Reviewerly/exHarmony huggingface dataset:
Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP

Open-vocabulary semantic segmentation aims to segment an image into semantic regions according to text descriptions, which may not have been seen during training. Recent two-stage methods first generate class-agnostic mask proposals and then leverage pre-trained vision-language models, e.g., CLIP, to classify masked regions. We identify the performance bottleneck of this paradigm to be the pre-trained CLIP model, since it does not perform well on masked images. To address this, we propose to finetune CLIP on a collection of masked image regions and their corresponding text descriptions. We collect training data by mining an existing image-caption dataset (e.g., COCO Captions), using CLIP to match masked image regions to nouns in the image captions. Compared with the more precise and manually annotated segmentation labels with fixed classes (e.g., COCO-Stuff), we find our noisy but diverse dataset can better retain CLIP's generalization ability. Along with finetuning the entire model, we utilize the “blank” areas in masked images using a method we dub mask prompt tuning. Experiments demonstrate mask prompt tuning brings significant improvement without modifying any weights of CLIP, and it can further improve a fully finetuned model. In particular, when trained on COCO and evaluated on ADE20K-150, our best model achieves 29.6% mIoU, which is +8.5% higher than the previous state-of-the-art. For the first time, open-vocabulary generalist models match the performance of supervised specialist models in 2017 without dataset specific adaptations.
Ontological Smoothing for Relation Extraction with Minimal Supervision

Relation extraction, the process of converting natural language text into structured knowledge, is increasingly important. Most successful techniques use supervised machine learning to generate extractors from sentences that have been manually labeled with the relations' arguments. Unfortunately, these methods require numerous training examples, which are expensive and time-consuming to produce. This paper presents ontological smoothing, a semi-supervisedtechnique that learns extractors for a set of minimally-labeledrelations. Ontological smoothing has three phases. First, itgenerates a mapping between the target relations and a backgroundknowledge-base. Second, it uses distant supervision toheuristically generate new training examples for the targetrelations. Finally, it learns an extractor from a combination of theoriginal and newly-generated examples. Experiments on 65 relationsacross three target domains show that ontological smoothing candramatically improve precision and recall, even rivaling fully supervisedperformance in many cases.
Coherence, entanglement, and quantumness in closed and open systems with conserved charge, with an application to many-body localization

While the scaling of entanglement in a quantum system can be used to distinguish many-body quantum phases, it is usually hard to quantify the amount of entanglement in mixed states of open quantum systems, while measuring entanglement experimentally, even for the closed systems, requires in general quantum state tomography. In this work we show how to remedy this situation in system with a fixed or conserved charge, e.g., density or magnetization, due to an emerging relation between quantum correlations and coherence. First, we show how, in these cases, the presence of multipartite entanglement or quantumness can be faithfully witnessed simply by detecting coherence in the quantum system, while bipartite entanglement or bipartite quantum discord are implied by asymmetry (block coherence) in the system. Second, we prove that the relation between quantum correlations and coherence is also quantitative. Namely, we establish upper and lower bounds on the amount of multipartite and bipartite entanglement in a many-body system with a fixed local charge, in terms of the amount of coherence and asymmetry present in the system. Importantly, both for pure and mixed quantum states, these bounds are expressed as closed formulas, and furthermore, for bipartite entanglement, are experimentally accessible by means of the multiple quantum coherence spectra. In particular, in one-dimensional systems, our bounds may detect breaking of the area law of entanglement entropy. We illustrate our results on the example of a many-body localized system, also in the presence of dephasing.
Meta-F $$^\star $$ : Proof Automation with SMT, Tactics, and Metaprograms

We introduce Meta-F $$^{\star }$$ , a tactics and metaprogramming framework for the F $$^\star $$ program verifier. The main novelty of Meta-F $$^\star $$ is allowing the use of tactics and metaprogramming to discharge assertions not solvable by SMT, or to just simplify them into well-behaved SMT fragments. Plus, Meta-F $$^\star $$ can be used to generate verified code automatically. Meta-F $$^\star $$ is implemented as an F $$^\star $$ effect, which, given the powerful effect system of F $$^{\star }$$ , heavily increases code reuse and even enables the lightweight verification of metaprograms. Metaprograms can be either interpreted, or compiled to efficient native code that can be dynamically loaded into the F $$^\star $$ type-checker and can interoperate with interpreted code. Evaluation on realistic case studies shows that Meta-F $$^\star $$ provides substantial gains in proof development, efficiency, and robustness.
A novel hybrid backtracking search optimization algorithm for continuous function optimization

Stochastic optimization algorithm provides a robust and efficient approach for solving complex real world problems.Backtracking Search Optimization Algorithm (BSA) is a new stochastic evolutionary algorithm and the aim of this paper is to introduce a hybrid approach combining the BSA and Quadratic approximation (QA), called HBSAfor solving unconstrained non-linear, non-differentiable optimization problems.For the validity of the proposed method the results are compared with five state-of-the-art particle swarm optimization (PSO) variant approaches in terms of the numerical result of the solutions.The sensitivity analysis of the BSA control parameter (F) is also performed..
Trust-Based Privacy-Preserving Photo Sharing in Online Social Networks

With the development of social media technologies, sharing photos in online social networks has now become a popular way for users to maintain social connections with others. However, the rich information contained in a photo makes it easier for a malicious viewer to infer sensitive information about those who appear in the photo. How to deal with the privacy disclosure problem incurred by photo sharing has attracted much attention in recent years. When sharing a photo that involves multiple users, the publisher of the photo should take into all related users' privacy into account. In this paper, we propose a trust-based privacy preserving mechanism for sharing such coowned photos. The basic idea is to anonymize the original photo so that users who may suffer a high privacy loss from the sharing of the photo cannot be identified from the anonymized photo. The privacy loss to a user depends on how much he or she trusts the receiver of the photo. And the user's trust in the publisher is affected by privacy loss. The anonymiation result of a photo is controlled by a threshold specified by the publisher. We propose a greedy method for the publisher to tune the threshold, in the purpose of balancing between the privacy preserved by anonymization and the information shared with others. Simulation results demonstrate that the trust-based photo sharing mechanism is helpful to reduce the privacy loss, and the proposed threshold tuning method can bring a good payoff to the user.
A New Algorithm Based on Magic Square and a Novel Chaotic System for Image Encryption

Abstract This article introduces a simple and effective new algorithm for image encryption using a chaotic system which is based on the magic squares. This novel 3D chaotic system is invoked to generate a random key to encrypt any color image. A number of chaotic keys equal to the size of the image are generated by this chaotic system and arranged into a matrix then divided into non-overlapped submatrices. The image to be encrypted is also divided into sub-images, and each sub-image is multiplied by a magic matrix to produce another set of matrices. The XOR operation is then used on the resultant two sets of matrices to produce the encrypted image. The strength of the encryption method is tested in two folds. The first fold is the security analysis which includes key space analysis and sensitivity analysis. In the second fold, statistical analysis was performed, which includes the correlation coefficients, information entropy, the histogram, and analysis of differential attacks. Finally, the time of encryption and decryption was computed and show very good results.
Automated Computational Detection, Quantitation, and Mapping of Mitosis in Whole-Slide Images for Clinically Actionable Surgical Pathology Decision Support

Background: Determining mitotic index by counting mitotic figures (MFs) microscopically from tumor areas with most abundant MF (hotspots [HS]) produces a prognostically useful tumor grading biomarker. However, interobserver concordance identifying MF and HS can be poorly reproducible. Immunolabeling MF, coupled with computer-automated counting by image analysis, can improve reproducibility. A computational system for obtaining MF values across digitized whole-slide images (WSIs) was sought that would minimize impact of artifacts, generate values clinically relatable to counting ten high-power microscopic fields of view typical in conventional microscopy, and that would reproducibly map HS topography. Materials and Methods: Relatively low-resolution WSI scans (0.50 µm/pixel) were imported in grid-tile format for feature-based MF segmentation, from naturally occurring canine melanomas providing a wide range of proliferative activity. MF feature extraction conformed to anti-phospho-histone H3-immunolabeled mitotic (M) phase cells. Computer vision image processing was established to subtract key artifacts, obtain MF counts, and employ rotationally invariant feature extraction to map MF topography. Results: The automated topometric HS (TMHS) algorithm identified mitotic HS and mapped select tissue tiles with greatest MF counts back onto WSI thumbnail images to plot HS topographically. Influence of dye, pigment, and extraneous structure artifacts was minimized. TMHS diagnostic decision support included image overlay graphics of HS topography, as well as a spreadsheet and plot of tile-based MF count values. TMHS performance was validated examining both mitotic HS counting and mapping functions. Significantly correlated TMHS MF mapping and metrics were demonstrated using repeat analysis with WSI in different orientation (R2 = 0.9916) and by agreement with a pathologist (R2 = 0.8605) as well as through assessment of counting function using an independently tuned object counting algorithm (OCA) (R2 = 0.9482). Limits of agreement analysis support method interchangeability. MF counts obtained led to accurate patient survival prediction in all (n = 30) except one case. By contrast, more variable performance was documented when several pathologists examined similar cases using microscopy (pair-wise correlations, rho range = 0.7597-0.9286). Conclusions: Automated TMHS MF segmentation and feature engineering performance were interchangeable with both observer and OCA in digital mode. Moreover, enhanced HS location accuracy and superior method reproducibility were achieved using the automated TMHS algorithm compared to the current practice employing clinical microscopy.
Online-Dating Romance Scam in Malaysia: An Analysis of Online Conversations between Scammers and Victims

The study of online romance scam is still at its infancy in Malaysia, despite the increase in the number of reported cases in this country. This research primarily aims to identify the steps and strategies involved in the online romance scam in Malaysia. Apart from that, it also aims to identify the pattern of deceptive language used in online romance scam in Malaysia through a comprehensive linguistic analysis of actual online conversations between scammers and victims. The empirical investigation of this research focuses on the language strategies used by scammers as a modus operando in deceiving their targets. With the help of the Malaysian Police Department, a database of romance scam cases was gathered and established. From the database, 30 sets of online communication between scammers and 30 Malaysian victims were selected and analysed using content analysis method. One of the aspects involved in data analysis was scammers’ linguistic styles and patterns in manipulating their targets. This was analysed using Brown and Levinson Politeness Model as well as Whitty’s Scammers Persuasive Techniques Model. The findings indicate a common linguistic pattern and style of conversation used by online scammers in persuading and deceiving their victims.
Fuzzy Entropy Approaches for Image Segmentation

Images extracted from uncontrolled environments possesses different complexities that affects processing tasks. It also demerit the performance of segmentation approaches in specific when are used thresholding mechanism. The alternative is to use methods that are able to manage uncertainties and ambiguities presented in the pixel’s classification. Fuzzy entropy methods then are interesting alternatives that permits to handle the situations described above. This chapter then introduces the concepts that permits to use evolutionary algorithms to find the best configuration of fuzzy entropy approaches for images segmentation. This chapter is theoretical, but it also encourages the reader to experiment and apply the concepts using any evolutionary approach.
Analysis of Fish-Eye Lens Camera Self-Calibration

The fish-eye lens camera offers the advantage of efficient acquisition of image data through a wide field of view. However, unlike the popular perspective projection camera, a strong distortion effect appears as the periphery of the image is compressed. Such characteristics must be precisely analyzed through camera self-calibration. In this study, we carried out a fish-eye lens camera self-calibration while considering different types of test objects and projection models. Self-calibration was performed using the V-, A-, Plane-, and Room-type test objects. In the fish-eye lens camera, the V-type test object was the most advantageous for ensuring the accuracy of the principal point coordinates and focal length, because the correlations between parameters were relatively low. On the other hand, the other test objects were advantageous for ensuring the accuracy of distortion parameters because of the well-distributed image points. Based on the above analysis, we proposed, an accurate fish-eye lens camera self-calibration method that applies the V-type test object. The RMS-residuals of the proposed method were less than 1 pixel.
3D Strain and Elasticity Measurement of Layered Biomaterials by Optical Coherence Elastography based on Digital Volume Correlation and Virtual Fields Method

The three-dimensional (3D) mechanical property characterization of biological tissues is essential for physiological and pathological studies. A digital volume correlation (DVC) and virtual fields method (VFM) based 3D optical coherence elastography (OCE) method is developed to quantitatively measure the 3D full-field displacements, strains and elastic parameters of layered biomaterials assuming the isotropy and homogeneity of each layer. The integrated noise-insensitive DVC method can obtain the 3D strain tensor with an accuracy of 10%. Automatic segmentation of the layered materials is realized based on the full field strain and strain gradient. With the strain tensor as input, and in combination with the segmented geometry, the Young’s modulus and Poison’s ratio of each layer of a double-layered material and a pork specimen are obtained by the VFM. This study provides a powerful experimental method for the differentiation of various components of heterogeneous biomaterials, and for the measurement of biomechanics.
Types by Need

A cornerstone of the theory of $$\lambda $$ -calculus is that intersection types characterise termination properties. They are a flexible tool that can be adapted to various notions of termination, and that also induces adequate denotational models. Since the seminal work of de Carvalho in 2007, it is known that multi types (i.e. non-idempotent intersection types) refine intersection types with quantitative information and a strong connection to linear logic. Typically, type derivations provide bounds for evaluation lengths, and minimal type derivations provide exact bounds. De Carvalho studied call-by-name evaluation, and Kesner used his system to show the termination equivalence of call-by-need and call-by-name. De Carvalho’s system, however, cannot provide exact bounds on call-by-need evaluation lengths. In this paper we develop a new multi type system for call-by-need. Our system produces exact bounds and induces a denotational model of call-by-need, providing the first tight quantitative semantics of call-by-need.
Multilayer neural network synchronized secured session key based encryption in wireless communication

&lt;p&gt;Energy computation concept of multilayer neural network synchronized on derived transmission key based encryption system has been proposed for wireless transactions. Multilayer perceptron transmitting machines accepted same input array, which in turn generate a resultant bit and the networks were trained accordingly to form a protected variable length secret-key. For each session, different hidden layer of multilayer neural network is selected randomly and weights of hidden units of this selected hidden layer help to form a secret session key. A novel approach to generate a transmission key has been explained in this proposed methodology. The last thirty two bits of the session key were taken into consideration to construct the transmission key. Inverse operations were carried out by the destination perceptron to decipher the data. Floating frequency analysis of the proposed encrypted stream of bits has yielded better degree of security results. Energy computation of the processed nodes inside multi layered networks can be done using this proposed frame of work.&lt;/p&gt;
C3Ro: An efficient mining algorithm of extended-closed contiguous robust sequential patterns in noisy data

Sequential pattern mining has been the focus of many works, but still faces a tough challenge in the mining of large databases for both efficiency and apprehensibility of its resulting set. To overcome these issues, the most promising direction taken by the literature relies on the use of constraints, including the well-known closedness constraint. However, such a mining is not resistant to noise in data, a characteristic of most real-world data. The main research question raised in this paper is thus: how to efficiently mine an apprehensible set of sequential patterns from noisy data? In order to address this research question, we introduce 1) two original constraints designed for the mining of noisy data: the robustness and the extended-closedness constraints, 2) a generic pattern mining algorithm, C3Ro, designed to mine a wide range of sequential patterns, going from closed or maximal contiguous sequential patterns to closed or maximal regular sequential patterns. C3Ro is dedicated to practitioners and is able to manage their multiple constraints. C3Ro also is the first sequential pattern mining algorithm to be as generic and parameterizable. Extensive experiments have been conducted and reveal the high efficiency of C3Ro, especially in large datasets, over well-known algorithms from the literature. Additional experiments have been conducted on a real-world job offers noisy dataset, with the goal to mine activities. This experiment offers a more thorough insight into C3Ro algorithm: job market experts confirm that the constraints we introduced actually have a significant positive impact on the apprehensibility of the set of mined activities.
Teachers and learners in a time of big data

Policy and technological transformation have coalesced to usher in massive changes to educational systems over the past two decades. Teachers’ roles, subjectivities and professional identities have been subject to sweeping changes enabled by sophisticated forms of governance. Simultaneously, students have been recast as ‘learners’; like teachers, learners have become subject to new forms of governance, through technological surveillance and datafication. This paper focuses on the intersection of the metrics driven approach to education and the political as a way to re-think the future of schooling in more explicitly philosophical terms. This exploration starts with a critical examination of constructions of teachers, learners and the digital data-driven educational culture in order to explicate the futures being generated. The trajectory of this future is explored through reference to the techno-educational models currently being developed in Silicon Valley. Drawing on Deleuze’s notion of control societies we contribute to the ongoing philosophical investigation of the datafication of education; a necessary discussion if we are to explore the future implications of schooling in a technologically saturated world. We present consideration of the past, present and future, as three ways of considering alternatives to a datafied education system. Alternative conceptualisations of the future of schooling are possible which offer ways of understanding and politicising what happens when we impose data-driven accountabilities into people’s lives.
Real-Time Streaming Graph Embedding Through Local Actions

Recently, considerable research attention has been paid to graph embedding, a popular approach to construct representations of vertices in latent space. Due to the curse of dimensionality and sparsity in graphical datasets, this approach has become indispensable for machine learning tasks over large networks. The majority of the existing literature has considered this technique under the assumption that the network is static. However, networks in many applications, including social networks, collaboration networks, and recommender systems, nodes, and edges accrue to a growing network as streaming. A small number of very recent results have addressed the problem of embedding for dynamic networks. However, they either rely on knowledge of vertex attributes, suffer high-time complexity or need to be re-trained without closed-form expression. Thus the approach of adapting the existing methods designed for static networks or dynamic networks to the streaming environment faces non-trivial technical challenges.
Session-Based Recommender System for Sustainable Digital Marketing

Many companies operate e-commerce websites to sell fashion products. Some customers want to buy products with intention of sustainability and therefore the companies need to suggest appropriate fashion products to those customers. Recommender systems are key applications in these sustainable digital marketing strategies and high performance is the most necessary factor. This research aims to improve recommendation systems’ performance by considering item session and attribute session information. We suggest the Item Session-Based Recommender (ISBR) and the Attribute Session-Based Recommenders (ASBRs) that use item and attribute session data independently, and then we suggest the Feature-Weighted Session-Based Recommenders (FWSBRs) that combine multiple ASBRs with various feature weighting schemes. Our experimental results show that FWSBR with chi-square feature weighting scheme outperforms ISBR, ASBRs, and Collaborative Filtering Recommender (CFR). In addition, it is notable that FWSBRs overcome the cold-start item problem, one significant limitation of CFR and ISBR, without losing performance.