Reviewerly/exHarmony huggingface dataset:
CHUQI-Miner: Mining Correlated Quantitative High Utility Itemsets

To discover patterns of high importance in data, a popular data science task is high utility itemset mining (HUIM). It aims at discovering all sets of values that have a high utility (importance) in database records. A key application is to find products purchased together in online stores that yield a high profit (utility), as it can provide insights for marketing and product recommendation. But HUIM has two key limitations. First, the discovered patterns do not provide information about the quantities of items. But in real-life, quantities are important (e.g. buying 1 bread is not the same as buying 12 breads). Second, it is observed in real shopping data that many itemsets yield a high utility (profit) but contain weakly correlated items. Such itemsets can be misleading as their joint sale may just appear by chance. This paper addresses both issues by proposing a novel algorithm called CHUQI-Miner (Correlated High Utility Quantitative Itemset-Miner). It extends the state-of-the-art HUQI-Miner algorithm for quantitative high utility itemset mining with the bond correlation measure. This allows finding strongly correlated high utility itemsets with quantities. Experiments on retail data show that the algorithm is efficient and can filter a huge amount of spurious itemsets.
Sentiment and Context-refined Word Embeddings for Sentiment Analysis

Word embeddings have become the de-facto tool for representing text in natural language processing (NLP) tasks, as they can capture semantic and syntactic relations, unlike their precedents such as Bag-of-Words. Although word embeddings have been employed in various studies in recent years and proven to be effective in many NLP tasks, they are still immature for sentiment analysis, as they suffer from insufficient sentiment information. General word embedding models pre-trained on large corpora with methods such as Word2Vec or GloVe achieve limited success in domain-specific NLP tasks. On the other hand, training domain-specific word embeddings from scratch requires a high amount of data and computation power. In this work, we target both shortcomings of pre-trained word embeddings to boost the performance of domain-specific sentiment analysis tasks. We propose a model that refines pre-trained word embeddings with context information and leverages the sentiment scores of sentences obtained from a lexicon-based method to further improve performance. Experiment results on two benchmark datasets show that the proposed method significantly increases the accuracy of sentiment classification.
Automatic detection of behavioural codes in team interactions

This paper investigates the feasibility of the task of automatic behaviour coding of spoken interactions in teamwork settings. We introduce the coding schema used to classify the behaviours of the group members and the corpus we collected to assess the coding schema reliability in real teamwork meetings. The behaviours embedded in spoken utterances are modelled using a discriminative approach based on conditional random fields, and state-of-the-art neural networks based models. Moreover, we fine-tune publicly available language models to fit our target domain and task and demonstrate how this type of knowledge transfer improves classification models’ generalisation capacity. To utilise public resources, the AMI corpus was used for deploying the proposed framework. However, the models were evaluated on both AMI (matched task) and recordings of students solving an engineering challenge (mismatched task). Evaluation results reveal that neural networks are the best performing models in matched tasks, but that C R F models outperform them in mismatched tasks. Mitigating the effect of noisy data, by implementing a lightly supervised approach leads to relative improvements of 32% and 22%, in F 1 measures of C R F and B E R T , respectively. The proposed classifiers are used as a part of technological support to the training programme in collaborative skills for undergraduate students. • The viability of automatic verbal behaviour coding using speech transcriptions is investigated. • A new corpus with annotations following a precise metadiscourse coding scheme is introduced to the field of automatic behaviour analysis in teamwork meetings. The applied coding scheme is based on a well-studied psychological theory that is designed to wisely observe to provide valuable feedback. • A lightly supervised approach that produces a better version of manually coded training data is also proposed. Lexical and contextual text features are used to train sequential and neural network automatic metadiscourse tagging models. • The performance of these models is compared under different hierarchical levels of behaviour analysis. • To evaluate the proposed approach, AMI (a matched set) and a real application set taken from the ENG Challenge data (unmatched set), test sets are used.
The Relationship of HR Internal Service Quality on Employees' Satisfaction in the Banking Sector of Bharain

This study's main purpose is to explore the overall relationship between HR internal service quality and employee job satisfaction in Bahrain's banking sector. The non-random sampling technique “snowball” was utilized to collect the data from different banks operating in the kingdom of Bahrain. A total of 241 responses to the questionnaire were collected. The collected data were analyzed using both descriptive and inferential statistical analysis techniques. The findings reveal all the human resource department's internal service quality dimensions have a direct and positive relationship with employee job satisfaction in the banking sector. This study's findings provide insights for organizations, mainly for the banking sector, aiming to be competitive and responsive to the growing challenges around the world. Conclusions highlighted that the adoption of specific efforts and mechanisms to encourage fostering the introduction and implementation of the internal service quality concept within the banks are likely to result in the attainment of excellent performance and outcomes.
Lexicon-Grammar Tables for Modern Arabic Frozen Expressions

Frozen or idiomatic expressions play a very important role in natural language processing, they are very important in focusing the meaning and expressing it clearly and precisely, away from the ambiguity's problem. They also enrich the language with enormous possibilities to express different meanings. Thus, the interest of linguistics in the study of the meaning is no longer limited to the vocabulary, but rather to the frozen expressions. The Arabic language is very rich in frozen expressions. We can find them dispersed in Arabic books, such as the Quran, the linguistic heritage's and literary's books, the books of proverbs, etc., which has led some researchers to collect, classify and explain them. Indeed, several classifications have been proposed according to the needs of each linguist. Our aim is to create, for the modern Arabic language, lexicon-grammar tables of fixed and continuous frozen expressions such as "مسك الختام" (misku al-khitam; Save the best for last). For that, we start by collecting and studying these expressions (about 800 frozen expressions). Then, we classified them into two classes and create their lexicon-grammar tables that will be implemented in NooJ by generating their NooJ dictionaries.
Application of Chinese Traffic Sign Detection Based on Yolov4

With the social progress and the continuous development of science and technology, people have made great progress in the field of automatic driving and assisted driving. In automatic driving system, it is very important to achieve higher precision and faster detection and recognition for traffic sign. In this paper, an improved algorithm TSnet based on Yolov4 is proposed to realize the application of Chinese traffic sign detection in visual direction. We further optimize the detection performance of Yolov4 in this scene, restructure and adjust the network structure of Yolov4, use the idea of DenseNet to replace the residual unit with dense connection unit in the backbone network, so as to better extract features. Experiments show that our proposed network is superior to the original Yolov4 network, it can achieve the task of Chinese traffic sign detection better.
Steering Control and Path Planning of Two-Wheel Vehicle with Hazard Avoidance

This paper presents a shortest-path-to-target control method via differential speed control of a two-wheeled vehicle. The motion of the vehicle is guided by a path planning algorithm. The overall path length is reduced in real time by tuning the desired turning radius. The desired turning radius uses a fastest descending gradient of the path length to obtain a local optimum. Using the current turning radius as a feedback, a controller is designed to adjust the left and right wheel speeds to accurately track the desired turning radius. Eventually, the two-wheeled vehicle is driven to the target with a shortest path. Simulations are conducted to demonstrate the effectiveness of the proposed method.
(SP)$$^2$$Net for Generalized Zero-Label Semantic Segmentation

Generalized zero-label semantic segmentation aims to make pixel-level predictions for both seen and unseen classes in an image. Prior works approach this task by leveraging semantic word embeddings to learn a semantic projection layer or generate features of unseen classes. However, those methods rely on standard segmentation networks that may not generalize well to unseen classes. To address this issue, we propose to leverage a class-agnostic segmentation prior provided by superpixels and introduce a superpixel pooling (SP-pooling) module as an intermediate layer of a segmentation network. Also, while prior works ignore the pixels of unseen classes that appear in training images, we propose to minimize the log probability of seen classes alleviating biased predictions in those ignore regions. We show that our (SP)\(^2\)Net significantly outperforms the state-of-the-art on different data splits of PASCAL VOC 2012 and PASCAL-Context benchmarks.
Estimation of English Proficiency Based on Read-Aloud Performance and Linguistic Difficulty of Sentences

In this study, the English proficiency (EP) of learners was determined based on their read-aloud performance (RAP). RAP reflects EP as it demonstrates the basic linguistic skills required for recognizing lexical/syntactic structure, understanding the meaning, and making speech sounds depending on the sentence structure/meaning. Empirically, RAP test scores have been correlated with those of EP tests such as the Test of English as a Foreign Language or Test of English for International Communication. With the assumption that RAP can be evaluated based on accuracy, speech rate, and learners’ subjective judgment, in this study, it was examined which combinations of these RAP indices achieved the highest EP estimation; in addition, how EP estimation depended on learners’ EP was analyzed. For this, EP estimation was evaluated for learners at different EP levels. The experimental results showed that all combinations of RAP indices perform comparably in EP estimation, and that effective EP estimation differed by EP level.
Research on Object Detection Algorithm Based on UVA Aerial Image

In this paper a new object detection network is proposed to process UVA aerial images. The detection network based on single-stage object detection algorithm, and reduces the calculation of the network through cross phase partial connection modules. Resource consumption makes the network lighter, through multi-scale feature fusion, the ability to detect small objects of the network we proposed is improved.
A Study of Documents Management System Based on Web, Case Study: University

Nowadays, security access technology has reached a high point of development and research, although more may be deemed to be researched in this field. The aim of this project is to be developing a web application tool for Document Management, to simplify application procedures for students in diploma subject trying to help them increase the impact of their work. Given that the students to have so far applied for the thesis in classical way, despite the advancement in information technology and digitizing time, the aim has been to build this online application which will facilitate the application for thesis. Applying online will be more functional than the current application, which enables you to choose mentor online. Also based on finding shortcomings in the current application as the master thesis: Ways of physical access, greater engagement of professors and students, the way of communication student/mentor this has been one of the main reasons to develop this application. We have used laravel and other relevant of the PHP and MySQL ecosystem as framework.
A Novel Predictive Safety Criteria for Robust Collision Avoidance of Autonomous Robots

In this article, we propose a novel predictive strategy for collision avoidance of multiple mobile robots by precisely quantifying the robot’s impending collisions. Our proposed approach is based on formulating the collisions by predicting the robots’ states and provides generalized safety conditions that are more realistic measures for collisions. This is a novel safety condition that extends the previous methods that use distance or velocity for safety conditions into a more generalized format to effectively avoid underestimating or overestimating the collisions by explicitly considering surrounding objects’ motions. We formulate the safety condition based on the robots’ motion into a vector format and construct a predictive safety matrix. We then consolidate the safety condition based on the proposed safety matrix’s eigenvalues. To effectively incorporate the safety condition into a control design considering uncertainties in the input sensor measurements, we perform uncertainty analysis on our proposed safety condition, the results of which are further used in this article to design a robust collision avoidance and tracking controller. This tracking controller avoids collisions effectively by incorporating the proposed safety condition into a robust tracking control design that is mathematically proven to be stable and guaranteeing safe motion. We show that the proposed safety condition, by effectively quantifying the conflicts among the robots, avoids collisions while achieving improved tracking performance compared to other existing reactive methods. This method is also verified experimentally and is shown to be effective in real-time control with superior tracking performance to the existing methods.
A Space Joint calibration method for lidar and camera on self-driving car and its experimental verification

The joint calibration process of lidar and camera space is restricted by the accuracy of measurement tools, which results in large errors in calibration result. Based on the measurement error of the translation and rotation of each axis between the lidar coordinate system and the camera coordinate system, the projection mapping result of the lidar point cloud data on the camera image was simulated. According to the simulation results, the non-coupling influence law of each single element on the projection result was summarized. According to this law, a method of combining coarse adjustment and fine adjustment of lidar and camera space was proposed. This method was used for experimental verification. The experimental results show that the principle of the method is correct, and it can achieve fast and accurate lidar and camera space joint calibration.
A multi-step decision prediction model based on LightGBM

This paper is based on the 3rd place solution to IEEE BigData Cup 2021: RL based RecSys. Standing on the competition task of game-like multi-step recommendation, we have analyzed user data and task goals, and treated recommendation task as multi-classification. After comparing different models, the LightGBM multi-classification model is finally selected. In addition, we evaluate those features that potentially cause the greatest impact on the prediction of our model, making it possible to predict the users’ purchase sequence accurately.
Face Geometry and Handwritten Characters Based Biometric Text Steganography

Contemporary advancement in technologies has the responsibilities in the context of transmission, reproduction and manipulation of information. Thus an important protagonist formed by the fragment of information security vital. One of the information security contrivances in this literature is biometric information security. Steganography is an excellent prerogative for information diffusion over every public media by hiding the message. A new work in the literature of security has been proven with the help of handwritten atmosphere based text steganography algorithm and biometric based security. The biometric has been embedded to the text domain. The handwritten text based steganography procedure has been designed which can work in different Indian regional languages. Here the quantum approach has been assimilates before embedding and Revised SSCE code (Revised SSCE - Revised Secret Steganography Code for Embedding) used for enhancing the security.
AmbiTeam

AmbiTeam is an ambient display designed to communicate contextual or background information in the periphery of the user's awareness and only requires the user's attention when it is appropriate or desired.
Logistic Regression and Artificial Neural Network: A Comparative Study in Diagnosing Breast Cancer

Breast tumor is a common problem in gynecology. A reliable test for preoperative discrimination between benign and malignant breast tumor is highly helpful for clinicians in culling the malignant cells through felicitous treatment for patients. This paper is carried out to generate and estimate both logistic regression technique and Artificial Neural Network (ANN) technique to predict the malignancy of breast tumor, utilizing Wisconsin Diagnosis Breast Cancer Database (WDBC). Our aim in this Paper is: (i) to compare the diagnostic performance of both methods in distinguishing between malignant and benign patterns, (ii) to truncate the number of benign cases sent for biopsy utilizing the best model as an auxiliary implement, and (iii) to authenticate the capability of each model to recognize incipient cases as an expert system.
Lightweight EdDSA Signature Verification for the Ultra-Low-Power Internet of Things

EdDSA is a digital signature scheme based on elliptic curves in Edwards form that is supported in the latest incarnation of the TLS protocol (i.e. TLS version 1.3). The straightforward way of verifying an EdDSA signature involves a costly double-scalar multiplication of the form \(kP - lQ\) where P is a “fixed” point (namely the generator of the underlying elliptic-curve group) and Q is only known at run time. This computation makes a verification not only much slower than a signature generation, but also more memory demanding. In the present paper we compare two implementations of EdDSA verification using Ed25519 as case study; the first is speed-optimized, while the other aims to achieve low RAM footprint. The speed-optimized variant performs the double-scalar multiplication in a simultaneous fashion and uses a Joint-Sparse Form (JSF) representation for the two scalars. On the other hand, the memory-optimized variant splits the computation of \(kP - lQ\) into two separate parts, namely a fixed-base scalar multiplication that is carried out using a standard comb method with eight pre-computed points, and a variable-base scalar multiplication, which is executed by means of the conventional Montgomery ladder on the birationally-equivalent Montgomery curve. Our experiments with a 16-bit ultra-low-power MSP430 microcontroller show that the separated method is 24% slower than the simultaneous technique, but reduces the RAM footprint by 40%. This makes the separated method attractive for “lightweight” cryptographic libraries, in particular if both Ed25519 signature generation/verification and X25519 key exchange need to be supported.
Punching trajectory optimization method for warp-knitted vamp based on improved ant colony optimization algorithm and Radau pseudospectral method

Aiming at the problems of complex trajectory, low efficiency and high operational difficulty of the robot in multi-point punching of warp-knitted vamp, a method of optimizing punching trajectory based on improved ant colony optimization algorithm and Radau pseudospectral method is proposed. After obtaining the position coordinates of punching points, an improved ant colony optimization algorithm is used to calculate the punching sequence of the shortest path through all punching points, and then Radau pseudospectral method is used to solve the optimal trajectory of the laser punching robot. Improved ant colony optimization algorithm combines a distributed calculation method and the positive feedback mechanism. Radau pseudospectral method can transform the optimal control problems into nonlinear programming problems, and the combination of the two can quickly and reliably obtain the optimal solution. To verify the method, under the condition of selecting the same number and location of punching points, the experiments of Radau pseudospectral method to solve the trajectory planning of laser punching robot is carried out. The experimental results show that improved ant colony optimization algorithm can calculate the path of the vamp punching point in a shorter time and with high accuracy. Radau pseudospectral method can obtain smooth trajectories satisfying various constraints, which can meet the requirements of accuracy and efficiency in practical production.
Mask Wearing Specification Detection Based on Cascaded Convolutional Neural Network

Standardizing the wearing of masks is an important part of public health management. In order to solve the problem of low detection accuracy of wearing masks in complex light environment in public places, a mask detection method based on cascaded convolution neural network is proposed. Face location is carried out by using MTCNN algorithm, and the obtained face parts are classified at the first level to determine whether to wear a mask or not, and the Fast R-CNN algorithm is used to detect the part below the eyes of the object wearing the mask, and the second-level classification is used to determine whether the mask is standard or not. The experimental results show that in a multifaceted and complex background environment, the correct rates of testing whether to wear a mask and whether the mask is standard are 93.88% and 91.75% respectively, which can effectively detect whether the mask is standard or not.