Reviewerly/exHarmony huggingface dataset:
Flaws of hypercube-like ciphers

The class of block cryptographic XSLP-algorithms called "hypercube" is considered. For algorithms of this class, we obtain estimates for the dispersion index of a linear environment for any number of iterations. It is shown that when choosing a transformation P using generalized de Bruijn graphs for the algorithms under consideration, the avalanche effect may not occur, as a result of which the encryption key can be determined with laboriousness, which is significantly less than the laboriousness of total key testing.
Blockchain technology in libraries

Transfer from the Internet of information to the Internet of values based on the blochain-based trust protocol offers new opportunities for preservation and management of intellectual products. However, the advantages of blockchain-technology, e.g. distributed ledgers, absence of single point of failure, smartcontracts, etc., have not got into the focus of libraries. E-library technologies are reviewed in brief. The efforts to acquire blockchain-based decentralized information systems are described. Possible scenarios for using blockchain-technologies in the libraries of future are discussed.
In Defense of Image Pre-Training for Spatiotemporal Recognition

Image pre-training, the current de-facto paradigm for a wide range of visual tasks, is generally less favored in the field of video recognition. By contrast, a common strategy is to directly train with spatiotemporal convolutional neural networks (CNNs) from scratch. Nonetheless, interestingly, by taking a closer look at these from-scratch learned CNNs, we note there exist certain 3D kernels that exhibit much stronger appearance modeling ability than others, arguably suggesting appearance information is already well disentangled in learning. Inspired by this observation, we hypothesize that the key to effectively leveraging image pre-training lies in the decomposition of learning spatial and temporal features, and revisiting image pre-training as the appearance prior to initializing 3D kernels. In addition, we propose Spatial-Temporal Separable (STS) convolution, which explicitly splits the feature channels into spatial and temporal groups, to further enable a more thorough decomposition of spatiotemporal features for fine-tuning 3D CNNs. Our experiments show that simply replacing 3D convolution with STS notably improves a wide range of 3D CNNs without increasing parameters and computation on both Kinetics-400 and Something-Something V2. Moreover, this new training pipeline consistently achieves better results on video recognition with significant speedup. For instance, we achieve $$+0.6\%$$ top-1 of Slowfast on Kinetics-400 over the strong 256-epoch 128-GPU baseline while fine-tuning for only 50 epochs with 4 GPUs. The code and models are available at https://github.com/UCSC-VLAA/Image-Pretraining-for-Video .
An Study on the Effect of Tactile Sharing in Psychological Games on Decision Making

In the field of social psychology, many studies have been conducted to clarify human cooperative behavior using experimental games. In this study, we developed a Haptics interface that can share the input actions of others in real time, assuming communication in an online environment. And investigated the effect of sharing physical information on decision-making in an experimental game played by multiple players.
I VERBI SINTAGMATICI IN PARLANTI DI ITALIANO L1, L2 E BILINGUI: UN’INDAGINE SPERIMENTALE

The article investigates the use of Particle Verbs (PV) by different types of speakers: Italian L1 speakers, Italian L2 speakers with German mother tongue (L2ers) and bilingual speakers of Italian and Trentino dialects. After outlining the state of the art in the research on PV and the theoretical framework (section 1), the main differences, among PV in Italian, its dia- lects and Germanic languages, are highlighted (section 2). The differences are, as follows: the extension of the phenomenon, the semantic meanings, and the syntax of the construction. These three categories have been taken into account while performing the quantitative and qualitative analysis. The third section describes the experimental research, introduced by a paragraph outlining the research carried out on PVs and L2ers so far. The current study used a story-telling task to elicit semi-spontaneous data to analyze (methodology 3.1). The quantita- tive analysis pointed out a statistically significant difference of the use of PVs among the three groups (section 3.2), while, through the qualitative analysis, the emphasis has been put on the use of PVs according to their semantics and syntax (section 3.3). In the discussion (section 4), the findings are connected to the theoretical framework, confirming the hypothesis that multilingual competence plays a role in the frequency of use of these constructions.
VIEWPOINTS OF SCHOOL GRADUATES ABOUT THE PROCEDURE OF CHOOSING PROFESSION AND EMPLOYMENT

The article deals with the theoretical aspects of the problem of choosing a profession, in particular professional self-determination of young people studying. Some features of the regulatory documents of the federal and regional levels regulating career guidance activities that promote conscious and effective professional self-determination of a person are disclosed. The generalized results of a study of school graduates on the procedure for choosing a profession and employment in the city of Chelyabinsk 2019-2021 are presented. It is established that students initially form an interest in the university, and then there is a definition in the profession (specialty).
Speckle noise reduction in SAR images using type-II neuro-fuzzy approach

Synthetic aperture RADAR (SAR) images play a vital role in remote sensing applications and thus it insists the requirement of quality enhancement as it gets affected with speckle noise. It is a kind of noise that gets multiplied with pixel intensities due to interference of backscattering signal. In this paper, computational intelligence-based approach is proposed to remove speckle noise by preserving edges and texture information. In particular, the proposed system uses type-II neuro-fuzzy approach using pixel neighbourhood topologies. The performance efficiency of the proposed system is proved by comparing its results with existing methods.
ELETerm: A Chinese Electric Power Term Dataset

The domain-specific knowledge graph construction and its corresponding applications are gradually attracting the attention of researchers. However, the lack of professional knowledge and term datasets restricts the development of domain-specific knowledge graph. In the electric power field, knowledge graph has been verified effective in electric fault monitoring, power consumer service, and decision-making on dispatching. Although the electric power knowledge graph is of great application prospects, it is difficult for artificial intelligence experts to create professional knowledge and terms for knowledge graph construction. To assist the process of building electric power knowledge graph, we introduce a new Chinese electric term dataset (ELETerm) containing 10,043 terms. We make full use of reliable data resources from State Grid Jiangsu Electric Power Company Research Institute to extract terms. Our approach includes four stages: word extraction, candidate term selection, term expansion, and dataset generation. We give the statistics and analysis of the dataset. The dataset is publicly available under CC BY-SA 4.0 in github.
Political Argumentation by Reciting Poems in the Spring and Autumn Period of Ancient China

This paper introduces the Generalized Argumentation Theory which takes argumentation as a locally rational socio-cultural interaction governed by social norms and carried out through discourse between the members of a socio-cultural community in order to reason things out. Then we bring in the basic structure of generalized argumentation and the localized procedure of Generalized Argumentation Theory for studying the argumentative rules (social norms). On the basis of above introduction, we use the localized procedure to analyze a case of political argumentation by reciting poems in ancient China. By doing so, it’s indicated that political argumentation by reciting poems, in essence, takes cultural principle of Li of the Spring and Autumn Period as argumentative rules and uses poems to express ideas, in virtue of which arguments are accepted as locally rational by the politicians at that time. At last, the Little Red Book quotation fight, a similar political activity, is discussed briefly to show the significance that ancient Chinese political argumentation has on contemporary argumentation.
Artistic Portrait Applet, Robot, and Printer

In this paper, we present a series of demonstrations for artistic portrait drawing generation (APDG), including an applet, a robot, and a printer. To this end, we develop novel APDG algorithms which can translate a facial photo into high quality portraits of five artistic styles, i.e. the line-drawing, pen-drawing, pencil-drawing, abstract-drawing, and cartoon. Besides, we provide a number of templates to post-process the generated portraits. By simply pressing several buttons, users can obtain artistic portraits in the applet and order the robot/printer to draw/print them on beautiful postcards. The whole procedure only consumes about 2 min. Our demonstrations are easy to use and have achieved excellent user experience in various exhibitions.
GEOLOGICAL, GEOCHEMICAL, AND GEOPHYSICAL CHARACTERIZATION OF GOLD IN THE DAWSON PERALUMINOUS INTRUSIVE-RELATED SHEAR ZONE GOLD SYSTEM, CENTRAL COLORADO

Assessment of new and existing geologic mapping, geochemistry, and geophysics conducted during a 2016 to 2017 gold exploration program at Dawson, south of Canyon City in central Colorado, has led to a new model for middle Proterozoic age gahnite bearing base and precious metal mineralization in the Colorado Front Ranges. All of the known base and precious metal occurrences are associated with what can be interpreted as high temperature hydrothermalite fractionates of peraluminous granitioid intrusive complexes emplaced during flat subduction near the end of the Yavapai Orogenic epoch in Colorado between 1715 and 1690 Ma. At Dawson and nearby Green Mountain, two styles of metallization are present. An older copper-zinc (Ag) metal event associated with banded, weakly peraluminous biotite aplite occurs in the upper portions of an E-W trending, steeply south dipping shear zone named the Dawson shear. A second gold (Te-Se-Bi) locally bonanza grade precious metal event is associated with a later pink, medium to coarse grained, weakly foliated peraluminous granite. It generally occurs down dip from and overlaps with the base metal mineralization. The fractionation of what are inferred to be peraluminous hydrothermalites may have been driven by depressurization in tensile zones developed along the Dawson shear zone during NE-SW shortening strain. Many of these middle Proterozoic metal occurrences, including those at Dawson, were formerly misinterpreted as metamorphosed volcanogene massive sulfides, but their consistent association with peraluminous intrusions suggests otherwise. A literature survey of other gahnite bearing metallogeny such as Broken Hill in Australia and Franklin, New Jersey also indicates the presence of a robust peraluminous granitioid and pegmatoid association. Like Dawson, much of the textures and mineralogies in these deposits can be viewed as magmato-hydrohermal, immiscible, high temperature hydrothermalites hydrously differentiated from peraluminous melts evolved within the amphibolite isograd. This new perspective for the gahnite bearing base and precious metal metallogeny may uncover significant new exploration opportunities not only in Colorado but elsewhere on this planet in high grade orogenic metamorphic belts where gahnite and peraluminous magmatism co-exists.
An Efficient Sparse CNNs Accelerator on FPGA

Convolutional Neural Networks (CNNs) have achieved remarkable performance at a huge computational cost. By improving the model sparsity, it can effectively reduce the complexity. However, with deepening of sparsity, the problems of unbalanced workloads, computing fragmentation and mapping access conflict caused by irregular sparsity have become more and more remarkable. These problems pose great challenges for efficient computation of sparse CNN s. In order to make full use of two side of sparsity introduced by activations and weights, and overcome the above problems, this paper proposes an efficient sparse CNN s accelerator on FPGA to achieve the inference acceleration. We designed and implemented the accelerator on the Zynq UltraScale+ MPSoC ZCU102 evaluation board. By running AlexNet, VGG16 and ResNet50 networks on the accelerator to evaluated the peeformance. Experimental results show that the method proposed in this paper can achieve more than 97% reduction in collision rate and 2.35x improvement in computing performance and 9.37x improvement in energy efficiency.
A STEM FAMILY E-LEARNING FRAMEWORK TO INCREASE FAMILY ENGAGEMENT IN DISADVANTAGED COMMUNITIES

STEM family e-learning involves increasing the engagement in STEM home learning activities.STEM home learning activities range from exploring subjects such as science technology, engineering, and maths through a fun play based K-6 STEM curriculum.The family are trained to act as teachers, mentors, and coaches to the K-6 members.Families from educational disadvantaged areas are typically early school leavers and having the STEM capacity to support their children is a challenge.This research proposes a STEM Family e-Learning framework to increase family engagement in supporting the K-6 members engaged in STEM learning activities.The proposed framework combines training for volunteers and STEM learning activities provided to families.Training for volunteers focuses on how to work with families in disadvantaged communities.STEM learning activities involve providing the family with STEM programmes such as Virtual Robotic Coding Clubs, Card Challenges, e-Learning Programme, STEM Play and Learn, STEM Workshops, Showcases and Events.Parent and child evaluation data indicate that the activities stimulated interest in STEM for both children and their parents and increased confidence in STEM subjects.This research can potentially enhance the mainstreaming and extension of STEM e-learning to disadvantaged communities.
CLASSIFIER RANK - A NEW CLASSIFICATION ASSESSMENT METHOD

Most of the commonly used confusion matrix-based classification performance metrics, such as f1_score, MCC, and PRC, are sensitive to the class imbalance.To address this problem, we propose a novel classifier evaluation method, called classifier rank which provides the rank of the classifier in the space of all possible classifiers.To rank a classifier, we find the distribution of performance metrics conditional on arbitrary class ratio.However, some metrics like PRC are functions of a large sequence of confusion matrices whose joint distribution is difficult to estimate.Hence, we propose a directed binary tree model to effectively represent this large-scale joint distribution.As a result, we can estimate the classifier rank using graphical inference algorithms, such as Monte-Carlo algorithm.
The Difficulties of Writing Descriptive Text for Junior High Schoolers

During the current pandemic, some schools have to implement hybrid learning, where some students come to school, and some have to go online. After the teacher gave the learning materials, some students who took online classes still did not understand it was different from students who took offline classes. The research used the qualitative method. The data was collected from 33 students in the 7b class and ten selected students based on their descriptive English writing text. It is revealed that the students have achieved reasonable control in writing descriptive text. However, in terms of writing, some students make spelling mistakes, and the use of tenses still needs more understanding. The result showed that the difficulties of descriptive writing are spelling, tenses, punctuation, sentence pattern, pronoun, conjunction, and preposition. They must consider schematic structures and use the appropriate language. Thus, their text will be well-organized and understandable.
Implementing Low-cost Immersive 360° Video Technology to Promote Core Skills in Journalism Courses

Intentional training in storytelling is fundamental in journalism education, and effective storytelling requires empathy and perspective taking. Emerging media technologies, such as virtual reality and 360&deg; video, are noteworthy for their ability to enhance user sense of presence and to build empathy. Using cardboard virtual reality viewers, this qualitative study explored how immersive 360&deg; videos are relevant to journalism students&rsquo; core (i.e., emotional) skill acquisition in a classroom on crisis and trauma coverage. In this pilot study on applied instructional design, twenty-three student journal entries were analyzed, supported by the Cognitive and Affective Model of Immersive Learning (CAMIL). Key emerging themes included emotional impact, sense of presence, appeal of immersive experiences, and novelty. Pedagogical recommendations are presented to educators, instructional designers, and other audiences interested in integrating usable, existing, and affordable immersive content in journalism education.
Estimation of Traffic Density Using CNN with Simple Architecture

Traffic congestion might be a problem that is commonly encountered in large cities. Currently, most traffic control systems are still unable to capture traffic data, which means that traffic lights cannot be programmed to be adaptive. In this research paper, a traffic density estimation system based on Convolutional Neural Network was created. In order to do so, a video frame from a road surveillance camera was divided into several blocks. The CNN was then used to predict whether each of those blocks was occupied by vehicles. By doing so, the traffic density of each frame is able to be estimated. The result showed that the simplest CNN model, which only consisted of 27,074 weights and biases, achieved the accuracy of 97.47% and 96.57% towards training and validation data, respectively. The processing speed itself is decent since the system was able to run at approximately 15.52 frames per second.
A Process Framework for Applying Situational Method Engineering (SME) on OMG's Essence

Situational method engineering (SME) is an engineering process used to construct context-specific software development methods.The advantage of SMEs is to allow software development teams to work using a context-specific or situational method, that is, a method that suits their project characteristics.A situational method comprises method parts; each part has a context description that details the appropriate situation for applying that particular method.There are several types of method parts, such as method fragment, method chunk, method component, and method service.In this research, we adopt the concept of method chunk.We also use the modified metamodel from our previous study.Although there are advantages to applying SMEs, it does require extra effort.Method chunks are not easy to find, and a different notation decreases the method chunk's interoperability.This research proposes a process framework for applying SMEs.The framework's benefits are to guide method engineers in applying SMEs and provide a reference for software engineers to develop the supporting system.This framework use Essence language as a standard for method modeling to improve the interoperability of method chunks.We also apply the concept of service-oriented in the SME process to enhance the accessibility of method chunks by providing method chunk description as a service.Following the proposed framework, method engineers can extract method chunks from existing methods, publish them at a centralized publishing system to make them available as a service, and construct situational methods from selected method chunks.Software engineers can use the proposed framework to develop the supporting system.Our framework defines the complete processes for applying SMEs in a software project.The proposed framework has been validated by using the framework in a case study and building a prototype of the supporting system.Our objective is to validate the applicability of the proposed framework as a guideline.We conclude that the proposed framework is applicable, and in the end, it can support method engineers in applying SMEs in their software projects with less effort.
Effects of Global Discourse Coherence on Local Contextual Predictions

Context is vitally important for how we understand the world around us, and this is particularly so with language. We know that humans use a range of linguistic cues when understanding language to help tailor their expectations about upcoming linguistic material, contributing to the seamless nature of comprehension. Linguistic cues fall into two different levels of context – the local, sentence context and the wider, global context. Some linguistic cues that are considered to be global are knowledge about the world or the speaker. These have been found to facilitate semantic processing when they align with linguistic expectations through faster reading times of predictable content. One other global contextual cue that may be of use to how we represent meaning and understand the world around us is the coherence of a discourse. The current, pre-registered study investigates the interaction between discourse coherence and word predictability during language comprehension. To do so, we used an online, self-paced reading paradigm and had participants read three-sentence discourses that differed on the coherence of the final target sentence with the first two preamble sentences, and also on the predictability of a critical word within the target sentence. From our first experiment, we found that people were sensitive to the coherence of the overall three-sentence narrative, with slower reading times for trials that had less coherent preamble contexts. After exploration of our data, we pre-registered two further studies to investigate whether this effect is still present in more extreme experimental settings. For Experiment 2, we altered the ratio of less and more coherent contexts to see if people still make use of discourse coherence as a linguistic cue for informing their expectations when there was a greater proportion of more coherent trials. We found this to be the case. In Experiment 3, we replaced our less predictable critical words that were semantically relevant to the overall message of the target sentence with completely anomalous words. Here, we found people were faster to read the highly predictable critical words and slower to read the anomalous critical words when they first read more coherent contexts, but not when they first read less coherent contexts. This suggests that people are able to use relevant linguistic cues from both levels of context, and do so flexibly depending on the degree of contextual support at the global discourse level.
Self-supervised Contrastive learning for Zero-shot automatic retail checkout

Long queues at retail stores are a nightmare for every customer, and the present time-consuming checkout process degrades the overall shopping experience. In this paper, we propose an approach that can leverage the power of Deep Learning and Computer Vision to remove the time-taking human supervision from retail checkout systems. Specifically, we develop a Self-supervised Siamese network that takes the query image and original product image as input and verifies if the product is present in the query image. Previous approaches to developing automatic retail checkout systems in literature required data labeling and re-training the models every time a new product gets added to the retail database. In contrast, our model isn't trained on any labeled data, yet we show that our model generalizes to unseen products during the zero-shot evaluation phase, making it useful for real-world retail stores.