nils-herrmann/clean_scirepeval huggingface dataset:
Graph-Based Global Reasoning Networks

Globally modeling and reasoning over relations between regions can be beneficial for many computer vision tasks on both images and videos. Convolutional Neural Networks (CNNs) excel at modeling local relations by convolution operations, but they are typically inefficient at capturing global relations between distant regions and require stacking multiple convolution layers. In this work, we propose a new approach for reasoning globally in which a set of features are globally aggregated over the coordinate space and then projected to an interaction space where relational reasoning can be efficiently computed. After reasoning, relation-aware features are distributed back to the original coordinate space for down-stream tasks. We further present a highly efficient instantiation of the proposed approach and introduce the Global Reasoning unit (GloRe unit) that implements the coordinate-interaction space mapping by weighted global pooling and weighted broadcasting, and the relation reasoning via graph convolution on a small graph in interaction space. The proposed GloRe unit is lightweight, end-to-end trainable and can be easily plugged into existing CNNs for a wide range of tasks. Extensive experiments show our GloRe unit can consistently boost the performance of state-of-the-art backbone architectures, including ResNet [15, 16], ResNeXt , SE-Net [18] and DPN [9], for both 2D and 3D CNNs, on image classification, semantic segmentation and video action recognition task.
A Protocol-Ignorance Perspective on Incremental Deployability of Routing Protocols

New protocols for Internet inter-domain routing struggle to get widely adopted. Because the Internet consists of more than 50,000 autonomous systems (ASes), deployment of a new routing protocol has to be incremental. In this work, we study such incremental deployment. We first formulate the routing problem in regard to a metric of routing cost. Then, the paper proposes and rigorously defines a statistical notion of protocol ignorance that quantifies the inability of a routing protocol to accurately determine routing prices with respect to the metric of interest. The proposed protocol-ignorance model of a routing protocol is fairly generic and can be applied to routing in both inter-domain and intra-domain settings, as well as to transportation and other types of networks. Our model of protocol deployment makes our study specific to Internet interdomain routing. Through a combination of mathematical analysis and simulation, we demonstrate that the benefits from adopting a new inter-domain protocol accumulate smoothly during its incremental deployment. In particular, the simulation shows that decreasing the routing price by 25% requires between 43% and 53% of all nodes to adopt the new protocol. Our findings elucidate the deployment struggle of new inter-domain routing protocols and indicate that wide deployment of such a protocol necessitates involving a large number of relevant ASes into a coordinated effort to adopt the new protocol.
Impact of reviewer social interaction on online consumer review fraud detection

Online consumer reviews have become a baseline for new consumers to try out a business or a new product. The reviews provide a quick look into the application and experience of the business/product and market it to new customers. However, some businesses or reviewers use these reviews to spread fake information about the business/product. The fake information can be used to promote a relatively average product/business or can be used to malign their competition. This activity is known as reviewer fraud or opinion spam. The paper proposes a feature set, capturing the user social interaction behavior to identify fraud. The problem being solved is one of the characteristics that lead to fraud rather than detecting fraud. Neural network algorithm is used to evaluate the proposed feature set and compare it against the state-of-the-art feature sets in detecting fraud. The feature set considers the user’s social interaction on the Yelp platform to determine if the user is committing fraud. The neural network algorithm helps in comparing the feature set with other feature sets used to detect fraud. Any attempt to find the characteristics that lead to fraud has a prerequisite to be good enough to detect fraud as well. The F1 score obtained using neural networks is on par with all the well-known methods for detecting fraud, a value of 0.95. The effectiveness of the feature set is in rivaling the other approaches to fraud detection. A user’s social interaction on a digital platform such as Yelp is equally important in evaluating the user as social interaction is in real life. The characteristics that lead to fraud can be intuitively captured. The characteristics such as number of friends, number of followers and the number of times the user has provided a review which was helpful to multiple people provide the neural network with a base to form a relationship between opinion fraud and social interaction characteristics.
University – incubator firm knowledge flows : assessing their impact on incubator firm performance

Technology incubators are university-based technology initiatives that should facilitate knowledge flows from the university to the incubator firms. We thus investigate the research question of how knowledge actually flows from universities to incubator firms. Moreover, we assess the effect of these knowledge flows on incubator firm-level differential performance. Based on the resource-based view of the firm and the absorptive capacity construct, we advance the overarching hypothesis that knowledge flows should enhance incubator firm performance. Drawing on longitudinal and fine-grained firm-level data of 79 technology ventures incubated between 1998 and 2003 at the Advanced Technology Development Center, a technology incubator sponsored by the Georgia Institute of Technology, we find some support for knowledge flows from universities to incubator firms. Our evidence suggests that incubator firms’ absorptive capacity is an important factor when transforming university knowledge into
Efficiency of Accounting Information System and Performance Measures – Literature Review

Accounting information system had been widely used by many organizations to automate and integrate their business operations .The main objective s of many businesses to adopt this system are to improve their business efficiency and increase competitiveness. The qualitative characteristic of any Accounting Information System can be maintained if there is a sound internal control system. Internal control is run to ensure the achievement of operational goals and performance. Therefore the purpose of this study is to examine the efficiency of Accounting Information System on performance measures using the secondary data in which it was found that accounting information system is of great importance to both businesses and organization in which it helps in facilitating management decision making, internal controls ,quality of the financial report ,and it facilitates the company’s transaction and it also plays an important role in economic system, and the study recommends that businesses, firms and organization should adopt the use of AIS because adequate accounting information is essential for every effective decision making process and adequate information is possible if accounting information systems are run efficiently also, efficient Accounting Information Systems ensures that all levels of management get sufficient, adequate, relevant and true information for planning and controlling activities of the business organization.
End-to-End Relation Extraction Using Distant Supervision from External Semantic Repositories

In this paper, we extend distant supervision (DS) based on Wikipedia for Relation Extraction (RE) by considering (i) relations defined in external repositories, e.g. YAGO, and (ii) any subset of Wikipedia documents. We show that training data constituted by sentences containing pairs of named entities in target relations is enough to produce reliable supervision. Our experiments with state-of-the-art relation extraction models, trained on the above data, show a meaningful F1 of 74.29% on a manually annotated test set: this highly improves the state-of-art in RE using DS. Additionally, our end-to-end experiments demonstrated that our extractors can be applied to any general text document.
Special Issue: Knowledge, Knowing, and Organizations: Deliberate Learning and the Evolution of Dynamic Capabilities

This paper investigates the mechanisms through which organizations develop dynamic capabilities, defined as routinized activities directed to the development and adaptation of operating routines, and reflects upon the role of (1) experience accumulation, (2) knowledge articulation and (3) knowledge codification processes in the evolution of dynamic, as well as operational, routines. The argument is made that dynamic capabilities are shaped by the co-evolution of these learning mechanisms. At any point in time, firms adopt a mix of learning behaviors constituted by a semi-automatic accumulation of experience and by deliberate investments in knowledge articulation and codification activities. The relative effectiveness of these capability-building mechanisms is analyzed here as contingent upon selected features of the task to be learned, such as its frequency, homogeneity and degree of causal ambiguity, and testable hypotheses are derived. Somewhat counterintuitive implications of the analysis include the relatively superior effectiveness of highly deliberate learning processes, such as knowledge codification, at lower levels of frequency and homogeneity of the organizational task, in contrast with common managerial practice.
SimpleDS: A Simple Deep Reinforcement Learning Dialogue System

This paper presents SimpleDS, a simple and publicly available dialogue system trained with deep reinforcement learning. In contrast to previous reinforcement learning dialogue systems, this system avoids manual feature engineering by performing action selection directly from raw text of the last system and (noisy) user responses. Our initial results, in the restaurant domain, report that it is indeed possible to induce reasonable behaviours with such an approach that aims for higher levels of automation in dialogue control for intelligent interactive agents.
Advanced IEEE 802.11ax TCP aware scheduling under unreliable channels

In this paper we suggest advanced IEEE 802.11ax TCP-aware scheduling strategies for optimizing the AP operation under transmission of unidirectional TCP traffic. Our scheduling strategies optimize the performance using the capability for Multi User transmissions over the Uplink, first introduced in IEEE 802.11ax, together with Multi User transmissions over the Downlink. They are based on Transmission Opportunities (TXOP) and we suggest three scheduling strategies determining the TXOP formation parameters. In one of the strategies one can control the achieved Goodput vs. the delay. We also assume saturated WiFi transmission queues. We show that with minimal Goodput degradation one can avoid considerable delays.
Effect of Electronic Word-of-Mouth on Consumer Purchase Intention: The Perspective of Gender Differences

Electronic word of mouth is available to customers in different types of online consumer reviews, which can be used to help them make e-commerce purchasing decisions. Customers acknowledge that online consumer reviews help them to determine eWOM credibility and to make purchasing decisions. This study uses surveys and multiple regression analysis to create an extended Elaboration Likelihood Model that describes the relationship between customer expertise, involvement, and rapport to acceptance and use of electronic word of mouth in making purchasing decisions. The study focuses on the cultural effects of gender on the extended Elaboration Likelihood Model and purchasing decisions in e-commerce virtual communities. Study results show that involvement has the most significant effect on perceived eWOM credibility. Study results show that perceived eWOM credibility has a significant effect on eWOM acceptance and intent to purchase. Study results also show the male customers have different e-commerce shopping behaviors than female customers.
Designing Adversarially Resilient Classifiers using Resilient Feature Engineering

We provide a methodology, resilient feature engineering, for creating adversarially resilient classifiers. According to existing work, adversarial attacks identify weakly correlated or non-predictive features learned by the classifier during training and design the adversarial noise to utilize these features. Therefore, highly predictive features should be used first during classification in order to determine the set of possible output labels. Our methodology focuses the problem of designing resilient classifiers into a problem of designing resilient feature extractors for these highly predictive features. We provide two theorems, which support our methodology. The Serial Composition Resilience and Parallel Composition Resilience theorems show that the output of adversarially resilient feature extractors can be combined to create an equally resilient classifier. Based on our theoretical results, we outline the design of an adversarially resilient classifier.
Backplay: "Man muss immer umkehren"

Model-free reinforcement learning (RL) requires a large number of trials to learn a good policy, especially in environments with sparse rewards. We explore a method to improve the sample efficiency when we have access to demonstrations. Our approach, Backplay, uses a single demonstration to construct a curriculum for a given task. Rather than starting each training episode in the environment’s fixed initial state, we start the agent near the end of the demonstration and move the starting point backwards during the course of training until we reach the initial state. Our contributions are that we analytically characterize the types of environments where Backplay can improve training speed, demonstrate the effectiveness of Backplay both in large grid worlds and a complex four player zero-sum game (Pommerman), and show that Backplay compares favorably to other competitive methods known to improve sample efficiency. This includes reward shaping, behavioral cloning, and reverse curriculum generation.
Sentiment analysis of twitter data

Social networks are the main resources to gather information about people’s opinion and sentiments towards different topics as they spend hours daily on social medias and share their opinion. In this technical paper, we show the application of sentimental analysis and how to connect to Twitter and run sentimental analysis queries. We run experiments on different queries from politics to humanity and show the interesting results. We realized that the neutral sentiment for tweets are significantly high which clearly shows the limitations of the current works. Twitter sentiment analysis, Social Network analysis.
Validation in interpretive management accounting research

This paper extends and contributes to emerging debates on the validation of interpretive research (IR) in management accounting. We argue that IR has the potential to produce not only subjectivist, emic understandings of actors’ meanings, but also explanations, characterised by a certain degree of ‘‘thickness”. Mobilising the key tenets of the modern philosophical theory of explanation and the notion of abduction, grounded in pragmatist epistemology, we explicate how explanations may be developed and validated, yet remaining true to the core premises of IR. We focus on the intricate relationship between two arguably central aspects of validation in IR, namely authenticity and plausibility. Working on the assumption that validation is an important, but potentially problematic concern in all serious scholarly research, we explore whether and how validation efforts are manifest in IR using two case studies as illustrative examples. Validation is seen as an issue of convincing readers of the authenticity of research findings whilst simultaneously ensuring that explanations are deemed plausible. Whilst the former is largely a matter of preserving the emic qualities of research accounts, the latter is intimately linked to the process of abductive reasoning, whereby different theories are applied to advance thick explanations. This underscores the view of validation as a process, not easily separated from the ongoing efforts of researchers to develop explanations as research projects unfold and far from reducible to mere technicalities of following pre-specified criteria presumably minimising various biases. These properties detract from a view of validation as conforming to prespecified, stable, and uniform criteria and allow IR to move beyond the ‘‘crisis of validity” arguably prevailing in the social sciences.
Selfishness, Altruism and Message Spreading in Mobile Social Networks

Many kinds of communication networks, in particular social and opportunistic networks, rely at least partly on on humans to help move data across the network. Human altruistic behavior is an important factor determining the feasibility of such a system. In this paper, we study the impact of different distributions of altruism on the throughput and delay of mobile social communication system. We evaluate the system performance using four experimental human mobility traces with uniform and community-biased traffic patterns. We found that mobile social networks are very robust to the distributions of altruism due to the nature of multiple paths. We further confirm the results by simulations on two popular social network models. To the best of our knowledge, this is the first complete study of the impact of altruism on mobile social networks, including the impact of topologies and traffic patterns.
Postpartum depression: what we know.

Postpartum depression (PPD) is a serious mental health problem. It is prevalent, and offspring are at risk for disturbances in development. Major risk factors include past depression, stressful life events, poor marital relationship, and social support. Public health efforts to detect PPD have been increasing. Standard treatments (e.g., Interpersonal Psychotherapy) and more tailored treatments have been found effective for PPD. Prevention efforts have been less consistently successful. Future research should include studies of epidemiological risk factors and prevalence, interventions aimed at the parenting of PPD mothers, specific diathesis for a subset of PPD, effectiveness trials of psychological interventions, and prevention interventions aimed at addressing mental health issues in pregnant women.
Cognitive assisted living ambient system: a survey

The demographic change towards an ageing population is introducing significant impact and drastic challenge to our society. We therefore need to find ways to assist older people to stay independently and prevent social isolation of these population. Information and Communication Technologies (ICT) can provide various solutions to help older adults to improve their quality of life, stay healthier, and live independently for longer time. The term of Ambient Assist Living (AAL) becomes a field to investigate innovative technologies to provide assistance as well as healthcare and rehabilitation to senior people with impairment. The paper provides a review of research background and technologies of AAL.
Query and Output: Generating Words by Querying Distributed Word Representations for Paraphrase Generation

Task: Paraphrase generation Problem: The existing sequence-to-sequence model tends to memorize the words and the patterns in the training dataset instead of learning the meaning of the words. Therefore, the generated sentences are often grammatically correct but semantically improper. Proposal: a novel model based on the encoder-decoder framework, called Word Embedding Attention Network (WEAN). Our proposed model generates the words by querying distributed word representations (i.e. neural word embeddings), hoping to capturing the meaning of the according words. Example of RNN Generated  因天气原因,飞机备降太原机场。有乘客要求重新安检,机长决 定继续飞行,引起机组人员与未吸烟乘客冲突。 Last night, several people were caught to smoke on a flight of China United Airlines from Chendu to Beijing. Later the flight temporarily landed on Taiyuan Airport. Some passengers asked for a security check but were denied by the captain, which led to a collision between crew and passengers. RNN: 中联航空机场发生爆炸致多人死亡。 China United Airlines exploded in the airport, leaving several people dead. Gold: 航班多人吸烟机组人员与乘客冲突。 Several people smoked on a flight which led to a collision between crew and passengers. Proposed Model Our Semantic Relevance Based neural model. It consists of decoder (above), encoder (below) and cosine similarity function.  Large Scale Chinese Short Text Summarization Dataset (LCSTS) Results of our model and baseline systems. Our models achieve substantial improvement of all ROUGE scores over baseline systems.  With careful calculation, there are many successful Internet companies in Shanghai, but few of them becomes giant company like BAT. This is also the reason why few Internet companies are listed in top hundred companies of paying tax. Some of them are merged, such as Ebay, Tudou, PPS, PPTV, Yihaodian and so on. Others are satisfied with segment market for years. Gold:为什么上海出不了互联网巨头? Why Shanghai comes out no giant company? RNN context:上海的互联网巨头。 Shanghai's giant company. SRB:上海鲜少互联网巨头的身影。 Shanghai has few giant companies. Proposed Model Text Representation Source text representation Vt = hN Generated summary representation Vs = sM − hN Semantic Relevance cosine similarity function cos Vs, Vt = Vt∙Vs Vt Vs Training Objective function L = −p y x; θ − λ cos Vs, Vt  Our work aims at improving semantic relevance of generated summaries and source texts for Chinese social media text summarization. Our model is able to transform the text and the summary into a dense vector, and encourage high similarity of their representation. Experiments show that our model outperforms baseline systems, and the generated summary has higher semantic relevance.
Artificial Neural Networks that Learn to Satisfy Logic Constraints

Logic-based problems such as planning, theorem proving, or puzzles like Soduko, typically involve combinatorial search and structured knowledge representation. Artificial neural networks (ANNs) are very successful statistical learners; however, for many years, they have been criticized for their weaknesses in representing and in processing complex structured knowledge which is crucial for combinatorial search and symbol manipulation. Two neural architectures are presented (Symmetric and RNN), which can encode structured relational knowledge in neural activation, and store bounded First Order Logic (FOL) constraints in connection weights. Both architectures learn to search for a solution that satisfies the constraints. Learning is done by unsupervised “practicing” on problem instances from the same domain, in a way that improves the network-solving speed. No teacher exists to provide answers for the problem instances of the training and test sets. However, the domain constraints are provided as prior knowledge to a loss function that measures the degree of constraint violations. Iterations of activation calculation and learning are executed until a solution that maximally satisfies the constraints emerges on the output units. As a test case, block-world planning problems are used to train networks that learn to plan in that domain, but the techniques proposed could be used more generally as in integrating prior symbolic knowledge with statistical learning.