Reviewerly/exHarmony huggingface dataset:
PERFORMANCE APPRAISAL OF EMPLOYEES IN TEXTILE INDUSTRIES IN KOZHIKODE

Even though exhibition examination is worried about assessing labourers' work execution, it simultaneously features the particular targets of an association. As the worker is being evaluated, the association likewise sets itself by looking at goals and guidelines of execution, audits the entire examination system and plan, and hierarchical qualities and culture. Execution examination is a genuine apparatus for associations to assess and expand the nature of instruction and preparation of their labour force to create deep-rooted learning examples and techniques to support efficiency through more extended working periods. Inspiration, as it connects with representative efficiency, is frequently behind the drive for execution and self-realization and gives open doors to higher efficiency. Efficiency is a significant proportion of objective accomplishment because finishing fewer assets increments hierarchical productivity. Utilizing the exploratory examination plan and 109 members, the consequence of the review of severe strength areas demonstrates the relationship between exhibition evaluation and worker efficiency. It recommends that the issue of execution examination in textile and garments industries in Kozhikode ought to be tended to. Considering the aftereffect of the review, the paper suggests that presentation evaluation ought to painstakingly survey the representative's assets and shortcomings against necessities for potential future higher obligations.
A Ranking Recommendation Algorithm Based on Dynamic User Preference

In recent years, hybrid recommendation techniques based on feature fusion have gained extensive attention in the field of list ranking. Most of them fuse linear and nonlinear models to simultaneously learn the linear and nonlinear features of entities and jointly fit user-item interactions. These methods are based on implicit feedback, which can reduce the difficulty of data collection and the time of data preprocessing, but will lead to the lack of entity interaction depth information due to the lack of user satisfaction. This is equivalent to artificially reducing the entity interaction features, limiting the overall performance of the model. To address this problem, we propose a two-stage recommendation model named A-DNR, short for Attention-based Deep Neural Ranking. In the first stage, user short-term preferences are modeled through an attention mechanism network. Then the user short-term preferences and user long-term preferences are fused into dynamic user preferences. In the second stage, the high-order and low-order feature interactions are modeled by a matrix factorization (MF) model and a multi-layer perceptron (MLP) model, respectively. Then, the features are fused through a fully connected layer, and the vectors are mapped to scores. Finally, a ranking list is output through the scores. Experiments on three real-world datasets (Movielens100K, Movielens1M and Yahoo Movies) show that our proposed model achieves significant improvements compared to existing methods.
Improving User Privacy in Identity-Based Encryption Environments

The promise of identity-based systems is that they maintain the functionality of public key cryptography while eliminating the need for public key certificates. The first efficient identity-based encryption (IBE) scheme was proposed by Boneh and Franklin in 2001; variations have been proposed by many researchers since then. However, a common drawback is the requirement for a private key generator (PKG) that uses its own master private key to compute private keys for end users. Thus, the PKG can potentially decrypt all ciphertext in the environment (regardless of who the intended recipient is), which can have undesirable privacy implications. This has led to limited adoption and deployment of IBE technology. There have been numerous proposals to address this situation (which are often characterized as methods to reduce trust in the PKG). These typically involve threshold mechanisms or separation-of-duty architectures, but unfortunately often rely on non-collusion assumptions that cannot be guaranteed in real-world settings. This paper proposes a separation architecture that instantiates several intermediate CAs (ICAs), rather than one (as in previous work). We employ digital credentials (containing a specially-designed attribute based on bilinear maps) as the blind tokens issued by the ICAs, which allows a user to easily obtain multiple layers of pseudonymization prior to interacting with the PKG. As a result, our proposed architecture does not rely on unrealistic non-collusion assumptions and allows a user to reduce the probability of a privacy breach to an arbitrarily small value.
EfficientNet-B0 Model for Face Mask Detection Based on Social Information Retrieval

The world was introduced to the term coronavirus at the end of 2019, following which everyone was thrown into stress and anxiety. The pandemic has been a complete disaster, wreaking devastation and resulting in a significant loss of human life throughout the world. The governments of various countries have issued guidelines and protocols to be followed for stopping the surge in cases (i.e., wearing masks). Amidst all this chaos, the only weapon is technology. So, the detection of face masks is important. The authors utilized a dataset that included images of individuals in society wearing and not wearing masks. They gathered the information required to train a model by using deep networks like EfficientNetB0, MobileNetV2, ResNet50, and InceptionV3. With EfficientNet-B0, they have been able to achieve an accuracy of 99.70% on a two-class classification issue. These methods make face mask detection easier and help in knowledge discovery. These technological breakthroughs may aid in information retrieval as well as help society and guarantee that such a healthcare disaster does not occur again.
A Derivative Matrix-Based Covert Communication Method in Blockchain

The data in the blockchain cannot be tampered with and the users are anonymous, which enables the blockchain to be a natural carrier for covert communication.However, the existing methods of covert communication in blockchain suffer from the predefined channel structure, the capacity of a single transaction is not high, and the fixed transaction behaviors will lower the concealment of the communication channel.Therefore, this paper proposes a derivation matrix-based covert communication method in blockchain.It uses dual-key to derive two types of blockchain addresses and then constructs an address matrix by dividing addresses into multiple layers to make full use of the redundancy of addresses.Subsequently, to solve the problem of the lack of concealment caused by the fixed transaction behaviors, divide the rectangular matrix into square blocks with overlapping regions and then encrypt different blocks sequentially to make the transaction behaviors of the channel addresses match better with those of the real addresses.Further, the linear congruence algorithm is used to generate random sequence, which provides a random order for blocks encryption, and thus enhances the security of the encryption algorithm.Experimental results show that this method can effectively reduce the abnormal transaction behaviors of addresses while ensuring the channel transmission efficiency.
Matter, meaning and semiotics

We inhabit two worlds – the world of matter and the world of meaning (see Halliday, ‘On matter and meaning: The two realms of human experience, 2005). In this article, these two worlds and the physical, biological, social and semiotic systems that connect them are investigated. In this respect, semiotic systems are the most complex because they involve physical systems (the material sign), biological systems (human beings), social systems (society and culture) and meaning itself. Semiotic frameworks need to take into account these various dimensions as changes in one system reverberate across the meta-system as a whole. With this in mind, the interplay between material and semiotic worlds from a social semiotic perspective, are explored with a focus on meaning and its significance in relation to human existence. Using examples from various industrial ages, the article explores how semiotic resources (in this case, in mathematics, science and computer programming languages) are organized to structure reality in specific ways, and how semiotic combinations and the technologies arising from those constructions have changed the course of human history. In this discussion, attention is paid to the role of visual communication, both in terms of visual semiotic resources (e.g. graphs, digital images) and visual aspects of multimodal texts. It thus becomes evident that the functionalities of any one semiotic resource (including language) must be viewed in relation to its collective co-deployment with other semiotic resources. Lastly, the author examines semiosis in the digital age and considers the social implications of the current digital ecosystem. In doing so, she conceptualizes digital technologies as a one-way mirror where members of society use digital media for every facet of their lives while being watched, analysed and manipulated by those who have designed and own the digital platforms. It is apparent that semiotics has a major role to play in terms of design, policymaking and activism around future digital technologies.
Mapping global dynamics of benchmark creation and saturation in artificial intelligence

Benchmarks are crucial to measuring and steering progress in artificial intelligence (AI). However, recent studies raised concerns over the state of AI benchmarking, reporting issues such as benchmark overfitting, benchmark saturation and increasing centralization of benchmark dataset creation. To facilitate monitoring of the health of the AI benchmarking ecosystem, we introduce methodologies for creating condensed maps of the global dynamics of benchmark creation and saturation. We curate data for 3765 benchmarks covering the entire domains of computer vision and natural language processing, and show that a large fraction of benchmarks quickly trends towards near-saturation, that many benchmarks fail to find widespread utilization, and that benchmark performance gains for different AI tasks are prone to unforeseen bursts. We analyze attributes associated with benchmark popularity, and conclude that future benchmarks should emphasize versatility, breadth and real-world utility.
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A resource efficient hardware accelerator for Bayesian neural network (BNN) named B2N2, Bernoulli random number based Bayesian neural network accelerator, is proposed. As neural networks expand their application into risk sensitive domains where mispredictions may cause serious social and economic losses, evaluating the NN’s confidence on its prediction has emerged as a critical concern. Among many uncertainty evaluation methods, BNN provides a theoretically grounded way to evaluate the uncertainty of NN’s output by treating network parameters as random variables. By exploiting the central limit theorem, we propose to replace costly Gaussian random number generators (RNG) with Bernoulli RNG which can be efficiently implemented on hardware since the possible outcome from Bernoulli distribution is binary. We demonstrate that B2N2 implemented on Xilinx ZCU104 FPGA board consumes only 465 DSPs and 81661 LUTs which corresponds to 50.9% and 14.3% reductions compared to Gaussian-BNN (Hirayama et al., 2020) implemented on the same FPGA board for fair comparison. We further compare B2N2 with VIBNN (Cai et al., 2018), which shows that B2N2 successfully reduced DSPs and LUTs usages by 50.9% and 57.9%, respectively. Owing to the reduced hardware resources, B2N2 improved energy efficiency by 7.50% and 57.5% compared to Gaussian-BNN (Hirayama et al., 2020) and VIBNN (Cai et al., 2018), respectively.
Strong Anonymous Communication System Based on Segment Routing Over SDN

Abstract Software defined networking (SDN) has brought a novel networking paradigm for achieving the goal of anonymous communication. In this paper, we propose a strong anonymous communication scheme based on segment routing (STAR), which does not need all forwarding devices to support OpenFlow protocol and is easy to deploy in SDNs. In STAR, by expanding the packet format of segment routing via Sphinx protocol, the required routing information is encrypted and hidden in the packet header to prevent the enemy from associating with the communication parties. Moreover, to avoid the adversary connecting the communication parties based on load information, the trust controller is employed as an auxiliary node to negotiate the symmetric key between the communication parties for encrypting the packet load. The theoretical analysis shows that, when the adversary compromises multiple intermediate nodes, a low correct linking probability and effective attack resistance are obtained, which proves the weak correlation and stronger anonymity of STAR. Besides, evaluation results confirm that, compared with existing anonymous systems, the proposed STAR can ensure stronger anonymity and higher throughput (83.7% for that of no anonymity) by just introducing very small communication latency (microseconds) and resource cost. Especially for the large-volume data in large-scale SDNs, the advantages will be more obvious.
EIoT-PBFT: A multi-stage consensus algorithm for IoT edge computing based on PBFT

With the introduction of edge computing into the field of Internet of Things (IoT), the Cognitive Internet of Things (CIoT) has emerged as the next-generation solution for trust and intelligent reasoning in the IoT. That also puts blockchain, with its unique consensus mechanism, transparency and trustworthiness, on the stage of IoT applications. At present, not much research is focused on blockchain's application in CIoT, whose development is to a large extent restricted by the inefficiency of the consensus algorithm. Considering the characteristics of CIoT, a multi-stage consensus algorithm of EIoT-PBFT is proposed on the basis of PBFT algorithm, which includes the Grouping stage, Scoring stage and Consensus reaching stage. EIoT-PBFT meets the IoT edge computing setup by adopting a two-phase improved PBFT algorithm and a scoring mechanism based on both location and reputation, thus achieving a great increase in consensus efficiency. Evaluation results show that EIoT-PBFT takes 36.4% less time than PBFT for a single consensus, and the performance remains stable over the 2500 node configurations we set up. Moreover, at a scale of 1000 nodes, the number of edge nodes to be configured to reduce the number of system communications by 90% compared to the PBFT algorithm is only 5, making blockchain more customized for CIoT settings.
Automated detection of IVC filters on radiographs with deep convolutional neural networks

To create an algorithm able to accurately detect IVC filters on radiographs without human assistance, capable of being used to screen radiographs to identify patients needing IVC filter retrieval.A primary dataset of 5225 images, 30% of which included IVC filters, was assembled and annotated. 85% of the data was used to train a Cascade R-CNN (Region Based Convolutional Neural Network) object detection network incorporating a pre-trained ResNet-50 backbone. The remaining 15% of the data, independently annotated by three radiologists, was used as a test set to assess performance. The algorithm was also assessed on an independently constructed 1424-image dataset, drawn from a different institution than the primary dataset.On the primary test set, the algorithm achieved a sensitivity of 96.2% (95% CI 92.7-98.1%) and a specificity of 98.9% (95% CI 97.4-99.5%). Results were similar on the external test set: sensitivity 97.9% (95% CI 96.2-98.9%), specificity 99.6 (95% CI 98.9-99.9%).Fully automated detection of IVC filters on radiographs with high sensitivity and excellent specificity required for an automated screening system can be achieved using object detection neural networks. Further work will develop a system for identifying patients for IVC filter retrieval based on this algorithm.
Recognizing the Indian Cautionary Traffic Signs using<scp>GAN</scp>, Improved Mask<scp>R‐CNN</scp>, and Grab Cut

Summary The detection and classification of traffic signs is a major challenge for self‐driving vehicles. The task can be narrowed down to detecting and classifying Indian Cautionary Traffic Signs (ICTS). In this proposed work, the difficulty of detecting and identifying Indian Cautionary Traffic Signs is addressed, and sincere attempts have been made to attain a possible solution using a Generative Adversarial Network (GAN), Improved Mask R‐CNN, and GrabCut algorithms. Data augmentation is done using Cascade Pyramid Generative Adversarial Network (CP‐GAN) to upsize the data set. The Mask R‐CNN with certain adoptions termed Improved mask RCNN is used in conjunction with the Grab‐Cut method to handle ICTS detection and identification through automatic end‐to‐end learning. Initially, Improved Mask R‐CNN generates a pixel‐by‐pixel segmentation mask for each item in the input sample. Masks developed using Improved Mask R‐CNN are not always clean, that is, some background pixels are often seen in the foreground segmentation. Hence, the generated masks are refined using the Grab Cut algorithm to enhance image segmentation. This combined approach works well in isolating the traffic signs from the ground truth images. Improved Mask R‐CNN attained better performance in the overall performance of traffic sign detection in the Indian data‐set. This method recognizes 40 different types of cautionary traffic signs from the unique Indian data set. The results are provided for complicated traffic sign categories that have not been addressed before. The proposed technique is trained and evaluated on ICTS, GTSDB, STSD, LISA, and DITS data sets, and it is also compared against cutting‐edge object recognition methods like Mask RCNN, Faster RCNN, SSD, and YOLOv3.
An improved term weighting method based on relevance frequency for text classification

As a vital step of text classification (TC) task, the assignment of term weight has a great influence on the performance of TC. Currently, masses of term weighting methods can be utilized, such as term frequency-inverse documents frequency and term frequency-relevance frequency (TF-RF). It can be found that they are both consisted of local part (TF) and global part (e.g., IDF, RF). However, most of these methods adopt the logarithmic processing on their respective global parts, so it is natural to consider whether the logarithmic processing applies to all these methods or not. Actually, for a specific term weighting method, due to its different ratio of local weight and global weight resulting from logarithmic processing, it usually shows diverse text classification results on different text sets, which shows poor robustness. To explore the influence of logarithmic processing imposed on the TC performance of term weighting methods, TF-RF is selected as the representative because it can achieve relatively stable performance among these methods adopting logarithmic processing. Then, in order to balance the local part and global part of TF-RF, an improved term weighting method based on TF-RF is proposed, named as term frequency-exponential relevance frequency (TF-ERF). And two groups of experiments are conducted on TF-ERF and other existing term weighting methods based on two general standard corpora. The results show that the improved term weighting method TF-ERF has better text classification performance and robustness.
Semi-supervised learning with dropouts

Graph Laplacian manifold regularization exploits the intrinsic geometrical properties of the underlying graph over both labeled and unlabeled data to approximate an optimal smooth prediction function. The regularization works on the assumption that abundant unlabeled data add information to the model, which increases accuracy and generalization. However, a large number of unlabeled data points with few labeled data points results in function degeneration which restricts generalization. Higher order regularization through iterated Laplacian semi-norm resolves this problem, but it is not computationally intensive and not scalable. In this paper, we propose dropouts regularization by appropriately balancing the supervised loss and intrinsic regularization terms to enforce smoothness. This is done by increasing the labeled data points by assigning temporary soft-labels to a subset of unlabeled set iteratively. This is followed by the determination of the final labels. Localized Procrustes analysis is, then, used to increase the accuracy of label assignment. Experiments on both synthetic and real-world dataset show that the proposed model outperforms the existing graph Laplacian and its variants by a significant margin.
Rhizomer: Interactive semantic knowledge graphs exploration

Rhizomer helps researchers and practitioners explore knowledge graphs available as Semantic Web data by performing the three data analysis tasks: overview, zoom and filter, and details-on-demand. This approach makes it easier for users to get an idea about the overall structure and intricacies of a dataset, when compared to existing approaches and even without prior knowledge. Rhizomer is helpful for data reusers, who want to know about the reuse opportunities of a given dataset, and for knowledge graph creators, who can check if the generated data follow their expectations. Rhizomer has been applied in many scenarios, from research and commercial projects to teaching.
A two-phase differential evolution for minimax optimization

The optimum of a minimax optimization problem is the minimum of maximal outputs in all possible scenarios. A minimax optimization problem includes two decision spaces: the scenario space and the solution space. When optimizing this kind of problem, an algorithm should consider three issues: (1) in the scenario space, how to decide which promising individuals to be optimized; (2) in the solution space, how to avoid discarding promising individuals; (3) how to properly allocate the optimization resources to these two spaces. Bearing these in mind, a two-phase differential evolution algorithm is proposed. To address the first issue, it optimizes a better individual with a higher probability instead of updating the best one directly. The second issue is addressed as follows. On the one hand, individuals with better objective function values are modified less frequently; On the other hand, an archive-based comparison strategy is developed to avoid selecting an offspring that owns a good objective function value but has not been optimized adequately in the scenario space. To properly monitor these two phases, the optimization resources are allocated dynamically. Experiments on benchmark test functions and an open problem in epidemic spreading control over complex networks demonstrate that the proposed method is competitive.