nils-herrmann/clean_scirepeval huggingface dataset:
Sentiment analysis on Weibo data

With the development of the Internet, people share their emotion statuses or attitudes on online social websites, leading to an explosive rise on the scale of data. Mining sentiment information behind these data helps people know about public opinions and social trends. In this paper a sentiment analysis algorithm adapting to Weibo (Microblog) data is proposed. Given that a Weibo post is usually short, LDA model is used to generate text features based on semantic information instead of text structure. To decide the sentiment polar and degree, SVR model is used here. Experiment shows the algorithm performs well on Weibo data.
Privacy-Preserving Adaptive Multi-channel Communications Under Timing Constraints

Currently, privacy leakage has become a great concern when a web-based application is applied. The threats deriving from communications are generally caused by implementing lower-level security protocols. However, using a higher-level privacy protection approach is often restricted by the efficiency requirements, since an enhanced security level needs a longer execution time than the approach offering a lower-level privacy protection. This work focuses on this issue and proposes a new approach that provides an adaptive multi-channel communications solution. The proposed approach is called Dynamic Multi-Channel Communications (DMC2) model, which is designed to dynamically determine the transport layer protocols in terms of the timing constraints. We consider four contemporary deployed protocol types the optional channels and our method can produce optimal solutions to maximizing the privacy level. Our experimental evaluations have examined the performance of DMC2, which demonstrates its privacy protection capability and the adaptability.
An Analysis of Information Security Event Managers

The most effective security starts with real time visibility into all activity on all systems, networks, database and applications. In this paper the focus in on structured data however, some semi-structured and unstructured data is also explored. Whether the source is from network traffic, user activity, or the application user, any variation from normal of abnormal activity could indicate that a threat is imminent and that your data or infrastructure is at risk. In the last several years, there has been a disturbing trend in which attackers are innovating much faster than the defenders. There has been a commercialization of malware with attack kits available through underground forums for anyone who wants to perpetrate any variety of attacks. Large botnets are available for rent, allowing attackers to send spam or launch DDos (distributed denial-of-service) attacks. Many attackers reuse malware and command and control (C & C) and methods, adapting their products over time to keep ahead of the anti malware industry and security professionals. This paper surveys ESMs (Enterprise Security Managers) and cyber-attack case studies.
Cloud Infrastructure Resource Allocation for Big Data Applications

Increasing popular big data applications bring about invaluable information, but along with challenges to industrial community and academia. Cloud computing with unlimited resources seems to be the way out. However, this panacea cannot play its role if we do not arrange fine allocation for cloud infrastructure resources. In this paper, we present a multi-objective optimization algorithm to trade off the performance, availability, and cost of Big Data application running on Cloud. After analyzing and modeling the interlaced relations among these objectives, we design and implement our approach on experimental environment. Finally, three sets of experiments show that our approach can run about 20 percent faster than traditional optimization approaches, and can achieve about 15 percent higher performance than other heuristic algorithms, while saving 4 to 20 percent cost.
Energy efficient scheduling with probability and task migration considerations for soft real-time systems

The main challenges for embedded real-time systems which use battery as their power supply are both to satisfy the requirements of real-time systems and minimize the energy consumption. This paper studies the energy saving problem for DAG (Directed Acyclic Graph) tasks in soft real-time systems with heterogeneous multicore processors. Since soft real-time systems can tolerate occasional time violations and tasks are completed before deadlines with a given probability, this paper proposes a novel processor and voltage assignment scheme - Adaptive Processor and Voltage Assignment with Probability (APVAP) to realize the minimum energy consumption which can satisfy the requirements of time constraints under the given probability. Most of previous work focuses on multicore processor task assignment for predecessor and successor (P-S) tasks. However, this paper introduced affinity to indicate successor tasks can be re-allocated to more appropriate cores according to task features and workload. Besides, this paper introduces the concept of data migration energy (DME) to compute the transmission energy when a task is migrated to a different core and adopts Ratio between Time and Energy (RTE) to determine the most suitable tasks for migration to reduce energy consumption at the cost of execution time. The experimental results demonstrate that our approach outperforms state-of-the-art algorithms in this field (maximum improvement of 30.7%).
An LOF-Based Adaptive Anomaly Detection Scheme for Cloud Computing

One of the most attractive things about cloud computing from the perspective of business people is that it provides an effective means to outsource IT. The behaviors of business applications on cloud are constantly evolving due to technical upgrading, cloud migration as well as social outbreaks. These changes bring the challenge of detecting anomalies during the change of applications on cloud. LOF (Local Outlier Factor) algorithm has already been proven as the most promising outlier detection method for detecting network intrusions. To improve the performance of detection, LOF needs a complete set of normal behaviors of business applications, which is usually not available in cloud computing. We present an adaptive anomaly detection scheme for cloud computing based on LOF. Our scheme learns behaviors of applications both in training and detecting phase. It is adaptive to the change during detecting. The adaptability of our scheme reduces demand of efforts on collecting training data before detecting. It also enables the ability to detect contextual anomalies. Experimental results show that our scheme can effectively detect contextual anomalies with relatively low computational overhead.
Energy Optimization With Dynamic Task Scheduling Mobile Cloud Computing

The smartphone is a typical cyberphysical system (CPS). It must be low energy consuming and highly reliable to deal with the simple but frequent interactions with the cloud, which constitutes the cloud-integrated CPS.  Dynamic voltage scaling (DVS) has emerged as a critical technique to leverage power management by lowering the supply voltage and frequency of processors. In this paper, based on the DVS technique, we propose a novel  Energy-aware Dynamic Task Scheduling (EDTS) algorithm to minimize the total energy consumption for smartphones, while satisfying stringent time constraints and the probability constraint for applications. Experimental results indicate that the EDTS algorithm can significantly reduce energy consumption for CPS, as compared to the critical path scheduling method and the parallelism-based scheduling algorithm.
A Strategy to Improve the Efficiency of I/O Intensive Application in Cloud Computing Environment

An I/O intensive application is one of the most common applications in cloud computing, which is a dominant type in some occasions. However, few studies have focused on how to improve the processing efficiency of such specific type of applications. We have found out the performance bottlenecks based on the features obtained from I/O-intensive applications. By analyzing the features of files, we put forward a packagebased strategy. It means all small sized files are packaged before the specific I/O operations by using this kind of applications. This can remarkably shorten the time of addressing files and fully use of the I/O resources. To verify the strategy, we have done extensive experiments. The experimental results show the packaging strategy can efficiently improve the processing efficiency of I/O-intensive applications in cloud computing, which can be beneficial for increasing the resource efficiency of cloud data centers.
Efficient Pattern Detection for Embedded Optical Bio-sensing System

To enable pattern detection in optical signals from a novel optical biosensor used in medical embedded system, we propose a set of efficient algorithms and their corresponding implementation on FPGA (field programmable gate array). The optical biosensor is a porous silicon micro cavity membrane, which can generate different optical reflectance spectra for varieties of molecule solutions. In measured reflectance spectra of the membrane, our design is able to detect the shift of the resonant dip which is considered as the pattern to distinguish target molecule solution of different concentration. According to measured results, besides the much higher sensitivity of the novel optical sensor than classic electrochemical methods, our FPGA based implementation of our detection algorithms also shows significant speedup over software implementation on PC. The small chip area cost of FPGA implementation of our detection algorithms further ensures feasibility of ASIC (application specific integrated circuits) to incorporate both sensors and signal processing in near future.
Minimizing Update Bits of NVM-Based Main Memory Using Bit Flipping and Cyclic Shifting

Non-Volatile Memory (NVM) has many advantages, such as non-volatility, high density, shock-resistivity, and low leakage power, and disadvantages, such as the relatively high write energy and limited write endurance. It also has the feature of writing more slowly than reading. We design a read-modify-write strategy called MinFS for NVM-based main memory to reduce the number of update bits and prolong NVM lifetime. The MinFS sets one flip bit and multiple shift bits for one word to re-encode the word. The flip bit indicates whether the word has been flipped or not, while the shift bits show how many bits the word has been circularly shifted. For a write request, the MinFS first computes all possible encoded data of new data with different flip or shift values and reads out old data. Then it calculates hamming distances between old data and each encoded data. The encoded data with the minimal distance is found and written into NVM. In order to reduce the overhead, we design a hardware unit to support parallel computation of multiple hamming distances. Experimental results show that the MinFS scheme can reduce the number of update bits by 16.9% and 14.6% compared with Flip-N-Write and Min-Shift on average under real benchmarks, respectively.
Traffic big data based path planning strategy in public vehicle systems

Public vehicle (PV) systems will be efficient traffic-management platforms in future smart cities, where PVs provide ridesharing trips with balanced QoS (quality of service). PV systems differ from traditional ridesharing due to that the paths and scheduling tasks are calculated by a server according to passengers' requests, and all PVs corporate with each other to achieve higher transportation efficiency. Path planning is the primary problem. The current path planning strategies become inefficient especially for traffic big data in cities of large population and urban area. To ensure real-time scheduling, we propose one efficient path planning strategy with balanced QoS (e.g., waiting time, detour) by restricting search area for each PV, so that a large number of computation is saved. Simulation results based on the Shanghai (China) urban road network show that, the computation can be reduced by 34% compared with the exhaustive search method since many requests violating QoS are excluded.
ORCA: An offloading framework for I/O-intensive applications on clusters

This paper presents an offloading framework - ORCA - to map I/O-intensive code to a cluster that consists of computing and storage nodes. To reduce data transmission among computing and storage nodes. our offloading framework partitions and schedules CPU-bound and I/O-bound modules to computing nodes and active storage nodes, respectively. From developer's perspective, ORCA helps them to deal with execution-path control, offloading executable code, and data sharing over a network. Powered by the offloading APIs, developers without any I/O offloading or network programming experience are allowed to write new I/O-intensive code running efficiently on clusters. We implement the ORCA framework on a cluster to quantitatively evaluate performance improvements offered by our approach. We run five real-world applications on both homogeneous and heterogeneous computing environments. Experimental results show ORCA speeds up the performance of all the five tested applications by a factor of up to 90.1% with an average of 75.5%. Moreover, the results confirm that ORCA reduces network burden imposed by I/O-intensive applications by a factor of anywhere between 35 to 68.
An Investigation on Cyber Security Threats and Security Models

Cyber security has been used interchangeably for information security, where later considers the role of the human in the security process while former consider this as an additional dimension and also, focus person has a potential target. However, such discussion on cyber security has important implication as it focuses on the ethical part of the society as a whole. To address the issue of cyber security, various frameworks and models have been developed. It also introduces the concepts of cyber security in terms of its framework, workforces and information related to protecting personal information in the computer. This paper reviews these models along with their limitations and review the past techniques used to mitigate these threats. Furthermore, the report also provides recommendations for future research.
Data Allocation for Hybrid Memory With Genetic Algorithm

The gradually widening speed disparity between CPU and memory has become an overwhelming bottleneck for the development of chip multiprocessor systems. In addition, increasing penalties caused by frequent on-chip memory accesses have raised critical challenges in delivering high memory access performance with tight power and latency budgets. To overcome the daunting memory wall and energy wall issues, this paper focuses on proposing a new heterogeneous scratchpad memory architecture, which is configured from SRAM, MRAM, and Z-RAM. Based on this architecture, we propose a genetic algorithm to perform data allocation to different memory units, therefore, reducing memory access cost in terms of power consumption and latency. Extensive and experiments are performed to show the merits of the heterogeneous scratchpad architecture over the traditional pure memory system and the effectiveness of the proposed algorithms.
Optimal Big Data Sharing Approach for Tele-Health in Cloud Computing

The rapid development of tele-health systems have received driving engagements from various emerging techniques, such as big data and cloud computing. Sharing data among multiple tele-health systems is an adaptive approach for improving service quality via the network-based technologies. However, current implementations of data sharing in cloud computing is still facing the restrictions caused by the networking capacities and virtual machine switches. In this paper, we focus on the problem of data sharing obstacles in cloud computing and propose an approach that uses dynamic programming to produce optimal solutions to data sharing mechanisms. The proposed approach is called Optimal Telehealth Data Sharing Model (OTDSM), which considers transmission probabilities, maximizing network capacities, and timing constraints. Our experimental results have proved the flexibility and adoptability of the proposed method.
What is the spatial extent of an object?

This paper discusses the question: Can we improve the recognition of objects by using their spatial context? We start from Bag-of-Words models and use the Pascal 2007 dataset. We use the rough object bounding boxes that come with this dataset to investigate the fundamental gain context can bring. Our main contributions are: (I) The result of Zhang et al. in CVPR07 that context is superfluous derived from the Pascal 2005 data set of 4 classes does not generalize to this dataset. For our larger and more realistic dataset context is important indeed. (II) Using the rough bounding box to limit or extend the scope of an object during both training and testing, we find that the spatial extent of an object is determined by its category: (a) well-defined, rigid objects have the object itself as the preferred spatial extent.  It is concluded that object localisation, if done sufficiently precise, helps considerably in the recognition of objects for the Pascal 2007 dataset.
Extreme Clicking for Efficient Object Annotation

Manually annotating object bounding boxes is central to building computer vision datasets, and it is very time consuming (annotating ILSVRC [53] took 35s for one high-quality box [62]). It involves clicking on imaginary comers of a tight box around the object. This is difficult as these comers are often outside the actual object and several adjustments are required to obtain a tight box. We propose extreme clicking instead: we ask the annotator to click on four physical points on the object: the top, bottom, left- and right-most points. This task is more natural and these points are easy to find. We crowd-source extreme point annotations for PASCAL VOC 2007 and 2012 and show that (1) annotation time is only 7s per box, 5 × faster than the traditional way of drawing boxes [62]: (2) the quality of the boxes is as good as the original ground-truth drawn the traditional way: (3) detectors trained on our annotations are as accurate as those trained on the original ground-truth. Moreover, our extreme clicking strategy not only yields box coordinates, but also four accurate boundary points. We show (4) how to incorporate them into GrabCut to obtain more accurate segmentations than those delivered when initializing it from bounding boxes: (5) semantic segmentations models trained on these segmentations outperform those trained on segmentations derived from bounding boxes.
Situational object boundary detection

Intuitively, the appearance of true object boundaries varies from image to image. Hence the usual monolithic approach of training a single boundary predictor and applying it to all images regardless of their content is bound to be suboptimal. In this paper we therefore propose situational object boundary detection: We first define a variety of situations and train a specialized object boundary detector for each of them using . Then given a test image, we classify it into these situations using its context, which we model by global image appearance. We apply the corresponding situational object boundary detectors, and fuse them based on the classification probabilities. In experiments on ImageNet [35], Microsoft COCO [24], and Pascal VOC 2012 segmentation [13] we show that our situational object boundary detection gives significant improvements over a monolithic approach. Additionally, our method substantially outperforms  on semantic contour detection on their SBD dataset.
Training Object Class Detectors with Click Supervision

Training object class detectors typically requires a large set of images with objects annotated by bounding boxes. However, manually drawing bounding boxes is very time consuming. In this paper we greatly reduce annotation time by proposing center-click annotations: we ask annotators to click on the center of an imaginary bounding box which tightly encloses the object instance. We then incorporate these clicks into existing Multiple Instance Learning techniques for weakly supervised object localization, to jointly localize object bounding boxes over all training images. Extensive experiments on PASCAL VOC 2007 and MS COCO show that: (1) our scheme delivers high-quality detectors, performing substantially better than those produced by weakly supervised techniques, with a modest extra annotation effort, (2) these detectors in fact perform in a range close to those trained from manually drawn bounding boxes, (3) as the center-click task is very fast, our scheme reduces total annotation time by 9x to 18x.
Real-Time Visual Concept Classification

As datasets grow increasingly large in content-based image and video retrieval, computational efficiency of concept classification is important. This paper reviews techniques to accelerate concept classification, where we show the trade-off between computational efficiency and accuracy. As a basis, we use the Bag-of-Words algorithm that in the 2008 benchmarks of TRECVID and PASCAL lead to the best performance scores. We divide the evaluation in three steps: 1) Descriptor Extraction, where we evaluate SIFT, SURF, DAISY, and Semantic Textons. 2) Visual Word Assignment, where we compare a k-means visual vocabulary with a Random Forest and evaluate subsampling, dimension reduction with PCA, and division strategies of the Spatial Pyramid. 3) Classification, where we evaluate the χ2, RBF, and Fast Histogram Intersection kernel for the SVM. Apart from the evaluation, we accelerate the calculation of densely sampled SIFT and SURF, accelerate nearest neighbor assignment, and improve accuracy of the Histogram Intersection kernel. We conclude by discussing whether further acceleration of the Bag-of-Words pipeline is possible. Our results lead to a 7-fold speed increase without accuracy loss, and a 70-fold speed increase with 3% accuracy loss. The latter system does classification in real-time, which opens up new applications for automatic concept classification. For example, this system permits five standard desktop PCs to automatically tag for 20 classes all images that are currently uploaded to Flickr.
We Don’t Need No Bounding-Boxes: Training Object Class Detectors Using Only Human Verification

Training object class detectors typically requires a large set of images in which objects are annotated by boundingboxes. However, manually drawing bounding-boxes is very time consuming. We propose a new scheme for training object detectors which only requires annotators to verify bounding-boxes produced automatically by the learning algorithm. Our scheme iterates between re-training the detector, re-localizing objects in the training images, and human verification. We use the verification signal both to improve re-training and to reduce the search space for re-localisation, which makes these steps different to what is normally done in a weakly supervised setting. Extensive experiments on PASCAL VOC 2007 show that (1) using human verification to update detectors and reduce the search space leads to the rapid production of high-quality bounding-box annotations, (2) our scheme delivers detectors performing almost as good as those trained in a fully supervised setting, without ever drawing any bounding-box, (3) as the verification task is very quick, our scheme substantially reduces total annotation time by a factor 6×-9×.
Device selective quantization for reversible wavelets

Compression with reversible embedded wavelets (CREW) consists of three major parts: a wavelet transform, an embedded context model, and a binary entropy coder. The wavelet transform is a special implementation of high quality wavelet which provides perfect reconstruction with finite precision coefficients. The context model describes the wavelet coefficients in a particular order with conditional probability estimates. Finally the FSM-coder, a binary entropy coder with functionality similar to the QM-coder, is used to generate the final code stream.