Reviewerly/exHarmony huggingface dataset:
Leveraging Multi-view Inter-passage Interactions for Neural Document Ranking

The configuration of 512 window size prevents transformers from being directly applicable to document ranking that requires larger context. Hence, recent works propose to estimate document relevance with fine-grained passage-level relevance signals. A limitation of such models, however, is that scoring each passage independently falls short in modeling inter-passage interactions and leads to unsatisfactory results. In this paper, we propose a Multiview inter-passage Interaction based Ranking model (MIR), to combine intra-passage interactions and inter-passage interactions in a complementary manner. The former captures local semantic relations inside each passage, whereas the latter draws global dependencies between different passages. Moreover, we represent inter-passage relationships via multi-view attention patterns, allowing information propagation at token, sentence, and passage-level. The representations at different levels of granularity, being aware of global context, are then aggregated into a document-level representation for ranking. Experimental results on two benchmarks show that modeling inter-passage interactions brings substantial improvements over existing passage-level methods.
APE-V: Athlete Performance Evaluation using Video

Athletes typically undergo regular evaluations by trainers and coaches to assess performance and injury risk. One of the most popular movements to examine in athletes needing lower extremity strength and power is the vertical jump. Specifically, maximal effort countermovement and drop jumps performed on bilateral force plates provide a wealth of metrics. However, the expense of the equipment and expertise needed to interpret the results limits their use. Computer vision techniques applied to videos of such movements are a less expensive alternative for extracting complementary metrics. Blanchard et al. [4] collected a dataset of 89 athletes performing these movements and showcased how OpenPose could be applied to the data. However, athlete error calls into question 46.2&#x0025; of movements &#x2014; in these cases, an expert assessor would have the athlete redo the movement to eliminate the error. Here, we augmented [4] with expert labels of error and established benchmark performance on automatic error identification. In total, 14 different types of errors were identified by trained annotators. Our benchmark models identified errors with an F1 score of 0.710 and a Kappa of 0.457 (Kappa measures accuracy over chance). All code and augmented labels can be found at https://blanchard-lab.github.io/apev.github.io/.
Rhyme in classical Latin poetry: Stylistic or stochastic?

Abstract This study offers the first broad quantitative analysis of the use of rhyme in classical Latin hexameter and elegiac verse. The data and tools developed for the analysis are released under a permissive open source license. These include software to create an accurate phonetic transcription of Latin verse from the Musisque Deoque corpus; a system for scoring rhyme via phonetic similarity; and a system for generating large amounts of metrically correct, stochastic Latin verse (useful for analysis baselines). Further to this, some initial analysis is performed: first via descriptive statistics and then with two unsupervised multivariate analyses using dimension reduction methods. The study examines nineteen works by twelve authors, comprising about 96,000 lines. First and foremost, the results suggest that rhyme was consciously used by classical authors, but to different extents and in different ways. There is a solid and detectable stylistic separation between the use of rhyme in elegy and epic, and possibly also between satire and the rest. Within genres, authors can be stylistically separated with a small set of features. On the negative side, it appears that the stylistic signal from rhyme is fairly faint, and so forensic analysis (e.g. for authorship attribution) is not presently recommended on texts that are shorter than several thousand lines.
V-EduGram: Voice-based Control Technique of Hologram Technology in Education

Education is one of the most critical fields in people&#x0027;s life that largely affects the development and progress of societies, and different methods are used to transfer the knowledge and information to the students, usually under the guidance of some educators. The use of images and visual displaying methods in education is of actual worth; visual means help us learn, explain tough concepts, and attract attention. The visual content is either displayed in a two-dimensional &#x201C;2D&#x201D; way which may include some problems, or in a three-dimensional &#x201C;3D&#x201D; way that is considered the proper way to display the visual information because it corresponds to human vision. Hologram technology is the one of the technologies that display 3D visual content, usually, depending on the reflections of light or laser beams so that people are able to see the 3D content directly with their eyes. The usage of Hologram technology in education as a tool to display visual information in a three-dimensional way outperforms other used ways and is considered to positively affect educational outcomes. Given the situation in which information is transferred from the teacher to students in education and using Hologram&#x0027;s technology to display visual educational content to them, teachers face a difficulty in the process of controlling the display of content on the Hologram projector while teaching students. This research studies the development of the process of controlling the display of the Holograms on the Hologram projector and gives teachers the ability to control the device using the voice commands.
Resource-efficient Hybrid X-formers for Vision

Although transformers have become the neural architectures of choice for natural language processing, they require orders of magnitude more training data, GPU memory, and computations in order to compete with convolutional neural networks for computer vision. The attention mechanism of transformers scales quadratically with the length of the input sequence, and unrolled images have long sequence lengths. Plus, transformers lack an inductive bias that is appropriate for images. We tested three modifications to vision transformer (ViT) architectures that address these shortcomings. Firstly, we alleviate the quadratic bottleneck by using linear attention mechanisms, called X-formers (such that, X ∈{Performer, Linformer, Nyströmformer}), thereby creating Vision X-formers (ViXs). This resulted in up to a seven times reduction in the GPU memory requirement. We also compared their performance with FNet and multi-layer perceptron mixers, which further reduced the GPU memory requirement. Secondly, we introduced an inductive prior for images by replacing the initial linear embedding layer by convolutional layers in ViX, which significantly increased classification accuracy without increasing the model size. Thirdly, we replaced the learnable 1D position embeddings in ViT with Rotary Position Embedding (RoPE), which increases the classification accuracy for the same model size. We believe that incorporating such changes can democratize transformers by making them accessible to those with limited data and computing resources.
Cluster-Aware Heterogeneous Information Network Embedding

Heterogeneous Information Network (HIN) embedding refers to the low-dimensional projections of the HIN nodes that preserve the HIN structure and semantics. HIN embedding has emerged as a promising research field for network analysis as it enables downstream tasks such as clustering and node classification. In this work, we propose VaCA-HINE for joint learning of cluster embeddings as well as cluster-aware HIN embedding. We assume that the connected nodes are highly likely to fall in the same cluster, and adopt a variational approach to preserve the information in the pairwise relations in a cluster-aware manner. In addition, we deploy contrastive modules to simultaneously utilize the information in multiple meta-paths, thereby alleviating the meta-path selection problem - a challenge faced by many of the famous HIN embedding approaches. The HIN embedding, thus learned, not only improves the clustering performance but also preserves pairwise proximity as well as the high-order HIN structure. We show the effectiveness of our approach by comparing it with many competitive baselines on three real-world datasets on clustering and downstream node classification.
Haze Level Evaluation Using Dark and Bright Channel Prior Information

Haze level evaluation is highly desired in outdoor scene monitoring applications. However, there are relatively few approaches available in this area. In this paper, a novel haze level evaluation strategy for real-world outdoor scenes is presented. The idea is inspired by the utilization of dark and bright channel prior (DBCP) for haze removal task. The change between hazy and haze-free scenes in bright channel could serve as haze level indicator, and we name it as DBCP-I. The variation of contrast between dark and bright channels in a single hazy image also contains useful information to reflect haze level. By searching for a segmentation threshold, a metric called DBCP-II is proposed. Combining the strengths of the above two indicators, a hybrid metric named DBCP-III is constructed to achieve better performance. The experiment results on public real-world benchmark datasets show the advantages of the proposed methods in terms of assessment accuracy with human subjective ratings. The study is first-of-its-kind with preliminary exploration in the field of haze level evaluation for real outdoor scenes, and it has a great potential to promote research in autonomous driving and automatic air quality monitoring. The open-source codes of the proposed algorithms are free to download.
Constructing a mobile visual search framework for Dunhuang murals based on fine-tuned CNN and ontology semantic distance

Purpose Dunhuang murals are rich in cultural and artistic value. The purpose of this paper is to construct a novel mobile visual search (MVS) framework for Dunhuang murals, enabling users to efficiently search for similar, relevant and diversified images. Design/methodology/approach The convolutional neural network (CNN) model is fine-tuned in the data set of Dunhuang murals. Image features are extracted through the fine-tuned CNN model, and the similarities between different candidate images and the query image are calculated by the dot product. Then, the candidate images are sorted by similarity, and semantic labels are extracted from the most similar image. Ontology semantic distance (OSD) is proposed to match relevant images using semantic labels. Furthermore, the improved DivScore is introduced to diversify search results. Findings The results illustrate that the fine-tuned ResNet152 is the best choice to search for similar images at the visual feature level, and OSD is the effective method to search for the relevant images at the semantic level. After re-ranking based on DivScore, the diversification of search results is improved. Originality/value This study collects and builds the Dunhuang mural data set and proposes an effective MVS framework for Dunhuang murals to protect and inherit Dunhuang cultural heritage. Similar, relevant and diversified Dunhuang murals are searched to meet different demands.
A Hill-PlayFair Hybrid Based on High order Matrices and 16 x 16 PlayFair tables

Cryptographic systems are either symmetric or asymmetric. Symmetric cryptographic systems use one common key for both encryption and decryption. Asymmetric cryptographic models use different keys, one for encryption and another for decryption. A product cipher is a hybrid cipher built from combining two or more ciphers. A product cipher built from the combination of symmetric and asymmetric ciphers, supposedly is much stronger than the ciphers used to build it. This paper explores the creation of a product cipher built from the combination of the Hill (asymmetric) cipher and the Playfair (symmetric) cipher after some modification to each. The Hill cipher commonly use matrix keys to encrypt the 26 letters of the alphabet through substitution by other alphabetic characters. In this paper, we propose the use of dynamically generated high order matrix keys to encrypt the 256 ASCII characters. Determining the inverse matrices of higher order matrix keys is a complex task to many, making brute force attack harder. On the contrary, the Playfair model traditionally uses a 5 x 5 table to encrypt 25 alphabetic characters, reading I and J as one character. We propose a revised Playfair model which supports the 256 characters by using a 16 x 16 table. An experiment was administered to evaluate the performance of the hybrid model with respect to RAM demands and CPU time. Results indicate that the performances of the product cipher are better than the sum of the performances of its building blocks.
A Geometry Information Enhanced Unet for Tumor Segmentation

Currently in clinical practice, tumor segmentation contributes to diagnosis and determination of radiotherapy area, leading to higher efficiency. In order to offer doctors help in lesion analysis and measurement, segmenting tumors from medical images is investigated in this paper. We propose a geometry information enhanced Unet to solve the problem of edge perception in medical images. In this network, we devise a method of extracting differential geometry information at the input of Unet, which makes full use of the tissue edge information of images to improve the segmentation accuracy of the network. This network makes the edge of tumors clearer by using Jacobian determinant and Laplace operator. Experiments on BraTS2018 dataset are performed to demonstrate that our network has superior performance to the baseline. And we apply our method to the clinical liver tumor CT data to explore practicality of our model in other tumor types or other medical image modality.
Decentralized Data Flows for the Functional Scalability of Service-Oriented IoT Systems

Abstract Horizontal and vertical scalability have been widely studied in the context of computational resources. However, with the exponential growth in the number of connected objects, functional scalability (in terms of the size of software systems) is rapidly becoming a central challenge for building efficient service-oriented Internet of Things (IoT) systems that generate huge volumes of data continuously. As systems scale up, a centralized approach for moving data between services becomes infeasible because it leads to a single performance bottleneck. A distributed approach avoids such a bottleneck, but it incurs additional network traffic as data streams pass through multiple mediators. Decentralized data exchange is the only solution for realizing totally efficient IoT systems, since it avoids a single performance bottleneck and dramatically minimizes network traffic. In this paper, we present a functionally scalable approach that separates data and control for the realization of decentralized data flows in service-oriented IoT systems. Our approach is evaluated empirically, and the results show that it scales well with the size of IoT systems by substantially reducing both the number of data flows and network traffic in comparison with distributed data flows.
Manipuri–English comparable corpus for cross-lingual studies

This paper presents Mni-EnCC, a temporal alligned Manipuri–English comparable corpus, to facilitate cross-lingual studies between Manipuri and English. Mni-EnCC has been created by collating text from two publicly published news sources in internet namely Sangai Express and Poknapham in Manipur. Though, both the publishers publish news in Manipuri and English editions, they are not the translation of each other. Almost all of the Manipuri editions are created using proprietary tools which generate texts in customized non-standard and non-unicode encodings. We develop tools to transform the non-unicode text into unicode text to generate the Manipuri corpus. We then verify and time aligned all the articles using a semi-automated process. Furthermore, the quality of the Mni-EnCC is evaluated using two premier cross-lingual studies: bilingual dictionary induction and machine translation. Experimental observations provide encouraging results making it as a suitable dataset for future cross-lingual studies on between Manipuri and English language pair. With an objective to promote cross-lingual studies in Manipuri–English, we also plan to release the corpus and supporting Unicode conversion tool.
A Further Exploration of the Natural Incorporation of Motives in BDI Architectures

Belief-Desire-Intention (BDI) is a well known model for designing agents behaving intelligently and in a flexible manner. The concept of goal is an important BDI agent&#x2019;s component that plays a central role in this flexibility of behavior. Besides, as the concept of motive is another relevant one for the agent&#x2019;s behavioral flexibility and as the BDI model originally does not include motives, some extensions of the BDI agent, with a new component for expressing the motive, have been proposed. In a recent work, it has been shown that the concept of motive can be specified via the goal concept, so it is not required to add a new component to express a motive. In this paper, we further explore this idea at the architectural level of a BDI agent. In particular, we look at how the added values of incorporating motives in BDI agents, can be obtained via the goal concept.
Research on Insulator Defect Detection Method Based on Image Processing and Watershed Algorithm

Disc-shaped suspension insulators are widely used in substations and transmission and distribution lines of various voltage levels. Its main function is to achieve insulation and wire fixation in high-voltage overhead transmission and distribution lines. During operation, in a complicated environment such as pollution, temperature and humidity, insulator damage and chip-dropping failures often occur. The exploration of non-contact insulator defect detection methods has important theoretical significance and practical value. This paper takes the visible light image of suspended insulators as the research object. In this paper, the image region is extracted according to the shape and texture of the insulator, and according to the difference of computer vision parameters of various defects, an improved watershed algorithm is selected to realize the segmentation of the insulator image. Aiming at the possible defects such as breakage and chipping, this paper proposes the insulator defect detection criterion, so as to realize the automatic detection of the defect and the classification of the defect type. The research algorithm considers environmental factors and the efficiency and accuracy of algorithm feature extraction.
Efficient Recommender System for Kid’s Hobby Using Machine Learning

A recommendation system is playing a vital role in the social media world and is used nowadays in many applications which provide users with recommendations based on their preferences. With this enormous growth in the volume of information online these days, recommender systems are a helpful tool to overcome information loaded by users. The utilization of recommender systems is day-by-day increased as users are potentially influenced by many choice-based challenges. There are many types of recommendation systems with different applications and have successfully adopted, including healthcare, marketing, agriculture, media, and many more. This paper provides an application for parents where the parent is filling their preferences and based on Support Vector Machine algorithm of Machine Learning system will recommend which is hobby suitable for their kids. The system is implemented using the support vector machine algorithm where classification is done by finding the hyperplane for differentiating the classes. We have considered the distances between the nearest data points which helps to decide the right hyperplane. This proposed system provides an overview of our recommender systems, their types, different techniques, proposed system, and conclusion drawn. To check for the efficiency of different algorithms are used for recommendation system are discussed in the paper.
Training with growing sets: A comparative study

Abstract Being similar to and inspired from the process of human education, curriculum learning methods ‐or CL methods‐sort the input examples from easy to difficult, then add them to the training set in that order. Considering the fact that CL research is most concerned with determining the direction (from easy to difficult vs. from difficult to easy) and the criteria of this sorting, vast and various studies have emerged in the literature addressing both types of sorting. However, this results in a contradiction that demands finding a common aspect of ordering in both directions. This study argues that this required common aspect lies in the gradual enlargement of training. In other words, it is claimed that the success of CL methods does not depend on which criteria or in which direction the ordering is made. Extensive experiments have been conducted on various datasets using different deep learning models in order to test this claim. It was observed that random ordering had achieved competitive results with CL methods. Moreover, random ordering proved to be faster than other CL methods as it eliminates the cost of sorting computation. Based on these results, using random ordered growing sets as a baseline in future CL studies is recommended. Moreover, the possibly to improve the optimization performance via training with growing sets in theoretical perspective is also explained.
Memorize, Then Recall: A Generative Framework for Low Bit-Rate Surveillance Video Compression

Surveillance video applications grow dramatically in public safety and daily life, which often detect and recognize moving objects inside video signals. Existing surveillance video compression schemes are still based on traditional hybrid coding frameworks handling temporal redundancy by block-wise motion compensation mechanism, lacking the extraction and utilization of inherent structure information. In this paper, we alleviate this issue by decomposing surveillance video signals into the structure of a global spatio-temporal feature (memory) and skeleton for each frame (clue). The memory is abstracted by a recurrent neural network across Group of Pictures (GoP) inside one video sequence, representing appearance for elements that appeared inside GoP. While the skeleton is obtained by the specific pose estimator, it served as a clue for recalling memory. In addition, we introduce an attention mechanism to learn the relationships between appearance and skeletons. And we reconstruct each frame with an adversarial training process. Experimental results demonstrate that our approach can effectively generate realistic frames from appearance and skeleton accordingly. Compared with the latest video compression standard H.265, it shows much higher compression performance on surveillance video.
Appearance-based Gaze Estimation Using Multi-task Neural Network

Abstract In order to improve the gaze estimation accuracy through using the information in the eye image more adequately, an appearance-based gaze estimation neural network EyeNet has been proposed in this article. On the one hand, by decomposing the main task into two subtasks, this network could gain more information during the training stage. On the other hand, EyeNet could extract features in the image more efficiently by adding an auxiliary task which is detecting the iris center and eye ball center of the eye. Experiments show that by using these two tricks, the error decreased from 5.42 degree to 5.16 degree.