, a reshape unit for efficient tile-based execution. Implementation results for tiny YOLOv3 object detection show that the proposed DNN system achieves peak performance or 120 GOP/s on Xilinx Zynq UltraScale + XCZU9EG.
Painting Image Retrieval Method Based on Color and Texture Features

In the paint industry, querying a certain texture image is usually done by employees visually with their personal experience or with the help of a common image retrieval system, which cannot meet the needs of paint companies to query images accurately. In order to improve the accuracy of retrieval, an image retrieval algorithm is proposed for paint images with a wide variety of colors and complex texture information. For color features, a color autocorrelogram is selected; for texture features, a direction-improved uniform local binary pattern that considers the comparison of gray values between neighboring pixels is proposed to enhance texture directional feature recognition. The color and texture features are fused as feature descriptors to retrieve 216 insulated decorative integrated panel images. The experimental results show that the fused features are more suitable for describing particular paint images and have a higher average finding accuracy than other descriptive feature algorithms.
Innovative Application of Genetic Algorithms in the Computer Games

The traditional methods to solve the computer games problem are to use various search algorithms on the game search tree and to combine the situation evaluation to generate the corresponding methods. The paper introduces the innovative genetic algorithms to the computer games based on the traditional methods. Through the operations for the search tree such as selection, crossover and mutation, the better search tree can be gotten from the better solutions. At the same time, the application of the evaluation functions can produce the best steps in the current situation. The advanced and modified genetic algorithms are proved to be practical and applicative by experimentations and tests of computer games.
One-component quantum mechanics and dynamical leakage-free paths

Abstract We derive an exact one-component equation of motion for the probability amplitude of a target time-dependent state, and use the equation to reformulate quantum dynamics and control for both closed and open systems. Using the one-component equation, we show that an unexpected time-dependent leakage-free path can be induced and we capture a necessary quantity in determining the effect of decoherence suppression. Our control protocol based on the nonperturbative leakage elimination operator provides a unified perspective connecting some subtle, popular, and important concepts of quantum control, such as dynamical decoupling, quantum Zeno effect, and adiabatic passage. The resultant one-component equation will promise significant advantages in both quantum dynamics and control.
Characterizing errors of integrated optical phase-shifting interferometer by P2VM method

Integrated optical phase-shifting interferometer is one of key devices that are frequently employed in astronomical interference instruments to measure complex coherent function of celestial object at certain spatial frequency, and further to achieve high-resolution imaging by synthetic aperture technology in last decades. Due to the presence of device fabrication error, calibration of an interferometer is indispensable, Pixel-to-Visibility Matrix (P2VM) method being a conventional approach. Up to present days, the P2VM is employed as a “all-in-one” method, which unify all device errors into one single matrix. However, characterization of device fabrication error at its various parts is often desired, to provide the basis for device optimization. In the current paper, a process to retrieve fabrication errors of the three main parts of the phase-shift interferometer, from an experimentally measured Pixel-to-Visibility Matrix (V2PM) is demonstrated. The results are expected to be useful in the process of optimizing the device structure and its manufacturing parameters.