Miracle-dz/UniFAR-FTUs huggingface dataset:
Advances in Information Provision from Wireless Sensor Networks for Irrigated Crops

Current advances in connected sensor technologies for near real-time environmental monitoring are transforming the quality of information provision to land managers. This “Third Industrial Revolution” that connects digital sensor data analytics with adaptive services aims to transform data processing for timely decision support. The information is needed to improve irrigation scheduling, because global demand for food relies heavily on irrigation and global freshwater resources are diminishing. Previously, practitioners used visual indicators, infrequent measurements or predictive water balance models to estimate irrigation schedules. Visual indicators and infrequent measurements are approximate, and predictive models require many inputs so that likely cumulative errors cause inaccuracies in scheduling. In contrast, wireless sensor networks enable near real-time continuous measurement of soil moisture at targeted positions providing the site-specific information required for precision irrigation scheduling and efficient freshwater management. This paper describes and compares the structure, build and implementation of Crossbow, DigiMesh, and LoRa systems to deliver information on spatio-temporal soil water status and crop stress to practitioners over smart phones and webpages to improve management of irrigated land. Our study found that the newer LoRA system has advantages over the other systems, especially on flat land, with furthest node range of >10 km and advanced communication protocols that can penetrate dense vegetation. The mesh networking of the DigiMesh and Crossbow systems was preferred in hilly terrain to communicate around hills, and allows easy expansion of the network. The Crossbow system is simpler to install but presents difficulties for third party sensor integration. All systems allowed a step change in our ability to track dynamic changes in soil hydraulic properties and crop stress, to improve irrigation water use efficiency.
Programming in Natural Language: Building Algorithms from Human Descriptions

Our work is where the Software Engineering meets the Human Computer Interaction and the End User Programming to aim for a major breakthrough by making machines programmable in ordinary and unrestricted language. In this paper, we provide a solution on how new algorithms can be recognized and learned from human descriptions. Our focus is to improve the interaction between humans and machines and enable the end user to instruct programmable devices, without having to learn a programming language. In a test-driven development, we created a platform that allows users to manipulate spreadsheet data by using natural language. Therefore, the system (i) enables end users to give instructions step-by-step, to avoid the complexity in full descriptions and give directly feedback of success (ii) creates an abstract meta model for user input during the linguistic analysis and (iii) independently interprets the meta model to code sequences that contain loops, conditionals, and statements. The context then places the recognized program component in the history. In this way, an algorithm is generated in an interactive process. One of the result can be the code sequence for algorithm, like well-known selection sort. We present a series of ontology structures for matching instructions to declare variables, loop, make decisions, etc. Furthermore, our system asks clarification questions when the human user is ambiguous. During the evaluation, 11 undergraduate students were asked to solve tasks by using natural language, and describe algorithms in three classes of complexity. Overall, the system was able to transform 60% of the user statements into code. Far from perfect, this research might lead to fundamental changes in computer use. Rather than merely consuming software, end users of the ever-increasing variety of digital devices could develop their own programs, potentially leading to novel, highly personalized, and plentiful solutions.
Analysis of flexible supported industrial robot on terminal accuracy

To meet comprehensive performance requirements of large workspace, lightweight, and low energy consumption, and flexible supported industrial robots emerge, which are usually composed of a six-degrees-of-rotational-freedom (6R) industrial robot and a flexible support. Flexible support greatly expands the motion range of the attached industrial robot. Flexible supported industrial robots have been adopted in surface coating of large structures such as aircrafts and rockets. However, the rigid–flexible coupling exists in these robot systems. When the industrial robot moves, the reaction force and torque of the robot disturb the flexible support and introduce vibration, which may result in the deterioration of the system’s terminal accuracy. This study focuses on both the robot body accuracy and system vibration suppression to improve the terminal accuracy of the flexible supported industrial robot. Firstly, based on kinematics analysis, accuracy of the industrial robot is investigated with the local conditioning index. Then, reaction force and torque ellipsoids are proposed with the deduced dynamic model to evaluate disturbances that the industrial robot applies to the flexible support. Considering these two aspects, the high-quality workspace of the flexible supported industrial robot is established. Numerical simulations show that reaction force and torque are effectively limited in the high-quality workspace, which greatly reduce the vibration energy and improve the terminal accuracy of the system.
Incorporation of Conceptual Metaphor Theory in Translation Pedagogy: A Case Study on Translating Simile-based Idioms

Acknowledging the necessity of achieving cognitive equivalence in the target culture in translation, this study attempts to demonstrate the efficacy of the incorporation of Conceptual Metaphor Theory (CMT) into the curriculum of translation classrooms for translating metaphorical language more accurately. With this aim, a case study was designed after the collection of 10 simile-based idioms constructed with white and black colours from English metaphor and idiom dictionaries. Among the 10, five idioms were with similar mapping conditions (SMC) and the other five were with different mapping conditions (DMC) with Turkish. Eighty students studying at the Department of Translation and Interpretation, Atılım University, were divided into two groups, experimental and control, and were assigned a translation task requiring them to translate 10 simile-based idioms from English to Turkish. Then, the experimental group received training on CMT and practised applying this theory for translation purposes. After the training, both groups were given a post-test and, later, the translations in the pre- and post-test were compared statistically in terms of their accuracy. The results revealed that CMT had a significant effect on achieving cognitive equivalence, especially while translating simile-based idioms with DMC, proving that CMT should become an inseparable part of translation courses.
Strengths of Exaggerated Tsunami-Originated Placenames: Disaster Subculture in Sanriku Coast, Japan

Disaster-originated placename is a kind of disaster subculture that is used for a practical purpose of identifying a location while reminding the past disaster experience. They are expected to transmit the risks and knowledge of high-risk low-frequency natural hazards, surviving over time and generations. This paper compares the perceptions to tsunami-originated placenames in local communities having realistic and exaggerated origins in Sanriku Coast, Japan. The reality of tsunami-originated placenames is first assessed by comparing the tsunami run-ups indicated in the origins and that of the tsunami in the Great East Japan Earthquake 2011 using GIS and digital elevation model. Considerable proportions of placenames had exaggerated origins, but the group interviews to local communities revealed that origins indicating unrealistic tsunami run-ups were more believed than that of the more realistic ones. We discuss that accurate hazard information will be discredited if it contradicts to the people’s everyday life and the desire for safety, and even imprecise and ambiguous information can survive if it is embedded to a system of local knowledge that consistently explains the various facts in a local area that requires explanation.
Saving Energy in Cellular IoT using Low-Power Wake-up Radios

The thesis work is based on power saving techniques to enhance the battery life of Machine Type Communication devices. The basic principle behind the introduced methods to save energy is to allow the Machine Type Communication devices only to perform its activities for a short time and go to sleep or inactive mode for rest of the time. The existing techniques save a part of device energy consumption which increases the battery life to some extent. To further improve the battery life of these devices, a low power Wake-up Receiver (WRx) is introduced additionally to wake up the power-hungry main receiver when data transfer is required. We started our analysis by evaluating the average power consumption for the existing solutions and compare them with the scenario where the wake-up receiver is integrated into the device. Different possibilities of the design of wake up signal for a wake-up receiver are studied and proposals are made for its specific use and it needs to be accommodated within the operating bandwidth in accordance with the Long Term Evolution signal parameters. The characteristics of the wake-up receiver which influences the parameters like the probability of miss, detection and false alarm are simulated with the wake-up signal designed and the results show a saving up to ninety-six percent of energy when compared to the reference cases considered in this thesis.
Energy Proportional Neural Network Inference with Adaptive Voltage and Frequency Scaling

This research presents the extension and application of a voltage and frequency scaling framework called Elongate to a high-performance and reconfigurable binarized neural network. The neural network is created in the FPGA reconfigurable fabric and coupled to a multiprocessor host that controls the operational point to obtain energy proportionality. Elongate instruments a design netlist by inserting timing detectors to enable the exploitation of the operating margins of a device reliably. The elongated neural network is re-targeted to devices with different nominal operating voltages and fabricated with 28 nm (i.e., Zynq) and 16nm (i.e., Zynq Ultrascale) feature sizes showing the portability of the framework to advanced process nodes. New hardware and software components are created to support the 16nm fabric microarchitecture and a comparison in terms of power, energy and performance with the older 28 nm process is performed. The results show that Elongate can obtain new performance and energy points that are up to 86 percent better than nominal at the same level of classification accuracy. Trade-offs between energy and performance are also possible with a large dynamic range of valid working points available. The results also indicate that the built-in neural network robustness allows operation beyond the first point of error while maintaining the classification accuracy largely unaffected.
Using Response Surface Methodology to Optimise Factors in Conjoint Experiments

Identifying relevant attributes or variables is the first objective of conjoint analysis in market research. As a result of technological development, today it is common for researchers to use sequential experimental methods for adjusting design factors in successive phases. In particular, in the field of consumer behaviour these models are used predominantly for assessing subjective perceptions relating to the attributes of different products with high sensorial components (e.g. food, drinks and personal care products). This paper illustrates the use of response surface methodology in conjoint experiments, allowing sequential research in which the evaluation of a choice set determines the weight of factors in the next choice set and continues until the optimum combination is achieved. To this end we have carried out a computer simulation to determine the optimal combination of ingredients for a sauce. The simulation shows that the model needs only a few steps to reach the optimal combination of ingredients. This result indicates that response surface methodology can be considered a useful tool in the field of market research and, in particular, in studies on consumer behaviour.
Analisis Perbandingan Konsumsi Energi dan Masa Hidup Jaringan pada Protokol LEACH, HEED, dan PEGASIS di Wireless Sensor Network

Wireless Sensor Network or WSN is a collection of nodes in the form of sensors which establish a network wirelessly. The function of WSN is to collect scattered data that is obtained from sensors which will be transmitted via Ad-Hoc communication to Base Station for further processing. In implementing it, WSN has several problems that occur. One of the problems is the energy consumption and lifetime of the sensor node which the sensor node uses energy only from the battery for its operation. In fact, on its application, the data is not taken once. Thus, it needs energy savings to reduce energy consumption, and the amount of enough energy to make the sensor nodes has a longer lifetime. One of the solutions to overcome the problem is to design a protocol from WSN so that sensor nodes can transmit data to Base Station effectively. In this final project, the simulation was done using three protocols, namely Low Energy Adaptive Clustering Hierarchy (LEACH), Hybrid Energy Efficient Distribute (HEED), and Power-Efficient Gathering in Sensor Information Systems (PEGASIS). The LEACH and HEED protocols used clustering method while PEGASIS used chain method. The analysis performed on these three protocols was energy consumption and network lifespan using the same parameters of Zigbee Xbee Pro and simulated on Matlab R2013a.
Event news detection and citizens community structure for disaster management in social networks

Purpose Nowadays, the event detection is so important in gathering news from social media. Indeed, it is widely employed by journalists to generate early alerts of reported stories. In order to incorporate available data on social media into a news story, journalists must manually process, compile and verify the news content within a very short time span. Despite its utility and importance, this process is time-consuming and labor-intensive for media organizations. Because of the afore-mentioned reason and as social media provides an essential source of data used as a support for professional journalists, the purpose of this paper is to propose the citizen clustering technique which allows the community of journalists and media professionals to document news during crises. Design/methodology/approach The authors develop, in this study, an approach for natural hazard events news detection and danger citizen’ groups clustering based on three major steps. In the first stage, the authors present a pipeline of several natural language processing tasks: event trigger detection, applied to recuperate potential event triggers; named entity recognition, used for the detection and recognition of event participants related to the extracted event triggers; and, ultimately, a dependency analysis between all the extracted data. Analyzing the ambiguity and the vagueness of similarity of news plays a key role in event detection. This issue was ignored in traditional event detection techniques. To this end, in the second step of our approach, the authors apply fuzzy sets techniques on these extracted events to enhance the clustering quality and remove the vagueness of the extracted information. Then, the defined degree of citizens’ danger is injected as input to the introduced citizens clustering method in order to detect citizens’ communities with close disaster degrees. Findings Empirical results indicate that homogeneous and compact citizen’ clusters can be detected using the suggested event detection method. It can also be observed that event news can be analyzed efficiently using the fuzzy theory. In addition, the proposed visualization process plays a crucial role in data journalism, as it is used to analyze event news, as well as in the final presentation of detected danger citizens’ clusters. Originality/value The introduced citizens clustering method is profitable for journalists and editors to better judge the veracity of social media content, navigate the overwhelming, identify eyewitnesses and contextualize the event. The empirical analysis results illustrate the efficiency of the developed method for both real and artificial networks.
Automatically ‘Verifying’ Discrete-Time Complex Systems through Learning, Abstraction and Refinement

Precisely modeling complex systems like cyber-physical systems is challenging, which often renders model-based system verification techniques like model checking infeasible. To overcome this challenge, we propose a method called LAR to automatically ‘verify’ such complex systems through a combination of learning, abstraction and refinement from a set of system log traces. We assume that log traces and sampling frequency are adequate to capture ‘enough’ behaviour of the system. Given a safety property and the concrete system log traces as input, LAR automatically learns and refines system models, and produces two kinds of outputs. One is a counterexample with a bounded probability of being spurious. The other is a probabilistic model based on which the given property is ‘verified’. The model can be viewed as a proof obligation, i.e., the property is verified if the model is correct. It can also be used for subsequent system analysis activities like runtime monitoring or model-based testing. Our method has been implemented as a self-contained software toolkit. The evaluation on multiple benchmark systems as well as a real-world water treatment system shows promising results.
SemiMap: A Semi-Folded Convolution Mapping for Speed-Overhead Balance on Crossbars

Crossbar architecture has been widely used in neural network (NN) accelerators, involving conventional and emerging devices. It performs well on the fully connected layer through efficient vector–matrix multiplication. Whereas, the advantages degrade on the convolutional layer with huge data reuse, since the execution speed and resource overhead are imbalanced when using existing fully unfolded or fully folded mapping strategy. To address this issue, we propose a novel semi-folded mapping (SemiMap) framework for implementing the convolution on crossbars. It simultaneously folds the physical resources along the row dimension of feature maps (FMs) and unfolds them along the column dimension. The former reduces the resource overhead, and the latter maintains the parallelism. An FM slicing scheme is further proposed to enable the processing of large-size image. Via our mapping framework, a row-by-row streaming pipeline for intraimage dataflow and periodical pipeline for interimage dataflow are easy to be obtained. To validate the idea, we build a many-crossbar architecture with several designs to guarantee the overall functionality and performance. Based on the measurement data of a fabricated chip, a mapping compiler and a cycle-accurate simulator are developed for the hardware simulation of large-scale networks. We evaluate the proposed SemiMap on various convolutional NNs across different network scale. ${>} 35 {\times }$ resource saving and several hundred times cycle reduction are demonstrated compared to the existing fully unfolded and fully folded strategies, respectively. This paper jumps out of the current extreme mapping schemes, and provides a balanced solution on how to efficiently deploy the computational graphs with data reuse on many-crossbar architecture.
Analysis of Recent Advances and Industrial Applications of PWM DC-DC Converters

This paper presents an inclusive review of recent advancements in PWM dc/dc converters. Pulse dc/dc converters are classified into two major groups galvanically isolated and galvanically non-isolated. The latter is further classified into buck, boost and boost-buck types. Galvanically isolated is classified into 5 major types such as forward, flyback, push-pull, half-bridge, bridge type. In this paper the advancements of Forward Converters without galvanic input-output separation, Forward Converters with galvanic input-output separation, Boost converter that steps up voltage, Push-pull converters and Cuk Converters have been discussed. These types of converters are mainly used for abridging power supply systems, segregating primary and secondary circuit and matching load with power supply.
Machine Learning algorithms in Health Questionnaires: Multiple Correspondence Analysis and Classification model

This paper describes the development of a unique multiple disease classification tool to expose and pre-screen for chronic diseases and can be applied to individual surveys. Through data mining of NHANES questionnaires, a transparent and straightforward model is developed which be could apply to future survey data. We show machine learning and dimensional reduction can be beneficial to survey data to determine the risk of multiple chronic diseases in individuals surveyed. The results are used to create a prototype tool that can predict the presence of multiple chronic diseases through fundamental questions. The number of questions are reduced to 5 questions to achieve an acceptable result. The researchers know of no tool currently available that delivers this kind of functionality.
Accounting Information System (AIS) AlignmentAnd Non-Financial Performance In Small Firms

The objective of this research is to investigate the effect of Accounting Information System (AIS) alignments on non-financial performance in Small and Medium Enterprises (SMEs). The result of this research is expected to help the owners of SMEs to understand the importance of AIS alignment to achieve non-financial performance. AIS alignment is influenced by several factors such as: organizational characteristics, owner commitment, and organizational strategies that effect on SMEs performance. The effect of AIS alignment on performance is explored using data collected from SMEs owners in the Special Administrative Region of Yogyakarta (DIY). The result of this research shows that AIS sophistication, owner commitment, and external IT expertise have significant effects on AIS alignment. AIS alignment also has significant effect on non-financial performance.