nils-herrmann/clean_scirepeval huggingface dataset:
Analysis of Financial Time Sequence using Hidden Markov Models

Financial time sequence analysis has been a popular research topic in the field of finance, data science and machine learning. It is a highly challenging due to the extreme complexity within the sequences. Mostly existing models are failed to capture its intrinsic information, factor and tendency. To improve the previous approaches, in this paper, we propose a Hidden Markov Model (HMMs) based approach to analyze the financial time sequence. The fluctuation of financial time sequence was predicted through introducing a dual-state HMMs. Dual-state HMMs models the sequence and produces the features which will be delivered to SVMs for prediction. Note that we cast a financial time sequence prediction problem to a classification problem. To evaluate the proposed approach, we use Shanghai Composite Index as the dataset for empirically experiments. The dataset was collected from 550 consecutive trading days, and is randomly split to the training set and test set. The extensively experimental results show that: when analyzing financial time sequence, the mean-square error calculated with HMMs was obviously smaller error than the compared GARCH approach. Therefore, when using HMM to predict the fluctuation of financial time sequence, it achieves higher accuracy and exhibits several attractive advantageous over GARCH approach.
Estimation and mitigation of current harmonics using improved control schemes

Algorithms are well presented for estimation of harmonics and their elimination in a three phase system for power quality improvement. This paper presents the application of KF-Adaline Algorithm for estimation of power system harmonics and shunt active power filter with hysteresis current controller based on space vector modulation for perfect elimination of harmonics. The space vector modulation (SVM) based hysteresis current controller (HCC) combines both the advantages of SVM and HCC. This controller determines a set of space vector from a region detector and then applies a space vector according to the Hysteresis current controller. Reference current extraction is done by the synchronous detection technique which is compared to the actual current and the error current is fed to the Shunt Active Power Filter (SAPF). Shunt Active Power Filter manages to provide better compensation signals to eliminate the harmonics for power quality improvement which is fed to the distribution system. Simulation results are provided to verify the better performance of SAPF due to the proposed controller.
Latency-driven design for FPGA-based convolutional neural networks

In recent years, Convolutional Neural Networks (ConvNets) have become the quintessential component of several state-of-the-art Artificial Intelligence tasks. Across the spectrum of applications, the performance needs vary significantly, from high-throughput image recognition to the very low-latency requirements of autonomous cars. In this context, FPGAs can provide a potential platform that can be optimally configured based on different performance requirements. However, with the increasing complexity of ConvNet models, the architectural design space becomes overwhelmingly large, asking for principled design flows that address the application-level needs. This paper presents a latency-driven design methodology for mapping ConvNets on FPGAs. The proposed design flow employs novel transformations over a Synchronous Dataflow-based modelling framework together with a latency-centric optimisation procedure in order to efficiently explore the design space targeting low-latency designs. Quantitative evaluation shows large improvements in latency when latency-driven optimisation is in place yielding designs that improve the latency of AlexNet by 73.54× and VGG16 by 5.61× over throughput-optimised designs.
A review of optimization method in face recognition: Comparison deep learning and non-deep learning methods

Currently, face recognition system is growing sustainably on a larger scope. A few years ago, face recognition was used as a personal identification with a limited scope, now this technology has grown in the field of security, in terms of preventing fraudsters, criminals, and terrorists. In addition, face recognition is also used in detecting how tired a driver is, reducing the occurrence of road accidents, as well as in marketing, advertising, health, and others. Many method are developed to give the best accuracy in face recognition. Deep learning approach become trend in this field because of stunning results, and fast computation. However, the problem about accuracy, complexity, and scalability become a challenges in face recognition. This paper focus on recognizing the importance of this technology, how to achieve high accuracy with low complexity. Deep learning and non-deep learning methods are discussed and compared to analyze their advantages and disadvantages. From critical analysis using experiment with YALE dataset, non-deep learning algorithm can reach up to 90.6% for low-high complexity and 94.67% in deep learning method for low-high complexity. Genetic algorithm combining with CNN and SVM was an optimization method for overcome accuracy and complexity problems.
A New Multi Criteria Decision Making Method: Approach of Logarithmic Concept (APLOCO)

The primary aim of the study is to introduce APLOCO method which is developed for the solution of multicriteria decision making problems both theoretically and practically. In this context, application subject of APLACO constitutes evaluation of investment potential of different cities in metropolitan status in Turkey. The secondary purpose of the study is to identify the independent variables affecting the factories in the operating phase and to estimate the effect levels of independent variables on the dependent variable in the organized industrial zones (OIZs), whose mission is to reduce regional development disparities and to mobilize local production dynamics. For this purpose, the effect levels of independent variables on dependent variables have been determined using the multilayer perceptron (MLP) method, which has a wide use in artificial neural networks (ANNs). The effect levels derived from MLP have been then used as the weight levels of the decision criteria in APLOCO. The independent variables included in MLP are also used as the decision criteria in APLOCO. According to the results obtained from APLOCO, Istanbul city is the best alternative in term of the investment potential and other alternatives are Manisa, Denizli, Izmir, Kocaeli, Bursa, Ankara, Adana, and Antalya, respectively. Although APLOCO is used to solve the ranking problem in order to show application process in the paper, it can be employed easily in the solution of classification and selection problems. On the other hand, the study also shows a rare example of the nested usage of APLOCO which is one of the methods of operation research as well as MLP used in determination of weights.
CookiExt: Patching the browser against session hijacking attacks

Session cookies constitute one of the main attack targets against client authentication on the Web. To counter these attacks, modern web browsers implement native cookie protection mechanisms based on the HttpOnly and Secure flags. While there is a general understanding about the effectiveness of these defenses, no formal result has so far been proved about the security guarantees they convey. With the present paper we provide the first such result, by presenting a mechanized proof of noninterference assessing the robustness of the HttpOnly and Secure cookie flags against both web and network attackers with the ability to perform arbitrary XSS code injection. We then develop CookiExt, a browser extension that provides client-side protection against session hijacking, based on appropriate flagging of session cookies and automatic redirection over HTTPS for HTTP requests carrying these cookies. Our solution improves over existing client-side defenses by combining protection against both web and network attacks, while at the same time being designed so as to minimise its effects on the user’s browsing experience. Finally, we report on the experiments we carried out to practically evaluate the effectiveness of our approach.
Reduction malarplasty using a zygomatic arch-lifting technique.

Broad or excessive malar protrusion is a trait associated with aggression and old age in Asian cultures. Although various methods of shifting the zygoma have been introduced for reduction malarplasty, soft tissue sagging and inadequate bony union still remain great challenges. We have devised an arch-lifting technique that helps overcome these issues. An analysis of surgical outcomes is presented herein.



A total of 54 patients subjected to lifting malar reductions between January 2013 and November 2014 were retrospectively reviewed. The reduction procedure entailed an L-shaped osteotomy of the zygomaticomaxillary junction via an intraoral approach. In addition, a prefabricated U-shaped microplate was applied for arch fixation in the lifted position. The follow-up period ranged from 6 to 18 months (average, 9.2 months), during which medical records, photographs, and facial bone computed tomography (CT) images were obtained to assess the postoperative results.



Patients were generally satisfied with aesthetic outcomes, thus rating the procedure excellent in terms of zygomatic prominence, midfacial width, symmetry, and resistance to cheek drooping. There were no major complications, such as facial nerve damage or trismus. An inadequate bony contact occurred in two instances due to unanticipated trauma, with immediate reduction and fixation thereafter. Minor wound infections developed in three patients but responded well to antibiotics.



Zygomatic reduction procedures must consider the dynamics of the adjacent muscles, which are stabilized through arch fixation. The use of our arch-lifting technique for reduction malarplasty efficiently elevates the zygomatic complex, thereby ensuring an adequate bone-to-bone contact. Predictable and accurate outcomes are thereby achieved.
Noise optimization of an inductively degenerated CMOS low noise amplifier

This paper presents a technique for substantially reducing the noise of a CMOS low noise amplifier implemented in the inductive source degeneration topology. The effects of the gate induced current noise on the noise performance are taken into account, and the total output noise is strongly reduced by inserting a capacitance of appropriate value in parallel with the amplifying MOS transistor of the LNA. As a result, very low noise figures become possible already at very low power consumption levels.
New Propeller-Type Tribocharging Device With Application to the Electrostatic Separation of Granular Insulating Materials

The aim of this paper is the development and functional optimization of a new propeller-type aerodynamic tribocharging device with application in the field of electrostatic separation. The originality of the system consists of its modular structure: one or several propellers can be stacked in the same device to provide appropriate tribocharging conditions to a wide variety of granular mixtures containing two or more of insulating materials. The study is conducted with samples of the following various insulating materials: polycarbonate, polyamide, acrylonitrile butadiene styrene, polyvinyl chloride, and high-impact polystyrene, grain sizes up to 4 mm in diameter, for several values of the propeller rotation speed, and of the mass of the particles in the tribocharging device, the wall of which are made of acetate, Polymethyl methacrylate (PMMA), or aluminum. The efficiency of the device is tested by processing the charged granular mixture in a metal-belt conveyor-type electrostatic separator. The aluminum-wall device enables better charging that PMMA and acetate. The mass introduced in the device has no significant effect on the outcome of the process, but the speed of the propellers does. Successful separation of a mixture of three insulating materials is reported.
AUGMENTED REALITY BROWSERS: A PROPOSAL FOR ARCHITECTURAL STANDARDIZATION

The technology evolution of smartphones, systems and the growing of telecommunications, allow the use of Augmented Reality in the exploration of geo-referenced information, complementing the real environment of the users with various types of content displayed on robust mobile cameras through applications called augmented reality browsers. Although this type of application is being in development and growing use by society, the technology, and especially its software architecture do not have any kind of standardization. This work presents concepts about augmented reality browsers for mobile devices. It shows the main aspects and applications of those types of reality. Besides that, the specific features of those types of architecture are discussed and compared, and new architecture, whose most relevant feature is the interoperability of applications in various platforms for mobile devices, are presented. This work has the objective to develop an architectural framework for the development of these browsers.
Internal crowdsourcing: conceptual framework, structured review, and research agenda

The use of IT-enabled crowdsourcing with employees in enterprises has increased substantially in recent years. This phenomenon, which we refer to as ‘internal crowdsourcing’, is distinct both from external crowdsourcing with end users and from hierarchy-based work with employees. A literature stream has emerged that corresponds with the increased relevance of internal crowdsourcing in practice. The purpose of this review paper of internal crowdsourcing is to provide conceptual development, synthesise the literature, and provide a research agenda. In the review reported in this paper, we systematically analysed and critically reviewed the literature in this domain published thus far (74 papers). We found useful findings and insights into a new and relevant IT-enabled phenomenon. At the same time, we also found conflicting definitions and conceptualisation, as well as research efforts that are not well integrated. The paper supports future research on internal crowdsourcing by providing improved conceptualisation, consolidating insights, and identifying important areas for future research.  (2016) 31, 166–184. doi:10.1057/jit.2016.14
An Access Control Framework for Cloud-Enabled Wearable Internet of Things

Internet of Things (IoT) has become a pervasive and diverse concept in recent years. IoT applications and services have given rise to a number of sub-fields in the IoT space. Wearable technology, with its particular set of characteristics and application domains, has formed a rapidly growing sub-field of IoT, viz., Wearable Internet of Things (WIoT). While numerous wearable devices are available in the market today, security and privacy are key factors for wide adoption of WIoT. Wearable devices are resource constrained by nature with limited storage, power, and computation. A Cloud-Enabled IoT (CEIoT) architecture, a dominant paradigm currently shaping the industry and suggested by many researchers, needs to be adopted for WIoT. In this paper, we develop an access control framework for cloud-enabled WIoT (CEWIoT) based on the Access Control Oriented (ACO) architecture recently developed for CEIoT in general. We first enhance the ACO architecture from the perspective of WIoT by adding an Object Abstraction Layer, and then develop our framework based on interactions between different layers of this enhanced ACO architecture. We present a general classification and taxonomy of IoT devices, along with brief introduction to various application domains of IoT and WIoT. We then present a remote health and fitness monitoring use case to illustrate different access control aspects of our framework and outline its possible enforcement in a commercial CEIoT platform, viz., AWS IoT. Finally, we discuss the objectives of our access control framework and relevant open problems.
Occupational Therapy in Mental Health Exploring the Safety and Therapeutic Effects of Deep Pressure Stimulation Using a Weighted Blanket

This paper presents the results of a concurrent, nested, mixed methods exploratory study on the safety and e ectiveness of the use of a 30 lb weighted blanket with a convenience sample of 32 adults. Safety is investigated measuring blood pressure, pulse rate, and pulse oximetry, and e ectiveness by electrodermal activity (EDA), the State Trait Anxiety Inventory-10 and an exit survey. The results reveal that Brian Mullen (E-mail: Bmullen@student.umass.edu), BS, is Graduate Research Assistant, Sundar Krishnamurty (E-mail: skrishna@ecs.umass.edu), PhD, is In terim Department Head and Associate Professor, and Robert X. Gao (E-mail: Gao@ecs.umass.edu), PhD, is Professor; all are at University of MassachusettsAmherst, Department of Mechanical & Industrial Engineering–ELAB Building, 160 Governors Drive, Amherst, MA 01003 . Tina Champagne (E-mail: Tina_Champagne@cooley-dickinson.org), MEd, OTR/L, is Occupational Therapy and Group Program Supervisor, and Debra Dickson (E-mail: Debra_Dickson@cooley-dickinson.org), APRN, BC, is Behavioral Health Clinical Nurse Specialist; both are at Cooley Dickinson Hospital, Acute Inpatient Behav ioral Health Department, 30 Locust Street, Northampton, MA 01060. Address correspondence to Tina Champagne at the above address. The authors wish to acknowledge and thank the UMASS-Amherst School of Nursing for providing use of the nursing lab and vital signs monitoring equipment for the pur poses of this study and to Dr. Keli Mu for his assistance with the revisions of this paper.  Therapy in Mental Health, Vol. 24(1) 2008 Available online at http://otmh.haworthpress.com  Data obtained on e ectiveness reveal 33% demonstrated lowering in EDA when using the weighted blanket, 63% reported lower anxiety after use, and 78% preferred the weighted blanket as a calming modality. The results of this study will be used to form the basis for subsequent research on the therapeutic in u ence of the weighted blanket with adults during an acute inpatient mental health admission.doi:10.1300/J004v24n01_05 [Article copies available for a fee from The Haworth Document Delivery Service: 1-800-HAWORTH. E-mail address: <docdelivery@haworthpress.com> Website: <http://www.HaworthPress. com>
A Meta-Analytic Review of Predictors of Job Performance for Salespeople

This meta-analysis evaluated predictors of both objective and subjective sales performance. Biodata measures and sales ability inventories were good predictors of the ratings criterion, with corrected rs of .52 and .45, respectively. Potency (a subdimension of the Big 5 personality dimension Extraversion) predicted supervisor ratings of performance (r = .28) and objective measures of sales (r — .26). Achievement (a component of the Conscientiousness dimension) predicted ratings (r = .25) and objective sales (r = .41). General cognitive ability showed a correlation of .40 with ratings but only .04 with objective sales. Similarly, age predicted ratings (r = .26) but not objective sales (r = —.06). On the basis of a small number of studies, interest appears to be a promising predictor of sales success.
Analyzing Predictive Features of Epileptic Seizures in Human Intracranial EEG Recordings

Epilepsy seizure prediction is a challenge that scientists have tried to overcome throughout many decades, using different state-of-the-art features and machine learning methods. If a forecasting system could predict and warn epilepsy patients of impeding seizures in real time, it would greatly improve their quality of life. Seizure prediction consists of two stages: feature extraction from the data and sample classification to interictal (non-seizure) or preictal (preseizure) state. EEG data is commonly used, as it is inexpensive, portable and it most clearly reflects the changes in the brain’s dynamics. While most studies focus on extracting novel features or using new classifiers, this Thesis focuses on ascertaining the most significant features among some that are commonly used in seizure prediction. Knowing which features influence the prediction results the most, helps to understand the inner workings of both the classifiers and the brain activity and to reduce the feature set in future research, making the classification process more effective. Intracranial EEG data of two patients was used in this Thesis with three classifiers from the scikit-learn library, which were combined with methods for evaluating feature importance. Moderately good to excellent prediction accuracies were achieved with these methods, which allowed to reliably analyze the feature importance results of the different classifiers.
Estimating the Success of Unsupervised Image to Image Translation

While in supervised learning, the validation error is an unbiased estimator of the generalization (test) error and complexity-based generalization bounds are abundant, no such bounds exist for learning a mapping in an unsupervised way. As a result, when training GANs and specifically when using GANs for learning to map between domains in a completely unsupervised way, one is forced to select the hyperparameters and the stopping epoch by subjectively examining multiple options. We propose a novel bound for predicting the success of unsupervised cross domain mapping methods, which is motivated by the recently proposed Simplicity Principle. The bound can be applied both in expectation, for comparing hyperparameters and for selecting a stopping criterion, or per sample, in order to predict the success of a specific cross-domain translation. The utility of the bound is demonstrated in an extensive set of experiments employing multiple recent algorithms. Our code is available at https: //github.com/sagiebenaim/gan bound.
The aberrant behavior checklist: a behavior rating scale for the assessment of treatment effects.

The development of a scale to assess drug and other treatment effects on severely mentally retarded individuals was described. In the first stage of the project, an initial scale encompassing a large number of behavior problems was used to rate 418 residents. The scale was then reduced to an intermediate version, and in the second stage, 509 moderately to profoundly retarded individuals were rated. Separate factor analyses of the data from the two samples resulted in a five-factor scale comprising 58 items. The factors of the Aberrant Behavior Checklist have been labeled as follows: (I) Irritability, Agitation, Crying; (II) Lethargy, Social Withdrawal; (III) Stereotypic Behavior; (IV) Hyperactivity, Noncompliance; and (V) Inappropriate Speech. Average subscale scores were presented for the instrument, and the results were compared with empirically derived rating scales of childhood psychopathology and with factor analytic work in the field of mental retardation.
Track-RNN : Joint Detection and Tracking Using Recurrent Neural Networks

As deep neural networks revolutionize many fundamental computer vision problems, there have not been many works using neural networks to track objects. In this project, we design and implement a tracking pipeline using convolutional neural networks and recurrent neural networks. Our model can handle detection and tracking jointly using appearance and motion features. We use MOT data challenge as a highly occluded single object tracking dataset. We demonstrate good qualitative and quantitative results of our model and discuss how to extend the pipeline to multi-object tracking.