nils-herrmann/clean_scirepeval huggingface dataset:
Discrete Structural Planning for Neural Machine Translation

Structural planning is important for producing long sentences, which is a missing part in current language generation models. In this work, we add a planning phase in neural machine translation to control the coarse structure of output sentences. The model first generates some planner codes, then predicts real output words conditioned on them. The codes are learned to capture the coarse structure of the target sentence. In order to obtain the codes, we design an end-to-end neural network with a discretization bottleneck, which predicts the simplified part-of-speech tags of target sentences. Experiments show that the translation performance are generally improved by planning ahead. We also find that translations with different structures can be obtained by manipulating the planner codes.
Learning to Generate Move-by-Move Commentary for Chess Games from Large-Scale Social Forum Data

This paper examines the problem of generating natural language descriptions of chess games. We introduce a new largescale chess commentary dataset and propose methods to generate commentary for individual moves in a chess game. The introduced dataset consists of more than 298K chess move-commentary pairs across 11K chess games. We highlight how this task poses unique research challenges in natural language generation: the data contain a large variety of styles of commentary and frequently depend on pragmatic context. We benchmark various baselines and propose an end-to-end trainable neural model which takes into account multiple pragmatic aspects of the game state that may be commented upon to describe a given chess move. Through a human study on predictions for a subset of the data which deals with direct move descriptions, we observe that outputs from our models are rated similar to ground truth commentary texts in terms of correctness and
Why people keep coming back to Facebook: Explaining and predicting continuance participation from an extended theory of planned behaviour perspective

a r t i c l e i n f o Keywords: Theory of planned behaviour Post-adoption Perceived value Facebook Social networking sites TPB SNS This study examines the continuance participation intentions and behaviour on Facebook, as a representative of Social Networking Sites (SNSs), from a social and behavioural perspective. The study extends the Theory of Planned Behaviour (TPB) through the inclusion of perceived value construct and utilizes the extended theory to explain users' continuance participation intentions and behaviour on Facebook. Despite the recent massive uptake of Facebook, our review of the related-literature revealed that very few studies tackled such technologies from the context of post-adoption as in this research. Using data from surveys of undergraduate and postgraduate students in Jordan (n=403), the extended theory was tested using statistical analysis methods. The results show that attitude, subjective norm, perceived behavioural control, and perceived value have significant effect on the continuance participation intention of post-adopters. Further, the results show that continuance participation intention and perceived value have significant effect on continuance participation behaviour. However, the results show that perceived be-havioural control has no significant effect on continuance participation behaviour of post-adopters. When comparing the extended theory developed in this study with the standard TPB, it was found that the inclusion of the perceived value construct in the extended theory is fruitful; as such an extension explained an additional 11.6% of the variance in continuance participation intention and 4.5% of the variance in continuance participation behaviour over the standard TPB constructs. Consistent with the research on value-driven post-adoption behaviour, these findings suggest that continuance intentions and behaviour of users of Facebook are likely to be greater when they perceive the behaviour to be associated with significant added-value (i.e. benefits outperform sacrifices). Since its introduction, the Internet has enabled entirely new forms of social interaction and activities, thanks to its basic features such as the prevalent usability and access. As the Internet is massively evolving over time, the World Wide Web or otherwise referred to as Web 1.0 has been transformed to the so-called Web 2.0. In fact, Web 2.0 refers to the second generation of the World Wide Web that facilitates information sharing, interoperability, user-centred design and collaboration. The advent of Web 2.0 has led to the development and evolution of Web-based communities, hosted services, and Web applications that work as a mainstream medium for value creation and exchange. Examples of Web …
Memristor-based neural networks

The synapse is a crucial element in biological neural networks, but a simple electronic equivalent has been absent. This complicates the development of hardware that imitates biological architectures in the nervous system. Now, the recent progress in the experimental realization of memristive devices has renewed interest in artificial neural networks. The resistance of a memristive system depends on its past states and exactly this functionality can be used to mimic the synaptic connections in a (human) brain. After a short introduction to memristors, we present and explain the relevant mechanisms in a biological neural network, such as long-term potentiation and spike time-dependent plasticity, and determine the minimal requirements for an artificial neural network. We review the implementations of these processes using basic electric circuits and more complex mechanisms that either imitate biological systems or could act as a model system for them. (Some figures may appear in colour only in the online journal)
Influence Maximization on Social Graphs: A Survey

Influence Maximization (IM), which selects a set of <inline-formula><tex-math notation="LaTeX">$k$</tex-math> <alternatives><inline-graphic xlink:href="li-ieq1-2807843.gif"/></alternatives></inline-formula> users (called seed set) from a social network to maximize the expected number of influenced users (called influence spread), is a key algorithmic problem in social influence analysis. Due to its immense application potential and enormous technical challenges, IM has been extensively studied in the past decade. In this paper, we survey and synthesize a wide spectrum of existing studies on IM from an <italic>algorithmic perspective</italic>, with a special focus on the following key aspects: (1) a review of well-accepted diffusion models that capture the information diffusion process and build the foundation of the IM problem, (2) a fine-grained taxonomy to classify existing IM algorithms based on their design objectives, (3) a rigorous theoretical comparison of existing IM algorithms, and (4) a comprehensive study on the applications of IM techniques in combining with novel context features of social networks such as topic, location, and time. Based on this analysis, we then outline the key challenges and research directions to expand the boundary of IM research.
Summarization system evaluation revisited: N-gram graphs

This article presents a novel automatic method (AutoSummENG) for the evaluation of summarization systems, based on comparing the character n-gram graphs representation of the extracted summaries and a number of model summaries. The presented approach is language neutral, due to its statistical nature, and appears to hold a level of evaluation performance that matches and even exceeds other contemporary evaluation methods. Within this study, we measure the effectiveness of different representation methods, namely, word and character n-gram graph and histogram, different n-gram neighborhood indication methods as well as different comparison methods between the supplied representations. A theory for the a priori determination of the methods' parameters along with supporting experiments concludes the study to provide a complete alternative to existing methods concerning the automatic summary system evaluation process.
Probabilistic Adaptive Computation Time

We present a probabilistic model with discrete latent variables that control the computation time in deep learning models such as ResNets and LSTMs. A prior on the latent variables expresses the preference for faster computation. The amount of computation for an input is determined via amortized maximum a posteriori (MAP) inference. MAP inference is performed using a novel stochastic variational optimization method. The recently proposed Adaptive Computation Time mechanism can be seen as an ad-hoc relaxation of this model. We demonstrate training using the generalpurpose Concrete relaxation of discrete variables. Evaluation on ResNet shows that our method matches the speed-accuracy trade-off of Adaptive Computation Time, while allowing for evaluation with a simple deterministic procedure that has a lower memory footprint.
PROBLEMS OF EMPLOYABILITY-A STUDY OF JOB – SKILL AND QUALIFICATION MISMATCH

1.1 CONCEPT OF EMPLOYABILITY Employability has emerged as a major element in Indian labor market and has now become the most common topic of discussion. One of the main impediments in economic growth of India is the insufficient supply of quality skills. According to World Bank, 2009, one of the major constraints most industries in India face is the skills shortage. Each year over 3 million graduates and post-graduates are added to the Indian workforce. Of these, only 25 percent of technical graduates and 10-15 percent of regular graduates are considered employable by the industry (NASSCOM Report). In the current business environment, there is a need of education for employability. Many public and private training institutions focus on the development of key skills and work experience among most of the proficient and non-proficient workforces which enhance their employability. The industrial by the trainees is the matter which concern employers the most, as stated by Nation‟s Higher  Level of productivity can be increased by providing industrial training to employers.
End-to-End Information Extraction without Token-Level Supervision

Most state-of-the-art information extraction approaches rely on token-level labels to find the areas of interest in text. Unfortunately, these labels are time-consuming and costly to create, and consequently, not available for many real-life IE tasks. To make matters worse, token-level labels are usually not the desired output, but just an intermediary step. End-to-end (E2E) models, which take raw text as input and produce the desired output directly, need not depend on token-level labels. We propose an E2E model based on pointer networks, which can be trained directly on pairs of raw input and output text. We evaluate our model on the ATIS data set, MIT restaurant corpus and the MIT movie corpus and compare to neural baselines that do use token-level labels. We achieve competitive results, within a few percentage points of the baselines, showing the feasibility of E2E information extraction without the need for token-level labels. This opens up new possibilities, as for many tasks currently addressed by human extractors, raw input and output data are available, but not token-level labels.
RGB-D to CAD Retrieval with ObjectNN Dataset

The goal of this track is to study and evaluate the performance of 3D object retrieval algorithms using RGB-D data. This is inspired from the practical need to pair an object acquired from a consumer-grade depth camera to CAD models available in public datasets on the Internet. To support the study, we propose ObjectNN, a new dataset with well segmented and annotated RGB-D objects from SceneNN [HPN∗16] and CAD models from ShapeNet  The evaluation results show that the RGB-D to CAD retrieval problem, while being challenging to solve due to partial and noisy 3D reconstruction, can be addressed to a good extent using deep learning techniques, particularly, convolutional neural networks trained by multi-view and 3D geometry. The best method in this track scores 82% in accuracy.
Compact 24-GHz Doppler radar module for non-contact human vital-sign detection

A compact 24-GHz Doppler radar module is developed in this paper for non-contact human vital-sign detection. The 24-GHz radar transceiver chip, transmitting and receiving antennas, baseband circuits, microcontroller, and Bluetooth transmission module have been integrated and implemented on a printed circuit board. For a measurement range of 1.5 m, the developed radar module can successfully detect the respiration and heartbeat of a human adult.
Rice disease identification using pattern recognition techniques

The techniques of machine vision are extensively applied to agricultural science, and it has great perspective especially in the plant protection field, which ultimately leads to crops management. The paper describes a software prototype system for rice disease detection based on the infected images of various rice plants. Images of the infected rice plants are captured by digital camera and processed using image growing, image segmentation techniques to detect infected parts of the plants. Then the infected part of the leaf has been used for the classification purpose using neural network. The methods evolved in this system are both image processing and soft computing technique applied on number of diseased rice plants.
The digital scribe

Current generation electronic health records suffer a number of problems that make them inefficient and associated with poor clinical satisfaction. Digital scribes or intelligent documentation support systems, take advantage of advances in speech recognition, natural language processing and artificial intelligence, to automate the clinical documentation task currently conducted by humans. Whilst in their infancy, digital scribes are likely to evolve through three broad stages. Human led systems task clinicians with creating documentation, but provide tools to make the task simpler and more effective, for example with dictation support, semantic checking and templates. Mixed-initiative systems are delegated part of the documentation task, converting the conversations in a clinical encounter into summaries suitable for the electronic record. Computer-led systems are delegated full control of documentation and only request human interaction when exceptions are encountered. Intelligent clinical environments permit such augmented clinical encounters to occur in a fully digitised space where the environment becomes the computer. Data from clinical instruments can be automatically transmitted, interpreted using AI and entered directly into the record. Digital scribes raise many issues for clinical practice, including new patient safety risks. Automation bias may see clinicians automatically accept scribe documents without checking. The electronic record also shifts from a human created summary of events to potentially a full audio, video and sensor record of the clinical encounter. Digital scribes promisingly offer a gateway into the clinical workflow for more advanced support for diagnostic, prognostic and therapeutic tasks.
Asymmetric Algorithms and Symmetric Algorithms: A Review

In these days securing a network is an important issue. Many techniques are provided to secure network. Cryptographic is a technique of transforming a message into such form which is unreadable, and then retransforming that message back to its original form. Cryptography works in two techniques: symmetric key also known as secret-key cryptography algorithms and asymmetric key also known as public-key cryptography algorithms. In this paper we are reviewing different symmetric and asymmetric algorithms.
Efficiently Trainable Text-to-Speech System Based on Deep Convolutional Networks with Guided Attention

This paper describes a novel text-to-speech (TTS) technique based on deep convolutional neural networks (CNN), without use of any recurrent units. Recurrent neural networks (RNN) have become a standard technique to model sequential data recently, and this technique has been used in some cutting-edge neural TTS techniques. However, training RNN components often requires a very powerful computer, or a very long time, typically several days or weeks. Recent other studies, on the other hand, have shown that CNN-based sequence synthesis can be much faster than RNN-based techniques, because of high parallelizability. The objective of this paper is to show that an alternative neural TTS based only on CNN alleviate these economic costs of training. In our experiment, the proposed Deep Convolutional TTS was sufficiently trained overnight (15 hours), using an ordinary gaming PC equipped with two GPUs, while the quality of the synthesized speech was almost acceptable.
Blockchain government-a next form of infrastructure for the twenty-first century

Today, more than 100 blockchain projects created to transform government systems are being conducted in more than 30 countries. What leads countries rapidly initiate blockchain projects? I argue that it is because blockchain is a technology directly related to social organization; Unlike other technologies, a consensus mechanism form the core of blockchain. Traditionally, consensus is not the domain of machines but rather humankind. However, blockchain operates through a consensus algorithm with human intervention; once that consensus is made, it cannot be modified or forged. Through utilization of Lawrence Lessig’s proposition that “Code is law,” I suggest that blockchain creates “absolute law” that cannot be violated. This characteristic of blockchain makes it possible to implement social technology that can replace existing social apparatuses including bureaucracy. In addition, there are three close similarities between blockchain and bureaucracy. First, both of them are defined by the rules and execute predetermined rules. Second, both of them work as information processing machines for society. Third, both of them work as trust machines for society. Therefore, I posit that it is possible and moreover unavoidable to replace bureaucracy with blockchain systems. In conclusion, I suggest five principles that should be adhered to when we replace bureaucracy with the blockchain system: 1) introducing Blockchain Statute law; 2) transparent disclosure of data and source code; 3) implementing autonomous executing administration; 4) building a governance system based on direct democracy and 5) making Distributed Autonomous Government(DAG).
Comparison of Invariant Manifolds for Model Reduction in Chemical Kinetics

A modern approach to model reduction in chemical kinetics is often based on the notion of slow invariant manifold. The goal of this paper is to give a comparison of various methods of construction of slow invariant manifolds using a simple Michaelis-Menten catalytic reaction. We explore a recently introduced Method of Invariant Grids (MIG) for iteratively solving the invariance equation. Various initial approximations for the grid are considered such as Quasi Equilibrium Manifold, Spectral Quasi Equilibrium Manifold, Intrinsic Low Dimensional Manifold and Symmetric Entropic Intrinsic Low Dimensional Manifold. Slow invariant manifold was also computed using the Computational Singular Perturbation (CSP) method. A comparison between MIG and CSP is also reported. PACS (2006): 05.20.Dd, 82.37.-j
Cyber attacking tactical radio networks

Cyber attacks in the Internet are common knowledge for even nontechnical people. Same attack techniques can also be used against any military radio networks in the battlefield. This paper describes a test setup that can be used to test tactical radio networks against cyber vulnerabilities. The test setup created is versatile and can be adapted to any command and control system on any level of the OSI model. Test setup uses as much publicly or commercially available tools as possible. Need for custom made components is minimized to decrease costs, to decrease deployment time and to increase usability. With architecture described, same tools used in IP network testing can be used in tactical radio networks. Problems found in any level of the system can be fixed in co-operation with vendors of the system. Cyber testing should be adapted as part of acceptance tests of any new military communication system.
A 28-Gb/s 4-Tap FFE/15-Tap DFE Serial Link Transceiver in 32-nm SOI CMOS Technology

This paper presents a 28-Gb/s transceiver in 32-nm SOI CMOS technology for chip-to-chip communications over high-loss electrical channels such as backplanes. The equalization needed for such applications is provided by a 4-tap baud-spaced feed-forward equalizer (FFE) in the transmitter and a two-stage peaking amplifier and 15-tap decision-feedback equalizer (DFE) in the receiver. The transmitter employs a source-series terminated (SST) driver topology which doubles the speed of existing half-rate designs. The high-frequency boost provided by the peaking amplifier is enhanced by adopting a structure with capacitively coupled parallel input stages and active feedback. A capacitive level-shifting technique is introduced in the half-rate DFE which allows a single current-integrating summer to drive the four parallel paths used for speculating the first two DFE taps. Error-free signaling at 28 Gb/s is demonstrated with the transceiver over a channel with 35 dB loss at half-baud frequency. In a four-port core configuration, the power consumption at 28 Gb/s is 693 mW/lane.
Distributional semantic pre-filtering in context-aware recommender systems

Context-aware recommender systems improve context-free recommenders by exploiting the knowledge of the contextual situation under which a user experienced and rated an item. They use data sets of contextually-tagged ratings to predict how the target user would evaluate (rate) an item in a given contextual situation, with the ultimate goal to recommend the items with the best estimated ratings. This paper describes and evaluates a pre-filtering approach to context-aware recommendation, called distributional-semantics pre-filtering (DSPF), which exploits in a novel way the distributional semantics of contextual conditions to build more precise context-aware rating prediction models. In DSPF, given a target contextual situation (of a target user), a matrix-factorization predictive model is built by using the ratings tagged with the contextual situations most similar to the target one. Then, this model is used to compute rating predictions and identify recommendations for that specific target contextual situation. In the proposed approach, the definition of the similarity of contextual situations is based on the distributional semantics of their composing conditions: situations are similar if they influence the user’s ratings in a similar way. This notion of similarity has the advantage of being directly derived from the rating data; hence it does not require a context taxonomy. We analyze the effectiveness of DSPF varying the specific method used to compute the situation-to-situation similarity. We also show how DSPF can be further improved by using clustering techniques. Finally, we evaluate DSPF on several contextually-tagged data sets and demonstrate that it outperforms state-of-the-art context-aware approaches.
Resource-based theories of competitive advantage : A ten-year retrospective

The resource-based view can be positioned relative to at least three theoretical traditions: SCPbased theories of industry determinants of firm performance, neo-classical microeconomics, and evolutionary economics. In the 1991 article, only the first of these ways of positioning the resourcebased view is explored. This article briefly discusses some of the implications of positioning the resource-based view relative to these other two literatures; it also discusses some of the empirical implications of each of these different resource-based theories.