Reviewerly/exHarmony huggingface dataset:
GA+DDPG+HER: Genetic Algorithm-Based Function Optimizer in Deep Reinforcement Learning for Robotic Manipulation Tasks

Agents can base decisions made using reinforcement learning (RL) on a reward function. The selection of values for the learning algorithm parameters can, nevertheless, have a substantial impact on the overall learning process. In order to discover values for the learning parameters that are close to optimal, we extended our previously proposed genetic algorithm-based Deep Deterministic Policy Gradient and Hindsight Experience Replay approach (referred to as GA+DDPG+HER) in this study. On the robotic manipulation tasks of FetchReach, FetchSlide, FetchPush, FetchPick&Place, and DoorOpening, we applied the GA+DDPG+HER methodology. Our technique GA+DDPG+HER was also used in the AuboReach environment with a few adjustments. Our experimental analysis demonstrates that our method produces performance that is noticeably better and occurs faster than the original algorithm. We also offer proof that GA+DDPG+HER beat the current approaches. The final results support our assertion and offer sufficient proof that automating the parameter tuning procedure is crucial and does cut down learning time by as much as 57%.
Client Selection and Bandwidth Allocation for Federated Learning: An Online Optimization Perspective

Federated learning (FL) can train a global model from clients' local data set, which can make full use of the computing resources of clients and performs more extensive and efficient machine learning on clients with protecting user information requirements. Many existing works have focused on optimizing FL accuracy within the resource constrained in each individual round, however there are few works comprehensively consider the optimization for latency, accuracy and energy consumption over all rounds in wireless federated learning. Inspired by this, in this paper, we investigate FL in wireless networks where client selection and bandwidth allocation are two crucial factors which significantly affect the latency, accuracy and energy consumption of clients. We formulate the optimization problem as a mixed-integer problem, which is to minimize the cost of time and accuracy within the long-term energy constrained over all rounds. To address this optimization problem, we propose a per-round energy drift plus cost (PEDPC) algorithm from an online perspective, and the performance of the PEDPC algorithm is verified in simulation results in terms of latency, accuracy and energy consumption in IID and NON-IID data distributions.
An XGBoost-Based Knowledge Tracing Model

Abstract The knowledge tracing (KT) model is an effective means to realize the personalization of online education using artificial intelligence methods. It can accurately evaluate the learning state of students and conduct personalized instruction according to the characteristics of different students. However, the current knowledge tracing models still have problems of inaccurate prediction results and poor features utilization. The study applies XGBoost algorithm to knowledge tracing model to improve the prediction performance. In addition, the model also effectively handles the multi-skill problem in the knowledge tracing model by adding the features of problem and knowledge skills. Experimental results show that the best AUC value of the XGBoost-based knowledge tracing model can reach 0.9855 using multiple features. Furthermore, compared with previous knowledge tracing models used deep learning, the model saves more training time.
To pay or not to pay: An exploratory analysis of sextortion in the context of romance fraud

Romance fraud, where an offender uses the guise of a genuine relationship for monetary gain, affects millions globally. Further to financial losses, evidence suggests victims may also be targeted for sextortion, where the offender either has images or claims to possess indecent images of a person and attempts to extort them for money. Research exploring the association between romance fraud and sextortion is limited, with little known about the characteristics of victims and their potential for financial loss. This article addresses this gap through a quantitative analysis of those who reported romance fraud which included a threat of sextortion. It identifies those who paid in response to the sextortion threat by a variety of situational and demographic variables and locates this group in the broader context of romance fraud. The implications of this study for an understanding of online fraud victimization are explored in detail.
Multi-frame super-resolution of remote sensing images using attention-based GAN models

Multi-frame super-resolution (MFSR) of remote sensing (RS) imageries becomes a critical research topic with the launch of new satellites having video capturing capability and the advancement of artificial intelligence techniques. In this study, an attention-based Generative Adversarial Network (GAN) algorithm is proposed for the multi-frame remote sensing image super-resolution (MRSISR). Firstly, we introduced an attention module to the generator and designed a space-based net that worked on every single frame for better temporal information extraction. Secondly, we proposed a novel attention module for better spatial and spectral information extraction. Thirdly, we applied an attention-based discriminator for the discriminative ability improvement of the discriminator. We implemented several experiments with the state-of-the-art models and the proposed approach using SpaceNet7 and Jilin-1 datasets. We quantitatively and qualitatively compared the results of different multi-frame super-resolution models.
Non-interactive Zero-Knowledge Proofs to Multiple Verifiers

In this paper, we study zero-knowledge (ZK) proofs for circuit satisfiability that can prove to n verifiers at a time efficiently. The proofs are secure against the collusion of a prover and a subset of t verifiers. We refer to such ZK proofs as multi-verifier zero-knowledge (MVZK) proofs and focus on the case that a majority of verifiers are honest (i.e., $$t<n/2$$ ). We construct efficient MVZK protocols in the random oracle model where the prover sends one message to each verifier, while the verifiers only exchange one round of messages. When the threshold of corrupted verifiers $$t<n/2$$ , the prover sends $$1/2+o(1)$$ field elements per multiplication gate to every verifier; when $$t<n(1/2-\epsilon )$$ for some constant $$0<\epsilon <1/2$$ , we can further reduce the communication to O(1/n) field elements per multiplication gate per verifier. Our MVZK protocols demonstrate particularly high scalability: the proofs are streamable and only require a memory proportional to what is needed to evaluate the circuit in the clear.
A generalized multi-aspect distance metric for mixed-type data clustering

Distance calculation is straightforward when working with pure categorical or pure numerical data sets. Defining a unified distance to improve the clustering performance for a mixed data set composed of nominal, ordinal, and numerical attributes is very challenging due to the attributes' different natures. In this study, we proposed a new measure of distance for a mixed-type data set that regards inter-attribute information and intra-attribute information depending on the type of attributes. In this regard, entropy and Jensen–Shannon divergence concepts were used to exploit the inter-attribute information of categorical-categorical and categorical-numerical attributes, respectively. Also, a modified version of Mahalanobis distance was proposed to consider the intra- and inter-attribute information of numerical attributes. We also introduced a unified framework based on mutual information to control attributes' contribution to distance measurement. The proposed distance in conjunction with spectral clustering was extensively evaluated concerning various categorical, numerical, and mixed-type benchmark data sets, and the results demonstrated the efficacy of the proposed method.
ALPHA: Audit that learns from previously hand-audited ballots

A risk-limiting election audit (RLA) offers a statistical guarantee: if the reported electoral outcome is incorrect, the audit has a known maximum chance (the risk limit) of not correcting it before it becomes final. BRAVO (Lindeman, Stark and Yates (In Proceedings of the 2011 Electronic Voting Technology Workshop/Workshop on Trustworthy Elections (EVT/WOTE'11) (2012) USENIX)), based on Wald's sequential probability ratio test for the Bernoulli parameter, is the simplest and most widely tried method for RLAs, but it has limitations. It cannot accommodate sampling without replacement or stratified sampling which can improve efficiency and are sometimes required by law. It applies only to ballot-polling audits which are less efficient than comparison audits. It applies to plurality, majority, supermajority, proportional representation, and instant-runoff voting (IRV, using RAIRE (Blom, Stuckey and Teague (In Electronic Voting (2018) 17–34 Springer))) but not to other social choice functions for which there are RLA methods. And while BRAVO has the smallest expected sample size among sequentially valid ballot-polling-with-replacement methods when the reported vote shares are exactly correct, it can require arbitrarily large samples when the reported reported winner(s) really won but the reported vote shares are incorrect. ALPHA is a simple generalization of BRAVO that: (i) works for sampling with and without replacement, with and without weights, with and without stratification, and for Bernoulli sampling; (ii) works not only for ballot polling but also for ballot-level comparison, batch polling, and batch-level comparison audits; (iii) works for all social choice functions covered by SHANGRLA (Stark (In Financial Cryptography and Data Security (2020) Springer)), including approval voting, STAR-Voting, proportional representation schemes, such as D'Hondt and Hamilton, IRV, Borda count, and all scoring rules, and (iv) in situations where both ALPHA and BRAVO apply, requires smaller samples than BRAVO when the reported vote shares are wrong but the outcome is correct—five orders of magnitude in some examples. ALPHA includes the family of betting martingale tests in RiLACS (Waudby-Smith, Stark and Ramdas (In Electronic Voting. E-Vote-ID 2021 (2021) Springer)) with a different betting strategy parametrized as an estimator of the population mean and explicit flexibility to accommodate sampling weights and population bounds that change with each draw. A Python implementation is provided.
A multi-criteria decision analysis model for selecting an optimum customer service chatbot under uncertainty

Chatbots are increasingly integrated into customer support systems due to recent developments in artificial intelligence. Chatbots can provide quick and tailored services to users, including employees and consumers. This paper presents a decision-making framework for chatbot selection in the telecommunication industry. The most suitable chatbot is chosen using a new combination strategy combining Combined Compromise Solution (CoCoSo) and analytic hierarchy process (AHP) in the context of single-valued neutrosophic sets (SVNSs). The proposed method uses the AHP to generate attribute weights (collected from previous studies), and CoCoSo ranks the options and selects the best chatbot. The AHP-CoCoSo is an efficient multi-criteria decision-making model for solving complex under uncertainty. The usefulness and viability of the proposed model are statistically demonstrated by solving an explanatory case study of chatbot selection in telecommunication. The method's resilience and strength are further demonstrated through sensitivity analysis and comparisons with previously proposed approaches. The findings of this study reveal that the approach used might lead to more realistic results when dealing with uncertain information.
Quantum computing for future real-time building HVAC controls

Buildings contribute to more than 70% of overall U.S. electricity usage and greenhouse gas (GHG) emissions. HVAC systems in buildings often consume more than 40% of the total building energy usage. To reduce such high energy use, numerous control strategies including optimal and predictive controls have been developed and demonstrated. To achieve a near real-time solution, most previous research has simplified the non-linearity of building thermodynamics and provided an approximate optimal solution. The future HVAC control optimizes more connected devices in buildings, which requires a rapid and accurate response, not only to the building itself but also to the grid signals. It also poses the challenge of solving non-linear problems with discrete variables. With the recent development of quantum computers, this has become feasible. In this paper, we developed a new optimization solution based on quantum annealing for model predictive control (MPC) of a rooftop unit (RTU). Compared to traditional optimization methods, we obtained similar solutions with less than 2% differences and improved computational speed from hours to seconds. We also demonstrated an 80% reduction in total electricity consumption and a 21% reduction in electricity bills by considering day-ahead price time-of-use demand response signals. Quantum computing has proven capable of solving large-scale non-linear discrete optimization problems for building energy systems.
Measuring security development in information technologies: A scientometric framework using arXiv e-prints

We study security-development patterns in computer-science technologies through (i) the security attention among technologies, (ii) the relation between technological change and security development, and (iii) the effect of opinion on security development. We perform a scientometric analysis on arXiv e-prints (n=340,569) related to 20 computer-science technology categories. Our contribution is threefold. First, we characterize both processes of technological change and security development: while most technologies follow a logistic-growth process, the security development follows an AR(1) process or a random walk with positive drift. Moreover, over the lifetime of computer-science technologies, the security development surges at a late stage. Second, we document no relation between the technological change and the security development. Third, we identify an inverse relation between security attention and experts’ opinion. Along with these results, we introduce new methods for modeling security-development patterns for broader sets of technologies.
Experimental study of terrain coverage of an autonomous chaotic mobile robot

In this work, the design and implementation of an autonomous mobile robot that can cover given workspaces, is presented. The motion commands are produced by a bit sequence, which is created from the combination, through an XOR function, of two separate chaotic random bitstreams. The implementation of the proposed method is performed by a robotic platform which is controlled through a microcontroller (Arduino Uno). Also, an ultrasonic sensor is inserted into the robotic platform with basic aim to locate and avoid the boundaries of the workspace. The robot’s behavior is studied experimentally in different scenarios with various starting positions. The experimental results confirm that this approach, with an appropriate sensor for avoiding collision with the boundaries, can obtain very satisfactory results in regard to the fast scanning of the robot’s workspace with unpredictable way.
Learning depth via leveraging semantics: Self-supervised monocular depth estimation with both implicit and explicit semantic guidance

Self-supervised monocular depth estimation has shown great success in learning depth using only images for supervision. In this paper, we propose to enhance self-supervised depth estimation with semantics and propose a novel learning scheme, which incorporates both implicit and explicit semantic guidances. Specifically, we propose to relate depth distributions to the semantic category information by proposing a Semantic-aware Spatial Feature Modulation (SSFM) scheme, which implicitly modulates the semantic and depth features in a joint learning framework. The modulation parameters are generated from semantic labels to acquire category-level guidance. Meanwhile, a semantic-guided ranking loss is proposed to explicitly constrain the estimated depth borders using the corresponding segmentation labels. To avoid the impact brought by erroneous segmentation labels, both robust sampling strategy and prediction uncertainty weighting are proposed for the ranking loss. Extensive experimental results show that our method produces high-quality depth maps with semantically consistent depth distributions and accurate depth edges, outperforming the state-of-the-art methods by significant margins.
Ontology for experimentation of human-building interactions using virtual reality

Scientific experiments significantly enhance the understanding of human-building interactions in building and engineering research. Recently, conducting virtual reality (VR) experiments has gained acceptance and popularity as an approach to studying human-building interactions. However, little attention has been given to the standardization of the experimentations. Proper standardization can promote the reusability, replicability, and repeatability of VR experiments and accelerate the maturity of this emerging experimentation method. Responding to such needs, the authors proposed a virtual human-building interaction experimentation ontology (VHBIEO). It is an ontology at the domain level, extending the ontology of scientific experiments (EXPO) to standardize virtual human-building interaction experimentation. It was developed based on state-of-the-art ontology development approaches. Competency questions (CQs) were used to derive requirements and regulate the development. Semantic Web technologies were applied to make VHBIEO machine-readable, accessible, and processable. VHBIEO incorporates an application view (APV) to support the inclusion of unique information for particular applications. The authors performed taxonomy evaluations to assess the consistency, completeness, and redundancy, affirming no occurrence of errors in its structure. Application evaluations were applied for investigating its ability to standardize and support generating of machine-readable, accessible, and processable information. Application evaluations also verified the capability of APV to support the inclusion of unique information.
Fun and frustrating: Students’ perspectives on practising speaking English with virtual humans

Speaking in a foreign language is considered challenging to both teach and learn. Virtual humans (VHs), as conversational agents (CAs), provide opportunities to practise speaking skills. Lower secondary school students (N = 25) engaged in an AI-based spoken dialogue system (SDS) and interacted verbally with VHs in simulated everyday-life scenarios to solve given tasks. Our analysis is based on system-generated metrics and self-reported experiences collected through questionnaires, logbooks, and interviews. Thematic analysis resulted in seven themes, revolving around the speaking practice method, scenarios and technology, which, in combination with descriptive statistics, enabled a deeper understanding of the students' experiences. The results indicate that, on average, they found it easy, fun, and safe, but sometimes frustrating in scenarios not always relevant to their everyday lives. Factors suggested as underlying the levels of experienced frustration include technical issues and constraints with the system, such as not being understood or heard as expected. The findings suggest that lower secondary school students conversing with VHs in the SDS in an institutional educational context facilitated a beneficial opportunity for practising speaking skills, especially pronunciation and interaction in dialogues, aligning with the key principles of second language acquisition (SLA) for language development.
Application of Satellite Remote Sensing, UAV-Geological Mapping, and Machine Learning Methods in the Exploration of Podiform Chromite Deposits

The irregular and sporadic occurrence of chromite pods in podiform chromite deposits (PCD), especially in mountainous terranes with rough topography, necessitates finding innovative methods for reconnaissance and prospecting. This research combines several remote sensing methods to discriminate the highly serpentinized peridotites hosting chromite pods from the other barren ultramafic and mafic cumulates. The case study is the area of the Sabzevar Ophiolite (NE Iran), which hosts several known chromite and other mineral deposits. The integration of satellite images [e.g., Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite sensor, Landsat series, and Sentinel-2] coupled with change detection, band rationing, and target detection algorithms [including the Spectral Angle Mapper (SAM)] were used to distinguish potential lithological units hosting chromites. Results have been verified by an initial on-field checking and compared with the high-resolution (GSD ~6 cm) orthomosaic images obtained by the processing of photographs taken from an Unmanned Aerial Vehicle (UAV) at a promising area of 35 km2. The combination of visual interpretation and supervised classification by machine learning methods [Support Vector Machine (SVM)] yielded the production of a geological map, in which the lithological units and structures are outlined, including the crust-mantle transition zone units, mafic cumulates, crosscutting dykes, and mantle sequences. The validation of the results was performed through a second phase, made up of field mapping, sampling, chemical analysis, and microscopic studies, leading to the discovery of new chromite occurrences and mineralized zones. All ultramafic units were classified into four groups based on the degree of serpentinization, represented by the intensity of their average spectral reflectance. Based on their presumed protolith, the highly serpentinized ultramafics and serpentinites were classified into two main categories (dunite or harzburgite). The serpentinite with probable dunitic protolith, discriminated for a peculiar Fe-rich Ni-bearing lateritic crust, is more productive for chromite prospecting. This is particularly true at the contact with mafic dykes, akin to some worldwide chromite deposits. The results of our work highlight the potential of multi-scale satellite and UAV-based remote sensing to find footprints of some chromite mineral deposits.