Reviewerly/exHarmony huggingface dataset:
Mapping Monotonic Restrictions in Inductive Inference

In inductive inference we investigate computable devices (learners) learning formal languages. In this work, we focus on monotonic learners which, despite their natural motivation, exhibit peculiar behaviour. A recent study analysed the learning capabilities of strongly monotone learners in various settings. The therein unveiled differences between explanatory (syntactically converging) and behaviourally correct (semantically converging) such learners motivate our studies of monotone learners in the same settings. While the structure of the pairwise relations for monotone explanatory learning is similar to the strongly monotone case (and for similar reasons), for behaviourally correct learning a very different picture emerges. In the latter setup, we provide a self-learning class of languages showing that monotone learners, as opposed to their strongly monotone counterpart, do heavily rely on the order in which the information is given, an unusual result for behaviourally correct learners.
Assessment of virtual environments for alcohol Relapse Prevention in a less immersive and cost-effective setup: A qualitative study

Relapse prevention (RP) - helping people to develop relevant coping skills in high-risk situations that challenge abstinence - is an important part of alcohol use disorder treatment. Recreating personalised, high-risk situations can be challenging in clinical contexts. Virtual Reality Therapy (VRT) has the potential to offer immersive exposure to relevant, interactive stimuli presented in Virtual Environments (VEs). The use of VRT in RP remains unexplored. In this study, 10 casual and 13 high-risk drinkers assessed, via unstructured interviews, the realism of three 'high-risk' VEs: a home, a supermarket and a pub, and the extent to which they induced alcohol temptation when presented in a less immersive, cost-effective setup. Template analysis revealed that proximal (alcohol stimuli) and contextual (stimuli typically associated with alcohol) cues, and a sense of presence within the VEs, were key aspects to inducing realism and alcohol temptation. High-risk drinkers were tempted to drink in any VE and regular drinkers primarily in a social, pub VE. Temptation to smoke was induced in smokers. The results suggest that the VEs may help people with alcohol or comorbid tobacco misuse to practice coping with craving, refusal skills (saying 'no' to prompts to drink) and emotion regulation in social, private and alcohol vending contexts. The interconnections of realism, presence, alcohol temptation and related cues discussed here can inform future VRT applications for alcohol treatment.
RETRACTED ARTICLE: Utilizing Index-Based Periodic High Utility Mining to Study Frequent Itemsets

The potential employability in different applications has garnered more significance for Periodic High-Utility Itemset Mining (PHUIM). It is to be noted that the conventional utility mining algorithms focus on an itemset’s utility value rather than that of its periodicity in the transaction. A MEAN periodicity measure is added to the minimum (MIN) and maximum (MAX) periodicity to incorporate the periodicity feature into PHUIM in this proposed work. The MEAN-periodicity measure brings a new dimension to the periodicity factor and is arrived at by dividing itemset’s period value by the total number of transactions in that dataset. Further, an algorithm to mine Index-Based Periodic High Utility Itemset Mining (IBPHUIM) from the database using an indexing approach is also proposed in this paper. The proposed IBPHUIM algorithm employs a projection-based technique and indexing procedure to increase memory and execution speed efficiency. The proposed model avoids redundant database scans by generating sub-databases using an indexing data structure. The proposed IBPHUIM model has experimented with test datasets, and the results drawn show that the proposed IBPHUIM model performs considerably better.
Web Mining Techniques and Technologies: A Landscape View

Abstract Web mining is the process of discovering important information and patterns in a weblog file. And contains loads of information. It consists of pages of various formats (HTML documents, images, etc.). And the data for this record is still increasing at a very high rate. So the process of retrieving information from it is a very difficult task. In order to obtain the required information from the network. This requires mining the weblog file. In this paper, the researcher conducted a review of previous studies and what researchers have dealt with in web prospecting. Explain web mining concepts. And how to obtain information and the visitors’ patterns through the techniques and algorithms used in Web usage mining.
Brief Industry Paper: Towards Real-Time 3D Object Detection for Autonomous Vehicles with Pruning Search

In autonomous driving, 3D object detection is es-sential as it provides basic knowledge about the environment. However, as deep learning based 3D detection methods are usually computation intensive, it is challenging to support realtime 3D object detection on edge-computing devices in selfdriving cars with limited computation and memory resources. To facilitate this, we propose a compiler-aware pruning search framework, to achieve real-time inference of 3D object detection on the resource-limited mobile devices. Specifically, a generator is applied to sample better pruning proposals in the search space based on current proposals with their performance, and an evaluator is adopted to evaluate the sampled pruning proposal performance. To accelerate the search, the evaluator employs Bayesian optimization with an ensemble of neural predictors. We demonstrate in experiments that for the first time, the pruning search framework can achieve real-time 3D object detection on mobile (Samsung Galaxy S20 phone) with state-of-the-art detection performance.
Flexible Metasurface QR Code for Simultaneous Identification and Sensing

This article presents a novel approach for using a flexible QR code as a metasurface for sensing and identification functionalities. This is achieved by developing conventional QR code, in part or completely, with functional materials such as thermochromic and photochromic papers. The physical stimuli (e.g., temperature) related colour changes on the QR metasurface were visually detected by the smartphone camera and analysed using a custom app. While the colour variation indicates the physical sensing parameter of the product with which QR code is used, the main QR code itself is used as the product identifier. The marriage of multiple functionalities in the conventional QR code, with no change in the code itself or addition of new sensors, is an attractive approach for attaining more functionality with lesser resources, cost-efficiency and sustainable ICT.
Using Genetic Improvement to Retarget quantum Software on Differing Hardware

Quantum computers are rapidly developing to a point where they can solve problems faster than any classical computation. In addition, competing standards for languages, models and architectures are also being created. These standards are often bespoke and aimed at optimizing around a single algorithm or problem. This can make it very difficult to reuse these them should the original hardware become unavailable or obsolete. We demonstrate a method that can compile circuits more generally across hardware constraints with the use of a genetic improvement inspired search technique that includes a realistic model of the hardware. We show that this method is effective at selecting gates that can be more easily implemented and run compared to generic optimization. This method reduces the total chance of failure. To ensure that these results are practical, empirical results are generated using different IBM hardware and a selection of real algorithms.
A Simple and Automatic Typesetting Method Based on BM Value of Interface Aesthetics and Genetic Algorithm

After more than ten years of development, the evaluation system of interface aesthetics has been gradually applied to the evaluation of the layout of various interface elements, but it is still difficult to guide the actual interface design. In this paper, starting from Measure of Balance, one of the evaluation indexes of interface aesthetics, and combining with the genetic algorithm, a simple automatic typesetting algorithm was constructed on Grasshopper (Rhino) Visual programming platform, and the results of its optimization were evaluated with satisfaction. The evaluation results show that this automatic typesetting algorithm produces some satisfactory results, and it can be better applied to the automatic typesetting of simple graphics and text. However, for complex graphic typesetting, the automatic typesetting algorithm needs to be further optimized, because this algorithm is only based on the BM value of interface aesthetics. When the BM value is maximum, only the balance degree is optimal, which does not represent that the overall interface aesthetics is optimal. Therefore, it is necessary to introduce other interface aesthetics evaluation indexes to supplement and build a more mature comprehensive index of interface aesthetics before it can be better applied to the actual interface design.
Hybrid Chinese text classification model based on pretraining model

Abstract In order to solve the problems of fuzzy boundary of Chinese words, polysemy of one word and the inability of traditional models to reflect the importance of each word in the text, a hybrid text classification model based on Ernie CNN and bilstm attention (mecba) is proposed. Firstly, Ernie model is used to generate the corresponding word vector, which can retain rich semantic information and enhance the semantic representation of words. Then, the word vector is input into CNN to extract local information, and the context feature is extracted by bilstm attention, and the output of the two is spliced; Finally, softmax classifier is used for classification. The experimental results show that the model is better than CNN, bilstm and other classification models, and can effectively improve the performance of Chinese text classification.
Design and Implementation of Project Cost Management Information System Based on Web

In order to improve the efficiency of project cost information management and the overall economic benefits of the company, this paper designs a project cost information management system based on web integrated with the actual needs of project cost management. The system is based on B/S architecture and MVC development, using Ajax technology to achieve real-time interaction between web pages and database without refreshing. Then, according to the specific business, the process of project cost is divided into several stages, and the design and implementation of key modules in each stage are described in detail. The system running test results show that the project cost management system designed in this paper can interconnect data with other project systems, which greatly improves the speed and efficiency of project estimation, and its practicability and effectiveness meet the expected requirements.
Research Report: Building a File Observatory for Secure Parser Development

Parsing untrusted data is notoriously challenging. Failure to handle maliciously crafted data correctly can (and does) lead to a wide range of vulnerabilities. The Language-theoretic security (LangSec) philosophy seeks to obviate the need for developers to apply ad hoc solutions by, instead, offering formally correct and verifiable input handling throughout the software development lifecycle. One of the key components in developing secure parsers is a broad coverage corpus that enables developers to understand the problem space for a given format and to use, potentially, as seeds for fuzzing and other automated testing. In this paper, we offer an update on work initially reported at the LangSec 2020 conference on the development of a file observatory to gather and enable analysis on a diverse collection of files at scale. The initial focus of the observatory is on Portable Document Format (PDF) files and file formats typically embedded in PDFs. In this paper, we report on the addition of a bug tracker corpus and new analytic methods.
‘Toward a Global Social Contract for Trade’ - a Rawlsian approach to Blockchain Systems Design and Responsible Trade Facilitation in the New Bretton Woods era

Imminent changes to the international monetary system alongside a shift toward more egalitarian principles of justice in commercial contracts for trade are now taking place. Such changes however do not sufficiently account for circumstances of hardship, or black-swan events such as COVID-19, whereby the relative losers of trading arrangements should continue to receive outcomes which are not only efficient, but also fair and resilient. We argue that the ‘Society-in-the-Loop’ (SITL) social contract paradigm, in conjunction with the use of Strategic Responsible Innovation Management (StRIM), can together provide a solution for improving distributive justice in trade. Through collaboration with a locally based trade facilitation company, we describe the innovation-planning phase of a blockchain smart contract solution based on Derek Leben's idea of a ‘Rawlsian Algorithm’ (2017). It is demonstrated how this can be used to strengthen the algorithmic fairness of commercial contract implementation in accordance with existing ISO 20022 standards. Since currently no formal design framework exists for modeling blockchain oriented software (BOS), an agile development approach is adopted which takes account of the substantial difference between traditional software development and smart contracts. This method involves the construction of UML Use Case, Sequence, and Class diagrams, with a view to blockchain specificities. Evaluation and feedback from the company is also considered.
Multi-objective Optimization of Spectrum Sensing and Power Allocation Based on Improved Slime Mould Algorithm

Abstract In order to improve the throughput of sub-users of cognitive radio networks and reduce the interference to primary users, firstly, we consider the spectrum sensing time, decision threshold, and power allocation factors of joint sub-users, and construct the average network throughput and average interference as a multi-objective optimization problem, and secondly, propose a multi-objective optimization algorithm based on improved sticky bacterium algorithm (SMA) to model and solve this problem to obtain the best the sensing time, decision threshold and power. Finally, the proposed method is compared with the NSGA-II algorithm, and the simulation results show that the proposed method can effectively search for the optimal solutions of the spectrum sensing parameters to maximize the average network throughput and minimize the average interference.
Standard Dialogue Structure and Frequent Patterns in the Agent Dialogue System

To design a dialogue well and easily implement it into an autonomous dialogue system, a dialogue with simple components in which interaction with the partners is smoothly achieved is required. In designing interactions to use in an autonomous dialogue system, we consider that there are typical dialogue structures that convey agency and facilitate the dialogue. In this paper, we focused on surveying such dialogue structures and defined this structure as the "Standard Dialogue Structure" for human-agent dialogue. It is constructed using the "Turn" that is one turn-taking between the dialogue participants, the "Topic" that is a series of Turns in which a single question is resolved, the "Topic-shift" that is short interaction to build consensus before moving to the next Topic, and the "Scene" that puts together each component. Using dialogue scenarios created based on the health checklist for elderly people, we confirmed that the Standard Dialogue Structure can be used in question-and-answer dialogues led by the agent. Furthermore, we investigated the frequent patterns of dialogue in the Topic by using the utterances in the Turns which were labeled by a four-quadrant impression. The results indicated that these frequent patterns could be an effective pattern for facilitating dialogue. Therefore, we investigate the possibility of expressing agency and promoting dialogue using these frequent patterns in the autonomous dialogue systems. In conclusion, we have shown that the Standard Dialogue Structure is possible to easily implement dialogues that effectively promote interaction, even for a novice in dialogue design.
Straightness metric for measurement of turbulence-induced distortion in long-range imaging

Algorithms used for mitigation of the effects of atmospheric turbulence on video sequences often rely on a process for creating a reference image to register all of the frames. Because such a pristine image is generally not available, no-reference image quality metrics can be used to identify frames in a sequence that have minimum distortion. Here, we propose a metric that quantifies image warping by measuring image straightness based on line detection. The average length of straight lines in a frame is used to select best frames in a sequence and to generate a reference frame for a subsequent dewarping algorithm. We perform tests with this metric on simulated data that exhibits varying degrees of distortion and blur and spans normalized turbulence strengths between 0.75 and 4.5. We show, through these simulations, that the metric can differentiate between weak and moderate turbulence effects. We also show in simulations that the optical flow that uses a reference frame generated by this metric produces consistently improved image quality. This improvement is even higher when we employ the metric to guide optical flow that is applied to three real video sequences taken over a 7 km path.
Difficulties and Disparities to Distance Learning During Covid-19 Period for Deaf Students –A Proposed Method to Eradicate Inequalities

During this critical period the humankind faces, one of the most affected daily activities is the education. Teachers and students of all education levels had to adopt new technological means to overcome the distance and to continue their education. Above and beyond the new prospects that this situation arises, also many problems were raised. For example, the special care education e.g., deaf or hard hearing students, face more difficulties than in ordinary situations. These students either read the lips or have to see their interlocutor's whole body, in order to understand what he/she is saying but this nowadays is almost inevitable. In the unlikely scenario that their school is open, all classroom participants including teacher must wear a face mask, while in distance learning they can see only a small window of their teacher and most likely only his/her face. This paper presents the ways of communication of deaf and hard hearing people and proposes a novel educational tool that with the proper use should help them overcome this difficult situation.
The Recognition Framework of Deep Kernel Learning for Enclosed Remote Sensing Objects

Remote sensing image target recognition is used in various fields, such as ships, tanks, airplanes, and vehicles, which are closed targets. The features of these targets include target outlines that are obvious and target discriminant features that are significantly different from the surrounding environment, and the targets are characterized as small and dense. Therefore, the recognition of these types of targets is a popular topic. We proposed a recognition framework consisting of a remote sensing image target recognition method based on deep saliency kernel learning analysis, which uses a target region extraction method based on the visual saliency mechanism and implements a nonlinear deep kernel learning saliency feature analysis method to realize target extraction and recognition. Experimental results show that a 95.9% recognition rate is achieved for SAR remote sensing target recognition on the public MSTAR data set, a 96% recognition rate on the UC Merced Land Use data set, and an 85% recognition rate on a self-built visible light remote sensing image data set. The recognition framework can be used for video recognition.
Determination parameter of exponential function based positive number

Abstract The relationship between variables can be made a model or function. The use of the function is as a tool for making a prediction of the response, provided the value of the predictor is known. A function has independent variable, dependent variable, and parameter. The function discussed is an exponential function with base number in positive number. Furthermore, it is developed in the form of the exponential function with one parameter, the exponential function with two parameters and the exponential regression model. To create a function required some parameters. We need a certain method to find parameter. The accuracy of obtained function model shows the goodness of the function in approaching the distribution of the data. The method used to find parameter using the sum square error and logarithm function. Using logarithm function produces a linear function. Computation of parameters using R and Matlab programs. To measure the value of model accuracy using the mean square error. The purpose of this study is to find parameter of the exponential function with one parameter, the exponential function with two parameters and the exponential regression model. The presentation of the result of this discussion is in form of table and picture in the hope that it is easy to understand and interesting. The output of the exponential function with one parameter shows that the base value is 1.050021, the parameter value α is 7.969737 and the prediction accuracy value is 99.996%. The exponential function with two parameters shows that the base value is 0.5, the parameter value α is 7.969737, and the parameter value β is - 0.071, while for the base value is 10, the parameter value α is 7.969737, and the parameter value β is 0.02. The prediction accuracy of the exponential function with two parameters is 99.996%. While the exponential regression model shows that the base value is 0.5, the parameter value α is 128.336, and the parameter value β is -55.828. The prediction accuracy of the exponential regression model is 84.8%.
WIP: An Onion-Based Routing Protocol Strengthening Anonymity

Anonymous Communication Networks (ACNs) are networks in which, beyond data confidentiality, also traffic flow confidentiality is provided. The most popular routing approach for ACNs also used in practice is Onion. Onion is based on multiple encryption wrapping combined with the proxy mechanism (relay nodes). However, it offers neither sender anonymity nor recipient anonymity in a global passive adversary model, simply because the adversary can observe (at the first relay node) the traffic coming from the sender, and (at the last relay node) the traffic delivered to the recipient. This may also cause a loss of relationship anonymity if timing attacks are performed. This paper presents Onion-Ring, a routing protocol that improves anonymity of Onion in the global adversary model, by achieving sender anonymity and recipient anonymity, and thus relationship anonymity.