Miracle-dz/UniFAR-FTUs huggingface dataset:
IOT based Home Automation using Arduino and NODEMCU

: Internet of Things (IOT) related applications have emerged as a significant field for both engineers and researchers, reflecting the magnitude and impact of data-related problems to be solved in contemporary business organizations especially in cloud computing. This paper reviews some of the technical opportunities offered and the technical challenges faced by the IoT in the house hold applications. Arduino Board is one of the significant objects in this ecosystem, where it is being used by many as an open source platform to build electronics projects. You need to understand that Arduino is a microcontroller, not a full-fledged computer, so you need to integrate the Arduino with wireless modules, motor drivers etc. for the suitable functioning of the project. For this, the IoT application should be directly loaded into the flash memory of the Arduino itself and also you should connect other wireless modules to transform it to an IoT device. In the advancement of technologies controlling and monitoring electrical appliances using laptop, computer with the help of internet connection is possible. So it gives a more space at a home, university and industrial controlling electrical appliances everywhere in the world. By using Internet of Things we can control many devices such as light, power plug, Fan, computer, security system and etc. It reduces human efforts and power efficiency. The main objective of internet of things is used to help specially challenged people and old age people to control electrical appliances and security purpose. IoT is very useful for these people in crucial situations. There are two ways to access these process WIFI connectivity (or) it is connected to a router. This process is done in low cost & controlling many devices in a simple circuit. To the Arduino, an ultrasonic sensor that detects the movement of a person is used in unison with a Piezo buzzer. An effort is being made to use a GSM module with the Arduino. This will be used to send and receive messages. A message would be sent to the authorised number whenever an unwanted movement is detected by the sensor. On detection of an unauthorised movement, first the buzzer starts sounds alarm at a particular tone and then the message is sent to the user/owner. In addition to this, an effort would be made to use a Wi-Fi module also. This Wi-Fi module is used to connect to the Internet in order to send and receive the data. The received data is projected onto a cloud based server dashboard to detect flow of intruder in the system. Lastly, a cloud storage system called pCloud.com is used to store the information, documentation and research related to this project.
Trip Characteristics Analysis of the Effects of a Travel Behavior Change Program

Travel behavior change programs (TBCP) are measures or actions specifically designed to motivate people to reduce their car use. This study contributes to the literature on rigorous evaluation of the effects of participating in TBCP. In particular, we analyze the effect of TBCP in relation to a potential for a reduction in car use. In contrast to previous studies, the effect of TBCP is analyzed considering two characteristics of the trip: travel companion and time of trip. The TBCP, consisting of three persuasion actions customized to the needs of the participants in the research, was designed and implemented in Valencia (Spain). To evaluate the effects of participating in the TBCP, a two-wave panel survey was carried out, which collected activity-travel scheduling process data from participants before and after the implementation of the TBCP. To properly identify the effects of taking part in the TBCP, participants were divided into two groups: those who were involved in the TBCP, and those who did not participate in any persuasion action (control group, CG). Descriptive and confirmatory analysis, which included the use of sample selection panel data models with treatment effects and random parameters, have been developed. The results suggest that the individuals most affected by participating in TBCP are those carrying out solo and afternoon trips. If traveling with companions, those who do so with household members are more influenced by participating in TBCP. Important research and transportation policy implications are derived from these results.
The Identity Crisis of Hard Cider

In the past 5 years, the hard cider industry in the U.S. has undergone a sudden and dramatic growth period. This boom initially revealed challenges on the cider-specific apple supply side, but issues on the hard cider demand side have also emerged. This mixed methods study conducted in Vermont, a crucial player of the U.S. hard cider industry, addresses the gaps in the literature both on the apple supply side, and on the hard cider demand side. On the apple supply side, fourteen semi-structured interviews demonstrated that neither a long-term formalized contract nor a cooperative model (the two strategic partnership mechanisms used by world’s leading industries to manage cider-specific apple production) are appropriate for the current Vermont industry context. On the hard cider demand side, cider makers expressed high interest in working under a geographical indication (GI) label to develop consumers’ hard cider literacy and increase demand. This research further indicates that GIs can act as a powerful economic development tool. Introducing hard cider GIs could address current hard cider industry issues on both the supply side and the demand side.
Generation of optical Nyquist pulses with pre-programmed rectangular-shape spectrum

Nyquist pulses, which are defined as responses of a Nyquist filter, can be used in time-division multiplexing transmission which can simultaneously achieve ultrahigh data rate and spectral efficiency. Generally, the methods of Nyquist pulse generation are based on optical Nyquist filters, optical parametric amplifier effect and electro-optical (EO) modulation. In this paper, we focus on the method of EO modulation. Traditionally the limitation of this method is the complex structure and driven signal synchronization between multiple EO modulators when cascaded EO modulators or special modulator structures are using to generate Nyquist pulses. To address this issue, we proposed a novel setup in which only one EO intensity modulator and an electrical arbitrary waveform generator (AWG) are employed. With this method, it is required less on devices. Furthermore, duty cycles of the ideal Nyquist pulses generated by this new method can be changed by using different tones number to drive the EO modulator. The duty cycles of Nyquist pulses we generated can set at 21%, 16% and 12.5% at the repetition of 2.5 GHz by programming the tones number at 2, 3 and 4 on the AWG. The narrowest pulse full width at half maximum is 50.2 ps, which the measured bandwidth is 22.5 GHz by the optical spectrum analyzer, are generated using only one EO intensity modulator with lower bandwidth down to 10 GHz. This method has a potential benefit to reduce the duty cycle further if we use a modulator with bandwidth more than 10 GHz.
Construction simulation analysis of 120m continuous rigid frame bridge based on Midas Civil

In this paper, a three-dimensional finite element model of a continuous rigid frame bridge with a main span of 120m is established by the simulation and analysis of Midas Civil software. The deflection and stress of the main beam in each construction stage of continuous beam bridge are simulated and analyzed, which provides a reliable technical guarantee for the safe construction of the bridge.
Six Cases

sis was made by histopathology . There were 6 cases of pituitary abscess . Two were rnale and 4 were female with an average age at 36.3year − old. Chief complaints were bilateral hemianopsia n 5 cases and headache and high fever {none . EnClocrinological dysfunction of pitu{tary was fbund in 3 cases . The duration from亡he onset of symptom to preoperative diagnosis ranged froIn 20 days to 8 months with an average of 4.4 months . The cause of abscess was meningitis in 2 cases , postoperative infection with pituitary adenoma in l case , sphenoid sinutitis in l case and unknown in the remaining 2 cases . For the surgical treatment, transsphenoidal pproach was chosen in 3 cases and transcranial approach in 3 cases . The confirmation of microorganism was obtained only in l case and the other 5 cases resulted in negative . The neurological deficit was all recovery postoperatively, but the endocrinopathy demonstrated no recovery . The fbllow −up period ranged from l year to 16 years with an average of 11,1years. The recurrence was not observed in any case . The mortality with pituitary abscess was high in the past decades but recent advancement of the treatment of infection e abled us to obtain no mortality as demonstrated in the present study . However , the endocrinopathy due to inflammation on pituitary glandwas irreversible , despite of that the neurological de且cits callsed by mass were well recovered . To improve the 血nctional outcome with pituitary abscess , caref ロ1 preoperative diagnosis and early surgical treatment are recommended . (Received December 11, 2000 ;accepted February 1,2001)
The Gender-Differential Effect of Financial Inclusion on Household Financial Resilience

Abstract This paper applies the kernel propensity score matching difference-in-differences method to examine gender-differential effects of financial inclusion on household financial resilience, using repeated cross-sectional data from two successive large-scale surveys of Ghanaian households. Applying standardised indices for financial inclusion and financial resilience, we find that financial inclusion significantly improves household financial resilience. Results from gender and locality disaggregated analyses suggest that the effect of financial inclusion on household resilience does not significantly vary by gender or locality. Results from different measures of financial inclusion show that savings and formal account ownership yield more pronounced resilience effect, with mobile money (m-money) exerting the least impact. Remittances via m-money – sending and receiving (a proxy for social capital) – provide significant financial resilience effects, with generally stronger effects in rural than in urban areas, especially for females.
Generating Interpretable Data-Based Explanations for Fairness Debugging using Gopher

Machine learning (ML) models, while increasingly being used to make life-altering decisions, are known to reinforce systemic bias and discrimination. Consequently, practitioners and model developers need tools to facilitate debugging for bias in ML models. We introduce Gopher, a system that generates compact, interpretable and causal explanations for ML model bias. Gopher identifies the top-k coherent subsets of the training data that are root causes for model bias by quantifying the extent to which removing or updating a subset can resolve the bias. We describe the architecture of Gopher and will walk the audience through real-world use cases to highlight how Gopher generates explanations that enable data scientists to understand how subsets of the training data contribute to the bias of a machine learning (ML) model. Gopher is available as open-source software; The code and the demonstration video are available at https://gopher-sys.github. io/.
Evaluating Multi-GPU Sorting with Modern Interconnects

GPUs have become a mainstream accelerator for database operations such as sorting. Most GPU sorting algorithms are single-GPU approaches. They neither harness the full computational power nor exploit the high-bandwidth P2P interconnects of modern multi-GPU platforms. The latest NVLink 2.0 and NVLink 3.0-based NVSwitch interconnects promise unparalleled multi-GPU acceleration. So far, multi-GPU sorting has only been evaluated on systems with PCIe 3.0. In this paper, we analyze serial, parallel, and bidirectional data transfer rates to, from, and between multiple GPUs on systems with PCIe 3.0/4.0, NVLink 2.0/3.0, and NVSwitch. We measure up to 35x higher parallel P2P throughput with NVLink 3.0-based NVSwitch over PCIe 3.0. To study GPU-accelerated sorting on today's hardware, we implement a P2P-based GPU-only (P2P sort) and a heterogeneous (HET sort) multi-GPU sorting algorithm and evaluate them on three modern platforms. We observe speedups over state-of-the-art parallel CPU radix sort of up to 14x for P2P sort and 9x for HET sort. On systems with fast P2P interconnects, P2P sort outperforms HET sort up to 1.65x. Finally, we show that overlapping GPU copy/compute operations does not mitigate the transfer bottleneck when sorting large out-of-core data.
Hunting Temporal Bumps in Graphs with Dynamic Vertex Properties

Given a time interval and a graph where vertices exhibit a property of interest (PoI) dynamically, an interesting question is: where (i.e., which part of the graph) and when (i.e., which time sub-interval) does the PoI occur frequently? To our knowledge, no work has been done to answer this question to date. We address this issue in this paper. Specifically, given (i) a time interval composed of multiple time slots and (ii) a graph where each vertex either exhibits or does not exhibit the PoI in each time slot, our objective is to find a pair of a connected sub-graph and a time sub-interval (which we refer to as a temporal bump), such that the discrepancy between the numbers of times that vertices in this sub-graph exhibit and do not exhibit the PoI during this time sub-interval is maximized. Due to the NP-hardness of this problem, initially, we propose two approximation algorithms. The first one achieves a tight approximation guarantee, at the cost of a weak scalability to the number of time slots. The second one achieves a strong scalability to the number of time slots, at the price of a loose approximation guarantee. Then, we propose two heuristic algorithms that have no non-trivial approximation guarantee, but produce similar solutions with, and are considerably faster than, the two approximation algorithms. Experiments on real datasets show that, in comparison with baselines built using related existing techniques, our algorithms hunt bumps with significantly higher discrepancies, while scaling well to large graphs, and thus are more suitable for answering the aforementioned question.
Serenade - Low-Latency Session-Based Recommendation in e-Commerce at Scale

Session-based recommendation predicts the next item with which a user will interact, given a sequence of her past interactions with other items. This machine learning problem targets a core scenario in e-commerce platforms, which aim to recommend interesting items to buy to users browsing the site. Session-based recommenders are difficult to scale due to their exponentially large input space of potential sessions. This impedes offline precomputation of the recommendations, and implies the necessity to maintain state during the online computation of next-item recommendations. We propose VMIS-kNN, an adaptation of a state-of-the-art nearest neighbor approach to session-based recommendation, which leverages a prebuilt index to compute next-item recommendations with low latency in scenarios with hundreds of millions of clicks to search through. Based on this approach, we design and implement the scalable session-based recommender system Serenade, which is in production usage at bol.com, a large European e-commerce platform. We evaluate the predictive performance of VMIS-kNN, and show that Serenade can answer a thousand recommendation requests per second with a 90th percentile latency of less than seven milliseconds in scenarios with millions of items to recommend. Furthermore, we present results from a three week long online A/B test with up to 600 requests per second for 6.5 million distinct items on more than 45 million user sessions from our e-commerce platform. To the best of our knowledge, we provide the first empirical evidence that the superior predictive performance of nearest neighbor approaches to session-based recommendation in offline evaluations translates to superior performance in a real world e-commerce setting.
A Hierarchical DBSCAN Method for Extracting Microservices from Monolithic Applications

The microservices architectural style offers many advantages such as scalability, reusability and ease of maintainability. As such microservices has become a common architectural choice when developing new applications. Hence, to benefit from these advantages, monolithic applications need to be redesigned in order to migrate to a microservice based architecture. Due to the inherent complexity and high costs related to this process, it is crucial to automate this task. In this paper, we propose a method that can identify potential microservices from a given monolithic application. Our method takes as input the source code of the source application in order to measure the similarities and dependencies between all of the classes in the system using their interactions and the domain terminology employed within the code. These similarity values are then used with a variant of a density-based clustering algorithm to generate a hierarchical structure of the recommended microservices while identifying potential outlier classes. We provide an empirical evaluation of our approach through different experimental settings including a comparison with existing human-designed microservices and a comparison with 5 baselines. The results show that our method succeeds in generating microservices that are overall more cohesive and that have fewer interactions in-between them with up to 0.9 of precision score when compared to human-designed microservices.
An Accurate Visible Light Positioning System Using Regenerated Fingerprint Database Based on Calibrated Propagation Model

This paper reports the development of a practical visible light positioning (VLP) system using received signal strength. The indoor localization system is accurate and easy to train and calibrate despite using fingerprinting technique. The VLP system consists of cheap photodiode-based receiver and consumer grade LED luminaires. The impact of distance metrics used to compute the weights of the weighted $K$ -nearest neighbor (WKNN) algorithm on the localization accuracy of the VLP is investigated. Experimental results show that square chord distance is the most robust and accurate metric and significantly outperforms the commonly used Euclidean distance metric. A room-scale implementation shows that a mean error of 2.2 cm and a 90-percentile error of 4.9 cm within a 3.3 $\text {m} \times 2.1$ m 2-D floor space are achievable. However, the high localization accuracy comes at the cost of requiring 187 offline measurements to construct the fingerprint database. A method for estimating an optical propagation model using only a handful of measurements is developed to address this problem. This leads to the creation of a dense and accurate fingerprinting database through fabricated data. The performance of the VLP system does not degrade noticeably when the localization is performed with the fabricated data. A mean error of 2.7 cm and a 90-percentile error of 5.7 cm are achievable with only 12 offline measurements.
Faster and Better Solution to Embed Lp Metrics by Tree Metrics

Hierarchically Separated Tree (HST) is the most popular solution to embed a metric space into a tree metric. By using HSTs, many optimization problems, which are hard on defined metrics, become easier to get good approximation bounds with respect to the effectiveness, e.g., task assignment, trip planning, and facility location planning. Existing work focuses on constructing HSTs for arbitrary metric spaces, which makes a general-purpose algorithm take at least O(n2)-time to get tight distortion guarantees O(log(n)). Here, distortion is a prevalent measurement of HSTs' effectiveness and usability. However, we observe that (1) in many applications that HSTs are applied, only Lp metrics are used (e.g., Euclidean space), (2) the state-of-the-art solution is still time-consuming to construct HSTs for large-scale data, and (3) distortions of existing algorithms are only satisfactory for high-dimensional data. Thus, in this paper, we are motivated to study the Embedding Lp metrics through Tree metrics (ELT) problem. We aim to design a faster algorithm than O(n2) time to construct HSTs with not only O(log(n)) distortion guarantees but also good and robust empirical results. Specifically, we first present a divide-and-conquer based general framework and prove that it has a distortion guarantee of O(log(n)). To achieve a better time complexity than O(n2), we next design two optimization techniques: reducing to nearest neighbor search (by indexing) and sampling. Finally, extensive experiments demonstrate that our algorithm DCsam outperforms the state-of-the-art algorithms by a large margin in terms of both distortion and running time.
Exploring self-tracking practices for those with lived experience of bipolar disorder: Learning from combined principles of Patient and Public Involvement and HCI

Bipolar Disorder (BD) is a complex, cyclical and chronic mental illness where self-tracking is central to self-management. Mobile technology is often leveraged to support this. Limited research has investigated the everyday practices of self-tracking for BD, and it is unclear how the normative ontology that is seen in existing self-tracking technology discourses (e.g. the Quantified Self movement) is applicable to the domain of mental health. Combining principles of Patient and Public Involvement (PPI)—a staple research design principle in mental healthcare—with design and HCI-oriented research approaches, we conducted interviews and workshops with people with lived experience of BD to explore reasons and methods for self-tracking, and challenges and opportunities for technology. Our results describe recommendations for the design of self-tracking mental health technology. We also reflect upon the complex role of researchers working at the intersection of emerging mental health technologies, the principles of PPI, and HCI research.
SIEVE: A Space-Efficient Algorithm for Viterbi Decoding

Can we get speech recognition tools to work on limited-memory devices? The Viterbi algorithm is a classic dynamic programming (DP) solution used to find the most likely sequence of hidden states in a Hidden Markov Model (HMM). While the algorithm finds universal application ranging from communication systems to speech recognition to bioinformatics, its scalability has been scarcely addressed, stranding it to a space complexity that grows with the number of observations. In this paper, we propose SIEVE (Space Efficient Viterbi), a reformulation of the Viterbi algorithm that eliminates its space-complexity dependence on the number of observations to be explained. SIEVE discards and recomputes parts of the DP solution for the sake of space efficiency, in divide-and-conquer fashion, without incurring a time-complexity overhead. Our thorough experimental evaluation shows that SIEVE is highly effective in reducing the memory usage compared to the classic Viterbi algorithm, while avoiding the runtime overhead of a naïve space-efficient solution.