Reviewerly/exHarmony huggingface dataset:
Semantic-aware Retrieval and Recommendation based on the Dirichlet Compound Language Model

Abstract Traditional bag-of-word Information Retrieval (IR) has led to quasi-standard models (e.g. TF-IDF, BM25) that are applied in the context of recommendation. While the benefits of semantic-aware models are clear, there is no standard semantic IR model, and this prevents the usage of semantic retrieval for tasks such as recommendation. We propose a ranking framework by quantifying parameters of the Dirichlet Compound Model (DCM) and using query performance prediction to aggregate scores of semantic extensions of DCM. Instead of employing heuristics, we introduce reasonable aggregation standards which are expandable to the recommendation task by considering user interaction (e.g. rating) as a semantic event. This approach helps to bridge the gap between advanced IR and recommendation. One of the main findings of this paper is that the concept-based extension of DCM consistently outweighed both terms-only and concept-only baselines concerning biomedical data. The DCM-based recommender system is also shown to be effective when applied on MovieLens, Book Crossing and LastFM. By bringing together recommendation (prediction) and retrieval, we lay the grounds for generalizable and hybrid algorithms. This paper shows how advanced but well-established probability distributions can be transparently leveraged in the standardization of semantic ranking methods, especially for applications in which recommendation (notification) is critical.
Digital Skill: Optimizing the Utilization of Information Technology by Pesantren University in Alumni Tracking Activities

Digital skill is the ability to discover, evaluate, use, share and create content using digital devices. The implementation of alumni tracing based on website is a form of implementing digital skills in educational communication. This research was conducted to determine the implementation of a tracer study at Universitas Darussalam (UNIDA) Gontor as a boarding school-based university by optimizing information technology according to design standards and methodologies set by the Directorate General of Belmawa Ristekdikti RI. This study uses a qualitative case study approach because the researcher wants to dig deeper into the phenomenon of optimizing information technology in Pesantren in carrying out alumni tracking. Collecting data through interviews and observations with the manager of the tracer study at the university level and the person in charge of the tracer study of the study program. The data analysis technique was carried out based on the theory of Milles and Huberman which consisted of data reduction, data presentation and drawing conclusions. Source and method triangulation was used as a technique for measuring the validity of the data in this study. The results showed that the implementation of the tracer study at Darussalam Gontor University had optimized the use of information technology in the form of a google form. In addition, a website-based alumni tracking system is currently being developed. This study uses the Theory of Planned Behavior as a research framework. With regard to technology adoption, there are 3 things that are extracted from the research subjects, namely abilities, knowledge, and resources owned by the implementer of the UNIDA Gontor tracer study, 3) the resources have been well prepared for the adjustment to the standards and methodology of the tracer study set by the Directorate General of Belmawa Ristekdikti. The recommendation given by the researcher is that there is a need to increase skills and knowledge for optimizing the use of a website-based alumni tracking system and providing appropriate resources.
A Fast Retrieval Algorithm for Person Re-identification Based on Hash

Abstract Aiming at the problem of slow retrieval speed due to a large amount of data in person re-identification, this paper introduces Taylor expansion into a deep neural network, and obtains Taylor deep neural network for feature extraction, which is combined with the proposed hash probability coding algorithm, and get a fast retrieval algorithm for person re-identification based on the hash. The algorithm can not only extract the feature map information well but also perform image retrieval at a faster speed, which is a new method to solve the fast image retrieval of person re-identification. First, a Taylor deep neural network framework is proposed, and the feature representation extracted by each layer of the deep network is fused by Taylor expansion weighting to retain the overall features of the image and make the feature representation more comprehensive ; then, the triplet loss function Triplet Loss is optimized to improve the When the distance between groups cannot be optimized, the group loss function Group Loss is proposed. Finally, a new hash probability compression coding method is used to make the image retrieval fast. Experiments show that the algorithm model in this paper is 26.7% and 8.18% higher than the BaseLine model on the large data sets Cuhk03 and Market1501, respectively. It also has a good retrieval speed on images retrieved by person re-identification..
Design and Implementation of Web Socket-based Monitoring Module for Cloud Broadcasting Management System

Cloud broadcasting changes the traditional TV broadcasting mode and will be used on a large scale in the future. Cloud broadcasting management system is to unify the cloud-based TV broadcasting platform and OB equipment. In order to solve the problem of real-time monitoring and control of new OB Vans equipment and cloud-based broadcasting platform in this management system, a real-time monitoring system including device, server and user is implemented based on WebSocket protocol, which solves the problem of pushing messages between users of management system and each OB and platform equipment. This paper expatiates the overall structure of the scheme, designs and realizes the functions of each module.
Breast Cancer Screening Using Deep Learning

Breast cancer is a disease in which the cells lining the ducts or lobules of the breast glandular tissue begin to grow out of control. Early detection increases the probability of being overcome by the patient. Breast cancer screening seems to be one of the best ways to provide early care for these patients, especially those who are asymptomatic. On the other hand, it has been indicated that between 10-30% of breast cancer studies are misclassified, mainly due to errors in interpretation (52% ), errors in image search (43%), and poor technical quality (5% ). Nowadays deep learning techniques have given great results in different medical imaging studies. This paper aims to present an analysis and evaluation of machine learning techniques for (1) screening for the presence of breast cancer and (2) classification of the level of malignancy if present. The deep convolutional neural networks analyzed are VGG-16, VGG-19, MobiINet-V2, and DenseNet-121. These architectures were used as binary classifiers, the model suggests the presence of a malignant tumor or not. K-Nearest-Neighbor (KNN), Random Forest, Gradient Boosting, and AutoML, were evaluated as multi-class classifiers for disease grade classification. The comparative analysis was performed by standardizing the test environment with the same number of epochs and analyzing the values obtained in terms of accuracy, training time, and F1. It was concluded that the DenseNet-121 was the best classifier of the presence of malignancy or not, however, for considering healthy and diagnosed cases MobilNet-V2 showed better results but with a small difference. Random Forest was the best classifier for a grade of malignancy.
Understanding the Role of Mixup in Knowledge Distillation: An Empirical Study

Mixup is a popular data augmentation technique based on creating new samples by linear interpolation between two given data samples, to improve both the generalization and robustness of the trained model. Knowledge distillation (KD), on the other hand, is widely used for model compression and transfer learning, which involves using a larger network's implicit knowledge to guide the learning of a smaller network. At first glance, these two techniques seem very different, however, we found that "smoothness" is the connecting link between the two and is also a crucial attribute in understanding KD's interplay with mixup. Although many mixup variants and distillation methods have been proposed, much remains to be understood regarding the role of a mixup in knowledge distillation. In this paper, we present a detailed empirical study on various important dimensions of compatibility between mixup and knowledge distillation. We also scrutinize the behavior of the networks trained with a mixup in the light of knowledge distillation through extensive analysis, visualizations, and comprehensive experiments on image classification. Finally, based on our findings, we suggest improved strategies to guide the student network to enhance its effectiveness. Additionally, the findings of this study provide insightful suggestions to researchers and practitioners that commonly use techniques from KD. Our code is available at https://github.com/hchoi71/MIX-KD.
Design Artificial Intelligence Convergence Teaching and Learning Model CP3 and Evaluations

In this paper, CP3 model (Converged model of Problem recognition, Plan and Play) is developed to perform the artificial intelligence convergence education as a teaching and learning model for elementary school students. The convergence education was applied to actual classes with five subjects: data collection and analysis, understanding sorting algorithms, understanding sequential structures, understanding repetitive structures, and procedural thinking. When the class was conducted using the CP3 model, the overall score is improved by 41.6% compared to the general classes. There were improvements of 53% of male students and 33% of female students, and male students in the lower grades participates more actively in Artificial Intelligence convergence classes. When the satisfaction of the class with CP3 model is analyzed, the interest level is improved by 83%, the problem-solving ability is improved by 70%, the satisfaction level is improved by 68.5%, the understanding level is improved by 64%, and the expectation level is improved by 68%. The overall satisfaction to the class is very high when the subjects and objects closely familiar in daily life are used due to the characteristics of the lower grade students, and the result is more effective when playable elements are applied. However, for low-grade students, they are still experiencing a little difficulties in classes with complex classes like CP3. Considering the characteristics of low-grade students, simple algorithms with a topic closely related to daily life would make a better result.
Analysis of the Relationship among Career Education Program Participation and Satisfaction, Work Value, Career Goal Setting in Higher Education

This study aimed to analyze the causal model of career education program participation, satisfaction, and career goal setting mediated by work values in higher education. Also, this study analyzed the differences between departments. In this study, data from the 2018 Graduate Occupational Mobility Survey (GOMS), the most recent data, was used. The Graduates Occupational Mobility Survey is the largest short-term panel survey of a representative sample of Korean Graduates. The GOMS is conducted annually and has its results compiled each year. Structural equation modeling and multiple SEM were used for this study. The results were as follows. First, career education program participation had a statistically significant positive effect on extrinsic and intrinsic work values and career goal setting. Second, career education program satisfaction had a statistically significant positive effect on intrinsic work values and career goal setting, except for extrinsic work value. In addition, only the intrinsic work values positively affected career goal setting. Third, the career education program satisfaction impacted intrinsic work values, and the intrinsic work value impact on career goal setting was the difference between department and majors. In conclusion, this study proposed the provision of career education programs considering the difference between departments and majors in higher education.
Collateral facilitation in humans and language models

Are the predictions of humans and language models affected by similar things? Research suggests that while comprehending language, humans make predictions about upcoming words, with more predictable words being processed more easily. However, evidence also shows that humans display a similar processing advantage for highly anomalous words when these words are semantically related to the preceding context or to the most probable continuation. Using stimuli from 3 psycholinguistic experiments, we find that this is also almost always also the case for 8 contemporary transformer language models (BERT, ALBERT, RoBERTa, XLM-R, GPT-2, GPT-Neo, GPT-J, and XGLM). We then discuss the implications of this phenomenon for our understanding of both human language comprehension and the predictions made by language models.
It's TEEtime: A New Architecture Bringing Sovereignty to Smartphones

Modern smartphones are complex systems in which control over phone resources is exercised by phone manufacturers, OS vendors, and users. These stakeholders have diverse and often competing interests. Barring some exceptions, users entrust their security and privacy to OS vendors (Android and iOS) and need to accept their constraints. Manufacturers protect their firmware and peripherals from the OS by executing in the highest privilege and leveraging dedicated CPUs and TEEs. OS vendors need to trust the highest privileged code deployed by manufacturers. This division of control over the phone is not ideal for OS vendors and is even more disadvantageous for the users. Users are generally limited in what applications they can install on their devices, in the privacy model and trust assumptions of the existing applications, and in the functionalities that applications can have. We propose TEEtime, a new smartphone architecture based on trusted execution allowing to balance the control different stakeholders exert over phones. More leveled control over the phone means that no stakeholder is more privileged than the others. In particular, TEEtime makes users sovereign over their phones: It enables them to install sensitive applications in isolated domains with protected access to selected peripherals alongside an OS. TEEtime achieves this while maintaining compatibility with the existing smartphone ecosystem and without relying on virtualization; it only assumes trust in a phone's firmware. TEEtime is the first TEE architecture that allows isolated execution domains to gain protected and direct access to peripherals. TEEtime is based on Armv8-A and achieves peripheral isolation using a novel mechanism based on memory and interrupt controller protection. We demonstrate the feasibility of our design by implementing a prototype of TEEtime, and by running exemplary sensitive applications.
Generating Clear Images From Images With Distortions Caused by Adverse Weather Using Generative Adversarial Networks

We presented a method for improving computer vision tasks on images affected by adverse weather conditions, including distortions caused by adherent raindrops. Overcoming the challenge of applying computer vision to images affected by adverse weather conditions is essential for autonomous vehicles utilizing RGB cameras. For this purpose, we trained an appropriate generative adversarial network and showed that it was effective at removing the effect of the distortions, in the context of image reconstruction and computer vision tasks. We showed that object recognition, a vital task for autonomous driving vehicles, is completely impaired by the distortions and occlusions caused by adherent raindrops and that performance can be restored by our de-raining model. The approach described in this paper could be applied to all adverse weather conditions.
Plausibility Verification For 3D Object Detectors Using Energy-Based Optimization

Environmental perception obtained via object detectors have no predictable safety layer encoded into their model schema, which creates the question of trustworthiness about the system's prediction. As can be seen from recent adversarial attacks, most of the current object detection networks are vulnerable to input tampering, which in the real world could compromise the safety of autonomous vehicles. The problem would be amplified even more when uncertainty errors could not propagate into the submodules, if these are not a part of the end-to-end system design. To address these concerns, a parallel module which verifies the predictions of the object proposals coming out of Deep Neural Networks are required. This work aims to verify 3D object proposals from MonoRUn model by proposing a plausibility framework that leverages cross sensor streams to reduce false positives. The verification metric being proposed uses prior knowledge in the form of four different energy functions, each utilizing a certain prior to output an energy value leading to a plausibility justification for the hypothesis under consideration. We also employ a novel two-step schema to improve the optimization of the composite energy function representing the energy model.
On the characterization of some non-abelian simple groups using codegree set

Let $G$ be a finite group and $\chi\in \irr(G)$. The codegree of $\chi$ is defined as $\cod(\chi)=\frac{|G:\ker(\chi)|}{\chi(1)}$ and $\cod(G)=\{\cod(\chi) \ |\ \chi\in \irr(G)\}$ is called the set of codegrees of $G$. In this paper, we show that the set of codegrees of $\Sy_4(4), \U_4(2)$, $\Sy_4(q)\ (q \geq 4)$, $\U_4(3)$, ${}^2\F_4(2)'$, $\J_3$, $\G_2(3)$, $\A_9$, $\J_2$, $\PSL(4,3)$, $\McL$, $\Sy_4(5)$, $\G_2(4)$, $\HS$, $\ON$ and $\M_{24}$ determines the group up to isomorphism.
Deep Learning Methods for Software Requirement Classification: A Performance Study on the PURE dataset

Requirement engineering (RE) is the first and the most important step in software production and development. The RE is aimed to specify software requirements. One of the tasks in RE is the categorization of software requirements as functional and non-functional requirements. The functional requirements (FR) show the responsibilities of the system while non-functional requirements represent the quality factors of software. Discrimination between FR and NFR is a challenging task. Nowadays Deep Learning (DL) has entered all fields of engineering and has increased accuracy and reduced time in their implementation process. In this paper, we use deep learning for the classification of software requirements. Five prominent DL algorithms are trained for classifying requirements. Also, two voting classification algorithms are utilized for creating ensemble classifiers based on five DL methods. The PURE, a repository of Software Requirement Specification (SRS) documents, is selected for our experiments. We created a dataset from PURE which contains 4661 requirements where 2617 requirements are functional and the remaining are non-functional. Our methods are applied to the dataset and their performance analysis is reported. The results show that the performance of deep learning models is satisfactory and the voting mechanisms provide better results.
Biomedical Multi-hop Question Answering Using Knowledge Graph Embeddings and Language Models

Biomedical knowledge graphs (KG) are heterogenous networks consisting of biological entities as nodes and relations between them as edges. These entities and relations are extracted from millions of research papers and unified in a single resource. The goal of biomedical multi-hop question-answering over knowledge graph (KGQA) is to help biologist and scientist to get valuable insights by asking questions in natural language. Relevant answers can be found by first understanding the question and then querying the KG for right set of nodes and relationships to arrive at an answer. To model the question, language models such as RoBERTa and BioBERT are used to understand context from natural language question. One of the challenges in KGQA is missing links in the KG. Knowledge graph embeddings (KGE) help to overcome this problem by encoding nodes and edges in a dense and more efficient way. In this paper, we use a publicly available KG called Hetionet which is an integrative network of biomedical knowledge assembled from 29 different databases of genes, compounds, diseases, and more. We have enriched this KG dataset by creating a multi-hop biomedical question-answering dataset in natural language for testing the biomedical multi-hop question-answering system and this dataset will be made available to the research community. The major contribution of this research is an integrated system that combines language models with KG embeddings to give highly relevant answers to free-form questions asked by biologists in an intuitive interface. Biomedical multi-hop question-answering system is tested on this data and results are highly encouraging.
Safety-Constrained Policy Transfer with Successor Features

In this work, we focus on the problem of safe policy transfer in reinforcement learning: we seek to leverage existing policies when learning a new task with specified constraints. This problem is important for safety-critical applications where interactions are costly and unconstrained policies can lead to undesirable or dangerous outcomes, e.g., with physical robots that interact with humans. We propose a Constrained Markov Decision Process (CMDP) formulation that simultaneously enables the transfer of policies and adherence to safety constraints. Our formulation cleanly separates task goals from safety considerations and permits the specification of a wide variety of constraints. Our approach relies on a novel extension of generalized policy improvement to constrained settings via a Lagrangian formulation. We devise a dual optimization algorithm that estimates the optimal dual variable of a target task, thus enabling safe transfer of policies derived from successor features learned on source tasks. Our experiments in simulated domains show that our approach is effective; it visits unsafe states less frequently and outperforms alternative state-of-the-art methods when taking safety constraints into account.
Constructing Dynamical Systems to Model Higher Order Ising Spin Interactions and their Application in Solving Combinatorial Optimization Problems

The Ising model provides a natural mapping for many computationally hard combinatorial optimization problems (COPs). Consequently, dynamical system-inspired computing models and hardware platforms that minimize the Ising Hamiltonian, have recently been proposed as a potential candidate for solving COPs, with the promise of significant performance benefit. However, the Ising model, and consequently, the corresponding dynamical system-based computational models primarily consider quadratic interactions among the nodes. Computational models considering higher order interactions among Ising spins remain largely unexplored. Therefore, in this work, we propose dynamical-system-based computational models to consider higher order (>2) interactions among the Ising spins, which subsequently, enables us to propose computational models to directly solve many COPs that entail such higher order interactions (COPs on hypergraphs). Specifically, we demonstrate our approach by developing dynamical systems to compute the solution for the Boolean NAE-K-SAT (K is greater than 3) problem as well as solve the Max-K-Cut of a hypergraph. Our work advances the potential of the physics-inspired 'toolbox' for solving COPs.