Reviewerly/exHarmony huggingface dataset:
Automated Search for Block Cipher Differentials: A GPU-Accelerated Branch-and-Bound Algorithm

In this paper, we propose a GPU-accelerated branch-and-bound algorithm. The proposed approach substantially increases the performance of the differential cluster search. We were able to derive a branch enumeration and evaluation kernel that is 5.95 times faster than its CPU counterpart. To showcase its practicality, the proposed algorithm is applied on TRIFLE-BC, a 128-bit block cipher. By incorporating a meet-in-the-middle approach with the proposed GPU kernel, we were able to improve the search efficiency (on 20 rounds of TRIFLE-BC) by approximately 58 times as compared to the CPU-based approach. Differentials consisting of up to 50 million individual characteristics can be constructed for 20 rounds of TRIFLE, leading to slight improvements to the overall differential probabilities. Even for larger rounds (43 rounds), the proposed algorithm is still able to construct large clusters of over 500 thousand characteristics. This result depicts the practicality of the proposed algorithm in constructing large differentials even for a 128-bit block cipher, which could be used to improve cryptanalytic findings against other block ciphers in the future.
Not All Webcams Are Created Equal [From the Screen of Stone]

Almost all laptops computers and tablets have built-in webcams. However, most of these have 720p resolution (the equivalent of "standard definition" for television). A web cam with 1080p HD ("high definition") resolution will yield a noticeably sharper image, particularly for the situations that are common in video conferencing where the face fills most of the frame. Furthermore, that 1080p resolution needs to be supported at a 60 Hz refresh rate. Slower refresh rates (e.g., the 30 Hz rates used by some webcams) result in "jerky" motions when the person moves. A good webcam will have an auto focus feature. This keeps the face (or whatever else is the primary object in the field of view) automatically in focus regardless of the distance from the camera or of any motion. Many webcams also come with software that allows zooming and centering the image.
New Constructions of MDS Twisted Reed-Solomon Codes and LCD MDS Codes

Maximum distance separable (MDS) codes are optimal where the minimum distance cannot be improved for a given length and code size. Twisted Reed-Solomon codes over finite fields were introduced in 2017, which are generalization of Reed-Solomon codes. Twisted Reed-Solomon codes can be applied in cryptography which prefer the codes with large minimum distance. MDS codes can be constructed from twisted Reed-Solomon codes, and most of them are not equivalent to Reed-Solomon codes. In this paper, we first generalize twisted Reed-Solomon codes to generalized twisted Reed-Solomon codes, then we give some new explicit constructions of MDS (generalized) twisted Reed-Solomon codes. In some cases, our constructions can get MDS codes with the length longer than the constructions of previous works. Linear complementary dual (LCD) codes are linear codes that intersect with their duals trivially. LCD codes can be applied in cryptography. This application of LCD codes renewed the interest in the construction of LCD codes having a large minimum distance. We also provide new constructions of LCD MDS codes from generalized twisted Reed-Solomon codes.
Unsupervised Deep-Learning Based Deformable Image Registration: A Bayesian Framework

Unsupervised deep-learning (DL) models were recently proposed for deformable image registration tasks. In such models, a neural-network is trained to predict the best deformation field by minimizing some dissimilarity function between the moving and the target images. After training on a dataset without reference deformation fields available, such a model can be used to rapidly predict the deformation field between newly seen moving and target images. Currently, the training process effectively provides a point-estimate of the network weights rather than characterizing their entire posterior distribution. This may result in a potential over-fitting which may yield sub-optimal results at inference phase, especially for small-size datasets, frequently present in the medical imaging domain. We introduce a fully Bayesian framework for unsupervised DL-based deformable image registration. Our method provides a principled way to characterize the true posterior distribution, thus, avoiding potential over-fitting. We used stochastic gradient Langevin dynamics (SGLD) to conduct the posterior sampling, which is both theoretically well-founded and computationally efficient. We demonstrated the added-value of our Basyesian unsupervised DL-based registration framework on the MNIST and brain MRI (MGH10) datasets in comparison to the VoxelMorph unsupervised DL-based image registration framework. Our experiments show that our approach provided better estimates of the deformation field by means of improved mean-squared-error ($0.0063$ vs. $0.0065$) and Dice coefficient ($0.73$ vs. $0.71$) for the MNIST and the MGH10 datasets respectively. Further, our approach provides an estimate of the uncertainty in the deformation-field by characterizing the true posterior distribution.
Origami Voting: A Non-cryptographic Approach to Transparent Ballot Verification

Over the past four decades, fear of election manipulation and hacking has spurred the security technology community to propose a variety of voting systems to implement verifiable voting. Most of these rely on hard to understand cryptographic protocols, which can affect whether users actually verify their selections. Three-Ballot and Vote/Anti-Vote/Vote, two related systems among the few non-cryptographic end-to-end verifiable voting systems, made improvements in security while eliminating complex protocols. They unfortunately suffered from usability issues, and although they did not require cryptographic primitives, they still relied on electronic devices. To address this, we introduce three folded-paper based systems that allow verifiable voting and resist common attacks despite not relying on any cryptography or electronic devices. The proposals are based on 1) semi-translucent ballots, 2) masking tape, or 3) folding and punching. These Origami voting methods help users understand the underlying mechanisms and give them a direct geometric approach to verification.
Need Assessment of Mathematics Learning Module Based on Information Technology for Junior High School

This study aims to analyze the need for teaching materials in the form of Flipbook as a media of information technology K-13 mathematics learning for junior high school students in class VIII semester II. The subjects of this research are 27 students of eight grades in Junior High School 2 Sukoharjo. This research is motivated by the limited available teaching materials and lack of innovation in teaching materials, so it tends to make students bored with mathematics. Data collection techniques using non-test with research instruments are a questionnaire that needs analysis and interviews. The analysis consists of 3 stages, namely front-end analysis, student analysis, and teaching material analysis The research findings show that (1) the lack of additional textbooks / other references in learning; (2) have never used learning media and the lack of facilities; (3) teachers and students need information technology-based mathematics teaching media.
Open Software Framework for Collaborative Development of No Reference Image and Video Quality Metrics

This paper describes ongoing work within the video quality experts group (VQEG) to develop no-reference (NR) audiovisual video quality analysis (VQA) metrics. VQEG provides an open forum that encourages knowledge sharing and collaboration. The VQEG no-reference Metric (NORM) group’s goal is to develop open-source NR-VQA metrics that meet industry requirements for scope, accuracy, and capability. This paper presents industry specifications from discussions at VQEG face-to-face meetings among industry, academic, and government participants. This paper also announces an open software framework for collaborative development of NR image quality Analysis (IQA) and VQA metrics &lt;ext-link ext-link-type="url" xlink:href="https://github.com/NTIA/NRMetricFramework"&gt;&lt;https://github.com/NTIA/NRMetricFramework&gt;&lt;/ext-link&gt;. This framework includes the support tools necessary to begin research and avoid common mistakes. VQEG’s goal is to produce a series of NR-VQA metrics with progressively improving scope and accuracy. This work draws upon and enables IQA metric research, as both use the human visual system to analyze the quality of audiovisual media on modern displays. Readers are invited to participate.
Redundancy Detection Of Sentence Pairs In The Software Requirements Specification Documents With Semantic Approach

Abstract Software requirements specification document must complete, unambiguous and must correct because can lead to failure in software development. It must contain sentences not redundant. Meyer’s seven sins explain some errors in software requirements specification document, one of which is a Noise. Variant of the noise is redundancy. Redundancy is repeating the same information, but using different terms or phrases, thus giving the impression that new information. This research to analyze performance redundancy detection in sentences pair on software requirement specification documents using WordNet based semantic similarity methods. This study tries to develop a framework that has been proposed in previous studies in different framework that is to detected redundancy in sentences without fact extraction. The proposed approach and evaluation process uses Kappa values to determine whether reliable framework that can be used to detect redundancy in sentence pairs by using experiments in two scenarios to get good results. Our experimental result show the performance of proposed framework can used to help reduce redundancy when creating software requirements specification documents and may contributes to enhance the quality of document. Shown with good kappa values and interpretation of the Kappa value is substantial agreement. It means many agreements between experts and framework.
A Large-Scale Chinese Short-Text Conversation Dataset

The advancements of neural dialogue generation models show promising results on modeling short-text conversations. However, training such models usually needs a large-scale high-quality dialogue corpus, which is hard to access. In this paper, we present a large-scale cleaned Chinese conversation dataset, LCCC, which contains a base version (6.8million dialogues) and a large version (12.0 million dialogues). The quality of our dataset is ensured by a rigorous data cleaning pipeline, which is built based on a set of rules and a classifier that is trained on manually annotated 110K dialogue pairs. We also release pre-training dialogue models which are trained on LCCC-base and LCCC-large respectively. The cleaned dataset and the pre-training models will facilitate the research of short-text conversation modeling. All the models and datasets are available at https://github.com/thu-coai/CDial-GPT.
Weight Determination Model for Social Networks in a Trust-Enhanced Recommender System

Social networks (SNs) among users are proven to be beneficial in enhancing the quality of recommender systems. But “information cocoons” in an SN make recommendations homogeneity and vision narrow. We constructed a recommender network in which the trust relationship is derived from a social network viewpoint and found that the trust relation is measured more appropriate by group interactions in the SN than by using individual interactions. Through analyzing characters of fuzzy measures, we weigh non-additive trust relationships between groups in the trust network. Further, a weight determination model for the trust network in recommender systems, on the basis of the knowledge of trust values, is developed for obtaining fuzzy measures for the Choquet integral. The novelty of this model is that it applies parameters necessary to describe the trust complementary and redundancy between experts and Choquet integral and it also utilizes fuzzy measures to deduce the effect of trust complementary and redundancy in recommendations. The model can help remove deviations caused by interactions, and improve the accuracy and objectivity of recommendations.
Enhanced Family Tree: Evolving Research and Expression: <i>Best Paper Award</i>

Enhanced Family Tree reimagines the possibilities of family trees with an evolving series of exhibits. The authors’ works combine genealogical data, visualization, 3D technologies and interactivity to explore and display ancient genealogical relationships. Their new approach may reveal questionable relationships in genealogical records. Moreover, the authors’ use of an organic metaphor of a “tree” can be further extended to increase public understanding and engagement. The audience's questions arising from this project show increased curiosity and nuanced questioning about their own family origins and development.
Feature Selective Anchor-Free Module for Single-Shot Object Detection

We motivate and present feature selective anchor-free (FSAF) module, a simple and effective building block for single-shot object detectors. It can be plugged into single-shot detectors with feature pyramid structure. The FSAF module addresses two limitations brought up by the conventional anchor-based detection: 1) heuristic-guided feature selection; 2) overlap-based anchor sampling. The general concept of the FSAF module is online feature selection applied to the training of multi-level anchor-free branches. Specifically, an anchor-free branch is attached to each level of the feature pyramid, allowing box encoding and decoding in the anchor-free manner at an arbitrary level. During training, we dynamically assign each instance to the most suitable feature level. At the time of inference, the FSAF module can work jointly with anchor-based branches by outputting predictions in parallel. We instantiate this concept with simple implementations of anchor-free branches and online feature selection strategy. Experimental results on the COCO detection track show that our FSAF module performs better than anchor-based counterparts while being faster. When working jointly with anchor-based branches, the FSAF module robustly improves the baseline RetinaNet by a large margin under various settings, while introducing nearly free inference overhead. And the resulting best model can achieve a state-of-the-art 44.6% mAP, outperforming all existing single-shot detectors on COCO.
Nudging Customers to Engage in Loyal Behaviour with Reference to Supermarkets / Hypermarkets in Lebanon

The main objective of this investigation is to determine the predictors of customer loyalty through the eyes of owners and managers of supermarkets / hypermarkets in Lebanon. The constructed causal model used for this research is a modified model of Laura Bassett's customer journey model (Bassett 2013, 2). It provides a logical causal model for the antecedents and consequences of customer support in Supermarkets / Hypermarkets in Lebanon. Concepts of personalize customer interactions are borrowed from Six Sigma -quality management theory-, and concepts related to customer support are borrowed from Steve Blank's Lean philosophies of marketing strategy while concepts related to understanding consumer behaviour are borrowed from implementing the CRM analytical approach. All of these concepts are used in constructing the questionnaire and building the causal model. Findings of this investigation provide strategic insights and practical thinking that have important implications for understanding and overcoming obstacles facing customer retention.
Deep State-Space Gaussian Processes

This paper is concerned with a state-space approach to deep Gaussian process (DGP) regression. We construct the DGP by hierarchically putting transformed Gaussian process (GP) priors on the length scales and magnitudes of the next level of Gaussian processes in the hierarchy. The idea of the state-space approach is to represent the DGP as a non-linear hierarchical system of linear stochastic differential equations (SDEs), where each SDE corresponds to a conditional GP. The DGP regression problem then becomes a state estimation problem, and we can estimate the state efficiently with sequential methods by using the Markov property of the state-space DGP. The computational complexity scales linearly with respect to the number of measurements. Based on this, we formulate state-space MAP as well as Bayesian filtering and smoothing solutions to the DGP regression problem. We demonstrate the performance of the proposed models and methods on synthetic non-stationary signals and apply the state-space DGP to detection of the gravitational waves from LIGO measurements.
The use of innovation in teaching the subject of “Social ecology” in schools

This article reveals the educational and health-saving potential of the “Social Ecology” discipline, the significant experience of teaching which has been accumulated by Armenian teachers. It reveals the objectives, the integrative nature of the content of the subject, the technology of studying and evaluating the results of mastering the didactic material. The innovative search for educators to increase the productivity of studying the subject of “Social Ecology” is characterized.
On the issue of forming and evaluating personal digital literacy

Various approaches of foreign and Russian researchers to the study of digital literacy are considered; the directions of digital literacy are highlighted, they include media literacy, information literacy, information security and digital skills; the basic principles of forming and evaluating digital literacy are defined.
Generation of 8 Gb/s physical random numbers based on spontaneous chaotic oscillation of GaAs/Al&lt;sub&gt;0.45&lt;/sub&gt;Ga&lt;sub&gt;0.55&lt;/sub&gt;As superlattices

&lt;sec&gt;Secret key is required in secure communications, and random numbers are generally used as keys to encrypt the original information. So it is crucial for cryptography and secure communication to generate the physical random number, which is completely safer than pseudo random number. However, Existing physical random number generator systems are difficult to satisfy the requirements of high-speed communication due to their complexity, large size, and limited equipment bandwidth. The GaAs/Al&lt;sub&gt;0.45&lt;/sub&gt;Ga&lt;sub&gt;0.55&lt;/sub&gt; superlattices is based on a structure formed by the alternating growth of two semiconductor materials, and has a good low-dimensional structure for studying the nonlinear behavior of electrons. Recent studies have shown that the GaAs/Al&lt;sub&gt;0.45&lt;/sub&gt;Ga&lt;sub&gt;0.55&lt;/sub&gt; superlattices under the DC voltage could appear chaos current oscillation states in some certain voltage ranges.&lt;/sec&gt;&lt;sec&gt;An all-solid-state real-time high-speed physical true random number generator based on superlattices is presented. The superlattices, excited by appropriate DC bias voltage, could generate a high-frequency chaotic oscillation signal as a source of physical entropy. A multi-bit analog-to-digital converter with 2 GHz sampling frequency is used for quantization to generate 12-bit binary random bits. Then, the field programmable gate array extracts the lowest 4 bits as valid bits and inverts bits to improve its randomness, and finally a true random number with a real-time rate of 8 Gbit/s is obtained. To obtained a superlattices signal with a higher degree of chaos, the Lyapunov exponent was used to assist in selecting a more suitable DC bias. The random number generated by the superlattices, owning excellent statistical characteristics, could pass the test of the random number industry standard (NIST SP 800-22), which is expected to be miniaturized and integrated on high-speed communication equipment.&lt;/sec&gt;
Accurate Detection of Distorted Pectoral Muscle in Mammograms Using Specific Patterned Isocontours

Automatic detection of the pectoral muscle in mammograms is widely used in computer-aided diagnostic (CAD) systems for breast cancer. The pectoral muscle region has some prominent features such as the upper corner position, high density, and triangular shape. But, these features may be distorted due to the masses, artifacts, skin folds, and overlapping tissues, and other reasons. Despite recent developments in CAD technology, accurate detection of distorted pectoral muscle images remains a challenging task. In this study, we proposed an automatic method that uses a divided topographic representation to detect distorted pectoral muscle boundaries. After the preprocessing stage, firstly an isocontour map is generated and then divided into horizontal blocks. The contours of the pectoral muscle boundary in the blocks often reveal specific patterns in terms of location, geometric and topological features. We developed a new segmentation algorithm, rule-based contour detection (RBCD), to detect these specific patterned isocontours. The method applied to two datasets consisting of 84 and 201 mammogram images from MIAS and Inbreast databases respectively. Besides, some distorted pectoral muscle samples selected from these datasets were used to further analyze the performance of the proposed method. The mean False-Positive and the mean False-Negative rates of the proposed method for MIAS and Inbreast datasets were 0.92%, 1.26%, and 2.34%, 1.15%, respectively. The quantitative and qualitative results for the distorted pectoral muscle samples show that the proposed method outperformed the compared methods.
Research on Stress Monitoring Index of Hydraulic Tunnel Lining Based on Numerical Simulation

Abstract In order to study the lining stress index of hydraulic tunnel, this paper mainly analyzes the safety monitoring data of a hydraulic tunnel, uses ANSYS finite element software for modeling, uses Flac 3D software for numerical calculation, and uses numerical analysis method to develop hydraulic tunnel G1 -18 Stress monitoring indicators for lining of section. The results show that the actual measured value is basically within the control range of the data calculation value. When determining the monitoring index of the steel bar stress, the result of the numerical calculation can be used as the index, but the monitoring index of the top steel bar meter should use the maximum monitoring value as the index.