Mapping Policy Research Articles

Post-MORDM: Mapping policies to synthesize optimization and robustness results for decision-maker compromise

This paper introduces post-MORDM, a decision-support framework that augments Many Objective Robust Decision Making (MORDM). MORDM often creates an intractable number of environmental management policies, characterized by decision variable, objective, and robustness values. This large number of policies inhibits decision support, causing disagreements among decision-makers. Post-MORDM addresses these challenges via the Self-Organizing Map (SOM), synthesizing MORDM data as layers organized in a map-like coordinate system. It uses the SOM to cluster policies, discover salient characteristics, and assess cause-effect relationships between decision-maker choices (i.e. decision variable values) and performance (objective and robustness values). Overall, the goal of post-MORDM is to create a structured platform that encourages negotiation and compromise. We demonstrate post-MORDM with a case study of two illustrative decision-makers for reservoir operation policy in the Colorado River Basin, USA. Post-MORDM helps communicate tradeoffs between storage and delivery objectives, relate tradeoffs to shortage policies, and identify mutually feasible policies.

EnforceSNN: Enabling resilient and energy-efficient spiking neural network inference considering approximate DRAMs for embedded systems.

Spiking Neural Networks (SNNs) have shown capabilities of achieving high accuracy under unsupervised settings and low operational power/energy due to their bio-plausible computations. Previous studies identified that DRAM-based off-chip memory accesses dominate the energy consumption of SNN processing. However, state-of-the-art works do not optimize the DRAM energy-per-access, thereby hindering the SNN-based systems from achieving further energy efficiency gains. To substantially reduce the DRAM energy-per-access, an effective solution is to decrease the DRAM supply voltage, but it may lead to errors in DRAM cells (i.e., so-called approximate DRAM). Toward this, we propose EnforceSNN, a novel design framework that provides a solution for resilient and energy-efficient SNN inference using reduced-voltage DRAM for embedded systems. The key mechanisms of our EnforceSNN are: (1) employing quantized weights to reduce the DRAM access energy; (2) devising an efficient DRAM mapping policy to minimize the DRAM energy-per-access; (3) analyzing the SNN error tolerance to understand its accuracy profile considering different bit error rate (BER) values; (4) leveraging the information for developing an efficient fault-aware training (FAT) that considers different BER values and bit error locations in DRAM to improve the SNN error tolerance; and (5) developing an algorithm to select the SNN model that offers good trade-offs among accuracy, memory, and energy consumption. The experimental results show that our EnforceSNN maintains the accuracy (i.e., no accuracy loss for BER ≤ 10−3) as compared to the baseline SNN with accurate DRAM while achieving up to 84.9% of DRAM energy saving and up to 4.1x speed-up of DRAM data throughput across different network sizes.

(Offloading) QoE-Aware Application Mapping and Energy-Aware Module Placement in Fog Computing + Offloading

Fog computing is a potential solution for the Internet of Things in close connection with things and end-users. Fog computing will easily transfer sensitive data without delaying distributed devices. Moreover, fog computing is more in real-time streaming applications, sensor networks, IoT which need high speed and reliable internet connectivity. Due to the heterogeneous and distributed characteristics, finley distributing the task with computation offloading is a challenging task. Developing an efficient QoE-aware application mapping policy is challenging due to the different user interests. The energy consumption would usually increase after such an algorithm and policy are implemented. In this paper, we enhanced the future from the previous QoE paper by proposing a computation offloading algorithm. The proposed algorithm is to prevent overloading on fog devices. Our proposed solution has been evaluated and compared with other existing solutions, the results show that our proposed solution performs better in terms of execution time, energy consumption, and network usage.

Bengkulu Coffee Production Development

As an agricultural community, in general, Bengkulu province has gardening. The largest plantations for the community are oil palm, rubber and coffee commodities. People depend their lives on abundant natural potentials but have not been able to manage them properly to stimulate added value. All potentials are still managed traditionally so that productivity is very low. The province of Bengkulu is the 3rd largest national coffee producer, but so far Bengkulu coffee is sold through Lampung and Palembang so it is not known outside. The problems are: The low income and welfare of the coffee farmers in Bengkulu province; The promotion and branding of Bengkulu coffee has not been recognized either nationally or internationally. The intention of compiling the Bengkulu coffee development master plan. One of the activities carried out is conducting research or mapping potential and mapping policy directions in the field of coffee development in Bengkulu Province. The aim of compiling the Bengkulu coffee development master plan a) Increasing the income of coffee farmers in Bengkulu province, b) Increasing the promotion and branding of Bengkulu Coffee at the national and international levels c) Increasing the knowledge of farmers and field officers towards coffee cultivation technology farmers: d) Increasing the role and function of farmer institutions in plantation areas coffee; e) Preparing technological innovations to increase coffee production and quality; The scope of activities is focused on coffee development in the Bengkulu area from upstream, production, downstream and provincial, district / city governments as well as Stake Holders / stakeholders.

ARCH-GARCH Analysis: An Approach to Determine The Price Volatility of Red Chili

Red chili is an agricultural commodity with high price volatility. Several previous studies stated that volatility was caused by weather effect on red chili production and shocks on public consumption. However, the other research stated that volatility was caused by the government’s import of red chili. This research aimed to analyze the price volatility of red chili in Semarang Regency on January 2019 to February 2020. The ARCH-GARCH method was applied in this study. This research showed that the price volatility of red chili occurred at the beginning, middle, and end of the year due to climate change, changes in public consumption patterns on religious holidays, and oversupply. However, the prevalence of Indonesia’s imports of red chili did not affect the price volatility. The government is suggested to implement a mapping policy and planting patterns to ensure the supply of red chili.

Data Classification and Access Control in Indonesia One Map Policy Geoportal

Indonesia has a data access policy on one map policy geoportal. Users at each level of government have different access rights to data. The President has full rights to view and downloads the data, while the governor or regent can only see certain attributes and download only its region on the same data. Data in one map policy geoportal must be able to facilitate the differences in access rights. The one map policy geoportal uses a service query system to facilitate the classification of access to process view data. In the process of downloading data geoportal using database queries. The choice of download data through the database is based on the results of the test download data from the database and service. These results indicate that downloading data through the database is faster than downloading data through a service.

A Fast Precision Tuning Solution for Always-On DNN Accelerators

Due to the non-volatility nature of Resistive RAM (ReRAM), dynamic operations in the arrays contribute to a much larger portion of power in ReRAM based neural networks than static power. To reduce the dynamic power of in-situ operations with neural parameters, precisiontuning is considered a viable approach of approximate computing to trade-off excessive computation exactness for power and efficiency gains. However, the switching overhead of precision tuning in hardware severely impacts its effectiveness when the systems need to quickly react to the change of environment, user constraint or input quality. This work for the first time investigates the feasibility of agile precision tuning for neural network accelerators to benefit from approximate computing. The proposed Computing in Memory (CiM) CNN accelerators fully utilize the normally-off characteristics of memristor crossbars to achieve instant network precision tuning without worrying about the model reloading penalty. The ReRAM-based accelerator, with the proposed neural parameter mapping policy and the novel mixed-model training method, induces negligible precision-switching latency and power consumption when compared with traditional variable precision accelerators. In evaluation with state-of-the-art workloads, the proposed ReRAM DNN architecture saves 58.3%-62.47% area overhead over the baseline design. We also leverage the proposed ReRAM accelerator architecture to build a novel always-on Key-Word Spotting (KWS) system. The KWS design can switch between different precision modes to capture the relevant sound with high accuracy. The experimental results show the precisionadjustable KWS architecture saves considerable operating energy when fed with realistic test-sets of audio data.

Distributed Cost-Aware Fault-Tolerant Load Balancing in Geo-Distributed Data Centers

Many critical e-commerce and financial services running on distributed data centers require high availability. Recent surveys show that the frequency and duration of failures (either partial or complete) at a data center are increasing over the years. However, most of the literature does not address fault-tolerance in data centers. In this paper, we address the problem of load balancing in fault-tolerant data centers while minimizing the operating cost (due to energy consumption). We formally model the problem using linear optimization and propose a distributed two-stage algorithm to solve it. In the first stage, the proposed workload shifting algorithm distributes the load on a failed data center across the remaining ones with a marginal increase in the operating cost. Subsequently, the min-cost network flow model is used to derive a request mapping policy, where the quality of service (QoS) requirements are considered using a set of constraints on the delay. We evaluate the proposed algorithms using real-world data for the demand and the energy consumption cost (both brown and green energy). Results show that the proposed algorithm has low computational complexity, yet exactly gives the cost obtained by the global optimal solution.

A Parallel Deep Reinforcement Learning Framework for Controlling Industrial Assembly Lines

Decision-making in a complex, dynamic, interconnected, and data-intensive industrial environment can be improved with the assistance of machine-learning techniques. In this work, a complex instance of industrial assembly line control is formalized and a parallel deep reinforcement learning approach is presented. We consider an assembly line control problem in which a set of tasks (e.g., vehicle assembly tasks) needs to be planned and controlled during their execution, with the aim of optimizing given key performance criteria. Specifically, the aim will be that of planning the task in order to minimize the total time taken to execute all the tasks (also called cycle time). Tasks run on workstations in the assembly line. To run, tasks need specific resources. Therefore, the tackled problem is that of optimally mapping tasks and resources to workstations, and deciding the optimal execution times of the tasks. In doing so, several constraints need to be respected (e.g., precedence constraints among the tasks, constraints on needed resources to run tasks, deadlines, etc.). The proposed approach uses deep reinforcement learning to learn a tasks/resources mapping policy that is effective in minimizing the resulting cycle time. The proposed method allows us to explicitly take into account all the constraints, and, once training is complete, can be used in real time to dynamically control the execution of tasks. Another motivation for the proposed work is in the ability of the used method to also work in complex scenarios, and in the presence of uncertainties. As a matter of fact, the use of deep neural networks allows for learning the model of the assembly line problem, in contrast with, e.g., optimization-based techniques, which require explicitly writing all the equations of the model of the problem. In order to speed up the training phase, we adopt a learning scheme in which more agents are trained in parallel. Simulations show that the proposed method can provide effective real-time decision support to industrial operators for scheduling and rescheduling activities, achieving the goal of minimizing the total tasks’ execution time.

Post-Mordm: Mapping Policies to Synthesize Optimization and Robustness Results for Decision-Maker Compromise

Post-Mordm: Mapping Policies to Synthesize Optimization and Robustness Results for Decision-Maker Compromise

Shuttle-Exploiting Attacks and Their Defenses in Trapped-Ion Quantum Computers

Trapped-ion (TI) quantum bits are a front-runner technology for quantum computing. TI systems with multiple interconnected traps can overcome the hardware connectivity issue inherent in superconducting qubits and can solve practical problems at scale. With a sufficient number of qubits on the horizon, the multi-programming model for Quantum Computers (QC) has been proposed where multiple users share the same QC for their computing. Multi-programming is enticing for quantum cloud providers as it can maximize device utilization, throughput, and profit for clouds. Users can also benefit from the short wait queue. However, shared access to quantum computers can create new security issues. This paper presents one such vulnerability in shared TI systems that require <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">shuttle</i> operations for communication among traps. Repeated shuttle operations increase quantum bit energy and degrade the reliability of computations (fidelity). We show adversarial program design approaches requiring numerous shuttles. We propose a random and systematic methodology for adversary program generation. Our analysis shows shuttle-exploiting attacks can substantially degrade the fidelities of victim programs by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\approx 2 \times $ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\approx 63 \times $ </tex-math></inline-formula> . Finally, we present several countermeasures such as adopting a hybrid initial mapping policy, padding victim programs with dummy qubits, and capping maximum shuttles.

Making illegality visible: The governance dilemmas created by visualising illegal palm oil plantations in Central Kalimantan, Indonesia

This study focuses on how Indonesia’s One Map Policy renders illegal palm oil plantations in Indonesia visible and the governance dilemmas this creates. Using Central Kalimantan as a case study, we first draw on spatial data to visualise the extent of illegal palm oil plantations on forest land. The vast majority of illegal palm oil is large plantations, with illegal independent smallholdings constituting just 0.4%. We then draw on key stakeholder interviews to analyse the governance dilemmas such visualisations create. We explore stakeholder perspectives of the new Omnibus Law and other attempts to legalise illegality. Four governance scenarios that emphasise the interests of either business, smallholders, environments, or adopt a multi-stakeholder perspective are developed and measured according to their different social and ecological land use implications. In the interests of promoting sustainable and effective governance for forests, peatlands and palm oil production, we caution against the pro-business option currently favoured by the Indonesian government that aims to legalise illegal plantations and which risks the reassignment of forests for commercial production. Our article outlines alternative policy solutions, including an approach that seeks to balance business and environmental interests while also paying heed to sustainable development needs. This approach could be applied in other contexts similarly struggling with the governance dilemmas about what to do when widespread land use illegalities are made visible.

Indonesia’s 13558 islands: A new census from space and a first step towards a One Map for Small Islands Policy

Indonesia is the largest country made only of islands, with previous census identifying between 13,000 and 25,000 islands. The current official figure puts the number at 17,508 islands. Landsat satellite images were used to provide a documented consistent inventory of the country’s islands. A total of 13,558 islands were mapped considering a minimum size of about 0.001 km2-with 87% and 39% of the islands covering less than 1 and 0.01 km2 respectively. These numbers highlight the dominance of small to very small islands. Beyond providing new accurate spatial statistics, we argued that the island census could be the foundation of a One Map for Small Islands Policy. Indonesia launched its One Map Policy in 2017 to mitigate land use conflicts in the largest of the islands, but small islands have their own development, social and conservation issues, and an extension of the One Map Policy vision to Small Islands is warranted.

Desimination of Technology Assistance for the Development of Road Network Geospatial Information in Murtigading Village, Bantul

To realize sustainable village development, the village government is obliged to prepare the Village Medium-Term Development Plan (RPJM). Considering that the RPJM of Murtigading Village, Sanden District, Bantul Regency is still in the form of ordinary digital data, mapping and making maps of geospatial information for earth-oriented village infrastructure is very important in supporting integrated development planning and in accordance with the One Map Policy. This service activity aims to assist the Village Administration in mapping and developing thematic geospatial information (IGT) for roads and public infrastructure in Murtigading Village using the Quantum GIS (QGIS) version 3.10.1 program. The data used is a map of Indonesia's Earth and Quickbird imagery of Bantul Regency. This activity involved Murtigading Village Pamong, 18 Padukuhan Heads, and KPMD, both in preliminary surveys, data collection and mapping, making IGT roads and other public infrastructure with QGIS, synchronization and dissemination of geospatial information data. The results of the activity are in the form of a Geographical Information System (GIS)-based IGT map containing data on the distribution of locations, names, history, current network conditions, geometrics, and road pavements as well as public facilities in shape file (shp) format as well as thematic analog maps with a scale of 1:2000, 1:3000, 1:3,500, 1:4000, and 1:7000 which are expected to be guidelines in planning and prioritizing the development of village infrastructure that is right on target.&#x0D;

Achieving Fairness-Aware Two-Level Scheduling for Heterogeneous Distributed Systems

In a heterogeneous distributed system composed of various types of computing platforms such as supercomputers, grids, and clouds, a two-level scheduling approach can be used to effectively distribute resources of the platforms to users in the first-level, and map tasks of the users in nodes for each platform in the second-level for executing many-task applications. When scheduling heterogeneous resources, service providers of the system should consider the fairness among multiple users as well as the system efficiency. However, the fairness cannot be achieved by simply distributing an equal amount of resources from each platform to every user. In this paper, we investigate how to address the fairness issue among multiple users in a heterogeneous distributed system. We present three first-level resource allocation policies of a provider affinity first policy, an application affinity first policy, and a platform affinity based round-robin policy, and two second-level task mapping policies of a most affected first policy and a co-runner affinity based round-robin policy. Using trace-based simulations, we evaluate the performance of various combinations of the first and second level scheduling policies. Our extensive simulation results demonstrate that the first-level policy plays a crucial role to achieve relatively good fairness.

Law enforcement access to electronic evidence across borders: mapping policy approaches and emerging reform initiatives

Abstract With the ubiquity of cloud computing, criminal investigations today—including exclusively domestic ones—often require access to data across borders. However, the traditional system of cross-border legal cooperation—the Mutual Legal Assistance system—is ill-suited to this development. There is a growing consensus that this system is unsustainable and needs to be reformed or replaced with new alternatives. That is where the consensus ends, however. Despite the shared understanding of the problem and repeated calls for reform or replacement of the traditional system, there is little agreement on what these reforms or alternative approaches should look like. What one can witness instead is the proliferation of uncoordinated initiatives that could lead to further jurisdictional conflict and legal uncertainty. The purpose of the present contribution is to map and examine these various initiatives based on the approaches they follow in addressing the challenges in obtaining electronic evidence across borders—issues that are referred to broadly in this article as ‘cross-border data access [CBDA] problem’. It tries to answer two questions: what approaches can best explain the proliferation of initiatives aimed at improving law enforcement access to electronic evidence across borders? To what extent are these initiatives apt to address the CBDA problem? This article develops and distinguishes between four approaches—reformist, unilateralist, internationalist and nuanced—that can best explain the current and emerging initiatives. It then examines the suitability and sustainability of these approaches against their stated objectives and some key principles that have enjoyed extensive support in policy and academic discussions.

A Sensitive File Abnormal Access Detection Method Based on Application Classification

As the access control mode of notepad files cannot meet the requirements of risk control for sensitive file hierarchical access, this paper proposes an application classification-based detection method for abnormal access to sensitive files. The application classification and file classification, access control policy mapping, and basic and preset policy detection are designed. Combining the operating system’s identification control of different applications at runtime, we monitor the abnormal access of sensitive files by hierarchical applications. The cross-access experiment of different levels of application to different sensitive files verified the effectiveness and security of hierarchical access control strategy and sensitive file abnormal access detection and reduced the risk of disclosure of sensitive files.

WITHDRAWN: A comparative performance analysis of phase change memory as main memory and DRAM

Abstract A group of new non-volatile memory technologies are emerging, with features that make them worthy of consideration for various sections of the hierarchy of memory, including the main memory. We are discussing the state of STT-RAM, ReRAM and PCM technologies in this paper, which are three of the front runners in this new technology community. We also used Intel's binary instrumentation system Pin to simulate PCM output used as main memory and compare it to DRAM to explore three research questions. In the first instance, in the case of we evaluate a data mapping policy where the stack of an application is mapped to DRAM and the heap is mapped to PCM. we tested horizontally integrated PCM and DRAM. we notice that this mapping has no advantages in the case of my simulation since most of the stack is kept in the cache continuously, which makes the DRAM end up being unused. Furthermore, between PCM and DRAM, we compared the read latency and find an average PCM increase of 48 percent.

Improving the Lives of Indigenous Communities through Mapping: A Case Study from Indonesia

Overlapping land use and tenure uncertainties are prevalent in Indonesia. The One Map Initiative at the Village Level (Inisiatif Satu Peta di Tingkat Desa; ITUPEDE) supports Indonesia’s One Map Policy to address these problems, primarily through participatory mapping. It helped the Gajah Bertalut indigenous community to map its territories and improve land-use planning based on local wisdom. It helped make land boundaries clearer and mapped more inclusively. Agreements restricting deforestation in specified areas were enacted and adequately implemented.

UPAYA PEMERINTAH INDONESIA DALAM MENGHADAPI HEGEMONI CHINA DI KAWASAN LAUT CHINA SELATAN PADA PEMERINTAHAN JOKO WIDODO TAHUN 2016 – 2019

China claims Indonesia’s sovereign in Natuna sea, which is located next to South China Sea (SCS). China insists that part of the Natuna sea belongs to China based on Nine-Dash Line map policy which makes it a claim to the South China Sea area. The Nine-Dash Line map was formed based on historical of China’s dynasty. The China’s hegemony is also shown by the islands’ exploration and management in the South China Sea. The conflict between China and Indonesia has been going on for quite a long. It is marked by the China’s actions in sending their fishing vessels to cross the Exclusive Economic Zone (EEZ) and doing the illegal fishing in Natuna sea. There are several confrontation issues between China’s vessel and Indonesia sea in Joko Widodo’s reign, but they are undaunted. They show implicit support to Indonesia’s sovereignty. This research used descriptive method to describe the conflicts in the South China Sea. There were 2 (two) concepts and 1 (one) theory in this research. Those were the Concept of National Interest, the Concept of Sovereignty and Geopolitical Theory. The results of this reasearch showed that the government in Joko Widodo’s reign has attempts in many ways in the face of China’s hegemony that was built in South China Sea, especially in Natuna Sea area. The attempts included the President's assertive demeanor by visiting Natuna, also holding the military training and development. In fact, Indonesia continued its diplomatic attempt in carrying out its role as an honest broker to the South China Sea conflict.