Reviewerly/exHarmony huggingface dataset:
SufRec, an algorithm for mining association rules: Recursivity and task parallelism

Authors:
raw_author_name: Abdelkader Mokkadem
raw_affiliation_string: Laboratoire de Mathématiques de Versailles
raw_author_name: Mariane Pelletier
raw_affiliation_string: Laboratoire de Mathématiques de Versailles
raw_author_name: Louis Raimbault
raw_affiliation_string: Laboratoire de Mathématiques de Versailles

Abstract: The mining of frequent itemsets and association rules is a core problem in data mining and an essential task in data analysis. In this paper, we present SufRec, a new algorithm for finding frequent itemsets and association rules. We give two versions. In both versions, the mining of the frequent itemsets is decomposed as a sequence of tasks. The first version (the SufRecDep algorithm) proceeds the tasks successively, each one using the results of the previous ones. The second version (the SufRecInd algorithm) performs the tasks independently of each other. Both versions are recursive with respect to the items and have thus the advantage to be particularly efficient for updating the mining process when new items are added to the database or when others are excluded. Moreover, the task-independent processing of SufRecInd makes it very easy to build parallel versions of SufRec. We present two parallel SufRec algorithms. With the first one, P-FIFO-SufRec, the processors perform the tasks according to the order in which the items appear in the database, preserving the recursive nature of SufRec. For second one, P-PAST-SufRec, the set of items is fixed and the tasks are pre-assigned to each processor. In order to evaluate the performance of SufRec, we carry out a consequent experimental study. In particular, we compare its running times with those of well-known parallel and non-parallel frequent itemsets mining algorithms.
Computing recommendations from free-form text

Authors:
raw_author_name: Lukas Eberhard
raw_affiliation_string: Graz University of Technology, Graz, Austria
raw_author_name: Simon Walk
raw_affiliation_string: Avery Dennison - atma.io, Graz, Austria
raw_author_name: Denis Helic
raw_affiliation_string: Modul University Vienna, Vienna, Austria

Abstract: While searching for consumer goods, users frequently ask for suggestions from their peers by writing short free-form textual requests. For example, when searching for movies users may ask for “Drama movies with a mind-bending story and a surprise ending, such as Fight Club” in one of the many online discussion boards. Despite the recent developments in large language models (LLMs) and natural language processing (NLP), modern recommender systems still struggle to process such requests. Therefore, in this paper we evaluate several approaches for annotating structured information from such short, free-form natural language user texts to calculate recommendations. We set up this evaluation as a two phase processes including (a) identification of the best NLP approach to identify key elements of users’ requests, and (b) assessment of the quality of recommendations computed with such elements. For our evaluation, we use a gold-standard reddit movie recommendation dataset consisting of annotations, manually created by crowdworkers who extracted keywords, actor names and movie titles. Using this dataset we evaluate a collection of more than 30 NLP and five recommender approaches. In addition, we perform an ablation study to assess relative annotation importance for movie recommendations. We find that domain-specific deep learning models, trained on a subset of data as well as embedding-based recommendation approaches are able to match the recommendation performance of recommendations computed from manual annotations. These promising results warrant further investigation in automatic processing of short free-form texts for computation of recommendations. Specifically, we provide insights into which NLP models and configurations work best for automatically annotating free text to compute (movie) recommendations, hence substantially reducing the search space for combinations of NLP and recommendation algorithms in the movie and potentially other domains.
MLIBT: A multi-level improvised binarization technique for Tamizhi inscriptions

Authors:
raw_author_name: Monisha Munivel
raw_affiliation_string: Sri Sivasubramaniya Nadar College of Engineering, Chennai, India

Abstract: The Tamizhi inscriptions, one of the earliest ever discovered, is predominantly found on memorial stones and caves which dates 5th century BCE to 3rd century CE. Today's generations need ways to interpret the script in order to know the historical figures and events because the Tamizhi script evolved into the modern Tamil script over time. Currently, only few epigraphists are available to manually decode the inscriptions into modern Tamil. Hence, there is a need for an alternate way to preserve this cultural heritage. Image processing is one such digital technology that enables binarization on inscription images, and the retrieved text may then be utilized to convert them to the needed target language. Nevertheless, binarization of Tamizhi inscription images are highly complex due to aging, environmental factors, handwritten, similar foreground & background and uneven size and shapes of the stones. Also, due to the small dataset, deep learning techniques are inapplicable. Furthermore, existing approaches produce poor results for Tamizhi inscriptions since they can only be used on flat stone backgrounds and require sufficient light illumination for effective binarization. This research suggests a multi-level improvised binarization solution for Tamizhi inscription images to address these challenges. It achieves this utilizing post-processing with shrink and swell filters together with an improved median filter with modified adaptive thresholding. Outperforming the current binarization techniques, which achieved a maximum accuracy of about 74%, MLIBT produced an accuracy of around 92.19%.
Temporal-Spatial Correlation Attention Network for Clinical Data Analysis in Intensive Care Unit

Authors:
raw_author_name: Weizhi Nie
raw_affiliation_string: School of Electrical and Information Engineering, Tianjin University, China
raw_author_name: Dan Song
raw_affiliation_string: School of Electrical and Information Engineering, Tianjin University, Tianjin, China
raw_author_name: Yunpeng Bai
raw_affiliation_string: Department of Cardiac surgery, Tianjin University Chest Hospital, China

Abstract: Recent advancements in medical information technology have enabled electronic health records (EHRs) to store comprehensive clinical data which has ushered healthcare into the era of "big data". However, medical data are rather complicated, making problem-solving in healthcare be limited in scope and comprehensiveness. The rapid development of deep learning in recent years has opened up opportunities for leveraging big data in healthcare. In this article we introduce a temporal-spatial correlation attention network (TSCAN) to address various clinical characteristic prediction problems, including mortality prediction, length of stay prediction, physiologic decline detection, and phenotype classification. Leveraging the attention mechanism model's design, our approach efficiently identifies relevant items in clinical data and temporally correlated nodes based on specific tasks, resulting in improved prediction accuracy. Additionally, our method identifies crucial clinical indicators associated with significant outcomes, which can inform and enhance treatment options. Our experiments utilize data from the publicly accessible Medical Information Mart for Intensive Care (MIMIC-IV) database. Finally, our approach demonstrates notable performance improvements of 2.0% (metric) compared to other SOTA prediction methods. Specifically, we achieved an impressive 90.7% mortality rate prediction accuracy and 45.1% accuracy in length of stay prediction.
Dual-branch counting method for dense crowd based on self-attention mechanism

Authors:
raw_author_name: Yongjie Wang
raw_affiliation_string: The 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050081, People's Republic of China
raw_author_name: Feng Wang
raw_affiliation_string: The 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050081, People's Republic of China

Abstract: A dense crowd counting method based on self-attention mechanism with dual-branch fusion network is proposed in this paper. Our method aims to address the problems of large variations in head scales and complex backgrounds in dense crowd images. This method combines the CNN and Transformer network frameworks and consists of shallow feature extraction network, dual-branch fusion network, and deep feature extraction network. The VGG16 network is employed by the shallow feature extraction network to extract low-level features. A multi-scale CNN branch and a Transformer branch built on an improved self-attention module make up the dual-branch fusion network, which collects local and global information on crowd areas, respectively. The Transformer network, which is based on a mixed attention module, is employed by the deep feature extraction network to further separate complicated backgrounds and concentrate on crowd areas. Both counting-level weakly supervised and location-level fully supervised methods are employed in the experiments. On four widely used datasets, the results demonstrate that the proposed method outperforms the most recent research. Our method has a higher counting accuracy with low parameter volumes and a counting accuracy of 89.1% under full supervision when compared to existing weakly supervised methods. The results of the experiments demonstrate that the method has excellent crowd counting performance and can accurately count in high-density and high-occlusion scenes.
Development of an automatic grammar checker for Yorùbá word processing using Government and Binding Theory

Authors:
raw_author_name: Abisola Olayiwola
raw_affiliation_string: Department of Computer Engineering, Olabisi Onabanjo University, Ago-Iwoye, Nigeria
raw_author_name: Ajibola Oyedeji
raw_affiliation_string: Department of Computer Engineering, Olabisi Onabanjo University, Ago-Iwoye, Nigeria

Abstract: Grammar checkers have been developed for well-resourced languages such as French, German and English. However, there is a dearth of automatic grammar checkers for African languages including Standard Yorùbá (SY) and consequently no standard software application. This study developed a software system for Yorùbá grammar checking. Requirements for a Yorùbá grammar checking system were investigated by gathering information on Yorùbá syntax from Textbooks and Journals. Based on the information gathered, a computational model was formulated using the theoretical framework of Government and Binding (GB). The formulated system was designed using the Context Free Grammar Tool in Automata Theory. The system was implemented using the Python programming language version 3.5 and Natural Language Tool kit (NLTK). The system was tested for accuracy using a total of three hundred sentences. In detecting grammatically correct and incorrect sentences, an accuracy of 86.67%, 88.89% and 88.89% were achieved for simple, complex and compound sentences respectively. The grammar checker has been able to function as an automatic grammatical error checker of Yorùbá digital text. Its implementation in software applications has the potential to increase the quality of Yorùbá word processing. Also, the dictionary of words created can be used in other areas of human language processing.
ATFA: Adversarial Time–Frequency Attention network for sensor-based multimodal human activity recognition

Authors:
raw_author_name: Haotian Feng
raw_affiliation_string: College of Computer Science and Technology, Jilin University, Changchun, 130012, China
raw_author_name: Qiang Shen
raw_affiliation_string: College of Computer Science and Technology, Jilin University, Changchun, 130012, China
raw_author_name: Rui Song
raw_affiliation_string: School of Artificial Intelligence, Jilin University, Changchun, 130012, China
raw_author_name: Lida Shi
raw_affiliation_string: School of Artificial Intelligence, Jilin University, Changchun, 130012, China
raw_author_name: Hao Xu
raw_affiliation_string: College of Computer Science and Technology, Jilin University, Changchun, 130012, China

Abstract: Human activity recognition (HAR) is a key component of mobile and ubiquitous computing. With the increasing number of sensors embedded in devices such as smartphones and smartwatches, models are enhanced to recognize more complex human behaviors. However, this also leads to increased data heterogeneity, caused by diverse user backgrounds, health conditions, and sensing environments, which makes it difficult to align the multimodal data distributions across individuals. This paper proposes a novel unsupervised domain adaptation framework via Adversarial Time-Frequency Attention (ATFA) to efficiently adapt models to new users. In particular, the proposed attention-based modality fusion module captures and fuses important modalities based on their context to reduce redundant information. Additionally, the network explores frequency domain features to improve performance in recognizing human activities. Extensive experiments are conducted on three publicly available HAR datasets to demonstrate the superiority of our proposed method compared to state-of-the-art baselines.
Exploring the optimality of byte-wise permutations of a piccolo-type block cipher

Authors:
raw_author_name: Shion Utsumi
raw_affiliation_string: University of Hyogo, Japan
raw_author_name: Motoki Nakahashi
raw_affiliation_string: University of Hyogo, Japan
raw_author_name: Kosei Sakamoto
raw_affiliation_string: Mitsubishi Electric Corporation, Japan; University of Hyogo, Japan
raw_author_name: Takanori Isobe
raw_affiliation_string: University of Hyogo, Japan

Abstract: Piccolo is a lightweight block cipher based on a 16-bit word 4-line generalized Feistel structure. Piccolo adopts byte-wise round permutation (RP) instead of the typical word-based RP to improve diffusion. In this paper, we explore the optimality of byte-based RP from the viewpoint of security. We evaluate the security of differential, linear, impossible differential, and integral attacks for all byte-wise RPs using mixed integer linear programming (MILP). We show that the RP of Piccolo is optimal in terms of the number of rounds required to guarantee security against such attacks. In addition, we introduce new two classes of RPs that require 7 rounds for security against impossible differential attacks, which is one round less than required by Piccolo. These new classes require 7/9 and 8/8 rounds to guarantee security against differential/linear attacks, respectively, which is more rounds than required by Piccolo.
TIEN: Temporal interest-aware evolution model for “Next Item Recommendation”

Authors:
raw_author_name: Yunfei Yin
raw_affiliation_string: College of Computer Science, Chongqing University, Chongqing 400044, China

Abstract: The “next item recommendation” is used to predict the next item that a user may be interested in and its main challenges is identifying the user's interests. The current state-of-the-art methods mostly adopt time-series-based recommendation methods but ignore the role of time intervals in the interest evolution process. This motivated us to propose a new Time Interest-aware Evolutionary Network model (TIEN) to predict the next item that a user may be interested in. TIEN highlights the role of time intervals in the interest evolution process and divides interests into long-term interests and short-term interests. In TIEN: (1) We devise a time interval-based attention network TAN to capture the user's interests, which employs item embeddings, behavior sequence embeddings, and time intervals to calculate the user's interest attention; (2) We devise an ATT-GRU network based on candidate activation vectors to capture the interest evolution process, which skillfully leverages the reset gate, hidden layer state, item information, time intervals, etc. to participate in interest evolution; (3) We introduce a dual-network collaborative training mechanism, where the obtained interests of users are treated as weights to guide the interest evolution process. Experimental results on multiple benchmark datasets show that TIEN has higher performance than the current state-of-the-art methods.
Fused robust geometric nonparallel hyperplane support vector machine for pattern classification

Authors:
raw_author_name: Kai Qi
raw_affiliation_string: College of Mathematics and Statistics, Chongqing University, Chongqing, 401331, China
raw_author_name: Hu Yang
raw_affiliation_string: College of Mathematics and Statistics, Chongqing University, Chongqing, 401331, China

Abstract: Recently, introducing nonconvex loss functions in support vector machine (SVM) to improve the robustness against varies noises has been drawing much attention. In this paper, we first construct a new robust capped asymmetric elastic net (CaEN) loss function. Second, we describe a novel robust Huberized kernel-based (HK) loss function and theoretically demonstrate several important properties, such as smoothness, boundness and the trade-off between the standard least squares and the truncated least squares. Finally, we apply the CaEN loss and the HK loss into elastic net nonparallel hyperplane SVM (ENNHSVM) to develop a fused robust geometric nonparallel SVM (FRGNHSVM). The proposed FRGNHSVM not only inherits the advantages of ENNHSVM but also improves the robustness of classification problems. An efficient Pegasos-based DC (difference of convex functions) algorithm is implemented to solve the FRGNHSVM optimization problem. In comparison with four famous SVMs, including Lagrangian SVM, twin SVM, pinball SVM and C-loss twin SVM, experimental results on simulations and twelve UCI datasets show that the proposed FRGNHSVM can often improve more than 5% average prediction accuracy. Moreover, the performance of the high prediction accuracy of FRGHNSVM is more significant along with the ratio of label noise increasing, indicating its superiority in dealing with label-contaminated datasets.
Context-aware Multi-level Question Embedding Fusion for visual question answering

Authors:
raw_author_name: Shengdong Li
raw_affiliation_string: School of Information, Renmin University of China, Beijing 100872, PR China
raw_author_name: Chen Gong
raw_affiliation_string: School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, PR China
raw_author_name: Yuqing Zhu
raw_affiliation_string: Department of Computer Science, California State University Los Angeles, CA 90032, USA; Zhejiang Lab, Hangzhou Zhejiang 311121, PR China
raw_author_name: Chuanwen Luo
raw_affiliation_string: Engineering Research Center for Forestry-Oriented Intelligent Information Processing, National Forestry and Grassland Administration, 100083, PR China; School of Information Science and Technology, Beijing Forestry University, Beijing 100083, PR China
raw_author_name: Yi Hong
raw_affiliation_string: Engineering Research Center for Forestry-Oriented Intelligent Information Processing, National Forestry and Grassland Administration, 100083, PR China; School of Information Science and Technology, Beijing Forestry University, Beijing 100083, PR China
raw_author_name: Xueqiang Lv
raw_affiliation_string: Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, Beijing Information Science and Technology University, 100101, PR China

Abstract: Question model has been widely concerned as the cornerstone of constructing Visual Question Answering (VQA) models. Existing question models attempt to exploit word context to extract multi-level concepts for modeling multi-level questions. However, they still have many defects. For example, most question models utilize simple fusion methods to fuse shallow modules and extract parameter-unshared low-level concepts, leading to poor modeling of multi-level questions; although some question models use deep bidirectional Transformer encoder in external knowledge transfer and BERT for multi-level questions, their complexity is still high. To solve these issues, we propose a novel low-complex contextual question model, termed Context-aware Multi-level Question Embedding Fusion (CMQEF). We formalize its concepts and theories, deduce its modeling process, optimization process and feature extraction process, analyze its low complexity and high expressiveness, and prove that it defines a new way to solve parameter non-sharing for extracting parameter-shared multi-level concepts and optimize the tradeoff between expressiveness and complexity in question models. Extensive experiments on VQAv2 and VQA-CPv2 validate that comparing with the state-of-the-art, our CMQEF outperforms it on SANs and UpDn, reduces the language priors of SANs and UpDn, and has preferable interpretability and applicability. Our code is available at https://github.com/lsdruc/CMQEF-for-VQA.
AEGraph: Node attribute-enhanced graph encoder method

Authors:
raw_author_name: Kang Sun
raw_affiliation_string: College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
raw_author_name: Liqing Qiu
raw_affiliation_string: College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
raw_author_name: Wenxiu Zhao
raw_affiliation_string: College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China

Abstract: Graph representation learning faces challenges in node classification tasks due to the intricate interplay between node categories, topological structure, significant characteristics, node attributes, and label information. However, prevailing graph convolutional networks (GCNs) exhibit inefficiencies in information propagation and need more robustness when dealing with missing node attributes. To surmount these issues, attribute-based dynamic wandering and attribute enhancement methods are proposed. First, the Node Attribute Random Wandering (NARW) method is proposed to obtain the node sequences. The NARW method fully considers the node-to-node attribute information to determine the wandering direction of the sequences. Subsequently, the node attribute enhanced graph encoder model (AEGraph) is proposed, which aims to improve the node classification performance by combining the graph structure and node attribute information during the information propagation process. Each iteration process includes node label information aggregation and node label information update. Finally, to fully consider the local and global information of the graph, this paper reduces the noise generated by information propagation during the iteration process. The model proposed in this paper shows promising results with a low labeling rate. Experimental results show that the method achieves better results and significant improvements on different datasets.