Reviewerly/exHarmony huggingface dataset:
Security-enhanced multiple-image encryption based on modified iterative phase retrieval algorithm with structured phase mask in Fresnel domain

A security-enhanced multiple-image encryption method is proposed based on modified iterative phase retrieval algorithm with structured phase mask in Fresnel domain in this paper, where each plaintext is encoded to only one independent phase-only mask with the help of the used structured phase mask, and a group of phase-only masks retrieved by multiple plaintexts are integrated into the ciphertext with the noise-like distribution by using the phase mask multiplexing technique. In the proposed method, the Fresnel diffraction distances and the wavelength of light as well as the optical parameters of the structured phase mask such as focal length, topological charge number are all used as digital keys to expand the key space. Most importantly, the sensitivities of the digital keys are substantially improved by mapping the digital keys with the initial values of chaotic system thanks to the nonlinear, pseudo-random, high sensitivity of initial value of special phase generated by chaotic system, contributing to an extremely high security of multiple-image encryption method. Moreover, computational simulations are performed to demonstrate the feasibility of the proposed security-enhanced multiple-image encryption method, and the simulation results confirm that the proposed method exhibits high effectiveness and strong robustness.
Digital image correlation method based on quasi-conformal mapping for large deformation measurement

Digital image correlation (DIC) has the advantages of non-contact full-field measurement and high measurement accuracy in optical mechanics, but still faces challenges in large deformation measurement. This work developed an initial estimation algorithm, using geometric distortion as an indicator. And the more accurate deformation is adjusted by the modified inverse compositional Gauss-Newton (IC-GN) algorithm that introduces the inverse mapping of the initial estimation. The simulation experiment proves that the proposed method has good measurement ability for large deformation, and the accuracy is about 0.02 pixels. The uniaxial tensile test of vulcanized rubber and the three-point bending test of the polycarbonate sheet are used to verify the effectiveness of the method in this paper.
DUASVS: A Mobile Data Saving Strategy in Short-Form Video Streaming

Fueled by the emerging short video applications (e.g., TikTok), streaming short-form videos nowadays is ubiquitous among mobile users. During the viewing, one common action is to scroll the screen to switch videos, which is a handy operation for the viewers to quickly search for content of interest. However, our empirical measurements reveal that frequent video switching can result in nearly half of the mobile data quota being used for transferring the video data that is never watched. This problem is called data loss in this work. Given the immense cost of the network infrastructure, such a high proportion of data loss is financially tremendous to both mobile users and streaming vendors. To tackle the problem, this study proposes a novel system called Data Usage Aware Short Video Streaming (DUASVS), where a new Integrated Learning is used to capture the characters of past network conditions and then trains intelligent adaptation models to reduce data loss and save data usage. Extensive evaluations show that DUASVS is able to save 70.7%∼83.2% of mobile data usage without incurring any QoE degradation. Moreover, the system exhibits strong robustness, performing consistently over a wide range of network environments as well as video streaming sessions.
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si470.svg"><mml:mrow><mml:mi>∊</mml:mi></mml:mrow></mml:math>-Kernel-free soft quadratic surface support vector regression

In this paper, we propose a new regression method called the ∊-kernel-free soft quadratic surface support vector regression (∊-SQSSVR). After converting the n-dimensional regression problem into the (n+1)-dimensional classification problem, the principle of maximizing the sum of relative geometrical margin of each training point is used to construct our optimization problem, where the quadratic surface is restricted to be a hyperparaboloid by setting both the (n+1)-th row and (n+1)-th column of the corresponding matrix to be zero. The existence and uniqueness of the optimal solution to both primal and dual problems are also addressed. It should be pointed out that our model is nonlinear and kernel-free, so it does not need to select kernel function and corresponding parameters. At the same time, it is highly interpretable. In addition, our model is still a quadratic convex programming problem similar to the standard SVR. To visualize the effectiveness of our ∊-SQSSVR, 6 artificial datasets and 15 benchmark datasets are implemented in numerical experiments. The results show that our method is less time-consuming and as good as the nonlinear standard SVR with kernel function in comprehensive performances.
Feasibility of using QR code for registration &amp; evaluation of training and its ability to increase response rate – The learners' perception

Taking learners' attendance and obtaining an evaluation of teaching is a routine activity performed by teachers. The traditional method of taking attendance using pen and paper posed a huge challenge during the COVID-19 pandemic. This has been time-consuming as compared to pre-COVID as well as frustrating for learners waiting in a queue for their turn to sign the register. Quick Response (QR) Codes were used to complete registration to buy back the time consumed using traditional methods of registration. Learners' evaluations are used as an instrument to evaluate teaching quality. At the researcher's workplace, a traditional paper-based evaluation method has been used for decades. However, over time a significant decrease in the response rate of evaluations was noticed. The pandemic provided an opportunity of using QR Codes to obtain learners' evaluation of teaching quality. This study assessed the learners' perception of using QR Codes for registration and evaluation, and the likelihood of learners completing the evaluation surveys, thus increasing the response rate. Participants of the study were asked to complete an electronic survey to help assess their perception of using QR Codes and a comparison was made between the responses gathered using paper-based evaluations over 5 months and QR Code evaluations over the same 5 months in the following year. The results of this study demonstrate that using QR Codes for registration and evaluation is easy and straightforward, thus increasing the likelihood of learners completing the evaluation. The comparison between paper-based and QR Code evaluations confirms that a substantial increase in response rate can be achieved by using QR Code evaluations.
Bearing-only 2D maneuvering target tracking using smart interacting multiple model filter

In this paper, along with reviewing and analyzing the maneuvering target tracking model, the multiple-model Interacting Multiple Model algorithm is used to solve the maneuvering target tracking problem in the presence of measurement noise. In addition, for reliable estimation another method is proposed, which uses higher-order Markov models to describe the system behavior precisely. It means that the previous two models are used to predict the next model of target in order to present a more better algorithm than the first-order IMM algorithm. In this approach, two models are employed. For each model Extended Kalman Filter is used to randomly estimate states of the target. The final estimation of the maneuvering target consists of these two models. Final target estimation is obtained from a weighted sum of all state estimates. In addition, target tracking is presented with two modes for noise measurement: one is an adaptive method and the other is an assignment of an integer amount considering problem circumstances. In the end, the results are compared.
A new anisotropic fourth-order diffusion equation model based on image features for image denoising

&lt;p style='text-indent:20px;'&gt;Image denoising has always been a challenging task. For performing this task, one of the most effective methods is based on variational PDE. Inspired by the LLT model, we first propose a new adaptive LLT model by adding a weighted function, and then we propose a class of fourth-order diffusion equations based on the new functional. Owing to the adaptive function, the new functional is better than the LLT model and other fourth-order models in terms of edge preservation. While generalizing the Euler-Lagrange equation of the new functional, we discuss a new fourth-order diffusion framework for image denoising. Different from those of other fourth-order diffusion models, the new diffusion coefficients depend on the first-order and second-order derivatives, which can preserve edges and smooth images, respectively. Regarding numerical implementations, we first design an explicit scheme for the proposed model. However, fourth-order diffusion equations require strict stability conditions, and the number of iterations needed is considerable. Consequently, we apply the fast explicit diffusion algorithm (FED) to the explicit scheme to reduce the time consumption of the proposed approach. Furthermore, the additive operator splitting (AOS) scheme is applied for the numerical implementation, and it is the most efficient among all of our algorithms. Finally, compared with other models, the new model exhibits superior effectiveness and efficiency.&lt;/p&gt;
Realtime Object Detection of Pantry Objects Using YOLOv5 Transfer Learning in Varying Lighting and Orientation

This paper describes the use of YOLOv5 transfer learning from the COCO dataset to train and deploy a custom model to detect select pantry objects in various lighting and orientations using an original custom dataset with applied brightness and saturation augmentations. The results show that the trained model using the custom dataset obtained an mAP(0.5) of 0.9948 at 87 epochs of training and an accuracy of 100% detections and 95% average confidence level for the Koko Krunch set and an accuracy of 100% detections and 91% average confidence level for the Lady's Choice Mayonnaise set during validation.
Empirical analysis of federated learning in heterogeneous environments

Federated learning (FL) is becoming a popular paradigm for collaborative learning over distributed, private datasets owned by non-trusting entities. FL has seen successful deployment in production environments, and it has been adopted in services such as virtual keyboards, auto-completion, item recommendation, and several IoT applications. However, FL comes with the challenge of performing training over largely heterogeneous datasets, devices, and networks that are out of the control of the centralized FL server. Motivated by this inherent setting, we make a first step towards characterizing the impact of device and behavioral heterogeneity on the trained model. We conduct an extensive empirical study spanning close to 1.5K unique configurations on five popular FL benchmarks. Our analysis shows that these sources of heterogeneity have a major impact on both model performance and fairness, thus shedding light on the importance of considering heterogeneity in FL system design.
Scoring of Large-Margin Embeddings for Speaker Verification: Cosine or PLDA?

The emergence of large-margin softmax cross-entropy losses in training deep speaker embedding neural networks has triggered a gradual shift from parametric back-ends to a simpler cosine similarity measure for speaker verification. Popular parametric back-ends include the probabilistic linear discriminant analysis (PLDA) and its variants. This paper investigates the properties of margin-based cross-entropy losses leading to such a shift and aims to find scoring back-ends best suited for speaker verification. In addition, we revisit the pre-processing techniques which have been widely used in the past and assess their effectiveness on large-margin embeddings. Experiments on the state-of-the-art ECAPA-TDNN networks trained with various large-margin softmax cross-entropy losses show a substantial increment in intra-speaker compactness making the conventional PLDA superfluous. In this regard, we found that constraining the within-speaker covariance matrix could improve the performance of the PLDA. It is demonstrated through a series of experiments on the VoxCeleb-1 and SITW core-core test sets with 40.8% equal error rate (EER) reduction and 35.1% minimum detection cost (minDCF) reduction. It also outperforms cosine scoring consistently with reductions in EER and minDCF by 10.9% and 4.9%, respectively.
Digital health care services in post COVID-19 scenario: modeling the enabling factors

Purpose The recent pandemic of COVID-19 has posed challenges for delivering essential and desirable health-care services for the masses. Digital health-care services initiated by several hospitals and health practitioners promise efficient and safe health care in the new normal post-COVID era but need a supportive enabling ecosystem. Therefore, this study aims toward identifying and modeling the key enabling factors for digital health-care services. Design/methodology/approach A total of nine factors were identified from the literature review and verified by the domain experts which can enable the wider acceptance of digital health-care services. The identified factors were then modeled with the help of the total interpretive structural modeling (TISM) approach and fuzzy Matrices d’Impacts Croises Multiplication Appliquée à un Classement (MICMAC) and a meaningful contextual relationship were developed for the factors. Findings This study reflects that the trust of patients is required for the acceptance of digital health care. Quality of patient care and affordability cum accessibility of online services will define mass engagement. Hospital staff resilience, hospital care service capacity, strategic partnerships and collaborations supported by technology and regulatory structure are the major factors defining the enabling ecosystem. Originality/value This study has its uniqueness in the way the TISM approach and fuzzy MICMAC are used for modeling the enabling factors toward growth and acceptance of digital health-care services in the days to come in developing nations. The focus of this study can be considered as relevant for the study interested in investigating the role of cognitive dimensions in influencing actors’ behaviors and decisions.
Can Blockchain technology help small islands achieve sustainable tourism? A perspective on North Cyprus

Purpose The purpose of this study is to explore the presence and use of Blockchain technologies (BCTs) in the North Cyprus hotel and tourism sectors by eliciting stakeholder perspectives on BCT's role in sustainable tourism development and the current state of its application in North Cyprus. Design/methodology/approach A qualitative methodology was used and primary data gathered via telephone interviews with sixteen managers of casino hotels. The researchers also used an observation technique for data collection in order to better analyse the existing case situation. Findings Although the findings show similarities in the stakeholders' awareness of cryptocurrencies, they lack overall knowledge of BCTs. The responses of participants indicate a lack of knowledge regarding the use of BCTs, particularly the areas in which they can be used. North Cyprus' tourism and hotel sectors are significantly behind the curve in terms of adopting innovative technologies, due to traditional operational concerns and to the limited scope of policy development. Research limitations/implications Policymakers should consider the practical implications for both the sector's and the country's resources in order to maximize environmental and financial gains. Practical implications The promotion of sustainable tourism can be facilitated by the wider adoption of BCTs as it could enable operators to manage their network relationships and business operations in a more socially and environmentally responsible manner. Originality/value No prior studies were found that evaluate the presence of BCTs in support of tourism development in North Cyprus. This study will assist practitioners in comprehending the practical implications and will enable policymakers to develop sector-specific policies.
Squeezy-Feely: Investigating Lateral Thumb-Index Pinching as an Input Modality

From zooming on smartphones and mid-air gestures to deformable user interfaces, thumb-index pinching grips are used in many interaction techniques. However, there is still a lack of systematic understanding of how the accuracy and efficiency of such grips are affected by various factors such as counterforce, grip span, and grip direction. Therefore, in this paper, we contribute an evaluation (N = 18) of thumb-index pinching performance in a visual targeting task using scales up to 75 items. As part of our findings, we conclude that the pinching interaction between the thumb and index finger is a promising modality also for one-dimensional input on higher scales. Furthermore, we discuss and outline implications for future user interfaces that benefit from pinching as an additional and complementary interaction modality.
Image Manipulation Localization Using Attentional Cross-Domain CNN Features

Along with the advancement of manipulation technologies, image modification is becoming increasingly convenient and imperceptible. To tackle the challenging image tampering detection problem, this article presents an attentional cross-domain deep architecture, which can be trained end-to-end. This architecture is composed of three convolutional neural network (CNN) streams to extract three types of features, including visual perception, resampling, and local inconsistency features, from spatial and frequency domains. The multitype and cross-domain features are then combined to formulate hybrid features to distinguish manipulated regions from nonmanipulated parts. Compared with other deep architectures, the proposed one spans a more complementary and discriminative feature space by integrating richer types of features from different domains in a unified end-to-end trainable framework and thus can better capture artifacts caused by different types of manipulations. In addition, we design and train a module called tampering discriminative attention network (TDA-Net) to highlight suspicious parts. These part-level representations are then integrated with the global ones to further enhance the discriminating capability of the hybrid features. To adequately train the proposed architecture, we synthesize a large dataset containing various types of manipulations based on DRESDEN and COCO. Experiments on four public datasets demonstrate that the proposed model can localize various manipulations and achieve the state-of-the-art performance. We also conduct ablation studies to verify the effectiveness of each stream and the TDA-Net module.
Relation Extraction as Open-book Examination

Pre-trained language models have contributed significantly to relation extraction by demonstrating remarkable few-shot learning abilities. However, prompt tuning methods for relation extraction may still fail to generalize to those rare or hard patterns. Note that the previous parametric learning paradigm can be viewed as memorization regarding training data as a book and inference as the close-book test. Those long-tailed or hard patterns can hardly be memorized in parameters given few-shot instances. To this end, we regard RE as an open-book examination and propose a new semiparametric paradigm of retrieval-enhanced prompt tuning for relation extraction. We construct an open-book datastore for retrieval regarding prompt-based instance representations and corresponding relation labels as memorized key-value pairs. During inference, the model can infer relations by linearly interpolating the base output of PLM with the non-parametric nearest neighbor distribution over the datastore. In this way, our model not only infers relation through knowledge stored in the weights during training but also assists decision-making by unwinding and querying examples in the open-book datastore. Extensive experiments on benchmark datasets show that our method can achieve state-of-the-art in both standard supervised and few-shot settings
Sound black-box checking in the LearnLib

In black-box checking (BBC) incremental hypotheses on the behavior of a system are learned in the form of finite automata, using information from a given set of requirements, specified in Linear-time Temporal Logic (LTL). The LTL formulae are checked on intermediate automata and potential counterexamples are validated on the actual system. Spurious counterexamples are used by the learner to refine these automata. We improve BBC in two directions. First, we improve checking lasso-like counterexamples by assuming a check for state equivalence. This provides a sound method without knowing an upper-bound on the number of states in the system. Second, we propose to check the safety portion of an LTL property first, by deriving simple counterexamples using monitors. We extended LearnLib's system under learning API to make our methods accessible, using LTSmin as model checker under the hood. We illustrate how LearnLib's most recent active learning algorithms can be used for BBC in practice. Using the RERS 2017 challenge, we provide experimental results on the performance of all LearnLib's active learning algorithms when applied in a BBC setting. We will show that the novel incremental algorithms TTT and ADT perform the best. We also provide experiments on the efficiency of various BBC strategies.
A Machine Learning Based Email Spam Classification Framework Model: Related Challenges and Issues

Spam emails, also known as non-self, are unsolicited commercial emails or fraudulent emails sent to a particular individual or company, or to a group of individuals. Machine learning algorithms in the area of spam filtering is commonly used. There has been a lot of effort to render spam filtering more efficient in classifying e-mails as either ham (valid messages) or spam (unwanted messages) through the ML classifiers. We may recognize the distinguishing features of the material of documents. Much important work has been carried out in the area of spam filtering which cannot be adapted to various conditions and problems which are limited to certain domains. Our analysis contrasts the positives methods as well as some shortcomings of current ML methods and open spam filters study challenges. We suggest some of the new ongoing approaches towards deep leaning as potential tactics that can tackle the challenge of spam emails efficiently.
Kaspersky: The State of Industrial Cyber security

When it comes to information security, many manufacturers, utilities and other firms operating in the industrial sector are coming late to the party. A legacy of isolated operational technology (OT) systems running on obscure, proprietary architectures has resulted in too many believing they are immune to the cyber risks facing business IT. They are not.