Miracle-dz/UniFAR-FTUs huggingface dataset:
Scheduling Low-Utilized Real-Time Systems with End-to-End Timing Constraints

End-to-end delay is one of the most important timing constraints in distributed real-time systems (DRTS) [1], especially in the area of wireless sensor network (WSN) or Internet of Things (IoT). Since we may need to collect data from sensor nodes and react immediately. Thus, tasks must be executed in a distance-constrained manner. That is, the temporal distance between any two consecutive executions of a task should always be less than a certain amount of time. In DRTS, transactions are decomposed into a group of tasks, and periodic model might not be efficient enough, since the temporal distance between two consecutive executions of task could be two times of its period in the worst case. Moreover, an execution will not be always ready in a period, which might incur extra end-to-end delay in DRTS. Pinwheel scheduling algorithms have been designed to schedule tasks with distance constraint. But meeting distance constraints in a node does not guarantee minimized end-to-end delay of a transaction. Therefore, DSr, a distributed pinwheel scheduling algorithm, focuses on reducing end-to-end delay systematically and synchronously [2]. Although the pinwheel scheduling algorithms provide simple scheduling bounds and approaches for fully utilized tasks, for the simpler sensor nodes with limited hardware timer support, it might not be easy to execute the pinwheel scheduling algorithms accordingly and guarantee the synchronous results. We find that there exists a simple feasible algorithm (e.g. First In First Out, FIFO) with tight scheduling bound. Although it is always schedulable only in low-utilized systems using FIFO, it results in shorter endto- end delays than DSr in most cases with high utilization. To simplify, we focus only on the system of two nodes with the same utilization. As shown in Figure 1, we simulate the total end-to-end delay with different number of transactions, n, and utilization, ρ, and find that FIFO outperforms DSr. That means transactions can be finished earlier using FIFO. Furthermore, we also find that the relative length of execution time effects the schedulability. As shown in Figure 2, where r stands for the largest ratio of length of execution times, the smaller r, the closer length of execution times, or the lower ρ, as in WSN, outperforms. Therefore, we believe FIFO has large potential in DRTS, especially in low-utilized DRTS, which commonly presents the case of WSN [3] or IoT.
Optimization WIFI indoor positioning KNN algorithm location-based fingerprint

Location-based services have been deep into all aspects of life and it provides a convenient and efficient service experience for people. Currently, technology is relatively mature and widely used in the outdoor positioning. By contrast, for indoor positioning, although there are a lot of hot technology, but they are mostly insufficient lead to it is hard to popularize. So how to improve the popularity of indoor positioning in the case of improve the positioning accuracy has became a hot research topoc. This paper analyzes and studies several typical fingerprint localization algorithm, including NN, KNN and WKNN, and then propose an algorithmic improvement program, it introduces signal propagation model, finds and narrows the K-gon.
Mosaic synaptopathy and functional defects in Cav1.4 heterozygous mice and human carriers of CSNB2.

Stylianos Michalakis1, Lior Shaltiel1, Vithiyanjali Sothilingam2, Susanne Koch1, Verena Schludi1, Stefanie Krause1, Christina Zeitz3,4,5, Isabelle Audo3,4,5,6,7, Marie-Elise Lancelot3,4,5, Christian Hamel8, Isabelle Meunier8, Markus N. Preising9, Christoph Friedburg9, Birgit Lorenz9, Nawal Zabouri10, Silke Haverkamp10, Marina Garcia Garrido2, Naoyuki Tanimoto2, Mathias W. Seeliger2, Martin Biel1 and Christian A. Wahl-Schott1,∗
What is literariness ? Empirical traces of reading

It is now widely maintained that the concept of "literariness" has been critically examined and found deficient. Prominent literary theorists have argued that there are no special characteristics that distinguish literature from other texts. Similarly, cognitive science has often subsumed literary understanding within a general theory of discourse processing. However, a review of empirical studies of literary readers reveals traces of literariness that appear irreducible to either of these explanatory frameworks. Our analysis of readers' responses to several literary texts (short stories and poems) indicates processes beyond the explanatory power of current inferential or situation models. Such findings suggest a three-component model of literariness involving foregrounded text features, readers' defamiliarizing responses to them, and the modification of personal meaning as a consequence. We argue that feeling, not cognition, is the primary vehicle for the processes of literary understanding. What is literariness? Empirical traces of reading What sort of activity is the reading of literature? There are several different answers to this question, depending on the theoretical commitment of the respondent. It may, for example, be explained as a type of discourse processing amenable to cognitive
Parole and the moral self: Moral change mitigates responsibility

ABSTRACT Recent studies demonstrate a moral self effect: continuity in moral values is crucial to ascriptions of identity in and over time. Since Locke, personal identity has been referred to as a ‘forensic’ concept, meaning that it plays a role in attributions of moral responsibility. If moral values are crucial to identity over time, then perceived changes in a person’s set of values may reduce responsibility for past deeds. To test this, we examined the moral self effect in parole contexts. In this empirical article, we conducted two experiments, in which participants were significantly more likely to grant parole to agents who underwent a moral change as opposed to mere behavioral change. We conclude by discussing possible objections and implications of these philosophical results for the Lockean view of personal identity.
Effects of a structured 20-session slow-cortical-potential-based neurofeedback program on attentional performance in children and adolescents with attention-deficit hyperactivity disorder: retrospective analysis of an open-label pilot-approach and 6-month follow-up

Objective The aim of this approach was to conduct a structured electroencephalography-based neurofeedback training program for children and adolescents with attention-deficit hyperactivity disorder (ADHD) using slow cortical potentials with an intensive first (almost daily sessions) and second phase of training (two sessions per week) and to assess aspects of attentional performance. Patients and methods A total of 24 young patients with ADHD participated in the 20-session training program. During phase I of training (2 weeks, 10 sessions), participants were trained on weekdays. During phase II, neurofeedback training occurred twice per week (5 weeks). The patients’ inattention problems were measured at three assessment time points before (pre, T0) and after (post, T1) the training and at a 6-month follow-up (T2); the assessments included neuropsychological tests (Alertness and Divided Attention subtests of the Test for Attentional Performance; Sustained Attention Dots and Shifting Attentional Set subtests of the Amsterdam Neuropsychological Test) and questionnaire data (inattention subscales of the so-called Fremdbeurteilungsbogen für Hyperkinetische Störungen and Child Behavior Checklist/4–18 [CBCL/4–18]). All data were analyzed retrospectively. Results The mean auditive reaction time in a Divided Attention task decreased significantly from T0 to T1 (medium effect), which was persistent over time and also found for a T0–T2 comparison (larger effects). In the Sustained Attention Dots task, the mean reaction time was reduced from T0–T1 and T1–T2 (small effects), whereas in the Shifting Attentional Set task, patients were able to increase the number of trials from T1–T2 and significantly diminished the number of errors (T1–T2 & T0–T2, large effects). Conclusion First positive but very small effects and preliminary results regarding different parameters of attentional performance were detected in young individuals with ADHD. The limitations of the obtained preliminary data are the rather small sample size, the lack of a control group/a placebo condition and the open-label approach because of the clinical setting and retrospective analysis. The value of the current approach lies in providing pilot data for future studies involving larger samples.
Anthocyanins in Black Raspberries Prevent Esophageal Tumors in Rats

Diets containing freeze-dried black raspberries (BRB) suppress the development of N-nitrosomethylbenzylamine (NMBA)–induced tumors in the rat esophagus. Using bioassay-directed fractionation, the anthocyanins in BRB were found to be the most active constituents for down-regulation of carcinogen-induced nuclear factor-κB and activator protein-1 expression in mouse epidermal cells in vitro. The present study was undertaken, therefore, to determine if the anthocyanins contribute to the chemopreventive activity of BRB in vivo. F344 rats consumed diets containing either (a) 5% whole BRB powder, (b) an anthocyanin-rich fraction, (c) an organic solvent-soluble extract (a–c each contained ∼3.8 μmol anthocyanins/g diet), (d) an organic-insoluble (residue) fraction (containing 0.02 μmol anthocyanins/g diet), (e) a hexane extract, and (f) a sugar fraction (e and f had only trace quantities of anthocyanins), all derived from BRB. Animals were fed diets 2 weeks before treatment with NMBA and throughout the bioassay. Control rats were treated with NMBA only. Animals were killed at week 30, and esophageal tumors were enumerated. The anthocyanin treatments (diet groups a–c) were about equally effective in reducing NMBA tumorigenesis in the esophagus, indicating that the anthocyanins in BRB have chemopreventive potential. The organic-insoluble (residue) fraction (d) was also effective, suggesting that components other than berry anthocyanins may be chemopreventive. The hexane and sugar diets were inactive. Diet groups a, b, and d all inhibited cell proliferation, inflammation, and angiogenesis and induced apoptosis in both preneoplastic and papillomatous esophageal tissues, suggesting similar mechanisms of action by the different berry components.
Improved Evaluation Algorithm for Performance Prediction with Error Analysis

With the help of a proper performance model and evaluation algorithm, the performance metrics of information systems can be determined at the early stages of the development process. In our work, the response time and the throughput performance metrics of multi-tier Web applications have been predicted based on a queueing model. The goal of our work is to extend the mean-value analysis (MVA) algorithm according to the investigation of the thread pool. Web applications have been tested with concurrent user sessions in order to validate the proposed algorithm in different versions of ASP.NET environment. Moreover, error analysis has been performed to demonstrate the accuracy of the proposed algorithm.
Warfare in Biodiversity Hotspots

Abstract: Conservation efforts are only as sustainable as the social and political context within which they take place. The weakening or collapse of sociopolitical frameworks during wartime can lead to habitat destruction and the erosion of conservation policies, but in some cases, may also confer ecological benefits through altered settlement patterns and reduced resource exploitation. Over 90% of the major armed conflicts between 1950 and 2000 occurred within countries containing biodiversity hotspots, and more than 80% took place directly within hotspot areas. Less than one‐third of the 34 recognized hotspots escaped significant conflict during this period, and most suffered repeated episodes of violence. This pattern was remarkably consistent over these 5 decades. Evidence from the war‐torn Eastern Afromontane hotspot suggests that biodiversity conservation is improved when international nongovernmental organizations support local protected area staff and remain engaged throughout the conflict. With biodiversity hotspots concentrated in politically volatile regions, the conservation community must maintain continuous involvement during periods of war, and biodiversity conservation should be incorporated into military, reconstruction, and humanitarian programs in the world's conflict zones.
A novel method for object detection based on graph theory

Ideal object detection result in an image is an optimal free shape sub-window that tightly covers the object of interest. However, the sub-windows considered in widely-used sliding window method are limited to rectangles. This paper proposed a new graph-theoretic method which allowed the detection sub-window to be any shape for object detection. Firstly, local features responses were calculated by using locality-constrained linear coding (LLC) technique. Then the proposed method take advantage of local feature response and boundary information to construct an objective function for the whole image and global optimal solution is obtained by graph cut algorithm. We provided results on two challenging object detection datasets, and demonstrated that the proposed method can obtained better spatial support and higher detection precision than existing sliding window method.
Wireless sensor networks for forest environmental monitoring

In this paper we present an in-depth science-centric 20-day wireless sensor network deployment at Purple Mountain area, a forest located near city of Nanjing, for real-world forest environmental surveillance. A micro monitoring system is developed which cover the node hardware design, sensor network layout, and the capabilities for remote data access and analysis. The currently network consists 18 nodes deployed in two areas within Purple Mountain area flowing forest surveillance information into a database. This application serves for further work in a new means to monitor forest environmental using this wireless sensor networks (WSN) technology.
Subconscious performance goals: Investigating the moderating effect of negative goal-discrepancy feedback

ABSTRACT Building on theories of conscious goals and feedback, we investigated the moderating effect of negative feedback on the relation between subconscious goals and performance. In two lab experiments, we manipulated subconscious performance goals and negative feedback about personal performance as well as social comparison information. In Study 1 (n = 80), subconscious goals positively influenced performance in an attention and concentration task when participants had received no feedback and negatively when participants had been confronted with negative performance feedback. In Study 2 (n = 90), additional comparison feedback indicating a higher performance of others led to higher performance of participants with versus without subconscious performance goals. The moderating effect of feedback was visible in self-efficacy, and we found partial support for its mediating role.
Upstream Open Reading Frames Located in the Leader of Protein Kinase Mζ mRNA Regulate Its Translation

For protein synthesis that occurs locally in dendrites, the translational control mechanisms are much more important for neuronal functioning than the transcription levels. Here, we show that uORFs (upstream open reading frames) in the 5′ untranslated region (5′UTR) play a critical role in regulation of the translation of protein kinase Mζ (PKMζ). Elimination of these uORFs activates translation of the reporter protein in vitro and in primary cultures of rat hippocampal neurons. Using cell-free translation systems, we demonstrate that translational initiation complexes are formed only on uORFs. Further, we address the mechanism of translational repression of PKMζ translation, by uORFs. We observed an increase in translation of the reporter protein under the control of PKMζ leader in neuronal culture during non-specific activation by picrotoxin. We also show that such a mechanism is similar to the mechanism seen in cell stress, as application of sodium arsenite to neuron cultures induced translation of mRNA carrying PKMζ 5′UTR similarly to picrotoxin activation. Therefore, we suppose that phosphorylation of eIF2a, like in cell stress, is a main regulator of PKMζ translation. Altogether, our findings considerably extend our understanding of the role of uORF in regulation of PKMζ translation in activated neurons, important at early stages of LTP.
More than just money: Patterns of disaggregated welfare expenditure in the enlarged Europe

This article demonstrates the potential of the disaggregated expenditure approach in comparative welfare state analysis by applying it for comparing patterns of welfare spending across 28 European countries. An initial factor analysis shows that welfare states differ primarily along their emphasis either on cash transfers for the elderly or on social services and cash transfers for the working-age population. European welfare states cluster along these two spending dimensions in a way that to a great extent coincides with the well-known delineation of welfare regimes based on institutional characteristics. Furthermore, the results attest to the emergence of a variety of welfare arrangements in the post-communist region, yet with a general orientation toward a Bismarckian or conservative model. The results of this analysis demonstrate that disaggregated welfare expenditure measures retain considerable importance in elucidating the realities of contemporary welfare policy.
UKPDIPF: Lexical Semantic Approach to Sentiment Polarity Prediction in Twitter Data

We present a sentiment classification system that participated in the SemEval 2014 shared task on sentiment analysis in Twitter. Our system expands tokens in a tweet with semantically similar expressions using a large novel distributional thesaurus and calculates the semantic relatedness of the expanded tweets to word lists representing positive and negative sentiment. This approach helps to assess the polarity of tweets that do not directly contain polarity cues. Moreover, we incorporate syntactic, lexical and surface sentiment features. On the message level, our system achieved the 8th place in terms of macroaveraged F-score among 50 systems, with particularly good performance on the LifeJournal corpus (F1=71.92) and the Twitter sarcasm (F1=54.59) dataset. On the expression level, our system ranked 14 out of 27 systems, based on macro-averaged F-score.
Mobile Learning Perception of Graduate Student Teachers: A Case Study in Magister Program of Educational Technology of University of Sebelas Maret

In the context of higher education institutions in Indonesia, some of schools authority refuses the implementation of mobile learning. Although m-learning is widely used in some school in Indonesia and considered as an effective educational tool, it is not yet fully utilized in most Indonesian school. Before designing a mobile learning system, it is necessary to assess the perception of target users towards mobile learning since their perception will influence the successful the system. This research aims to investigate the perception of students of educational technology program who currently are being teachers in several level of educations towards mobile learning to ascertain their readiness to apply a mobile learning system in their teaching. The respondents of this study are 18 teachers who are being students of Magister Program on Educational Technology in e-learning & mobile learning class in even semester. This research is conducted with a survey related to implementation of mobile learning. The results show that teachers in Indonesia had positive perception and were looking forward to apply mobile learning in their teaching. Knowledge and learning method issues were not obstacles for them to use a mobile learning as well as financial obstacle. The readiness for mobile learning of teachers are vary based on the school level even though they have been taught mobile learning subject. The findings surprisingly show that accessibility of device may be a big problem on implementing mobile learning in class.
A Real-time Intrusion Detection System by Integrating Hadoop and Naive Bayes Classification

There is a meteoric rise in intrusions that penetrate highly secure networks. Furthermore, a successful intrusion serves as a motivation factor for aspiring black-hat hackers to launch their own intrusion campaigns. Security experts are now facing the complications of growing network data generated by their networks. Almost all businesses have an online platform through which multitudes of companies have reaped enormous profits. Hence, such networks that churn out terabytes/gigabytes of data (big data) remain as an indispensable asset for companies. To process the huge network traffic data we require powerful machines that are often too expensive. In this paper, we present our intrusion detection system that employs a Naive Bayes algorithm that runs in a distributed manner. The classifier uses the Apache Hadoop and HStreaming APIs to detect intrusions in real time. The network traffic data generated by non-commercial packet sniffers serve as input to our classifier. Our aim is to develop a distributed, scalable, fault tolerant and reliable system. The system is designed in such way that it can run effortlessly on a cluster comprised of obsolete hardware. The results denote that our system's performance is acceptable in real case scenarios.
APACHE IV Is Superior to MELD Scoring System in Predicting Prognosis in Patients after Orthotopic Liver Transplantation

This study aims to compare the efficiency of APACHE IV with that of MELD scoring system for prediction of the risk of mortality risk after orthotopic liver transplantation (OLT). A retrospective cohort study was performed based on a total of 195 patients admitted to the ICU after orthotopic liver transplantation (OLT) between February 2006 and July 2009 in Guangzhou, China. APACHE IV and MELD scoring systems were used to predict the postoperative mortality after OLT. The area under the receiver operating characteristic curve (AUC) and the Hosmer-Lemeshow C statistic were used to assess the discrimination and calibration of APACHE IV and MELD, respectively. Twenty-seven patients died during hospitalization with a mortality rate of 13.8%. The mean scores of APACHE IV and MELD were 42.32 ± 21.95 and 18.09 ± 10.55, respectively, and APACHE IV showed better discrimination than MELD; the areas under the receiver operating characteristic curve for APACHE IV and MELD were 0.937 and 0.694 (P < 0.05 for both models), which indicated that the prognostic value of APACHE IV was relatively high. Both models were well-calibrated (The Hosmer-Lemeshow C statistics were 1.568 and 6.818 for APACHE IV and MELD, resp.; P > 0.05 for both). The respective Youden indexes of APACHE IV, MELD, and combination of APACHE IV with MELD were 0.763, 0.430, and 0.545. The prognostic value of APACHE IV is high but still underestimates the overall hospital mortality, while the prognostic value of MELD is poor. The function of the APACHE IV is, thus, better than that of the MELD.
Data Management in Mobile P2P Systems

We focus on effective data sharing in mobile P2P system. In order to realize data sharing, data have to be stored in the system so it can be retrieved by every peer. Our data sharing mechanism consists of a data storing method, a query processing method, and a data dissemination method. We adopt a limited search approach for the query processing in order to retrieve data effectively in highly mobile environment. Therefore, the goal of the data storing method is to distribute data in wide area. We adopt data hashing approach for the data storing method and propose data distribution algorithm HID and redundancy control algorithm ARw/D. Our results show that HID with ARw/D performs significantly well.