Reviewerly/exHarmony huggingface dataset:
SR-DSFF and FENet-ReID: A Two-Stage Approach for Cross Resolution Person Re-Identification

In real-life scenarios, the accuracy of person re-identification (Re-ID) is subject to the limitation of camera hardware conditions and the change of image resolution caused by factors such as camera focusing errors. People call this problem cross-resolution person Re-ID. In this paper, we improve the recognition accuracy of cross-resolution person Re-ID by enhancing the image enhancement network and feature extraction network. Specifically, we treat cross-resolution person Re-ID as a two-stage task: the first stage is the image enhancement stage, and we propose a Super-Resolution Dual-Stream Feature Fusion sub-network, named SR-DSFF, which contains SR module and DSFF module. The SR-DSFF utilizes the SR module recovers the resolution of the low-resolution (LR) images and then obtains the feature maps of the LR images and super-resolution (SR) images, respectively, through the dual-stream feature fusion with learned weights extracts and fuses feature maps from LR and SR images in the DSFF module. At the end of SR-DSFF, we set a transposed convolution to visualize the feature maps into images. The second stage is the feature acquisition stage. We design a global-local feature extraction network guided by human pose estimation, named FENet-ReID. The FENet-ReID obtains the final features through multistage feature extraction and multiscale feature fusion for the Re-ID task. The two stages complement each other, making the final pedestrian feature representation has the advantage of accurate identification compared with other methods. Experimental results show that our method improves significantly compared with some state-of-the-art methods.
Enhanced lithological mapping of Durba-Araba basement blocks, along the eastern margin of the Central Gulf of Suez Rift, Egypt, using Landsat-8 Data

Abstract The eastern margin of the central Gulf of Suez rift exposes unique rotated basement blocks of Gabal Durba-Araba. According to geological maps, these blocks are composed of Dokhan volcanic, older, and younger granites cut by basaltic dikes and overlayed by a tilted sedimentary succession of Paleozoic to quaternary. Herein, we propose a workflow of stepwise preprocessing and classification procedures for Landsat-8 (OLI) data verified by control points from previous maps to update and improve lithological discrimination in converging areas that require further field mapping. The workflow applies digital image-processing techniques, including spectral signature analysis, band-ratio transformation, maximum likelihood, and Mahalanobis supervised classifications. The accuracy assessment of the lithological mapping reaches 86.6% and 75.7% for maximum likelihood and Mahalanobis classifications, respectively. By running the Tactical Hyperspectral Operations Resource (THOR) algorithm on the classified images, we could accurately map and modify the lithological boundaries for the following rock units: (1) gneiss-amphibolite-schist, tonalite, and granodiorite at Gabal Araba; (2) granitic gneiss, granodiorite, biotite-muscovite-leucogranite, and riebeckite-syenite-albitite at Gabal Abu Haswa; (3) Dokhan volcanic, granite biotite-muscovite-leucogranite, granites riebeckite-syenite-albitite, Pyroclastic breccia and lava flow, and NW tertiary dike at Gabal Abu Durba.
PENGARUH MEDIA PLOTAGON TERHADAP HASIL BELAJAR SISWA KELAS V PADA MATA PELAJARAN IPA MATERI KALOR DI SDN CIBUNTU 01

The development of education in Indonesia is undergoing rapid changes, therefore we as Indonesian citizens must prepare ourselves so as not to be out of date. In the teaching and learning process sometimes there are students who feel bored and bored when the lesson takes place so that they have difficulty in understanding the material presented by the teacher and cause low student learning outcomes. This study aims to determine whether or not plotagon media has an effect on the learning outcomes of fifth graders at SDN Cibuntu 01. The sample in this study was class V at SDN Cibuntu 01 which consisted of 2 groups of data, namely the experimental class of 27 students and the control class of 25 students. The instrument in this study used a learning outcomes test sheet in the form of multiple choice. The experimental design used in this study was the Posttest-Only Control Design method. Data analysis used independent sample t-test. It is obtained that the value of sig. 0.000 &lt; 0.05 of Sig.(2-tailed). So it can be concluded that plotagon media can affect the value of student learning outcomes at SDN Cibuntu 01.
Technopreneur and Digital Economy as a Driving Force for the Economic Development in Palestine

This paper addresses the main issues of Technopreneur, knowledge and digital economy and their possible role in economic development in Palestine. Human capital is a main pillar for knowledge economy, in addition to other pillars like appropriate infrastructure and policies. In general, Palestine has limited natural resources. However, it has high percentage of educated individuals with university and post-graduate degrees especially in technology-related studies. This forms a good base for proper human capital for future knowledge economy. Several initiatives and efforts have been carried out to promote entrepreneurship and technopreneurship in Palestine as a mean to contribute to economic development and advancement, however without official stated strategy or policy. Digital economy, together with Technopreneurship spirit would form the base for economic development in Palestine. This could be achieved through the adoption of a proposed framework of possible policy and strategy, which addresses the connections between various elements of such framework.
Scalable Exploration for Neural Online Learning to Rank with Perturbed Feedback

Deep neural networks (DNNs) demonstrates significant advantages in improving ranking performance in retrieval tasks. Driven by the recent developments in optimization and generalization of DNNs, learning a neural ranking model online from its interactions with users becomes possible. However, the required exploration for model learning has to be performed in the entire neural network parameter space, which is prohibitively expensive and limits the application of such online solutions in practice.
Convolutional neural network for noise reduction to improve the quality of images obtained using active-pulse television measuring systems

Abstract The paper presents a software implementation of an image recovery algorithm using a convolutional neural network. An example of using the developed algorithm in the context of noise reduction of images obtained using active-pulse television measuring systems is presented. The result obtained with a convolutional neural network is better than classical filtering algorithms (PSNR higher by 2.01 dB).
ZeroMatcher

Entity Matching (EM) aims to find data instances from different sources that refer to the same real-world entity. The existing EM techniques can be either costly or tailored for a specific data type. We present ZeroMatcher, a cost-off entity matching system, which supports (i) handling EM tasks with different data types, including relational tables and knowledge graphs; (ii) keeping its EM performance always competitive by enabling the sub-modules to be updated in a lightweight manner, thus reducing development costs; and (iii) performing EM without human annotations to further slash the labor costs. First, ZeroMatcher automatically suggests users a set of appropriate modules for EM according to the data types of the input datasets. Users could specify the modules for the subsequent EM process according to their preferences. Alternatively, users are able to customize the modules of ZeroMatcher. Then, the system proceeds to the EM task, where users can track the entire EM process and monitor the memory usage changes in real-time. When the EM process is completed, ZeroMatcher visualizes the EM results from different aspects to ease the understanding for users. Finally, ZeroMatcher provides EM results evaluation, enabling users to compare the effectiveness among different parameter settings.
Design of Chinese Corpus Based on Semantic Mining Algorithm

In order to improve the practical effect of the Chinese corpus, this paper combines the semantic mining algorithm to design the Chinese corpus, proposes an ontology adaptive algorithm based on content learning, and conducts in-depth research on the model of the algorithm. Firstly, in view of the heterogeneity of web information structure and the chaotic nature of information organization, this paper proposes a web content extraction method to effectively remove noise information. Moreover, this paper analyzes an ontology-based text content parsing method, which uses the semantic parsing capability of the ontology to improve the semantic parsing capability of the text. Secondly, this paper proposes the BFA algorithm to optimize the BP neural network. The experimental research shows that the Chinese corpus system based on semantic mining proposed in this paper has a good practical effect and meets the actual needs of Chinese teaching and translation.
Analysis of Children’s Sports Heuristic Teaching Based on Deep Learning

The “pursuit of deep learning” is mentioned among the recent trends driving the key trends driving educational technology in schools. “Deep learning” is widely used as a term, and classroom teaching has begun to focus more and more on deep learning. The heuristic teaching method is gradually accepted and used by educators all over the world with its scientific teaching mode and novel teaching methods. In today’s children’s physical education classroom, the heuristic teaching method has achieved certain results and effects, but in the process of trying, there is still room for development and improvement. Based on the deep learning model, this research will improve the existing heuristic teaching methods, through the experimental research on children's physical education classroom, observe the data results obtained by the deep learning-based children's physical education heuristic teaching, and analyze according to the results, so as to achieve the effect of heuristic teaching. A multilabel classification model ALSTM-LSTM is proposed according to the algorithm adaptation method in the multi-label learning method. The experimental results obtained an accuracy of 95.1%, which is higher than other deep learning models, and also reached the best in the evaluation indicators of precision, recall, and F1 score.
Hierarchical Transformers for Group-Aware Sequential Recommendation: Application in MOBA Games

In recent years, several recommendation systems have been introduced to improve the user experience of players in video games. In Multiplayer Online Battle Arena (MOBA) games, a popular game genre, these systems are useful for recommending items for a character during a match. Current approaches focus on recommending a fixed set of items based on a character and the other participants. However, a MOBA match is an inherently sequential process, where its past decisions define the current ones. Therefore, it would be desirable to obtain a contextual recommendation considering a specific situation and the previously consumed items. To fill this gap, in this work, we propose HT4Rec for group-aware sequential item recommendation. It consists of a contextual encoder that generates a character-item representation contextualized by the other participants involved in the game, followed by a sequential encoder that captures sequential patterns of the data to recommend the next item. In this way, HT4Rec provides a flexible and unified attention-based network structure to capture both general and long-term preferences. Our evaluations on a Dota 2 video game dataset demonstrate that HT4Rec outperforms well-known sequential recommendation methods on various evaluation metrics. Additional experiments unveil the most important parts of our model and the most relevant inputs according to the attention mechanism, which could be used to interpret the suggested items. Furthermore, we demonstrate that HT4Rec could be applied to a different scenario (movie recommendation) than MOBA games, showing better results than the baseline models.
Survey on Cloud Auditing by Using Integrity Checking Algorithm and Key Validation Mechanism

Cloud computing can be used to access and storing data and delivery of different services over the internet. Using cloud storage, users can remotely store their data. Cloud service provider (CSP) provides data owners to store and access their valuable data in the cloud server and offers them to make use of on-demand data access without maintaining a local copy of their data. Even though this service avoids the data owners from making use of their third-party auditor, it certainly possesses serious security threats in maintaining the data owners cloud data. In addition, integrity is also an important issue in maintaining the data owners data stored in the cloud server. This survey presents an overview of integrity check and continuous auditing. The review work based on creating secure clouds by continuous auditing and cloud certification system (CCS) is used for high level security. This survey helps to provide the security by continuous auditing and overcome the integrity issues and to avoid the attacks by integrity checking algorithm and key validation mechanism. In this survey paper, various researchers’ ideas based on integrity checking and key schemes have been analyzed as literature review.
Quality-aided Annotation Service Selection in MLaaS Market

The vibrant markets offering data annotation services are fast-growing and play an important part in machine learning. While many multi-label prediction services are available, it is challenging for consumers to decide which services to use for their own tasks and budgets due to the heterogeneity in those services’ labeling categories, labeling quality and price. In this paper, we focus on a practical problem of obtaining high-quality multi-label annotation data from multiple services within a budget constraint. We propose a framework that firstly parameterizes the labeling generation based on the constructed Probabilistic Graph Model, and designs an Expectation Maximization(EM)-based iteration algorithm to estimate the service labeling quality and task truth distribution. Then we transform the annotation service selection strategy into an adaptive submodular maximization coverage problem, which motivates us to design an adaptive random greedy algorithm with a constant approximation ratio 1−1/e. We evaluate our design on both real-world experiments and a series of simulations on various machine learning models and real datasets. These experiments will show that our method has more accuracy and reliability improvements.
Virtual Reality, 3D Recreations and 3D Printing in Social Sciences Education

Virtual reality is a technology for the relocation and interaction of users with a digital environment created by computers through an interface that recognizes actions carried out in the real world. It allows users a multimodal experience, making possible immersion in a digital world. On the other hand, 3D modeling is a process of design and representation of an object (3D model) in three dimensions with specialized software, a model that could become tangible through 3D printing. Virtual reality, 3D modeling, and 3D printing technologies, with appropriate teaching proposals and appropriate tools for process monitoring, offer students new ways of interacting with contents of social sciences, geography, and history. There are more resources for the management of classes with VR devices and specific VR applications for teaching. In general, virtual reality and 3D modeling/painting in class allows both interaction and several opportunities for learning adapted to the needs of students.
Dual Pseudo Supervision for Semi-Supervised Text Classification with a Reliable Teacher

In this paper, we study the semi-supervised text classification (SSTC) by exploring both labeled and extra unlabeled data. One of the most popular SSTC techniques is pseudo-labeling which assigns pseudo labels for unlabeled data via a teacher classifier trained on labeled data. These pseudo labeled data is then applied to train a student classifier. However, when the pseudo labels are inaccurate, the student classifier will learn from inaccurate data and get even worse performance than the teacher. To mitigate this issue, we propose a simple yet efficient pseudo-labeling framework called Dual Pseudo Supervision (DPS), which exploits the feedback signal from the student to guide the teacher to generate better pseudo labels. In particular, we alternately update the student based on the pseudo labeled data annotated by the teacher and optimize the teacher based on the student's performance via meta learning. In addition, we also design a consistency regularization term to further improve the stability of the teacher. With the above two strategies, the learned reliable teacher can provide more accurate pseudo-labels to the student and thus improve the overall performance of text classification. We conduct extensive experiments on three benchmark datasets (i.e., AG News, Yelp and Yahoo) to verify the effectiveness of our DPS method. Experimental results show that our approach achieves substantially better performance than the strong competitors. For reproducibility, we will release our code and data of this paper publicly at https://github.com/GRIT621/DPS.
DDEN: A Heterogeneous Learning-to-Rank Approach with Deep Debiasing Experts Network

Learning-to-Rank(LTR) is widely used in many Information Retrieval(IR) scenarios, including web search and Location Based Services(LBS) search. However, most existing LTR techniques mainly focus on homogeneous ranking. Taking QAC in Dianping search as an example, heterogeneous documents including suggested queries (SQ) and Point-of-Interests(POI) need to be ranked and presented to enhance user experience. New challenges are faced when conducting heterogeneous ranking, including inconsistent feature space and more serious position bias caused by distinct representation spaces. Therefore, we propose Deep Debiasing Experts Network (DDEN), a novel heterogeneous LTR approach based on Mixture-of-Experts architecture and gating network, to deal with the inconsistent feature space of documents in ranking system. Furthermore, DDEN mitigates the position bias by adopting adversarial-debiasing framework embedded with heterogeneous LTR techniques. We conduct reproducible experiments on industrial datasets from Dianping, one of the largest local life platforms, and deploy DDEN in online application. Results show that DDEN substantially improves ranking performance in offline evaluation and boost the overall click-through rate in online A/B test by 2.1%.
Study on the Dilemma and Countermeasures of Rural Revitalization in Deeply Poor Mountainous Areas of China

Under the guidance of the rural revitalization strategy of China’s government, the battle against poverty paid off to some extent, promoting the comprehensive development of the deeply poverty areas effectively. Nowadays, the local rural revitalization has reached a critical point since the coming “Post 2020” era. Having analyzed the current situation of rural poverty revitalization in Shangzhou District of Shangluo City, Through a large number of investigations, literature research, quantitative and qualitative analysis and other research methods, I found that there are still problems on weak foundation for poverty alleviation, difficulty in poverty alleviation, low population quality, backward perspectives, imbalance in industrial investments. Besides, there are many causes of poverty, and the task of returning back to poverty is arduous. In addition, the local income is limited and there is a large capital gap. From this perspective, it is proposed to increase government policy guidance, actively promote large-scale industrial development, promoting the diversified and integrated development of poverty alleviation industries, strengthening vocational training and education for poor households, doing a good job in poverty dynamic management, eliminating multi-directional financing from the source, strengthening project management. These would aid it in attaining a complete victory in rural revitalization.
Telling the Technical Services Story: Communicating Value

"Telling the Technical Services Story: Communicating Value." Journal of Electronic Resources Librarianship, 34(2), pp. 161–162
TAJA Corpus: Linguistically Tagged Written Algerian Judeo-Arabic Corpus

Abstract The Tagged Algerian Judeo-Arabic ( TAJA ) corpus is the first linguistically annotated corpus of any Judeo-Arabic dialect regardless of geography and period. The corpus is a genre-diverse collection of written Modern Algerian Judeo-Arabic texts, encompassing translations of the Bible and of liturgical texts, commentaries and original Judeo-Arabic books and journals. The TAJA corpus was manually annotated with parts-of-speech ( POS ) tags and detailed morphology tags. The goal of the new corpus is twofold. First, it preserves this endangered Judeo-Arabic language, expanding on previous fieldwork and going beyond the study of individual written texts. The corpus has already enabled us to make strides towards a grammar of written Algerian Judeo-Arabic. Second, this tagged corpus serves as a foundation for the development of Judeo-Arabic-specific Natural Language Processing ( NLP ) tools, which allow automatic POS tagging and morphological annotation of large collections of yet untapped texts in Algerian Judeo-Arabic and other Judeo-Arabic varieties.
LPDA: A new classification method based on linear programming

The search of separation hyperplanes is an efficient way to find rules with classification purposes. This paper presents an alternative mathematical programming formulation to existing methods to find a discriminant hyperplane. The hyperplane H is found by minimizing the sum of all the distances to the area assigned to the group each individual belongs to. It results in a convex optimization problem for which we find an equivalent linear programming problem. We demonstrate that H exists when the centroids of the two groups are not equal. The method is effective dealing with low and high dimensional data where reduction of the dimension is proposed to avoid overfitting problems. We show the performance of this approach with different data sets and comparisons with other classifications methods. The method is called LPDA and it is implemented in a R package available in https://github.com/mjnueda/lpda.
Context Adaptive Personalized Privacy for Location-based Systems

Mobile devices are augmented with advanced computing and sensing capabilities. Due to mobility, they are surrounded by dynamically changing environmental conditions. The immense context information flowing from their environment has paved the way for improved user experience. As users are central to the mobile computing paradigm, context information typically contains sensitive personal data, including user location. Users may need varying levels of privacy at different places and times. Since, as human beings, we are not capable of handling continuously flowing data, it is essential to automatically configure device privacy settings across changing contexts. In this paper, we propose a Context-Adaptive and Personalized Privacy-Preserving System (CAPPPS). It adapts to the changes in location, date, and time as the user context. The system adjusts privacy parameters for mobile device location concerning user privacy requirements while using a location-based system (LBS). We adopt a differential privacy approach to perturb user locations. We evaluated the effectiveness of CAPPPS using actual user trajectories. Results show that CAPPPS is effective for providing adaptive and personalized location privacy based on privacy parameters for changing user context.