Reviewerly/exHarmony huggingface dataset:
New Teaching Approaches and Reform Countermeasures of Career Development Planning and Employment Guidance Courses in Colleges and Universities

Affected by the COVID-19 and the economic downturn, the employment problem of college students has become increasingly prominent.Improving the inherent mode of college students' career development courses and improving the quality of course construction is one of the effective ways to achieve high-quality employment of college students.Based on this, college students' career development planning and career guidance courses need to optimize the curriculum system, update the curriculum content, improve teaching methods, and build a scientific concept of employment and career selection with a positive attitude and reasonable planning.Based on the actual situation of contemporary college students, this paper analyzes the reform direction and path countermeasures of this course, aiming to provide feasible suggestions and recommendations for the reform of college career development planning curriculum.
Design and Implementation of Time-lock Wallet using Blockchain Technology

In this work, authors have presented a design and implementation of the Time Lock Wallet. This Time lock model n This is done by using Blockchain Technology. The main goal behind time-locked wallets is to lock the money for a specific amount of time. After the stated date has passed, only the designated person/beneficiary may take money out of the wallet.
Penerapan Pengelompokkan Produktivitas Hasil Pertanian Menggunakan Algoritma K-Means

Since ancient times, Indonesia has always been rich in agricultural products such as rice, soybeans, corn, peanuts, cassava, and sweet potatoes. In addition, there are also products from agriculture that are referred to as trade crop agricultural products, namely tea, coffee, coconut, quinine, cloves, sugar cane, rubber, and others. The agricultural sector in 2021 will grow by 1.84% and contribute 13.28% to the national economy. Then in 2022, the agricultural sector will show consistency with a positive growth of 1.37% and contribute 12.98% to the national economy. Then it is necessary to group the productivity of agricultural products using the k-means clustering method to group data on the highest and lowest yield types according to the District in Bojonegoro so that the types of agricultural products that are most productive and less productive can be identified. The method used in this study is K-Means cluster analysis by first determining the number of groups to be formed. In this study, the data used is secondary data on agricultural products originating from One Bojonegoro Data. The food crops in question are rice, shallots, soybeans, large chilies, corn, and so on. From the results of grouping agricultural products based on the year of production, the best types of crops will be known, and which districts will produce the most productive food crops so that the distribution of food crops in Bojonegoro District can be controlled. Productivity grouping of agricultural products can be used as a strategy to increase agricultural yields
NTPP-MVSNet: Multi-View Stereo Network Based on Neighboring Tangent Plane Propagation

Although learning-based multi-view stereo algorithms have produced exciting results in recent years, few researchers have explored the specific role of deep sampling in the network. We posit that depth sampling accuracy more directly impacts the quality of scene reconstruction. To address this issue, we proposed NTPP-MVSNet, which utilizes normal vector and depth information from neighboring pixels to propagate tangent planes. Based on this, we obtained a more accurate depth estimate through homography transformation. We used deformable convolution to acquire continuous pixel positions on the surface and 3D-UNet to account for the regression of depth and normal vector maps without consuming additional GPU memory. Finally, we applied homography transformation to complete the mapping of the imaging plane and the neighborhood surface tangent plane to generate a depth hypothesis. Experimental trials on the DTU and Tanks and Temples datasets demonstrate the feasibility of NTPP-MVSNet, and ablation experiments confirm the superior performance of our deep sampling methodology.
Pendampingan Pembuatan Video Game untuk SMAN 3 Batusangkar

Student creativity in the field of research can develop a method that has excellent potential to be utilized by the wider community. SMAN 3 Batusangkar students have high creativity and interest in participating in research olympiads, such as designing a video game structure as a therapeutic tool to help children with Attention Deficit Hyperactivity Disorder (ADHD) in problems with hyperactivity, impulsivity, and inattention. The designed video game is part of their research plan proposed in the Indonesian Student Research Olympiad. However, the lack of understanding of the students in making video games with the concept of visual attention for children with ADHD to be used in research is a problem in itself. Therefore, the community service team in the Information Systems Department, Universitas Andalas, assisted in making video games to increase the understanding of the SMAN 3 Batusangkar team on making video games. This activity aimed to disseminate process knowledge related to making simple video games to support innovations that benefit society. Assistance was carried out intensively at the Basic Laboratory of the Information Systems Department to help the SMAN 3 Batusangkar team complete a series of research plans proposed for the Olympics, especially at stages related to making video games.
Novices cannot fill the examiners’ shoes: Evidence of footwear examiners’ expertise in shoe comparisons

The value of a footwear examiner’s opinion centres on their ability to determine whether a particular shoe made an impression with greater accuracy than a novice. However, there has been limited research on the expertise of footwear examiners and the accuracy and reproducibility of their decisions. In the current study, we measured the accuracy and consensus of 31 footwear examiners versus a comparison group of 29 novices. Participants completed 20 ground truth known mock shoe comparisons. Results demonstrated that footwear examiners were more accurate than novices, regardless of comparison difficulty. Overall, on trials where probative decisions were given, examiners made false identifications and false exclusions on a total of 3% and 2% of trials, while novices made false identifications and false exclusions on a total of 19% and 17% of trials. Examiners also demonstrated better consensus in their opinions than novices, although both groups demonstrated low levels of agreement in their responses and variability in their interpretation of the conclusion scale. In summary, these findings support the proposition that footwear examiners show expert-level performance in matching known and unknown footwear impressions. These performance estimates may help the criminal justice system to appropriately value footwear examination evidence.
Pixellayer- A novel approach for stitching digital Images and Videos

Image stitching and video stitching are the techniques to solve the problem of overlapping fields of view in images and videos using AI technologies. The creations of panoramic images using computer vision are primarily focused on creating panoramas using a library of images. The basic idea of this research is to propose a suitable method to meet the needs of stitching two or more images and further generate a 360° visualization of that stitched image. This method performs stitching of real-time images using live wireless cameras. Proposed approach ‘Pixellayer’ also removes the usage of a wide-angle wireless camera for stitching which makes this approach efficient in terms of cost. It includes the feature of frames per second and captures the live movement of an object in a real-time environment. It also solves the problem of blind spots of security wireless cameras and creating cinematic effects.
Teachers' Efforts in Overcoming Difficulties Learning to Read in Elementary School Students

This study aims to explain teachers' efforts in overcoming reading learning difficulties in grade II students of Gonda State Elementary School, Baubau City. This type of research is qualitative research with descriptive methods. The study subjects used were grade II teachers, with 23 students consisting of 11 female and 12 male students. The method or technique of data collection used is observation, interview, and documentation. Data analysis is carried out by reducing data, presenting data, and drawing conclusions. The research results on teachers' efforts in overcoming reading learning difficulties in grade II students of Gonda State Elementary School in Baubaubau City have two steps: choosing and implementing effective learning strategies and learning methods. The learning strategy used is introducing the alphabet to students first, Utilizing reading corners, conducting tutoring, and habituating to reading together during the learning process. The learning method is by approaching students and giving reading practice tasks and giving interesting reading spelling books. The forms of teacher efforts are expected to effectively overcome students' reading learning difficulties, especially in grade II students of Gonda State Elementary School in Baubau City.
ZKFlow: Private Transactions in Corda with ZKP

Corda is a permissioned distributed ledger platform that enables enterprises to manage their business deals and obligations in a decentralized manner. Its unique ledger structure, which requires sharing the content of a transaction only to participating parties, contributes to the protection of privacy-sensitive transaction data. Despite improved privacy guarantees, the platform cannot assure complete data protection due to two main issues: the leakage of transaction data to the validating notaries and the leakage of historical transaction data in the backchain to future participants. These issues can be handled using cryptographic techniques whose adoption in Corda requires a dedicated protocol design. In this paper, we present a protocol that overcomes the problem of data leakage in the notarization and transaction backchain validation of Corda transactions using zero-knowledge proofs (ZKPs). Our protocol enables the platform participants to validate their transactions without revealing their content to the non-trusted parties. We provide the technical details of our protocol and show how ZKPs can be efficiently adopted into the Corda ecosystem.
Complexity analysis of deep learning image classification methods

Brest cells malignancy is one of the major reasons of women death in all over the world.The malignant cells calculation is now more accurate and easy with the deep learning approaches developed for image classification.By use of these deep learning methods early detection of breast cancer is possible.This article shows different approaches of deep learning i.e. logistic regression, sequential minimal optimization, random forest and hyper parameter technique and comparison between these to show that deep neural technique is more accurate than other approaches.
Detection of Human Falls via Computer Vision for Elderly Care – An I3D/RNN Approach

As the population continues to age, the number of elderly individuals is increasing at an unprecedented rate as the active age group in developed countries continues to shrink. This demographic shift has resulted in a shortage of resources and limitations in the provision of adequate elderly care. Among the many health risks faced by the elderly, falls are a common and serious problem, which can lead to injuries, hospitalizations, and even death. Despite the prevalence of falls, traditional manual detection methods are often unreliable and inadequate. In response to this, this paper proposes an automatic fall detection system based on video cameras that uses a unique combination of an inflated 3D convolutional neural network (I3D) and a recurrent neural network (RNN). This model was evaluated using multiple fall detection datasets, including a newly developed dataset of simulated falls. The results of this study demonstrate that this hybrid model is highly effective at detecting falls and is on par with other state-of-the-art systems, boasting 94% F1 score and 96% recall values. This technology has the potential to significantly improve the quality of life and safety of the elderly population by providing timely and accurate detection of falls.
Education System in Russia in 2022

In 2022, all pandemic restrictions were lifted from the Russian education system as it switched to full-time mode. At the same time, the outcomes that emerged during the periods of prolonged lockdowns remained, especially distance learning. In the new environment, they are used partially but allow to work remotely, for example, university lecturers (to give lectures remotely, being on business trip, etc.) or researchers can remotely participate in scientific conferences and seminars. As a result, the audiences outreach for various seminars and webinars has increased dramatically. Simultaneously, in 2022, the Russian education system faced new challenges associated with the special military operation (SMO) in Ukraine and the sanctions pressure on Russia caused by the SMO. This led to substantive, structural, organizational and economic changes in the functioning of different levels of the education system.
Analysis and Design of Precision-Scalable Computation Array for Efficient Neural Radiance Field Rendering

Neural Radiance Field (NeRF), a disruptive method for 3D representation and rendering, is extremely popular in the field of computer graphics and computer vision in the past three years. The most distinctive feature of NeRF models is their scene representation property, making it possible to quantize the models according to the complexity of the representing scenes. This paper proposes a novel approach to improve the efficiency of NeRF rendering by adopting precision-scalable computation. We first analyze and validate the idea of scene-dependent quantization for NeRF models. Based on that, we further propose look-up table (LUT) processing element (PE)-based precision-scalable computation unit designs. To evaluate the performance of different precision-scalable computing units, we implement these designs and compare the corresponding area, power, speed and energy efficiency. We also compare the proposed designs with existing approaches as well as the fixed precision approach for NeRF rendering tasks. The comparison results show that energy efficiency can be significantly improved by using precision-scalable computation for NeRF.
We Need to Communicate: Communicating Attention Network for Semantic Segmentation of High-Resolution Remote Sensing Images

Traditional models that employ CNNs as encoders do not sufficiently combine high-level features and low-level features. However, high-level features are rich in semantic information but lack spatial detail, while low-level features are the opposite. Therefore, the integrated utilization of multi-level features and the bridging of the gap between them is crucial to promote the accuracy of semantic segmentation. To address this issue, we presented communicating mutual attention (CMA) and communicating self-attention (CSA) modules to enhance the interaction and fusion of different levels of feature maps. On the one hand, CMA aggregates the global context information of high-level features into low-level features and embeds the spatial detail localization characteristics of low-level features in high-level features. On the other hand, the CSA module is deployed to integrate the spatially detailed representation of low-level features into the attention map of high-level features. We have experimented with the communicating attention network (CANet), a U-net-like network composed of multiple CMA and CSA modules, on the ISPRS Vaihingen and Potsdam datasets with mean F1-scores of 89.61% and 92.60%, respectively. The results demonstrate that CANet embodies superior performance in the semantic segmentation task of remote sensing of images.
PERBEDAAN HASIL BELAJAR DAN MINAT SISWA MENGGUNAKAN MEDIA MACROMEDIA FLASH DAN MEDIA POWTOON PADA MATERI LARUTAN ELEKTROLIT DAN NON-ELEKTROLIT

This study aims to identify differences in learning outcomes and interests of students who are taught using Macromedia Flash and Powtoon media for electrolyte and non-electrolyte solutions. The population of this study were all students of class X MIPA, totaling 3 classes. Sampling was done by purposive sampling. Data on student learning outcomes were examined for validity, difficulty, discriminating power, and reliability using an objective test instrument consisting of 20 questions. To measure student interest, the data used a questionnaire filled in by the observer. Pretest, posttest, and students' interest in the two experimental classes were determined based on normality test data and homogeneity test. In the Experimental Class I and Experimental Class II, it was found that the pretest, posttest, and students' interest in the two experimental classes were normally distributed and homogeneous. Next, a hypothesis test was carried out using a two-party Independent Sample T-Test. To test the first hypothesis, the results were sig. 0.024 (sig. &lt;0.05) and to test the hypothesis II, the results were sig. 0.000 (sig. &lt;0.05), which means that there is a significant difference in the learning outcomes and interests of students taught with Macromedia Flash Media using the Problem Based Learning model compared to the learning outcomes and interests of students taught with Powtoon Media using the Problem Based Learning model in the material electrolyte and non-electrolyte solutions.
Deep Gated Multi-modal Fusion for Image Privacy Prediction

With the rapid development of technologies in mobile devices, people can post their daily lives on social networking sites such as Facebook, Flickr, and Instagram. This leads to new privacy concerns due to people’s lack of understanding that private information can be leaked and used to their detriment. Image privacy prediction models are developed to predict whether images contain sensitive information (private images) or are safe to be shared online (public images). Despite significant progress on this task, there are still some crucial problems that remain to be solved. Firstly, images’ content and tags are found to be useful modalities to automatically predict images’ privacy. To date, most image privacy prediction models use single modalities (image-only or tag-only), which limits their performance. Secondly, we observe that current image privacy prediction models are surprisingly vulnerable to even small perturbations in the input data. Attackers can add small perturbations to input data and easily damage a well-trained image privacy prediction model. To address these challenges, in this article, we propose a new decision-level Gated multi-modal fusion (GMMF) approach that fuses object, scene, and image tags modalities to predict privacy for online images. In particular, the proposed approach identifies fusion weights of class probability distributions generated by single-modal classifiers according to their reliability of the privacy prediction for each target image in a sample-by-sample manner and performs a weighted decision-level fusion, so that modalities with high reliability are assigned with higher fusion weights while ones with low reliability are restrained with lower fusion weights. The results of our experiments show that the gated multi-modal fusion network effectively fuses single modalities and outperforms state-of-the-art models for image privacy prediction. Moreover, we perform adversarial training on our proposed GMMF model using multiple types of noise on input data (i.e., images and/or tags). When some modalities are failed by input data with noise attacks, our approach effectively utilizes clean modalities and minimizes negative influences brought by degraded ones using fusion weights, achieving significantly stronger robustness over traditional fusion methods for image privacy prediction. The robustness of our GMMF model against data noise can even be generalized to more severe noise levels. To the best of our knowledge, we are the first to investigate the robustness of image privacy prediction models against noise attacks. Moreover, as the performance of decision-level multi-modal fusion depends highly on the quality of single-modal networks, we investigate self-distillation on single-modal privacy classifiers and observe that transferring knowledge from a trained teacher model to a student model is beneficial in our proposed approach.
PENERAPAN METODE THINK PAIR SHARE DALAM UPAYA MENINGKATKAN HASIL BELAJAR SISWA

Abstract&#x0D; Mathematics material becomes a 'scourge' for some students. Monotonous learning adds to the boredom of children learning mathematics. Mathematics teachers are required to be a fun educator figure so that the material can be absorbed properly. Based on the results of initial observations at SDN Kalianget Barat 1, it was found that from the number of students as many as 16 students in grade 6, there were 10 students who did not complete theme 1, sub-theme 1, mathematics. The tendency is that they think negatively about mathematics. As if mathematics is a scary subject, difficult, complicated and full of counting numbers. Even though teaching mathematics is not only with numbers that make students bored and less attractive. Teaching mathematics in elementary school (SD) classes does require a special method to make it fun and challenging. The Think Pair Share method is tried to be a model to overcome such problems. This improvement uses classroom action research (CAR), with the hope that the Think Pair Share method can improve student learning outcomes. Based on the results of the study that the use of the Think Pair Share method with good implementation can improve learning outcomes, it is stated from the results of the presentation that in the first cycle the number of children who completed as many as 4 people or 25%, while in the second cycle, the number of students who completed as many as 12 people or 75%, meaning the increase is read as much as 50%. From these data it can be concluded that the implementation of the Think Pair Share learning method can improve learning outcomes.
Encoding of security properties for transparent consent data processing

User consent is a pervasive challenge over the digital platform. After the introduction of the framework such as GDPR and the Indian Digital Personal Data Protection Bill (DPDPB-2022, Draft), consent becomes indispensable. As per DPDPB, it is the data fiduciary responsibility to obtain the user's consent before data collection and data processing. The existing techniques of consent processing are not transparent and adhere to data protection goals. Data fiduciaries may misuse the collected data for purposes other than specified in the consent. Therefore, a robust model is necessary to achieve the framework's consent processing objective. In this paper, we have described that encoding of requisite security and privacy properties will ascertain stronger consent compliance. We formalize these properties as Proofs of Consent (PoC) and categorized them into three layers. The acquisition of a higher layer will minimize adversarial risks and ascertain greater transparency. Based on this, we have proposed a model shielded consent manager (SCM) using blockchain state channel and other cryptographic primitives for retrieval of consent to grant permissions to access Android resources. SCM include parameters as per the framework, satisfies the security properties such as integrity of consent, non-deniability by users, auditability of logs in data processing, and provides finer visualization of user's consents. The simulation of the contract is done using solidity, truffle and ganache test network, and the feasibility of practical implementation is analyzed to show the efficacy of the model.