Miracle-dz/UniFAR-FTUs huggingface dataset:
Fine-Grained Privacy-Preserving Spatiotemporal Matching in Mobile Social Networks

With the rapid popularization of mobile smartphone and its built-in location-aware devices, people are possible to establish trust relationships with each other by matching their interests, hobbies, experiences, or spatiotemporal profiles. However, the contradiction between the possibility of personal sensitive information leaking and the growing privacy concerns of users restricts the widespread use of direct matching schemes. To addressthis problem, lots of privacy-preserving matching schemes were proposed recently years. These schemes ensure users find the perfect matcher(s) without revealing extra unnecessary personal information. And yet, at the same time, it is inevitable that they produce more computation amount and communication traffic compare with former direct matching schemes. For mobile application scenarios, it is a heavy burden since power is limited. Particularly, for spatiotemporal matching, the situation is much worse due to the number of elements in users' spatiotemporal profiles will be very large as time goes on. Another outstanding issue in spatiotemporal matching is that how to define two users are neighboring. So, how to achieve an efficient and exactly privacy-preserving spatiotemporal matching remains an open question. In this paper, we propose a fine-grained privacypreserving spatiotemporal matching in Mobile Social Networks. Our scheme decreases the spatiotemporal matching error, as well as promotes the efficiency of matchmaking. Thorough security analysis and evaluation results indicate that our scheme is effective and efficient.
Thread-Aware Adaptive Prefetcher on Multicore Systems

Most processors employ hardware data prefetching techniques to hide memory access latencies. However, the prefetching requests from different threads on a multicore processor can cause severe interference with prefetching and/or demand requests of others. The data prefetching can lead to significant performance degradation due to shared resource contention on shared memory multicore systems. This article proposes a thread-aware data prefetching mechanism based on low-overhead runtime information to tune prefetching modes and aggressiveness, mitigating the resource contention in the memory system. Our solution has three new components: (1) a self-tuning prefetcher that uses runtime feedback to dynamically adjust data prefetching modes and arguments of each thread, (2) a filtering mechanism that informs the hardware about which prefetching request can cause shared data invalidation and should be discarded, and (3) a limiter thread acceleration mechanism to estimate and accelerate the critical thread which has the longest completion time in the parallel region of execution. On a set of multithreaded parallel benchmarks, our thread-aware data prefetching mechanism improves the overall performance of 64-core system by 13% over a multimode prefetch baseline system with two-level cache organization and conventional modified, exclusive, shared, and invalid-based directory coherence protocol. We compare our approach with the feedback directed prefetching technique and find that it provides 9% performance improvement on multicore systems, while saving the memory bandwidth consumption.
A stellar flare during the transit of the extrasolar planet OGLE-TR-10b

We report a stellar flare occurring during a transit of the exoplanet OGLE-TR-10b, an event not previously reported in the literature. This reduces the observed transit depth, particularly in the u'-band, but flaring could also be significant in other bands and could lead to incorrect planetary parameters. We suggest that OGLE-TR-10a is an active planet-hosting star and has an unusually high X-ray luminosity.
Dermatomyositis Leading to Necrotizing Vasculitis: A Perfect Response to Applied Therapy

Dermatomyositis is an idiopathic inflammatory myopathy that cause skin and muscle complications. The ethiology is not understood well yet. Released cytokines including interferon and interleukins are suggested to make inflammatory responses in the skin or muscle. Muscle weakness and skin lesions including heliotrope rash, shawl sign and Gottron’s papules are the most common symptoms. A biopsy (muscle or skin) is always the most reliable method for diagnosis. Corticosteroids in association with immunosuppressive agents are used as standard treatment. The patient was a 30 years old woman who got involved with dermatomyositis for 10 years. She has been under therapy with Methotrexate, Prednisolon and Azathioprine until she came to us suffering from progressive skin lesions. Experiments and examinations were normal except the lesions and detected lipoatrophy. Because of immune cells infiltration and observations necrotizing vasculitis was diagnosed. After three month of high dose prednisolon and intravenous cyclophosphamide therapy the lesions vanished remarkable. True and immediate diagnosis gives physicians the chance not only to assess the best treatment but have adequate time to apply the procedure. However shortening the therapy and diminishing morbidity of the disease need more investigations and efforts.
Enhancing the Performance and Fairness of Shared DRAM Systems with Parallelism-Aware Batch Scheduling

Enhancing the Performance and Fairness of Shared DRAM Systems with Parallelism-Aware Batch Scheduling Onur Mutlu Thomas Moscibroda Microsoft Research Abstract In a chip-multiprocessor (CMP) system, the DRAM system is shared among cores. In a shared DRAM system, requests from a thread can not only delay requests from other threads by causing bank/bus/row-buffer conflicts but they can also destroy other threads’ DRAM-bank-level parallelism. Requests whose latencies would otherwise have been overlapped could effectively become serialized. As a result both fairness and system throughput degrade, and some threads can starve for long time periods. This paper proposes a fundamentally new approach to designing a shared DRAM controller that provides quality of service to threads, while also improving system throughput. Our parallelism-aware batch scheduler (PAR-BS) design is based on two key ideas. First, PAR-BS processes DRAM requests in batches to provide fairness and to avoid starvation of requests. Second, to optimize system throughput, PAR-BS employs a parallelism-aware DRAM scheduling policy that aims to process requests from a thread in parallel in the DRAM banks, thereby reducing the memory-related stall-time experienced by the thread. PAR-BS seamlessly incorporates support for system-level thread priorities and can provide different service levels, including purely opportunistic service, to threads with different priorities. We evaluate the design trade-offs involved in PAR-BS and compare it to four previously proposed DRAM scheduler designs on 4-, 8-, and 16-core systems. Our evaluations show that, averaged over 100 4-core workloads, PAR-BS improves fairness by 1.11X and system throughput by 8.3% compared to the best previous scheduling technique, Stall-Time Fair Memory (STFM) scheduling. Based on simple request prioritization rules, PAR-BS is also simpler to implement than STFM.
Child Care and the Labor Supply of Married Women Reduced Form Evidence

This paper empirically analyzes family demands for market and nonmarket child care services and the impact of these demands on the work effort of married women. The paper first develops a general model of child care and labor force participation. The model predicts that higher wages increase the likelihood of labor force participation and that higher costs decrease the likelihood of child care utilization. The paper then develops a three-equation, reduced-form econometric specification of the general model. The equations in the specification are estimated simultaneously using 1985 data from the Survey of Income Program Participation. The estimates reveal that the cost of market child care has a strong negative effect on the labor supply of married women.
Receptor activity-modifying protein-dependent effects of mutations in the calcitonin receptor-like receptor: implications for adrenomedullin and calcitonin gene-related peptide pharmacology

Receptor activity‐modifying proteins (RAMPs) define the pharmacology of the calcitonin receptor‐like receptor (CLR). The interactions of the different RAMPs with this class B GPCR yield high‐affinity calcitonin gene‐related peptide (CGRP) or adrenomedullin (AM) receptors. However, the mechanism for this is unclear.
A De-compositional Approach to Regular Expression Matching for Network Security Applications

Regular expressions are a very common tool for network security applications because they can match precisely and maintain high matching speed even for many simultaneous patterns. Their core feature is efficient representation as an automaton, where much of the interaction between patterns can be pre-computed and aggregated. Many algorithms have been devised to try and improve this pre-computation to not take exponential space while keeping high performance, but none has met all the requirements of fast, automated construction, small memory image, and high matching speed. We present Match Filtering, a technique for de-composing regular expressions into segments that can be matched independently, while a stateful post-processing engine filters these matches to eliminate those that do not correspond to matches of the original regular expression. Using standard CPU instructions, the post-processing engine can more efficiently represent constructs that would require a multiplicative increase in automaton states. Because the pre-processing is simple, automaton construction can be automated and fast, and because most on-line processing is done by a DFA, its matching speed is close to that of a DFA alone. We demonstrate experimentally 30× smaller, fast (seconds, not minutes) automaton construction and 43% faster matching speeds than state-of-the-art software algorithms.
Teaching about religion: A mixed methods study of teachers' attitudes, knowledge, and preparation, with a focus on Islam and Muslims

The subject of religion in public schools has always been controversial. America is a religiously diverse society, and one of the fundamental documents of this country, the Constitution of the United States, prohibits the establishment of a single religion which may seek to influence or control the ―free exercise‖ of all religions. Indeed the discussion of religion in school is very extensive. The subject includes, but is not limited to the discussion of students‘ religious expression, prayer in school, students‘ religious accommodations, the right of the student to distribute religious literature, as well as the rights of students to form religious clubs. The objective of this research study was to refine the very broad discussion of religion and religious liberties in public schools to the narrow subject of how teaching about religion is viewed in the public schools. Furthermore, given the current world‘s conflict with members of the Islamic faith and the increasing Muslim population in the United States, the study about Islam is a fundamental subject of inquiry for today‘s students who require a more global outlook. Primary to the study of whether teaching about religion is constitutional, which it is, an examination of how teachers, administrators, and school board members approach the subject, implement the policy, and prepare teachers for the task, was the focus of this research. Using a mixed methods methodology, I collected and analyzed quantitative and qualitative data from a sample total of
Want a coffee?: predicting users' trails

Twitter and Foursquare are two well-connected platforms for sharing information where growing numbers of users post location-related messages. In contrast to the longitude-latitude geotags commonly used online, e.g., on photos and tweets, new place-tags containing category information show more human-readable high-level information rather than a pair of coordinates. This grants an opportunity for better understanding users' physical locations which can be used as context to facilitate other applications, e.g., location context-aware advertisement. In this paper, we verify the assumption that users' current trails contain cues of their future routes. The results from the preliminary experiments show promising performance of a basic Markov Chain-based model.
Entropy Based Grey Relational Analysis Method for Multi- Attribute Decision Making under Single Valued Neutrosophic Assessments

In this paper we investigate multi-attribute decision making problem with single-valued neutrosophic attribute values. Crisp values are inadequate to model real life situation due to imprecise information frequently used in decision making process. Neutrosophic set is one such tool that can handle these situations. The rating of all alternatives is expressed with single-valued neutrosophic set which is characterised by truth-membership degree, indeterminacy-membership degree, and falsity-membership degree. Weight of each attribute is completely unknown to decision maker. We extend the grey relational analysis method to neutrosophic environment and apply it to multi-attribute decision making problem. Information entropy method is used to determine the unknown attribute weights. Neutrosophic grey relational coefficient is determined by using Hamming distance between each alternative to ideal neutrosophic estimates reliability solution and the ideal neutrosophic estimates un-reliability solution. Then neutrosophic relational degree is defined to determine the ranking order of all alternatives. Finally, an example is provided to illustrate the application of the proposed method.
Programming Temporally Integrated Distributed Embedded Systems

Discrete-event (DE) models are formal system specifications that have analyzable deterministic behaviors in terms of event values and time stamps. However, since time is only a modeling property, they are primarily used in performance modeling and simulation. In this paper, we extend discrete-event models with the capability of mapping certain events to physical time and propose them as a programming model, called PTIDES. We seek analysis tools and execution strategies that can preserve the deterministic behaviors specified in DE models without paying the penalty of totally ordered executions. This is particularly intriguing in time synchronized distributed systems since there is a consistent global notion of time and intrinsic parallelism among the nodes. Based on causality analysis of DE systems, we define relevant dependency and relevant orders to enable outof-order executions without hurting determinism and without requiring backtracking. For a given network characteristic, we can check statically whether deploying the model in the network can preserve the real-time properties in the specification.
Deficits in Degraded Facial Affect Labeling in Schizophrenia and Borderline Personality Disorder

Although deficits in facial affect processing have been reported in schizophrenia as well as in borderline personality disorder (BPD), these disorders have not yet been directly compared on facial affect labeling. Using degraded stimuli portraying neutral, angry, fearful and angry facial expressions, we hypothesized more errors in labeling negative facial expressions in patients with schizophrenia compared to healthy controls. Patients with BPD were expected to have difficulty in labeling neutral expressions and to display a bias towards a negative attribution when wrongly labeling neutral faces. Patients with schizophrenia (N = 57) and patients with BPD (N = 30) were compared to patients with somatoform disorder (SoD, a psychiatric control group; N = 25) and healthy control participants (N = 41) on facial affect labeling accuracy and type of misattributions. Patients with schizophrenia showed deficits in labeling angry and fearful expressions compared to the healthy control group and patients with BPD showed deficits in labeling neutral expressions compared to the healthy control group. Schizophrenia and BPD patients did not differ significantly from each other when labeling any of the facial expressions. Compared to SoD patients, schizophrenia patients showed deficits on fearful expressions, but BPD did not significantly differ from SoD patients on any of the facial expressions. With respect to the type of misattributions, BPD patients mistook neutral expressions more often for fearful expressions compared to schizophrenia patients and healthy controls, and less often for happy compared to schizophrenia patients. These findings suggest that although schizophrenia and BPD patients demonstrate different as well as similar facial affect labeling deficits, BPD may be associated with a tendency to detect negative affect in neutral expressions.
Energy efficient information collection with the ARIMA model in wireless sensor networks

This paper is aimed at saving energy in information collection in wireless sensor networks. Our method is to keep sensor nodes from transmitting redundant information, which can be predicted by the sink node. By utilizing the ARIMA model for prediction, we propose an energy efficient information collection scheme. Since the ARIMA model is capable of modeling a wide variety of complex time series by simply adjusting parameters, we believe it can be used in various applications without requiring extra human labor in model selection case by case. The effectiveness of our proposed method is demonstrated with a real application based on the MICA2 platform
Phonetic Models for Generating Spelling Variants

Proper names, whether English or non-English, have several different spellings when transliterated from a non-English source language into English. Knowing the different variations can significantly improve the results of name-searches on various source texts, especially when recall is important. In this paper we propose two novel phonetic models to generate numerous candidate variant spellings of a name. Our methods show threefold improvement over the baseline and generate four times as many good name variants compared to a human while maintaining a respectable precision of 0.68.
A new decision fusion technique for image classification

In this study, we introduce a new image classification technique using decision fusion. The proposed technique, called Meta-Fuzzified Yield Value (Meta-FYV), is based on two-layer Stacked Generalization (SG) architecture [1]. At the base-layer, the system, receives a set of feature vectors of various dimensions and dynamical ranges and outputs hypotheses through fuzzy transformations. Then, the hypotheses created by the base layer transformations are concatenated for building a regression equation at meta-layer. Experimental evidence indicates that the Meta-FYV is superior compared to one of the most successful Fuzzy SG methods, introduced by Akbas [2].
Application of fuzzy logic technology for spatial load forecasting

Utilities are required to provide reliable power to customers. In the design stages, utilities need to plan ahead for anticipated future load growth under different possible scenarios. Their decisions and designs can affect the gain or loss of millions of dollars for their companies as well as customer satisfaction and future economic growth in their territory. This paper proposes and describes the general methodology to use fuzzy logic to fuse the available information for spatial load forecasting. The proposed scheme can provide distribution planners other alternatives to aggregate their information for spatial load forecasting.
First Results from Further Experimental Comparisons between Pittsburgh and Michigan Fuzzy Classifier Systems for Mobile Robotics

This paper presents new work carried out in simulation on a performance comparison between the Michigan and Pittsburgh Fuzzy Classifier System approaches to a control problem in mobile robotics. First results from this work, combined with thoughts on our previous work, indicate that changes in the problem formulation can swing the balance between whether the Michigan or Pittsburgh approaches give the best results, in terms of fast and robust convergence on high performance solutions. In the context of the evolutionary and learning tasks overall, the changes could appear minor, in this case modifying the sensor range of the robot. However, such changes can fundamentally modify the processes of a fuzzy controller in a given environment and, thereby, the characteristics of fitness evaluations. However, these early results are not yet statistically significant, a larger experimental programme is required to achieve this.
Fault-tolerant control strategy for actuator faults using LPV techniques: Application to a two degree of freedom helicopter

Fault-tolerant control strategy for actuator faults using LPV techniques: Application to a two degree of freedom helicopter In this paper, a Fault Tolerant Control (FTC) strategy for Linear Parameter Varying (LPV) systems that can be used in the case of actuator faults is proposed. The idea of this FTC method is to adapt the faulty plant instead of adapting the controller to the faulty plant. This approach can be seen as a kind of virtual actuator. An integrated FTC design procedure for the fault identification and fault-tolerant control schemes using LPV techniques is provided as well. Fault identification is based on the use of an Unknown Input Observer (UIO). The FTC controller is implemented as a state feedback controller and designed using polytopic LPV techniques and Linear Matrix Inequality (LMI) regions in such a way as to guarantee the closed-loop behavior in terms of several LMI constraints. To assess the performance of the proposed approach, a two degree of freedom helicopter is used.
TimeAudit: Blind dissection and analysis of transaction delays in enterprise networks

Transaction delay is in many cases the most important performance metric of networked applications because it is a manifestation of both network and end host (client or server) performance and it often directly reflects the user experience. We introduce TimeAudit, a tool for enterprise network administrators that analyzes transaction delays from TCP/IP packet header traces. TimeAudit computes the transaction delays and dissects them into client, server, and network related parts, and in this way enables drilling down to the origins of the application-level performance of services. Furthermore, it applies statistical analysis to reveal patterns and anomalies from the delay data. TimeAudit relies solely on the behavior of TCP and, unlike SNMP-based management tools, for instance, it does the analysis “blindly” requiring no explicit knowledge of the applications whose traffic it analyzes. We apply it to a traffic trace captured at an enterprise network of 345 hosts and 56 servers. This exercise demonstrates that it is able to pinpointing performance trouble spots so that the enterprise network admin could take further action.
M-LEARNING PILOT AT SOFIA UNIVERSITY

Many universities have designed specialized Learning Management Systems in order to facilitate the management of education, the access to knowledge and educational resources, and the communications with all stakeholders involved. With the wide spread of mobile technologies nowadays, new challenges emerge for adapting the available systems to the demands of mobile users. The authors present the results of a pilot project of the Department of Software Engineering of Sofia University, Bulgaria, aimed at adapting to the challenges of m-learning.