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This study aims to examine the economic empowerment model applied by the Minangkabau community, which is known as a society that holds firmly to values, traditions, culture and religion, besides that Minangkabau people have long been known as Minang merchants who adhere to a communal economic system. The cooperative economic system implemented by the Minangkabau community is able to maintain the economic stability of the Minangkabau community, it is necessary to conduct a study to see the model of economic empowerment of the Minangkabau community. The research method used is library research by collecting data through reference books, published scientific journals and other literature to see the applied economic empowerment model. The data collection method is carried out by collecting study materials, reading, making research notes and reviewing study materials related to the research focus. The findings of this study are that the Minangkabau community's economy is stable and able to withstand economic shocks by applying several models of economic empowerment, namely mutual cooperation, cooperation, wandering and trading.Keywords: Empowerment, Minangkabau, Economic Aspect

The concept of community development is a necessity in the field of special education. Special education is a challenge in itself for implementers of social welfare programs. This study aims to propose a community development model in providing education for people with Down syndrome so that the entire community can enjoy educational development programs. The method used in this research is descriptive qualitative to reveal facts and contribute thoughts in community development in the field of education. Data mining was carried out with a literacy approach and policy considerations derived by the government in order to obtain a critical analysis and recommendations for the concept of education for people with Down syndrome. The results of this study indicate several recommendations that can be applied to implement community development programs in the field of education and applications in the form of inclusive schools in order to create an equitable and equitable education scheme.

This study aims to explore the role of the community in protecting land. Land in the Tengger community has been the spotlight since an increase in tourist visits to the Bromo area, which has raised many questions and curiosity about the land preservation model. In collecting data and analysis, the researcher used a qualitative descriptive approach. It was further carried out in-depth interviews with several key informants to answer the research question. The research results show that the productive soil conditions make the Tengger community closely prevent the land from living sustainably. Land conditions supported by the community in understanding traditional values as a legacy from their ancestors discouraged people from selling because this is a prohibition unviolated. The Ngadisari’s Village Government has made an effort to protect the land in response to the challenges of buying and selling land by issuing customary regulations with recognition. The traditional rules regarding the prohibition of the land of sale become legal rules and have legal force.

With the progress of machine learning, the demand for realistic data with high-quality annotations has been thriving. In order to generalize well, considerable amounts of data are required, especially realistic ground-truth data, for tasks such as object detection and scene segmentation. Such data can be difficult, time-consuming, and expensive to collect. This article presents a dataset of household objects and box scenes commonly found in warehouse environments. The dataset was obtained using a robotic setup with four different cameras. It contains reconstructed objects and scenes, as well as raw RGB and depth images, camera poses, pixel-wise labels of objects directly in the RGB images, and 3D bounding boxes with poses in the world frame. Furthermore, raw calibration data are provided, together with the intrinsic and extrinsic parameters for all the sensors. By providing object labels as pixel-wise masks, 3D, and 2D object bounding boxes, this dataset is useful for both object recognition and instance segmentation. The realistic scenes provided will serve for learning-based algorithms applied to scenarios where boxes of objects are often found, such as in the logistics sector. Both the dataset and the tools for data processing are published and available online.

Current research on visual place recognition mostly focuses on aggregating local visual features of an image into a single vector representation. Therefore, high-level information such as the geometric arrangement of the features is typically lost. In this paper, we introduce a novel learning-based approach to place recognition, using RGB-D cameras and line clusters as visual and geometric features. We state the place recognition problem as a problem of recognizing clusters of lines instead of individual patches, thus maintaining structural information. In our work, line clusters are defined as lines that make up individual objects, hence our place recognition approach can be understood as object recognition. 3D line segments are detected in RGB-D images using state-of-the-art techniques. We present a neural network architecture based on the attention mechanism for frame-wise line clustering. A similar neural network is used for the description of these clusters with a compact embedding of 128 floating point numbers, trained with triplet loss on training data obtained from the InteriorNet dataset. We show experiments on a large number of indoor scenes and compare our method with the bag-of-words image-retrieval approach using SIFT and SuperPoint features and the global descriptor NetVLAD. Trained only on synthetic data, our approach generalizes well to real-world data captured with Kinect sensors, while also providing information about the geometric arrangement of instances., Accepted for International Conference on 3D Vision (3DV) 2020

The Philippines is a primarily agricultural nation in Southeast Asia, despite rapid industrialization in many areas of the archipelago...

Robust and accurate pose estimation is crucial for many applications in mobile robotics. Extending visual Simultaneous Localization and Mapping (SLAM) with other modalities such as an inertial measurement unit (IMU) can boost robustness and accuracy. However, for a tight sensor fusion, accurate time synchronization of the sensors is often crucial. Changing exposure times, internal sensor filtering, multiple clock sources and unpredictable delays from operation system scheduling and data transfer can make sensor synchronization challenging. In this paper, we present VersaVIS, an Open Versatile Multi-Camera Visual-Inertial Sensor Suite aimed to be an efficient research platform for easy deployment, integration and extension for many mobile robotic applications. VersaVIS provides a complete, open-source hardware, firmware and software bundle to perform time synchronization of multiple cameras with an IMU featuring exposure compensation, host clock translation and independent and stereo camera triggering. The sensor suite supports a wide range of cameras and IMUs to match the requirements of the application. The synchronization accuracy of the framework is evaluated on multiple experiments achieving timing accuracy of less than 1 ms . Furthermore, the applicability and versatility of the sensor suite is demonstrated in multiple applications including visual-inertial SLAM, multi-camera applications, multi-modal mapping, reconstruction and object based mapping., Sensors, 20 (5), ISSN:1424-8220

We use a 2.5 GHz clocked quantum key distribution system to perform long-distance and high-speed quantum key distribution. Taking benefit from superconducting detectors optimized for each operation regime and low-loss fiber, we achieve state-of-the-art performance.

Yogyakarta is a famous city for its slogan "Jogya Berhati Nyaman", but that slogan it has not really be perceived its comfort by all levels of society. Especially for those who have special needs or often called people with disabilities. People with disabilities are a group that often gets discriminated in every activity of life. Not only in society, people with disabilities often get discrimination to get their rights in public service and get a job. The difficulty of access in getting jobs will have a direct effect on their survival. Whereas equality in gaining access to this work is the right of every citizen, not least for the disabilities. So that based on that problem, Muhammadiyah as the largest religious organization in Yogyakarta want to empower the marginalized people (Disabilities). Muhammadiyah in its empowerment uses the strategy of Dakwah Bil-Hal to empower people with disabilities to get out of their handcuff problems. Dakwah Bil-Hal is an empowerment activity that emphasizes concrete actions to solve existing problems. This action aims to be able to raise the praise, dignity, and welfare of marginalized groups (people with disabilities in Yogyakarta). The main purpose of this paper is to explore and analyze Dakwah Bil-Hal that Muhammadiyah does in solving the problem of the disabilities with access to get jobs and how challenges facing Muhammadiyah in its empowerment. Data collection was done by interview, documentation study, and literature study. The result of this research, it is concluded that Muhammadiyah empowerment has succeeded in delivering the disability groups to better economic condition than before. Where is the empowerment strategy that is done by way of; Mentoring of assisted groups, Training and special skills, Granting business loan assistance.

This study aims to explore information on improving community welfare after converting agricultural land to oil palm land. Many of the villagers carry out land conversion to increase income as a means of fulfilling their daily needs, although in reality not all of them get welfare. The method used in this research is descriptive qualitative. Data mining was carried out by interview and documentation. Interviews were conducted with smallholders who converted land to oil palm, stakeholders and local government. The results of the interview were then triangulated with documentation obtained from library searches in the form of books, articles and previous research. The results of this study indicate an increase in the welfare of the community after land conversion but with very binding rules both from the state and from the prevailing market rules.

Object finding in clutter is a skill that requires perception of the environment and in many cases physical interaction. In robotics, interactive perception defines a set of algorithms that leverage actions to improve the perception of the environment, and vice versa use perception to guide the next action. Scene interactions are difficult to model, therefore, most of the current systems use predefined heuristics. This limits their ability to efficiently search for the target object in a complex environment. In order to remove heuristics and the need for explicit models of the interactions, in this work we propose a reinforcement learning based active and interactive perception system for scene exploration and object search. We evaluate our work both in simulated and in real-world experiments using a robotic manipulator equipped with an RGB and a depth camera, and compare our system to two baselines. The results indicate that our approach, trained in simulation only, transfers smoothly to reality and can solve the object finding task efficiently and with more than 88% success rate., Comment: IEEE International Conference on Robotics and Automation (ICRA), 2020

Research has been conducted to determine the Minang domain arabica coffee sensory quality profile, which can be used as a reference for specialty coffee. Seed processing is carried out at the research location (Solok Regency, South Solok Regency, Pasaman, Agam, and Fifty Cities) until the grain stage is wet (washed). Fianda Coffee Roastery carries out the coffee roasting process. Coffee uses W600i Roaster with a long roast method with two treatments, namely temperature 1800C for 15 minutes, temperature B 2000C for 10 minutes. The taste test was conducted at a two-door cafe and the Indonesian Coffee and Cocoa Research Institute Laboratory, Jember, East Java, following the SCAA. The test results showed that dry coffee beans' moisture content for all treatments was

Recent years have seen an increasing interest in exploring alternative techniques to conventional grinding methods such as fragmentation by high voltage discharges. Although pulsed power has already been applied to break down complex composite materials, there is currently no systematic comparison between different types of discharge regimes such as electrohydraulic and electrodynamic fragmentation. The aim of this work is to present such a comparison based on the electrohydraulic and electrodynamic fragmentation of copper indium diselenide (CIS) photovoltaic modules for potential indium recovery. High voltage discharges are performed in a process vessel filled with demineralized water at ambient conditions. After fragmentation, individual fractions are weighed, milled and the indium content is determined by X-ray fluorescence (XRF). Both the electrohydraulic and the electrodynamic approach are suitable to efficiently separate thin-film photovoltaic composite material into its constituent layers. The separation result is not dependent on the voltage level, but only on the total pulse energy applied. Since the generation of discharges with higher voltages requires a higher investment into plant properties such as insulation and generator, a comminution at lower voltages, i.e., electrohydraulic fragmentation, is preferable to electrodynamic fragmentation at higher voltages. To reveal the specific strengths of the two processes compared here, further comparative work with different composite materials is required.

Enabling autonomous robots to interact in unstructured environments with dynamic objects requires manipulation capabilities that can deal with clutter, changes, and objects' variability. This paper presents a comparison of different reinforcement learning-based approaches for object picking with a robotic manipulator. We learn closed-loop policies mapping depth camera inputs to motion commands and compare different approaches to keep the problem tractable, including reward shaping, curriculum learning and using a policy pre-trained on a task with a reduced action set to warm-start the full problem. For efficient and more flexible data collection, we train in simulation and transfer the policies to a real robot. We show that using curriculum learning, policies learned with a sparse reward formulation can be trained at similar rates as with a shaped reward. These policies result in success rates comparable to the policy initialized on the simplified task. We could successfully transfer these policies to the real robot with only minor modifications of the depth image filtering. We found that using a heuristic to warm-start the training was useful to enforce desired behavior, while the policies trained from scratch using a curriculum learned better to cope with unseen scenarios where objects are removed., Comment: 8 pages, video available at https://youtu.be/ii16Zejmf-E

Using multiple sensors often requires the knowledge of static transformations between those sensors. If these transformations are unknown, hand-eye calibration is used to obtain them. Additionally, sensors are often unsynchronized, thus requiring time-alignment of measurements. This alignment can further be hindered by having sensors that fail at providing useful data over a certain time period. We present an end-to-end calibration framework to solve the hand-eye calibration. After an initial time-alignment step, we use the time-aligned pose estimates to perform the static transformation estimation based on different prefiltering methods, which are robust to outliers. In a final step, we employ a non-linear optimization to locally refine the calibration and time-alignment. Successful application of this estimation framework is demonstrated on multiple robotic systems with different sensor configurations. This framework is released as open source software together with the datasets.

Robots that are operating for extended periods of time need to be able to deal with changes in their environment and represent them adequately in their maps. In this paper, we present a novel 3D reconstruction algorithm based on an extended Truncated Signed Distance Function (TSDF) that enables to continuously refine the static map while simultaneously obtaining 3D reconstructions of dynamic objects in the scene. This is a challenging problem because map updates happen incrementally and are often incomplete. Previous work typically performs change detection on point clouds, surfels or maps, which are not able to distinguish between unexplored and empty space. In contrast, our TSDF-based representation naturally contains this information and thus allows us to more robustly solve the scene differencing problem. We demonstrate the algorithms performance as part of a system for unsupervised object discovery and class recognition. We evaluated our algorithm on challenging datasets that we recorded over several days with RGB-D enabled tablets. To stimulate further research in this area, all of our datasets are publicly available3.

Arthur Lewis's structural change theory focused on the development shows the process of change which occurs from the traditional economic society towards the industrial economic society. The development of traditional economies in the countryside into the industrial economy in urban areas has a serious impact on equitable distribution of ongoing economic development. The villagers initially worked in the agricultural sector moved to the city then due to an absorption of labor in the industrial sector. Massive urbanization is a serious impact caused by this change, so that there is a vacant labor force in the countryside and an increase in unemployment in urban areas. The problem caused by structural changes in economic development can be overcome by the local wisdom approach of the local community. In this paper, the case is taken in Pariaman community especially the Tabuik ceremony as an annual routine. Tabuik in Pariaman requires that the community remaining in Pariaman and undergo a routine as an agricultural community because there are many special needs and rituals requiring ceremonial materials from the agricultural field. The local wisdom of people in Pariaman is not involved in significant urbanization cases. Tabuik as a control the flow of globalization makes the community and economic activities manageable as of the development in Pariaman with traditional agricultural economic models and adequate labor naturally.

Coconut milk is the main raw material needed for processing galamai. Galamai quality is strongly influenced by the quality of the milk used. In the mechanical processing of coconut milk, coconut milk produced is influenced by the amount of additional water and frequency milling. The process of adding the coconut milk in the manufacture of galamai there are two phases to dissolve “saka” and thick coconut milk to dissolve rice flour and Glutinous rice flour. Not yet known how much additional water volume and frequency of the mechanical squeezer milling machine to produce coconut milk coconut meat that is suitable for the manufacture of galamai. The research method is design completely randomized design (CRD) with two factors based on the results of the study treatment Gives the best results of treatment A2B3 (weight ratio and weight addition of grated coconut water is 1: 1 and frequency milling 3 times).

This article presents a method of probabilistically computing species sensitivity distributions (SSD) that is well-suited to cope with distinct data scarcity and variability. First, a probability distribution that reflects the uncertainty and variability of sensitivity is modeled for each species considered. These single species sensitivity distributions are then combined to create an SSD for a particular ecosystem. A probabilistic estimation of the risk is carried out by combining the probability of critical environmental concentrations with the probability of organisms being impacted negatively by these concentrations. To evaluate the performance of the method, we developed SSD and risk calculations for the aquatic environment exposed to triclosan. The case studies showed that the probabilistic results reflect the empirical information well, and the method provides a valuable alternative or supplement to more traditional methods for calculating SSDs based on averaging raw data and/or on using theoretical distributional forms. A comparison and evaluation with single SSD values (5th-percentile [HC5]) revealed the robustness of the proposed method.

An elementary step towards a quantitative assessment of the risks of new compounds or pollutants (chemicals, materials) to the environment is to estimate their environmental concentrations. Thus, the calculation of predicted environmental concentrations (PECs) builds the basis of a first exposure assessment. This paper presents a probabilistic method to compute distributions of PECs by means of a stochastic stationary substance/material flow modeling. The evolved model is basically applicable to any substance with a distinct lack of data concerning environmental fate, exposure, emission and transmission characteristics. The model input parameters and variables consider production, application quantities and fate of the compounds in natural and technical environments. To cope with uncertainties concerning the estimation of the model parameters (e.g. transfer and partitioning coefficients, emission factors) as well as uncertainties about the exposure causal mechanisms (e.g. level of compound production and application) themselves, we utilized and combined sensitivity and uncertainty analysis, Monte Carlo simulation and Markov Chain Monte Carlo modeling. The combination of these methods is appropriate to calculate realistic PECs when facing a lack of data. The proposed model is programmed and carried out with the computational tool R and implemented and validated with data for an exemplary case study of flows of the engineered nanoparticle nano-TiO"2 in Switzerland.

IbM of Mangosteen UKM group is one of Agricultural Polytechnic State of Payakumbuh’s proposal program which has aim to increase the productivity value and competitiveness also to dig up the mangosteen local food potention in line with functional food paradigm development. Prebiotic and probiotic mangosteen food technology as the food quality for life safety is one of future plan effort to dig up local food potention that is Safe, Healthy, Intact and Halal (Allowed in Islamic way). By this IbM program is expected to change people’s mindset so they have willingness, be able and can to process the mangosteen to be product which about to add economical value and various functional food using technology in processing. Mangosteen (Garcinia mangostana.L) aka Queen of tropical fruit is a fruit that has many superiorities compared to another fruits like natural colour source that can be used as food coloring, also to be utilized as anti-oxidant, anti-diarrhea, anti-cancer and others. The Methods done are field survey, Focus Group Discussion (FGD), Demonstration, illumination, training that focused to the products and methods with guidance system also effective monitoring in creating functional food products and always to be preferred. In its implementation Mitra’s group in society contributing as the business doer in IbM activity. College has role giving innovation and technology transfer and evaluation and guidance to ‘Mitra’. The result expected is prebiotic and probiotic mangosteen food products like mangosteen yogurt, Mangosteen syrup, Mangosteen-dried extract and mangosteen cake, service, journal, and book.

There are so many ways to do in reducing cholesterol concentration in blood. Yogurt and strawberry juice have ability to repair blood lipid profile at hypercholesterolemia. Research and lactobacillus development potention as the cholesterol killing probiotic that to be mixed with strawberry juice is necessary to do to support community health. The purpose of this reasearch was measuring the mixture of yogurt and strawberry juice effectiveness to repair blood lipid profile. The design of this research that to be used was experimental by pretest and posted randomized control design progam. The sample of this research was thirty white mice (Rattus Norvegicus). Cholesterol Total, HDL and LDL to be analyzed by WI-M-KK2 BLK-SB methode (Clinical Chemstry Auto Analyzer) using tool Selectra E Auto Analyzer. The data was analyzed by Anova test, continued by LSD test using computer program . The result after giving mixture of yogurt and strawberry juice with the dosage 2ml and 3ml per day could obviously reduce LDL Level (p 0,05). The differentiation in dosis giving showed unmeaning reduction result (p>0,05). Conclusion, the mixture of yogurt and strawberry juice could repair blood lipid profile, where it’s proved to reduce drastically choleterol Total Level, LDL in 30 days.