nils-herrmann/clean_scirepeval huggingface dataset:
Experiences before things: a primer for the (yet) unconvinced

While things (i.e., technologies) play a crucial role in creating and shaping meaningful, positive experiences, their true value lies only in the resulting experiences. It is about what we can do and experience with a thing, about the stories unfolding through using a technology, not about its styling, material, or impressive list of features. This paper explores the notion of "experiences" further: from the link between experiences, well-being, and people's developing post-materialistic stance to the challenges of the experience market and the experience-driven design of technology.
LATERAL RECESS SYNDROME AND COMPUTED TOMOGRAPHY

It is easy to make the diagnosis of lateral recess syndrome(LRS} when radiological and clinical findings are evaluated together. Nerve roots are compressed at the lateral recess; centrally at the subarticular region by superior articular facet hypertrophy. congenital stenosis or posteriolateral osteophytes; at the distal portion of the lateral recess. foraminal stenosis, extreme lateral disc herniations, or osteophytic formations. cause nerve root entrapment. In this syndrome low back and leg pain are sclerotomaJ. and intermittent in nature. Typically it is not affacted by the Valsalva manoeuvre. Pain is alleviated on sitting and neuroradiological findings are chronic arid insignificant. In neuroradiological evaluation plain radiography. poly tomography and myelography have significant importance. Similar results can be obtained by poly tomography in addition to computed tomography(CT). Differential diagnosis is difficult by myelography and the findings appear as root amputation or flattening.
What does your selfie say about you?

Selfies refer to self-portraits taken by oneself using a digital camera or a smartphone. They become increasingly popular in social media. However, little is known about how selfies reflect their owners’ personality traits and how people judge others’ personality from selfies. In this study, we examined the association between selfies and personality by measuring participants’ Big Five personality and coding their selfies posted on a social networking site. We found specific cues in selfies related to agreeableness, conscientiousness, neuroticism, and openness. We also examined zero-acquaintance personality judgment and found that observers had moderate to strong agreement in their ratings of Big Five personality based on selfies. However, they could only accurately predict selfie owners’ degree of openness. This study is the first to reveal personality-related cues in selfies and provide a picture-coding scheme that can be used to analyze selfies. We discussed the difference between personality expression in selfies and other types of photos, and its possible relationship with impression management of social media users.
Emotional Human Machine Conversation Generation Based on SeqGAN

In recent years, artificial intelligence has made a significant breakthrough and progress in the field of humanmachine conversation. However, how to generate high-quality, emotional and subhuman conversation still a troublesome work. The key factor of man-machine dialogue is whether the chatbot can give a good response in content and emotional level. How to ensure that the robot understands the user’s emotions, and consider the user’s emotions then give a satisfactory response. In this paper, we add the emotional tags to the post and response from the dataset respectively. The emotional tags, as the emotional tags of post and response, represent the emotions expressed by this sentence. The purpose of our emotional tags is to make the chatbot understood the emotion of the input sequence more directly so that it has a recognition of the emotional dimension. In this paper, we apply the mechanism of GAN network on our conversation model. For the generator: We make full use of Encoder-Decoder structure form a seq2seq model, which is used to generate a sentence’s response. For the discriminator: distinguish between the human-generated dialogues and the machine-generated ones.The outputs from the discriminator are used as rewards for the generative model, pushing the system to generate dialogues that mostly resemble human dialogues. We cast our task as an RL(Reinforcement Learning) problem, using a policy gradient method to reward more subhuman conversational sequences, and in addition we have added an emotion tags to represent the response we want to get, which we will use as a rewarding part of it, so that the emotions of real responses can be closer to the emotions we specify. Our experiment shows that through the introduction of emotional intelligence, our model can generate responses appropriate not only in content but also in emotion, which can be used to control and adjust users emotion. Compared with our previous work, we get a better performance on the same data set, and we get less ’’safe’’ response than before, but there will be a certain degree of existence.
MODELING THE PHYSICAL CHARACTERISTICS OF NAND FLASH MEMORY

Flash memory has gained tremendous popularity in recent years. A variant of flash, referred to as NAND flash, is widely used in consumer electronics products, such as cell-phones and music players while NAND flash based Solid-State Disks (SSDs) are increasingly displacing hard disk drives as the primary storage device in laptops, desktops, and even data centers. Computer architects have recently begun exploring the use NAND flash, from SSD organizations to disk caches and even new flash-based server architectures. In order to study this design space, architects require simulation tools that can provide detailed insights into the behavior of flash memory. This thesis presents two such tools that model two important characteristics of NAND flash: power consumption and endurance. The first tool, called FlashPower, is a microarchitecture level modeling tool that provides a detailed analytical power model for Single-Level Cell (SLC) based NAND flash memory. FlashPower estimates the power consumed by a NAND flash memory chip during its various operating modes. We have validated FlashPower against published chip power measurements and show that they are comparable. Results from a design space exploration using FlashPower indicate that the values of bits being read or written into NAND flash memory have a significant impact on energy dissipation. The second tool, called Flash EnduraNCE (FENCE), models the endurance characteristics of NAND flash and captures the impact of stress and recovery on NAND flash memory cells. Using FENCE, we show that the recovery process, which prior studies on flash based SSDs have not considered, significantly boosts endurance. Using a set of real enterprise workloads, we show that this recovery process allows for orders of magnitude higher endurance than those given in datasheets. Our results indicate that, under realistic usage conditions, SSDs that use standard wear-
Is preprocessing of text really worth your time for online comment classification?

A large proportion of online comments present on public domains are usually constructive, however a significant proportion are toxic in nature. The comments contain lot of typos which increases the number of features manifold, making the ML model difficult to train. Considering the fact that the data scientists spend approximately 80% of their time in collecting, cleaning and organizing their data , we explored how much effort should we invest in the preprocessing (transformation) of raw comments before feeding it to the state-of-the-art classification models. With the help of four models on Jigsaw toxic comment classification data, we demonstrated that the training of model without any transformation produce relatively decent model. Applying even basic transformations, in some cases, lead to worse performance and should be applied with caution.
Fiber Bragg Grating Sensors toward Structural Health Monitoring in Composite Materials: Challenges and Solutions

Nowadays, smart composite materials embed miniaturized sensors for structural health monitoring (SHM) in order to mitigate the risk of failure due to an overload or to unwanted inhomogeneity resulting from the fabrication process. Optical fiber sensors, and more particularly fiber Bragg grating (FBG) sensors, outperform traditional sensor technologies, as they are lightweight, small in size and offer convenient multiplexing capabilities with remote operation. They have thus been extensively associated to composite materials to study their behavior for further SHM purposes. This paper reviews the main challenges arising from the use of FBGs in composite materials. The focus will be made on issues related to temperature-strain discrimination, demodulation of the amplitude spectrum during and after the curing process as well as connection between the embedded optical fibers and the surroundings. The main strategies developed in each of these three topics will be summarized and compared, demonstrating the large progress that has been made in this field in the past few years.
A 1.5V Low Power CMOS LNA Design

A 1. 5 V 0.18mum CMOS LNA for GPS applications has been designed with fully differential topology. Under such a low supply voltage, the fully differential LNA has been simulated, it provides a series of good results in Noise figure, Linearity and Power consumption. The LNA achieves a Noise figure of 1. 5 dB, voltage gain of 32 dB, Power dissipation of 6 mW, and the input reflection coefficient (Sn) is -23 dB.
Awareness of Menstrual Abnormality Amongst College Students in Urban Area of Ile-Ife, Osun State, Nigeria

Disturbances of menstrual bleeding are major social and medical problem for women and account for high percentage of gynecological visit.



The objective of the study was to document menstrual abnormalities experienced by female college students, their awareness and health seeking behavior.



A cross-sectional survey was undertaken, 400 students were selected using stratified sampling technique and interviewed using semi-structured self-administered questionnaire. Inferential statistical analysis such as Chi-square test and logistic regressions were carried out.



The mean age at menarche was 14.18 years. Irregular menstrual cycles were reported in 9.0%. Dysmenorrhea was present in 62.5%, and 12.5% reported school absenteeism. Students' awareness of menstrual abnormalities was poor (29%). A few of them (10.5%) decided to seek help for menstrual abnormalities. The awareness of students on menstrual abnormalities was significantly influenced by their age (OR = 2.33, P = 0.03); however, age at menarche and level of study did not influence their awareness (OR = 0.45, P = 0.24 and OR = 1.42, P = 0.12). History of dysmenorrheal (OR = 10.2, P = 0.001) and academic disturbance (OR = 5.45, P = 0.001) had significant influence on the health seeking behavior of the students.



There was a general lack of information about menstrual issues and when to seek help. There is a need to educate female college students about menstrual issues in order to improve their health seeking behavior as regards menstrual abnormalities.
Cross-Layer Self-Similar Coflow Scheduling for Machine Learning Clusters

In recent years, many companies have developed various distributed computation frameworks for processing machine learning (ML) jobs in clusters. Networking is a well-known bottleneck for ML systems and the cluster demands efficient scheduling for huge traffic (up to 1GB per flow) generated by ML jobs. Coflow has been proven an effective abstraction to schedule flows of such data-parallel applications. However, the implementation of coflow scheduling policy is constrained when coflow characteristics are unknown a prior, and when TCP congestion control misinterprets the congestion signal leading to low throughput. Fortunately, traffic patterns experienced by some ML jobs support to speculate the complete coflow characteristic with limited information. Hence this paper summarizes coflow from these ML jobs as self-similar coflow and proposes a decentralized self-similar coflow scheduler Cicada. Cicada assigns each coflow a probe flow to speculate its characteristics during the transportation and employs the Shortest Job First (SJF) to separate coflow into strict priority queues based on the speculation result. To achieve full bandwidth for throughput- sensitive ML jobs, and to guarantee the scheduling policy implementation, Cicada promotes the elastic transport-layer rate control that outperforms prior works. Large-scale simulations show that Cicada completes coflow 2.08x faster than the state-of-the-art schemes in the information-agnostic scenario.
Top-k Matching Queries for Filter-Based Profile Matching in Knowledge Bases

Finding the best matching job offers for a candidate profile or, the best candidates profiles for a particular job offer, respectively constitutes the most common and most relevant type of queries in the Human Resources sector. This technically requires to investigate top-k queries on top of knowledge bases and relational databases. We propose in this paper a top-k query algorithm on relational databases able to produce effective and efficient results. The approach is to consider the partial order of matching relations between jobs and candidates profiles together with an efficient design of the data involved. In particular, the focus on a single relation, the matching relation, is crucial to achieve the expectations.
Picture tags and world knowledge: learning tag relations from visual semantic sources

This paper studies the use of everyday words to describe images. The common saying has it that 'a picture is worth a thousand words', here we ask which thousand? The proliferation of tagged social multimedia data presents a challenge to understanding collective tag-use at large scale -- one can ask if patterns from photo tags help understand tag-tag relations, and how it can be leveraged to improve visual search and recognition. We propose a new method to jointly analyze three distinct visual knowledge resources: Flickr, ImageNet/WordNet, and ConceptNet. This allows us to quantify the visual relevance of both tags learn their relationships. We propose a novel network estimation algorithm, Inverse Concept Rank, to infer incomplete tag relationships. We then design an algorithm for image annotation that takes into account both image and tag features. We analyze over 5 million photos with over 20,000 visual tags. The statistics from this collection leads to good results for image tagging, relationship estimation, and generalizing to unseen tags. This is a first step in analyzing picture tags and everyday semantic knowledge. Potential other applications include generating natural language descriptions of pictures, as well as validating and supplementing knowledge databases.
Critical Review: In children with phonological/articulation disorders, do non-speech oral motor exercises improve speech production compared to direct speech therapy?

This critical review examines the evidence evaluating the efficacy of non-speech oral motor exercises (NSOMEs) as a treatment approach for children with phonological/articulation disorders. Research studies include one randomized clinical trial design, one single group pre-test post-test design and one single subject design. Overall, the evidence does not support the use of NSOMEs to treat children with phonological/articulation disorders. Future and clinical recommendations are discussed.
Second-Order Belief Hidden Markov Models

Hidden Markov Models (HMMs) are learning methods for pattern recognition. The probabilistic HMMs have been one of the most used techniques based on the Bayesian model. First-order probabilistic HMMs were adapted to the theory of belief functions such that Bayesian probabilities were replaced with mass functions. In this paper, we present a second-order Hidden Markov Model using belief functions. Previous works in belief HMMs have been focused on the first-order HMMs. We extend them to the second-order model.
AUTHENTICATION OF PAPER CURRENCY AND CONVERSION INTO LOWER DENOMINATIONS

Over the past few year, as a result of the great technological advances in color printing, duplicating and scanning, counterfeiting problems have become more and more serious. In the past, only the printing house has the ability to make counterfeit paper currency, but today it is possible for any person to print counterfeit bank notes simply by using a computer and a laser printer at house. Therefore the issue of efficiently distinguishing counterfeit banknotes from genuine ones via automatic machines has become more and more important. There is a need to design a system that is helpful in recognition of paper currency notes with fast speed and in less time.
Radar signal recognition algorithm based on entropy theory

With the increasingly complex electromagnetic environment of communication, as well as the gradually increased radar signal types, how to effectively identify the types of radar signals at low SNR becomes a hot topic. A radar signal recognition algorithm based on entropy features, which describes the distribution characteristics for different types of radar signals by extracting Shannon entropy, Singular spectrum Shannon entropy and Singular spectrum index entropy features, was proposed to achieve the purpose of signal identification. Simulation results show that, the algorithm based on entropies has good anti-noise performance, and it can still describe the characteristics of signals well even at low SNR, which can achieve the purpose of identification and classification for different radar signals.
Immediate effect of Ayres's sensory integration-based occupational therapy intervention on children with autism spectrum disorders.

This study examined the effects of Ayres' sensory integration intervention on the behavior and task engagement of young children with autism spectrum disorders (ASD). Clinical observations and caregiver reports of behavior and engagement also were explored to help guide future investigations.



This single-subject study used an ABAB design to compare the immediate effect of Ayres's sensory integration and a play scenario on the undesired behavior and task engagement of 4 children with ASD.



No clear patterns of change in undesired behavior or task management emerged through objective measurement. Subjective data suggested that each child exhibited positive changes during and after intervention.



When effects are measured immediately after intervention, short-term Ayres's sensory integration does not have a substantially different effect than a play scenario on undesired behavior or engagement of young children with ASD. However, subjective data suggest that Ayres's sensory integration may produce an effect that is evident during treatment sessions and in home environments.
Efficient Hierarchical Embedding for Learning Coherent Visual Styles

The visionary Steve Jobs said, “A lot of times, people don’t know what they want until you show it to them.” A powerful recommender system not only shows people similar items, but also helps them discover what they might like, and items that complement what they already purchased. In this paper, we attempt to instill a sense of “intention” and “style” into our recommender system, i.e., we aim to recommend items that are visually complementary with those already consumed. By identifying items that are visually coherent with a query item/image, our method facilitates exploration of the long tail items, whose existence users may be even unaware of. This task is formulated only recently by Julian et al. , with the input being millions of item pairs that are frequently viewed/bought together, entailing noisy style coherence. In the same work, the authors proposed a Mahalanobisbased transform to discriminate a given pair to be sharing a same style or not. Despite its success, we experimentally found that it’s only able to recommend items on the margin of different clusters, which leads to limited coverage of the items to be recommended. Another limitation is it totally ignores the existence of taxonomy information that is ubiquitous in many datasets like Amazon the authors experimented with. In this report, we propose two novel methods that make use of the hierarchical category metadata to overcome the limitations identified above. The main contributions are listed as following.
Movie Genre Classification from Plot Summaries Using Bidirectional LSTM

Movie plot summaries are expected to reflect the genre of movies since many spectators read the plot summaries before deciding to watch a movie. In this study, we perform movie genre classification from plot summaries of movies using bidirectional LSTM (Bi-LSTM). We first divide each plot summary of a movie into sentences and assign the genre of corresponding movie to each sentence. Next, using the word representations of sentences, we train Bi-LSTM networks. We estimate the genres for each sentence separately. Since plot summaries generally contain multiple sentences, we use majority voting for the final decision by considering the posterior probabilities of genres assigned to sentences. Our results reflect that, training Bi-LSTM network after dividing the plot summaries into their sentences and fusing the predictions for individual sentences outperform training the network with the whole plot summaries with the limited amount of data. Moreover, employing Bi-LSTM performs better compared to basic Recurrent Neural Networks (RNNs) and Logistic Regression (LR) as a baseline.
Acute systemic inflammation increases arterial stiffness and decreases wave reflections in healthy individuals.

Aortic stiffness is a marker of cardiovascular disease and an independent predictor of cardiovascular risk. Although an association between inflammatory markers and increased arterial stiffness has been suggested, the causative relationship between inflammation and arterial stiffness has not been investigated.



One hundred healthy individuals were studied according to a randomized, double-blind, sham procedure-controlled design. Each substudy consisted of 2 treatment arms, 1 with Salmonella typhi vaccination and 1 with sham vaccination. Vaccination produced a significant (P<0.01) increase in pulse wave velocity (at 8 hours by 0.43 m/s), denoting an increase in aortic stiffness. Wave reflections were reduced significantly (P<0.01) by vaccination (decrease in augmentation index of 5.0% at 8 hours and 2.5% at 32 hours) as a result of peripheral vasodilatation. These effects were associated with significant increases in inflammatory markers such as high-sensitivity C-reactive protein (P<0.001), high-sensitivity interleukin-6 (P<0.001), and matrix metalloproteinase-9 (P<0.01). With aspirin pretreatment (1200 mg PO), neither pulse wave velocity nor augmentation index changed significantly after vaccination (increase of 0.11 m/s and 0.4%, respectively; P=NS for both).



This is the first study to show through a cause-and-effect relationship that acute systemic inflammation leads to deterioration of large-artery stiffness and to a decrease in wave reflections. These findings have important implications, given the importance of aortic stiffness for cardiovascular function and risk and the potential of therapeutic interventions with antiinflammatory properties.