nils-herrmann/clean_scirepeval huggingface dataset:
Efficacy of Cannabis-Based Medicines for Pain Management: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.

The management of chronic pain is a complex challenge worldwide. Cannabis-based medicines (CBMs) have proven to be efficient in reducing chronic pain, although the topic remains highly controversial in this field.



This study's aim is to conduct a conclusive review and meta-analysis, which incorporates all randomized controlled trials (RCTs) in order to update clinicians' and researchers' knowledge regarding the efficacy and adverse events (AEs) of CBMs for chronic and postoperative pain treatment.



A systematic review and meta-analysis.



An electronic search was conducted using Medline/Pubmed and Google Scholar with the use of Medical Subject Heading (MeSH) terms on all literature published up to July 2015. A follow-up manual search was conducted and included a complete cross-check of the relevant studies. The included studies were RCTs which compared the analgesic effects of CBMs to placebo. Hedges's g scores were calculated for each of the studies. A study quality assessment was performed utilizing the Jadad scale. A meta-analysis was performed utilizing random-effects models and heterogeneity between studies was statistically computed using I² statistic and tau² test.



The results of 43 RCTs (a total of 2,437 patients) were included in this review, of which 24 RCTs (a total of 1,334 patients) were eligible for meta-analysis. This analysis showed limited evidence showing more pain reduction in chronic pain -0.61 (-0.78 to -0.43, P < 0.0001), especially by inhalation -0.93 (-1.51 to -0.35, P = 0.001) compared to placebo. Moreover, even though this review consisted of some RCTs that showed a clinically significant improvement with a decrease of pain scores of 2 points or more, 30% or 50% or more, the majority of the studies did not show an effect. Consequently, although the primary analysis showed that the results were favorable to CBMs over placebo, the clinical significance of these findings is uncertain. The most prominent AEs were related to the central nervous and the gastrointestinal (GI) systems.



Publication limitation could have been present due to the inclusion of English-only published studies. Additionally, the included studies were extremely heterogeneous. Only 7 studies reported on the patients' history of prior consumption of CBMs. Furthermore, since cannabinoids are surrounded by considerable controversy in the media and society, cannabinoids have marked effects, so that inadequate blinding of the placebo could constitute an important source of limitation in these types of studies.



The current systematic review suggests that CBMs might be effective for chronic pain treatment, based on limited evidence, primarily for neuropathic pain (NP) patients. Additionally, GI AEs occurred more frequently when CBMs were administered via oral/oromucosal routes than by inhalation. Cannabis, CBMs, chronic pain, postoperative pain, review, meta-analysis.
Social Media, Traditional Media, and Music Sales

Motivated by the growing importance of social media, this paper examines the relationship between new media, old media, and sales, in the context of the music industry. In particular, we study the interplay between blog buzz, radio play and music sales, at both the album and song levels of analysis. We employ the Panel Vector Auto-regression (PVAR) methodology, an extension of vector auto-regression to panel data. We find that radio play is consistently and positively related to future sales at both the song and album levels. Blog buzz, however, is not related to album sales and negatively related to song sales, suggesting that sales displacement due to free online sampling dominates any positive word-of-mouth effects of song buzz on sales. Further, the negative relationship between song buzz and sales is stronger for niche music relative to mainstream music, and for less popular songs within albums. We discuss the implications of these results for both research and practice regarding the role of new media in the music industry.
Deep artifact learning for compressed sensing and parallel MRI

Compressed sensing MRI (CS-MRI) from single and parallel coils is one of the powerful ways to reduce the scan time of MR imaging with performance guarantee. However, the computational costs are usually expensive. This paper aims to propose a computationally fast and accurate deep learning algorithm for the reconstruction of MR images from highly down-sampled k-space data.  Based on the topological analysis, we show that the data manifold of the aliasing artifact is easier to learn from a uniform subsampling pattern with additional low-frequency k-space data. Thus, we develop deep aliasing artifact learning networks for the magnitude and phase images to estimate and remove the aliasing artifacts from highly accelerated MR acquisition.  The aliasing artifacts are directly estimated from the distorted magnitude and phase images reconstructed from subsampled k-space data so that we can get an aliasing-free images by subtracting the estimated aliasing artifact from corrupted inputs. Moreover, to deal with the globally distributed aliasing artifact, we develop a multi-scale deep neural network with a large receptive field.  The experimental results confirm that the proposed deep artifact learning network effectively estimates and removes the aliasing artifacts. Compared to existing CS methods from single and multi-coli data, the proposed network shows minimal errors by removing the coherent aliasing artifacts. Furthermore, the computational time is by order of magnitude faster.  As the proposed deep artifact learning network immediately generates accurate reconstruction, it has great potential for clinical applications.
An application of cascaded 3D fully convolutional networks for medical image segmentation

Recent advances in 3D fully convolutional networks (FCN) have made it feasible to produce dense voxel-wise predictions of volumetric images. In this work, we show that a multi-class 3D FCN trained on manually labeled CT scans of several anatomical structures (ranging from the large organs to thin vessels) can achieve competitive segmentation results, while avoiding the need for handcrafting features or training class-specific models. To this end, we propose a two-stage, coarse-to-fine approach that will first use a 3D FCN to roughly define a candidate region, which will then be used as input to a second 3D FCN. This reduces the number of voxels the second FCN has to classify to ∼10% and allows it to focus on more detailed segmentation of the organs and vessels. We utilize training and validation sets consisting of 331 clinical CT images and test our models on a completely unseen data collection acquired at a different hospital that includes 150 CT scans, targeting three anatomical organs (liver, spleen, and pancreas). In challenging organs such as the pancreas, our cascaded approach improves the mean Dice score from 68.5 to 82.2%, achieving the highest reported average score on this dataset. We compare with a 2D FCN method on a separate dataset of 240 CT scans with 18 classes and achieve a significantly higher performance in small organs and vessels. Furthermore, we explore fine-tuning our models to different datasets. Our experiments illustrate the promise and robustness of current 3D FCN based semantic segmentation of medical images, achieving state-of-the-art .
The factor structure of generalized workplace harassment.

We describe the development and psychometric characteristics of the Generalized Workplace Harassment Questionnaire (GWHQ), a 29-item instrument developed to assess harassing experiences at work in five conceptual domains: verbal aggression, disrespect, isolation/exclusion, threats/bribes, and physical aggression. Over 1700 current and former university employees completed the GWHQ at three time points. Factor analytic results at each wave of data suggested a five-factor solution that did not correspond to the original five conceptual factors. We suggest a revised scoring scheme for the GWHQ utilizing four of the empirically extracted factors: covert hostility, verbal hostility, manipulation, and physical hostility. Covert hostility was the most frequently experienced type of harassment, followed by verbal hostility, manipulation, and physical hostility. Verbal hostility, covert hostility, and manipulation were found to be significant predictors of psychological distress.
Error correction, sensory prediction, and adaptation in motor control.

Motor control is the study of how organisms make accurate goal-directed movements. Here we consider two problems that the motor system must solve in order to achieve such control. The first problem is that sensory feedback is noisy and delayed, which can make movements inaccurate and unstable. The second problem is that the relationship between a motor command and the movement it produces is variable, as the body and the environment can both change. A solution is to build adaptive internal models of the body and the world. The predictions of these internal models, called forward models because they transform motor commands into sensory consequences, can be used to both produce a lifetime of calibrated movements, and to improve the ability of the sensory system to estimate the state of the body and the world around it. Forward models are only useful if they produce unbiased predictions. Evidence shows that forward models remain calibrated through motor adaptation: learning driven by sensory prediction errors.
Social Network Security Issues : Social Engineering and Phishing Attacks

Social Networking sites have grown in popularity with the number of users who utilize the medium to share aspects of their personal lives increasing yearly. Security issues have been of concern to users, site designers, as well as Security specialists who must find ways to defend corporate networks from malicious attacks initiated from these sites. Social engineering attacks can lead to targeted spear phishing attacks, all with a monetary motive at the root. This study describes and discusses some of these security issues.
Changes in women's feelings about their romantic relationships across the ovulatory cycle

According to the dual mating hypothesis, women possess two overlapping suites of mate-choice mechanisms: one leading to preferences for sexually desirable men who have high-fitness genes and one leading to preferences for men who are able to invest in a woman and her children. Evidence increasingly demonstrates that women's preference for sexual desirability (but not investment attractiveness) increases when women are most fertile within the ovulatory cycle. Little is known, however, about the implications of these preference shifts for women's relationships with their long-term partners. Using luteinizing hormone tests to verify ovulation, across two studies (Samples 1 and 2), we found that women whose partners were relatively low in sexual desirability felt less close to their partner (Samples 1 and 2) and were more critical of their partner's faults (Sample 2) on high-fertility days of the cycle just prior to ovulation compared with low-fertility days of the cycle. Women whose partners were relatively high in sexual desirability felt closer to their partner (Sample 1) and more satisfied with their relationship (Sample 2) on high- than low-fertility days of the cycle. There were no such shifts in women's commitment to their relationship. Therefore, partner sexual desirability predicts women's high-fertility assessments of relationship quality but not their intentions to stay in their relationship, consistent with the dual mating hypothesis. These findings suggest that variations across the ovulation cycle in women's reproductive hormones play an important role in relationship dynamics.
Low-Bit-Rate Speech Coding

Low-bit-rate speech coding, at rates below 4 kb/s, is needed for both communication and voice storage applications. At such low rates, full encoding of the speech waveform is not possible; therefore, low-rate coders rely instead on parametric models to represent only the most perceptually-relevant aspects of speech. While there are a number of different approaches for this modeling, all can be related to the basic linear model of speech production, where an excitation signal drives a vocal tract filter. The basic properties of the speech signal and of human speech perception can explain the principles of parametric speech coding as applied in early vocoders. Current speech modeling approaches, such as mixed excitation linear prediction, sinusoidal coding, and waveform interpolation, use more sophisticated versions of these same concepts. Modern techniques for encoding the model parameters, in particular using the theory of vector quantization, allow the encoding of the model information with very few bits per speech frame. Successful standardization of low-rate coders has enabled their widespread use for both military and satellite communications, at rates from 4 kb/s all the way down to 600 b/s. However, the goal of tollquality low-rate coding continues to provide a research challenge. This work was sponsored by the Defense Advanced Research Projects Agency under Air Force Contract FA8721-05-C-0002 . Opinions, interpretations, conclusions, and recommendat ions are those of the authors and are not necessarily endorsed by the U nited States Government.
A Trust-Based Secure Routing Scheme Using the Traceback Approach for Energy-Harvesting Wireless Sensor Networks

The Internet of things (IoT) is composed of billions of sensing devices that are subject to threats stemming from increasing reliance on communications technologies. A Trust-Based Secure Routing (TBSR) scheme using the traceback approach is proposed to improve the security of data routing and maximize the use of available energy in Energy-Harvesting Wireless Sensor Networks (EHWSNs). The main contributions of a TBSR are (a) the source nodes send data and notification to sinks through disjoint paths, separately; in such a mechanism, the data and notification can be verified independently to ensure their security. (b) Furthermore, the data and notification adopt a dynamic probability of marking and logging approach during the routing. Therefore, when attacked, the network will adopt the traceback approach to locate and clear malicious nodes to ensure security. The probability of marking is determined based on the level of battery remaining; when nodes harvest more energy, the probability of marking is higher, which can improve network security. Because if the probability of marking is higher, the number of marked nodes on the data packet routing path will be more, and the sink will be more likely to trace back the data packet routing path and find malicious nodes according to this notification. When data packets are routed again, they tend to bypass these malicious nodes, which make the success rate of routing higher and lead to improved network security. When the battery level is low, the probability of marking will be decreased, which is able to save energy. For logging, when the battery level is high, the network adopts a larger probability of marking and smaller probability of logging to transmit notification to the sink, which can reserve enough storage space to meet the storage demand for the period of the battery on low level; when the battery level is low, increasing the probability of logging can reduce energy consumption. After the level of battery remaining is high enough, nodes then send the notification which was logged before to the sink. Compared with past solutions, our results indicate that the performance of the TBSR scheme has been improved comprehensively; it can effectively increase the quantity of notification received by the sink by 20%, increase energy efficiency by 11%, reduce the maximum storage capacity needed by nodes by 33.3% and improve the success rate of routing by approximately 16.30%.
Revisiting Visual Question Answering Baselines

•Study of Visual Question Answering has led to the exploration of complex models designed to do multi-modal ’reasoning’ and ’attention’. •We show that a basic approach designed to exploit answer bias performs well despite being much simpler: – reaches state-of-the-art performance on Visual7W Telling. –performs competitively on VQA Real multiple choice. •Surprisingly, the model does ’well’ even when missing the image or question.
The World Health Organization Adult ADHD Self-Report Scale (ASRS): a short screening scale for use in the general population.

A self-report screening scale of adult attention-deficit/hyperactivity disorder (ADHD), the World Health Organization (WHO) Adult ADHD Self-Report Scale (ASRS) was developed in conjunction with revision of the WHO Composite International Diagnostic Interview (CIDI). The current report presents data on concordance of the ASRS and of a short-form ASRS screener with blind clinical diagnoses in a community sample.



The ASRS includes 18 questions about frequency of recent DSM-IV Criterion A symptoms of adult ADHD. The ASRS screener consists of six out of these 18 questions that were selected based on stepwise logistic regression to optimize concordance with the clinical classification. ASRS responses were compared to blind clinical ratings of DSM-IV adult ADHD in a sample of 154 respondents who previously participated in the US National Comorbidity Survey Replication (NCS-R), oversampling those who reported childhood ADHD and adult persistence.



Each ASRS symptom measure was significantly related to the comparable clinical symptom rating, but varied substantially in concordance (Cohen's kappa in the range 0.16-0.81). Optimal scoring to predict clinical syndrome classifications was to sum unweighted dichotomous responses across all 18 ASRS questions. However, because of the wide variation in symptom-level concordance, the unweighted six-question ASRS screener outperformed the unweighted 18-question ASRS in sensitivity (68.7% v. 56.3%), specificity (99.5% v. 98.3%), total classification accuracy (97.9% v. 96.2%), and kappa (0.76 v. 0.58).



Clinical calibration in larger samples might show that a weighted version of the 18-question ASRS outperforms the six-question ASRS screener. Until that time, however, the unweighted screener should be preferred to the full ASRS, both in community surveys and in clinical outreach and case-finding initiatives.
The Rise of Ransomware

Ransomware continues to be one of the most crucial cyber threats and is actively threatening IT users around the world. In recent years, it has become a phenomenon and traumatic threat to individuals, governments and organizations. Ransomwares not only penalized computational operations, it also mercilessly extorts huge amount of money from the victims if the victims want to regain back access to the system and files. As such, the cybercriminals are making millions of profits and keep on spreading new variants of ransomware. This paper discusses about ransomware and some related works in fighting this threat.
A survey on context-aware web service systems

This survey aims to study and analyze current techniques and methods for context-aware web service systems, to discuss future trends and propose further steps on making web services systems context-aware.  The paper analyzes and compares existing context-aware web service-based systems based on techniques they support, such as context information modeling, context sensing, distribution, security and privacy, and adaptation techniques. Existing systems are also examined in terms of application domains, system type, mobility support, multi-organization support and level of web services implementation.  Supporting context-aware web service-based systems is increasing. It is hard to find a truly context-aware web service-based system that is interoperable and secure, and operates on multi-organizational environments. Various issues, such as distributed context management, context-aware service modeling and engineering, context reasoning and quality of context, security and privacy issues have not been well addressed.  The number of systems analyzed is limited. Furthermore, the survey is based on published papers. Therefore, up-to-date information and development might not be taken into account.  Existing surveys do not focus on context-awareness techniques for web services. This paper helps to understand the state of the art in context-aware techniques for web services that can be employed in the future of services which is built around, amongst others, mobile devices, web services, and pervasive environments.
Very Deep Convolutional Networks for Natural Language Processing

The dominant approach for many NLP tasks are recurrent neura l networks, in particular LSTMs, and convolutional neural networks. However , these architectures are rather shallow in comparison to the deep convolutional n etworks which are very successful in computer vision. We present a new archite ctur for text processing which operates directly on the character level and uses o nly small convolutions and pooling operations. We are able to show that the performa nce of this model increases with the depth: using up to 29 convolutional layer s, we report significant improvements over the state-of-the-art on several public t ext classification tasks. To the best of our knowledge, this is the first time that very de ep convolutional nets have been applied to NLP.
Network Analysis of Intrinsic Functional Brain Connectivity in Alzheimer's Disease

Functional brain networks detected in task-free ("resting-state") functional magnetic resonance imaging (fMRI) have a small-world architecture that reflects a robust functional organization of the brain. Here, we examined whether this functional organization is disrupted in Alzheimer's disease (AD). Task-free fMRI data from 21 AD subjects and 18 age-matched controls were obtained. Wavelet analysis was applied to the fMRI data to compute frequency-dependent correlation matrices. Correlation matrices were thresholded to create 90-node undirected-graphs of functional brain networks. Small-world metrics (characteristic path length and clustering coefficient) were computed using graph analytical methods. In the low frequency interval 0.01 to 0.05 Hz, functional brain networks in controls showed small-world organization of brain activity, characterized by a high clustering coefficient and a low characteristic path length. In contrast, functional brain networks in AD showed loss of small-world properties, characterized by a significantly lower clustering coefficient (p<0.01), indicative of disrupted local connectivity. Clustering coefficients for the left and right hippocampus were significantly lower (p<0.01) in the AD group compared to the control group. Furthermore, the clustering coefficient distinguished AD participants from the controls with a sensitivity of 72% and specificity of 78%. Our study provides new evidence that there is disrupted organization of functional brain networks in AD. Small-world metrics can characterize the functional organization of the brain in AD, and our findings further suggest that these network measures may be useful as an imaging-based biomarker to distinguish AD from healthy aging.