nils-herrmann/clean_scirepeval huggingface dataset:
Passive UHF RFID-Based Localization Using Detection of Tag Interference on Smart Shelf

In this paper, we present a novel radio-frequency identification (RFID) smart shelf that accurately locates tagged objects using standard passive UHF RFID tags. This standard-based commercial off-the-shelf approach provides significant advantages over custom HF RFID and other near-field RFID approaches, including reduced tag costs, minimal infrastructure costs, and simple operation. In order to achieve accurate location sensing of objects sitting on the shelf, we utilize a novel localization algorithm that utilizes detected changes in a tag's readability to infer the presence of neighboring tags. According to our experimentation results, with a single RFID reader antenna for two wooden shelves of size 91 cm × 152 cm, our smart-shelf system estimates nine box-level object locations with an average error of just 18.48 cm, which is a 71% improvement in accuracy compared with the previously published k nearest neighbor (KNN) algorithm.
Comdb2: Bloomberg's Highly Available Relational Database System

Comdb2 is a distributed database system designed for geographical replication and high availability. In contrast with the latest trends in this field, Comdb2 o↵ers full transactional support, a standard relational model, and the expressivity of SQL. Moreover, the system allows for rich stored procedures using a dialect of Lua. Comdb2 implements a serializable system in which reads from any node always return current values. Comdb2 provides transparent High Availability through built-in service discovery and sophisticated retry logic embedded in the standard API. In addition to the relational data model, Comdb2 implements queues for publisher-to-subscriber message delivery. Queues can be combined with table triggers for timeconsistent log distribution, providing functionality commonly needed in modern OLTP. In this paper we give an overview of our last twelve years of work. We focus on the design choices that have made Comdb2 the primary database solution within our company, Bloomberg LP (BLP).
Sentiment Classification at Discourse Segment Level: Experiments on multi-domain Arabic corpus

Sentiment classification aims to determine whether the semantic orientation of a text is positive, negative or neutral. It can be tackled at several levels of granularity: expression or phrase level, sentence level, and document level. In the scope of this research, we are interested in the sentence and sub-sentential level classification which can provide very useful trends for information retrieval and extraction applications, Question Answering systems and summarization tasks. In the context of our work, we address the problem of Arabic sentiment classification at sub-sentential level by (i) building a high coverage sentiment lexicon with semi-automatic approach; (ii) creating a large multi-domain annotated sentiment corpus segmented into discourse segments in order to evaluate our sentiment approach; and (iii) applying a lexicon-based approach with an aggregation model taking into account advanced linguistic phenomena such as negation and intensification. The results that we obtained are considered good and close to state of the art results in English language.
peepHole : Securing your homes with Raspberry Pi and Amazon Echo

In recent years, the field of Internet of Things (IoT) has seen significant investments made by the research community and the industry. Specifically, the Smart Home space has been a prime focus with the introduction of devices such as Amazon Echo, Google Home, Samsung Smart Things among others. The growth of an industry results in innovative, economic, and advanced solutions. In this paper, we focus on smart home security and how to build a robust, cost-effective system that can be widely used. We power our system using Amazon Echo, Amazon’s cloud services, its speech services. A Raspberry Pi with a camera module is used as the hardware component for providing home security. We describe the different components of our product and we show that our system works effectively to identify the person at the doorstep, and have thus named our system peepHole. Intenet of Things, Smart Homes, Home Security, Amazon Web Services, Amazon Echo, Raspberry Pi, Face Verification
Leveraging Big Data and Business Analytics

F ueled by the growing popularity of social media, e-commerce, and an increased interest in business collaboration, there’s been an explosion of data. However, enterprises don’t always know how to use this “big data” to make (or automate) complex decisions, resulting in a business advantage. Powerful analytics techniques can help enterprises deal with complex decisions by providing new insights, creating a virtuous cycle by spurring an interest in and demand for better techniques, tools, and approaches for leveraging big data and business analytics.
Big data analytics enhanced healthcare systems: a review

There is increased interest in deploying big data technology in the healthcare industry to manage massive collections of heterogeneous health datasets such as electronic health records and sensor data, which are increasing in volume and variety due to the commoditization of digital devices such as mobile phones and wireless sensors. The modern healthcare system requires an overhaul of traditional healthcare software/hardware paradigms, which are ill-equipped to cope with the volume and diversity of the modern health data and must be augmented with new “big data” computing and analysis capabilities. For researchers, there is an opportunity in healthcare data analytics to study this vast amount of data, find patterns and trends within data and provide a solution for improving healthcare, thereby reducing costs, democratizing health access, and saving valuable human lives. In this paper, we present a comprehensive survey of different big data analytics integrated healthcare systems and describe the various applicable healthcare data analytics algorithms, techniques, and tools that may be deployed in wireless, cloud, Internet of Things settings. Finally, the contribution is given in formation of a convergence point of all these platforms in form of SmartHealth that could result in contributing to unified standard learning healthcare system for future.
Approximate computing: Challenges and opportunities

Approximate computing is gaining traction as a computing paradigm for data analytics and cognitive applications that aim to extract deep insight from vast quantities of data. In this paper, we demonstrate that multiple approximation techniques can be applied to applications in these domains and can be further combined together to compound their benefits. In assessing the potential of approximation in these applications, we took the liberty of changing multiple layers of the system stack: architecture, programming model, and algorithms. Across a set of applications spanning the domains of DSP, robotics, and machine learning, we show that hot loops in the applications can be perforated by an average of 50% with proportional reduction in execution time, while still producing acceptable quality of results. In addition, the width of the data used in the computation can be reduced to 10-16 bits from the currently common 32/64 bits with potential for significant performance and energy benefits. For parallel applications we reduced execution time by 50% using relaxed synchronization mechanisms. Finally, our results also demonstrate that benefits compounded when these techniques are applied concurrently. Our results across different applications demonstrate that approximate computing is a widely applicable paradigm with potential for compounded benefits from applying multiple techniques across the system stack. In order to exploit these benefits it is essential to re-think multiple layers of the system stack to embrace approximations ground-up and to design tightly integrated approximate accelerators. Doing so will enable moving the applications into a world in which the architecture, programming model, and even the algorithms used to implement the application are all fundamentally designed for approximate computing.
Multi-modal Semantic Place Classification

The ability to represent knowledge about space and its position therein is crucial for a mobile robot. To this end, topological and semantic descriptions are gaining popularity for augmenting purely metric space representations. In this paper we present a multi-modal place classification system that allows a mobile robot to identify places and recognize semantic categories in an indoor environment. The system effectively utilizes information from different robotic sensors by fusing multiple visual cues and laser range data. This is achieved using a high-level cue integration scheme based on a Support Vector Machine (SVM) that learns how to optimally combine and weight each cue. Our multi-modal place classification approach can be used to obtain a real-time semantic space labeling system which integrates information over time and space. We perform an extensive experimental evaluation of the method for two different platforms and environments, on a realistic off-line database and in a live experiment on an autonomous robot. The results clearly demonstrate the effecThe International  No. 00, Xxxxxxxx 2009,  The Author(s), 2009.  http://www.sagepub.co.uk/journalsPermissions.nav Figures 1–15, 17, 18 appear in color online: http://ijr.sagepub.com tiveness of our cue integration scheme and its value for robust place classification under varying conditions. KEY WORDS—recognition, sensor fusion, localization, multi-modal place classification, sensor and cue integration, semantic annotation of space
Emerging roles of the brain's default network.

During periods of inactivity, global metabolism does not decrease in the brain, and small but consistent increases in activity occur in a specific set of regions called the "default network". Although much is known about the topological and connectional properties of the default network, its functions remain a matter of debate. Functional neuroimaging and electrophysiological studies have suggested two apparently contrasting functions for this network: spontaneous cognition and monitoring the environment. Spontaneous cognition, however, is by default situated in a given external context, an external milieu to which we align ourselves and which must be monitored. This review integrates recent literature suggesting that the two proposed functions of the default network functions need not to be mutually exclusive, and that spontaneous cognition and monitoring of the environment represent complementary instances of conscious experiences occurring during idle moments of daily life.
Clinicopathological Findings of Chronic Rejection in a Face Grafted Patient.

Skin chronic rejection (CR) in vascularized composite allotransplantation has not been included in the Banff classification yet. We report a face-transplant patient who developed cutaneous clinicopathologic changes suggestive of CR.



The recipient was a 27-year-old man with severe disfigurement of the lower face due to a pyrotechnic explosion. He received a facial allograft, including mandible, cheeks, lips, and chin, in November 2009. Immunosuppression included antithymocyte globulins and bone-marrow infusion then steroids, tacrolimus, and mycophenolate mofetil.



During the first posttransplant year the acute rejection episodes were characterized by reversible oedema and erythema of the graft. Subsequently, the patient developed primary asymptomatic Epstein-Barr virus (EBV) infection, followed by EBV+ B-cell lymphoma and hepatic EBV-associated posttransplant smooth muscle tumors; therefore, the immunosuppressive treatment was greatly reduced. Since the second posttransplant year, the allografted facial skin became progressively sclerotic and presented pigmented macules on a background of hypopigmentation and teleangiectasias, resulting in a poikilodermatous aspect. Skin biopsies showed epidermal atrophy, basal cell vacuolization, and diffuse dermal sclerosis in the absence of significant dermal cell infiltration. The dermal capillaries showed thickened walls and narrowed lumina, whereas the large vessels did not show significant alterations. Neither donor-specific antibodies nor vascular Cd4 deposits were detected.A dysfunction of the graft functions occurred. It was evidenced by a decrease in mouth opening and modification of some phonemes although lip closure was still possible allowing food intake.



This is the first report suggestive of CR in a face allotransplantation after immunosuppression minimization.
A Comparison of MIMO Techniques in Downlink Millimeter Wave Cellular Networks With Hybrid Beamforming

Large antenna arrays will be needed in future millimeter wave (mmWave) cellular networks, enabling a large number of different possible antenna architectures and multiple-input multiple-output (MIMO) techniques. It is still unclear which MIMO technique is most desirable as a function of different network parameters. This paper, therefore, compares the coverage and rate performance of hybrid beamforming enabled multiuser (MU) MIMO and single-user spatial multiplexing (SM) with single-user analog beamforming (SU-BF). A stochastic geometry model for coverage and rate analysis is proposed for MU-MIMO mmWave cellular networks, taking into account important mmWave-specific hardware constraints for hybrid analog/digital precoders and combiners, and a blockage-dependent channel model which is sparse in angular domain. The analytical results highlight the coverage, rate, and power consumption tradeoffs in multiuser mmWave networks. With perfect channel state information at the transmitter and round robin scheduling, MU-MIMO is usually a better choice than SM or SU-BF in mmWave cellular networks. This observation, however, neglects any overhead due to channel acquisition or computational complexity. Incorporating the impact of such overheads, our results can be re-interpreted so as to quantify the minimum allowable efficiency of MU-MIMO to provide higher rates than SM or SU-BF.
The Deep Ritz method: A deep learning-based numerical algorithm for solving variational problems

We propose a deep learning based method, the Deep Ritz Method, for numerically solving variational problems, particularly the ones that arise from partial differential equations. The Deep Ritz method is naturally nonlinear, naturally adaptive and has the potential to work in rather high dimensions. The framework is quite simple and fits well with the stochastic gradient descent method used in deep learning. We illustrate the method on several problems including some eigenvalue problems.
Brain activation during human male ejaculation.

Brain mechanisms that control human sexual behavior in general, and ejaculation in particular, are poorly understood. We used positron emission tomography to measure increases in regional cerebral blood flow (rCBF) during ejaculation compared with sexual stimulation in heterosexual male volunteers. Manual penile stimulation was performed by the volunteer's female partner. Primary activation was found in the mesodiencephalic transition zone, including the ventral tegmental area, which is involved in a wide variety of rewarding behaviors. Parallels are drawn between ejaculation and heroin rush. Other activated mesodiencephalic structures are the midbrain lateral central tegmental field, zona incerta, subparafascicular nucleus, and the ventroposterior, midline, and intralaminar thalamic nuclei. Increased activation was also present in the lateral putamen and adjoining parts of the claustrum. Neocortical activity was only found in Brodmann areas 7/40, 18, 21, 23, and 47, exclusively on the right side. On the basis of studies in rodents, the medial preoptic area, bed nucleus of the stria terminalis, and amygdala are thought to be involved in ejaculation, but increased rCBF was not found in any of these regions. Conversely, in the amygdala and adjacent entorhinal cortex, a decrease in activation was observed. Remarkably strong rCBF increases were observed in the cerebellum. These findings corroborate the recent notion that the cerebellum plays an important role in emotional processing. The present study for the first time provides insight into which regions in the human brain play a primary role in ejaculation, and the results might have important implications for our understanding of how human ejaculation is brought about, and for our ability to improve sexual function and satisfaction in men.
State-Space Modeling of a Class ${\bf E}^{\bf 2}$  Converter for Inductive Links

This paper presents a state-space-based analysis of a Class E2 converter for wireless power systems based on a two-coil inductive link. The Class E2 converter consists of a 200-kHz Class E inverter as the primary coil driver and a voltage-driven Class E synchronous rectifier at the secondary coil of the inductive link. A piecewise linear seventh-order state-space model is used to calculate several parameters and values to achieve optimum switching operation of the Class E inverter and the Class E rectifier. Simulation results are presented to compare the accuracy of the state-space modeling approach with the established analytical approach. For validation of the state-space analysis, an investigation of the influence of variation of coil alignment and load for a 20-W Class E2 converter prototype system is performed by means of a novel compensation method that maintains optimum switching conditions irrespective of variations. Experimental results are presented to confirm the accuracy of the state-space modeling approach over a wide range of operational conditions and the utility of the compensation method.
Comparison of database replication techniques based on total order broadcast

In this paper, we present a performance comparison of database replication techniques based on total order broadcast. While the performance of total order broadcast-based replication techniques has been studied in previous papers, this paper presents many new contributions. First, it compares with each other techniques that were presented and evaluated separately, usually by comparing them to a classical replication scheme like distributed locking. Second, the evaluation is done using a finer network model than previous studies. Third, the paper compares techniques that offer the same consistency criterion (one-copy serializability) in the same environment using the same settings. The paper shows that, while networking performance has little influence in a LAN setting, the cost of synchronizing replicas is quite high. Because of this, total order broadcast-based techniques are very promising as they minimize synchronization between replicas.
WeChecker: efficient and precise detection of privilege escalation vulnerabilities in Android apps

Due to the rapid increase of Android apps and their wide usage to handle personal data, a precise and large-scaling checker is in need to validate the apps' permission flow before they are listed on the market. Several tools have been proposed to detect sensitive data leaks in Android apps. But these tools are not applicable to large-scale analysis since they fail to deal with the arbitrary execution orders of different event handlers smartly. Event handlers are invoked by the framework based on the system state, therefore we cannot pre-determine their order of execution. Besides, since all exported components can be invoked by an external app, the execution orders of these components are also arbitrary. A naive way to simulate these two types of arbitrary execution orders yields a permutation of all event handlers in an app. The time complexity is O(n!) where n is the number of event handlers in an app. This leads to a high analysis overhead when n is big. To give an illustration,  found 50.73 entry points of 44 unique class types in an app on average. In this paper we propose an improved static taint analysis to deal with the challenge brought by the arbitrary execution orders without sacrificing the high precision. Our analysis does not need to make permutations and achieves a polynomial time complexity. We also propose to unify the array and map access with object reference by propagating access paths to reduce the number of false positives due to field-insensitivity and over approximation of array access and map access.
 We implement a tool, WeChecker, to detect privilege escalation vulnerabilities [7] in Android apps. WeChecker achieves 96% precision and 96% recall in the state-of-the-art test suite DriodBench (for compairson, the precision and recall of FlowDroid  are 86% and 93%, respectively). The evaluation of WeChecker on real apps shows that it is efficient (average analysis time of each app: 29.985s) and fits for large-scale checking.
Design of a distributed energy-efficient clustering algorithm for heterogeneous wireless sensor networks

The clustering Algorithm is a kind of key technique used to reduce energy consumption. It can increase the scalability and lifetime of the network. Energy-efficient clustering protocols should be designed for the characteristic of heterogeneous wireless sensor networks. We propose and evaluate a new distributed energy-efficient clustering scheme for heterogeneous wireless sensor networks, which is called DEEC. In DEEC, the cluster-heads are elected by a probability based on the ratio between residual energy of each node and the average energy of the network. The epochs of being cluster-heads for nodes are different according to their initial and residual energy. The nodes with high initial and residual energy will have more chances to be the cluster-heads than the nodes with low energy. Finally, the simulation results show that DEEC achieves longer lifetime and more effective messages than current important clustering protocols in heterogeneous environments.
I Spot a Bot : Building a binary classifier to detect bots on Twitter

It has been estimated that up to 50% of the activity on Twitter comes from bots : algorithmically-automated accounts created to advertize products, distribute spam, or sway public opinion. It is perhaps this last intent that is most alarming; studies have found that up to 20% of the Twitter activity related to the 2016 U.S. presidential election came from suspected bot accounts, and there has been evidence of bots used to spread false rumors about French presidential candidate Emmanuel Macron and to escalate a recent conflict in Qatar . Detecting bots is necessary in order to identify bad actors in the “Twitterverse” and protect genuine users from misinformation and malicious intents. This has been an area of research for several years, but current algorithms still lag in performance relative to humans . Given a Twitter user’s profile and tweet history, our project was to build a binary classifier that identifies a given user as “bot” or “human.” The end-user application for a classifier such as this one would be a web plug-in for the browser that can score a given account in real-time (See page 5 for mock-ups). All of the raw inputs required to classify a public Twitter account via our algorithm are available for download from the Twitter API; in fact, our check_screenname.py program is a working prototype that uses the API to classify a given Twitter user handle within seconds. It is our opinion that a product like this is sorely needed for the average Twitter consumer.