nils-herrmann/clean_scirepeval huggingface dataset:
Efficacy of psychotherapeutic interventions to promote forgiveness: a meta-analysis.

This meta-analysis addressed the efficacy of psychotherapeutic interventions to help people forgive others and to examine moderators of treatment effects.



Eligible studies reported quantitative data on forgiveness of a specific hurt following treatment by a professional with an intervention designed explicitly to promote forgiveness. Random effects meta-analyses were conducted using k = 53 posttreatment effect sizes (N = 2,323) and k = 41 follow-up effect sizes (N = 1,716) from a total of 54 published and unpublished research reports.



Participants receiving explicit forgiveness treatments reported significantly greater forgiveness than participants not receiving treatment (Δ+ = 0.56 [0.43, 0.68]) and participants, receiving alternative treatments (Δ+ = 0.45 [0.21, 0.69]). Also, forgiveness treatments resulted in greater changes in depression, anxiety, and hope than no-treatment conditions. Moderators of treatment efficacy included treatment dosage, offense severity, treatment model, and treatment modality. Multimoderator analyses indicated that treatment dosage (i.e., longer interventions) and modality (individual > group) uniquely predicted change in forgiveness compared with no-treatment controls. Compared with alternative treatment conditions, both modality (individual > group) and offense severity were marginally predictive (ps < .10) of treatment effects.



It appears that using theoretically grounded forgiveness interventions is a sound choice for helping clients to deal with past offenses and helping them achieve resolution in the form of forgiveness. Differences between treatment approaches disappeared when controlling for other significant moderators; the advantage for individual interventions was most clearly demonstrated for Enright-model interventions, as there have been no studies of individual interventions using the Worthington model.
Parathyroid Hormone PTH(1-34) Formulation that Enables Uniform Coating on a Novel Transdermal Microprojection Delivery System

Assess formulation parameters to enable >24-h continuous accurate and uniform coating of PTH(1-34) on a novel transdermal microprojection array delivery system. Surface activity and rheology of the liquid formulation was determined by contact angle measurement and cone-plate viscometry. The formulation’s delivery performance was assessed in vivo using the hairless guinea pig model. Peptide gelation was investigated by rheological and viscoelastic behavior changes. Accurate and uniform coating was achieved by formulating the liquid formulation to a preferred contact angle range of 30–60° with a surfactant and by establishing a Newtonian fluid (defined as a fluid maintaining a constant viscosity with shear rate and time) with a viscosity of ≥20 cps via adjusting the peptide concentration and using an appropriate acidic counterion. A non-volatile acidic counterion was found critical to compensate for the loss of the volatile acetate counterion to maintain the peptide formulation’s solubility upon rehydration in the skin. Finally, the 15.5% w/w PTH(1-34) concentration was found to be the most physically stable formulation (delayed gelation) in the roll-coating reservoir. With a properly designed coating reservoir for shear force reduction, the liquid formulation could last for more than 24 h without gelation. The study successfully offered scientific rationales for developing an optimal liquid formulation for a novel titanium microprojection array coating process. The resultant formulation has an enduring physical stability (>24 h) in the coating reservoir and maintained good in vivo dissolution performance.
Overcoming Security Challenges in Microservice Architectures

The microservice architectural style is an emerging trend in software engineering that allows building highly scalable and flexible systems. However, current state of the art provides only limited insight into the particular security concerns of microservice system. With this paper, we seek to unravel some of the mysteries surrounding microservice security by: providing a taxonomy of microservices security; assessing the security implications of the microservice architecture; and surveying related contemporary solutions, among others Docker Swarm and Netflix security decisions. We offer two important insights. On one hand, microservice security is a multi-faceted problem that requires a layered security solution that is not available out of the box at the moment. On the other hand, if these security challenges are solved, microservice architectures can improve security; their inherent properties of loose coupling, isolation, diversity, and fail fast all contribute to the increased robustness of a system. To address the lack of security guidelines this paper describes the design and implementation of a simple security framework for microservices that can be leveraged by practitioners. Proof-of-concept evaluation results show that the performance overhead of the security mechanisms is around 11%.
Delay Analysis of Hybrid WiFi-LiFi System

Heterogeneous wireless networks are capable of effectivel y leveraging different access technologies to provide a wid e variety of coverage areas. In this paper, the coexistence of WiFi and visible light communication (VLC) is investigated as a para digm. The delay of two configurations of such heterogeneous system has been evaluated. In the first configuration, the non-aggregat ed system, any request is either allocated to WiFi or VLC. While in the se cond configuration, the aggregated system, each request is s plit into two pieces, one is forwarded to WiFi and the other is forw arded to VLC. Under the assumptions of Poisson arrival proce ss of requests and the exponential distribution of requests si ze, it is mathematically proved that the aggregated system p rovides lower minimum average system delay than that of the non-aggregate d system. For the non-aggregated system, the optimal traffic allo ation ratio is derived. For the aggregated system, an efficient sol ution for the splitting ratio is proposed. Empirical result show that the solution proposed here incurs a delay penalty (less than 3%) over the optimal result.
Corrective 3D reconstruction of lips from monocular video

In facial animation, the accurate shape and motion of the lips of virtual humans is of paramount importance, since subtle nuances in mouth expression strongly influence the interpretation of speech and the conveyed emotion. Unfortunately, passive photometric reconstruction of expressive lip motions, such as a kiss or rolling lips, is fundamentally hard even with multi-view methods in controlled studios. To alleviate this problem, we present a novel approach for fully automatic reconstruction of detailed and expressive lip shapes along with the dense geometry of the entire face, from just monocular RGB video. To this end, we learn the difference between inaccurate lip shapes found by a state-of-the-art monocular facial performance capture approach, and the true 3D lip shapes reconstructed using a high-quality multi-view system in combination with applied lip tattoos that are easy to track. A robust gradient domain regressor is trained to infer accurate lip shapes from coarse monocular reconstructions, with the additional help of automatically extracted inner and outer 2D lip contours. We quantitatively and qualitatively show that our monocular approach reconstructs higher quality lip shapes, even for complex shapes like a kiss or lip rolling, than previous monocular approaches. Furthermore, we compare the performance of person-specific and multi-person generic regression strategies and show that our approach generalizes to new individuals and general scenes, enabling high-fidelity reconstruction even from commodity video footage.
Text Steganography in SMS

One of the services used in mobile phone is the short message service (SMS) which is widely used by the public in all parts of the world especially in Asia and Europe. This service enables people to write and exchange short messages via mobile phone. Due to the limited size of SMS, lack of a proper keyboard on the mobile phone and to improve the speed of typing, new abbreviations have been invented for different words and phrases which has lead to the invention of a new language called SMS-texting. One of the main issues in communication is information security and privacy. There are many methods for secret communication and many researchers are working on steganography. In steganography the data is hidden in a cover media such as picture or text. The present paper offers a new method for secret exchange of information through SMS by using and developing abbreviation text steganography with the use of the invented language of SMS-texting. This project has been implemented by J2ME (Java 2 Micro Edition) programming language and tested on a Nokia N71 mobile phone.
Finger printing on the web

Nowadays, companies such as Google  and Facebook  are present on most websites either through advertising services or through social plugins. They follow users around the web collecting information to create unique user profiles. This serious threat to privacy is a major cause for concern, as this reveals that there are no restrictions on the type of information that can be collected, the duration it can be stored, the people who can access it and the purpose of use or misuse. In order to determine if there indeed is any statistically significant evidence that fingerprinting occurs on certain web services, we integrated 2 software namely, FPBlock [6], a prototype browser extension that blocks web-based device fingerprinting, and AdFisher , an automated tool that sending out thousands of automated Web browser instances, in such a manner so that the data collected by FP-Block could be fed to AdFisher for analysis.
Exosomes in tumor microenvironment: novel transporters and biomarkers

Tumor microenvironment (TME) plays an integral part in the biology of cancer, participating in tumor initiation, progression, and response to therapy. Exosome is an important part of TME. Exosomes are small vesicles formed in vesicular bodies with a diameter of 30–100 nm and a classic “cup” or “dish” morphology. They can contain microRNAs, mRNAs, DNA fragments and proteins, which are shuttled from a donor cell to recipient cells. Exosomes secreted from tumor cells are called tumor-derived (TD) exosomes. There is emerging evidence that TD exosomes can construct a fertile environment to support tumor proliferation, angiogenesis, invasion and premetastatic niche preparation. TD exosomes also may facilitate tumor growth and metastasis by inhibiting immune surveillance and by increasing chemoresistance via removal of chemotherapeutic drugs. Therefore, TD-exosomes might be potential targets for therapeutic interventions via their modification or removal. For example, exosomes can serve as specific delivery vehicles to tumors of drugs, small molecules, or agents of prevention and gene therapy. Furthermore, the biomarkers detected in exosomes of biological fluids imply a potential for exosomes in the early detection and diagnosis, prediction of therapeutic efficacy, and determining prognosis of cancer. Although exosomes may serve as cancer biomarkers and aid in the treatment of cancer, we have a long way to go before we can further enhance the anti-tumor therapy of exosomes and develop exosome-based cancer diagnostic and therapeutic strategies.
Design of a Water Environment Monitoring System Based on Wireless Sensor Networks

A water environmental monitoring system based on a wireless sensor network is proposed. It consists of three parts: data monitoring nodes, data base station and remote monitoring center. This system is suitable for the complex and large-scale water environment monitoring, such as for reservoirs, lakes, rivers, swamps, and shallow or deep groundwaters. This paper is devoted to the explanation and illustration for our new water environment monitoring system design. The system had successfully accomplished the online auto-monitoring of the water temperature and pH value environment of an artificial lake. The system's measurement capacity ranges from 0 to 80 °C for water temperature, with an accuracy of ±0.5 °C; from 0 to 14 on pH value, with an accuracy of ±0.05 pH units. Sensors applicable to different water quality scenarios should be installed at the nodes to meet the monitoring demands for a variety of water environments and to obtain different parameters. The monitoring system thus promises broad applicability prospects.
Design of a Multi-Sensor Cooperation Travel Environment Perception System for Autonomous Vehicle

This paper describes the environment perception system designed for intelligent vehicle SmartV-II, which won the 2010 Future Challenge. This system utilizes the cooperation of multiple lasers and cameras to realize several necessary functions of autonomous navigation: road curb detection, lane detection and traffic sign recognition. Multiple single scan lasers are integrated to detect the road curb based on Z-variance method. Vision based lane detection is realized by two scans method combining with image model. Haar-like feature based method is applied for traffic sign detection and SURF matching method is used for sign classification. The results of experiments validate the effectiveness of the proposed algorithms and the whole system.
Demand Queries with Preprocessing

Given a set of items and a submodular set-function f that determines the value of every subset of items, a demand query assigns prices to the items, and the desired answer is a set S of items that maximizes the pro t, namely, the value of S minus its price. The use of demand queries is well motivated in the context of combinatorial auctions. However, answering a demand query (even approximately) is NP-hard. We consider the question of whether exponential time preprocessing of f prior to receiving the demand query can help in later answering demand queries in polynomial time. We design a preprocessing algorithm that leads to approximation ratios that are NP-hard to achieve without preprocessing. We also prove that there are limitations to the approximation ratios achievable after preprocessing, unless NP ⊂ P/poly.
IMAGE PROCESSING TECHNIQUES FOR THE ENHANCEMENT OF BRAIN TUMOR PATTERNS

Brain tumor analysis is done by doctors but its grading gives different conclusions which may vary from one doctor to another. So for the ease of doctors, a research was done which made the use of software with edge detection and segmentation methods, which gave the edge pattern and segment of brain and brain tumor itself. Medical image segmentation had been a vital point of research, as it inherited complex problems for the proper diagnosis of brain disorders. In this research, it provides a foundation of segmentation and edge detection, as the first step towards brain tumor grading. Current segmentation approaches are reviewed with an emphasis placed on revealing the advantages and disadvantages of these methods for medical imaging applications. The use of image segmentation in different imaging modalities is also described along with the difficulties encountered in each modality.
Spherical super wideband (SWB) monopole antenna with micromachined tapered feeding line

A spherical super wideband (SWB) monopole antenna is designed, fabricated and characterized. The low frequency cutoff is mainly determined by the diameter of the conductive sphere monopole antenna at its quarter wavelength, where the sphere serves as a main radiator. The upper cutoff is greatly enlarged by using the tapered feeding line between the coaxial and the sphere, which is fabricated using thick photopatternable polyurethane (D50, MacDerimid Inc.) and 3D multidirectional UV lithography. The implemented SWB antenna has a 10dB-bandwidth between 2.4 and 23.2 GHz (a ratio bandwidth of 9.7:1), and an omni-directional radiation pattern with a maximum gain of approximately 2.9 dBi at 10 GHz.
Data Mining: Web Data Mining Techniques, Tools and Algorithms: An Overview

Web data mining became an easy and important platform for retrieval of useful information. Users prefer World Wide Web more to upload and download data. As increasing growth of data over the internet, it is getting difficult and time consuming for discovering informative knowledge and patterns. Digging knowledgeable and user queried information from unstructured and inconsistent data over the web is not an easy task to perform. Different mining techniques are used to fetch relevant information from web (hyperlinks, contents, web usage logs). Web data mining is a sub discipline of data mining which mainly deals with web. Web data mining is divided into three different types: web structure, web content and web usage mining. All these types use different techniques, tools, approaches, algorithms for discover information from huge bulks of data over the web. Web data mining; hyperlinks; usage logs; contents; patterns
Deep Convolutional Matching

We propose a new matching algorithm, called DeepMatching, to compute dense correspondences between images. DeepMatching relies on a deep, multilayer, convolutional architecture designed for matching images. The proposed matching algorithm can handle non-rigid deformations and repetitive textures, and can therefore efficiently determine dense correspondences in the presence of significant changes between images. We evaluate the performance of DeepMatching, in comparison with state-of-the-art matching algorithms, on resp. the Mikolajczyk (Mikolajczyk et al 2005), the MPI-Sintel (Butler et al 2012) and the Kitti (Geiger et al 2013) datasets. The performance measure is “Accuracy@T ”, that is the percentage of correct pixels for a specified error threshold of T pixels. DeepMatching outperforms the state-of-the-art algorithms in terms of accuracy@T and shows excellent results in particular for repetitive textures. We also propose a method for estimating optical flow, called DeepFlow, by integrating a DeepMatching term in a variational energy minimization approach. Robustness to large displacements is obtained due to this matching term. DeepFlow obtains competitive performance on public benchmarks for optical flow estimation. In particular, DeepFlow obtains state-of-the-art results on the MPI-Sintel dataset (Butler et al 2012).
Risk-averse reinforcement learning for algorithmic trading

We propose a general framework of risk-averse reinforcement learning for algorithmic trading. Our approach is tested in an experiment based on 1.5 years of millisecond time-scale limit order data from NASDAQ, which contain the data around the 2010 flash crash. The results show that our algorithm outperforms the risk-neutral reinforcement learning algorithm by 1) keeping the trading cost at a substantially low level at the spot when the flash crash happened, and 2) significantly reducing the risk over the whole test period.
Gatekeeping Algorithms with Human Ethical Bias: The ethics of algorithms in archives, libraries and society

In the age of algorithms, I focus on the question of how to ensure algorithms that will take over many of our familiar archival and library tasks, will behave according to human ethical norms that have evolved over many years. I start by characterizing physical archives in the context of related institutions such as libraries and museums. In this setting I analyze how ethical principles, in particular about access to information, have been formalized and communicated in the form of ethical codes, or: codes of conducts. After that I describe two main developments: digitalization, in which physical aspects of the world are turned into digital data, and algorithmization, in which intelligent computer programs turn this data into predictions and decisions. Both affect interactions that were once physical but now digital. In this new setting I survey and analyze the ethical aspects of algorithms and how they shape a vision on the future of archivists and librarians, in the form of algorithmic documentalists, or: codementalists. Finally I outline a general research strategy, called IntERMEeDIUM, to obtain algorithms that obey are human ethical values encoded in code of ethics.
Color Image Denoising Using Wavelet soft Thresholding

In this paper we proposed a new approach for color Image denoising using wavelet thresholding. The denoising process rejects noise by thresholding in the wavelet domain.Wavelet analysis is powerful tool for Image Denoising when Image are to be viewed or processed at multiple resolution Wavelet Transform is the mathematical tool for denoising. Discrete wavelet transform has the benefit of giving a joint time frequency representation of the signal. Also it is suitable for both stationary and non-stationary signals and is the most appropriate System in the field of signal detection. Discrete wavelet transform is implemented through multi resolution an alysis and digital filter banks. The proposed method was applied by using MATLAB R2013a with color images contaminated by white Gaussian noise.  Gaussian, Salt & Pepper, Poisson & speckle Noise, 2D filtering, Wavelet Thresholding.
Compliance through Informed Consent: Semantic Based Consent Permission and Data Management Model

The General Data Protection Regulations (GDPR) imposes greater restrictions on obtaining valid user consents involving the use of personal data. A semantic model of consent can make the concepts of consent explicit, establish a common understanding and enable re-use of consent. Therefore, forming a semantic model of consent will satisfy the GDPR requirements of specificity and unambiguity and is an important step towards ensuring compliance. In this paper, we discuss obtaining an open vocabulary of expressing consent leveraging existing semantic models of provenance, processes, permission and obligations. We also present a reference architecture for the management of data processing according to consent permission. This data management model utilizes the open vocabulary of consent and incorporates the change of context into the data processing activity. By identifying and incorporating changes to the relational context between data controllers and data subjects into the data processing model, it aims to improve the integration of data management across different information systems specifically adhering to the GDPR and helping controllers to demonstrate compliance.
MMCF: Multimodal Collaborative Filtering for Automatic Playlist Continuation

Automatic playlist continuation (APC) is a common task of music recommender systems, enabling the automatic discovery of tracks that fit into a given playlist. To recommend a coherent list of tracks to users, it is important to capture the underlying characteristics of a playlist. Unfortunately, existing recommender models suffer from several problems: (1) They tend to misinterpret tracks that appear rarely in a playlist (popularity bias) (2) they cannot extend user's playlist that consists of very few tracks (cold-start problem), and (3) they neglect the context of a playlist such as the sequence of tracks or playlist title (context-aware continuation). This year's ACM RecSys Challenge'18 aimed to find new solutions to tackle these problems.
 In this paper, we propose a multimodal collaborative filtering model to deal effectively with diverse data. This consists of two components: (1) an autoencoder using both the playlist and its categorical contents and (2) a character-level convolutional neural network using the playlist title only. By simultaneously analyzing the playlist and the categorical contents, our model successfully addresses the cold-start and popularity bias problems. In addition, we consider the context of a playlist by utilizing its title, thus enhancing the prediction of well-suited tracks. In the challenge, our team "hello world!" was ranked the 2nd place, scoring 0.224, 0.394, and 1.928 for the three evaluation metrics, respectively. Our implementation code is publicly available at https://github.com/hojinYang/spotify_recSys_challenge_2018.