Reviewerly/exHarmony huggingface dataset:
Motivation and Design of the Conversational Components of DraftAgent for Human-Agent Negotiation

In sync with the significant interest in industry and the general populace about future potential of human-AI collaboration [14], academic researchers have been pushing the frontier of new modalities of peer-level and ad-hoc human agent collaboration [4,15]. We have been particularly interested in research on agents representing human users in negotiating deals with other human and autonomous agents [6,11,13]. We present the design motivation and key components of the conversational aspect of our agent entry into the Human-Agent League(HAL) (http://web.tuat.ac.jp/~katfuji/ANAC2020/cfp/ham_cfp.pdf )of the 2020 Automated Negotiation Agent Competition (ANAC). We explore how language can be used to promote human-agent collaboration even in the domain of a competitive negotiation. We present small scale in-lab testing to demonstrate the potential of our approach.
Blockchain Applications in Power Industry

Blockchain is a transparent, secure and decentralized transaction and data management technology, which has been utilized in wide range of industries including banking, insurance healthcare and energy. This technology offers benefits such as decentralization, sustainability, reliability, transparency, interoperability and elimination of intermediaries for the power industry. In addition blockchain can provide solutions for peer-to-peer transactions in retail and wholesale power market. This technology facilitates interactions among small generators in micro-grids, prosumers and consumers using tokens and cryptocurrencies. In this paper, principles and concepts of blockchain are overviewed and a taxonomy of its applications in power industry is proposed. In addition, for each application category, number of notable projects/products are introduced.
PPE Circuits: Formal Definition to Software Automation

Pairing-based cryptography is widely used for its efficiency and functionality. When designing pairing-based schemes, one common task is to devise algorithms for verifying a set of untrusted group elements with respect to a set of trusted group elements. One might be searching for a verification algorithm for a signature scheme or a method for verifying an IBE/ABE private key with respect to the IBE/ABE public parameters. In ACM CCS 2019 Hohenberger Vusirikala, the AutoPPE software tool was introduced for automatically generating a set of pairing product equations (PPEs) that can verify the correctness of a set of pairing group elements with respect to a set of trusted group elements. This task is non-trivial. Some schemes (e.g., those based on dual system encryption) provably do not support any efficient algorithm for verifying the private keys with respect to the public parameters. Other schemes (e.g., the Boyen-Waters anonymous IBE) were left in a gray area by Hohenberger-Vusirikala (CCS 19) -- no conjunction of PPEs was known for testing them, but no proof of untestability either.
USING THE CUBORO CONSTRUCTOR IN TEACHING YOUNGER PUPILS IN MODELING AT INFORMATICS LESSONS

The article describes the possibilities of using the Cuboro constructor in the process of forming the concepts of "model" and "modeling" at informatics lessons in primary school. Definitions of concepts, classification of models on various bases are given. A system of practical design exercises to familiarize younger pupils with full-scale (subject) and information models is presented. The proposed system of exercises is built on the principle "from simple to complex": working with ready-made models (assembling a structure according to a sample, image, diagram, table, technical drawing; defining a cube by verbal description, by drawing, by a conventional image; copying a conventional image of a cube and etc.), partially constructed models (performing tasks with redundant or missing data, tasks for finding and eliminating errors; transforming a structure according to specified conditions; searching for errors in the image of cubes; addition, transformation of a graphic model), independent construction of models (creating individually or in groups of functional structures; construction of verbal and sign models in the form of a verbal description; execution of technical drawings without a sample as a result of mental construction; execution according to the scheme of a virtual structure in a computer or mobile application without using real cubes, etc.).
Label Propagation from camera image to point cloud BEV

Road detection based on deep learning methods is an important task in the field of unmanned driving using 3D Lidar data. The deep learning (DL) methods need enough data to train DL model. In KITTI dataset, it provides enough 3d lidar and camera image data for road detection, but it only made manual annotations in camera images. In this paper, we introduce a new label propagation algorithm for road region label from camera image to 3D lidar data presented as point cloud bird's eye view (BEV). Firstly, we introduce how to convert 3D lidar data into point cloud BEV; then we project the point cloud onto the camera image with road label to get the label in the point cloud and present the label on the point cloud BEV. But in some complicated road scenes, label propagation based on geometric space mapping may cause inconsistent labels. Finally, we use a fully connected conditional random field (CRF) for optimization to obtain prefect label propagation results. In order to verify the effectiveness of the label propagation algorithm, we will use the label propagation results before and after optimization to train a road detection model based on fully convolution network, and compare and analyze its performance. The accuracy result of road detection using CRF optimized label data is 1.5% higher than that without optimized data.
SMPR: Single-stage multi-person pose regression

Existing multi-person pose estimators can be roughly divided into two-stage approaches (top-down and bottom-up approaches) and one-stage approaches. The two-stage methods either suffer high computational redundancy for additional person detectors or group keypoints heuristically after predicting all the instance-free keypoints. The recently proposed single-stage methods do not rely on the above two extra stages but have lower performance than the latest bottom-up approaches. In this work, a novel single-stage multi-person pose regression, termed SMPR, is presented. It follows the paradigm of dense prediction and predicts instance-aware keypoints from every location. Besides feature aggregation, we propose better strategies to define positive pose hypotheses for training which all play an important role in dense pose estimation. The network also learns the scores of estimated poses. The pose scoring strategy further improves the pose estimation performance by prioritizing superior poses during non-maximum suppression (NMS). We show that our method not only outperforms existing single-stage methods but also be competitive with the latest bottom-up methods, with 70.2 AP and 77.5 AP75 on the COCO test-dev pose benchmark. The code is available at https://github.com/cmdi-dlut/SMPR .
Towards an Answer Set Programming Methodology for Constructing Programs Following a Semi-Automatic Approach – Extended and Revised version

Answer Set Programming (ASP) is a successful rule-based formalism for modeling and solving knowledge-intense combinatorial (optimization) problems. Despite its success in both academic and industry, open challenges like automatic source code optimization, and software engineering remains. This is because a problem encoded into an ASP might not have the desired solving performance compared to an equivalent representation. Motivated by these two challenges, this paper has three main contributions. First, we propose a developing process towards a methodology to implement ASP programs, being faithful to existing methods. Second, we present ASP encodings that serve as the basis from the developing process. Third, we demonstrate the use of ASP to reverse the standard solving process. That is, knowing answer sets in advance, and desired strong equivalent properties, “we” exhaustively reconstruct ASP programs if they exist. This paper was originally motivated by the search of propositional formulas (if they exist) that represent the semantics of a new aggregate operator. Particularly, a parity aggregate. This aggregate comes as an improvement from the already existing parity (xor) constraints from xorro, where lacks expressiveness, even though these constraints fit perfectly for reasoning modes like sampling or model counting. To this end, this extended version covers the fundaments from parity constraints as well as the xorro system. Hence, we delve a little more in the examples and the proposed methodology over parity constraints. Finally, we discuss our results by showing the only representation available, that satisfies different properties from the classical logic xor operator, which is also consistent with the semantics of parity constraints from xorro.
About of the asymptotical stability of solutions of systems of ordinary differential equations

The concept of partial derivatives of numbers is considered to study the stability of solutions of systems of differential equations. The conditions and criteria for the use of partial and external derived numbers are proposed. This makes it possible to investigate the behavior of a function of several variables, without requiring its differentiability, but using only information about partial derivative numbers. This reduces the restrictions on the degree of smoothness of the functions being studied. The proposed method can be used to obtain the necessary or sufficient conditions for the stability of solutions of systems of differential equations.
A Face Recognition Approach Based on Computer Vision

Most face recognition algorithms are easily affected by external factors, or require the recognizer to stand in a fixed position, so the recognition is slow and the accuracy is not very high. This paper implements an application platform of face recognition based on Open CV. Firstly, the face image is collected and preprocessed by optimizing the algorithms of Eigenfaces, Fisherfaces and LBP platform, which are used for face recognition by cooperation with each other. In addition to the restrictions on the recognition of the target to be detected by the face recognition system, eye location is added to the traditional AdaBoost based face detection algorithm to realize the function of face module training and face recognition. After testing, the system is proved to have a friendly interface, stable operation, good robustness to face location and environmental light changes, and it can detect and recognize face in real time quickly and accurately.
Image Segmentation With Language Referring Expression and Comprehension

Image segmentation with language referring expression can complete object segmentation based on expression text. Existing image segmentation methods show good results on high-performance computers, but most robot systems need real-time and high accuracy. At present, most methods cannot meet these requirements well. Therefore, we propose a high-precision and real-time deep learning network that integrates the two tasks of image segmentation with language referring expression and referring expression comprehension and then treats them as two branches. Specifically, the proposed network first merges the two tasks. The feature maps of different scales extracted by each branch are fed back to the two branches to obtain prediction results. These two tasks promote and restrict each other. Experiments show that our method has better real-time performance and higher accuracy than existing methods.
Causal Feature Selection

Feature selection is a process of the data preprocessing task in business intelligence (BI), analytics, and data mining that urges for new methods that can handle with high dimensionality. One alternative that have been researched to deal with the curse of dimensionality is causal feature selection. Causal feature selection is not based on correlation, but the causality relationship among variables. The main goal of this chapter is to present, based on the issues identified on other methods, a new strategy that considers attributes beyond those that compounds the Markov blanket of a node and calculate the causal effect to ensure the causality relationship.
Factorization and Malleability of RSA Moduli, and Counting Points on Elliptic Curves Modulo N

In this paper we address two different problems related with the factorization of an RSA (Rivest–Shamir–Adleman cryptosystem) modulus N. First we show that factoring is equivalent, in deterministic polynomial time, to counting points on a pair of twisted Elliptic curves modulo N. The second problem is related with malleability. This notion was introduced in 2006 by Pailler and Villar, and deals with the question of whether or not the factorization of a given number N becomes substantially easier when knowing the factorization of another one N′ relatively prime to N. Despite the efforts done up to now, a complete answer to this question was unknown. Here we settle the problem affirmatively. To construct a particular N′ that helps the factorization of N, we use the number of points of a single elliptic curve modulo N. Coppersmith’s algorithm allows us to go from the factors of N′ to the factors of N in polynomial time.
Forensic Analysis in Access Control

We pose and study forensic analysis in the context of access control systems in a manner that prior work has not. Forensics seeks to answer questions about past states of a system, and thereby provides important clues and evidence in the event of a security incident. Access control deals with who may perform what action on a resource and is a critical security function. Our focus is access control systems that allow for changes to the authorization state to be delegated to potentially untrusted users. We argue that this context in access control is an important one in which to consider forensic analysis, and observe that it is a natural complement of safety analysis, which has been considered extensively in the literature. We pose the forensic analysis problem for such access control systems abstractly, and instantiate it for three schemes from the literature: a well-known access matrix scheme, a role-based scheme, and a discretionary scheme. We identify the computational complexity of forensic analysis, and compare it to that of safety analysis for each of the schemes. We consider also the notion of logs, i.e., data that can be collected over time to aid forensic analysis.
Towards Accurate Identification and Removal of Shirorekha from Off-line Handwritten Devanagari word Documents

Shirorekha identification and removal is an important and a challenging pre-processing stage in almost all machine interpretations for handwritten Devanagari documents. Within this area of investigation, all studies are designed based on traditional image processing techniques. Which are mainly based on hand-engineering and learn local transformations only. However, it can also be viewed as a supervised classification task in which each pixel, in a document, is examined/ queried so that those classified as shirorekha are removed. For this purpose, we extended this area of investigation by designing an encoder-decoder based convolutional neural network (EDCNN). Which have demonstrated, from various studies, that they learn image intricacies very well. The contribution of this work is three-fold, first, we created our own handwritten word dataset comprising of words with and without shirorekha, such that, effective training takes place. Next, we trained the proposed network with binary as well as in gray scale formats. Finally, we demonstrated that the proposed approach is accurate and generalizable.
Educational Resources for Promoting Talent in Quantum Computing

Quantum physics has a deep impact in today's economy, with a historical role in industry sectors ranging from chemicals to electronics. Today, Quantum Computing advents to industrial relevance, signaling a renewed demand for a quantum-enabled workforce. Despite this clear interest, novelty and complexity frequently limit the exposure of young students to this topic, threatening the engagement of potential future practitioners and leaders. We advocate that quantum computing concepts can, effectively, be introduced at the primary and secondary school level. We substantiate this by compiling in this paper a number of publicly available resources, of our creation and from third parties, tested in real classes, for integrating Quantum Computing to the education for K-12 age groups.
LowLAG: Low-latency hardware accelerator of a sound effect with system-on-chip design

Many sound effects such as Autotune are very popular among artists to be used in live concerts. However, latency problems may occur when using these effects. This latency may affect the artists negatively. Latency of audio processing in CPU-based systems is a significant issue and does not provide minimal worst-case latency values for many high-end musical devices to be used in live concerts. This paper investigates how to achieve a low-latency audio processing with hardware and software co-design methodology in FPGA-based systems by designing an application-specific system-on-chip (SoC) architecture containing a hardware accelerator to generate a sound effect. The accelerator is connected to an ARM processor core within a Zynq-based programmable SoC platform. The ARM processor core is responsible (1) to handle data movement operations, (2) to enable or disable the sound effect over captured audio signal and (3) to perform some of the operations of the sound effect algorithm. The sound effect algorithm is designed in MATLAB & Simulink and corresponding hardware is generated through the HDL Coder. Our main objective is to reduce the audio latency of sound effect processing from input to output by designing a sound effect-specific hardware IP core via MATLAB & Simulink and incorporating it on a custom SoC architecture. SoC solution is prototyped in ZedBoard that contains ZC7020 Zynq chip and this low-end embedded SoC system outperforms the CPU-based solution.
Coalgebras on Digital Images

In this article, we investigate the fundamental properties of coalgebras with coalgebra comultiplications, counits, and coalgebra homomorphisms of coalgebras over a commutative ring R with identity 1R based on digital images with adjacency relations. We also investigate a contravariant functor from the category of digital images and digital continuous functions to the category of coalgebras and coalgebra homomorphisms based on digital images via the category of unitary R-modules and R-module homomorphisms.
The Dark Web

The deep web (also called deepnet, the invisible web, dark web, or the hidden web) refers to world wide web content that is not part of the surface web, which is indexed by standard search engines. The more familiar “surface” web contains only a small fraction of the information available on the internet. The deep web contains much of the valuable data on the web, but is largely invisible to standard web crawling techniques. Besides it being the huge source of information, it also provides the rostrum for cybercrime like by providing download links for movies, music, games, etc. without having their copyrights. This article aims to provide context and policy recommendations pertaining to the dark web. The dark web's complete history, from its creation to the latest incidents and the way to access and their sub forums are briefly discussed with respective to the user perspective.
Metric Semantics for Concurrent Languages Designed in Continuation-Passing Style

We present a semantic domain for designing concurrent languages in continuation-passing style (CPS). We offer denotational semantics designed in CPS for languages based on Milner's CCS extended with mechanisms which support multiparty interactions. We establish a collection of properties which can describe the behavior of concurrent systems with multiparty interactions. The semantic investigation is accomplished in the mathematical framework of complete metric spaces.
A Lightweight Exchangeable Encryption Scheme for IoT Devices Based on Vigenere Cipher and MLS Keystream

An exchangeable encryption scheme which is lightweight and secure suitable for IoT devices is proposed. The security of the encryption scheme is mainly based on random keystream generated from the Meitei lock sequence (MLS). The encryption scheme is lightweight in the sense that it does not require any memory buffer during its execution and also, it does not involve any complex mathematical operations. It is also size preserving, i.e., plaintext and ciphertext sizes are the same. Also, there is no upper limit for the key size, and hence, breaking it using brute-force attack is theoretically impossible. Moreover, tracking key from the ciphertext is also not possible because of the use of MLS, which produces different random keystreams when there is any slight difference in any of password elements. The encryption scheme is also fast as it does not involve any complex mathematical operation and any rounds of permutation and substitution operations. The encryption scheme has been applied for encryption of text, speech, and image. Experimental results based on correlation coefficient measures show that the proposed encryption is secure.
Quantum Convolutional Neural Network On Scale Chaology

In this paper we propose a quantum convolutional neural network(QCNN) based chaotic metric classification for chaology scaling-down images in the tensorflow quantum framework. The chaology image is properly downscaled with the measurement of spatial occupancy and granularity introduced by fractal dimension and the dimensionality of the overall input space is reduce before it is fed into the QCNNs quantum circuit for state preparation, quantum convolution and quantum pooling. The experimental results show that QCNN, Hybrid QCNN and Hybrid QCNN with multiple quantum filters have achieved relatively high accuracies on more than 94%, and convergence of these three kinds of QCNN classifier has a small fluctuation which indicates that through this scale transformation the discrepancy among the different quantum hybrid classifiers is minimized to some extent.
Text Categorisation Through Dimensionality Reduction Using Wavelet Transform

This paper proposes a new method of dimensionality reduction when performing Text Classification, by applying the discrete wavelet transform to the document-term frequencies matrix. We analyse the features provided by the wavelet coefficients from the different orientations: (1) The high energy coefficients in the horizontal orientation correspond to relevant terms in a single document. (2) The high energy coefficients in the vertical orientation correspond to relevant terms for a single document, but not for the others. (3) The high energy coefficients in the diagonal orientation correspond to relevant terms in a document in comparison to other terms. If we filter using the wavelet coefficients and fulfil these three conditions simultaneously, we can obtain a reduced vocabulary of the corpus, with less dimensions than in the original one. To test the success of the reduced vocabulary, we recoded the corpus with the new reduced vocabulary and we obtained a statistically relevant level of accuracy for document classification.