nils-herrmann/clean_scirepeval huggingface dataset:
Examining Crowd Work and Gig Work Through the Historical Lens of Piecework

The internet is empowering the rise of crowd work, gig work, and other forms of on--demand labor. A large and growing body of scholarship has attempted to predict the socio--technical outcomes of this shift, especially addressing three questions: begin{inlinelist} item What are the complexity limits of on-demand work?, item How far can work be decomposed into smaller microtasks?, and item What will work and the place of work look like for workers' end {inlinelist} In this paper, we look to the historical scholarship on piecework --- a similar trend of work decomposition, distribution, and payment that was popular at the turn of the nth{20} century --- to understand how these questions might play out with modern on--demand work. We identify the mechanisms that enabled and limited piecework historically, and identify whether on--demand work faces the same pitfalls or might differentiate itself. This approach introduces theoretical grounding that can help address some of the most persistent questions in crowd work, and suggests design interventions that learn from history rather than repeat it.
Generic Text Summarization Using Probabilistic Latent Semantic Indexing

This paper presents a strategy to generate generic summary of documents using Probabilistic Latent Semantic Indexing. Generally a document contains several topics rather than a single one. Summaries created by human beings tend to cover several topics to give the readers an overall idea about the original document. Hence we can expect that a summary containing sentences from better part of the topic spectrum should make a better summary. PLSI has proven to be an effective method in topic detection. In this paper we present a method for creating extractive summary of the document by using PLSI to analyze the features of document such as term frequency and graph structure. We also show our results, which was evaluated using ROUGE, and compare the results with other techniques, proposed in the past.
Emerging frameworks for tangible user interfaces

For more than thirty years, people have relied primarily on screen-based text and graphics to interact with computers. Whether the screen is placed on a desk, held in one’s hand, worn on one’s head, or embedded in the physical environment, the screen has cultivated a predominantly visual paradigm of humancomputer interaction. In this chapter, we discuss a growing space of interfaces in which physical objects play a central role as both physical representations and controls for digital information. We present an interaction model and key characteristics for such “tangible user interfaces,” and explore these characteristics in a number of interface examples. This discussion supports a newly integrated view of both recent and previous work, and points the way towards new kinds of computationally-mediated interfaces that more seamlessly weave together the physical and digital worlds.
Color Iris Authentication using Color Models

Iris authentication is a popular method where persons are accurately authenticated. During authentication phase the features are extracted which are unique. Iris authentication uses IR images for authentication. This proposed work uses color iris images for authentication. Experiments are performed using ten different color models. This paper is focused on performance evaluation of color models used for color iris authentication. This proposed method is more reliable which cope up with different noises of color iris images. The experiments reveals the best selection of color model used for iris authentication. The proposed method is validated on UBIRIS noisy iris database. The results demonstrate that the accuracy is 92.1%, equal error rate of 0.072 and computational time is 0.039 seconds.
Access point assignment in hybrid LiFi and WiFi networks in consideration of LiFi channel blockage

As a promising complement to the increasingly congested wireless fidelity (WiFi) system, light fidelity (LiFi) has recently drawn massive attention due to the wide and unregulated visible light spectrum. Also, the light waves cannot penetrate walls which enables interference avoidance between compartments. Meanwhile, LiFi access points (APs) have a much shorter range, relative to WiFi APs. A hybrid network can combine the high-speed transmission of LiFi and the ubiquitous coverage of WiFi. For such a network, access point assignment (APA) becomes challenging because of the completely overlaying coverage of different networks. In addition, LiFi is susceptible to channel blockage due to opaque obstacles, adding difficulties to the APA issue. In this paper, a novel APA method is proposed for hybrid LiFi and WiFi networks in consideration of LiFi channel blockage for the first time. By exploiting users' statistical information of channel blockage, the proposed method formulates the issue as an optimisation problem that maximises the system throughput over a period of time. Results show that the proposed method can improve throughput by up to 90% over the conventional load balancing method.
An analytical model for deflection of flexible needles during needle insertion

This paper presents a new needle deflection model that is an extension of prior work in our group based on the principles of beam theory. The use of a long flexible needle in percutaneous interventions necessitates accurate modeling of the generated curved trajectory when the needle interacts with soft tissue. Finding a feasible model is important in simulators with applications in training novice clinicians or in path planners used for needle guidance. Using intra-operative force measurements at the needle base, our approach relates mechanical and geometric properties of needle-tissue interaction to the net amount of deflection and estimates the needle curvature. To this end, tissue resistance is modeled by introducing virtual springs along the needle shaft, and the impact of needle-tissue friction is considered by adding a moving distributed external force to the bending equations. Cutting force is also incorporated by finding its equivalent sub-boundary conditions. Subsequently, the closed-from solution of the partial differential equations governing the planar deflection is obtained using Green's functions. To evaluate the performance of our model, experiments were carried out on artificial phantoms.
"And We Will Fight For Our Race!" A Measurement Study of Genetic Testing Conversations on Reddit and 4chan

Rapid progress in genomics has enabled a thriving market for “direct-to-consumer” genetic testing, whereby people have access to their genetic information without the involvement of a healthcare provider. Companies like 23andMe and AncestryDNA, which provide affordable health, genealogy, and ancestry reports, have already tested tens of millions of customers. At the same time, alas, far-right groups have also taken an interest in genetic testing, using them to attack minorities and prove their genetic “purity.” However, the relation between genetic testing and online hate has not really been studied by the scientific community. To address this gap, we present a measurement study shedding light on how genetic testing is discussed on Web communities in Reddit and 4chan. We collect 1.3M comments posted over 27 months using a set of 280 keywords related to genetic testing. We then use Latent Dirichlet Allocation, Google’s Perspective API, Perceptual Hashing, and word embeddings to identify trends, themes, and topics of discussion. Our analysis shows that genetic testing is discussed frequently on Reddit and 4chan, and often includes highly toxic language expressed through hateful, racist, and misogynistic comments. In particular, on 4chan’s politically incorrect board (/pol/), content from genetic testing conversations involves several alt-right personalities and openly antisemitic memes. Finally, we find that genetic testing appears in a few unexpected contexts, and that users seem to build groups ranging from technology enthusiasts to communities using it to promote fringe political views.
Neuromuscular and athletic performance following core strength training in elite youth soccer: Role of instability.

Cross-sectional studies revealed that inclusion of unstable elements in core-strengthening exercises produced increases in trunk muscle activity and thus potential extra stimuli to induce more pronounced performance enhancements in youth athletes. Thus, the purpose of the study was to investigate changes in neuromuscular and athletic performance following core strength training performed on unstable (CSTU) compared with stable surfaces (CSTS) in youth soccer players. Thirty-nine male elite soccer players (age: 17 ± 1 years) were assigned to two groups performing a progressive core strength-training program for 9 weeks (2-3 times/week) in addition to regular in-season soccer training. CSTS group conducted core exercises on stable (i.e., floor, bench) and CSTU group on unstable (e.g., Thera-Band® Stability Trainer, Togu© Swiss ball) surfaces. Measurements included tests for assessing trunk muscle strength/activation, countermovement jump height, sprint time, agility time, and kicking performance. Statistical analysis revealed significant main effects of test (pre vs post) for trunk extensor strength (5%, P < 0.05, d = 0.86), 10-20-m sprint time (3%, P < 0.05, d = 2.56), and kicking performance (1%, P < 0.01, d = 1.28). No significant Group × test interactions were observed for any variable. In conclusion, trunk muscle strength, sprint, and kicking performance improved following CSTU and CSTS when conducted in combination with regular soccer training.
Hierarchical Compositional Visual Concepts

The natural world is infinitely diverse, yet this diversity arises from a relatively small set of coherent properties and rules, such as the laws of physics or chemistry. We conjecture that biological intelligent systems are able to survive within their diverse environments by discovering the regularities that arise from these rules primarily through unsupervised experiences, and representing this knowledge as abstract concepts. Such representations possess useful properties of compositionality and hierarchical organisation, which allow intelligent agents to recombine a finite set of conceptual building blocks into an exponentially large set of useful new concepts. This paper describes SCAN (Symbol-Concept Association Network), a new framework for learning such concepts in the visual domain. We first use the previously published β-VAE (Higgins et al., 2017a) architecture to learn a disentangled representation of the latent structure of the visual world, before training SCAN to extract abstract concepts grounded in such disentangled visual primitives through fast symbol association. Our approach requires very few pairings between symbols and images and makes no assumptions about the choice of symbol representations. Once trained, SCAN is capable of multimodal bi-directional inference, generating a diverse set of image samples from symbolic descriptions and vice versa. It also allows for traversal and manipulation of the implicit hierarchy of compositional visual concepts through symbolic instructions and learnt logical recombination operations. Such manipulations enable SCAN to invent and learn novel visual concepts through recombination of the few learnt concepts.
Learning Deep Object Detectors from 3D Models

Crowdsourced 3D CAD models are easily accessible online, and can potentially generate an infinite number of training images for almost any object category. We show that augmenting the training data of contemporary Deep Convolutional Neural Net (DCNN) models with such synthetic data can be effective, especially when real training data is limited or not well matched to the target domain. Most freely available CAD models capture 3D shape but are often missing other low level cues, such as realistic object texture, pose, or background. In a detailed analysis, we use synthetic CAD images to probe the ability of DCNN to learn without these cues, with surprising findings. In particular, we show that when the DCNN is fine-tuned on the target detection task, it exhibits a large degree of invariance to missing low-level cues, but, when pretrained on generic ImageNet classification, it learns better when the low-level cues are simulated. We show that our synthetic DCNN training approach significantly outperforms previous methods on the benchmark PASCAL VOC2007 dataset when learning in the few-shot scenario and improves performance in a domain shift scenario on the Office benchmark.
DUORC: TOWARDS COMPLEX LANGUAGE UNDER-

We propose DuoRC, a novel dataset for Reading Comprehension (RC) that motivates several new challenges for neural approaches in language understanding beyond those offered by existing RC datasets. DuoRC contains 186,089 unique question-answer pairs created from a collection of 7680 pairs of movie plots where each pair in the collection reflects two versions of the same movie one from Wikipedia and the other from IMDb written by two different authors. We asked crowdsourced workers to create questions from one version of the plot and a different set of workers to extract or synthesize answers from the other version. This unique characteristic of DuoRC where questions and answers are created from different versions of a document narrating the same underlying story, ensures by design, that there is very little lexical overlap between the questions created from one version and the segments containing the answer in the other version. Further, since the two versions have different levels of plot detail, narration style, vocabulary, etc., answering questions from the second version requires deeper language understanding and incorporating external background knowledge. Additionally, the narrative style of passages arising from movie plots (as opposed to typical descriptive passages in existing datasets) exhibits the need to perform complex reasoning over events across multiple sentences. Indeed, we observe that state-ofthe-art neural RC models which have achieved near human performance on the SQuAD dataset (Rajpurkar et al., 2016b), even when coupled with traditional NLP techniques to address the challenges presented in DuoRC exhibit very poor performance (F1 score of 37.42% on DuoRC v/s 86% on SQuAD dataset). This opens up several interesting research avenues wherein DuoRC could complement other RC datasets to explore novel neural approaches for studying language understanding.
- 9359 ( Volume-4 , Issue-7 ) Enhancing the Security of Identity and Access Management in Cloud Computing using Elliptic Curve Cryptography Assist

loud computing is the new technology that aims to provide software, hardware, bandwidth, and virtualized resources on demand via the internet based on the principle of pay per use. There are many challenges that facing the cloud computing, one of the main concerns is managing the identity and access control. This paper focuses on improving the security of identity and access management (IAM) in cloud computing. The proposed scheme combines the security of Identity-Based Cryptography (IBC), Trusted cloud (TC), and Elliptic Curve Cryptography (ECC). IBC is suitable choice for IAM because it reduce the key management complexities, and an ECC is robust algorithm and resistance against attacks.  Cloud computing, Cryptography, Elliptic Curve, Identity and Access
Testing the Generalization Power of Neural Network Models Across NLI Benchmarks

Neural network models have been very successful for natural language inference, with the best models reaching 90% accuracy in some benchmarks. However, the success of these models turns out to be largely benchmark specific. We show that models trained on natural language inference dataset drawn from one benchmark fail to perform well in others, even if the notion of inference assumed in these benchmark tasks is the same or similar. We train five state-of-the-art neural network models on different datasets and show that each one of these fail to generalize outside of the respective benchmark. In light of these results we conclude that the current neural network models are not able to generalize in capturing the semantics of natural language inference, but seem to be overfitting to the specific dataset.
Yersinia ruckeri, the causative agent of enteric redmouth disease in fish

Enteric redmouth disease (ERM) is a serious septicemic bacterial disease of salmonid fish species. It is caused by Yersinia ruckeri, a Gram-negative rod-shaped enterobacterium. It has a wide host range, broad geographical distribution, and causes significant economic losses in the fish aquaculture industry. The disease gets its name from the subcutaneous hemorrhages, it can cause at the corners of the mouth and in gums and tongue. Other clinical signs include exophthalmia, darkening of the skin, splenomegaly and inflammation of the lower intestine with accumulation of thick yellow fluid. The bacterium enters the fish via the secondary gill lamellae and from there it spreads to the blood and internal organs. Y. ruckeri can be detected by conventional biochemical, serological and molecular methods. Its genome is 3.7 Mb with 3406-3530 coding sequences. Several important virulence factors of Y. ruckeri have been discovered, including haemolyin YhlA and metalloprotease Yrp1. Both non-specific and specific immune responses of fish during the course of Y. ruckeri infection have been well characterized. Several methods of vaccination have been developed for controlling both biotype 1 and biotype 2 Y. ruckeri strains in fish. This review summarizes the current state of knowledge regarding enteric redmouth disease and Y. ruckeri: diagnosis, genome, virulence factors, interaction with the host immune responses, and the development of vaccines against this pathogen.
Clinical Question Answering using Key-Value Memory Networks and Knowledge Graph

We describe our clinical question answering system implemented for the Text Retrieval Conference (TREC 2016) Clinical Decision Support (CDS) track. We submitted five runs using a combination of knowledge-driven (based on a curated knowledge graph) and deep learning-based (using key-value memory networks) approaches to retrieve relevant biomedical articles for answering generic clinical questions (diagnoses, treatment, and test) for each clinical scenario provided in three forms: notes, descriptions, and summaries. The submitted runs were varied based on the use of notes, descriptions, or summaries in association with different diagnostic inferencing methodologies applied prior to biomedical article retrieval. Evaluation results demonstrate that our systems achieved best or close to best scores for 20% of the topics and better than median scores for 40% of the topics across all participants considering all evaluation measures. Further analysis shows that on average our clinical question answering system performed best with summaries using diagnostic inferencing from the knowledge graph whereas our key-value memory network model with notes consistently outperformed the knowledge graph-based system for notes and descriptions. ∗The author is also affiliated with Worcester Polytechnic Institute (szhao@wpi.edu). †The author is also affiliated with Northwestern University (kathy.lee@eecs.northwestern.edu). ‡The author is also affiliated with Brandeis University (aprakash@brandeis.edu).
Cryptocurrency Mining - Transition to Cloud

Cryptocurrency, a form of digital currency that has an open and decentralized system and uses cryptography to enhance security and control the creation of new units, is touted to be the next step from conventional monetary transactions. Many cryptocurrencies exist today, with Bitcoin being the most prominent of them. Cryptocurrencies are generated by mining, as a fee for validating any transaction. The rate of generating hashes, which validate any transaction, has been increased by the use of specialized machines such as FPGAs and ASICs, running complex hashing algorithms like SHA-256 and Scrypt, thereby leading to faster generation of cryptocurrencies. This arms race for cheaper-yet-efficient machines has been on since the day the first cryptocurrency, Bitcoin, was introduced in 2009. However, with more people venturing into the world of virtual currency, generating hashes for this validation has become far more complex over the years, with miners having to invest huge sums of money on employing multiple high performance ASICs. Thus the value of the currency obtained for finding a hash did not justify the amount of money spent on setting up the machines, the cooling facilities to overcome the enormous amount of heat they produce and electricity required to run them. The next logical step in this is to utilize the power of cloud computing. Miners leasing super computers that generate hashes at astonishing rates that have a high probability of profits, with the same machine being leased to more than one person on a time bound basis is a win-win situation to both the miners, as well as the cloud service providers. This paper throws light on the nuances of cryptocurrency mining process, the traditional machines used for mining, their limitations, about how cloud based mining is the logical next step and the advantage that cloud platform offers over the traditional machines. Cryptocurrency; Bitcoin mining; Cloud mining; Double Spending; Profitability
Prediction-based portfolio optimization model using neural networks

This work presents a new prediction-based portfolio optimization model tha t can capture short-term investment opportunities. We used neural network predictors to predict stock s’ returns and derived a risk measure, based on the prediction errors, that have the same statistical foundation o f he mean-variance model. The efficient diversification effects holds thanks to the selection of predictor s with low and complementary pairwise error profiles. We employed a large set of experiments with real data from the Brazilian sto ck market to examine our portfolio optimization model, which included the evaluation of the Normality o f the prediction errors. Our results showed that it is possible to obtain Normal prediction errors with non-Normal time series of stock returns, and that the prediction-based portfolio optimization model to ok advantage of short term opportunities, outperforming the mean-variance model and beating the m arket index.
The pretesting effect: do unsuccessful retrieval attempts enhance learning?

Testing previously studied information enhances long-term memory, particularly when the information is successfully retrieved from memory. The authors examined the effect of unsuccessful retrieval attempts on learning. Participants in 5 experiments read an essay about vision. In the test condition, they were asked about embedded concepts before reading the passage; in the extended study condition, they were given a longer time to read the passage. To distinguish the effects of testing from attention direction, the authors emphasized the tested concepts in both conditions, using italics or bolded keywords or, in Experiment 5, by presenting the questions but not asking participants to answer them before reading the passage. Posttest performance was better in the test condition than in the extended study condition in all experiments--a pretesting effect--even though only items that were not successfully retrieved on the pretest were analyzed. The testing effect appears to be attributable, in part, to the role unsuccessful tests play in enhancing future learning.