Reviewerly/exHarmony huggingface dataset:
Development of an E-Commerce Chatbot for a University Shopping Mall

Chatbots have been used in many fields ranging from education to healthcare and are also used in e-commerce settings. This research aims at developing a web-based chatbot called Hebron for the Covenant University Community Mall. The chatbot is developed using Python and React.js as the programming languages and MySQL (Structured Query Language) server as the database to give a structure to the e-commerce datasets and Admin Portal process. The e-commerce chatbot application for Covenant University Shopping Mall (CUSM) seeks to provide an easy, smart, and comfortable shopping experience for the Covenant University Community.
Efficient Continuous Big Data Integrity Checking for Decentralized Storage

Decentralized storage powered by blockchain is becoming a new trend that allows data owners to outsource their data to remote storage resources offered by various storage providers. Unfortunately, unqualified storage providers easily encounter unpredictable downtime due to security threats, such as malicious attacks or system failures, which is unacceptable in many real-time or data-driven applications. As a result, continuous data integrity should be guaranteed in decentralized storage, which ensures that data is intact and available for the entire storage period. However, this requires frequent checking for long time periods and incurs heavy burdens of both communication and computation, especially in a big data scenario. In this paper, we propose an efficient continuous big data integrity checking approach for decentralized storage. We design a data-time sampling strategy that randomly checks the integrity of multiple files at each time slot with high checking probability. Furthermore, to tackle the fairness problem derived from the sampling strategy, we propose a fair approach by designing an arbitration algorithm with the verifiable random function. Security analysis shows the security of our approach under the random oracle model. Evaluation and experiments demonstrate that our approach is more efficient in the big data scenario compared with the state-of-the-arts.
DatingSec: Detecting Malicious Accounts in Dating Apps Using a Content-Based Attention Network

Dating apps have gained tremendous popularity during the past decade. Compared with traditional offline dating means, dating apps ease the process of partner finding significantly. While bringing convenience to hundreds of millions of users, dating apps are vulnerable to become targets of adversaries. In this article, we focus on malicious user detection in dating apps. Existing methods overlooked the signals hidden in the textual information of user interactions, particularly the interplay of temporal-spatial behaviors and textual information, leading to limited detection performance. To tackle this, we propose DatingSec, a novel malicious user detection system for dating apps. Concretely, DatingSec leverages long short-term memory neural networks (LSTM) and an attentive module to capture the interplay of users' temporal-spatial behaviors and user-generated textual content. We evaluate DatingSec on a real-world dataset collected from Momo, a widely used dating app with more than 180 million users. Experimental results show that DatingSec outperforms state-of-the-art methods and achieves an F1-score of 0.857 and AUC of 0.940.
Certificateless-Based Anonymous Authentication and Aggregate Signature Scheme for Vehicular Ad Hoc Networks

Development of Internet of Vehicles (IoV) has aroused extensive attention in recent years. The IoV requires an efficient communication mode when the application scenarios are complicated. To reduce the verifying time and cut the length of signature, certificateless aggregate signature (CL-AS) is used to achieve improved performance in resource-constrained environments like vehicular ad hoc networks (VANETs), which is able to make it effective in environments constrained by bandwidth and storage. However, in the real application scenarios, messages should be kept untamed, unleashed, and authentic. In addition, most of the proposed schemes tend to be easy to attack by signers or malicious entities which can be called coalition attack. In this paper, we present an improved certificateless-based authentication and aggregate signature scheme, which can properly solve the coalition attack. Moreover, the proposed scheme not only uses pseudonyms in communications to prevent vehicles from revealing their identity but also achieves considerable efficiency compared with state-of-the-art work, certificateless signature (CLS), and CL-AS schemes. Furthermore, it demonstrates that when focused on the existential forgery on adaptive chosen message attack and coalition attack, the proposed schemes can be proved secure. Also, we show that our scheme exceeds existing certification schemes in both computing and communication costs.
Using stable isotopes of Cu, Mo, S, and 87Sr/86Sr in hydrogeochemical mineral exploration as tracers of porphyry and exotic copper deposits

Groundwaters collected from the undisturbed Picaron Cu–Mo porphyry and exotic prospect (Atacama region, Chile) were analyzed for major and trace elements, along with isotopes of Cu, Mo, S, and 87Sr/86Sr. Sulfide minerals at Picaron have undergone oxidation by meteoric waters, resulting in the generation of acid and the lateral and downward migration of dissolved trace cations (Cd, Co, Cu, Ni, Pb, and Zn) and (oxy)anions (As, Mo, Sb, and Se). This paper presents a case study utilizing stable metal isotopes in hydrogeochemical mineral exploration to ascertain the possible hydrogeochemical footprints that can be detected around known mineralization. The groundwaters are weakly brackish (TDS < 3500 mg/L), reflected in relatively low trace metal concentrations. Despite this, the groundwaters reflect the dissolution of ore, as well as alteration and wall rock minerals proximal to porphyry mineralization with elevated dissolved concentrations of Ca, Mg, HCO3−, and SO42−. δ65Cu values range from 0.41 to 2.80‰, with values above 1‰ proximal to porphyry mineralization reflecting Cu fractionation during sulfide oxidation and the highest values (up to 2.80‰) proximal to exotic mineralization likely from Cu desorption. Downgradient, δ65Cu values decrease; possibility resulting from adsorption of the heavier 65Cu onto reactive surfaces or mixing with other sources. δ98Mo values proximal to porphyry mineralization (2.41 and 2.42‰) are much higher than the regional average for molybdenite reported in previous studies (0.11‰), suggesting acid neutralization and isotopic fractionation related to adsorption may be occurring close to the source. Much lower δ98Mo values downgradient (<1.0‰) are likely indicative of Mo input from surrounding country rocks. The hydrogeochemical trends observed in the Picaron groundwaters highlight the chemical processes of mineral dissolution, precipitation, and recharge pathways and indicate that the leaching of the porphyry mineralization could be the source of exotic mineralization. Ultimately, the results indicate the need for a multiple isotope approach to hydrogeochemical mineral exploration.
Evaluating existing water supply reservoirs as small-scale pumped hydroelectric storage options – A case study in Connecticut

Considering the reduction of steep power ramps caused by renewable energy penetration, the present study evaluates the potential of utilizing existing water supply infrastructure as small-scale pumped-hydroelectric storage (PHS) units. A novel methodology is developed that estimates the total storage capacity via the available space in five water supply systems in Connecticut (United States), by solely employing water level measurements and considering a water release schedule that favors power-demand peak reduction. The study culminates in the estimation of optimal areal requirements for hypothesized solar farms neighboring the studied systems, based on deficit minimization between storage capacity and energy generation over the study period. Analysis reveals that, in most cases, the seasonal pattern of the storage capacity is in accordance with the behavior of the corresponding hydraulic head timeseries, showing higher values in winter/autumn. Given certain assumptions regarding the infrastructure, a yearly average of approximately 95 MWh/day can be stored as an intersystem aggregate, illustrating that the amalgamation of existing water supply reservoirs could provide significant energy storage capacity to complement substation variability. Finally, the area covered by PV arrays increases proportionally with the storage capacity, while the calculated solar energy output demonstrates significant variability relative to the PHS storage potential.
The effectiveness of GitHub cloud services for implementing a programming training project: students’ point of view

Abstract In today’s IT industry, it is important to develop the ability of IT students to collaboratively develop software, professional and personal skills. An effective method for developing such skills in future IT specialists is to organize different types of educational projects related to different programming technologies during the execution of mini projects, group and individual project assignments, term papers, academic training within the academic disciplines. The paper summarizes the results of a pedagogical study involving 29 expert students who study Computer Science and Software Engineering and used cloud service for GitHub collaborative IT development projects. The research findings testify, the most effective characteristics of this service, according to experts, identified the possibility of collaborative development of software (i1), the convenience of bug tracking (i3) and the convenience of the code editor (i7). It offers examples and results of using GitHub cloud service in the process of executing educational projects by future IT specialists.
Text detection and script identification in natural scene images using deep learning

The detection of text in an image and identification of its language are important tasks in optical character recognition. Such tasks are challenging, particularly in natural scene images. Previous studies have been conducted with a focus on convolutional neural networks for script identification. In other studies, fully convolutional networks (FCNs) have been used for model enhancement and not as classifiers. In this study, we use FCNs for both model enhancement and classification. The proposed methodology improves the Efficient and Accurate Scene Text Detector by adding new FCN branches for script identification. Moreover, whereas most end-to-end (e2e) methods train the text detection and script identification models separately, we propose two e2e methods for jointly training the models, namely, multi-channel mask (MCM) and multi-channel segmentation (MCS). The results show that the performance of an MCM is similar to that of other state-of-the-art methods, whereas MCS outperforms existing methods with recall values of 54.34% and 81.13%, when using the ICDAR MLT 2017 and MLe2e datasets, respectively.
Transfer Learning with Radio Frequency Signals

Transfer learning has allowed for more widespread adaptation and expanded use of deep learning models in fields such as computer vision and speech recognition. The radio frequency (RF) domain has lagged behind and has yet to experience similar gains. Yet, at the same time, transfer learning is more relevant to tasks the RF domain. Few large labeled RF datasets exist and when they are available there is typically a difference between the dataset, i.e., the source data, and any target data application due to the myriad of variances that are introduced to RF signals, systems, and environments. Transfer learning focuses directly on this disparity between source and target data and changes in task, making it an ideal candidate to enable more widespread adaptation of deep learning within the RF domain. In this paper we explore several applications of transfer learning to RF signals using both synthetic and real-world communications signals. We focus on an RF fingerprinting task, using our RiftNet model as the base of our deep learning models. This paper presents approaches and results for RF transfer learning in the context of pre-trained models, re-tuning, unsupervised feature learning, and novel device detection. Moving beyond the methods and results presented in this paper, we hope this work motivates others to explore the potential and benefits of RF applications of transfer learning.
The formation of educational environment in foreign language training of energy engineering students by means of project technology

Abstract The article deals with the results of experimental work concerning the educational environment formation that is focused on the foreign language training of future energy engineering students. Project learning technology is chosen as the means of formation. A model of the educational environment for the metalanguage studying of the speciality of energy engineering students has been developed. In the educational process, educational projects with elements of professional orientation were implemented in accordance with the subject of the discipline “Business Foreign Language”, and the language abilities and internal potential of students were taken into account. The preparedness of energy engineering students to integrate knowledge of professionally oriented disciplines into a foreign language environment for solving project tasks of communicative nature has been determined. According to the results of the pedagogical experiment, it was established that in the process of project activity the mastering level of lexical and speech competences as the components of foreign language competence of students has significantly increased. The developed educational environment is characterized by differentiation, individuality, independence, autonomy, informativeness, creativity, which allows to create the necessary conditions for successful learning of foreign language, development of creative thinking, communicative skills of energy engineering students, formation of foreign, communicative, professional, informational, project, research competencies.
Single-Image super-resolution - When model adaptation matters

In recent years, impressive advances have been made in single-image super-resolution. Deep learning is behind much of this success. Deep(er) architecture design and external prior modeling are the key ingredients. The internal contents of the low-resolution input image are neglected with deep modeling, despite earlier works that show the power of using such internal priors. In this paper, we propose a variation of deep residual convolutional neural networks, which has been carefully designed for robustness and efficiency in both learning and testing. Moreover, we propose multiple strategies for model adaptation to the internal contents of the low-resolution input image and analyze their strong points and weaknesses. By trading runtime and using internal priors, we achieve improvements from 0.1 to 0.3 dB PSNR over the reported results on standard datasets. Our adaptation especially favors images with repetitive structures or high resolutions. It indicates a more practical usage when our adaption approach applies to sequences or videos in which adjacent frames are strongly correlated in their contents. Moreover, the approach can be combined with other simple techniques, such as back-projection and enhanced prediction, to realize further improvements.
Development of the Ability for Professional Interaction in Future Engineers at a Research University

The paper states that the development of the ability for professional communication in a future engineer is a prerequisite for him becoming a skilled specialist, as well as a significant factor in his future professional activity. The solution of production problems and fruitful interdisciplinary team work are facilitated by the communicative and rhetorical qualities of an engineer who is ready for productive business communication and effective presentation of his position, as well as the presentation of work results. The future engineers' professional communication ability should be developed at the university level. We proposed the rationale for its effective development through the creation of a set of relevant pedagogical conditions: the introduction of Rhetoric as an additional discipline or the modernization of the program of an existing subject with a similar focus; the use of specially designed educational techniques and methods for the development of communicative and rhetorical competence; training university staff in the conditions of advanced training; creation by the university of conditions for students' active research and social activities; cooperation of educational institutions with large enterprises, companies and research centers. The paper also provides a pedagogical experiment conducted under the aegis of the Kazan National Research Technological University. We have developed the recommendations for the selection and structuring of the material as well as methods for developing the future engineers' ability to professional communication at a research university; we have also identified problems that require further consideration, including the search for innovative methods.
Explaining Fear of Identity Theft Victimization Using a Routine Activity Approach

The current study aims to estimate and explain citizens’ fear of identity theft victimization by examining data collected from a nationally representative sample of South Korean residents. Specifically, we compared participants’ levels of fear of identity theft victimization with fear of other types of crime using paired-samples t-tests. We found that fear of identity theft victimization is significantly higher than fear of other types of crime. Drawing on routine activity theory, we explored the relationship between victimization (i.e., identity theft), online proximity to motivated offenders (i.e., phishing), online exposure to motivated offenders (e.g., online banking and shopping), target suitability (e.g., downloading pirated media), and fear of identity theft victimization. Results from ordinal logistic regression models suggest that victimization and online exposure to motivated offenders were significantly related to fear of identity theft victimization.
Particle Swarm Optimization—An Adaptation for the Control of Robotic Swarms

Particle Swarm Optimization (PSO) is a numerical optimization technique based on the motion of virtual particles within a multidimensional space. The particles explore the space in an attempt to find minima or maxima to the optimization problem. The motion of the particles is linked, and the overall behavior of the particle swarm is controlled by several parameters. PSO has been proposed as a control strategy for physical swarms of robots that are localizing a source; the robots are analogous to the virtual particles. However, previous attempts to achieve this have shown that there are inherent problems. This paper addresses these problems by introducing a modified version of PSO, as well as introducing new guidelines for parameter selection. The proposed algorithm links the parameters to the velocity and acceleration of each robot, and demonstrates obstacle avoidance. Simulation results from both MATLAB and Gazebo show close agreement and demonstrate that the proposed algorithm is capable of effective control of a robotic swarm and obstacle avoidance.
Compressing Visual-linguistic Model via Knowledge Distillation

Despite exciting progress in pre-training for visual-linguistic (VL) representations, very few aspire to a small VL model. In this paper, we study knowledge distillation (KD) to effectively compress a transformer based large VL model into a small VL model. The major challenge arises from the inconsistent regional visual tokens extracted from different detectors of Teacher and Student, resulting in the misalignment of hidden representations and attention distributions. To address the problem, we retrain and adapt the Teacher by using the same region proposals from Student’s detector while the features are from Teacher’s own object detector. With aligned network inputs, the adapted Teacher is capable of transferring the knowledge through the intermediate representations. Specifically, we use the mean square error loss to mimic the attention distribution inside the transformer block, and present a token-wise noise contrastive loss to align the hidden state by contrasting with negative representations stored in a sample queue. To this end, we show that our proposed distillation significantly improves the performance of small VL models on image captioning and visual question answering tasks. It reaches 120.8 in CIDEr score on COCO captioning, an improvement of 5.1 over its non-distilled counterpart; and an accuracy of 69.8 on VQA 2.0, a 0.8 gain from the baseline. Our extensive experiments and ablations confirm the effectiveness of VL distillation in both pre-training and fine-tuning stages.
Reversible Data Hiding Based on Adaptive Multiple Histograms Modification

Pixel value ordering prediction has been verified as an effective mechanism to exploit image redundancy for reversible data hiding (RDH) and numerous extensions have been devised. However, their performance is still unsatisfactory since the error modification is generally fixed and independent of image content. In this paper, a new RDH scheme is proposed by incorporating pixel distance to realize adaptive multiple histograms modification (AMHM). During exploiting the correlation between the largest/smallest pixel and any other one in the scope of pixel block, we propose to process every two correlated pixels successively following the ascending order of their distance. Specifically, the generated errors with a given distance are collected and verified. If they are all shiftable errors, the follow-up errors would be collected into the next sub-histogram. In this way, a histogram sequence is adaptively generated such that different modification mechanisms can be taken for different sub-histograms to achieve adaptive embedding. Finally, AMHM for conventional prediction-error expansion (PEE) and AMHM for 2D PEE have been both realized in this paper. Experimental results show that AMHM is of great significance to better exploit pixel correlation and the proposed scheme outperforms a series of the latest schemes.
Bayesian Deep Basis Fitting for Depth Completion with Uncertainty

In this work we investigate the problem of uncertainty estimation for image-guided depth completion. We extend Deep Basis Fitting (DBF) [54] for depth completion within a Bayesian evidence framework to provide calibrated perpixel variance. The DBF approach frames the depth completion problem in terms of a network that produces a set of low-dimensional depth bases and a differentiable least squares fitting module that computes the basis weights using the sparse depths. By adopting a Bayesian treatment, our Bayesian Deep Basis Fitting (BDBF) approach is able to 1) predict high-quality uncertainty estimates and 2) enable depth completion with few or no sparse measurements. We conduct controlled experiments to compare BDBF against commonly used techniques for uncertainty estimation under various scenarios. Results show that our method produces better uncertainty estimates with accurate depth prediction.