Reviewerly/exHarmony huggingface dataset:
Application of Weber-Fechner Law in Image Transmission in the Field of Onboard Communications

In the modern world, image transmission, including transmission of television and video signals, is carried out using a preliminary and restorative correction according to the law, which was due to the use of imaging devices on cathode ray tubes. This law differs from the law of dependence of the visibility threshold of a visual stimulus on its magnitude, but continues to be used for compatibility reasons. Therefore, in the work, instead of linearizing the sensations of the quantization scale of a television signal, it is proposed to optimize the choice of code combinations for image transmission. This approach can be effective in television systems without compression of video information with losses, for example, for transmitting signals over short distances or in channels for transmitting signals of the image of Earth remote sensing systems.
DPSelect: A Differential Privacy Based Guard Relay Selection Algorithm for Tor

Abstract Recent work has shown that Tor is vulnerable to attacks that manipulate inter-domain routing to compromise user privacy. Proposed solutions such as Counter-RAPTOR [29] attempt to ameliorate this issue by favoring Tor entry relays that have high resilience to these attacks. However, because these defenses bias Tor path selection on the identity of the client, they invariably leak probabilistic information about client identities. In this work, we make the following contributions. First, we identify a novel means to quantify privacy leakage in guard selection algorithms using the metric of Max-Divergence. Max-Divergence ensures that probabilistic privacy loss is within strict bounds while also providing composability over time. Second, we utilize Max-Divergence and multiple notions of entropy to understand privacy loss in the worst-case for Counter-RAPTOR. Our worst-case analysis provides a fresh perspective to the field, as prior work such as Counter-RAPTOR only analyzed average case-privacy loss. Third, we propose modifications to Counter-RAPTOR that incorporate worst-case Max-Divergence in its design. Specifically, we utilize the exponential mechanism (a mechanism for differential privacy) to guarantee a worst-case bound on Max-Divergence/privacy loss. For the quality function used in the exponential mechanism, we show that a Monte-Carlo sampling-based method for stochastic optimization can be used to improve multi-dimensional trade-offs between security, privacy, and performance. Finally, we demonstrate that compared to Counter-RAPTOR, our approach achieves an 83% decrease in Max-Divergence after one guard selection and a 245% increase in worst-case Shannon entropy after 5 guard selections. Notably, experimental evaluations using the Shadow emulator shows that our approach provides these privacy benefits with minimal impact on system performance.
Incremental learning of concept drift in Multiple Instance Learning for industrial visual inspection

Most Multiple Instance Learning (MIL) algorithms are designed with the assumption that the target concept is stationary in time, i.e. it is drawn from a stationary unknown distribution. However, in real industrial applications, like automatic visual inspection where defects may evolve, MIL has to deal with changing target concepts whose statistical characteristics may vary over time. Despite this fact, there is little discussion about how to learn from data in non-stationary environments (or data with concept drift) using multiple instance learners. In this work, an incremental MIL algorithm is proposed in order to learn non-stationary and recurrent target concepts in industrial visual inspection applications. Experiments on both synthetic and real-world datasets are conducted to test the performance of the proposed approach. Real-world datasets come from the automatic visual inspection task in industry. The experimental results show that the proposed approach is able to handle changing target concepts over time.
Triplet Loss Network for Unsupervised Domain Adaptation

Domain adaptation is a sub-field of transfer learning that aims at bridging the dissimilarity gap between different domains by transferring and re-using the knowledge obtained in the source domain to the target domain. Many methods have been proposed to resolve this problem, using techniques such as generative adversarial networks (GAN), but the complexity of such methods makes it hard to use them in different problems, as fine-tuning such networks is usually a time-consuming task. In this paper, we propose a method for unsupervised domain adaptation that is both simple and effective. Our model (referred to as TripNet) harnesses the idea of a discriminator and Linear Discriminant Analysis (LDA) to push the encoder to generate domain-invariant features that are category-informative. At the same time, pseudo-labelling is used for the target data to train the classifier and to bring the same classes from both domains together. We evaluate TripNet against several existing, state-of-the-art methods on three image classification tasks: Digit classification (MNIST, SVHN, and USPC datasets), object recognition (Office31 dataset), and traffic sign recognition (GTSRB and Synthetic Signs datasets). Our experimental results demonstrate that (i) TripNet beats almost all existing methods (having a similar simple model like it) on all of these tasks; and (ii) for models that are significantly more complex (or hard to train) than TripNet, it even beats their performance in some cases. Hence, the results confirm the effectiveness of using TripNet for unsupervised domain adaptation in image classification.
The Effects of Calibration Target, Screen Location, and Movement Type on Infant Eye‐Tracking Data Quality

During infant eye‐tracking, fussiness caused by the repetition of calibration stimuli and body movements during testing are frequent constraints on measurement quality. Here, we systematically investigated these constraints with infants and adults using EyeLink 1000 Plus. We compared looking time and dispersion of gaze points elicited by stimuli resembling commonly used calibration animations. The adult group additionally performed body movements during gaze recording that were equivalent to movements infants spontaneously produce during testing. In our results, infants’ preference for a particular calibration target did not predict data quality elicited by that stimulus, but targets exhibiting the strongest contrasts in their center or targets with globally distributed complexity resulted in the highest accuracy. Our gaze measures from the adult movement tasks were differentially affected by the type of movement as well as the location where the target appeared on the screen. These heterogeneous effects of movement on measures should be taken into account when planning infant eye‐tracking experiments. Additionally, to improve data quality, infants’ tolerance for repeated calibrations can be facilitated by alternating between precise calibration targets.
Are 3D virtual environments better than 2D interfaces in serious games performance? An explorative study for the assessment of executive functions

Executive functions refer to higher-order cognitive processes that supervise and guide goaldirected and adaptive behaviors in response to everyday situations.The traditional measures used to assess executive functions include paper-and pencil tests and/or computerized tests that have been found to have a moderate level of ecological validity in predicting real-world performance.Serious games (SG) represent a novel methodological approach, allowing investigating subjects' performance in real-simulated situations.Serious games are computer games which primary purposes include investigating human behaviors and changes.Furthermore, SG can also vary according to the technology used and the interaction.Indeed, a SG can be rendered via a non-immersive screen-based (2D) or via an immersive virtual reality game (3D).Starting from these premises, we compared a narrative-contextualized SG in 2D and 3D, correlating them with traditional tests.Findings showed different condition correlations with the traditional tasks and the comparison between the two systems have revealed that 3D is able to generate lower reaction times, higher correct answers, and lower perseverative responses in attentional abilities, inhibition control, and cognitive shifting than 2D condition.The present study yielded evidence on the use of more ecological tools to identify the functional cognitive status in real-simulated contexts along with traditional evaluation.
An image hiding scheme in a 2-dimensional coupled map lattice of matrices

An image hiding scheme in a 2-dimensional coupled map lattice of matrices is presented in this paper. Scalar variables at each node of the lattice are replaced by nilpotent matrices. The spatiotemporal divergence process is employed to hide the secret digital image in the state map of the nodal variables. The presented image hiding scheme does not require the computation of the difference image between two patterns produced by the perturbed and the unperturbed initial conditions. Computational experiments are used to demonstrate the efficacy of the presented technique.
Overview of end-to-end speech recognition

In the 1960s, automatic speech recognition has been widely studied. In the past, HMM has been the mainstream of the acoustic model. With the development of machine learning, neural network is introduced to the speech recognition, relying on neural network's strong learning ability. Thus the acoustic model of DNN has significantly improved the voice recognition rate, compared to the HMM model. In order to simplify the traditional speech recognition system, the end-to-end speech recognition method is proposed. This paper mainly introduces and analyzes the end-to-end system, and the main two models of CTC and attention, as well as the prospect of future speech recognition research.
Comments on a lightweight cloud auditing scheme: Security analysis and improvement

In a cloud storage service, public auditing mechanisms allow a third party to verify integrity of the outsourced data on behalf of data users without the need to retrieve data from the cloud server. Recently, Shen et al. proposed a new lightweight and privacy preserving cloud data auditing scheme which employs a third party medium to perform time-consuming operations on behalf of users. The authors have claimed that the scheme meets the security requirements of public auditing mechanisms. In this paper, we show that Shen et al.'s scheme is insecure by proposing two attacks on the scheme. In the first attack, an outside adversary can modify some messages in transmission to the cloud server and forge a valid authenticator on the modified data block. In the second attack, the dishonest cloud server arbitrarily manipulates the received data blocks, and in both attacks data manipulation is not detected by the auditor in the verification phase. Accordingly, the scheme is insecure for cloud storage auditing. We next enhance the security of Shen et al.'s scheme to overcome the proposed attacks, evaluate the performance and perform experiments to demonstrate the practicality of the improved scheme.
Re-implementing a legacy system

Re-implementation is one of the alternatives to migrate a legacy software system next to conversion, wrapping and redevelopment. It is a compromise solution between automated conversion and complete redevelopment. The technical architecture can be revised and the code replaced, but the functional architecture – the use cases remains as it was. The challenge of this approach is to preserve the functionality while changing the technical implementation. This approach is taken when conversion is not feasible and redevelopment is too expensive or too great a risk. It entails more than a 1:1 transformation but less than a total rewrite. The same components remain with different contents. In this paper the case for reimplementation is presented and the process described. The tools required to support the process are identified and their use illustrated. Finally, two industrial case studies are presented, one with a VisualAge/ PL/I-DB2 system and one with a COBOL-IMS application.
Projection-free kernel principal component analysis for denoising

Kernel principal component analysis (KPCA) forms the basis for a class of methods commonly used for denoising a set of multivariate observations. Most KPCA algorithms involve two steps: projection and preimage approximation. We argue that this two-step procedure can be inefficient and result in poor denoising. We propose an alternative projection-free KPCA denoising approach that does not involve the usual projection and subsequent preimage approximation steps. In order to denoise an observation, our approach performs a single line search along the gradient descent direction of the squared projection error. The rationale is that this moves an observation towards the underlying manifold that represents the noiseless data in the most direct manner possible. We demonstrate that the approach is simple, computationally efficient, robust, and sometimes provides substantially better denoising than the standard KPCA algorithm.
A survey on association rule mining based on evolutionary algorithms

Searching for patterns in large database is one of the major tasks in data mining. This can be achieved by using association rule mining, which usually tends to find out the relations in an exhaustive manner. In real life, many data mining tasks require optimization between multiple objectives concurrently. Consequently over the years a large number of researches have been conducted on various techniques for efficient association rule mining and among them the field of evolutionary technique is growing rapidly with its large-scale applications and exceptional result. This research includes a systematical structured review of a wide range of state-of-the-art and recent multi-objective evolutionary algorithms (MOEAs) in terms of their chromosome representation, genetic operators and initial population, which are applied to categorical, quantitative and fuzzy rule mining problems. A lucid comparative study on various MOEA-based approaches includes computational complexity and applications within the context of this research. Finally, a guideline toward future studies on MOEA approaches has been presented that incorporates a general discussion of the current state of art literatures, newly rising study area (Interactive MOEA) along with their limitations and numerous possibilities.
An optimization model for poverty Alleviation fund audit mode based on BP neural network

Tracking audit of poverty alleviation policy is an important guarantee to win the battle against poverty. Effective prevention and control of audit risk in tracking audit of poverty alleviation policy is the key to ensure the improvement of audit quality. According to the characteristics of financi al audit, this paper analyzes the main factors affecting the operation of loan enterprises, puts forward a neural network model for financial audit, and gives the solution of the model. This paper takes poverty alleviation audit as an example, centering on the goal of full coverage of the audit, and based on poverty alleviation data, integrating relevant data in relevant fields to build poverty alleviation fund audit, so as to achieve full coverage of poverty alleviation audit. Utilize emerging technologies, give full play to the role of full coverage of audit supervision and supervision on precision poverty alleviation, and participate in project consulting and auditing in advance, real-time online tracking audit, and post-performance performance evaluation audit. We will promote the construction of accurate poverty alleviation information, strengthen economic responsibility audits, and more effectively monitor the authenticity of poverty alleviation funds, effectively implement poverty alleviation projects, and achieve accurate poverty alleviation efficiently. The model can better help the auditors to accurately determine the basic status of the audit object and provide a strong guarantee for quickly determining the audit focus.
A role for optics in AI hardware

Experiments show how an all-optical version of an artificial neural network — a type of artificial-intelligence system — could potentially deliver better energy efficiency can conventional computing approaches. Tackling the energy efficiency of computing technology.
Two-Dimensional Phase Unwrapping via Balanced Spanning Forests

Phase unwrapping is the process of recovering a continuous phase signal from an original signal wrapped in the ($-\pi$,$\pi$] interval. It is a critical step of coherent signal processing, with applications such as synthetic aperture radar, acoustic imaging, magnetic resonance, X-ray crystallography, and seismic processing. In the field of computational optics, this problem is classically treated as a norm-minimization problem, in which one seeks to minimize the differences between the gradients of the original wrapped signal and those of the continuous unwrapped signal. When the L0-norm is considered, the number of differences should be minimized, leading to a difficult combinatorial optimization problem. We propose an approximate model for the L0-norm phase unwrapping problem in 2D, in which the singularities of the wrapped phase image are associated with a graph where the vertices have $-1$ or $+1$ polarities. The objective is to find a minimum-cost balanced spanning forest where the sum of the polarities is equal to zero in each tree. We introduce a set of primal and dual heuristics, a branch-and-cut algorithm, and a hybrid metaheuristic to efficiently find exact or heuristic solutions. These approaches move us one step closer to optimal solutions for 2D L0-norm phase unwrapping; such solutions were previously viewed, in the signal processing literature, as highly desirable but not achievable.
A multilevel logistic hidden Markov model for learning under cognitive diagnosis

Students who wish to learn a specific skill have increasing access to a growing number of online courses and open-source educational repositories of instructional tools, including videos, slides, and exercises. Navigating these tools is time-consuming and the search itself can hinder the learning of the skill. Educators are hence interested in aiding students by selecting the optimal content sequence for individual learners, specifically which skill one should learn next and which material one should use to study. Such adaptive selection would rely on pre-knowledge of how the learners' and the instructional tools' characteristics jointly affect the probability of acquiring a certain skill. Building upon previous research on Latent Transition Analysis and Learning Trajectories, we propose a multilevel logistic hidden Markov model for learning based on cognitive diagnosis models, where the probability that a learner acquires the target skill depends not only on the general difficulty of the skill and the learner's mastery of other skills in the curriculum but also on the effectiveness of the particular learning tool and its interaction with mastery of other skills, captured by random slopes and intercepts for each learning tool. A Bayesian modeling framework and an MCMC algorithm for parameter estimation are proposed and evaluated using a simulation study.
Applying the FAHP to Improve the Performance Evaluation Reliability of Software Defect Classifiers

Today's software complexity makes developing defect-free software almost impossible.Consequently, developing classifiers to classify software modules into defective and non-defective before software releases have attracted great interest in academia and software industry alike.Although many classifiers have been proposed, no one has been proven superior over others.The major reason is that while a research shows that classifier A is better than classifier B, we can find other research that shows the opposite.These conflicts are usually triggered when researchers report results using their preferable performance evaluation measures such as, recall and precision.Although this approach is valid, it does not examine all possible facets of classifiers performance characteristics.Thus, the performance evaluation might improve or deteriorate if researchers choose other performance measures.As a result, software developers usually struggle to select the most suitable classifier to use in their projects.The goal of this paper is to apply the fuzzy analytical hierarchy process (FAHP) as a popular multicriteria decision-making technique to reliably evaluate classifiers' performance.This evaluation framework incorporates a wider spectrum of performance measures to evaluate classifiers performance rather than relying on selected preferable measures.The results show that this approach will increase software developers' confidence in research outcomes and help them in avoiding false conclusions and infer reasonable boundaries for them.We exploited 22 popular performance measures and 11 software defect classifiers.The analysis was carried out using KNIME data mining platform and 12 software defect data sets provided by the NASA metrics data program (MDP) repository.
Visual Communication Methods and Tools for Sustainability Performance Assessment: Linking Academic and Industry Perspectives

Effective decision-making based on quantitative analysis is limited by analysts’ abilities to visualize and interpret data and information, a critical challenge in sustainability performance assessment. Non-experts, including students and marketing/finance professionals, often lack experience in interpreting complex data and results, increasing the difficulty of evaluating product sustainability performance. A lack of robust and clear visualization frameworks represents a barrier in advancing the efficacy of sustainability analyses, the tools used for such analyses, and the decisions based on them, exacerbated by the gap between research and industry practice. Emerging and ongoing efforts are investigated by reviewing the research literature on the visual communication of quantitative sustainability performance information and by collecting direct input from sustainability assessment practitioners. While the communication of complicated and uncertain results is an active area of research, prior visualization tool development efforts in support of sustainable product, process, and supply chain design decision-making have found little commercial application. Viewed as a thorny challenge by practitioners, our analysis did not find universal guidance for visualization, but it did uncover best practices for presenting actionable sustainability analysis results that could provide a starting point. Based on the intersection of the literature analysis and primary source interviews, we provide a snapshot of the current state in the field and present potential research avenues.
Bot Detection on Online Social Networks Using Deep Forest

Nowadays, social networks are widely used not only by humans, but also by bots (automated agents). Recent studies have reported that bot accounts are used across different dimensions and in different granularities (e.g. performing terrorist propaganda, spreading misinformation and polluting content). Bot detection has become a significant challenge, especially on online social networks. Today, researchers through Twitter are attempting to propose approaches for bot detection. However, they are confronted with certain challenges owing to the problems inherent to text and the use of language-dependent features. Therefore, we defined a set of statistical features which proved their importance in our work. Our proposed features are based on how much the others interact with the posts of the user, when the user interacts and how much the user interacts. We demonstrated that the mere use of information from the metadata of user profiles and the metadata of posts with a Deep Forest algorithm is sufficient in order to detect bot accounts accurately. In fact, this yielded an Accuracy result of 97.55%.