nils-herrmann/clean_scirepeval huggingface dataset:
Automatic Semantic Content Removal by Learning to Neglect

We introduce a new system for automatic image content removal and inpainting. Unlike traditional inpainting algorithms, which require advance knowledge of the region to be filled in, our system automatically detects the area to be removed and infilled. Region segmentation and inpainting are performed jointly in a single pass. In this way, potential segmentation errors are more naturally alleviated by the inpainting module. The system is implemented as an encoder-decoder architecture, with two decoder branches, one tasked with segmentation of the foreground region, the other with inpainting. The encoder and the two decoder branches are linked via neglect nodes, which guide the inpainting process in selecting which areas need reconstruction. The whole model is trained using a conditional GAN strategy. Comparative experiments show that our algorithm outperforms state-of-the-art inpainting techniques (which, unlike our system, do not segment the input image and thus must be aided by an external segmentation module.
Practical Digital Libraries: Books, Bytes, and Bucks

Bargaining with reading habit is no need. Reading is not kind of something sold that you can take or not. It is a thing that will change your life to life better. It is the thing that will give you many things around the world and this universe, in the real world and here after. As what will be given by this practical digital libraries books bytes and bucks, how can you bargain with the thing that has many benefits for you?
Collaborative Multi-Level Embedding Learning from Reviews for Rating Prediction

We investigate the problem of personalized reviewbased rating prediction which aims at predicting users’ ratings for items that they have not evaluated by using their historical reviews and ratings. Most of existing methods solve this problem by integrating topic model and latent factor model to learn interpretable user and items factors. However, these methods cannot utilize word local context information of reviews. Moreover, it simply restricts user and item representations equivalent to their review representations, which may bring some irrelevant information in review text and harm the accuracy of rating prediction. In this paper, we propose a novel Collaborative Multi-Level Embedding (CMLE) model to address these limitations. The main technical contribution of CMLE is to integrate word embedding model with standard matrix factorization model through a projection level. This allows CMLE to inherit the ability of capturing word local context information from word embedding model and relax the strict equivalence requirement by projecting review embedding to user and item embeddings. A joint optimization problem is formulated and solved through an efficient stochastic gradient ascent algorithm. Empirical evaluations on real datasets show CMLE outperforms several competitive methods and can solve the two limitations well.
A Case Study for Grain Quality Assurance Tracking based on a Blockchain Business Network

One of the key processes in Agriculture is quality measurement throughout the transportation of grains along its complex supply chain. This procedure is suitable for failures, such as delays to final destinations, poor monitoring, and frauds. To address the grain quality measurement challenge through the transportation chain, novel technologies, such as Distributed Ledger and Blockchain, can bring more efficiency and resilience to the process. Particularly, Blockchain is a new type of distributed database in which transactions are securely appended using cryptography and hashed pointers. Those transactions can be generated and ruled by special network-embedded software – known as smart contracts – that may be public to all nodes of the network or may be private to a specific set of peer nodes. This paper analyses the implementation of Blockchain technology targeting grain quality assurance tracking in a real scenario. Preliminary results support a potential demand for a Blockchainbased certification that would lead to an added valuation of around 15% for GM-free soy in the scope of a Grain Exporter Business Network in Brazil.
Definite Clause Grammars for Language Analysis - A Survey of the Formalism and a Comparison with Augmented Transition Networks

A clear andpowerfulformalism for describing languages, both natural and artificial, follows f iom a method for expressing grammars in logic due to Colmerauer and Kowalski. This formalism, which is a natural extension o f context-free grammars, we call "definite clause grammars" (DCGs). A DCG provides not only a description of a language, but also an effective means for analysing strings o f that language, since the DCG, as it stands, is an executable program o f the programming language Prolog. Using a standard Prolog compiler, the DCG can be compiled into efficient code, making it feasible to implement practical language analysers directly as DCGs. This paper compares DCGs with the successful and widely used augmented transition network (ATN) formalism, and indicates how ATNs can be translated into DCGs. It is argued that DCGs can be at least as efficient as ATNs, whilst the DCG formalism is clearer, more concise and in practice more powerful
LNG decision making approaches compared.

Hazard zones associated with LNG handling activities have been a major point of contention in recent terminal development applications. Debate has reflected primarily worst case scenarios and discussion of these. This paper presents results from a maximum credible event approach. A comparison of results from several models either run by the authors or reported in the literature is presented. While larger scale experimental trials will be necessary to reduce the uncertainty, in the interim a set of base cases are suggested covering both existing trials and credible and worst case events is proposed. This can assist users to assess the degree of conservatism present in quoted modeling approaches and model selections.
The role of microbiome in central nervous system disorders

Mammals live in a co-evolutionary association with the plethora of microorganisms that reside at a variety of tissue microenvironments. The microbiome represents the collective genomes of these co-existing microorganisms, which is shaped by host factors such as genetics and nutrients but in turn is able to influence host biology in health and disease. Niche-specific microbiome, prominently the gut microbiome, has the capacity to effect both local and distal sites within the host. The gut microbiome has played a crucial role in the bidirectional gut-brain axis that integrates the gut and central nervous system (CNS) activities, and thus the concept of microbiome-gut-brain axis is emerging. Studies are revealing how diverse forms of neuro-immune and neuro-psychiatric disorders are correlated with or modulated by variations of microbiome, microbiota-derived products and exogenous antibiotics and probiotics. The microbiome poises the peripheral immune homeostasis and predisposes host susceptibility to CNS autoimmune diseases such as multiple sclerosis. Neural, endocrine and metabolic mechanisms are also critical mediators of the microbiome-CNS signaling, which are more involved in neuro-psychiatric disorders such as autism, depression, anxiety, stress. Research on the role of microbiome in CNS disorders deepens our academic knowledge about host-microbiome commensalism in central regulation and in practicality, holds conceivable promise for developing novel prognostic and therapeutic avenues for CNS disorders.
Security Attacks and Solutions in Vehicular Ad Hoc Networks: a Survey

Vehicular Ad hoc Networks (VANETs) have emerged recently as one of the most attractive topics for researchers and automotive industries due to their tremendous potential to improve traffic safety, efficiency and other added services. However, VANETs are themselves vulnerable against attacks that can directly lead to the corruption of networks and then possibly provoke big losses of time, money, and even lives. This paper presents a survey of VANETs attacks and solutions in carefully considering other similar works as well as updating new attacks and categorizing them into different classes.
A brief history of bioinformatics.

It is easy for today's students and researchers to believe that modern bioinformatics emerged recently to assist next-generation sequencing data analysis. However, the very beginnings of bioinformatics occurred more than 50 years ago, when desktop computers were still a hypothesis and DNA could not yet be sequenced. The foundations of bioinformatics were laid in the early 1960s with the application of computational methods to protein sequence analysis (notably, de novo sequence assembly, biological sequence databases and substitution models). Later on, DNA analysis also emerged due to parallel advances in (i) molecular biology methods, which allowed easier manipulation of DNA, as well as its sequencing, and (ii) computer science, which saw the rise of increasingly miniaturized and more powerful computers, as well as novel software better suited to handle bioinformatics tasks. In the 1990s through the 2000s, major improvements in sequencing technology, along with reduced costs, gave rise to an exponential increase of data. The arrival of 'Big Data' has laid out new challenges in terms of data mining and management, calling for more expertise from computer science into the field. Coupled with an ever-increasing amount of bioinformatics tools, biological Big Data had (and continues to have) profound implications on the predictive power and reproducibility of bioinformatics results. To overcome this issue, universities are now fully integrating this discipline into the curriculum of biology students. Recent subdisciplines such as synthetic biology, systems biology and whole-cell modeling have emerged from the ever-increasing complementarity between computer science and biology.
Supervised Neural Models Revitalize the Open Relation Extraction

Open relation extraction (ORE) remains a challenge to obtain a semantic representation by discovering arbitrary relation tuples from the un-structured text. However, perhaps due to limited data, previous extractors use unsupervised or semi-supervised methods based on pattern matching, which heavily depend on manual work or syntactic parsers and are inefficient or error-cascading. Their development has encountered bottlenecks. Although a few people try to use neural network based models to improve the ORE task performance recently, it is always intractable for ORE to produce supervised systems based on various neural architectures. We analyze and review the neural ORE methods. Further, we construct a large-scale automatically tagging training set and design a tagging scheme to frame ORE as a supervised sequence tagging task. A hybrid neural sequence tagging model (NST) is proposed which combines BiLSTM, CNN and CRF to capture the contextual temporal information, local spatial information, and sentence level tag information of the sequence by using the word and part-of-speech embeddings. Experiments on multiple datasets show that our method is better than most of the existing pattern-based methods and other neural networks based models.
Using Ant Colony System to Consolidate VMs for Green Cloud Computing

High energy consumption of cloud data centers is a matter of great concern. Dynamic consolidation of Virtual Machines (VMs) presents a significant opportunity to save energy in data centers. A VM consolidation approach uses live migration of VMs so that some of the under-loaded Physical Machines (PMs) can be switched-off or put into a low-power mode. On the other hand, achieving the desired level of Quality of Service (QoS) between cloud providers and their users is critical. Therefore, the main challenge is to reduce energy consumption of data centers while satisfying QoS requirements. In this paper, we present a distributed system architecture to perform dynamic VM consolidation to reduce energy consumption of cloud data centers while maintaining the desired QoS. Since the VM consolidation problem is strictly NP-hard, we use an online optimization metaheuristic algorithm called Ant Colony System (ACS). The proposed ACS-based VM Consolidation (ACS-VMC) approach finds a near-optimal solution based on a specified objective function. Experimental results on real workload traces show that ACS-VMC reduces energy consumption while maintaining the required performance levels in a cloud data center. It outperforms existing VM consolidation approaches in terms of energy consumption, number of VM migrations, and QoS requirements concerning performance.
A ka-band butler matrix with antenna array based on micromachined rectangular coaxial structures

A Ka-band 4×4 Butler matrix feeding a 4-element linear antenna array has been presented in this paper. The Butler matrix is based on a rectangular coaxial structure, constructed using five layers of gold coated micromachined silicon slices. The patch antennas are of an air-filled microstrip type, and spaced by half a wavelength at 38 GHz to form the array. The demonstrated device is 26 mm by 23 mm in size and 1.5 mm in height. The measured return losses at all input ports are better than −10 dB between 34.4 and 38.3 GHz. The measured radiation pattern of one beam has shown good agreement with the simulations.
Machine Learning : The High-Interest Credit Card of Technical Debt

Machine learning offers a fantastically powerful toolkit f or building complex systems quickly. This paper argues that it is dangerous to think of these quick wins as coming for free. Using the framework of technical debt , we note that it is remarkably easy to incur massive ongoing maintenance costs at the system level when applying machine learning. The goal of this paper is hig hlight several machine learning specific risk factors and design patterns to b e avoided or refactored where possible. These include boundary erosion, entanglem ent, hidden feedback loops, undeclared consumers, data dependencies, changes i n the external world, and a variety of system-level anti-patterns. 1 Machine Learning and Complex Systems Real world software engineers are often faced with the chall enge of moving quickly to ship new products or services, which can lead to a dilemma between spe ed of execution and quality of engineering. The concept of technical debtwas first introduced by Ward Cunningham in 1992 as a way to help quantify the cost of such decisions. Like incurri ng fiscal debt, there are often sound strategic reasons to take on technical debt. Not all debt is n ecessarily bad, but technical debt does tend to compound. Deferring the work to pay it off results in i ncreasing costs, system brittleness, and reduced rates of innovation. Traditional methods of paying off technical debt include re factoring, increasing coverage of unit tests, deleting dead code, reducing dependencies, tighten ng APIs, and improving documentation . The goal of these activities is not to add new functionality, but to make it easier to add future improvements, be cheaper to maintain, and reduce the likeli hood of bugs. One of the basic arguments in this paper is that machine learn ing packages have all the basic code complexity issues as normal code, but also have a larger syst em-level complexity that can create hidden debt. Thus, refactoring these libraries, adding bet ter unit tests, and associated activity is time well spent but does not necessarily address debt at a systems level. In this paper, we focus on the system-level interaction betw e n machine learning code and larger systems as an area where hidden technical debt may rapidly accum ulate. At a system-level, a machine learning model may subtly erode abstraction boundaries. It may be tempting to re-use input signals in ways that create unintended tight coupling of otherw ise disjoint systems. Machine learning packages may often be treated as black boxes, resulting in la rge masses of “glue code” or calibration layers that can lock in assumptions. Changes in the exte rnal world may make models or input signals change behavior in unintended ways, ratcheting up m aintenance cost and the burden of any debt. Even monitoring that the system as a whole is operating s intended may be difficult without careful design.
Beyond General Intelligence ( IQ ) and Emotional Intelligence ( EQ ) : The Role of Cultural Intelligence ( CQ ) on Cross-Border Leadership Effectiveness in a Globalized World

Emphasizing the importance of cross-border effectiveness in the contemporary globalized world, we propose that cultural intelligence—the leadership capability to manage effectively in culturally diverse settings—is a critical leadership competency for those with cross-border responsibilities. We tested this hypothesis with multisource data, including multiple intelligences, in a sample of 126 Swiss military officers with both domestic and cross-border leadership responsibilities. Results supported our predictions: (1) general intelligence predicted both domestic and cross-border leadership effectiveness; (2) emotional intelligence was a stronger predictor of domestic leadership effectiveness, and (3) cultural intelligence was a stronger predictor of cross-border leadership effectiveness. Overall,
Speech Recognition Using Deep Learning Algorithms

Automatic speech recognition, translating of spoken words into text, is still a challenging task due to the high viability in speech signals. Deep learning, sometimes referred as representation learning or unsupervised feature learning, is a new area of machine learning. Deep learning is becoming a mainstream technology for speech recognition and has successfully replaced Gaussian mixtures for speech recognition and feature coding at an increasingly larger scale. The main target of this course project is to applying typical deep learning algorithms, including deep neural networks (DNN) and deep belief networks (DBN), for automatic continuous speech recognition.
Generative Adversarial Active Learning for Unsupervised Outlier Detection

Outlier detection is an important topic in machine learning and has been used in a wide range of applications. In this paper, we approach outlier detection as a binary-classification issue by sampling potential outliers from a uniform reference distribution. However, due to the sparsity of data in high-dimensional space, a limited number of potential outliers may fail to provide sufficient information to assist the classifier in describing a boundary that can separate outliers from normal data effectively. To address this, we propose a novel Single-Objective Generative Adversarial Active Learning (SO-GAAL) method for outlier detection, which can directly generate informative potential outliers based on the mini-max game between a generator and a discriminator. Moreover, to prevent the generator from falling into the mode collapsing problem, the stop node of training should be determined when SO-GAAL is able to provide sufficient information. But without any prior information, it is extremely difficult for SO-GAAL. Therefore, we expand the network structure of SO-GAAL from a single generator to multiple generators with different objectives (MO-GAAL), which can generate a reasonable reference distribution for the whole dataset. We empirically compare the proposed approach with several state-of-the-art outlier detection methods on both synthetic and real-world datasets. The results show that MO-GAAL outperforms its competitors in the majority of cases, especially for datasets with various cluster types or high irrelevant variable ratio. The experiment codes are available at: https://github.com/leibinghe/GAAL-based-outlier-detection
A teamwork model for understanding an agile team: A case study of a Scrum project

Software development depends significantly on team performance, as does any process that involves human interaction.  Most current development methods argue that teams should self-manage. Our objective is thus to provide a better understanding of the nature of self-managing agile teams, and the teamwork challenges that arise when introducing such teams.  We conducted extensive fieldwork for 9 months in a software development company that introduced Scrum. We focused on the human sensemaking, on how mechanisms of teamwork were understood by the people involved.  We describe a project through Dickinson and McIntyre’s teamwork model, focusing on the interrelations between essential teamwork components. Problems with team orientation, team leadership and coordination in addition to highly specialized skills and corresponding division of work were important barriers for achieving team effectiveness.  Transitioning from individual work to self-managing teams requires a reorientation not only by developers but also by management. This transition takes time and resources, but should not be neglected. In addition to Dickinson and McIntyre’s teamwork components, we found trust and shared mental models to be of fundamental importance.
A Highly Integrated 60 GHz 6-Channel Transceiver With Antenna in Package for Smart Sensing and Short-Range Communications

This work presents a highly integrated 57-64 GHz 4-channel receiver 2-channel transmitter chip targeting short range sensing and large bandwidth communications. The chip is housed in an embedded wafer level ball grid array package. The package includes 6 integrated patch antennas realized with a metal redistribution layer. The receiver patch antennas have a combined antenna gain of ≈10 dBi while each transmitter antenna has a gain of ≈6 dBi. The chip features a wide tuning range integrated VCO with a measured phase noise lower than -80 dBc/Hz at 100 kHz offset. Each of the differential transmitter channels shows a measured output power of 2-5 dBm over the complete frequency range. In addition, one transmitter channel features a modulator that can be digitally programmed to operate in either radar or communication mode. Each of the receiver channels has a measured conversion gain of 19 dB, a single-side-band noise figure of less than 10 dB and an input referred 1 dB compression point of less than 10 dBm. With all channels turned on the chip consumes a current of 300 mA from a 3.3 V supply. The functionality of the chip is demonstrated for both sensing and short range wireless communications.