nils-herrmann/clean_scirepeval huggingface dataset:
An Analysis of Encoder Representations in Transformer-Based Machine Translation

The attention mechanism is a successful technique in modern NLP, especially in tasks like machine translation. The recently proposed network architecture of the Transformer is based entirely on attention mechanisms and achieves new state of the art results in neural machine translation, outperforming other sequence-to-sequence models. However, so far not much is known about the internal properties of the model and the representations it learns to achieve that performance. To study this question, we investigate the information that is learned by the attention mechanism in Transformer models with different translation quality. We assess the representations of the encoder by extracting dependency relations based on self-attention weights, we perform four probing tasks to study the amount of syntactic and semantic captured information and we also test attention in a transfer learning scenario. Our analysis sheds light on the relative strengths and weaknesses of the various encoder representations. We observe that specific attention heads mark syntactic dependency relations and we can also confirm that lower layers tend to learn more about syntax while higher layers tend to encode more semantics.
Understanding Grounded Language Learning Agents

Neural network-based systems can now learn to locate the referents of words and phrases in images, answer questions about visual scenes, and even execute symbolic instructions as first-person actors in partially-observable worlds. To achieve this so-called grounded language learning, models must overcome certain well-studied learning challenges that are also fundamental to infants learning their first words. While it is notable that models with no meaningful prior knowledge overcome these learning obstacles, AI researchers and practitioners currently lack a clear understanding of exactly how they do so. Here we address this question as a way of achieving a clearer general understanding of grounded language learning, both to inform future research and to improve confidence in model predictions. For maximum control and generality, we focus on a simple neural network-based language learning agent trained via policy-gradient methods to interpret synthetic linguistic instructions in a simulated 3D world. We apply experimental paradigms from developmental psychology to this agent, exploring the conditions under which established human biases and learning effects emerge. We further propose a novel way to visualise and analyse semantic representation in grounded language learning agents that yields a plausible computational account of the observed effects.
The requirements, challenges, and technologies for 5G of terrestrial mobile telecommunication

In this article, we summarize the 5G mobile communication requirements and challenges. First, essential requirements for 5G are pointed out, including higher traffic volume, indoor or hotspot traffic, and spectrum, energy, and cost efficiency. Along with these changes of requirements, we present a potential step change for the evolution toward 5G, which shows that macro-local coexisting and coordinating paths will replace one macro-dominated path as in 4G and before. We hereafter discuss emerging technologies for 5G within international mobile telecommunications. Challenges and directions in hardware, including integrated circuits and passive components, are also discussed. Finally, a whole picture for the evolution to 5G is predicted and presented.
Experimental Investigation of Light-Gauge Steel Plate Shear Walls

This paper describes the prototype design, specimen design, experimental setup, and experimental results of three steel plate shear wall concepts. Prototype light-gauge steel plate shear walls are designed as seismic retrofits for a hospital st area of high seismicity, and emphasis is placed on minimizing their impact on the existing framing. Three single-story test spe designed using these prototypes as a basis, two specimens with flat infill plates (thicknesses of 0.9 mm ) and a third using a corrugat infill plate (thickness of 0.7 mm). Connection of the infill plates to the boundary frames is achieved through the use of b combination with industrial strength epoxy or welds, allowing for mobility of the infills if desired. Testing of the systems is don quasi-static conditions. It is shown that one of the flat infill plate specimens, as well as the specimen utilizing a corrugated in achieve significant ductility and energy dissipation while minimizing the demands placed on the surrounding framing. Exp results are compared to monotonic pushover predictions from computer analysis using a simple model and good agreement DOI: 10.1061/
Privado: Practical and Secure DNN Inference

Recently, cloud providers have extended support for trusted hardware primitives such as Intel SGX. Simultaneously, the field of deep learning is seeing enormous innovation and increase in adoption. In this paper, we therefore ask the question: “Can third-party cloud services use SGX to provide practical, yet secure DNN Inference-as-a-service? ” Our work addresses the three main challenges that SGX-based DNN inferencing faces, namely, security, ease-of-use, and performance. We first demonstrate that side-channel based attacks on DNN models are indeed possible. We show that, by observing access patterns, we can recover inputs to the DNN model. This motivates the need for PRIVADO, a system we have designed for secure inference-as-aservice. PRIVADO is input-oblivious: it transforms any deep learning framework written in C/C++ to be free of input-dependent access patterns. PRIVADO is fullyautomated and has a low TCB: with zero developer effort, given an ONNX description, it generates compact C code for the model which can run within SGX-enclaves. PRIVADO has low performance overhead: we have used PRIVADO with Torch, and have shown its overhead to be 20.77% on average on 10 contemporary networks.
An Overview of Internet of Things (IoT) and Data Analytics in Agriculture: Benefits and Challenges

The surge in global population is compelling a shift toward smart agriculture practices. This coupled with the diminishing natural resources, limited availability of arable land, increase in unpredictable weather conditions makes food security a major concern for most countries. As a result, the use of Internet of Things (IoT) and data analytics (DA) are employed to enhance the operational efficiency and productivity in the agriculture sector. There is a paradigm shift from use of wireless sensor network (WSN) as a major driver of smart agriculture to the use of IoT and DA. The IoT integrates several existing technologies, such as WSN, radio frequency identification, cloud computing, middleware systems, and end-user applications. In this paper, several benefits and challenges of IoT have been identified. We present the IoT ecosystem and how the combination of IoT and DA is enabling smart agriculture. Furthermore, we provide future trends and opportunities which are categorized into technological innovations, application scenarios, business, and marketability.
Programming curvature using origami tessellations.

Origami describes rules for creating folded structures from patterns on a flat sheet, but does not prescribe how patterns can be designed to fit target shapes. Here, starting from the simplest periodic origami pattern that yields one-degree-of-freedom collapsible structures-we show that scale-independent elementary geometric constructions and constrained optimization algorithms can be used to determine spatially modulated patterns that yield approximations to given surfaces of constant or varying curvature. Paper models confirm the feasibility of our calculations. We also assess the difficulty of realizing these geometric structures by quantifying the energetic barrier that separates the metastable flat and folded states. Moreover, we characterize the trade-off between the accuracy to which the pattern conforms to the target surface, and the effort associated with creating finer folds. Our approach enables the tailoring of origami patterns to drape complex surfaces independent of absolute scale, as well as the quantification of the energetic and material cost of doing so.
Detecting insurance claims fraud using machine learning techniques

The insurance industries consist of more than thousand companies in worldwide. And collect more than one trillions of dollars premiums in each year. When a person or entity make false insurance claims in order to obtain compensation or benefits to which they are not entitled is known as an insurance fraud. The total cost of an insurance fraud is estimated to be more than forty billions of dollars. So detection of an insurance fraud is a challenging problem for the insurance industry. The traditional approach for fraud detection is based on developing heuristics around fraud indicator. The auto\vehicle insurance fraud is the most prominent type of insurance fraud, which can be done by fake accident claim. In this paper, focusing on detecting the auto\vehicle fraud by using, machine learning technique. Also, the performance will be compared by calculation of confusion matrix. This can help to calculate accuracy, precision, and recall.
Application of Technology Acceptance Model ( TAM ) in M-Banking Adoption in Kenya

Mobile phones with Mobile Commerce technology are becoming more readily available in Kenya. Similarly many financial institutions and mobile phone service providers are teaming up to provide banking services to customers via the mobile phone. However the number of people who choose to adopt or use such technologies is still relatively low. Therefore there is need to assess the acceptance of such technologies to establish factors that hinder or promote their acceptance. This study applied Technology Acceptance Model to examine the factors that influence the adoption of M-banking in Kenya. The study specifically focused on the evaluation of MKesho, an M-banking application in Kenya. A survey was conducted to gather data which was coded in SPSS 16. Confirmatory Factor Analysis was used to analyze the data and Structural Equation Modeling using Analysis of Moment Structures was used to validate the research model. Out of a total of 450 questionnaires distributed to M-Kesho users, 395 were returned and validated. The analysis revealed that Perceived Ease of Use, Perceived Usefulness, Perceived Self Efficacy and Perceived Credibility significantly influenced customers‘ attitude towards usage of M-banking. The results of the data analysis contributes to the body of knowledge by demonstrating that the above factors are critical in attitude towards usage of M-banking in a developing country context. The implications of the results form a good basis for providing practical recommendations to the banking industry, and directions for further work.
Problematic Video Game Use: Estimated Prevalence and Associations with Mental and Physical Health

A nationwide survey was conducted to investigate the prevalence of video game addiction and problematic video game use and their association with physical and mental health. An initial sample comprising 2,500 individuals was randomly selected from the Norwegian National Registry. A total of 816 (34.0 percent) individuals completed and returned the questionnaire. The majority (56.3 percent) of respondents used video games on a regular basis. The prevalence of video game addiction was estimated to be 0.6 percent, with problematic use of video games reported by 4.1 percent of the sample. Gender (male) and age group (young) were strong predictors for problematic use of video games. A higher proportion of high frequency compared with low frequency players preferred massively multiplayer online role-playing games, although the majority of high frequency players preferred other game types. Problematic use of video games was associated with lower scores on life satisfaction and with elevated levels of anxiety and depression. Video game use was not associated with reported amount of physical exercise.
AdaBoost learning for fabric defect detection based on HOG and SVM

In this paper, a novel fabric detect detection scheme based on HOG and SVM is proposed. Firstly, each block-based feature of the image is encoded using the histograms of orientated gradients (HOG), which are insensitive to various lightings and noises. Then, a powerful feature selection algorithm, AdaBoost, is performed to automatically select a small set of discriminative HOG features in order to achieve robust detection results. In the end, support vector machine (SVM) is used to classify the fabric defects. Experimental results demonstrate the efficiency of our proposed algorithm.
Social Affordance Tracking over Time - A Sensorimotor Account of False-Belief Tasks

False-belief task have mainly been associated with the explanatory notion of the theory of mind and the theory-theory. However, it has often been pointed out that this kind of highlevel reasoning is computational and time expensive. During the last decades, the idea of embodied intelligence, i.e. complex behavior caused by sensorimotor contingencies, has emerged in both the fields of neuroscience, psychology and artificial intelligence. Viewed from this perspective, the failing in a false-belief test can be the result of the impairment to recognize and track others’ sensorimotor contingencies and affordances. Thus, social cognition is explained in terms of lowlevel signals instead of high-level reasoning. In this work, we present a generative model for optimal action selection which simultaneously can be employed to make predictions of others’ actions. As we base the decision making on a hidden state representation of sensorimotor signals, this model is in line with the ideas of embodied intelligence. We demonstrate how the tracking of others’ hidden states can give rise to correct falsebelief inferences, while a lack thereof leads to failing. With this work, we want to emphasize the importance of sensorimotor contingencies in social cognition, which might be a key to artificial, socially intelligent systems.
Performance Evaluation of a large 3 D Fingerprint database

It is necessary to have a realistic estimation of its performance prior to the large scale deployment of any biometric system. In the domain of fingerprint biometric, 3D fingerprint scan technology has been developing very fast. However, there is no 3D fingerprint database publicly available for research purpose. To evaluate the matching performance of 3D fingerprints and the compatibility of 2D and 3D fingerprints comprehensively, we have established a large fingerprint database using two commercial fingerprint sensors. The database consists of both 3D fingerprints and their corresponding 2D fingerprints. We have carried out several verification experiments using a commercial fingerprint identification software. The results can be served as performance criterion of the database, which will be released publicly together with the database in late 2014.
An Improved Playfair Cipher Cryptographic Substitution Algorithm

The goal of this research is to find the efficient and most widely used cryptographic algorithms form the history, investigating one of its merits and demerits which have not been modified so far. Perception of cryptography, its techniques such as transposition & substitution and Steganography were discussed. Our main focus is on the Playfair Cipher, its advantages and disadvantages. Finally, we have proposed a few methods to enhance the playfair cipher for more secure and efficient cryptography.
Unsupervised P2P Rental Recommendations via Integer Programming

Due to the sparseness of quality rating data, unsupervised recommender systems are used in many applications in Peer to Peer (P2P) rental marketplaces such as Airbnb, FlipKey, and HomeAway. We present an integer programming based recommender systems, where both accommodation benefits and community risks of lodging places are measured and incorporated into an objective function as utility measurements. More specifically, we first present an unsupervised fused scoring method for quantifying the accommodation benefits and community risks of a lodging with crowd-sourced geo-tagged data. In order to the utility of recommendations, we formulate the unsupervised P2P rental recommendations as a constrained integer programming problem, where the accommodation benefits of recommendations are maximized and the community risks of recommendations are minimized, while maintaining constraints on personalization. Furthermore, we provide an efficient solution for the optimization problem by developing a learning-to-integer-programming method for combining aggregated listwise learning to rank into branching variable selection. We apply the proposed approach to the Airbnb data of New York City and provide lodging recommendations to travelers. In our empirical experiments, we demonstrate both the efficiency and effectiveness of our method in terms of striving a trade-off between the user satisfaction, time on market, and the number of reviews, and achieving a balance between positive and negative sides.
Understanding the Linux Kernel

Linux scheduling is based on the time-sharing technique already introduced in the section "CPU's Time Sharing" in Chapter 5, Timing Measurements: several processes are allowed to run "concurrently," which means that the CPU time is roughly divided into "slices," one for each runnable process.[1] Of course, a single processor can run only one process at any given instant. If a currently running process is not terminated when its time slice or quantum expires, a process switch may take place. Time-sharing relies on timer interrupts and is thus transparent to processes. No additional code needs to be inserted in the programs in order to ensure CPU time-sharing.
Developing limits for driving under cannabis.

Development of a rational and enforceable basis for controlling the impact of cannabis use on traffic safety.



An international working group of experts on issues related to drug use and traffic safety evaluated evidence from experimental and epidemiological research and discussed potential approaches to developing per se limits for cannabis.



In analogy to alcohol, finite (non-zero) per se limits for delta-9-tetrahydrocannabinol (THC) in blood appear to be the most effective approach to separating drivers who are impaired by cannabis use from those who are no longer under the influence. Limited epidemiological studies indicate that serum concentrations of THC below 10 ng/ml are not associated with an elevated accident risk. A comparison of meta-analyses of experimental studies on the impairment of driving-relevant skills by alcohol or cannabis suggests that a THC concentration in the serum of 7-10 ng/ml is correlated with an impairment comparable to that caused by a blood alcohol concentration (BAC) of 0.05%. Thus, a suitable numerical limit for THC in serum may fall in that range.



This analysis offers an empirical basis for a per se limit for THC that allows identification of drivers impaired by cannabis. The limited epidemiological data render this limit preliminary.
A Deep Reinforced Sequence-to-Set Model for Multi-Label Text Classification

Multi-label text classification (MLTC) aims to assign multiple labels to each sample in the dataset. The labels usually have internal correlations. However, traditional methods tend to ignore the correlations between labels. In order to capture the correlations between labels, the sequence-tosequence (Seq2Seq) model views the MLTC task as a sequence generation problem, which achieves excellent performance on this task. However, the Seq2Seq model is not suitable for the MLTC task in essence. The reason is that it requires humans to predefine the order of the output labels, while some of the output labels in the MLTC task are essentially an unordered set rather than an ordered sequence. This conflicts with the strict requirement of the Seq2Seq model for the label order. In this paper, we propose a novel sequence-toset framework utilizing deep reinforcement learning, which not only captures the correlations between labels, but also reduces the dependence on the label order. Extensive experimental results show that our proposed method outperforms the competitive baselines by a large margin.
LayoutGAN: Generating Graphic Layouts with Wireframe Discriminators

Layout is important for graphic design and scene generation. We propose a novel Generative Adversarial Network, called LayoutGAN, that synthesizes layouts by modeling geometric relations of different types of 2D elements. The generator of LayoutGAN takes as input a set of randomly-placed 2D graphic elements and uses self-attention modules to refine their labels and geometric parameters jointly to produce a realistic layout. Accurate alignment is critical for good layouts. We thus propose a novel differentiable wireframe rendering layer that maps the generated layout to a wireframe image, upon which a CNN-based discriminator is used to optimize the layouts in image space. We validate the effectiveness of LayoutGAN in various experiments including MNIST digit generation, document layout generation, clipart abstract scene generation and tangram graphic design.
Engineering three-dimensional bone tissue in vitro using biodegradable scaffolds: investigating initial cell-seeding density and culture period.

New three-dimensional (3D) scaffolds for bone tissue engineering have been developed throughout which bone cells grow, differentiate, and produce mineralized matrix. In this study, the percentage of cells anchoring to our polymer scaffolds as a function of initial cell seeding density was established; we then investigated bone tissue formation throughout our scaffolds as a function of initial cell seeding density and time in culture. Initial cell seeding densities ranging from 0.5 to 10 x 10(6) cells/cm(3) were seeded onto 3D scaffolds. After 1 h in culture, we determined that 25% of initial seeded cells had adhered to the scaffolds in static culture conditions. The cell-seeded scaffolds remained in culture for 3 and 6 weeks, to investigate the effect of initial cell seeding density on bone tissue formation in vitro. Further cultures using 1 x 10(6) cells/cm(3) were maintained for 1 h and 1, 2, 4, and 6 weeks to study bone tissue formation as a function of culture period. After 3 and 6 weeks in culture, scaffolds seeded with 1 x 10(6) cells/cm(3) showed similar tissue formation as those seeded with higher initial cell seeding densities. When initial cell seeding densities of 1 x 10(6) cells/cm(3) were used, osteocalcin immunolabeling indicative of osteoblast differentiation was seen throughout the scaffolds after only 2 weeks of culture. Von Kossa and tetracycline labeling, indicative of mineralization, occurred after 3 weeks. These results demonstrated that differentiated bone tissue was formed throughout 3D scaffolds after 2 weeks in culture using an optimized initial cell density, whereas mineralization of the tissue only occurred after 3 weeks. Furthermore, after 6 weeks in culture, newly formed bone tissue had replaced degrading polymer.
Building Virtual and Augmented Reality museum exhibitions

A system that allows museums to build and manage Virtual and Augmented Reality exhibitions based on 3D models of artifacts is presented. Dynamic content creation based on pre-designed visualization templates allows content designers to create virtual exhibitions very efficiently. Virtual Reality exhibitions can be presented both inside museums, e.g. on touch-screen displays installed inside galleries and, at the same time, on the Internet. Additionally, the presentation based on Augmented Reality technologies allows museum visitors to interact with the content in an intuitive and exciting manner.