Reviewerly/exHarmony huggingface dataset:
Study and Implementation of the Fuzzy Mamdani and Sugeno Methods in Decision Making on Selection of Outstanding Students at the South Aceh Polytechnic

The selection of students who excel academically is a measure of the quality of education in an educational institution. In the implementation of education, student achievement becomes a measure of the success of education at the institution. Achievement is the result of hard work by following the rules given by a certain person or institution with certain criteria and conditions to determine if someone is eligible to be chosen as an award recipient. This research uses the Mamdani Fuzzy method or Fuzzy inference system and Sugeno Fuzzy, to design this decision-making system is to design the decision results from the rules compiled using Matlab software for testing. The South Aceh Polytechnic holds outstanding student elections every year, the selection of students with achievements that are carried out every year only focuses on the GPA value in academic achievement without looking at the determining factors which are more precise. In the research that will be conducted the selection must meet the eligibility criteria as a student who gets an award or as an outstanding student at the South Aceh Polytechnic involving variables in the form of Cumulative Achievement Index, Final Project value, Extracurricular, and Attendance. Research is expected to be a tool in the selection process for candidate participants who compete in the selection of outstanding students at the South Aceh Polytechnic so that relevant departments in universities can determine the eligibility of prospective students as award recipients as students with the best achievement scores by considering the appropriate determining factors. The expected output using the Mamdani and Sugeno fuzzy method can become an intellectual property system so that this system can be selected as a guideline by the South Aceh Polytechnic academic for determining outstanding students. The results of the research that have been obtained show that the level of truth is 86.82% for the Mamdani method while the implementation of Sugeno Fuzzy logic is obtained a level of truth of 71.88%
Impact of detuning and dephasing on a laser-corrected subnatural-linewidth single-photon source

The elastic scattering peak of a resonantly driven two-level system has been argued to provide narrow-linewidth antibunched photons. Although independent measurements of spectral width on the one hand and antibunching, on the other hand, do seem to show that this is the case, a joint measurement reveals that only one or the other of these attributes can be realised in the direct emission. We discuss a scheme which interferes the emission with a laser to produce simultaneously single photons of subnatural linewidth. In particular, we consider the effect of dephasing and of the detuning between the driving laser and/or the detector with the emitter. We find that our scheme brings such considerable improvement as compared to the standard schemes as to make it the best single-photon source in terms of all-order multi-photon suppression by several orders of magnitudes. While the scheme is particularly fragile to dephasing, its superiority holds even for subnatural-linewidth emission down to a third of the radiative lifetime.
Direct experimental test of forward and reverse uncertainty relations

This paper shows experimental tests of state dependent uncertainty relations for the product as well as the sum of variances of two incompatible observables for photonic qutrits. These uncertainty relations are independent of any optimization and much tighter than the Robertson-Schrodinger uncertainty relation and other ones found in current literature. Also the authors test the state dependent reverse uncertainty relations for the sum of variances of two incompatible observables.
IIITBH-IITP@CL-SciSumm20, CL-LaySumm20, LongSumm20

In this paper, we present the IIIT Bhagalpur and IIT Patna team's effort to solve the three shared tasks namely, CL-SciSumm 2020, CL-LaySumm 2020, LongSumm 2020 at SDP 2020.The themes of these tasks are to generate medium-scale, lay and long summaries, respectively, for scientific articles.For the first two tasks, unsupervised systems are developed, while for the third one, we have developed a supervised system.The performances of all the systems are evaluated on the associated datasets with the shared tasks in term of well-known ROUGE metric.
Parameter regimes for surpassing the PLOB bound with error-corrected qudit repeaters

A potential quantum internet would open up the possibility of realizing numerous new applications, including provably secure communication. Since losses of photons limit long-distance, direct quantum communication and widespread quantum networks, quantum repeaters are needed. The so-called PLOB-repeaterless bound [Pirandola et al., Nat. Commun. 8, 15043 (2017)] is a fundamental limit on the quantum capacity of direct quantum communication. Here, we analytically derive the quantum-repeater gain for error-corrected, one-way quantum repeaters based on higher-dimensional qudits for two different physical encodings: Fock and multimode qudits. We identify parameter regimes in which such quantum repeaters can surpass the PLOB-repeaterless bound and systematically analyze how typical parameters manifest themselves in the quantum-repeater gain. This benchmarking provides a guideline for the implementation of error-corrected qudit repeaters.
The impact of cloud services pricing on provider profit, consumer surplus, and social welfare

The providers of cloud solutions create rentals for computing power and disk space by means of the protected Internet-connections while their consumers pay only for the resources chosen with options of flexible scaling, assuming rather adequate substitution and development for their digital infrastructure without legal property rights for hardware or software. As a result, the users of cloud products get ready services, and their providers obtain the resources necessary for further development of their products. The work is devoted to computing the total gain of all participants in such a market at various pricing models.
Extracting Relations between Radiotherapy Treatment Details

We present work on extraction of radiotherapy treatment information from the clinical narrative in the electronic medical records.Radiotherapy is a central component of the treatment of most solid cancers.Its details are described in nonstandardized fashions using jargon not found in other medical specialties, complicating the already difficult task of manual data extraction.We examine the performance of several state-of-the-art neural methods for relation extraction of radiotherapy treatment details, with a goal of automating detailed information extraction.The neural systems perform at 0.82-0.88macro-average F1, which approximates or in some cases exceeds the inter-annotator agreement.To the best of our knowledge, this is the first effort to develop models for radiotherapy relation extraction and one of the few efforts for relation extraction to describe cancer treatment in general.
Information System Development Based-on ERP and RAD Methods: Application For Activities Information Broadcasting

As technology develops, information systems become very important in institutions. Information systems support the delivery of information quickly and accurately. The system is a reference at the Persada Islamic Boarding School Ahmad Dahlan University to develop an information delivery system using applications integrated. Manual systems cause the information to be less relevant, requiring an integrated and comprehensive system that can effectively and efficiently deliver information to students. The application developed using Enterprise Resource Planning (ERP) and Rapid Application Development (RAD) methods. The application of these methods requires two main steps, including determining ERP and RAD for implementation. The process for obtaining an ERP method requires steps such as Material Requirement Planning, Close-Loop, and Manufacturing Resource Planning, and Enterprise Resource Planning. The RAD method requires steps such as Requirement Planning, RAD Design Workshop, and Implementation. Application testing used black-box and alpha testing. Each of these tests obtained an accuracy of 95% and 97.7%. Thus, this application can be implemented in Persada very well on mobile and desktop platforms. Besides, the app used information broadcasting to users in real-time for any information in Persada.
Higher-order and long-range synchronization effects for classification and computing in oscillator-based spiking neural networks

In the circuit of two thermally coupled VO2 oscillators, we studied a higher-order synchronization effect, which can be used in object classification techniques to increase the number of possible synchronous states of the oscillator system. We developed the phase-locking estimation method to determine the values of subharmonic ratio and synchronization effectiveness. In our experiment, the number of possible synchronous states of the oscillator system was twelve, and subharmonic ratio distributions were shaped as Arnold’s tongues. In the model, the number of states may reach the maximum value of 150 at certain levels of coupling strength and noise. The long-range synchronization effect in a one-dimensional chain of oscillators occurs even at low values of synchronization effectiveness for intermediate links. We demonstrate a technique for storing and recognizing vector images, which can used for reservoir computing. In addition, we present the implementation of analog operation of multiplication, the synchronization-based logic for binary computations and the possibility to develop the interface between spike neural network and a computer. Based on the universal physical effects, the high-order synchronization can be applied to any spiking oscillators with any coupling type, enhancing the practical value of the presented results to expand spike neural network capabilities.
iPregel: Vertex-centric programmability vs memory efficiency and performance, why choose?

The vertex-centric programming model, designed to improve the programmability in graph processing application writing, has attracted great attention over the years. Multiple shared memory frameworks that have implemented the vertex-centric interface all expose a common tradeoff: programmability against memory efficiency and performance. Our approach consists in preserving vertex-centric programmability, while implementing optimisations missing from FemtoGraph, developing new ones and designing these so they are transparent to a user’s application code, hence not impacting programmability. We therefore implemented our own shared memory vertex-centric framework iPregel, relying on in-memory storage and synchronous execution. In this paper, we evaluate it against FemtoGraph, whose characteristics are identical, but also an asynchronous counterpart GraphChi and the vertex-subset-centric framework Ligra. Our experiments include three of the most popular vertex-centric benchmark applications over 4 real-world publicly accessible graphs, which cover all orders of magnitude between a million to a billion edges. We then measure the execution time and the peak memory usage. Finally, we evaluate the programmability of each framework by comparing it against the original Pregel, Google’s closed-source implementation that started the whole area of vertex-centric programming. Experiments demonstrate that iPregel, like FemtoGraph, does not sacrifice vertex-centric programmability for additional performance and memory efficiency optimisations, which contrasts with GraphChi and Ligra. Sacrificing vertex-centric programmability allowed the latter to benefit from substantial performance and memory efficiency gains. However, experiments demonstrate that iPregel is up to 2300 times faster than FemtoGraph, as well as generating a memory footprint up to 100 times smaller. These results greatly change the situation; Ligra and GraphChi are up to 17,000 and 700 times faster than FemtoGraph but, when comparing against iPregel, this maximum speed-up drops to 10. Furthermore, on PageRank, it is iPregel that proves to be the fastest overall. When it comes to memory efficiency, the same observation applies; Ligra and GraphChi are 100 and 50 times lighter than FemtoGraph, but iPregel nullifies these benefits: it provides the same memory efficiency as Ligra and even proves to be 3 to 6 times lighter than GraphChi on average. In other words, iPregel demonstrates that preserving vertex-centric programmability is not incompatible with a competitive performance and memory efficiency.
Unsupervised object-centric video generation and decomposition in 3D

A natural approach to generative modeling of videos is to represent them as a composition of moving objects. Recent works model a set of 2D sprites over a slowly-varying background, but without considering the underlying 3D scene that gives rise to them. We instead propose to model a video as the view seen while moving through a scene with multiple 3D objects and a 3D background. Our model is trained from monocular videos without any supervision, yet learns to generate coherent 3D scenes containing several moving objects. We conduct detailed experiments on two datasets, going beyond the visual complexity supported by state-of-the-art generative approaches. We evaluate our method on depth-prediction and 3D object detection---tasks which cannot be addressed by those earlier works---and show it out-performs them even on 2D instance segmentation and tracking.
ARTU / TU Wien and Artificial Researcher@ LongSumm 20

In this paper, we present our approach to solve the LongSumm 2020 Shared Task, at the 1st Workshop on Scholarly Document Processing.The objective of the long summaries task is to generate long summaries that cover salient information in scientific articles.The task is to generate abstractive and extractive summaries of a given scientific article.In the proposed approach, we are inspired by the concept of Argumentative Zoning (AZ) that defines the main rhetorical structure in scientific articles.We define two aspects that should be covered in scientific paper summary, namely Claim/Method and Conclusion/Result aspects.We use Solr index to expand the sentences of the paper abstract.We formulate each abstract sentence in a given publication as query to retrieve similar sentences from the text body of the document itself.We utilize a sentence selection algorithm described in previous literature to select sentences for the final summary that covers the two aforementioned aspects.
A Dynamical Central Limit Theorem for Shallow Neural Networks

Recent theoretical works have characterized the dynamics of wide shallow neural networks trained via gradient descent in an asymptotic mean-field limit when the width tends towards infinity. At initialization, the random sampling of the parameters leads to deviations from the mean-field limit dictated by the classical Central Limit Theorem (CLT). However, since gradient descent induces correlations among the parameters, it is of interest to analyze how these fluctuations evolve. Here, we use a dynamical CLT to prove that the asymptotic fluctuations around the mean limit remain bounded in mean square throughout training. The upper bound is given by a Monte-Carlo resampling error, with a variance that that depends on the 2-norm of the underlying measure, which also controls the generalization error. This motivates the use of this 2-norm as a regularization term during training. Furthermore, if the mean-field dynamics converges to a measure that interpolates the training data, we prove that the asymptotic deviation eventually vanishes in the CLT scaling. We also complement these results with numerical experiments.
Medical Concept Normalization in User-Generated Texts by Learning Target Concept Embeddings

Medical concept normalization helps in discovering standard concepts in free-form text i.e., maps health-related mentions to standard concepts in a clinical knowledge base.It is much beyond simple string matching and requires a deep semantic understanding of concept mentions.Recent research approach concept normalization as either text classification or text similarity.The main drawback in existing a) text classification approach is ignoring valuable target concepts information in learning input concept mention representation b) text similarity approach is the need to separately generate target concept embeddings which is time and resource consuming.Our proposed model overcomes these drawbacks by jointly learning the representations of input concept mention and target concepts.First, we learn input concept mention representation using RoBERTa.Second, we find cosine similarity between embeddings of input concept mention and all the target concepts.Here, embeddings of target concepts are randomly initialized and then updated during training.Finally, the target concept with maximum cosine similarity is assigned to the input concept mention.Our model surpasses all the existing methods across three standard datasets by improving accuracy up to 2.31%.
Gravitational models explain shifts on human visual attention

Abstract Visual attention refers to the human brain’s ability to select relevant sensory information for preferential processing, improving performance in visual and cognitive tasks. It proceeds in two phases. One in which visual feature maps are acquired and processed in parallel. Another where the information from these maps is merged in order to select a single location to be attended for further and more complex computations and reasoning. Its computational description is challenging, especially if the temporal dynamics of the process are taken into account. Numerous methods to estimate saliency have been proposed in the last 3 decades. They achieve almost perfect performance in estimating saliency at the pixel level, but the way they generate shifts in visual attention fully depends on winner-take-all (WTA) circuitry. WTA is implemented by the biological hardware in order to select a location with maximum saliency, towards which to direct overt attention. In this paper we propose a gravitational model to describe the attentional shifts. Every single feature acts as an attractor and the shifts are the result of the joint effects of the attractors. In the current framework, the assumption of a single, centralized saliency map is no longer necessary, though still plausible. Quantitative results on two large image datasets show that this model predicts shifts more accurately than winner-take-all.
Counterexample-Guided Learning of Monotonic Neural Networks

The widespread adoption of deep learning is often attributed to its automatic feature construction with minimal inductive bias. However, in many real-world tasks, the learned function is intended to satisfy domain-specific constraints. We focus on monotonicity constraints, which are common and require that the function's output increases with increasing values of specific input features. We develop a counterexample-guided technique to provably enforce monotonicity constraints at prediction time. Additionally, we propose a technique to use monotonicity as an inductive bias for deep learning. It works by iteratively incorporating monotonicity counterexamples in the learning process. Contrary to prior work in monotonic learning, we target general ReLU neural networks and do not further restrict the hypothesis space. We have implemented these techniques in a tool called COMET. Experiments on real-world datasets demonstrate that our approach achieves state-of-the-art results compared to existing monotonic learners, and can improve the model quality compared to those that were trained without taking monotonicity constraints into account.
Crawling in Rogue’s Dungeons with (Partitioned) A3C

Rogue is a famous dungeon-crawling video-game of the 80ies, the ancestor of its gender. Rogue-like games are known for the necessity to explore partially observable and always different randomly-generated labyrinths, preventing any form of level replay. As such, they serve as a very natural and challenging task for reinforcement learning, requiring the acquisition of complex, non-reactive behaviors involving memory and planning. In this article we show how, exploiting a version of A3C partitioned on different situations, the agent is able to reach the stairs and descend to the next level in 98% of cases.
Functorial Question Answering

Distributional compositional (DisCo) models are functors that compute the meaning of a sentence from the meaning of its words.We show that DisCo models in the category of sets and relations correspond precisely to relational databases.As a consequence, we get complexity-theoretic reductions from semantics and entailment of a fragment of natural language to evaluation and containment of conjunctive queries, respectively.Finally, we define question answering as an NP-complete problem.
Enhancing Parameter Estimation Precision in a Dissipative Environment with Two‐Photon Driving

Abstract The frequency estimation of an optical cavity field suffering from an unavoidable dissipative environment is investigated. Generally, dissipative noise significantly reduces the precision. Here, it is found that two‐photon driving can improve measurement precision by resisting the noise. Moreover, over a long duration, the frequency uncertainty can be minimized with the right magnitude of the parametric two‐photon drive, which is in sharp contrast to the uncertainty tending to infinity without two‐photon driving. The results show that two‐photon driving can achieve ultrasensitive measurement in a dissipative environment under the long‐encoding‐time condition.
On the Impact of Perceived Vulnerability in the Adoption of Information Systems Security Innovations

A number of determinants predict the adoption of Information Systems (IS) security innovations.Amongst, perceived vulnerability of IS security threats has been examined in a number of past explorations.In this research, we examined the processes pursued in analysing the relationship between perceived vulnerability of IS security threats and the adoption of IS security innovations.The study uses Systematic Literature Review (SLR) method to evaluate the practice involved in examining perceived vulnerability on IS security innovation adoption.The SLR findings revealed the appropriateness of the existing empirical investigations of the relationship between perceived vulnerability of IS security threats on IS security innovation adoption.Furthermore, the SLR results confirmed that individuals who perceives vulnerable to an IS security threat are more likely to engage in the adoption an IS security innovation.In addition, the study validates the past studies on the relationship between perceived vulnerability and IS security innovation adoption.