Reviewerly/exHarmony huggingface dataset:
Ready for a Fair Deal

Canadian academic libraries are unionized environments, requiring collective organization and action to address labour conditions and contract negotiations. The University of Manitoba Faculty Association (UMFA) has 1264 members, including 52 archivists and librarians, and in 2021 resolved the longest strike in association history. The newly ratified agreement contained major gains to advance pay equity within the union, and the strike itself maintained UMFA historic high levels of participation and member engagement, in part due to the significant contributions of librarians and archivists. In this paper, three librarians who held distinct positions of leadership within UMFA, particularly during its 2021 strike, examine the unique strengths and difficulties of librarians and archivists working within a broader faculty union to make change. Relying on core competencies of librarianship, such as collaboration, consultation, communication, and leadership, the authors collectively and successfully filled central roles in the strategic direction, organizational foundation, and on-the-ground mobilization of the strike effort. The historical context for the labour climate and organizing history at the University of Manitoba is examined and demonstrates that core competencies of librarians and archivists are valuable and imperative skills in faculty union organizing. Librarians and archivists can use this narrative to inform the development of their own activism within their unions and workplaces, and to examine how their own skills may help enhance and improve their working conditions.
Searching Open-Source Vulnerability Function Based on Software Modularization

Vulnerable open-source component reuse can lead to security problems. At present, open-source component detection for binary programs can only reveal whether open-source components with vulnerabilities are reused, which cannot determine the specific location of vulnerabilities. To address this problem, we propose BMVul, an open-source vulnerability function detection based on the software modularization method, which is oriented to binary programs. BMVul performs binary modularization by the overlapping clustering method DBM based on directed graph, then uses feature comparison technology to carry out modular software component analysis. After creating open-source component vulnerability function set through function signature, BMVul detects vulnerability function in the binary modules reusing open-source components. The experimental results show that compared with the component detection based on Louvain modularization and B2SFinder, BMVul improves the precision by 3.16% and 59.57%, respectively. Moreover, the precision of unique binary module matching is improved by 39.43% compared with the Louvain method. The F1 score is improved by 8.45% compared to B2SFinder. Module-level detection narrows the search space of vulnerability functions, thereby reducing the workload of open-source vulnerability detection, which is of great significance for software security analysis.
An Intelligent Deep Neural Sentiment Classification Network

A Deep Neural Sentiment Classification Network (DNSCN) is developed in this work to classify the Twitter data unambiguously.It attempts to extract the negative and positive sentiments in the Twitter database.The main goal of the system is to find the sentiment behavior of tweets with minimum ambiguity.A well-defined neural network extracts deep features from the tweets automatically.Before extracting features deeper and deeper, the text in each tweet is represented by Bag-of-Words (BoW) and Word Embeddings (WE) models.The effectiveness of DNSCN architecture is analyzed using Twitter-Sanders-Apple2 (TSA2), Twitter-Sanders-Apple3 (TSA3), and Twitter-DataSet (TDS).TSA2 and TDS consist of positive and negative tweets, whereas TSA3 has neutral tweets also.Thus, the proposed DNSCN acts as a binary classifier for TSA2 and TDS databases and a multiclass classifier for TSA3.The performances of DNSCN architecture are evaluated by F1 score, precision, and recall rates using 5-fold and 10-fold crossvalidation.Results show that the DNSCN-WE model provides more accuracy than the DNSCN-BoW model for representing the tweets in the feature encoding.The F1 score of the DNSCN-BW based system on the TSA2 database is 0.98 (binary classification) and 0.97 (three-class classification) for the TSA3 database.This system provides better a F1 score of 0.99 for the TDS database.
Federated Blockchain Model for Cyber Intrusion Analysis in Smart Grid Networks

Smart internet of things (IoT) devices are used to manage domestic and industrial energy needs using sustainable and renewable energy sources.Due to cyber infiltration and a lack of transparency, the traditional transaction process is inefficient, unsafe and expensive.Smart grid systems are now efficient, safe and transparent owing to the development of blockchain (BC) technology and its smart contract (SC) solution.In this study, federated learning extreme gradient boosting (FL-XGB) framework has been developed along with BC to learn the intrusion inside the smart energy system.FL is best suited for a decentralized BC-enabled system to adapt learning models for trustworthy and reliable transactions.Many features and attributes of the Third International Knowledge Discovery and Data mining Tools Competition (KDD Cup 1999) dataset have been used in this study to perform experimental analysis.The likelihood of intrusions in the network is mathematically stated.The participant nodes run the BC based FL-Smart Contract (SC) algorithms to detect network intrusions.FL provided aggregated learning results from the experiment that was 99% accurate in predicting network intrusion.The experimentally determined block storage gain and retrieval gain were 97.5% and 95.4% respectively.The intrusion in the smart grid network was evaluated, and the data indicated that there was 1.2% illegal access.Moreover, the learning system's accuracy, retrieval and storage intrusions, legal access and transaction processing times were considered for comparison.The proposed system outperformed contemporary research-developed systems targeted for the same application.Therefore, this study provides a guaranteed intrusion learning system and secure transaction system for smart grids.
An Introduction to Deepfakes on Cryptographic Image Security

The level of protection afforded to the encryption key is directly proportional to the level of security afforded to the data being encrypted. Data transmission via networks is the primary application for encryption use. There have been many different methods developed and put into use, all of which are utilized for safeguarding sensitive image data from any kind of illegal access. Text, audio, video, graphics, and still photos are some of the numerous types of data that can be included within multimedia files. A rise in the use of multimedia content transmitted over the internet has resulted in an increase in the sortage for the content. The vast majority of current encryption algorithms are typically reserved for use with informations since they are mismatched with digitalized data. A block-based transformation technique was utilized for this project.
Macrocytic anemia: Myelogram results in military hospital Avicenna in Marrakech

Anemia is one of the most common health problems in the primary care setting. Macrocytosis in adults is defined as a red blood cell mean corpuscular volum &gt;95 fL. Macrocytic anemias are generally classified into megaloblastic or non-megaloblastic anemia. Retrospective descriptive study included 340 samples of myelogram received in the Hematology Labortory belonging to patients admitted to the Military hospital of Avicenna in Marrakech, over a period of 48 months (from January 2016 to January 2020), aimed to assess the etiological profile of macrocytic anemia in patients whose bone marrow smears were received in our laboratory. Out of 340 myelogram samples, 120 cases of macrocytic anemia were received. The male gender was predominant with a sex-ratio (M/F) at 3. The average age was 55 years old with extremes ranging from 29 to 91 years? The discovery of macrocytic anemia was fortuitous in 30% of cases. 47% of our population presented anemic syndrom, 14% hemorrhagic syndrome, 9% infectious syndrome. The hemoglobin varyied between 4,7 g/dL and 11,9 g/dL with an average of 7.2 g/dL. The mean corpuscular volume was 107 fL. The bone marrow smear confirmed megaloblastic anemia in 54,3% of cases of macrocytic anemia. The diversity and complexity of factors leading to macrocytic anemia preclude a single or uniform method of investigation. The investigative pattern must be tailored to the individual patient, giving importance to the clinical presentation.
Strategies for Targeting in Undercover Terrains: Modeling Multi-Source Data in Apuí Region, SW Amazon Craton, Brazil

Exploring covered terrains is a challenge that requires the integration of multiple sources of knowledge, particularly in the initial stages of mineral exploration. The Apuí region, located in the Southwestern Amazon Craton, has a small and constant gold production, despite the deep cover and limited geological knowledge. The gold is mainly hosted in quartz veins and breccias that cut Paleoproterozoic volcano-sedimentary sequences. The occurrences have similar characteristics to magmatic–hydrothermal deposits, such as a lack of regional metamorphism and intense hydrothermalization. We undertook a multi-source prospective investigation on different scales using 2D and 3D techniques to translate the footprints of the mineral system into mappable criteria. Gold prospectivity maps for the Juma District and Guida Target were produced by integrating geological, geochemical, and geophysical datasets in knowledge-driven fuzzy systems. Regional airborne magnetization vector inversion (MVI) models were utilized. The correlation between the drill cores and the magnetic susceptibility models highlighted a potential surface for gold mineralization associated with the boundary between a granitic intrusion and volcano-sedimentary rocks. The prospectivity maps reduced the search area, and the regional susceptibility models allowed for the reconnaissance of structures and bodies that may be related to gold mineralization at depth. The results present new strategies for increasing discovery performance in the Southern Amazon Craton under cover.
Evaluation of FractalDB Pre-training with Vision Transformers

Since the introduction of the Vision Transformer, many transformer-based networks have been proposed. Nakashima et al. showed that ViT and gMLP can be pre-trained in FractalDB and achieve the same level of accuracy as ImageNet-1k. We hypothesize that other Transformer networks may also benefit from pre-training in FractalDB. If this hypothesis is proven, it can be expected that improving FDSL-based datasets such as FractalDB will improve the accuracy of existing networks and those to be proposed in the future. Therefore, in this paper, we perform exhaustive experiments on pre-training results of representative Transformer networks on FractalDB.
Advanced Random Mix Augmentation : Data Augmentation Method for Improving Performance of Image Classification in Deep Learning Using Unduplicated Image Processing Combinations

Data augmentation is a commonly used method for improving deep learning models in image classification. By adding slightly modified images that do not change the label of the original image to the training data set, the trained model becomes more robust against diverse characteristics of the input image. In this study, we propose a new data augmentation method by improving a previously-known random augmentation method. Our method consists of three steps; 1) determine the set of image modification operators and the number of augmented images, 2) determine the sequence of the image modification operators so that no duplicated sequences are generated, and 3) apply the sequence to augment images. The variety of augmentation is further increased by randomly determining the level (intensity) and the weight of combining the sequences. We applied our method on the CIFAR dataset and show that our method outperforms existing methods.
The labor axiological problem in the technological unemployment context

The article deals with axiological problems caused by technological unemployment. For this purpose, the formation of the assumption that labor is a value is considered. Methodologically the research is based on M. Heideggers work "European Nihilism". The formation of labor is analyzed as the modern period value. The work ethic that emerged at that time inculcated a moral attitude positing that hard work should be valued, and a human is obliged to be committed to work. In postmodernity, a person's life is increasingly structured around self-realization in a competitive environment, and work becomes the main way to achieve this goal. The meaning of "postmodernity" concept is revealed. Within the context of this work, postmodernity will be defined as a transformational period of the personal work identification. The question under investigation is whether labor has remained valuable in postmodernity. The problem under consideration is the axiological problem of identifying a person and his values through work. Also the second axiological problem has been identified: the assumption of new value orientations in the professional life. It is assumed that a new principle of the work value is being formated. This principle can be established only through new knowledge about the essence of values and their evaluation conditions. This requires a higher personal awareness, which will help to reevaluate values and put new ones, then the former axiological aspects will be deposed and new ones will be formed. The basis of the value-setting new principle can be social focus, humanism, creative self-realization for the good of society.
Aging Effects on Perceptual Reading Span: Evidence from an Eye-Tracking Study

Objectives: The purpose of this study was to investigate how aging influences the perceptual span and reading through online reading measurement variables using an eye-tracking paradigm. Methods: This study had a total of 38 participants including 20 normal younger and 18 older adults. Offline (reading comprehension accuracy) and online (words per second, mean fixation duration) measurement variables were analyzed through sentence comprehension questions and perceptual span measurement tasks. Results: 1) There were not significant differences between the older adults and younger adults in reading comprehension accuracy. There was no cognitive demand due to the low difficulty of the task, and it did not affect the reading comprehension accuracy of each group. 2) There were significant differences between the older adults and younger adults in both words per second and mean fixation duration. The older group showed a decrease in encoding ability due to the normal aging process. Additionally, the performance of words per second and mean fixation duration was significantly lower under the one fixated word condition (1W) in both groups. 3) In the case of mean fixation duration, the older group fixed their gaze longer than the younger. The older group showed greater difficulty under the 1W condition when the cognitive load was increased by presenting limited language information. Conclusion: This study was intended to examine the perceptual span of older adults and the level which shows the aging-effect concretely. It was possible to predict that the perceptual span of the younger group was between one or two words to the right of the fixated word (W+1 and W+2), and that of the elderly was W+1.
Transforming metadata content guidelines and instructions to linked data

Among metadata-related standards, data content standards like metadata guidelines and instructions for creating metadata still remain in legacy forms. This study investigates a way to transform data content standards to linked data (LD) through conversion from other formats, while referring to the proposed layered framework. Under the basic policies for making LD on which this study is based, several principal matters were examined: (a) defining units to be assigned with Universal Resource Identifiers (URIs), (b) defining relationships among the instructions with URIs and (c) expressing instructed content in instructions properly with certain Resource Description Framework (RDF) properties. With the proper choice(s) for each matter, some actual standards were converted to LD: Resource Description and Access (RDA) and Dublin Core User Guide. The results showed that the adopted way of transforming data content standards to LD is valid and proper, and the resultant LD would be expected to be utilised in various manners.
Research, Representation, and Conservation of Mani Heaps: The Digitalization Projects

Abstract A kind of cultural heritage, a Mani heap is piled with many carving stones and used as a religious altar for prayer in daily life in Tibet. It has distinctive characteristics and high research value, providing extensive content and abundant information. This paper focuses on an overview of three digitization projects for Mani heaps. Based on the collected data of five field surveys, the three projects leverage digitization methods to introduce, analyze, and represent Mani heaps to support scholarly analysis and casual appreciation. The authors explore these projects to study, represent, and conserve Mani heaps, which are often ignored by researchers.
Air Target Threat Assessment: A Kernel Extreme Learning Machine Based on a Multistrategy Improved Sparrow Search Algorithm

Air strikes are among the main means of attack in modern warfare. To improve air defense capabilities and aid military decision-making, threat assessment models have been introduced. As the parameters of the kernel extreme learning machine (KELM) model need to be set individually, this study proposes a parameter learning strategy based on a multistrategy improved sparrow search algorithm (MISSA). First, a reasonable threat assessment model was established based on the capability and situation factors of air targets. Second, the sparrow search algorithm was improved in terms of population position initialization and position update strategy, incorporating tent chaos reverse learning, nonlinear inertia weights, a global search strategy, and adaptive t-distribution. The effectiveness of the MISSA strategy was verified using nine common benchmark functions. The results show that the proposed MISSA finds an effective balance between global and local searches. Moreover, when the MISSA is applied to solve the tuning problem of KELM, the values of mean absolute percentage error, mean square error, root mean square error, and mean absolute error for MISSA–KELM in the air target threat assessment problem are 2.013 × 10−2, 1.282 × 10−4, 1.132 × 10−2, and 8.316 × 10−3, respectively, all of which are higher than that of the other metaheuristic algorithms (e.g., ACWOA-KELM and HGWO-KELM). Therefore, the method proposed in this study can be used as a parameter-tuning tool for KELM, enabling KELM to perform better in practical applications.
How Do We Experience Digital Arts? An Exploration through Latour’s Modes of Existence

Abstract This article explores the divergence between the practice and theory of technology as observed through an interdisciplinary free-improvisation workshop that critically engaged with (digital) technology Bruno Latour’s “technical mode of existence” proposes an intriguing interpretation of this differentiation. What is it that we experience when we engage with “digital art”? How does this bear on conceptualizations of technologies? The “fictional” and “reference” modes of existence further aid in understanding the digital as it pervades culture and media. Using examples from music, visual arts, and observations from the workshop, dystopian visions of technology are disentangled and reconfigured. Embodied agency and kinesthesia play a major role in this process.
Method of gaze extraction in bionic vision

Aiming at the low efficiency of information processing in a robot vision system, our work studies how to extract fixed points during tasks so that significant and interesting target areas in the scene can be located. First, the architecture of a gaze extraction model of bionic vision is proposed, which consists of a spatial visual saliency model and a task-driven fixed point extraction model. The task-driven fixed point extraction process is represented as a closed-loop control problem. To couple exploration with the sampling process, a Q learning algorithm based on a random strategy is adopted, which is a top-down task-driven gaze extraction in the time dimension. It is fused with the spatial-based visual saliency model to form a spatiotemporal hybrid gaze extraction model to determine the fixed point in the final image. Finally, our qualitative image visualization experiment indicates that the fixed point extraction results of the model in two consecutive frames are close to the truth value obtained by an eye tracker. The quantitative area under the curve and average angular error data confirms the effectiveness of the model in predicting and extracting fixed points.
MixOOD: Improving Out-of-distribution Detection with Enhanced Data Mixup

Detecting out-of-distribution (OOD) inputs for deep learning models is a critical task when models are deployed in real-world environments. Recently, a large number of works have been dedicated to tackling the OOD detection problem. One of the most straightforward and effective ways is OOD training, which adds heterogeneous auxiliary data in the training stage. However, the extra auxiliary data cannot be involved arbitrarily. A high-quality and powerful auxiliary dataset must contain samples that belong to OOD but are close to in-distribution (ID), which can teach the model to learn more information about OOD samples, furthermore, distinguish OOD from ID. The key issue for this problem is how to simply acquire such distinctive OOD samples. In this article, we propose an enhanced Mixup-based OOD (MixOOD) detection strategy that can be attached to any threshold-based OOD detecting method. Different from the traditional Mixup designed for ID data augmentation, our proposed MixOOD generates augmented images with deliberately modified Mixup and then uses them as auxiliary OOD data to leverage the OOD detection. We test our method with classical OOD detecting approaches like Maximum Softmax Probability, Energy Score, and Out-of-distribution detector for Neural networks. Experiments show that models with MixOOD can better distinguish in- and out-of-distribution samples than the original version of each approach.