nils-herrmann/clean_scirepeval huggingface dataset:
NACHOS: Software Driven Hardware Assisted Memory Disambiguation for Accelerators
Venue: 2018 IEEE International Symposium on High Performance Computer Architecture (HPCA)
Year: 2018

Hardware accelerators have relied on the compiler to extract instruction parallelism but may waste significant energy in enforcing memory ordering and discovering memory parallelism. Accelerators tend to either serialize memory operations [43] or reuse power hungry load store queues (LSQs)   Recent works [11]  use the compiler for scheduling but continue to rely on LSQs for memory disambiguation. NACHOS is a hardware assisted software driven approach to memory disambiguation for accelerators. In NACHOS, the compiler classifies pairs of memory operations as NO alias (i.e. independent memory operations) MUST alias (i.e. ordering required) or MAY alias (i.e. compiler uncertain) We developed a compiler only approach called NACHOS SW that serializes memory operations both when the compiler is certain (MUST alias) and uncertain (MAY alias) Our study analyzes multiple stages of alias analysis on 135 acceleration regions extracted from SPEC2K, SPEC2k6, and PARSEC. NACHOS SW is en ergy efficient, but serialization limits performance; 18% 100% slowdown compared to an optimized LSQ. We then proposed NACHOS a low overhead, scalable, hardware comparator assist that dynamically verifies MAY alias and executes independent memory operations in parallel. NACHOS is a pay as you go approach where the compiler filters out memory operations to save dynamic energy, and the hardware dynamically checks to find MLP. NACHOS achieves performance comparable to an optimized LSQ; in fact, it improved performance in 6 benchmarks(6% 70% by reducing load to use latency for cache hits. NACHOS imposes no energy overhead in 15 out of 27 benchmarks i.e. compiler accurately determines all memory dependencies; the average energy overhead is ?6% of total (accelerator and L1 cache) in comparison, an optimized LSQ consumes 27% of total energy. NACHOS is released as free and open source software. Github: https:/github.com/sfu arch/ nachos
Analisis comparativo de las variables oferta, demanda e infraestructura del sistema de transporte publico colectivo urbano entre las ciudades de Bogota y Medellin entre los anos 2010 y 2018.
Year: 2019

El presente articulo tiene como finalidad identificar y comparar las variables de oferta, demanda e infraestructura del SITP de Bogota y SITVA de Medellin entre los anos 2010 y 2018, con el fin de determinar elementos que mejoren la organizacion del sistema masivo de transporte en la ciudad de Bogota con mucha mas organizacion para llegar al punto de tener mas oferta y suplir con la demanda, por consiguiente se requiere realizar un analisis sistematico de literatura ya que a traves de este se logra plantear una serie de alternativas que garanticen el mejoramiento del SITP de Bogota a traves del analisis de las variables estudiadas.
Understanding Narrative Texts: Inferences Made During Reading by Children With and Without Language Impairments
Year: 2011

Successful reading comprehension relies on a number of abilities, including decoding and using linguistic and world knowledge to link text propositions. While difficulties with reading comprehension are well documented for children with Specific Language Impairment (SLI) only a handful of studies have investigated the inferences children with SLI make as they read. The present study examines the inferences made by children with SLI during and after reading, with particular attention to causal inferences, considered critical to good comprehension. The common procedure for assessing inferences asking comprehension questions after reading was combined with the more novel approach of eliciting children's thoughts about the text as they read "think aloud" Twelve children with SLI (M age 10;2) were compared to 12 age matched peers with typical language development (TLD) The SLI group had lower scores than the TLD group on literal comprehension questions, and inferential questions requiring informational and causal inferences. The think aloud data were consistent with these findings, and additionally showed that the causal inferences of the SLI group were less accurate than in the TLD group. Nevertheless, both groups had higher scores on comprehension questions after engaging in the think aloud, with the improvement in the SLI group driven by a significant improvement in causal comprehension. The results are examined in light of children's performance on measures of oral language and working memory, and discussed in terms of reading comprehension theories. Practical applications of the think aloud procedure are also considered.
Beyond the Craft Ghetto: Harnessing Creative Industries to Support Development
Year: 2010

Creative industries and the creative economy are relatively new fields in terms of study and analysis. Understanding of their relationship to international development is even more embryonic. Using a crossdisciplinary approach, this paper attempts to summarise some of the key areas of thought, highlighting why this is an important area for additional research. It focuses on specific initiatives where public policy might pro actively support and influence the growth of a successful creative sector. The paper explores the importance of the digital revolution in enabling creative industries in developing countries to access global markets. It argues that the creative industries have the capacity to be a transformative influence through generating cultural confidence, which can impact on social and economic development.
Deep Convolutional Neural Networks for Sentiment Analysis of Short Texts
Venue: COLING
Year: 2014

Sentiment analysis of short texts such as single sentences and Twitter messages is challenging because of the limited contextual information that they normally contain. Effectively solving this task requires strategies that combine the small text content with prior knowledge and use more than just bag of words. In this work we propose a new deep convolutional neural network that exploits from characterto sentence level information to perform sentiment analysis of short texts. We apply our approach for two corpora of two different domains: the Stanford Sentiment Treebank (SSTb) which contains sentences from movie reviews; and the Stanford Twitter Sentiment corpus (STS) which contains Twitter messages. For the SSTb corpus, our approach achieves state of the art results for single sentence sentiment prediction in both binary positive/negative classification, with 85.7% accuracy, and fine grained classification, with 48.3% accuracy. For the STS corpus, our approach achieves a sentiment prediction accuracy of 86.4%
Culture's Constraints
Year: 2012

Recent research has identified a critical contrast between societies that are tight (i.e. societies that have strong norms and a low tolerance for deviant behavior) and those that are loose (i.e. societies that have weak norms and a high tolerance for deviant behavior) I review differences between tight and loose cultures from differences in ecological and historical conditions to the strength of everyday social situations and individual differences in psychological processes and discuss the implications of this cultural contrast for research and practice in psychological science.
Distributed Decision Fusion With M ary Source Coding On Sensor Observation and Uncoded Data Transmission
Venue: 2020 IEEE 17th Annual Consumer Communications Networking Conference (CCNC)
Year: 2020

M ary source coding scheme on sensor detection is able to improve the detection performance on the final decision made at the fusion center (FC) In this paper, a new $M$ ary source coding scheme using analog transmission is proposed for distributed binary detection. In the scheme, the source coding is through the quantization process, but the output of the quantizer is transmitted directly without digitalizing and coding process. In the FC, the linear combiner detection rule is adopted to make the final decision. The problem of huge channel bandwidth demand can be avoided by using the considered Mary source coding scheme. The goal of the proposed scheme is to minimize the decision errors at the FC via optimizing the region allocation. The error performances using maximum a posteriori (MAP) and equal gain combining (EGC) fusion rules are analyzed. The proposed $M$ ary source coding scheme is illustrated with numerical examples highlighting its significant improvement in error performance and enhanced information available at the FC when the transmission is via either additive white Gaussian noise (AWGN) or Rayleigh faded channel.
Protecting the Intellectual Property of Deep Neural Networks with Watermarking: The Frequency Domain Approach
Venue: 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)
Year: 2020

Similar to other digital assets, deep neural network (DNN) models could suffer from piracy threat initiated by insider and/or outsider adversaries due to their inherent commercial value. DNN watermarking is a promising technique to mitigate this threat to intellectual property. This work focuses on black box DNN watermarking, with which an owner can only verify his ownership by issuing special trigger queries to a remote suspicious model. However, informed attackers, who are aware of the watermark and somehow obtain the triggers, could forge fake triggers to claim their ownerships since the poor robustness of triggers and the lack of correlation between the model and the owner identity. This consideration calls for new watermarking methods that can achieve better trade off for addressing the discrepancy. In this paper, we exploit frequency domain image watermarking to generate triggers and build our DNN watermarking algorithm accordingly. Since watermarking in the frequency domain is high concealment and robust to signal processing operation, the proposed algorithm is superior to existing schemes in resisting fraudulent claim attack. Besides, extensive experimental results on 3 datasets and 8 neural networks demonstrate that the proposed DNN watermarking algorithm achieves similar performance on functionality metrics and better performance on security metrics when compared with existing algorithms.
Correction: Conversion of landfill gas to liquid fuels through a TriFTS (tri reforming and Fischer Tropsch synthesis) process: a feasibility study
Year: 2019

Biogas produced in landfills contains large amounts of methane (a potent greenhouse gas) and hence requires collection and treatment according to EPA regulations. Tri reforming of such biogas (CH4 CO2) is a combination of dry reforming, steam reforming, and partial oxidation to produce syngas (CO H2) This syngas can be converted to liquid hydrocarbons using Fischer Tropsch Synthesis (FTS) A novel technology of combining tri reforming and FTS (TriFTS) is proposed and utilized to convert landfill gas (LFG) to high value added liquid hydrocarbon fuels such as gasoline, diesel, and jet fuel. This article explores the feasibility of TriFTS from an experimental and process economics point of view. Landfill gas collected from a local landfill was used in this study. After removing contaminant gases such as H2S and NH3via condensation and adsorption, NiMg/Ce0.6Zr0.4O2 pellets were used in a tri reforming reactor to produce H2 and CO in a ratio suitable for FTS. The conversions of CH4 and CO2 were 99% and 60% respectively. The H2/CO molar ratio was 1.7. In the FTS section, a Co/SiO2 eggshell catalyst was used to synthesize liquid hydrocarbon with high selectivity for middle distillate cuts. The CO conversion in FTS was 71% and the liquid hydrocarbon product was similar to that of low sulfur diesel. The experimental results were used to conduct a preliminary economic analysis of a commercial scale TriFTS process. The results indicate that 45% of the energy contained in the LFG can be recovered in the liquid fuel generated, with the rest going to meet the energy demands of the conversion process including heat losses. The breakeven cost of diesel fuel produced was estimated as $3.24 per gal but reduces to $2.71 per gal if the LFG is assumed to be free of cost as would be the case for landfill operators. Additional renewable fuel credits will make the process even more economically attractive. This study suggests that conversion of LFG to liquid fuels is a promising new technology ripe for commercialization.
Pulmonary Blood Flow Increases in Damaged Regions Directly after Acid Aspiration in Rats
Venue: Anesthesiology
Year: 2013

After gastric aspiration events, patients are at risk of pulmonary dysfunction and the development of severe acute lung injury and acute respiratory distress syndrome, which may contribute to the development of an inflammatory reaction. The authors' aim in the current study was to investigate the role of the spatial distribution of pulmonary blood flow in the pathogenesis of pulmonary dysfunction during the early stages after acid aspiration. The authors analyzed the pulmonary distribution of radiolabeled microspheres in normal (n 6) and injured (n 12) anesthetized rat lungs using positron emission tomography, computed tomography, and histological examination. Injured regions demonstrate increased pulmonary blood flow in association with reduced arterial pressure and the deterioration of arterial oxygenation. After acid aspiration, computed tomography scans revealed that lung density had increased in the injured regions and that these regions colocalized with areas of increased blood flow. The acid was instilled into the middle and basal regions of the lungs. The blood flow was significantly increased to these regions compared with the blood flow to uninjured lungs in the control animals (middle region: 1.23 [1.1; 1.4] (median [25% 75% vs. 1.04 [1.0; 1.1] and basal region: 1.25 [1.2; 1.3] vs. 1.02 [1.0; 1.05] respectively) The increase in blood flow did not seem to be due to vascular leakage into these injured areas. The data suggest that 10 min after acid aspiration, damaged areas are characterized by increased pulmonary blood flow. The results may impact further treatment strategies, such as drug targeting.
Magnetic Mesoporous Calcium Sillicate/Chitosan Porous Scaffolds for Enhanced Bone Regeneration and Photothermal Chemotherapy of Osteosarcoma
Venue: Scientific Reports
Year: 2018

The development of multifunctional biomaterials to repair bone defects after neoplasm removal and inhibit tumor recurrence remained huge clinical challenges. Here, we demonstrate a kind of innovative and multifunctional magnetic mesoporous calcium sillicate/chitosan (MCSC) porous scaffolds, made of M type ferrite particles (SrFe12O19) mesoporous calcium silicate (CaSiO3) and chitosan (CS) which exert robust anti tumor and bone regeneration properties. The mesopores in the CaSiO3 microspheres contributed to the drug delivery property, and the SrFe12O19 particles improved photothermal therapy (PTT) conversion efficacy. With the irradiation of NIR laser, doxorubicin (DOX) was rapidly released from the MCSC/DOX scaffolds. In vitro and in vivo tests demonstrated that the MCSC scaffolds possessed the excellent anti tumor efficacy via the synergetic effect of DOX drug release and hyperthermia ablation. Moreover, BMP 2/Smad/Runx2 pathway was involved in the MCSC scaffolds promoted proliferation and osteogenic differentiation of human bone marrow stromal cells (hBMSCs) Taken together, the MCSC scaffolds have the ability to promote osteogenesis and enhance synergetic photothermal chemotherapy against osteosarcoma, indicating MCSC scaffolds may have great application potential for bone tumor related defects.
A Sensorimotor Map: Modulating Lateral Interactions for Anticipation and Planning
Year: 2006

Experimental studies of reasoning and planned behavior have provided evidence that nervous systems use internal models to perform predictive motor control, imagery, inference, and planning. Classical (model free) reinforcement learning approaches omit such a model; standard sensorimotor models account for forward and backward functions of sensorimotor dependencies but do not provide a proper neural representation on which to realize planning. We propose a sensorimotor map to represent such an internal model. The map learns a state representation similar to self organizing maps but is inherently coupled to sensor and motor signals. Motor activations modulate the lateral connection strengths and thereby induce anticipatory shifts of the activity peak on the sensorimotor map. This mechanism encodes a model of the change of stimuli depending on the current motor activities. The activation dynamics on the map are derived from neural field models. An additional dynamic process on the sensorimotor map (derived from dynamic programming) realizes planning and emits corresponding goal directed motor sequences, for instance, to navigate through a maze.
Jobs Study The Central Africa SME Fund, Democratic Republic of Congo.
Year: 2019

The Small and Medium Enterprise (SME) ventures program of the International Finance Corporation (IFC) invests in funds targeting International Development Association (IDA) fragile and frontier countries where acute development needs persist. The Central Africa SME fund (CASF) is one of the funds to which IFC has committed risk capital and technical assistance (TA) CASF provides private equity, long term debt and management expertise to SMEs in the Democratic Republic of Congo (DRC) and the Central African Republic (CAR) This study gives insight into the employment effects of CASF's investments in SMEs in the fragile state context of the DRC. The study is based on a sample of eleven portfolio SMEs located in Kinshasa. The structure of this report is as follows: Chapter 2 discusses the study's approach, data sources and limitations; Chapter 3 introduces the macro economic context of the DRC, as well as CASF and its portfolio; Chapter 4 presents the results of the value chain analysis of sample SMEs; Chapter 5 provides insights into indirect and induced jobs related to sample SMEs; Chapter 6 discusses how fragility in the DRC affects CASF's activities, and vice versa; and Chapter 7 presents the study's conclusions and recommendations.
Effects of Artificial Light Treatments on Growth, Mineral Composition, Physiology, and Pigment Concentration in Dieffenbachia maculata "Compacta" Plants
Venue: Sustainability
Year: 2019

Specific wavebands may allow precise control of plant growth. However, light sources must be carefully evaluated before the large scale use of supplemental light sources can be implemented. Dieffenbachia maculata "Compacta" plants were grown for 8 weeks in pots in a growth chamber under tightly controlled temperature and humidity in order to assess the effects of supplemental light. Three treatments were applied: (i) using 18 W fluorescent bulbs (T1) (ii) using the same bulbs with supplemental light emitting diodes (LEDs) (Pure Blue and Pure Red Mix Light Emitting Diodes (BR LEDs) (T2) and (iii) using high efficiency TL5 fluorescents (T3) Plant biomass, mineral composition, and physiological and photosynthetic parameters were assessed under each light treatment. Total plant dry weight was highest in plants grown under treatments T1 and T3. Other differences were observed between different light treatments, including variation in biomass partitioning as well as N and K concentrations in roots, stems, and leaves. Further, proline and indole 3 acetic acid (IAA) levels were higher in plants grown under the T1 treatment, whereas total soluble sugars and starch were higher in plants grown under treatment T3. Plants grown under treatment T1 had the lowest chlorophyll concentrations. No differences were observed in organ water content and P concentration. T2 was not the best treatment, as expected. The model proposed a linear regression between integrated use of spectral energy (IUSE) and total dry weight (TDW) which showed a good relationship with an R2 value of 0.83. Therefore, we recommend this methodology to discern the effects of the different spectral qualities on plant biomass.
IoTDefender: A Federated Transfer Learning Intrusion Detection Framework for 5G IoT
Venue: 2020 IEEE 14th International Conference on Big Data Science and Engineering (BigDataSE)
Year: 2020

5G and edge computing promote the development of Internet of Things (IoT) In the near future, 5G will be used as infrastructure to connect all walks of life. At the same time, numerous resource constrained IoT devices make attacks easier and more frequent, resulting in more and more serious harm. 5G needs to provide security for the IoT it carries. 5G IoT security faces three major challenges. First, due to the heterogeneity, diversity and personalization of IoT networks, it is impossible to use a single unified detection model. Second, data in various industries exists in the form of isolated islands so it is hard to share in the light of privacy protection. Third, data island makes some industries produce too little data to train a powerful intrusion detection model. Therefore, 5G needs a personalized, distributed and effective intrusion detection system that can integrate all IoT information under the premise of protecting the privacy of each IoT data.In this paper, we propose IoTDefender, an intrusion detection framework for 5G IoT based on federated transfer learning. 5G edge computing well supports the layered and distributed structure of IoTDefender. IoTDefender carries out data aggregation by federated learning and builds customized detection models by transfer learning. It enables all IoT networks to share information without leaking privacy. Consequently, IoTDefender owns excellent generalization ability, which can highly improve the detection of unknown attacks. The experimental results demonstrate that IoTDefender is more effective (91.93% detection accuracy on average) than traditional method. Furthermore, IoTDefender produces a lower false positive rate than that of a single unified model, which means it has advantages in personalization.
Real time human body tracking based on data fusion from multiple RGB D sensors
Venue: Multimedia Tools and Applications
Year: 2016

In this work we present a human pose estimation method based on the skeleton fusion and tracking using multiple RGB D sensors. The proposed method considers the skeletons provided by each RGB D device and constructs an improved skeleton, taking into account the quality measures provided by the sensors at two different levels: the whole skeleton and each joint individually. Then, each joint is tracked by a Kalman filter, resulting in a smooth tracking performance. We have also developed a new dataset consisting of six subjects performing seven different gestures, recorded with four Kinect devices simultaneously. Experimental results performed on this dataset show that the system obtains better smoothness results than the most representative methods found in the literature. The proposed system operates at a processing rate of 25 frames per second (including the whole algorithm loop, i.e. data acquisition and processing) without the explicit use of the multithreading capabilities of the system.