Reviewerly/exHarmony huggingface dataset:
Hand gesture recognition using multi-sensor information fusion

Accurately recognizing hand gestures has great significance in assisting human-computer interaction, enhancing user experience, and developing a human-centered ubiquitous system. Due to the inherent complexity of hand gestures, however, how to capture discriminant features of hand motions and build a gesture recognition model remains crucial. To this end, we herein propose a gesture recognition method based on multi-sensor information fusion. Specifically, we first use the accelerometer and surface electromyography (sEMG) sensor to capture the kinematic and physiological signals of hand motions. Afterward, we utilize the sliding window technique to segment the streaming sensor data and extract various features from each segment to return a feature vector. We then optimize a gesture recognition model with the feature vectors. Finally, comparative experiments are conducted on the collected dataset in terms of different machine learning models, different sensors, as well as different types of features. Results show the joint use of sEMG sensor and accelerometer achieves the average accuracy of 97.88% compared to the 90.38% of using sEMG sensor and 84.03% of using accelerometer among four classifiers, which indicates the effectiveness of multi-sensor fusion. Besides, we quantitatively investigate the impact of null gesture on a gesture recognizer.
GAN-based algorithm for efficient image inpainting

Global pandemic due to the spread of COVID-19 has post challenges in a new dimension on facial recognition, where people start to wear masks. Under such condition, the authors consider utilizing machine learning in image inpainting to tackle the problem, by complete the possible face that is originally covered in mask. In particular, autoencoder has great potential on retaining important, general features of the image as well as the generative power of the Generative Adversarial Network (GAN). The authors implement a combination of the two models, context encoders and explain how it combines the power of the two models and train the model with 50,000 images of influencers faces and yields a solid result that still contains space for improvements. Furthermore, the authors discuss some shortcomings with the model, their possible improvements, as well as some area of study for future investigation for applicative perspective, as well as directions to further enhance and refine the model.
DEVELOPMENT OF QUALITY INSTRUMENTS AND DATA COLLECTION TECHNIQUES

Data collection methods and tools are the techniques and tools used in research to collect various data which are processed quantitatively or qualitatively, then arranged systematically. Data collection is defined as research activities that aim to collect field data needed to answer research questions in qualitative research or to test hypotheses in quantitative research. Research instruments are tools that use various types of information in research to be collected, processed quantitatively or qualitatively and then arranged systematically. Qualitative researchers as human instruments function to determine research focus, select informants as data sources, collect data, assess data quality, analyze data, interpret data and make conclusions on all stimuli from the environment that must be estimated to be meaningful or not for research. Quantitative research uses test instruments, non-test instruments and test inventories. For disclosure of facts to become data, the instruments used must be of good quality in the sense of being valid and reliable so that the data obtained will be in accordance with the facts or circumstances when collecting, studying, studying the problem under study.
PENINGKATAN KETERAMPILAN LITERAS BACA TULIS MELALUI MEMBACA EKSTENSIF UNTUK MENINGKATKAN KEMAMPUAN BERFIKIR TINGKAT TINGGI DI SEKOLAH DASAR

Students have speed reading skills are very important if they want to progress and develop and not be left behind with the current situation. With this skill, students will not be left behind in responding to the rapid flow of information and ideas that daily flood print and electronic media. How to improve extensive reading skills in order to improve thinking skills by using several methods including preview, read, review with jump reading techniques, The purpose of conducting this study is to analyze extensive reading skills, and analyze changes in high-level thinking patterns using several methods including preview, read, review with jump reading techniques in order to improve the thinking skills of grade V students at SD Muhammadiyah 1 GKB Gresik. The study uses the action research method which includes two cycles. Each cycle consists of four of them that are planned, actioned, observed, and reflected. To conduct data analysis carried out quantitatively and qualitatively, Extensive reading can be used several methods of preview, read, review with jump reading techniques to improve the ability of thinking grade V students at SD Muhammadiyah 1 GKB Gresik has run optimally. The research data carried out by the analysis, about increasing extensive reading skills using 3 preview, read, review methods with jump reading techniques is 15%. The result of the average value of the first cycle is 68.68 and the average value of the second cycle is 81.10. Conclusion of the average value of extensive reading skills to improve thinking skills using 3 methods preview, read, review with jumping reading techniques increased well. There is a change in the level of thinking skills and behavior that students show, namely students are increasingly excited, active, serious, and interested in extensive reading learning using 3 methods of preview, read, review with jump reading techniques
Invisible Chains

Melissa came to speak to me after a lecture I gave to parents at her son’s school. Her son Neil was a fifth grader. She told me that Neil was a good student and had friends in school, but he plays Fortnite for many hours a day. At first, Melissa told him to stop playing, but he kept begging for more time. When she insisted, he yelled and got extremely upset. Melissa then installed an app to limit Neil’s computer time. After a while Melissa noticed that he was playing Fortnite well into the evening. It turned out that Neil had discovered the password and changed the settings. This happened multiple times. Sometimes Neil asked for extra computer time for homework, but Melissa found him playing Fortnite instead. Melissa told me she thought she was not so good with technology, and that she should have found a better app. She felt that maybe she could have prevented what happened next.
PENGEMBANGAN VIDEO PEMBELAJARAN PENDAMPING GAMES EDUKATIF WORDWALL UNTUK MATERI PAI SEKOLAH DASAR

This research is focused on PAI material and the target of this research is elementary school students. The research approach used is a mix method with a development design using a 4-D model formulated from Thiagarajan, Semmel and Semmel. This development model contains 4 steps, namely define, design, develop, and disseminate. Research data was obtained through interviews, questionnaires, and document analysis techniques. This research produces: 1) learning video product design made based on the results of questionnaire analysis of teacher needs for PAI SD learning media and based on PAI SD curriculum analysis by analyzing Basic Competencies. From this basis, learning video products are designed that are in accordance with the characteristics of elementary school students, 2) Products in the form of wordwall companion learning videos that last 8 minutes 44 seconds. 3) Product test results that get an assessment from users with a very good predicate, meaning that the product is very suitabel for use in PAI teaching. From the results of this research, learning videos can be further developed for other materials
Google's robot sorts out office waste

Cyber-lawyer Scott Shapiro believes there is more to beating hackers than getting better at programming. He tells David Adam why online security is as much about humans as technology and how understanding both can keep us safer online
Differentiation of Multi-lithotypes Olistostromal Occurrences Associated with Iron Enrichment and Mineralization Within the Ophiolites of Choman Area, NE Iraq

The discovery of multi-lithotypes olistostromes deposits associated with potentially economically viable iron ore mineralizations in the Rayat region of NE Iraq are described in this study. The depositional and differentiation-classification characteristics of multi-lithotypes of olistostromes with distinct depositional and characteristics are reported here through detailed petrographic, mineralogical, and geochemical data, that allowed for mineralization mechanisms assessment and associated hydrothermal and alteration processes. Ferruginous conglomerate lithotypes occur below the sandstone olistostromes lithotypes section of the investigated series. Through the ferruginous conglomerate olistostromes (FCO) lithotypes there is a layer of potentially iron ore containing iron oxide (up to %42.55), quartz with some minor spinel content. Below the FCO, there are the horizons of (FOpO and FSO), they are also rich in hematite quartz and calcite with some minor spinel content. A 2m thick transition zone (TZ), separate poorly mineralized ophiocarbonatic olistostrome (OpCO) from the FCO. The multi-lithotypes of olistostrome from bottom to top are (serpentinite olistostrome (SO), Ophiocarbonatic olistostrome (OpCO), ferruginous serpentinite olistostrome (FSO), ferruginous ophiocarbonatic olistostrome (FOpCO), ferruginous conglomerate olistostrome (FCO), and sandstone olistostrome (SstO). In addition to Fe, the other potential economic importance element includes nickel (507 to 5816 mg/kg) and cobalt (47.2 to 328.3 mg/kg). The findings indicate that the Rayat olistostromes such as (SO, FSO, OpCO, FOpCO, FCO, and SstO) and the transition- zone (TZ) originated from a mixture of early hydrothermal alteration of serpentinites. The finding of olistostromes in the Rayat, exposes NE Iraq as a potential mineral-rich secto
IC layouts patterns topological profiling using directional geometrical kernels

As the semiconductor manufacturing technology node scales down in the deep submicron domain, hotspot detection becomes more challenging and geo-contextually dependent than ever before. The need to profile IC layout patterns based on geometrical commonalities becomes a significant demand either during IC layout design or manufacturing phases. Identified hotspots during the manufacturing phase are usually correlated to specific geometrical configurations sensitive to the lithography process or other manufacturing processes. Accordingly, identifying similar geometrical configurations is an important step toward locating potential hotspots. Once these hotspots are identified, their patterns can be provided to the router to avoid using these patterns and find other valid alternatives. Furthermore, in the IC design sign-off phase or Design Rules Check (DRC), layout profiling can identify patterns with high commonalities to these problematic patterns that potentially lead to a yield loss. In this paper, we introduce automated IC layout patterns topological profiling approach using Directional Geometrical Kernels (DGKs) to capture the context of patterns around a Point-Of-Interest (POI) in an IC layout, such as a hotspot. The DGKs pattern representation provides a direct one-to-one mapping with physical geometrical measurements centered by the POI and doesn’t need further feature extraction models or maps used by other pixelized gridded imagebased or density-based representations, which are both time and computational resources-consuming. The DGKs are decomposed into topological and dimensional components. This makes the mechanism of patterns topological profiling not in need of complex models and can be precisely fine controlled to produce adequate patterns profiling granularity that is not easily approached by other patterns profiling alternatives.
A novel authentication scheme for PIoT based on aggregate signature

Secure access of massive terminals in PIoT is the key to realize the secure transmission of power information. However, the power system mostly adopts the certificate-based terminal access solutions, which need to query and verify the legitimacy of the certificate during authentication. The heavy burden of certificate management limits the application of low-cost and resource constrained terminals in PIoT. The SM9 algorithm takes user’s ID as the public key, omits the certificate management. The key escrow also meets the supervision needs of the superior and subordinate in power system. Based on the analysis of the security threats faced by huge amounts of terminal secure access, a three-layer framework is constructed for PIoT. The access layer sets up authentication nodes to manage the secure access processes of terminals. Aiming at the low efficiency problem of signature verification when a large number of terminals access, a SM9-based aggregation signature scheme is designed. The authentication node is responsible for aggregating multiple terminals’ signatures into one signature. Compared with other identity-based aggregated signature schemes, the proposed scheme is efficient in terms of computational performance.
Intelligent new model design under the organic integration of computer network and English teaching courses

Computer networks have brought a very great transformation to English teaching, this transformation has more advantages than disadvantages. In recent years, people have conducted in-depth theoretical and practical researches on the integration of information technology and college English courses, certain research results have been achieved. In this paper, we take the intelligent classroom formed after the organic integration of computer network and English teaching courses as an example and elaborate the design of the new model of intelligent teaching.
Improved vehicle detection based on YOLOv5s

Aiming at the problems of low detection accuracy, poor real-time and robustness in vehicle target detection in the field of autonomous driving and other fields of existing target detection algorithms, this paper proposes a model based on YOLOv5s and introduces an attention mechanism to fuse multi-scale features. The detection algorithm of YOLOv5s model adds a layer of CBAM(Convolutional Block Attention Module) before the SPPF of Backbone of the YOLOv5s model, and uses ACON(Acrivate or Not) to replace ReLU as the activation function of the network, which improves the detection accuracy and can be effectively applied to vehicle detection in complex scenes.
Reversible data hiding in encrypted image based on pixel prediction and group marking

In recent years, as people pay more and more attention to the protection of cloud data, reversible data hiding in encrypted image (RDHEI) has become the focus of research. This paper proposes a hiding scheme based on pixel prediction and group marking. First, the cover image is pre-processed, including block, grouping, pixel prediction, etc. Then, the embedding method and position are selected according to the parameters, and the marked map is generated. Finally, we encrypt the image and embed data in the encrypted pixels according to the marker map to obtain an image for transmission. For the receiver, separable data recovery is performed based on the key. The correct rate of secret data extraction is 100%, and the cover image can be recovered without distortion. This method does not require auxiliary information during decryption, but the decryption quality is good. At the same time, the method provides two embedding methods to provide a high embedding rate. Therefore, this method has advantages in image decryption quality, embedding rate, and separability.
Differentially private discrete multi-attributed network releasing

In the area of social network, different attributes have different effects on the structure of network. Most of the existing privacy protection methods for attributed networks ignore the situation which different attributes have different effects on the network structure. They protect the privacy of the attributes indiscriminately. In respect of the issues above, a differentially private discrete multi-attributed network releasing method is proposed. Firstly, a probability model of discrete multi-attributed network is structured and the correlation parameter between multiple attributes and network structure is defined. The factor with different effects of different attributes on network structure is added into the model. Then, the algorithm uses the correlation parameter to establish the partition model of metadata and divides the metadata into different groups. As the group has different network model and attribute between each other, the groups are independence. The differential privacy of discrete multi-attributed network is realized through sanitizing parameters of the model and allocating metadata using exponential mechanism. Finally, experiment on real datasets verifies that the algorithm can satisfy the characteristics of the discrete multi-attributed network. It can also improve the efficiency and data availability.
Adaptive method of process flow parameter values setting based on remote sensing image metadata

Remote sensing data processing involves many steps. This paper uses process flow to represent these processing steps. This paper combines remote sensing image metadata (RSIM) with case based reasoning (CBR) technology, proposes a parameter value adaptive method based on RSIM, and constructs a remote RSIM case model. It makes the representation of RSIM more intuitive and convenient. This method consists of two parts: case building and case similarity measurement. Case building refers to building RSIM into RSIM cases according to case models, and then classifying and saving each case. Case similarity measurement refers to the similarity measurement of metadata in the production process and each case under the flow. Finally, the parameter values will be configured according to the highest similarity. The method is tested in an actual remote sensing production system, and the experimental results show that the method has good applicability in the parameter value adaptation of remote sensing process production.
A method based on relaxation frequency for object recognition

This paper proposes a target recognition technique based on target relaxation frequency parameters. These parameters are calculated from the frequency response observed by multi-frequency magnetic induction. Firstly, we prepare multifrequency response and class labels of various materials. The least-square optimization method calculates the prepared materials' relaxation frequency characteristic parameter matrix. Secondly, the matrix is used in the linear regression method to calculate the regression model's undetermined coefficients. Then the coefficients help us to obtain the class label of the target. Finally, we compare the target label with the prepared class labels to identify the substance type of the target. The results show that the technique proposed in this paper can accurately identify and classify the substance type of the target, and the multi-frequency magnetic induction technology based on relaxation frequency can be applied in substance identification.
Research on path planning of mobile robot based on improved ant colony algorithm

Aiming at the problems of slow convergence speed and easy to fall into the local optimal solution in the path planning of mobile robots in the application of traditional ant colony algorithm, a strategy to update the pheromone increment according to the planned path and the historical optimal path is proposed, so as to improve the convergence of the algorithm. To avoid the algorithm getting stuck in stagnation, an adaptive pheromone volatility coefficient is introduced. It not only avoids the stagnation phenomenon in the search, but also improves the path convergence speed. Simulation results show that the improved algorithm has good convergence.
Research on the architecture of network text readability rating system based on streaming media

In the era of information explosion, language learners can easily obtain a large number of language learning materials. It is a common way to obtain learning materials of the target language learned from streaming media. However, streaming media platforms generally only inspect the compliance of the content in terms of politics and economy, while the inspection of language is relatively lacking. This makes it difficult for language learners to determine whether the text of the streaming media is suitable for their own language proficiency level. Therefore, it is necessary to research on the architecture of network text readability rating system based on streaming media. In this paper, PocketSphinx, SpeechRecognition and other add-on packages based on Python platform have conducted voice recognition on streaming media information collected from user equipment, and then conducted readability grade evaluation on the recognized text, and analyzed the language characteristics of the text, so that language learners can learn corresponding language knowledge.
Design and verification of convolutional neural network accelerator

The existing software implementation schemes of Convolutional Neural Networks (CNN) cannot meet the requirements of computing performance and power consumption. To further improve the energy efficiency of the deep neural network, improve throughput and reduce power consumption, a hardware accelerator based on a convolutional neural network was designed, and a verification platform was built for it. The platform has good reusability and can flexibly complete the verification work of the target chip under various modes and configurations, and perform performance evaluation and functional correctness verification on the chip. Through the board-level verification results, this design reduces power consumption by 12.36% compared with similar accelerators and improves hardware resource utilization by 13.87% while keeping the same conditions as clock frequency and bus bit width.
Darknet for 4-point homography estimation

Homography estimation is often an indispensable step in computer vision tasks that require multi-frame time-domain information. However, when we estimate the traditional homography matrix, the rotational and translational terms are often difficult to balance. In this paper, based on the 4-point homography parameter matrix, we reproduce the Synthetic COCO dataset (S-COCO) and the Photometrically Distorted Synthetic COCO dataset (PDS-COCO). Then, we use the Darknet in YOLOv3 as the backbone to design a deep network for 4-point homography estimation. Experiments show that compared with existing main one-stop methods, our proposed deep learning network achieves the best performance on the S-COCO dataset and excellent performance on the PDS-COCO dataset.
Hardware implementation of improved dark channel fog removal algorithm

In view of the low contrast and large colour deviation of the image captured by the camera in foggy weather, it is proposed to modify the parameters of atmospheric light and transmittance through dual channels, refine the transmittance by the tolerance mechanism, and obtain the dehazed image in reverse according to the atmospheric scattering model function. Combined with the parallel operation and high-speed characteristics of FPGA (Field Programmable Gate Array) hardware platform, the proposed algorithm is parallelized and simplified, and the implementation of dark channel dehazing hardware based on FPGA is designed. After testing by MATLAB and ModelSim, the restored image is clear and distortion-free.