Miracle-dz/UniFAR-FTUs huggingface dataset:
Arbitrary-Order Proximity Preserved Network Embedding

Network embedding has received increasing research attention in recent years. The existing methods show that the high-order proximity plays a key role in capturing the underlying structure of the network. However, two fundamental problems in preserving the high-order proximity remain unsolved. First, all the existing methods can only preserve fixed-order proximities, despite that proximities of different orders are often desired for distinct networks and target applications. Second, given a certain order proximity, the existing methods cannot guarantee accuracy and efficiency simultaneously. To address these challenges, we propose AROPE (arbitrary-order proximity preserved embedding), a novel network embedding method based on SVD framework. We theoretically prove the eigen-decomposition reweighting theorem, revealing the intrinsic relationship between proximities of different orders. With this theorem, we propose a scalable eigen-decomposition solution to derive the embedding vectors and shift them between proximities of arbitrary orders. Theoretical analysis is provided to guarantee that i) our method has a low marginal cost in shifting the embedding vectors across different orders, ii) given a certain order, our method can get the global optimal solutions, and iii) the overall time complexity of our method is linear with respect to network size. Extensive experimental results on several large-scale networks demonstrate that our proposed method greatly and consistently outperforms the baselines in various tasks including network reconstruction, link prediction and node classification.
Density Tradeoffs of Non-Volatile Memory as a Replacement for SRAM Based Last Level Cache

Increasing the capacity of the Last Level Cache (LLC) can help scale the memory wall. Due to prohibitive area and leakage power, however, growing conventional SRAM LLC already incurs diminishing returns. Emerging Non-Volatile Memory (NVM) technologies like Spin Torque Transfer RAM (STTRAM) promise high density and low leakage, thereby offering an attractive alternative for building large capacity LLCs. However these technologies have significantly longer write latency compared to SRAM, which interferes with reads and severely limits their performance potential. Despite the recent work showing the write latency reduction at NVM technology level, practical considerations like high yield and low bit error rates will result a significant loss of NVM density when these techniques are implemented. Therefore, improving the write latency while compromising on the density results in sub-optimal usage of the NVM technology. In this paper we present a novel STTRAM LLC design that mitigates the long write latency, thereby delivering SRAM like performance while preserving the benefits of high density. Based on a light-weight learning mechanism, our solution relieves LLC congestion through two schemes. Firstly, we propose write congestion aware bypass that eliminates a large fraction of writes. Despite dropping LLC hit rates which could severely degrade performance in a conventional LLC, our policy smartly modulates the bypass, overcomes the hit rate loss and delivers significant performance gain. Furthermore, our solution establishes a virtual hybrid cache that absorbs and eliminates the redundant writes, which otherwise might be repeatedly and slowly written to the NVM LLC. Detailed simulation of traditional SPEC CPU 2006 suite as well as important industry workloads running on a 4-core system shows that our proposal delivers on an average 26% performance improvement over a baseline LLC design using 8MB STTRAM, while reducing the memory system energy by 10%. Our design outperforms a similar area SRAM LLC by nearly 18%, thereby making NVM technology an attractive alternative for future high performance computing.
Citizens' Awareness, Acceptance and Use of Mobile Government Services in India: An Exploratory Research

The development of mobile technology is not only changing the way business is run, as evidenced by the fast growth of mobile commerce, but also facilitating the transformation in a way that governments deliver their services. Mobile government is one area that has received very little research, but it can have a tremendous impact on the way citizens engage with their government. This research will analyse the data gathered from 25 respondents through online survey on the various aspects of mobile government and in India and their views on some of the key questions related to the use of mobile government in the country.
Hardware Supported Permission Checks on Persistent Objects for Performance and Programmability

Non-Volatile Memory technologies are advancing rapidly and may replace DRAM in future systems. However, a key question is how programmers will use them to construct and manipulate persistent data. One possible approach gives programmers direct access to persistent memory using relocatable persistent pools that hold persistent objects which can be accessed using persistent pointers, called ObjectIDs. Prior work has shown that hardware-supported address translation for ObjectIDs provides significant performance improvement and simplifies programming, however these works did not consider the large overheads incurred to check permissions before accessing persistent objects. In this paper, we identify permission checking in hardware as a critical mechanism that must be included when translating ObjectIDs to addresses in order to simplify programming and fully benefit from hardware translation. To support it, we add a System Persistent Object Table (SPOT) to support translation and permissions checks on ObjectIDs. The SPOT holds all known pools, their physical address, and their permissions information in memory. When a program attempts to access a persistent object, the SPOT is consulted and permissions are verified without trapping to the operating system. We have implemented our new design in a cycle accurate simulator and compared it with software only approaches and prior work. We find that our design offers a compelling 3.3x speedup on average for microbenchmarks that access pools with the RANDOM pattern and 1.4x and 1.7x speedup on TPC-C and vacation, respectively, for the SEPARATE pattern.
A Study on Inappropriately Partitioned Commits — How Much and What Kinds of IP Commits in Java Projects? —

When we use code repositories, each commit should include code changes for only a single task and code changes for a single task should not be scattered over multiple commits. There are many studies on the former violation–often referred to as tangled commits–but the latter violation has been out of scope for MSR research. In this paper, we firstly investigate how much and what kinds of inappropriately partitioned commits in Java projects. Then, we propose a simple technique to detect such commits automatically. We also report evaluation results of the proposed technique.
Branching Bisimulation and Concurrent Object Verification

Linearizability and progress properties are key correctness notions for concurrent objects. This paper presents novel verification techniques for both property classes. The key of our techniques is based on the branching bisimulation equivalence. We first show that it suffices to check linearizability on the quotient object program under branching bisimulation. This is appealing, as it does not rely on linearization points. Further, by exploiting divergence-sensitive branching bisimilarity, our approach proves progress properties (e.g., lock-, wait-freedom) by comparing the concurrent to-be-verified object program against an abstract program consisting of atomic blocks. Our work thus enables the usage of well-known proof techniques for branching bisimulation to check the correctness of concurrent objects. The potential of our approach is illustrated by verifying linearizability and lock-freedom of 14 benchmark algorithms from the literature. Our experiments confirm one known bug and reveals one new bug.
Using WhatsApp to Support Communication in Teaching and Learning

Digital children are surrounded by various digital media such as Facebook (social media) and WhatsApp (instant messaging tools). Communication dynamics have been affected by these media. As a result, teaching and learning has been impacted by the emergence of these technological media. This study investigated the use of WhatsApp to support communication in teaching and learning in a small university, using questionnaires and interviews to gather data from 166 students and the 2 lecturers. The study found that when used to support communication in teaching and learning, WhatsApp enhances student-to-lecturer interaction, students-to-student interaction, student-to-lecturer intimacy and student-to-student intimacy. It also increased student’s interest in a course on condition that lecturers and other students create some sense of humour during teaching and learning on the WhatsApp platform. Due to unnecessary post by some students, it’s recommended that WhatsApp platform be improved such that group Administrators can restrict postings to groups (but viewing not block). Also it should be improved to include optional structured or unstructured conversation (especially for a group). Lecturers and students should add course related humour during interaction to sustain interest of slow paced students till they catch up.
Large-Scale Analysis of the Co-commit Patterns of the Active Developers in GitHub's Top Repositories

GitHub, the largest code hosting site (with 25 million public active repositories and contributions from 6 million active users), provides an unprecedented opportunity to observe the collaboration patterns of software developers. Understanding the patterns behind the social coding phenomena is an active research area where the insights gained can guide the design of better collaboration tools, and can also help to identify and select developer talent. In this paper, we present a large-scale analysis of the co-commit patterns in GitHub. We analyze 10 million commits made by 200 thousand developers to 16 thousand repositories, using 17 of the most popular programming languages over a period of 3 years. Although a large volume of data is included in our study, we pay close attention to the participation criteria for repositories and developers. We select repositories by reputation (based on star ranking), and we introduce the notion of active developer in GitHub (observing that a limited subset of developers is responsible for the vast majority of the commits). Using co-authorship networks, we analyze the co-commit patterns of the active developer network for each programming language. We observe that the active developer networks are less connected and more centralized than the general GitHub developer networks, and that the patterns vary significantly among languages. We compare our results to other collaborative environments (Wikipedia and scientific research networks), and we also describe the evolution of the co-commit patterns over time.
Dynamic Bike Reposition: A Spatio-Temporal Reinforcement Learning Approach

Bike-sharing systems are widely deployed in many major cities, while the jammed and empty stations in them lead to severe customer loss. Currently, operators try to constantly reposition bikes among stations when the system is operating. However, how to efficiently reposition to minimize the customer loss in a long period remains unsolved. We propose a spatio-temporal reinforcement learning based bike reposition model to deal with this problem. Firstly, an inter-independent inner-balance clustering algorithm is proposed to cluster stations into groups. Clusters obtained have two properties, i.e. each cluster is inner-balanced and independent from the others. As there are many trikes repositioning in a very large system simultaneously, clustering is necessary to reduce the problem complexity. Secondly, we allocate multiple trikes to each cluster to conduct inner-cluster bike reposition. A spatio-temporal reinforcement learning model is designed for each cluster to learn a reposition policy in it, targeting at minimizing its customer loss in a long period. To learn each model, we design a deep neural network to estimate its optimal long-term value function, from which the optimal policy can be easily inferred. Besides formulating the model in a multi-agent way, we further reduce its training complexity by two spatio-temporal pruning rules. Thirdly, we design a system simulator based on two predictors to train and evaluate the reposition model. Experiments on real-world datasets from Citi Bike are conducted to confirm the effectiveness of our model.
Analysis of Predictive Equations for Estimating Resting Energy Expenditure in a Large Cohort of Morbidly Obese Patients

The treatment of obesity requires creating an energy deficit through caloric restriction and physical activity. Energy needs are estimated assessing the resting energy expenditure (REE) that in the clinical practice is estimated using predictive equations. In the present cross sectional study, we compared, in a large cohort of morbidly obese patients, the accuracy of REE predictive equations recommended by current obesity guidelines [Harris-Benedict, WHO/FAO/ONU and Mifflin-St Jeor (MJ)] and/or developed for obese patients (Muller, Muller BC, Lazzer, Lazzer BC), focusing on the effect of comorbidities on the accuracy of the equations. Data on REE measured by indirect calorimetry and body composition were collected in 4,247 obese patients (69% women, mean age 48 ± 19 years, mean BMI 44 ± 7 Kg/m2) admitted to the Istituto Auxologico Italiano from 1999 to 2014. The performance of the equations was assessed in the whole cohort, in 4 groups with 0, 1, 2, or ≥ 3 comorbidities and in a subgroup of 1,598 patients with 1 comorbidity (47.1% hypertension, 16.7% psychiatric disorders, 13.3% binge eating disorders, 6.1% endocrine disorders, 6.4% type 2 diabetes, 3.5% sleep apnoea, 3.1% dyslipidemia, 2.5% coronary disease). In the whole cohort of obese patients, as well as in each stratum of comorbidity number, the MJ equation had the highest performance for agreement measures and bias. The MJ equation had the best performance in obese patients with ≥3 comorbidities (accuracy of 61.1%, bias of −89.87) and in patients with type 2 diabetes and sleep apnoea (accuracy/bias 69%/−19.17 and 66%/−21.67 respectively), who also have the highest levels of measured REE. In conclusion, MJ equation should be preferred to other equations to estimate the energy needs of Caucasian morbidly obese patients when measurement of the REE cannot be performed. As even MJ equation does not precisely predict REE, it should be better to plan the diet intervention by measuring rather than estimating REE. Future studies focusing on the clinical differences that determine the high inter-individual variability of the precision of the REE predictive equations (e.g., on the organ-tissue metabolic rate), could help to develop predictive equations with a better performance.
Secure Identity-Based Data Sharing and Profile Matching for Mobile Healthcare Social Networks in Cloud Computing

Cloud computing and social networks are changing the way of healthcare by providing real-time data sharing in a cost-effective manner. However, data security issue is one of the main obstacles to the wide application of mobile healthcare social networks (MHSNs), since health information is considered to be highly sensitive. In this paper, we introduce a secure data sharing and profile matching scheme for the MHSN in cloud computing. The patients can outsource their encrypted health records to cloud storage with an identity-based broadcast encryption technique, and share them with a group of doctors in a secure and efficient manner. We then present an attribute-based conditional data re-encryption construction which permits the doctors who satisfy the pre-defined conditions in the ciphertext to authorize the cloud platform to convert a ciphertext into a new ciphertext of an identity-based encryption scheme for specialist without leaking any sensitive information. Furthermore, we provide a profile matching mechanism in the MHSN based on identity-based encryption with an equality test, which helps patients to find friends in a privacy-preserving way and achieves flexible authorization on the encrypted health records with resisting the keywords guessing attack. Moreover, this mechanism reduces the computation cost on the patient side. The security analysis and experimental evaluation show that our scheme is practical for protecting the data security and privacy in the MHSN.
A Systematic Review of Renal Health in Healthy Individuals Associated with Protein Intake above the US Recommended Daily Allowance in Randomized Controlled Trials and Observational Studies

Abstract A systematic review was used to identify randomized controlled trials (RCTs) and observational epidemiologic studies (OBSs) that examined protein intake consistent with either the US RDA (0.8 g/kg or 10–15% of energy) or a higher protein intake (≥20% but <35% of energy or ≥10% higher than a comparison intake) and reported measures of kidney function. Studies (n = 26) of healthy, free-living adults (>18 y old) with or without metabolic disease risk factors were included. Studies of subjects with overt disease, such as chronic kidney, end-stage renal disease, cancer, or organ transplant, were excluded. The most commonly reported variable was glomerular filtration rate (GFR), with 13 RCTs comparing GFRs obtained with normal and higher protein intakes. Most (n = 8), but not all (n = 5), RCTs reported significantly higher GFRs in response to increased protein intake, and all rates were consistent with normal kidney function in healthy adults. The evidence from the current review is limited and inconsistent with regard to the role of protein intake and the risk of kidney stones. Increased protein intake had little or no effect on blood markers of kidney function. Evidence reported here suggests that protein intake above the US RDA has no adverse effect on blood pressure. All included studies were of moderate to high risk of bias and, with the exception of 2 included cohorts, were limited in duration (i.e. <6 mo). Data in the current review are insufficient to determine if increased protein intake from a particular source, i.e., plant or animal, influences kidney health outcomes. These data further indicate that, at least in the short term, higher protein intake within the range of recommended intakes for protein is consistent with normal kidney function in healthy individuals.
Sampling-based probabilistic inference emerges from learning in neural circuits with a cost on reliability

Neural responses in the cortex change over time both systematically, due to ongoing plasticity and learning, and seemingly randomly, due to various sources of noise and variability. Most previous work considered each of these processes, learning and variability, in isolation -- here we study neural networks exhibiting both and show that their interaction leads to the emergence of powerful computational properties. We trained neural networks on classical unsupervised learning tasks, in which the objective was to represent their inputs in an efficient, easily decodable form, with an additional cost for neural reliability which we derived from basic biophysical considerations. This cost on reliability introduced a tradeoff between energetically cheap but inaccurate representations and energetically costly but accurate ones. Despite the learning tasks being non-probabilistic, the networks solved this tradeoff by developing a probabilistic representation: neural variability represented samples from statistically appropriate posterior distributions that would result from performing probabilistic inference over their inputs. We provide an analytical understanding of this result by revealing a connection between the cost of reliability, and the objective for a state-of-the-art Bayesian inference strategy: variational autoencoders. We show that the same cost leads to the emergence of increasingly accurate probabilistic representations as networks become more complex, from single-layer feed-forward, through multi-layer feed-forward, to recurrent architectures. Our results provide insights into why neural responses in sensory areas show signatures of sampling-based probabilistic representations, and may inform future deep learning algorithms and their implementation in stochastic low-precision computing systems.
Concurrent Ranging with Ultra-Wideband Radios: From Experimental Evidence to a Practical Solution

To enable future location-aware Internet of Things (IoT) applications, Ultra-wideband (UWB) technology provides centimeter-accurate distance estimations. In the common case of a non-synchronized network, at least N·(N-1) message exchanges are required to derive the distance between N nodes. Enabling concurrent ranging between an initiator and an arbitrary number of responders can drastically reduce the amount of necessary transmissions and hence increases the efficiency of UWB systems. Although the feasibility of concurrent ranging has been proven experimentally, several key challenges still need to be addressed to practically implement concurrent ranging in real-world UWB systems, such as the automatic detection of multiple responses, the identification of a responder, as well as the detection of overlapping responses (especially in the presence of multipath components). In this paper, we provide a concurrent ranging solution tackling the aforementioned challenges. Among others, our solution enables (i) to detect responses in the CIR reliably, (ii) to encode the responder ID in the CIR to allow personalized ranging, as well as (iii) to mitigate the impact of overlapping responses and multipath components. We further show how the proposed solution increases the scalability of concurrent ranging in real-world UWB-based distributed systems.
Advances in the Development of Shape Similarity Methods and Their Application in Drug Discovery

Molecular similarity is a key concept in drug discovery. It is based on the assumption that structurally similar molecules frequently have similar properties. Assessment of similarity between small molecules has been highly effective in the discovery and development of various drugs. Especially, two-dimensional (2D) similarity approaches have been quite popular due to their simplicity, accuracy and efficiency. Recently, the focus has been shifted toward the development of methods involving the representation and comparison of three-dimensional (3D) conformation of small molecules. Among the 3D similarity methods, evaluation of shape similarity is now gaining attention for its application not only in virtual screening but also in molecular target prediction, drug repurposing and scaffold hopping. A wide range of methods have been developed to describe molecular shape and to determine the shape similarity between small molecules. The most widely used methods include atom distance-based methods, surface-based approaches such as spherical harmonics and 3D Zernike descriptors, atom-centered Gaussian overlay based representations. Several of these methods demonstrated excellent virtual screening performance not only retrospectively but also prospectively. In addition to methods assessing the similarity between small molecules, shape similarity approaches have been developed to compare shapes of protein structures and binding pockets. Additionally, shape comparisons between atomic models and 3D density maps allowed the fitting of atomic models into cryo-electron microscopy maps. This review aims to summarize the methodological advances in shape similarity assessment highlighting advantages, disadvantages and their application in drug discovery.