nils-herrmann/clean_scirepeval huggingface dataset:
Bootstrap Domain-Specific Sentiment Classifiers from Unlabeled Corpora

There is often the need to perform sentiment classification in a particular domain where no labeled document is available. Although we could make use of a general-purpose off-the-shelf sentiment classifier or a pre-built one for a different domain, the effectiveness would be inferior. In this paper, we explore the possibility of building domain-specific sentiment classifiers with unlabeled documents only. Our investigation indicates that in the word embeddings learned from the unlabeled corpus of a given domain, the distributed word representations (vectors) for opposite sentiments form distinct clusters, though those clusters are not transferable across domains. Exploiting such a clustering structure, we are able to utilize machine learning algorithms to induce a quality domain-specific sentiment lexicon from just a few typical sentiment words (“seeds”). An important finding is that simple linear model based supervised learning algorithms (such as linear SVM) can actually work better than more sophisticated semi-supervised/transductive learning algorithms which represent the state-of-the-art technique for sentiment lexicon induction. The induced lexicon could be applied directly in a lexicon-based method for sentiment classification, but a higher performance could be achieved through a two-phase bootstrapping method which uses the induced lexicon to assign positive/negative sentiment scores to unlabeled documents first, a nd t hen u ses those documents found to have clear sentiment signals as pseudo-labeled examples to train a document sentiment classifier v ia supervised learning algorithms (such as LSTM). On several benchmark datasets for document sentiment classification, our end-to-end pipelined approach which is overall unsupervised (except for a tiny set of seed words) outperforms existing unsupervised approaches and achieves an accuracy comparable to that of fully supervised approaches.
A review of bidirectional on-board charger topologies for plugin vehicles

On-board bidirectional battery charger is a key component in a plug-in vehicle with vehicle-to-grid (V2G) capability. In a high-performance on-board battery charger, volume and weight should be minimized. Battery charger topologies are generally classified under the two main categories of single-stage and two-stages. Each category has its own merits and drawbacks. This paper attempts to make a review of the existing bidirectional battery charger topologies.
An Energy Efficient Ant Colony System for Virtual Machine Placement in Cloud Computing

Virtual machine placement (VMP) and energy efficiency are significant topics in cloud computing research. In this paper, evolutionary computing is applied to VMP to minimize the number of active physical servers, so as to schedule underutilized servers to save energy. Inspired by the promising performance of the ant colony system (ACS) algorithm for combinatorial problems, an ACS-based approach is developed to achieve the VMP goal. Coupled with order exchange and migration (OEM) local search techniques, the resultant algorithm is termed an OEMACS. It effectively minimizes the number of active servers used for the assignment of virtual machines (VMs) from a global optimization perspective through a novel strategy for pheromone deposition which guides the artificial ants toward promising solutions that group candidate VMs together. The OEMACS is applied to a variety of VMP problems with differing VM sizes in cloud environments of homogenous and heterogeneous servers. The results show that the OEMACS generally outperforms conventional heuristic and other evolutionary-based approaches, especially on VMP with bottleneck resource characteristics, and offers significant savings of energy and more efficient use of different resources.
Assessing a Firm's Web Presence: A Heuristic Evaluation Procedure for the Measurement of Usability

Web site usability is a critical metric for assessing the quality of a firm’s Web presence. A measure of usability must not only provide a global rating for a specific Web site, ideally it should also illuminate specific strengths and weaknesses associated with site design. In this paper, we describe a heuristic evaluation procedure for examining the usability of Web sites. The procedure utilizes a comprehensive set of usability guidelines developed by Microsoft. We present the categories and subcategories comprising these guidelines, and discuss the development of an instrument that operationalizes the measurement of usability. The proposed instrument was tested in a heuristic evaluation study where 1,475 users rated multiple Web sites from four different industry sectors: airlines, online bookstores, automobile manufacturers, and car rental agencies. To enhance the external validity of the study, users were asked to assume the role of a consumer or an investor when assessing usability. Empirical results suggest that the evaluation procedure, the instrument, as well as the usability metric exhibit good properties. Implications of the findings for researchers, for Web site designers, and for heuristic evaluation methods in usability testing are offered. (Usability; Heuristic Evaluation; Microsoft Usability Guidelines; Human-Computer Interaction; Web Interface)
Bank Notes Authentication System based on Wavelet Features and Artificial Neural Network

Monetary transactions are integral part of our day to day activities, so currency authentication has become one of the active research area at present and it has vast potential applications. In this paper we introduced a system to verify the authentication of banknotes bench mark dataset using computer vision. We considered 1372 samples of various bank notes in our work. The technology of currency authentication aims to search and extract potential features of paper currency for efficient classification. Features were extracted from images that were taken from genuine and forged banknote-like specimens. They are classified using Artificial Neural network. The variance of Wavelet Transformed image (continuous), skewness of Wavelet Transformed image (continuous),curtosis of Wavelet Transformed image (continuous),entropy of image (continuous) features are extracted from images for accurate classification. Our proposed system able to authenticate with zero percent misclassification.
CopperDroid: Automatic Reconstruction of Android Malware Behaviors

Mobile devices and their application marketplaces drive the entire economy of the today’s mobile landscape. Android platforms alone have produced staggering revenues, exceeding five billion USD, which has attracted cybercriminals and increased malware in Android markets at an alarming rate. To better understand this slew of threats, we present CopperDroid, an automatic VMI-based dynamic analysis system to reconstruct the behaviors of Android malware. The novelty of CopperDroid lies in its agnostic approach to identify interesting OSand high-level Android-specific behaviors. It reconstructs these behaviors by observing and dissecting system calls and, therefore, is resistant to the multitude of alterations the Android runtime is subjected to over its life-cycle. CopperDroid automatically and accurately reconstructs events of interest that describe, not only well-known process-OS interactions (e.g., file and process creation), but also complex intraand inter-process communications (e.g., SMS reception), whose semantics are typically contextualized through complex Android objects. Because CopperDroid’s reconstruction mechanisms are agnostic to the underlying action invocation methods, it is able to capture actions initiated both from Java and native code execution. CopperDroid’s analysis generates detailed behavioral profiles that abstract a large stream of low-level—often uninteresting—events into concise, high-level semantics, which are well-suited to provide insightful behavioral traits and open the possibility to further research directions. We carried out an extensive evaluation to assess the capabilities and performance of CopperDroid on more than 2,900 Android malware samples. Our experiments show that CopperDroid faithfully reconstructs OSand Android-specific behaviors. Additionally, we demonstrate how CopperDroid can be leveraged to disclose additional behaviors through the use of a simple, yet effective, app stimulation technique. Using this technique, we successfully triggered and disclosed additional behaviors on more than 60% of the analyzed malware samples. This qualitatively demonstrates the versatility of CopperDroid’s ability to improve dynamic-based code coverage.
Scalable Recognition with a Vocabulary Tree

A recognition scheme that scales efficiently to a large number of objects is presented. The efficiency and quality is exhibited in a live demonstration that recognizes CD-covers from a database of 40000 images of popular music CD’s. The scheme builds upon popular techniques of indexing descriptors extracted from local regions, and is robust to background clutter and occlusion. The local region descriptors are hierarchically quantized in a vocabulary tree. The vocabulary tree allows a larger and more discriminatory vocabulary to be used efficiently, which we show experimentally leads to a dramatic improvement in retrieval quality. The most significant property of the scheme is that the tree directly defines the quantization. The quantization and the indexing are therefore fully integrated, essentially being one and the same. The recognition quality is evaluated through retrieval on a database with ground truth, showing the power of the vocabulary tree approach, going as high as 1 million images.
Chitosan in Non-Viral Gene Delivery : Role of Structure , Characterization Methods , and Insights in Cancer and Rare Diseases Therapies

Non-viral gene delivery vectors have lagged far behind viral ones in the current pipeline of clinical trials of gene therapy nanomedicines. Even when non-viral nanovectors pose less safety risks than do viruses, their efficacy is much lower. Since the early studies to deliver pDNA, chitosan has been regarded as a highly attractive biopolymer to deliver nucleic acids intracellularly and induce a transgenic response resulting in either upregulation of protein expression (for pDNA, mRNA) or its downregulation (for siRNA or microRNA). This is explained as the consequence of a multi-step process involving condensation of nucleic acids, protection against degradation, stabilization in physiological conditions, cellular internalization, release from the endolysosome (“proton sponge” effect), unpacking and enabling the trafficking of pDNA to the nucleus or the siRNA to the RNA interference silencing complex (RISC). Given the multiple steps and complexity involved in the gene transfection process, there is a dearth of understanding of the role of chitosan’s structural features (Mw and degree of acetylation, DA%) on each step that dictates the net transfection efficiency and its kinetics. The use of fully characterized chitosan samples along with the utilization of complementary biophysical and biological techniques is key to bridging this gap of knowledge and identifying the optimal chitosans for delivering a specific gene. Other aspects such as cell type and administration route are also at play. At the same time, the role of chitosan structural features on the morphology, size and surface composition of synthetic virus-like particles has barely been addressed. The ongoing revolution brought about by the recent discovery of CRISPR-Cas9 technology will undoubtedly be a game changer in this field in the short term. In the field of rare diseases, gene therapy is perhaps where the greatest potential lies and we anticipate that chitosans will be key players in the translation of research to the clinic.
Does public policy support or undermine climate change adaptation ? Exploring policy interplay across different scales of governance

Policy makers have now recognised the need to integrate thinking about climate change into all areas of public policy making. However, the discussion of ‘climate policy integration’ has tended to focus on mitigation decisions mostly taken at international and national levels. Clearly, there is also a more locally focused adaptation dimension to climate policy integration, which has not been adequately explored by academics or policy makers. Drawing on a case study of the UK, this paper adopts both a top-down and a bottom-up perspective to explore how far different sub-elements of policies within the agriculture, nature conservation and water sectors support or undermine potential adaptive responses. The top-down approach, which assumes that policies set explicit aims and objectives that are directly translated into action on the ground, combines a content analysis of policy documents with interviews with policy makers. The bottom-up approach recognises the importance of other actors in shaping policy implementation and involves interviews with actors in organisations within the three sectors. This paper reveals that neither approach offers a complete picture of the potentially enabling or constraining effects of different policies on future adaptive planning, but together they offer new perspectives on climate policy integration. These findings inform a discussion on how to implement climate policy integration, including auditing existing policies and ‘climate proofing’ new ones so they support rather than hinder adaptive planning.
Measuring the user experience on a large scale: user-centered metrics for web applications

More and more products and services are being deployed on the web, and this presents new challenges and opportunities for measurement of user experience on a large scale. There is a strong need for user-centered metrics for web applications, which can be used to measure progress towards key goals, and drive product decisions. In this note, we describe the HEART framework for user-centered metrics, as well as a process for mapping product goals to metrics. We include practical examples of how HEART metrics have helped product teams make decisions that are both data-driven and user-centered. The framework and process have generalized to enough of our company's own products that we are confident that teams in other organizations will be able to reuse or adapt them. We also hope to encourage more research into metrics based on large-scale behavioral data.
Evaluating Shallow and Deep Neural Networks for Network Intrusion Detection Systems in Cyber Security

Intrusion detection system (IDS) has become an essential layer in all the latest ICT system due to an urge towards cyber safety in the day-to-day world. Reasons including uncertainty in finding the types of attacks and increased the complexity of advanced cyber attacks, IDS calls for the need of integration of Deep Neural Networks (DNNs). In this paper, DNNs have been utilized to predict the attacks on Network Intrusion Detection System (N-IDS). A DNN with 0.1 rate of learning is applied and is run for 1000 number of epochs and KDDCup-‘99’ dataset has been used for training and benchmarking the network. For comparison purposes, the training is done on the same dataset with several other classical machine learning algorithms and DNN of layers ranging from 1 to 5. The results were compared and concluded that a DNN of 3 layers has superior performance over all the other classical machine learning algorithms.
Will Harmonizing Accounting Standards Really Harmonize Accounting ?

International harmonization of accounting standards appears to be inevitable. However, little evidence exists regarding whether harmonizing accounting standards will result in actual harmonization of accounting practices. Using a sample of non-US firms that adopt US GAAP to provide evidence on this issue, we find that most firms that adopt US GAAP adjust their accounting methods to those required by US GAAP. Properties of the firms’ accounting numbers also change significantly after adopting US GAAP, but do not fully converge towards that of US firms. In the cross-section, regulatory oversight is associated with more successful implementation of US GAAP; firm-specific capital market incentives are not. These results suggest that harmonizing accounting standards may result in more comparable accounting methods and numbers, but that effective regulatory oversight will be important in reaching this outcome.
Review Conversational Reading Comprehension

Seeking information about products and services is an important activity of online consumers before making a purchase decision. Inspired by recent research on conversational reading comprehension (CRC) on formal documents, this paper studies the task of leveraging knowledge from a huge amount of reviews to answer multi-turn questions from consumers or users. Questions spanning multiple turns in a dialogue enables users to ask more specific questions that are hard to ask within a single question as in traditional machine reading comprehension (MRC). In this paper, we first build a dataset and then propose a novel task-adaptation approach to encoding the formulation of CRC task into a pretrained language model. This task-adaptation approach is unsupervised and can greatly enhance the performance of the end CRC task that has only limited supervision. Experimental results show that the proposed approach is highly effective and has competitive performance as supervised approach.
Smartphone-Based Indoor Localization with Bluetooth Low Energy Beacons

Indoor wireless localization using Bluetooth Low Energy (BLE) beacons has attracted considerable attention after the release of the BLE protocol. In this paper, we propose an algorithm that uses the combination of channel-separate polynomial regression model (PRM), channel-separate fingerprinting (FP), outlier detection and extended Kalman filtering (EKF) for smartphone-based indoor localization with BLE beacons. The proposed algorithm uses FP and PRM to estimate the target's location and the distances between the target and BLE beacons respectively. We compare the performance of distance estimation that uses separate PRM for three advertisement channels (i.e., the separate strategy) with that use an aggregate PRM generated through the combination of information from all channels (i.e., the aggregate strategy). The performance of FP-based location estimation results of the separate strategy and the aggregate strategy are also compared. It was found that the separate strategy can provide higher accuracy; thus, it is preferred to adopt PRM and FP for each BLE advertisement channel separately. Furthermore, to enhance the robustness of the algorithm, a two-level outlier detection mechanism is designed. Distance and location estimates obtained from PRM and FP are passed to the first outlier detection to generate improved distance estimates for the EKF. After the EKF process, the second outlier detection algorithm based on statistical testing is further performed to remove the outliers. The proposed algorithm was evaluated by various field experiments. Results show that the proposed algorithm achieved the accuracy of <2.56 m at 90% of the time with dense deployment of BLE beacons (1 beacon per 9 m), which performs 35.82% better than <3.99 m from the Propagation Model (PM) + EKF algorithm and 15.77% more accurate than <3.04 m from the FP + EKF algorithm. With sparse deployment (1 beacon per 18 m), the proposed algorithm achieves the accuracies of <3.88 m at 90% of the time, which performs 49.58% more accurate than <8.00 m from the PM + EKF algorithm and 21.41% better than <4.94 m from the FP + EKF algorithm. Therefore, the proposed algorithm is especially useful to improve the localization accuracy in environments with sparse beacon deployment.
Geometry of Optimization and Implicit Regularization in Deep Learning

We argue that the optimization plays a crucial role in generalization of deep learning models through implicit regularization. We do this by demonstrating that generalization ability is not controlled by network size but rather by some other implicit control. We then demonstrate how changing the empirical optimization procedure can improve generalization, even if actual optimization quality is not affected. We do so by studying the geometry of the parameter space of deep networks, and devising an optimization algorithm attuned to this geometry.
Automatic knowledge extraction from documents

extraction from documents J. Fan A. Kalyanpur D. C. Gondek D. A. Ferrucci Access to a large amount of knowledge is critical for success at answering open-domain questions for DeepQA systems such as IBM Watsoni. Formal representation of knowledge has the advantage of being easy to reason with, but acquisition of structured knowledge in open domains from unstructured data is often difficult and expensive. Our central hypothesis is that shallow syntactic knowledge and its implied semantics can be easily acquired and can be used in many areas of a question-answering system. We take a two-stage approach to extract the syntactic knowledge and implied semantics. First, shallow knowledge from large collections of documents is automatically extracted. Second, additional semantics are inferred from aggregate statistics of the automatically extracted shallow knowledge. In this paper, we describe in detail what kind of shallow knowledge is extracted, how it is automatically done from a large corpus, and how additional semantics are inferred from aggregate statistics. We also briefly discuss the various ways extracted knowledge is used throughout the IBM DeepQA system.
Effect of a coteaching handwriting program for first graders: one-group pretest-posttest design.

We examined the effects of a cotaught handwriting and writing program on first-grade students grouped by low, average, and high baseline legibility. The program's aim was to increase legibility, handwriting speed, writing fluency, and written expression in students with diverse learning needs. Thirty-six first-grade students in two classrooms participated in a 12-wk handwriting and writing program cotaught by teachers and an occupational therapist. Students were assessed at pretest, posttest, and 6-mo follow-up using the Evaluation Tool of Children's Handwriting-Manuscript (ETCH-M) and the Woodcock-Johnson Writing Fluency and Writing Samples tests. Students made large gains in ETCH-M legibility (η² = .74), speed (η²s = .52-.65), Writing Fluency (η² = .58), and Writing Samples (η² = .59). Students with initially low legibility improved most in legibility; progress on the other tests was similar across low-, average-, and high-performing groups. This program appeared to benefit first-grade students with diverse learning needs and to increase handwriting legibility and speed and writing fluency.
Self-Organizing Maps In Natural Language Processing

Kohonen's Self-Organizing Map (SOM) is one of the most popular arti cial neural network algorithms. Word category maps are SOMs that have been organized according to word similarities, measured by the similarity of the short contexts of the words. Conceptually interrelated words tend to fall into the same or neighboring map nodes. Nodes may thus be viewed as word categories. Although no a priori information about classes is given, during the self-organizing process a model of the word classes emerges. The central topic of the thesis is the use of the SOM in natural language processing. The approach based on the word category maps is compared with the methods that are widely used in arti cial intelligence research. Modeling gradience, conceptual change, and subjectivity of natural language interpretation are considered. The main application area is information retrieval and textual data mining for which a speci c SOM-based method called the WEBSOM has been developed. The WEBSOM method organizes a document collection on a map display that provides an overview of the collection and facilitates interactive browsing.