Reviewerly/exHarmony huggingface dataset:
Overparameterization: A Connection Between Software 1.0 and Software 2.0

A new ecosystem of machine-learning driven applications, titled Software 2.0, has arisen that integrates neural networks into a variety of computational tasks. Such applications include image recognition, natural language processing, and other traditional machine learning tasks. However, these techniques have also grown to include other structured domains, such as program analysis and program optimization for which novel, domain-specific insights mate with model design. In this paper, we connect the world of Software 2.0 with that of traditional software - Software 1.0 - through overparameterization: a program may provide more computational capacity and precision than is necessary for the task at hand. In Software 2.0, overparamterization - when a machine learning model has more parameters than datapoints in the dataset - arises as a contemporary understanding of the ability for modern, gradient-based learning methods to learn models over complex datasets with high-accuracy. Specifically, the more parameters a model has, the better it learns.In Software 1.0, the results of the approximate computing community show that traditional software is also overparameterized in that software often simply computes results that are more precise than is required by the user. Approximate computing exploits this overparameterization to improve performance by eliminating unnecessary, excess computation. For example, one - of many techniques - is to reduce the precision of arithmetic in the application. In this paper, we argue that the gap between available precision and that that is required for either Software 1.0 or Software 2.0 is a fundamental aspect of software design that illustrates the balance between software designed for general-purposes and domain-adapted solutions. A general-purpose solution is easier to develop and maintain versus a domain-adapted solution. However, that ease comes at the expense of performance.We show that the approximate computing community and the machine learning community have developed overlapping techniques to improve performance by reducing overparameterization. We also show that because of these shared techniques, questions, concerns, and answers on how to construct software can translate from one software variant to the other.
Porting your VR title to oculus quest

Survios, a virtual reality (VR) game developer dedicated to building active, immersive experiences that push the limits of VR innovation, ported their VR boxing title, Creed: Rise to Glory, to the newly anticipated Oculus Quest. Porting to this new mobile VR platform is complex and demands extra creativity from developers when compared to porting to the previous generation of consoles. In this paper, Eugene Elkin, Senior Software Engineer at Survios, will share insights and learnings from the process, covering target hardware and its capabilities, rendering techniques, game optimization, and more.
Improving Students’ Achievement in Reading Comprehension by Applying Contextual Teaching and Learning (CTL)

This research aims to find out the significant improvement of the students achievement in reading comprehension by applying Contextual Teaching and Learning (CTL). The subject of this research was X grade of SMK Bandung-2 at Academic Year 2017/2018. It consisted of one class with 30 students as respondents. The objective was to discover the significant improvement of the students’ achievement in reading comprehension by applying Contextual Teaching and Learning (CTL). This research was conducted by using classroom action research. The instruments of collecting data were observation sheet, and Reading tests. The research finding is shown of that there was an improvement on the students’ reading achievement on narrative text from each cycle. It was showed from the mean of pre-test which was 56.5 with persentage 0%. After CTL was applied in the first cycle, there was an improvement of the result of the students’ in reading comprehension the mean was 69.16 with persentage 33.3%. It means that there was an improvement from pre-test to post-test I was 33.3%. And for the second cycle after reflection on the first cycle, there was an improvement of students’ mean which was 85 with persentage 100%. Based on the result showed that there was an improvement after applying CTL was 66.7%. It concluded that students’ achievement in reading comprehension by applying contextual teaching and learning (CTL) improved.
Development of editorial models applying augmented reality for children's learnin

The content of a written document is organized in a coherent, aesthetic and harmonious manner with the principles of editorial design. If it uses augmented reality, one of the new technologies must also be taken into account when generating the composition. In the present article we present a model of editorial design with augmented reality focused on influencing the learning process of middle-aged children. Its construction will be based on the golden ratio, the Fibonacci succession, following the Vignelli canon. The results will allow improving the model to develop future research projects.
Time-space lower bounds for two-pass learning

A line of recent works showed that for a large class of learning problems, any learning algorithm requires either super-linear memory size or a super-polynomial number of samples [11, 7, 12, 9, 2, 5]. For example, any algorithm for learning parities of size n requires either a memory of size Ω(n2) or an exponential number of samples [11].All these works modeled the learner as a one-pass branching program, allowing only one pass over the stream of samples. In this work, we prove the first memory-samples lower bounds (with a super-linear lower bound on the memory size and super-polynomial lower bound on the number of samples) when the learner is allowed two passes over the stream of samples. For example, we prove that any two-pass algorithm for learning parities of size n requires either a memory of size Ω(n1.5) or at least 2Ω([EQUATION]) samples.More generally, a matrix M : A x X → {-1, 1} corresponds to the following learning problem: An unknown element x ∈ X is chosen uniformly at random. A learner tries to learn x from a stream of samples, (a1, b1), (a2, b2) ..., where for every i, ai ∈ A is chosen uniformly at random and bi = M(ai, x).Assume that k, l, r are such that any submatrix of M of at least 2-k · |A| rows and at least 2-l · |X| columns, has a bias of at most 2-r. We show that any two-pass learning algorithm for the learning problem corresponding to M requires either a memory of size at least Ω(k · min[EQUATION]), or at least 2Ω(min[EQUATION]) samples.
Origin of Perl

Perl was developed in 1987 by Larry Wall. It was created because the tools that were available to Mr. Wall at the time (sed, C, awk, and Bourne shell) didn’t provide the sort of functionality that he required.
Improve Real-time Object Detection with Feature Enhancement

Real-time object detection is a critical task in the field of computer vision. Current methods suffers from pooling errors and classification imbalance. In this paper, we proposed a feature enhanced framework to improve the feature extraction for efficient real-time object detection. Particularly, the framework is compromised with five optimizations on feature enhancement: bilinear interpolation up-sampling, network fully sharing, single score map, upward rounding quantization and single layer output. We performed comprehensive experiments on a publicly available dataset DACSDC, and the achieved mean average precision (mAP) is 0.6317 with a throughput of 24.67 frame per second (FPS). Our work ranked the 6th out of 24 teams in the 2018 system design contest on the 55th Design Automatic Conference (DAC).
On Computational Complexity of Automorphism Groups in Classical Planning

Symmetry-based pruning is a family of powerful methods for reducing search effort in planning as heuristic search. Applying these methods requires first establishing an automorphism group that is then used for pruning within the search process. Despite the growing popularity of state-space symmetries in planning techniques, the computational complexity of finding the automorphism group of a compactly represented planning task has not been formally established. In a series of reductions, we show that computing the automorphism group of a grounded planning task is GI-hard. Furthermore, we discuss the presentations of these symmetry groups and list some of their drawbacks.
Understanding Reviewer Assignment Problem and its Issues and Challenges

One of the common issue faced in assigning proposals submitted to the conferences, journals etc. is the assignment of proposal to the appropriate reviewers. This is also termed as reviewer assignment problem (RAP). Here, where the expertise level of a reviewer who is involved in reviewing a proposal should be optimized to guarantee the selection of best suitable proposal. Choosing appropriate reviewer not only involves the expertise but also considers diversity and conflict of interests amongst them. In this direction, numerous solutions have been given by researchers in the past, but some of the issues persists in this field. The paper highlights a review on the existing techniques proposed by the researchers to solve reviewer assignment problem and various keyholes in the current scenario along-with the possible solutions.
Academic English Proficiency Assessment Using a Computerized Adaptive Test

This paper describes the steps to convert a paper-and-pencil English proficiency test for academic purposes, consisting of multiple choice items administered following the Admissible Probability Measurement Procedure [24], adopted by the graduate program at the Institute of Mathematics and Computer Sciences at the University of Sao Paulo (ICMC-USP), Brazil, to a computerized adaptive test (CAT) based on an Item Response Theory Model (IRT). Despite the fact that the program accepts various internationally recognized tests that attest non-native speakers English proficiency, such as the Test of English as a Foreign Language (TOEFL), the International English Language Testing System (IELTS) and the Cambridge English: Proficiency (CPE), for instance, its requirement is incoherent in public universities in Brazil due to the cost, which ranges from US$ 200.00 to US$ 300.00 per exam. The TAI-PI software (Computerized Adaptive Test for English Proficiency), which was developed in Java language and SQLite, started to be used to assess the English pro?ciency of students on the program from October, 2013. The statistical methodology used was defined considering the history and aims of the test and adopted Samejima's Graded Response Model [21], the Kullback-Leibler information criterion for item selection, the a posteriori estimation method for latent trait [2] and the Shadow Test approach [29] to impose restrictions (content and test length) on the test composition of each individual. A description of the test design, the statistical methods used, and the results of a real application of TAI-PI for graduate students are presented in this paper, as well as the validation studies of the new methodology for pass or fail classification, showing the good quality of the new evaluation system and examination of improvement using the IRT and CAT methods.
Complexity lower bounds for computing the approximately-commuting operator value of non-local games to high precision

We study the problem of approximating the commuting-operator value of a two-player non-local game. It is well-known that it is NP-complete to decide whether the classical value of a non-local game is 1 or 1 --- ∈, promised that one of the two is the case. Furthermore, as long as ∈ is small enough, this result does not depend on the gap ∈. In contrast, a recent result of Fitzsimons, Ji, Vidick, and Yuen shows that the complexity of computing the quantum value grows without bound as the gap ∈ decreases. In this paper, we show that this also holds for the commuting-operator value of a game. Specifically, in the language of multi-prover interactive proofs, we show that the power of MIPco(2, 1, 1, s) (proofs with two provers, one round, completeness probability 1, soundness probability s, and commuting-operator strategies) can increase without bound as the gap 1 --- s gets arbitrarily small.Our results also extend naturally in two ways, to perfect zero-knowledge protocols, and to lower bounds on the complexity of computing the approximately-commuting value of a game. Thus we get lower bounds on the complexity class PZK-MIPcoδ (2, 1, 1, s) of perfect zero-knowledge multi-prover proofs with approximately-commuting operator strategies, as the gap 1 --- s gets arbitrarily small. While we do not know any computable time upper bound on the class MIPco, a result of the first author and Vidick shows that for s = 1 --- 1/poly(f(n)) and δ = 1/poly(f(n)), the class MIPcoδ (2, 1, 1, s), with constant communication from the provers, is contained in TIME(exp(poly(f(n)))). We give a lower bound of coNTIME(f(n)) (ignoring constants inside the function) for this class, which is tight up to polynomial factors assuming the exponential time hypothesis.
A Real-Time Video Stream Stabilization System Using Inertial Sensor

This paper has the purpose to show a stabilization video-streaming methodology feasible to low-power wearable devices. Thanks to an Inertial Measurement Unit (IMU) mounted together with the camera we are ready to stabilize directly on video-stream without the delay and the complexity due to image processing used by classic software stabilization techniques. The IMU gives information about the angle rotation respect to the three main orthogonal axes of the camera; the wearable device transmits the video along with the IMU data synchronized frame per frame then a base station receives and stabilizes while renders the video. The result is that the shaking and the unwanted motions of the human body wearing the system are compensated giving a clear and stable video. Numeric results prove that the video is more stable: we cut-off the half of the motion noise in the scene.
Preliminary spectral design of a LED- and light bulb-based sun spectrum simulator

Sun simulator is a device which emits light with the same or very similar spectrum as the wavelength of sunlight. This device is extremely useful for performing consistent and reliable experiments with an artificial light source in a laboratory without sunlight. Sun simulators can be purchased; however, we wanted to design and develop a Sun simulator device that is more cost effective than the current models available on the market. For this purpose, the wavelengths of several different light sources have been experimented on. After analyzing the recorded spectra, we have performed Additive Spectrum Mixing (ASM) using LED lights and light bulbs. While the results were adequate, they were far from ideal. Our mission therefore was to find a better method of spectral mixing which is presented in this paper.
The case study in researching networks and collaborative arrangements

The case study method is a foundational method of network and collaboration research. This chapter highlights key features of the method, combined with a literature review on the use of this method in network and collaboration research - identifying eighty-two studies between 1988 and 2018. Main trends in the use of the method, like a gradual shift in the balance between single and multiple case studies, are discussed. Finally, conclusions are drawn about the future of the method: it is likely to remain a key method for future network and collaboration research, but more mixed-methods designs are called for.
Risk, Trustworthiness, and Justice: Understanding Blockchain Technologies in the Supply Chain

Organizations investing in multi-million-dollar, multi-stakeholder supply chain information systems struggle to make the adoption and implementation successful. Some projects fail because of technical caveats or because the system could not meet business-specific needs, but many of them fail because of poor management of the implementation process. Blockchain technologies enable authenticated transactions across a network of supply chain partners making the implementation more complex than most internally-focused technology adoptions. Blockchain technologies require all supply chain partners to implement, contribute and share information. Understanding how supply chain agents perceive blockchain technologies is a crucial step toward successful implementations. To test our hypotheses, we conducted a scenario-based role-playing experiment with industry professionals. Integrating design theory with classic diffusion processes, we examine how blockchain technology design features shape supply chain agents’ perceptions and subsequent willingness to use blockchain technologies. The findings show that the trustworthiness of the technology in regards to its competence and the distributive justice are key influencers of an agent’s willingness to use the technology.
Definite Clause Grammars with Parse Trees: Extension for Prolog

Definite Clause Grammars (DCGs) are a convenient way to specify possibly non-context-free grammars for natural and formal languages. They can be used to progressively build a parse tree as grammar rules are applied by providing an extra argument in the DCG rule's head. In the simplest way, this is a structure that contains the name of the used nonterminal. This extension of a DCG has been proposed for natural language processing in the past and can be done automatically in Prolog using term expansion.We extend this approach by a meta-nonterminal to specify optional and sequences of nonterminals, as these structures are common in grammars for formal, domain-specific languages. We specify a term expansion that represents these sequences as lists while preserving the grammar's ability to be used both for parsing and serialising, i.e. to create a parse tree by a given source code and vice-versa. We show that this mechanism can be used to lift grammars specified in extended Backus-Naur form (EBNF) to generate parse trees. As a case study, we present a parser for the Prolog programming language itself based only on the grammars given in the ISO Prolog standard which produces corresponding parse trees.
Designing an e-commerce recommender system based on collaborative filtering using a data mining approach

E-commerce recommender systems have been converted to a very important decision-making helper for customers, and provide online personalised recommendations using information technology and customers' information. In the meantime, collaborative filtering (CF) recommender systems are one of the key components of successful e-commerce systems. Despite the popularity and successes of CF, these systems still face a series of serious limitations, including cold start, sparsity of user-item matrix, scalability and change of user interest during the time, that impede exact recommendations to customers. Although much research has been presented to overcome these limitations, no comprehensive model is yet offered to reduce them: 1) customer segmentation based on LRFM variables in the level of product category to evaluate the length of customer relationship with the company, recency, frequency, and monetary of purchasing product categories; 2) extracting association rules based on user-category matrixes in the level of each cluster; 3) customer segmentation according to demographic variables; 4) change of user-item matrix and reduction of its dimensions; 5) developing a new similarity function by weighted combination of results of segmentation methods and CF. According to the gained results, the proposed system of this research has resulted in the removal of traditional CF constraints and presents more accurate and appropriate recommendations for the preferences of customers.
An Efficient User Authentication Model for IOT-based Healthcare Environment

Along with the large-scale proliferation of IoT-networks and information technology, users can obtain the information resources conveniently via intelligent device. Therefore authentication mechanism is a fundamental tool for ensuring secure communications and the validity of communicating party. Specially, focusing on healthcare applications based on IoT-networks.This paper proposes an efficient authentication model called 'Elngar authentication model' (EAM)-based IoT medical data system for anonymous users using elliptic curves cryptosystem (ECC) which achieves mutual authentication and forward security. Specifically, this paper certify the legitimacy of the proposed via employing BAN-logic, which is one of the important formal methods. Further, the performance comparison shows that EAM model is more suitable for IoT medical applications where efficiency and security concerned.
Computation of Approximate Solutions for Guided Sampling-Based Motion Planning of 3D Objects

Motion planning of 3D solid objects leads to a search in a 6D configuration space. Sampling-based planners randomly sample the configuration space and store the collision-free samples into a graph (roadmap) that can be searched by standard graph-search methods. The well-known issue of the sampling-based planners is the narrow passage problem. Narrow passages are small collision-free regions in the configuration space that are, due to their low volume, difficult to cover by the random samples, which prevents the sampling-based planners to find a path leading through the passages. By decreasing the size of the object, the relative volume of the narrow passages is increased, which helps to cover them more densely. This allows the planner to find an approximate solution, i.e., a solution feasible for the smaller object. The approximate solution can be then used to iteratively guide the sampling in the configuration space, while increasing the size of the object, until a solution for the original object is found. In this paper, we propose a modification of the iterative guiding process. To avoid a situation where the part of the guiding path is too close to obstacles of the configuration space, we shift it away from the obstacles. This requires to estimate the surface of the obstacle region, which is achieved by detecting its boundary configurations during the sampling process. Experiments have shown that the proposed modification outperforms the simple guiding using approximate solutions, as well as other related state-of-the-art planners.
Towards Integrated Failure Recovery for Web Service Composition

Web Service Composition (WSC) aims at facilitating the use of multiple Web Services for solving a given task. Automatic WSC is particularly focused on automating the composition process following specifications of involved actors. There are many initiatives developed in this direction, but most of them stop at describing the process of building the composition, while some also study the coordination of the execution. However, research of specific solutions in cases of failure of services is typically not integrated with composition algorithms and mostly relies on costly additional measures. Fault tolerance is important for developers relying on the compositions, since their applications are rarely for transient usage, requiring high availability over time. Naively re-computing the compositions from scratch can be a waste of resources for the composition engine. In this paper, we propose addressing web service faults by preparing backup compositions as part of a fallback mechanism, which efficiently integrates with the base composition algorithm.