Reviewerly/exHarmony huggingface dataset:
A framework for classifying breast cancer based on deep features integration and selection

Abstract Deep convolutional neural networks (DCNNs) are one of the most advanced techniques for classifying images in a range of applications. One of the most prevalent cancers that cause death in women is breast cancer. For survival rates to increase, early detection and treatment of breast cancer is essential. Deep learning (DL) can help radiologists diagnose and classify breast cancer lesions. This paper proposes a computer-aided system based on DL techniques for automatically classify breast cancer tumors in histopathological images. There are nine DCNN architectures used in this work. Four schemes are performed in the proposed framework to find the best approach. The first scheme consists of pre-trained DCNNs based on the transfer learning concept. The second scheme performs feature extraction of the DCNN architectures and uses a support vector machine (SVM) classifier for evaluation. The third one performs feature integration to show how the integrated deep features may enhance the SVM classifiers' accuracy. Finally, in the fourth scheme, the Chi-square ( χ 2) feature selection method is applied to reduce the large feature size in the feature integration step. The results of the proposed system present a promising performance for breast cancer classification with an accuracy of 99.24%. The system performance shows that the proposed tool is suitable to assist radiologists in diagnosing breast cancer tumors.
On the probability of (falsely) connecting two distinct components when learning a GGM

AbstractIn this paper, we extend the result on the probability of (falsely) connecting two distinct components when learning a GGM (Gaussian Graphical Model) by the joint regression based technique. While the classical method of regression based technique learns the neighbours of each node one at a time through a Lasso penalized regression, its joint modification, considered here, learns the neighbours of each node simultaneously through a group Lasso penalized regression.Keywords: GGM inferenceLassogroup Lasso2020 Mathematics Subject Classification: Primary: 62G05Secondary: 62-07 Additional informationFundingDaniela De Canditiis was supported by grant INdAM-GNCS Project 2022.
Assessing the content typicality and construct of Persian language proficiency test (PLPT) for non-Persian speakers: a corpus-informed study

Abstract Drawing on a growing body of research on the interface between corpus linguistics and second/foreign language testing and assessment, we adopted Peykare , a large-scale, annotated, Persian written language resource to evaluate the content (i.e., coverage and typicality) and construct validity of a Persian language proficiency test developed for certification of proficiency in Persian as a foreign language (PFL) of non-native speakers. Designed at the Research Center for Intelligent Signal Processing (RCISP), Peykare contains 35,058 text files over five linguistic varieties and 24 different registers of contemporary Persian. This study addresses how corpora, as rich database resources, can practically be applied to test validation purposes and insightfully inform the test life cycle. The results of content validity phase revealed evidence supporting content representativeness, relevance, and typicality of the test. The linkage between the corpus-extracted criterial features or parameters and those covered by the test was not, however, strongly evidenced by items measuring ezafeh constructions, homographs/homophones, PRO (proposition), and POST (postposition). The analysis of content typicality indicated chunks that did not closely conform to the corpus typical output. The construct validity phase, assessing the test hypothesized factor structure (i.e., hierarchical, unitary, correlated, and uncorrelated models) in two randomly split samples of PFL learners from Asian and European countries ( N =121), showed that the correlated model fit the data best in both samples. The results supported the presence of distinctive factors of receptive skills, providing empirical evidence for score interpretations of the corpus-based test.
A natural language processing based technique for sentiment analysis of college english corpus

The college English corpus can help us better master English, but how to obtain the desired information from a large number of English corpus has become the focus of information technology. Based on the natural language processing (NLP) technology, a sentiment analysis model is built in this article. An improved term frequency-inverse document frequency (TF-IDF) algorithm is proposed in this article, where the weighted average method is used to determine the emotional value of each emotional word. The inspirational words are used to obtain the English corpus's emotional tendency and emotional value. The results show that the model has high classification accuracy and operation efficiency when selecting feature words. Compared with the TF-IDF, the improved TF-IDF algorithm added the necessary information weight processing and word density weight processing to two new processing links, which can significantly improve the efficiency of college English learning.
Securing Cyber-Physical Spaces with Hybrid Analytics: Vision and Reference Architecture

Considering the massive increase in the number of crimes in the last decade, as well as the outlook toward smarter cities and more sustainable urban living, the emerging cyber-physical space (CPS) obtained by the interaction of such physical spaces with the cyber elements around them (e.g., think of Internet-of-Things devices or hyperconnected mobility), plays a key role in the protection of urban social living, e.g., social events or daily routines. For example, the hyperconnectedness of a CPS to many networks can lead to potential vulnerability. This vision paper aims to outline a vision and reference architecture where CPS protection is center-stage and where CPS models as well as so-called hybrid analytics work jointly to help the Law Enforcement Agents (LEAs), e.g., in event monitoring and early detection of criticalities. As a part of validating said reference architecture, we implement a case study in the scope of VISOR, a Dutch government project aimed at improving CPS protection using hybrid analytics. We conduct a field experiment in the Paaspop social event and festival grounds to test and select the most appropriate device configuration. There we experiment with a CPS protection pipeline featuring several components reflected in the reference architecture, e.g., the KGen middleware, a prototype tool to anonymize structured big data using genetic algorithms, and SENSEI, a framework for dark web marketplace analytics. We conclude that hybrid analytics offer a considerable ground for more sustainable CPS.
Automation of Vulnerability Information Extraction Using Transformer-Based Language Models

Identifying and mitigating vulnerabilities as rapidly and extensively as possible is essential for preventing security breaches. Thus, organizations and companies often store vulnerability information, expressed in natural language, and share them with other stakeholders. Disclosure and dissemination of this information in a structured and unambiguous format in a timely manner is crucial to prevent security attacks. Many existing automated vulnerability information extraction techniques use rule-based strategies like Pattern Matching and Part-of-Speech Tagging, and Machine Learning models built on Conditional Random Fields (CRF). There are also hybrid models that integrate NLP and pattern recognition to create semi-automated systems. We propose an alternative approach using Transformer models, including BERT, XLNet, RoBERTa, and DistilBERT, which have been shown to have promising performance in many NLP downstream tasks such as for Named Entity Recognition (NER) and Co-reference Resolution in an end-to-end neural architecture. We fine-tune several language representation models similar to BERT, on a labeled dataset from vulnerability databases, for the task of NER so that we can automatically extract security-related words and terms and phrases from descriptions of the vulnerabilities. Our approach allows us to extract complex features from the data without requiring feature selection, thus eliminating the need for domain-expert knowledge. It also outperforms the CRF-based models. Additionally, it is able to detect new information from vulnerabilities whose description text patterns differ from those specified by rule-based systems.
Rebooting IT Security Awareness – How Organisations Can Encourage and Sustain Secure Behaviours

Abstract Most organisations are using online security awareness training and simulated phishing attacks to encourage their employees to behave securely. Buying off-the-shelf training packages and making it mandatory for all employees to complete them is easy, and satisfies most regulatory and audit requirements, but does not lead to secure behaviour becoming a routine. In this paper, we identify the additional steps employees must go through to develop secure routines, and the blockers that stop a new behaviour from becoming a routine. Our key message is: security awareness as we know it is only the first step; organisations who want employees have to do more to smooth the path: they have to ensure that secure behaviour is feasible, and support their staff through the stages of the Security Behaviour Curve – concordance, self-efficacy, and embedding – for secure behaviour to become a routine. We provide examples of those organisational activities, and specific recommendations to different organisational stakeholders.
An Integrated Scheme of the Data-Driven Multi-Block FCMs for Modeling the Steady Turning Motion of Motorboats

The steady turning motion of merchant ships is modeled according to industry specifications. However, challenges arise when motorboats are modeled. This study proposes a novel data-driven multi-block fuzzy cognitive map (FCM) model trained based on sea trials with four ship states. The optimal positions sampled by two different types of sensors were modeled using inverse variance weighting, which takes both Cartesian coordinate transformation and the constraints of the locations of the apparatuses into consideration. Combining these samplings with a scheme of multi-block FCMs, we conducted a study on data from motorboat trials. Our results closely approximate the data from motorboat trials at sea and are validated by a generated dataset of the classical model. Furthermore, we reveal the characteristics of our scheme, including the number of data blocks, the boundary of each block, and the parameters of the FCM for each block. As opposed to the classical method, the proposed scheme is insensitive to speed. This study presents a promising step toward mining modeling information from ship trials.
New prospective on information entropy using different initial states of the atom–field interaction

We propose a new approach for introducing the long-life information entropy of the interaction between atom and electromagnetic field. Our approach is based on a comparison of the initial state of the field. The field is initialized in Pólya state, negative hypergeometric state, generalized nonclassical state, phase-like state and generalized hypergeometric state. Dynamics of entropy squeezing are examined. It is shown that a long-time approximately steady nonclassical behavior of the system can be obtained using specific values of the parameters associated with the initial state of system. An application of the developed method to obtain the long-lived information from different models is given.
Video in 10 Bits: Few-Bit VideoQA for Efficiency and Privacy

In Video Question Answering (VideoQA), answering general questions about a video requires its visual information. Yet, video often contains redundant information irrelevant to the VideoQA task. For example, if the task is only to answer questions similar to "Is someone laughing in the video?", then all other information can be discarded. This paper investigates how many bits are really needed from the video in order to do VideoQA by introducing a novel Few-Bit VideoQA problem, where the goal is to accomplish VideoQA with few bits of video information (e.g., 10 bits). We propose a simple yet effective task-specific feature compression approach to solve this problem. Specifically, we insert a lightweight Feature Compression Module (FeatComp) into a VideoQA model which learns to extract task-specific tiny features as little as 10 bits, which are optimal for answering certain types of questions. We demonstrate more than 100,000-fold storage efficiency over MPEG4-encoded videos and 1,000-fold over regular floating point features, with just 2.0–6.6% absolute loss in accuracy, which is a surprising and novel finding. Finally, we analyze what the learned tiny features capture and demonstrate that they have eliminated most of the non-task-specific information, and introduce a Bit Activation Map to visualize what information is being stored. This decreases the privacy risk of data by providing k-anonymity and robustness to feature-inversion techniques, which can influence the machine learning community, allowing us to store data with privacy guarantees while still performing the task effectively.
Can Bayesian Models of Cognition Show That We Are (Epistemically) Rational?

Abstract “According to [Bayesian] models” in cognitive neuroscience, says a recent textbook, “the human mind behaves like a capable data scientist.” Do they? That is, do such models show we are rational? I argue that Bayesian models of cognition, perhaps surprisingly, don’t and indeed can’t show that we are Bayes-rational. The key reason is that they appeal to approximations, a fact that carries significant implications. After outlining the argument, I critique two responses, seen in recent cognitive neuroscience. One says that the mind can be seen as approximately Bayes-rational, while the other reconceives norms of rationality.
Towards Improved Intermediate Layer Variational Inference for Uncertainty Estimation

DNNs have been excelling on many computer vision tasks, achieving many milestones and are continuing to prove their validity. It is important for DNNs to express their uncertainties to be taken into account for the whole application output, whilst maintaining their performance. The intermediate layer variational inference has been a promising approach to estimate uncertainty in real-time beating state-of-the-art approaches. In this work, we propose an enhancement of the intermediate layer variational inference by adding Dirichlet distribution to the network architecture. This improves, on one hand, the uncertainty estimation reliability and on the other hand can detect out-of-distribution samples. Results show that with the addition of Dirichlet distributions the DNN is able to maintain its segmentation performance whilst boosting its uncertainty estimation capabilities.
Strengthening Skeletal Action Recognizers via Leveraging Temporal Patterns

Skeleton sequences are compact and lightweight. Numerous skeleton-based action recognizers have been proposed to classify human behaviors. In this work, we aim to incorporate components that are compatible with existing models and further improve their accuracy. To this end, we design two temporal accessories: discrete cosine encoding (DCE) and chronological loss (CRL). DCE facilitates models to analyze motion patterns from the frequency domain and meanwhile alleviates the influence of signal noise. CRL guides networks to explicitly capture the sequence's chronological order. These two components consistently endow many recently-proposed action recognizers with accuracy boosts, achieving new state-of-the-art (SOTA) accuracy on two large datasets.
New cryptanalysis of LowMC with algebraic techniques

Abstract LowMC is a family of block ciphers proposed by Albrecht et al. at EUROCRYPT 2015, which is tailored specifically for FHE and MPC applications. At ToSC 2018, a difference enumeration attack was given for the cryptanalysis of low-data instances of full LowMCv2 with few applied S-boxes per round. Recently at CRYPTO 2021, an efficient algebraic technique was proposed to attack 4-round LowMC adopting a full S-box layer. Following these works, we present a new difference enumeration attack framework, which is based on our new observations on the LowMC S-box, to analyze LowMC instances with a full S-box layer. As a result, with only 3 chosen plaintexts, we can attack 4-round LowMC instances which adopt a full S-box layer with block size of 129, 192, and 255 bits, respectively. We show that all these attacks have either a lower time complexity or a higher success probability than those reported in the CRYPTO paper.
HiFace: Hybrid Task Learning for Face Reconstruction from Single Image

The task of 3D face reconstruction in the WCPA challenge requires a monocular image as input and outputs 3D face geometry, which has been a prevalent field for decades. Considerable works have been published, in which PerspNet significantly outperforms the other methods under perspective projection. However, as the UV coordinates distribute unevenly, the UV mapping process introduces inevitable precision degradation in dense regions of reconstructed 3D faces. Thus, we design a vertex refinement module to overcome the precision degradation. We also design a multi-task learning module to enhance 3D features. By carefully designing and organizing the vertex refinement module and the multi-task learning module, we propose a hybrid task learning based 3D face reconstruction method called HiFace. Our HiFace achieves the 2nd place in the final official ranking of the ECCV 2022 WCPA Challenge, which demonstrates the superiority of our HiFace.
Supervision by Landmarks: An Enhanced Facial De-occlusion Network for VR-Based Applications

Face possesses a rich spatial structure that can provide valuable cues to guide various face-related tasks. The eyes are considered an important socio-visual cue for effective communication. They are an integral feature of facial expressions as they are an important aspect of interpersonal communication. However, virtual reality headsets occlude a significant portion of the face and restrict the visibility of certain facial features, particularly the eye region. Reproducing this region with realistic content and handling complex eye movements such as blinks is challenging. Previous facial inpainting methods are not capable enough to capture subtle eye movements. In view of this, we propose a working solution to refine the reconstructions, particularly around the eye region, by leveraging inherent eye structure. We introduce spatial supervision and a novel landmark predictor module to regularize per-frame reconstructions obtained from an existing image-based facial de-occlusion network. Experiments verify the usefulness of our approach in enhancing the quality of reconstructions to capture subtle eye movements.
Which Expert Knows Best? Modulating Soft Learning with Online Batch Confidence for Domain Adaptive Person Re-Identification

Deploying a person re-identification (Re-ID) system in a real scenario requires adapting a model trained on one labeled dataset to a different environment, with no person identity information. This poses an evident challenge that can be faced by unsupervised domain adaptation approaches. Recent state-of-the-art methods adopt architectures composed of multiple models (a.k.a. experts), and transfer the learned knowledge from the source domain by clustering and assigning hard pseudo-labels to unlabeled target data. While this approach achieves outstanding accuracy, the clustering procedure is typically sub-optimal, and the experts are simply combined to learn in a collaborative way, thus limiting the final performance. In order to mitigate the effects of noisy pseudo-labels and better exploit experts' knowledge, we propose to combine soft supervision techniques in a novel multi-expert domain adaptation framework. We introduce a novel weighting mechanism for soft supervisory learning, named Online Batch Confidence, which takes into account expert reliability in an online per-batch basis. We conduct experiments across popular cross-domain Re-ID benchmarks proving that our model outperforms the current state-of-the-art results.
UnconFuse: Avatar Reconstruction from Unconstrained Images

The report proposes an effective solution about 3D human body reconstruction from multiple unconstrained frames for ECCV 2022 WCPA Challenge: From Face, Body and Fashion to 3D Virtual avatars I (track1: Multi-View Based 3D Human Body Reconstruction). We reproduce the reconstruction method presented in MVP-Human [19] as our baseline, and make some improvements for the particularity of this challenge. We finally achieve the score 0.93 on the official testing set, getting the 1st place on the leaderboard.
UDC-UNet: Under-Display Camera Image Restoration via U-shape Dynamic Network

Under-Display Camera (UDC) has been widely exploited to help smartphones realize full-screen displays. However, as the screen could inevitably affect the light propagation process, the images captured by the UDC system usually contain flare, haze, blur, and noise. Particularly, flare and blur in UDC images could severely deteriorate the user experience in high dynamic range (HDR) scenes. In this paper, we propose a new deep model, namely UDC-UNet, to address the UDC image restoration problem with an estimated PSF in HDR scenes. Our network consists of three parts, including a U-shape base network to utilize multi-scale information, a condition branch to perform spatially variant modulation, and a kernel branch to leverage the prior knowledge of the PSF. According to the characteristics of HDR data, we additionally design a tone mapping loss to stabilize network optimization and achieve better visual quality. Experimental results show that the proposed UDC-UNet outperforms the state-of-the-art methods in quantitative and qualitative comparisons. Our approach won second place in the UDC image restoration track of the MIPI challenge. Codes and models are available at https://github.com/J-FHu/UDCUNet .
Depth Completion Using Laplacian Pyramid-Based Depth Residuals

In this paper, we propose a robust and efficient depth completion network based on residuals. Unlike previous methods that directly predict a depth residual, we reconstruct high-frequency information in complex scenes by exploiting the efficiency of the Laplacian pyramid in representing multi-scale content. Specifically, the framework can be divided into two stages: sparse-to-coarse and coarse-to-fine. In the sparse-to-coarse stage, we only recover depth from the sparse depth map without using any additional color image, and downsample the result to filter out unreliable high-frequency information from the sparse depth measurement. In the coarse-to-fine stage, we use features extracted from both data modalities to model high-frequency components as a series of multi-scale depth residuals via a Laplacian pyramid. Considering the wide distribution of high-frequency information in the frequency domain, we propose a Global-Local Refinement Network (GLRN) to estimate depth residuals separately at each scale. Furthermore, to compensate for the structural information lost by coarse depth map downsampling and further optimize the results with the color image, we propose a novel and efficient Affinity decay spatial propagation network (AD-SPN), which is used to refine the depth estimation results at each scale. Extensive experiments on indoor and outdoor datasets demonstrate that our approach achieves state-of-the-art performance.