nils-herrmann/clean_scirepeval huggingface dataset:
Demo: OpenC2X — An open source experimental and prototyping platform supporting ETSI ITS-G5

Vehicular networking is at the corner from early research to final deployment. This phase requires more field testing and real-world experimentation. Most Field Operational Tests (FOTs) are based on proprietary commercial hardware that only allows for marginal modifications of the protocol stack. Furthermore, the roll-out of updated implementations for new or changing protocol standards often takes a prohibitively long time. We developed one of the first complete Open Source experimental and prototyping platform for vehicular networking solutions. Our system supports most of the ETSI ITS-G5 features and runs on standard Linux. New protocol features and updates could now easily be done by and shared with the vehicular networking R&D community.
Performance Improvement in Cloud Computing using Resource Clustering

Cloud computing is a computing paradigm in which th e various tasks are assigned to a combination of connections, software and services that can be acce ssed over the network. The computing resources and services can be efficiently delivered and utilized, making the vision of computing utility realizable. In various applications, execution of services with mo re number of tasks has to perform with minimum intertask communication. The applications are more likely to exhibit different patterns and levels and the distributed resources organize into various topolog ies for information and query dissemination. In a distributed system the resource discovery is a sign ifica t process for finding appropriate nodes. The earlier resource discovery mechanism in cloud system relies on the recent observations. In this study, resourc e usage distribution for a group of nodes with identi cal resource usages patterns are identified and kep t as a cluster and is named as resource clustering approac h. The resource clustering approach is modeled usin g CloudSim, a toolkit for modeling and simulating clo ud computing environments and the evaluation improves the performance of the system in the usage of the resources. Results show that resource clust ers are able to provide high accuracy for resource disc overy.
A Family of Droids-Android Malware Detection via Behavioral Modeling: Static vs Dynamic Analysis

Following the increasing popularity of the mobile ecosystem, cybercriminals have increasingly targeted mobile ecosystems, designing and distributing malicious apps that steal information or cause harm to the device's owner. Aiming to counter them, detection techniques based on either static or dynamic analysis that model Android malware, have been proposed. While the pros and cons of these analysis techniques are known, they are usually compared in the context of their limitations e.g., static analysis is not able to capture runtime behaviors, full code coverage is usually not achieved during dynamic analysis, etc. Whereas, in this paper, we analyze the performance of static and dynamic analysis methods in the detection of Android malware and attempt to compare them in terms of their detection performance, using the same modeling approach.To this end, we build on MAMADROID, a state-of-the-art detection system that relies on static analysis to create a behavioral model from the sequences of abstracted API calls. Then, aiming to apply the same technique in a dynamic analysis setting, we modify CHIMP, a platform recently proposed to crowdsource human inputs for app testing, in order to extract API calls' sequences from the traces produced while executing the app on a CHIMP virtual device. We call this system AUNTIEDROID and instantiate it by using both automated (Monkey) and usergenerated inputs. We find that combining both static and dynamic analysis yields the best performance, with $F -$measure reaching 0.92. We also show that static analysis is at least as effective as dynamic analysis, depending on how apps are stimulated during execution, and investigate the reasons for inconsistent misclassifications across methods.
Wearable Medical Sensor-Based System Design: A Survey

Wearable medical sensors (WMSs) are garnering ever-increasing attention from both the scientific community and the industry. Driven by technological advances in sensing, wireless communication, and machine learning, WMS-based systems have begun transforming our daily lives. Although WMSs were initially developed to enable low-cost solutions for continuous health monitoring, the applications of WMS-based systems now range far beyond health care. Several research efforts have proposed the use of such systems in diverse application domains, e.g., education, human-computer interaction, and security. Even though the number of such research studies has grown drastically in the last few years, the potential challenges associated with their design, development, and implementation are neither well-studied nor well-recognized. This article discusses various services, applications, and systems that have been developed based on WMSs and sheds light on their design goals and challenges. We first provide a brief history of WMSs and discuss how their market is growing. We then discuss the scope of applications of WMS-based systems. Next, we describe the architecture of a typical WMS-based system and the components that constitute such a system, and their limitations. Thereafter, we suggest a list of desirable design goals that WMS-based systems should satisfy. Finally, we discuss various research directions related to WMSs and how previous research studies have attempted to address the limitations of the components used in WMS-based systems and satisfy the desirable design goals.
The Three-Fold Axis of the HIV-1 Capsid Lattice Is the Species-Specific Binding Interface for TRIM5α

Rhesus TRIM5α (rhTRIM5α) potently restricts replication of human immunodeficiency virus type 1 (HIV-1). Restriction is mediated through direct binding of the C-terminal B30.2 domain of TRIM5α to the assembled HIV-1 capsid core. This host-pathogen interaction involves multiple capsid molecules within the hexagonal HIV-1 capsid lattice. However, the molecular details of this interaction and the precise site at which the B30.2 domain binds remain largely unknown. The human orthologue of TRIM5α (hsTRIM5α) fails to block infection by HIV-1 both in vivo and in vitro This is thought to be due to differences in binding to the capsid lattice. To map the species-specific binding surface on the HIV-1 capsid lattice, we used microscale thermophoresis and dual-focus fluorescence correlation spectroscopy to measure binding affinity of rhesus and human TRIM5α B30.2 domains to a series of HIV-1 capsid variants that mimic distinct capsid arrangements at each of the symmetry axes of the HIV-1 capsid lattice. These surrogates include previously characterized capsid oligomers, as well as a novel chemically cross-linked capsid trimer that contains cysteine substitutions near the 3-fold axis of symmetry. The results demonstrate that TRIM5α binding involves multiple capsid molecules along the 2-fold and 3-fold interfaces between hexamers and indicate that the binding interface at the 3-fold axis contributes to the well-established differences in restriction potency between TRIM5α orthologues.IMPORTANCE TRIM5α is a cellular protein that fends off infection by retroviruses through binding to the viruses' protein shell surrounding its genetic material. This shell is composed of several hundred capsid proteins arranged in a honeycomb-like hexagonal pattern that is conserved across retroviruses. By binding to the complex lattice formed by multiple capsid proteins, rather than to a single capsid monomer, TRIM5α restriction activity persists despite the high mutation rate in retroviruses such as HIV-1. In rhesus monkeys, but not in humans, TRIM5α confers resistance to HIV-1. By measuring the binding of human and rhesus TRIM5α to a series of engineered HIV-1 capsid mimics of distinct capsid lattice interfaces, we reveal the HIV-1 capsid surface critical for species-specific binding by TRIM5α.
How Privacy Flaws Affect Consumer Perception

We examine how consumers perceive publicized instances of privacy flaws and private information data breaches.Using three real-world privacy breach incidents, we study how these flaws affected consumers' future purchasing behavior and perspective on a company's trustworthiness. We investigate whether despite a lack of widespread privacy enhancing technology (PET) usage, consumers are taking some basic security precautions when making purchasing decisions. We survey 600participants on three well-known privacy breaches. Our results show that, in general, consumers are less likely to purchase products that had experienced some form of privacy breach.We find evidence of a slight bias toward giving products the consumers owned themselves more leeway, as suggested by the endowment effect hypothesis.
Approximating Rate-Distortion Graphs of Individual Data: Experiments in Lossy Compression and Denoising

Classical rate-distortion theory requires specifying a source distribution. Instead, we analyze rate-distortion properties of individual objects using the recently developed algorithmic rate-distortion theory. The latter is based on the noncomputable notion of Kolmogorov complexity. To apply the theory we approximate the Kolmogorov complexity by standard data compression techniques, and perform a number of experiments with lossy compression and denoising of objects from different domains. We also introduce a natural generalization to lossy compression with side information. To maintain full generality we need to address a difficult searching problem. While our solutions are therefore not time efficient, we do observe good denoising and compression performance.
ECU tendon “dislocation” in asymptomatic volunteers

Assess extensor carpi ulnaris (ECU) tendon position in the ulnar groove, determine the frequency of tendon “dislocation” with the forearm prone, neutral, and supine, and determine if an association exists between ulnar groove morphology and tendon position in asymptomatic volunteers. Axial proton density-weighted MR was performed through the distal radioulnar joint with the forearm prone, neutral, and supine in 38 asymptomatic wrists. The percentage of the tendon located beyond the ulnar-most border of the ulnar groove was recorded. Ulnar groove depth and length was measured and ECU tendon signal was assessed. 15.8 % of tendons remained within the groove in all forearm positions. In 76.3 %, the tendon translated medially from prone to supine. The tendon “dislocated” in 0, 10.5, and 39.5 % with the forearm prone, neutral and supine, respectively. In 7.9 % prone, 5.3 % neutral, and 10.5 % supine exams, the tendon was 51–99 % beyond the ulnar border of the ulnar groove. Mean ulnar groove depth and length were 1.6 and 7.7 mm, respectively, with an overall trend towards greater degrees of tendon translation in shorter, shallower ulnar grooves. The ECU tendon shifts in a medial direction when the forearm is supine; however, tendon “dislocation” has not been previously documented in asymptomatic volunteers. The ECU tendon medially translated or frankly dislocated from the ulnar groove in the majority of our asymptomatic volunteers, particularly when the forearm is supine. Overall greater degrees of tendon translation were observed in shorter and shallower ulnar grooves.
Part-Based Player Identification Using Deep Convolutional Representation and Multi-scale Pooling

This paper addresses the problem of automatic player identification in broadcast sports videos filmed with a single side-view medium distance camera. Player identification in this setting is a challenging task because visual cues such as faces and jersey numbers are not clearly visible. Thus, this task requires sophisticated approaches to capture distinctive features from players to distinguish them. To this end, we use Convolutional Neural Networks (CNN) features extracted at multiple scales and encode them with an advanced pooling, called Fisher vector. We leverage it for exploring representations that have sufficient discriminatory power and ability to magnify subtle differences. We also analyze the distinguishing parts of the players and present a part based pooling approach to use these distinctive feature points. The resulting player representation is able to identify players even in difficult scenes. It achieves state-of-the-art results up to 96% on NBA basketball clips.
Monitoring of Rotor-Bar Defects in Inverter-Fed Induction Machines at Zero Load and Speed

Rotor-cage fault detection in inverter-fed induction machines is still difficult nowadays as the dynamics introduced by the control or load influence the fault-indicator signals commonly applied. In addition, detection is usually possible only when the machine is operated above a specific load level to generate a significant rotor-current magnitude. This paper proposes a new method of detecting rotor-bar defects at zero load and almost at standstill. The method uses the standard current sensors already present in modern industrial inverters and, hence, is noninvasive. It is thus well suited as a start-up test for drives. By applying an excitation with voltage pulses using the switching of the inverter and then measuring the resulting current slope, a new fault indicator is obtained. As a result, it is possible to clearly identify the fault-induced asymmetry in the machine's transient reactances. Although the transient-flux linkage cannot penetrate the rotor because of the cage, the faulty bar locally influences the zigzag flux, leading to a significant change in the transient reactances. Measurement results show the applicability and sensitivity of the proposed method.
Analyzing the Digital Traces of Political Manipulation: The 2016 Russian Interference Twitter Campaign

Until recently, social media was seen to promote democratic discourse on social and political issues. However, this powerful communication platform has come under scrutiny for allowing hostile actors to exploit online discussions in an attempt to manipulate public opinion. A case in point is the ongoing U.S. Congress investigation of Russian interference in the 2016 U.S. election campaign, with Russia accused of, among other things, using trolls (malicious accounts created for the purpose of manipulation) and bots (automated accounts) to spread misinformation and politically biased information. In this study, we explore the effects of this manipulation campaign, taking a closer look at users who re-shared the posts produced on Twitter by the Russian troll accounts publicly disclosed by U.S. Congress investigation. We collected a dataset with over 43 million elections-related posts shared on Twitter between September 16 and November 9, 2016 by about 5.7 million distinct users. This dataset includes accounts associated with the identified Russian trolls. We use label propagation to infer the users' ideology based on the news sources they shared, to classify a large number of them as liberal or conservative with precision and recall above 90%. Conservatives retweeted Russian trolls significantly more often than liberals and produced 36 times more tweets. Additionally, most of the troll content originated in, and was shared by users from Southern states. Using state-of-the-art bot detection techniques, we estimated that about 4.9% and 6.2% of liberal and conservative users respectively were bots. Text analysis on the content shared by trolls reveals that they had a mostly conservative, pro-Trump agenda. Although an ideologically broad swath of Twitter users were exposed to Russian trolls in the period leading up to the 2016 U.S. Presidential election, it was mainly conservatives who helped amplify their message.
PAL, a tool for Pre-annotation and Active Learning

Many natural language processing systems rely on machine learning models that are trained on large amounts of manually annotated text data. The lack of sufficient amounts of annotated data is, however, a common obstacle for such systems, since manual annotation of text is often expensive and time-consuming. The aim of “PAL, a tool for Pre-annotation and Active Learning” is to provide a ready-made package that can be used to simplify annotation and to reduce the amount of annotated data required to train a machine learning classifier. The package provides support for two techniques that have been shown to be successful in previous studies, namely active learning and pre-annotation. The output of the pre-annotation is provided in the annotation format of the annotation tool BRAT, but PAL is a stand-alone package that can be adapted to other formats.
DBSCAN Clustering Algorithm Applied to Identify Suspicious Financial Transactions

Money laundering refers to disguise or conceal the source and nature of variety ill-gotten gains, to make it legalization. In this paper, we design and implement the anti-money laundering regulatory application system (AMLRAS), which can not only automate sorting and counting the money laundering cases in comprehension and details, but also collect, analyses and count the large cash transactions. We also adopt data mining techniques DBSCAN clustering algorithm to identify suspicious financial transactions, while using link analysis (LA) to mark the suspicious level. The presumptive approach is tested on large cash transaction data which is provided by a bank where AMLRAS has already been applied. The result proves that this method is automatable to detect suspicious financial transaction cases from mass financial data, which is helpful to prevent money laundering from occurring.
On the implementation of minimum redundancy prefix codes

Minimum redundancy coding (also known as Huffman coding) is one of the enduring techniques of data compression. Many efforts have been made to improve the efficiency of minimum redundancy coding, the majority based on the use of improved representations for explicit Huffman trees. In this paper, we examine how minimum redundancy coding can be implemented efficiently by divorcing coding from a code tree, with emphasis on the situation whenn is large, perhaps on the order of 10. We review techniques for devising minimum redundancy codes, and consider in detail how encoding and decoding should be accomplished. In particular, we describe a modified decoding method that allows improved decoding speed, requiring just a few machine operations per output symbol (rather than for each decoded bit), and uses just a few hundred bytes of memory above and beyond the space required to store an enumeration of the source alphabet.
Prevalence and type of pre-analytical problems for inpatients samples in coagulation laboratory.

Total quality in coagulation testing is a necessary requisite to achieve clinically reliable results. Evidence was provided that poor standardization in the extra-analytical phases of the testing process has the greatest influence on test results, though little information is available so far on prevalence and type of pre-analytical variability in coagulation testing.



The present study was designed to describe all pre-analytical problems on inpatients routine and stat samples recorded in our coagulation laboratory over a 2-year period and clustered according to their source (hospital departments).



Overall, pre-analytic problems were identified in 5.5% of the specimens. Although the highest frequency was observed for paediatric departments, in no case was the comparison of the prevalence among the different hospital departments statistically significant. The more frequent problems could be referred to samples not received in the laboratory following a doctor's order (49.3%), haemolysis (19.5%), clotting (14.2%) and inappropriate volume (13.7%). Specimens not received prevailed in the intensive care unit, surgical and clinical departments, whereas clotted and haemolysed specimens were those most frequently recorded from paediatric and emergency departments, respectively. The present investigation demonstrates a high prevalence of pre-analytical problems affecting samples for coagulation testing.



Full implementation of a total quality system, encompassing a systematic error tracking system, is a valuable tool to achieve meaningful information on the local pre-analytic processes most susceptible to errors, enabling considerations on specific responsibilities and providing the ideal basis for an efficient feedback within the hospital departments.
Efficient distance-based outlier detection on uncertain datasets of Gaussian distribution

Uncertain data management, querying and mining have become important because the majority of real world data is accompanied with uncertainty these days. Uncertainty in data is often caused by the deficiency in underlying data collecting equipments or sometimes manually introduced to preserve data privacy. This work discusses the problem of distance-based outlier detection on uncertain datasets of Gaussian distribution. The Naive approach of distance-based outlier on uncertain data is usually infeasible due to expensive distance function. Therefore a cell-based approach is proposed in this work to quickly identify the outliers. The infinite nature of Gaussian distribution prevents to devise effective pruning techniques. Therefore an approximate approach using bounded Gaussian distribution is also proposed. Approximating Gaussian distribution by bounded Gaussian distribution enables an approximate but more efficient cell-based outlier detection approach. An extensive empirical study on synthetic and real datasets show that our proposed approaches are effective, efficient and scalable.
A Survey on Reinforcement Learning Models and Algorithms for Traffic Signal Control

Traffic congestion has become a vexing and complex issue in many urban areas. Of particular interest are the intersections where traffic bottlenecks are known to occur despite being traditionally signalized. Reinforcement learning (RL), which is an artificial intelligence approach, has been adopted in traffic signal control for monitoring and ameliorating traffic congestion. RL enables autonomous decision makers (e.g., traffic signal controllers) to observe, learn, and select the optimal action (e.g., determining the appropriate traffic phase and its timing) to manage traffic such that system performance is improved. This article reviews various RL models and algorithms applied to traffic signal control in the aspects of the representations of the RL model (i.e., state, action, and reward), performance measures, and complexity to establish a foundation for further investigation in this research field. Open issues are presented toward the end of this article to discover new research areas with the objective to spark new interest in this research field.
Fast Rate Analysis of Some Stochastic Optimization Algorithms

In this paper, we revisit three fundamental and popular stochastic optimization algorithms (namely, Online Proximal Gradient, Regularized Dual Averaging method and ADMM with online proximal gradient) and analyze their convergence speed under conditions weaker than those in literature. In particular, previous works showed that these algorithms converge at a rate of O(lnT/T ) when the loss function is strongly convex, and O(1/ √ T ) in the weakly convex case. In contrast, we relax the strong convexity assumption of the loss function, and show that the algorithms converge at a rate O(lnT/T ) if the expectation of the loss function is locally strongly convex. This is a much weaker assumption and is satisfied by many practical formulations including Lasso and Logistic Regression. Our analysis thus extends the applicability of these three methods, as well as provides a general recipe for improving analysis of convergence rate for stochastic and online optimization algorithms.