Miracle-dz/UniFAR-FTUs huggingface dataset:
Simulation of phase-change random access memory with 35nm diameter of the TiN bottom electrode by finite element modeling

A three-dimensional finite element model for Phase-Change Random Access Memory (PCRAM) is established to simulate thermal and electrical distribution during RESET operation. The establishment of the model is highly in accordance with the manufacture of PCRAM cell in the 40nm process and the model is applied to simulate the RESET behaviors of 35 nm diameter of titanium nitride (TiN) bottom electrode in the conventional mushroom structure (MS). By the simulations of thermal and electrical distribution, the highest temperature is observed in TiN bottom electrode contactor and meanwhile the voltage of the TiN bottom electrode accounts for as high as 65 percent of the total voltage. It induces high RESET current which suggests that the thermoelectric conductivity of MS is crucial in improving the heating efficiency in RESET process. Simulation results of RESET current and high resistance distribution during RESET operation are close to the data from the actual measurement. However those two values of low resistance are slightly different, probably due to the interface resistance between Ge2Sb2Te5 (GST) and other materials and the resistance caused by microstructural defects. This work reveals the importance of the thermoelectrical properties of materials in PCRAM cells and improves the quality of PCRAM simulations in industrial application.
Reappraisal of the Glaciation of Northeastern Kansas

The glaciation of northeastern Kansas has attracted scientific attention since the mid-19th century and has played a conspicuous role for glacial stratigraphy and chronology. Since the 1990s, many studies have been conducted in Kansas, other mid-western states, and elsewhere around the world that shed additional light on the glaciation of Kansas as well as its global role. All glacial and glacially related deposits in northeastern Kansas are designated as the Independence Formation with an age range in the interval approximately 810,000 to 640,000 years ago corresponding to MIS 20-16 (latest Matuyama - early Brunhes chrons). The Independence Glaciation took place as two, large ice lobes - Minnesota and Dakota, which are interpreted as low-profile ice masses that were quite thin at their margins and advanced by surging over thawed or deformable beds. The Independence Glaciation corresponds in part with the Don Glaciation in Europe and to MIS 16, which is appropriately one of the greatest glacial phases in the entire Pleistocene Epoch. The Independence and Don glaciations represent the farthest southward expansions into their respective continental interiors.
Voltage regulation and power-saving method of asynchronous motor based on fuzzy control theory

Abstract The load rate of asynchronous motors is low, the operation efficiency is low, and the waste of electric energy is very serious. Therefore, a voltage regulation and electricity saving method for asynchronous motors based on fuzzy control theory is proposed. According to the principle of voltage regulation and power saving of asynchronous motor, the stator current and its variation of are selected as input variables, the deviation of current variation is selected as output variable, and a two-dimensional fuzzy control algorithm model is constructed. Fuzzy control theory is used for voltage regulation and power saving of asynchronous motors. The simulation results show that the proposed method is based on fuzzy control theory for voltage regulation and electricity saving of asynchronous motors; after the voltage regulation and electricity saving control, the phase current of the motor is lower, and the active power and reactive power of the motor are reduced. The power factor of the motor is higher, and the power-saving effect is remarkable.
GREEN CONSTITUTION SEBAGAI PENGUATAN NORMA HUKUM LINGKUNGAN DAN PEDOMAN LEGAL DRAFTING PERATURAN DAERAH DALAM RANGKA PRAKTIK-PRAKTIK TATA KELOLA PEMERINTAHAN YANG BAIK DI DAERAH

Abstract The purpose of this study is to formulate legal drafting a model policy formulation Regional Regulation based Green Legislation in order to develop the practices of good governance in the region through strengthening the role of the executive (such as the leading sector of legal department Sector, BLH, Central Java Regional Environment and Parliament through strategies, mechanisms, and empowerment potential, and improving procedures for preparing regulations for environment conservation attention. This study is a juridical non-doctrinal, with a qualitative approach. The method will be used through the stages of: mapping the existing condition into the formulation of legal drafting which has been used, mapping of potential executive (leading sector) and the Parliament, to evaluate regulations that have been generated , identify and evaluate the constraints faced in the policy formulation stage, identify opportunities and strategies and formulate a model formulation that can be developed. The emphasis in the context of formulation of legal drafting by the Government to produce a green-based regulation legislation. The results of research are: First, the preparation of formulations Legal Drafting regulations made by the Government in accordance with Act No. 12 of 2011 and Act No. 32 of 2004, but not all use the Paper of Academic regulation in the formulation so that the Legal Drafting Regional Regulation not meet the criteria of a good legal system. Second, participation has not been effective and not all regulation is preceded Academic Paper. Third, the constraints in the formulation of legal drafting regulations include limitations of the preparation of draft environmental preservation, the ability to assess the regulation, the ability to formulate an explicit policy, concrete , clear and operational. Kata kunci: Green Constitution , Legal Drafting
The Effects of Interspersed Retrieval Practice in Multiple-List Learning on Initially Studied Material

The forward testing effect (FTE) refers to the finding that retrieval practice of previously studied material can facilitate retention of newly studied material more than does restudy of the material. The goal of the present study was to examine how such retrieval practice affects initially studied, unpracticed material. To this end, we used two commonly applied versions of the FTE task, consisting of either three (Experiment 1) or five (Experiment 2) study lists. While study of list 1 was always followed by an unrelated distractor activity, study of list 2 (3-list version) or lists 2, 3, and 4 (5-list version) was followed by either interim restudy or retrieval practice of the immediately preceding list. After studying all lists, participants were either asked to recall the first or last study list. Results showed that, for both the three-list and five-list versions, interim retrieval practice led to a typical FTE, irrespective of whether unrelated or categorized study lists were used. Going beyond the prior work, interim retrieval practice was found to have no effect on initially studied, unpracticed material, regardless of the type of study material. The findings suggest that using interim retrieval practice as a study method can improve recall of the last studied list without incurring a cost for the initially studied material. Our results are difficult to align with the view that retrieval practice induces context change, but are consistent with the idea that retrieval practice can lead participants to employ superior encoding strategies.
Compact graph neural network models for node classification

Recent research on graph convolutional networks tend to increase the complexity and non-linearity of graph convolution operators. Many of these operators result in models exploiting a huge number of learnable parameters, that have to be tuned during the training phase. This aspect makes the application of these approaches on huge datasets challenging due to the considerable computational time required by the training phase. On the other hand, having a huge number of parameters limits the applicability of the models in many node classification problems, in particular in the semi-supervised setting where the number of samples for which the target is known is limited to a few dozen nodes. In this paper, we propose a simple and efficient operator dubbed Compact Multi-head Exponential Graph Convolution (CM-EGC). To limit the number of learnable parameters, the proposed model exploits a compact and structurally meaningful representation of the input node features resorting to truncated SVD matrix decomposition, while the expressiveness of the model is ensured by adopting a multi-head attention-based gating mechanism. We evaluated the CM-EGC on semi-supervised and fully-supervised node classification tasks considering 3 well-known benchmark datasets. The results show that even using an extremely compact model, the classification performance of the proposed approach is comparable and sometimes better than the state of the art.
A diversity personalization approach towards recommending POIs for Jeju island

In this paper, we propose a diversity personalization approach: we find out how important each user considers diversity of a travel destination when choosing where to visit. Then, we provide recommendations on tourist attractions by combining the score of the personalized diversity, predicted rating score and popularity of POI. We crawled TripAdvisor and Naver data to evaluate the proposed method. Experimental results show that the proposed method shows meaningful improvements in Recall, nDCG, and MRR in terms of top-1, top-2, and top-3 recommendations compared to several baselines.
Towards automated library migrations with error prone and refaster

Software projects use a significant number of external libraries to speed up development and deliver high-quality software in a cost-effective way. Requirements placed on external libraries can change over time and libraries may cease to be maintained or evolve in incompatible ways. Consequently, a library may no longer be fit for purpose and require replacement. Such a library migration is generally performed manually and may be complex, error-prone, and time-consuming, especially for large codebases. It follows that an automated approach to library migrations is preferred. We consider the suitability of Error Prone and Refaster, two Google tools capable of refactoring Java programs, for automated library migration. We propose extensions that improve the accuracy with which Refaster templates are matched, as well as the correctness of their replacement operations. We additionally propose a set of Error Prone extensions to facilitate rewriting method return types. Together they enable a new class of rewrite operations especially relevant to library migrations. For validation we consider a private industrial codebase as well as an actively developed open source codebase. We show that using out-of-the-box Error Prone and Refaster full migration of RxJava 2-referencing files is possible in only 35% and 0% of such files, respectively. Using the proposed extensions, we manage to fully migrate an additional 35% of the RxJava 2-referencing files in the former codebase, and an initial 39% of such files in the latter codebase.
Sybil-Based Attacks on Google Maps or How to Forge the Image of City Life

Location-based services (LBS) increasingly rely on participatory or crowd-sensed data: users voluntarily contribute data about their whereabouts and points of interest (POIs) and allow the LBS to capture the dynamically changing environment, e.g., how crowded specific places, streets, or public transportation are. Popular LBS applications do not offer strong security, less so for their participatory sensing (PS) and data contribution part. Openness favors participation and increases data, but it also makes attacks easier. Sporadic misbehavior incidents and the presumed user honesty should not be reassuring: an attacker could exploit the PS components and submit a large volume of forged data to dominate the PS-collected LBS data, locally or at a large scale. Individuals, organizations, or entire areas could be targeted, e.g., having customers diverted or causing public transportation routes or roads to appear congested. The lingering open question is whether such attacks can be perpetrated against well-established popular LBS with PS components. This paper affirms this: we investigate Google Maps, the single most popular application in this domain, and show a range of effective and scalable attacks based on very modest adversarial assumptions. We reverse-engineer the data submission process and automate attacks that craft and submit false data in volume and a targeted fashion. We collect evidence that our attacks work on POI crowdedness, traffic congestion levels, and public transportation crowdedness with extreme caution. We responsibly disclosed the attacks to Google, acknowledged them and awarded recognition. The attack methodology carries over to other LBS applications but, most importantly, raises awareness and motivates countermeasures, which we also outline here, for stronger LBS and PS security overall.
D-FEND: a diffusion-based fake news detection framework for news articles related to COVID-19

The social confusion caused by the recent pandemic of COVID-19 has been further facilitated by fake news diffused via social media on the Internet. For this reason, many studies have been proposed to detect fake news as early as possible. The content-based detection methods consider the difference between the contents of true and fake news articles. However, they suffer from the two serious limitations: (1) the publisher can manipulate the content of a news article easily, and (2) the content depends upon the language, with which the article is written. To overcome these limitations, the diffusion-based fake news detection methods have been proposed. The diffusion-based methods consider the difference among the diffusion patterns of true and fake news articles on social media. Despite its success, however, the lack of the diffusion information regarding to the COVID-19 related fake news prevents from studying the diffusion-based fake news detection methods. Therefore, for overcoming the limitation, we propose a diffusion-based fake news detection framework (D-FEND), which consists of four components: (C1) diffusion data collection, (C2) analysis of the data and feature extraction, (C3) model training, and (C4) inference. Our work contributes to the effort to mitigate the risk of infodemics during a pandemic by (1) building a new diffusion dataset, named CoAID+, (2) identifying and addressing the class imbalance problem of CoAID+, and (3) demonstrating that D-FEND successfully detects fake news articles with 88.89% model accuracy on average.
A fast unsupervised assignment of ICD codes with clinical notes through explanations

In healthcare industry a set of ICD codes are assigned to a clinical note (which can be patient visit, a discharge summary and the like) as part of medical coding process which is mandatory for medical care and patient billing. Most of this assignment task use supervised framework for which a collection of the clinical notes are a-priori labeled with ICD codes. But in lot of cases there may not be enough labeled texts, and transfer learning is not a viable option. These necessitate an unsupervised assignment of codes. In this work we propose an unsupervised method of assignment of codes to clinical notes which takes diagnosis description (DD) of a patient and assigns the most appropriate ICD codes to it. This framework employs a novel approach of finding similarity between a clinical note and a list of ICD codes, called Word Mover's Similarity (WMS), which is based on the concept of Word Mover's Distance (WMD) used to compute the distance between text documents. We also speed up our proposed algorithm by developing an approach to derive similarities with the provision of a faster computation. Moreover, using this framework one is able to explain the rationale behind the assignment codes to a DD. Lastly, we show the efficacy of our unsupervised approach on clinical notes containing 59k patient visits.
On hybrid front-haul for 5G NR mmWave indoor coverage

In this paper, we introduce a novel hybrid front-hauling approach for achieving effective 5G NR mmWave indoor coverage. The proposed hybrid front-haul architecture is characterized by the cascade of digital and analog links that ensures line-of-sight (LOS) transmissions for the indoor user equipment (UE). A novel precoder is defined to handle the corresponding cascaded cable-radio (C-R) channel. Simulation results indicate that the proposed front-hauling approach can efficiently fulfill spectral efficiency requirements of 5G NR; and the proposed precoder can work well for the cascaded C-R channel under the mmWave indoor coverage.
CHORS: hardening high-assurance security systems with trusted computing

High-assurance security systems require strong isolation from the untrusted world to protect the security-sensitive or privacy-sensitive data they process. Existing regulations impose that such systems must execute in a trustworthy operating system (OS) to ensure they are not collocated with untrusted software that might negatively impact their availability or security. However, the existing techniques to attest to the OS integrity fall short due to the cuckoo attack. We present and formally prove a novel defense against this attack. We implement it as part of an integrity monitoring and enforcement system that attests to the remote OS integrity 3.7× -- 8.5× faster than the existing integrity monitoring systems. We demonstrate its practicality by protecting a real-world eHealth application, and performing micro and macro-benchmarks.
Redundant dataflow applications on clustered manycore architectures

Increasing performance requirements in the embedded systems domain have encouraged a drift from singlecore to multicore processors. Cars are an example for complex embedded systems in which the use of multicores continues to grow. The requirements of software components running in modern cars are diverse. On the one hand there are safety-critical tasks like the airbag control, on the other hand tasks which do not have any safety-related requirements at all, for example those controlling the infotainment system. Trends like autonomous driving lead to tasks which are simultaneously safety-critical and computationally complex. To satisfy the requirements of modern embedded applications we developed a dataflow-based runtime environment (RTE) for clustered manycore architectures. The RTE is able to execute dataflow graphs in various redundancy configurations and with different schedulers. We implemented our RTE design on the Kalray Bostan Massively Parallel Processor Array and evaluated all possible configurations for three common computation tasks. To classify the performance of our RTE, we compared the non-redundant graph executions with OpenCL versions of the three applications. The results show that our RTE can come close or even surpass Kalray's OpenCL framework, although maximum performance was not the primary goal of our design.
OpenHaystack Mobile - Tracking Custom Find My Accessories on Smartphones

In 2021 OpenHaystack on macOS was the first step into liberating Apple's Find My technology to be integrated into any Bluetooth-capable device. By using custom firmware for microchips like the ESP32, it was possible to build custom trackable accessories similar to an Apple AirTag in size and functionality. OpenHaystack Mobile is a cross-platform mobile application that runs on Android and iOS. It extends previous work, which was tied to macOS, to be available to more users. It provides BLE-based location tracking functionality based on Apple's Find My network. Our app is built cross-platform capable by using the Flutter framework for the user interface and the cryptographic operations. The user experience is improved compared to a desktop-based app because it allows tracking lost valuables anywhere. Furthermore, the app allows to directly route users to lost devices with smartphone navigation apps like Google Maps.
Leveraging multimodal content for podcast summarization

Podcasts are becoming an increasingly popular way to share streaming audio content. Podcast summarization aims at improving the accessibility of podcast content by automatically generating a concise summary consisting of text/audio extracts. Existing approaches either extract short audio snippets by means of speech summarization techniques or produce abstractive summaries of the speech transcription disregarding the podcast audio. To leverage the multimodal information hidden in podcast episodes we propose an end-to-end architecture for extractive summarization that encodes both acoustic and textual contents. It learns how to attend relevant multimodal features using an ad hoc, deep feature fusion network. The experimental results achieved on a real benchmark dataset show the benefits of integrating audio encodings into the extractive summarization process. The quality of the generated summaries is superior to those achieved by existing extractive methods.
Evaluating the integration of datasets

Evaluation is a bottleneck in data integration processes: it is performed by domain experts through manual onerous data inspections. This task is particularly heavy in real business scenarios, where the large amount of data makes checking all integrated tuples infeasible. Our idea is to address this issue by providing the experts with an unsupervised measure, based on word frequencies, which quantifies how much a dataset is representative of another dataset, giving an indication of how good is the integration process. The paper motivates and introduces the measure and provides extensive experimental evaluations, that show the effectiveness and the efficiency of the approach.
Metrics to quantify software developer experience: a systematic mapping

The quality of the developers has a direct impact on the software product. Besides the code they write, an experienced developer may define architectural solutions essential to the system's maintainability. This condition is necessary to develop new features and fix defects in a timely and cost-effective manner. Some works have studied the impact of developer experience and use metrics to study the relationship with the maintainability and evolution of software. In this systematic mapping, we present a catalog of metrics that have been used to quantify developers' experience, found in 34 works. These metrics are classified by the type of experience they represent and the purpose of using them. The results showed that most of the selected studies use metrics based on counting developer activities in code.
Learning football player features using graph embeddings for player recommendation system

Football analytics is a field that has been growing incredibly over the years thanks to the improvement of technologies capturing data in sports events. Outcomes of football matches are highly affected by the in-game decisions of football manager such as defending and attacking strategies or substituting particular football players. That is why football player recommendation is an important decision making task to gain the best results from a football match. To assist the football managers in this decision making process, a system that recommends the most suitable football player to replace a certain player is proposed. Our proposed model utilizes passing information during a game to learn feature embeddings of football players. Using the learned feature embeddings, a k-nearest neighbors (k-NN) model, an XGBoost model and an artificial neural network (ANN) model are trained to recommend the most similar and suitable replacement for a football player. The novelty of this recommendation system is that learned embeddings generate high-quality representations of football players which yield high performance for player recommendation when a replacement is needed.