Reviewerly/exHarmony huggingface dataset:
Building a Role Specified Open-Domain Dialogue System Leveraging Large-Scale Language Models

Recent open-domain dialogue models have brought numerous breakthroughs.However, building a chat system is not scalable since it often requires a considerable volume of human-human dialogue data, especially when enforcing features such as persona, style, or safety.In this work, we study the challenge of imposing roles on open-domain dialogue systems, with the goal of making the systems maintain consistent roles while conversing naturally with humans.To accomplish this, the system must satisfy a role specification that includes certain conditions on the stated features as well as a system policy on whether or not certain types of utterances are allowed.For this, we propose an efficient data collection framework leveraging in-context few-shot learning of large-scale language models for building role-satisfying dialogue dataset from scratch.We then compare various architectures for open-domain dialogue systems in terms of meeting role specifications while maintaining conversational abilities.Automatic and human evaluations show that our models return few out-of-bounds utterances, keeping competitive performance on general metrics.We release a Korean dialogue dataset we built for further research 1 .
An automatic fault detection method of freight train images based on BD-YOLO

The automatic fault detection of freight train image is difficult to locate, and its average detection accuracy is low.In this paper, an object detection model Bidirectional You Only Look Once (BD-YOLO), is proposed.The process of BD-YOLO is divided into four steps.First, the feature extraction network extracts and separates image features.Second, multiscale features are fused to aggregate features of different scales.Third, prediction across scale modules is used to forecast the feature layers obtained after aggregation.Fourth, the final prediction result is obtained by decoding the prediction feature layer.The BD-YOLO model is trained using the mosaic data enhancement method and K-means clustering algorithm to improve detection accuracy and speed.Experimental results show that the mean average precision of BD-YOLO model is improved by 17.57%, on average, compared with the state-of-the-art object detection models on four types of data sets.The BD-YOLO model can immediately detect three typical faults, namely, movement of upper lever, offset of locking plate, and closing of truncated plug door handle.It has high detection rate, low false detection rate, and good robustness.
Computing Optimal Repairs of Quantified ABoxes w.r.t. Static EL TBoxes (Extended Version)

The application of automated reasoning approaches to Description Logic (DL) ontologies may produce certain consequences that either are deemed to be wrong or should be hidden for privacy reasons. The question is then how to repair the ontology such that the unwanted consequences can no longer be deduced. An optimal repair is one where the least amount of other consequences is removed. Most of the previous approaches to ontology repair are of a syntactic nature in that they remove or weaken the axioms explicitly present in the ontology, and thus cannot achieve semantic optimality. In previous work, we have addressed the problem of computing optimal repairs of (quantified) ABoxes, where the unwanted consequences are described by concept assertions of the lightweight DL EL. In the present paper, we improve on the results achieved so far in two ways. First, we allow for the presence of terminological knowledge in the form of an EL TBox. This TBox is assumed to be static in the sense that it cannot be changed in the repair process. Second, the construction of optimal repairs described in our previous work is best case exponential. We introduce an optimized construction that is exponential only in the worst case. First experimental results indicate that this reduces the size of the computed optimal repairs considerably.
Composable security for inter-satellite continuous-variable quantum key distribution in the terahertz band

Continuous-variable quantum key distribution (CVQKD) can be effectively compatible with off-the-shelf communication systems and has been proven to be the security against collective attacks in the finite-size regime and composability. In this paper, we classify three different trust levels for the loss and noise experienced by the sender and receiver. Based on these trust levels, we derive the composable finite-size security bounds of inter-satellite CVQKD in the terahertz (THz) band. We also show how these trust levels can nontrivially increase the composable secret key rates of THz-CVQKD and tolerate higher loss. Furthermore, the numerical simulations strongly support the feasibility of inter-satellite THz-CVQKD even in the worst trust level. This work provides an efficient path for building an inter-satellite quantum communication network.
Contrastive Spatio-Temporal Pretext Learning for Self-Supervised Video Representation

Spatio-temporal representation learning is critical for video self-supervised representation. Recent approaches mainly use contrastive learning and pretext tasks. However, these approaches learn representation by discriminating sampled instances via feature similarity in the latent space while ignoring the intermediate state of the learned representations, which limits the overall performance. In this work, taking into account the degree of similarity of sampled instances as the intermediate state, we propose a novel pretext task - spatio-temporal overlap rate (STOR) prediction. It stems from the observation that humans are capable of discriminating the overlap rates of videos in space and time. This task encourages the model to discriminate the STOR of two generated samples to learn the representations. Moreover, we employ a joint optimization combining pretext tasks with contrastive learning to further enhance the spatio-temporal representation learning. We also study the mutual influence of each component in the proposed scheme. Extensive experiments demonstrate that our proposed STOR task can favor both contrastive learning and pretext tasks and the joint optimization scheme can significantly improve the spatio-temporal representation in video understanding. The code is available at https://github.com/Katou2/CSTP.
Calibration method for an extended depth-of-field microscopic structured light system

This paper presents a calibration method for a microscopic structured light system with an extended depth of field (DOF). We first employed the focal sweep technique to achieve large enough depth measurement range, and then developed a computational framework to alleviate the impact of phase errors caused by the standard off-the-shelf calibration target (black circles with a white background). Specifically, we developed a polynomial interpolation algorithm to correct phase errors near the black circles to obtain more accurate phase maps for projector feature points determination. Experimental results indicate that the proposed method can achieve a measurement accuracy of approximately 1.0 μm for a measurement volume of approximately 2,500 μm (W) × 2,000 μm (H) × 500 μm (D).
On the Security of an ECC-Based Authentication Scheme for Internet of Drones

We demonstrate that the ECC-based authentication scheme for internet of drones (short for IoD-AS, published in <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IEEE Systems Journal</small> , Hussain et al., 2021) Amassing the security: An ECC-based authentication scheme for Internet of Drones) fails to prevent an adversary from launching drone impersonation attacks. We also demonstrate that an adversary can compromise the session key of a target user and her/his employed drone.
Gbit/s secure key generation and distribution based on the phase noise of an amplified spontaneous emission source

In this paper, a secure key generation and distribution scheme based on the phase noise of an amplified spontaneous emission source is proposed and experimentally verified. A giant key generator that contains two distribution arms is used to generate a specific beat dependent on the path length difference of the arms. Through the method of balanced subtraction of the local signals, the effect of the intensity noise has been mainly excluded, and the two legal users obtain the consistent differential signal that depended on the phase noise. Experiment results show that the correlation coefficient of the key signals reaches to about 0.89, and the bit generation rate of the scheme achieves to 3.06 Gbit/s under a length of 20 km standard single-mode optical fiber route, as the bit error rate stays under 0.02%. Moreover, the converted digital key stream has passed the NIST statistical test suite, which means that the scheme is inherently random in the statistical sense. With the excellent performance mentioned above, the proposed scheme provides a simple and efficient solution for the method of one-time pad.
Optimal ABox Repair w.r.t. Static EL TBoxes: from Quantified ABoxes back to ABoxes (Extended Version)

Errors in Description Logic (DL) ontologies are often detected when a reasoner computes unwanted consequences. The question is then how to repair the ontology such that the unwanted consequences no longer follow, but as many of the other consequences as possible are preserved. The problem of computing such optimal repairs was addressed in our previous work in the setting where the data (expressed by an ABox) may contain errors, but the schema (expressed by an EL TBox) is assumed to be correct. Actually, we consider a generalization of ABoxes called quantified ABoxes (qABoxes) both as input for and as result of the repair process. Using qABoxes for repair allows us to retain more information, but the disadvantage is that standard DL systems do not accept qABoxes as input. This raises the question, investigated in the present paper, whether and how one can obtain optimal repairs if one restricts the output of the repair process to being ABoxes. In general, such optimal ABox repairs need not exist. Our main contribution is that we show how to decide the existence of optimal ABox repairs in exponential time, and how to compute all such repairs in case they exist.
Neuromorphic photonic circuit modeling in Verilog-A

One of the significant challenges in neuromorphic photonic architectures is the lack of good tools to simulate large-scale photonic integrated circuits. It is crucial to perform simulations on a single platform to capture the circuit’s behavior in the presence of both optical and electrical components. Here, we adopted a Verilog-A based approach to model neuromorphic photonic circuits by considering both the electrical and optical properties. Verilog-A models for the primary optical devices, such as lasers, couplers, waveguides, phase shifters, and photodetectors, are discussed, along with studying the composite devices such as microring resonators. Model parameters for different optical devices are extracted and tuned by analyzing the measured data. The simulated and experimental results are also compared for validation of Verilog-A models. Finally, a single photonic neuron circuit is simulated by implementing input, weight, and non-linear activation function by using lasers, microring resonators, and modulator, respectively. Electro-optical rapid co-simulation would significantly improve the efficiency of optimizing the devices and provide an accurate simulation of the circuit performance.
Comparative study of Decision Tree, Random Forest and XGBoost performance in forecasting the power output of a photovoltaic system

In this paper, we make a comparative study of the performance of three methods for predicting the power output of a photovoltaic installation: Decision Tree, Random Forest and XGBoost. We performed these predictions in Python using as input meteorological data such as wind speed, sun position, temperature, direct irradiation, diffuse irradiation and reflected irradiation and as output data the power output of a 1000Wp panel. These data were downloaded from the PVGIS database for the city of Natitingou (Benin) and for a period of 12 years (from January 1st 2005 to December 31st 2016). We obtained as Mean Square Errors 2.195026, 3.058383 and 5.544319 respectively for the XGBoost, Random Forest and Decision Tree and for Regression Values 0.9999999194, 0.9999797366 and 0.9997013968 respectively for the XGBoost, Random Forest and Decision Tree. We conclude that all three models are effective for the forecasting task performed but that the XGBoost is the best performing model with Mean Square Error and Regression Value of 2.195026 and 0.9999999194 respectively.
Automatic Attendance System using Face Recognition Technique

Attendance system is very important in schools and colleges’ Manual attendance system has many difficulties like it may less accurate and critical to maintain. So, attendance system using face recognition technique increase the accuracy and also it required less time than other methods. There are many existing system for attendance such as face recognition using IoT, PIR sensors and so on. For face recognition, hardware devices also helpful. But challenge is that to maintain all the sensors properly without get damage. After studying all method and techniques we are trying to implement a system with Haar Cascade Algorithm which has highest accuracy among all. It is able to capture the images from 50-70cm. We are creating graphical user interface which capture the images, create the dataset and train the dataset on single click. After recognizing the face it will display name of student and roll number. That information stored in attendance sheet automatically with time and date.
A survey on partitional clustering using single-objective metaheuristic approach

Clustering is one of the important functions of data mining, which is used to analyse a large amount of data. It groups these set of data according to some similarity property such that data within the cluster are similar to each other and data between the clusters are dissimilar to each other. To obtain an optimal clustering result with the help of an optimisation algorithm is an emerging trend in data mining. The partitional clustering is one of the popularly used types of clustering algorithm. These algorithms often land in local optimum and number of clusters needs to be predefined. To encounter the above problem, optimisation algorithms such as metaheuristic algorithms are used as a suitable problem-solving paradigm. This paper presents an overview of single-objective metaheuristic algorithms used for partitional clustering problem and their applications. This paper even presents the research issues which can be dealt with in future.
Collimation testing of laser beams having different diameters using compact holographic lateral shearing interferometer

A quick and accurate way for testing collimation of laser beams is desired to achieve correct results in many optical instruments. Laser beam collimation is generally performed using glass wedge plates. For better accuracy, a set of wedge plates of different thicknesses and wedge angles are required to acquire first-hand visual information about collimation of laser beams having a wide range of diameters. Use of a compact holographic lateral shearing interferometer (HLSI) is demonstrated for collimation testing of laser beams having different beam diameters. The interferometer uses two holographic optical elements (HOEs) with provision of variable tilt in one of the HOEs to maintain a constant number of fringes in the interferograms for different diameters of input test beams. An angle of rotation of interference fringes from a reference position is measured to be 0.9 deg for a test beam of diameter 2 mm and 16 deg for the test beam of diameter 35 mm. We experimentally demonstrated range of optimum number of interference fringes in the interferogram for visual inspection of the collimation state of the test beam. Thus the proposed interferometer could replace a set of wedge plates generally required for collimation testing of beams having different diameters. The functioning of the interferometer for collimation testing of beams having different diameters is discussed and experimentally demonstrated.
A Differential Privacy Based (k-Ψ)-Anonymity Method for Trajectory Data Publishing

In recent years, mobile Internet technology and location based services have wide application. Application providers and users have accumulated huge amount of trajectory data. While publishing and analyzing user trajectory data have brought great convenience for people, the disclosure risks of user privacy caused by the trajectory data publishing are also becoming more and more prominent. Traditional k-anonymous trajectory data publishing technologies cannot effectively protect user privacy against attackers with strong background knowledge. For privacy preserving trajectory data publishing, we propose a differential privacy based (k-Ψ)-anonymity method to defend against re-identification and probabilistic inference attack. The proposed method is divided into two phases: in the first phase, a dummy-based (k-Ψ)-anonymous trajectory data publishing algorithm is given, which improves (k-δ)-anonymity by considering changes of threshold δ on different road segments and constructing an adaptive threshold set Ψ that takes into account road network information. In the second phase, Laplace noise regarding distance of anonymous locations under differential privacy is used for trajectory perturbation of the anonymous trajectory dataset outputted by the first phase. Experiments on real road network dataset are performed and the results show that the proposed method improves the trajectory indistinguishability and achieves good data utility in condition of preserving user privacy.
Bitcoin Script

In this chapter we will learn about the core element of Bitcoin, the language called Script. Script is an embedded programming language that runs inside every Bitcoin node and is responsible for processing transactions. Unlike most other programming languages, it wasn’t designed upfront with formalized grammar and syntax. Instead of using a proper notation technique like Backus-Naur to describe its syntax, Script was hard-coded in the very first version of Bitcoin.
SPLAT: Spherical Localization and Tracking in Large Spaces

When implementing an Augmented Reality (AR) interface, it is essential to track camera motion in order to precisely register the virtual overlay in the view of the user. However, unlike most indoor AR scenarios, in many outdoor scenarios the user maintains a static position performing mostly rotational movements. Simultaneous Localization and Mapping (SLAM) methods typically used to solve the tracking problem require significant translational camera motion to perform reliably. The magnitude of the required translation is proportional to the size of the scene, exacerbating this problem in large environments such as open places or stadiums. In this paper, we present an alternative SLAM method, which combines spherical Structure-from-Motion and a robust 3D tracking method. We compare our method to ORB SLAM2 in synthetic and real tests, and show that our method can track more reliably in large spaces, with simpler calculation due to the spherical motion constraint. We discuss this issue in the context of implementing an AR interface for live sport events in stadiums or other open environments, but possible application scenarios for our technique go beyond and can be applied to handheld AR in many outdoor environments.
Employee Commitment on Organizational Performance

Employee commitment plays the major part in the growth of the organization performance. Many organizations want to increase their performance. The employee commitment will boost the employee’s performance in the organization. The behavior of commitment changes can be occurred in the job satisfaction, motivation and job involvement. The data was collected by 100 employees are working in machine tools manufacturing industry in Vellore, Tamil Nadu, India. The consisting of items in Questionnaire are adopted from the previous researches was used in primary data collection. The correlations method by SPSS was conducted. In this article found that employees commitment have positive related in organizational performance. The employee commitment have positively related in the job satisfaction, motivation and job involvement. The organization needs to give more important to increase the employee’s commitment for high level performance.
Enhanced Whale Optimization Algorithm and Wavelet Transform for Image Steganography

In the interactive environment, information security is considered as the main issue with the development of information technology.Here, there is no protection for the messages transmitted to and from the receiver.A method called image steganography is used, which assures security to the concealed communication and protection of the information.In some of the receiver images, image steganography conceals the secret message and transmits the secret message so that the message is noticeable only to the transmitter and the receiver.Hence, this paper presents an algorithm for image steganography by exploiting sparse representation, and a method called Enhanced Whale Optimization Algorithm (WOA) in order to effectual selection of the pixels in order to embed the secret audio signal in the image.Enhanced WOA based pixel chosen process exploits a fitness function that is on the basis of the cost function.In order to evaluate the fitness, cost function computes the entropy, edge, and pixel intensity.Experimentation has been performed and a comparison of the proposed algorithm with the conventional algorithms regarding the PSNR and MSE.Moreover, it decides the proposed Enhanced WOA, as an effectual algorithm.
Machine Learning Approaches Used For Prediction In Diverse Fields

Although machine learning has long provided a powerful approach to prediction, its applicability has been somewhat emerging right now because of the large requirements in the various field. In recent years a number of new predictions with greatly reduced algorithm requirements have been developed. The purpose of this paper is to survey the various techniques that using now a days. The approaches of machine learning and the algorithms are included in this review. Several applications of the new techniques are discussed.
Study on data sparsity in social network-based recommender system

With the development of information technology and the expanding of information resources, it is more difficult for people to get the information that they are really interested in, which is so-called information overload. Recommender systems are regarded as an important approach to deal with information overload, because it can predict users' preferences according to users' records. Matrix factorisation is very successful in recommender systems, but it faces the terrible problem of data sparsity. In this paper, the authors deal with the sparsity problem from the perspective of adding more kinds of information from social networks, such as friendships and tags into the recommending model in order to alleviate the sparsity problem. The paper also validates the impacts of users' friendships, tags and neighbours of items on reducing the sparseness of the data and improving the accuracy of recommending by the experiments using the dataset from real life.