nils-herrmann/clean_scirepeval huggingface dataset:
Contextual and Position-Aware Factorization Machines for Sentiment Classification

While existing machine learning models have achieved great success for sentiment classification, they typically do not explicitly capture sentiment-oriented word interaction, which can lead to poor results for fine-grained analysis at the snippet level (a phrase or sentence). Factorization Machine provides a possible approach to learning element-wise interaction for recommender systems, but they are not directly applicable to our task due to the inability to model contexts and word sequences. In this work, we develop two Position-aware Factorization Machines which consider word interaction, context and position information. Such information is jointly encoded in a set of sentiment-oriented word interaction vectors. Compared to traditional word embeddings, SWI vectors explicitly capture sentiment-oriented word interaction and simplify the parameter learning. Experimental results show that while they have comparable performance with state-of-the-art methods for document-level classification, they benefit the snippet/sentence-level sentiment analysis.
Experience Replay Using Transition Sequences

Experience replay is one of the most commonly used approaches to improve the sample efficiency of reinforcement learning algorithms. In this work, we propose an approach to select and replay sequences of transitions in order to accelerate the learning of a reinforcement learning agent in an off-policy setting. In addition to selecting appropriate sequences, we also artificially construct transition sequences using information gathered from previous agent-environment interactions. These sequences, when replayed, allow value function information to trickle down to larger sections of the state/state-action space, thereby making the most of the agent's experience. We demonstrate our approach on modified versions of standard reinforcement learning tasks such as the mountain car and puddle world problems and empirically show that it enables faster, and more accurate learning of value functions as compared to other forms of experience replay. Further, we briefly discuss some of the possible extensions to this work, as well as applications and situations where this approach could be particularly useful.
2 Maturity Models in Business Process Management

Maturity models are a prospering approach to improving a company’s processes and business process management (BPM) capabilities. In fact, the number of corresponding maturity models is such high that practitioners and scholars run the risk of losing track. We therefore provide a systematic in-depth review of BPM maturity models.  The paper follows the accepted research process for literature reviews. We analyze a sample of 10 BPM maturity models according to a framework of general design principles. The framework particularly focuses on the applicability and usefulness of maturity models.  The analyzed maturity models sufficiently address basic design principles as well as principles for a descriptive purpose of use. The design principles for a prescriptive use, however, are hardly met. Thus, BPM maturity models provide limited guidance for identifying desirable maturity levels and for implementing improvement measures.  We are confident that this review covers the majority of publicly available BPM maturity models. As the number of corresponding maturity models seems to be constantly growing, exhaustiveness can hardly be guaranteed. Our results stimulate future research. Inter alia, adopters from industry require more elaborate support by means of ready-to-use and adaptable instruments for maturity assessment and improvement. We also reaffirm the need for maturity model consolidation in the field of BPM.  As existing literature reviews focus on process improvement or BPM in general, our findings extend current knowledge. They also increase transparency. Our results provide guidance for scholars and practitioners involved in the design, enhancement, or application of BPM maturity models.
Understanding and Controlling Memory in Recurrent Neural Networks

To be effective in sequential data processing, Recurrent Neural Networks (RNNs) are required to keep track of past events by creating memories. While the relation between memories and the network’s hidden state dynamics was established over the last decade, previous works in this direction were of a predominantly descriptive nature focusing mainly on locating the dynamical objects of interest. In particular, it remained unclear how dynamical observables affect the performance, how they form and whether they can be manipulated. Here, we utilize different training protocols, datasets and architectures to obtain a range of networks solving a delayed classification task with similar performance, alongside substantial differences in their ability to extrapolate for longer delays. We analyze the dynamics of the network’s hidden state, and uncover the reasons for this difference. Each memory is found to be associated with a nearly steady state of the dynamics which we refer to as a ’slow point’. Slow point speeds predict extrapolation performance across all datasets, protocols and architectures tested. Furthermore, by tracking the formation of the slow points we are able to understand the origin of differences between training protocols. Finally, we propose a novel regularization technique that is based on the relation between hidden state speeds and memory longevity. Our technique manipulates these speeds, thereby leading to a dramatic improvement in memory robustness over time, and could pave the way for a new class of regularization methods.
Nup153 Unlocks the Nuclear Pore Complex for HIV-1 Nuclear Translocation in Nondividing Cells.

Human immunodeficiency virus type 1 (HIV-1) displays the unique ability to infect nondividing cells. The capsid of HIV-1 is the viral determinant for viral nuclear import. To understand the cellular factors involved in the ability of HIV-1 to infect nondividing cells, we sought to find capsid mutations that allow the virus to infect dividing but not nondividing cells. Because the interaction of capsid with the nucleoporin protein 153 (Nup153) is important for nuclear import of HIV-1, we solved new crystal structures of hexameric HIV-1 capsid in complex with a Nup153-derived peptide containing a phenylalanine-glycine repeat (FG repeat), which we used to guide structure-based mutagenesis of the capsid-binding interface. HIV-1 viruses with mutations in these capsid residues were tested for their ability to infect dividing and nondividing cells. HIV-1 viruses with capsid N57 substitutions infected dividing but not nondividing cells. Interestingly, HIV-1 viruses with N57 mutations underwent reverse transcription but not nuclear translocation. The mutant capsids also lost the ability to interact with Nup153 and CPSF6. The use of small molecules PF74 and BI-2 prevented the interaction of FG-containing nucleoporins (Nups), such as Nup153, with the HIV-1 core. Analysis of integration sites in HIV-1 viruses with N57 mutations revealed diminished integration into transcriptionally active genes in a manner resembling that of HIV-1 in CPSF6 knockout cells or that of HIV-1-N74D. The integration pattern of the N57 mutant HIV-1 can be explained by loss of capsid interaction with CPSF6, whereas capsid interaction with Nup153 is required for HIV-1 to infect nondividing cells. Additionally, the observed viral integration profiles suggested that integration site selection is a multiparameter process that depends upon nuclear factors and the state of the cellular chromatin. One of the key advantages that distinguish lentiviruses, such as HIV-1, from all other retroviruses is its ability to infect nondividing cells. Interaction of the HIV-1 capsid with Nup153 and CPSF6 is important for nuclear entry and integration; however, the contribution of each of these proteins to nuclear import and integration is not clear. Using genetics, we demonstrated that these proteins contribute to different processes: Nup153 is essential for the HIV-1 nuclear import in nondividing cells, and CPSF6 is important for HIV-1 integration. In addition, nuclear factors such as CPSF6 and the state of the chromatin are known to be important for integration site selection; nevertheless, the preferential determinant influencing integration site selection is not known. This work demonstrates that integration site selection is a multiparameter process that depends upon nuclear factors and the state of the cellular chromatin.
Fraud Risk Modelling: Requirements Elicitation in the Case of Telecom Services

Telecom providers are losing tremendous amounts of money due to fraud risks posed to Telecom services and products. Currently, they are mainly focusing on fraud detection approaches to reduce the impact of fraud risks against their services. However, fraud prevention approaches should also be investigated in order to further reduce fraud risks and improve the revenue of Telecom providers. Fraud risk modelling is a fraud prevention approach aims at identifying the potential fraud risks, estimating the damage and setting up preventive mechanisms before the fraud risks lead to actual losses. In this paper, we highlight the important requirements for a usable and context-aware fraud risk modelling approach for Telecom services. To do so, we have conducted two workshops with experts from a Telecom provider and experts from multidisciplinary areas. In order to show and document the requirements, we present two exemplary Telecom fraud scenarios, analyse and estimate the impacts of fraud risks qualitatively.
Adaptive Appearance Rendering

We propose an approach to generate images of people given a desired appearance and pose. Disentangled representations of pose and appearance are necessary to handle the compound variability in the resulting generated images. Hence, we develop an approach based on intermediate representations of poses and appearance: our pose-guided appearance rendering network firstly encodes the targets’ poses using an encoder-decoder neural network. Then the targets’ appearances are encoded by learning adaptive appearance filters using a fully convolutional network. Finally, these filters are placed in the encoder-decoder neural networks to complete the rendering. We demonstrate that our model can generate images and videos that are superior to state-of-the-art methods, and can handle pose guided appearance rendering in both image and video generation.
Multidisciplinary Model for Smart Agriculture using Internet-of-Things ( IoT ) , Sensors , Cloud-Computing , Mobile-Computing & Big-Data Analysis

Although precision agriculture has been adopted in few countries; the agriculture industry in India still needs to be modernized with the involvement of technologies for better production, distribution and cost control. In this paper we proposed a multidisciplinary model for smart agriculture based on the key technologies: Internet-of-Things (IoT), Sensors, Cloud-Computing, MobileComputing, Big-Data analysis. Farmers, AgroMarketing agencies and Agro-Vendors need to be registered to the AgroCloud module through MobileApp module. AgroCloud storage is used to store the details of farmers, periodic soil properties of farmlands, agro-vendors and agro-marketing agencies, Agro e-governance schemes and current environmental conditions. Soil and environment properties are sensed and periodically sent to AgroCloud through IoT (Beagle Black Bone). Bigdata analysis on AgroCloud data is done for fertilizer requirements, best crop sequences analysis, total production, and current stock and market requirements. Proposed model is beneficial for increase in agricultural production and for cost control of Agro-products.
ETHICS , CHARACTER , AND AUTHENTIC TRANSFORMATIONAL LEADERSHIP BEHAVIOR

The morality of transformational leadership has been sharply questioned, particularly by libertarians, “grass roots” theorists, and organizational development consultants. This paper argues that to be truly transformational, leadership must be grounded in moral foundations. The four components of authentic transformational leadership (idealized influence, inspirational motivation, intellectual stimulation, and individualized consideration) are contrasted with their counterfeits in dissembling pseudo-transformational leadership on the basis of (1) the moral character of the leaders and their concerns for self and others; (2) the ethical values embedded in the leaders’ vision, articulation, and program, which followers can embrace or reject; and (3) the morality of the processes of social ethical choices and action in which the leaders and followers engage and collectively pursue. The literature on transformational leadership is linked to the long-standing literature on virtue and moral character, as exemplified by Socratic and Confucian typologies. It is related as well to the major themes of the modern Western ethical agenda: liberty, utility, and distributive justice Deception, sophistry, and pretense are examined alongside issues of transcendence, agency, trust, striving for congruence in values, cooperative action, power, persuasion, and corporate governance to establish the strategic and moral foundations of authentic transformational leadership.
Cross-lingual Coreference Resolution : A New Task for Multilingual Comparable Corpora

We introduce cross-lingual coreference resolution, the task of grouping entity mentions with a common referent in a multilingual corpus. Information, especially on the web, is increasingly multilingual. We would like to track entity references across languages without machine translation, which is expensive and unavailable for many language pairs. Therefore, we develop a set of models that rely on decreasing levels of parallel resources: a bitext, a bilingual lexicon, and a parallel name list. We propose baselines, provide experimental results, and analyze sources of error. Across a range of metrics, we find that even our lowest resource model gives a 2.5% F1 absolute improvement over the strongest baseline. Our results present a positive outlook for crosslingual coreference resolution even in low resource languages. We are releasing our crosslingual annotations for the ACE2008 ArabicEnglish evaluation corpus.
"Nice Picture Comment!" Graphicons in Facebook Comment Threads

Facebook has increasingly incorporated graphical means of communication such as emoticons, emoji, stickers, GIFs, images, and videos (‘graphicons’) into comment threads. Adapting methods of computer‐ mediated discourse analysis, we analyze the frequency and pragmatic functions of each graphicon type in threads sampled from public graphicon-focused Facebook groups. Six main functions emerged from the data: mention, reaction, tone modification, riffing, action, and narrative sequence. Reaction was most common, and emoji expressed the widest array of functions. We propose structural, social, and technical explanations for variation in graphicon use, and suggest improvements for the design of conversational graphical elements in social media systems.
A review on abnormal crowd behavior detection

Crowd analysis becomes very popular research topic in the area of computer vision. A growing requirement for smarter video surveillance of private and public space using intelligent vision systems which can differentiate what is semantically important in the direction of the human observer as normal behaviors and abnormal behaviors. People counting, people tracking and crowd behavior analysis are different stages for computer based crowd analysis algorithm. This paper focus on crowd behavior analysis which can detect normal behavior or abnormal behavior.
Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network

Twitter messages are increasingly used to determine consumer sentiment towards a brand. The existing literature on Twitter sentiment analysis uses various feature sets and methods, many of which are adapted from more traditional text classification problems. In this research, we introduce an approach to supervised feature reduction using n-grams and statistical analysis to develop a Twitter-specific lexicon for sentiment analysis. We augment this reduced Twitter-specific lexicon with brand-specific terms for brand-related tweets. We show that the reduced lexicon set, while significantly smaller (only 187 features), reduces modeling complexity, maintains a high degree of coverage over our Twitter corpus, and yields improved sentiment classification accuracy. To demonstrate the effectiveness of the devised Twitter-specific lexicon compared to a traditional sentiment lexicon, we develop comparable sentiment classification models using SVM. We show that the Twitter-specific lexicon is significantly more effective in terms of classification recall and accuracy metrics. We then develop sentiment classification models using the Twitter-specific lexicon and the DAN2 machine learning approach, which has demonstrated success in other text classification problems. We show that DAN2 produces more accurate sentiment classification results than SVM while using the same Twitter-specific lexicon.
The neurobiological consequences of early stress and childhood maltreatment

Early severe stress and maltreatment produces a cascade of neurobiological events that have the potential to cause enduring changes in brain development. These changes occur on multiple levels, from neurohumoral (especially the hypothalamic-pituitary-adrenal [HPA] axis) to structural and functional. The major structural consequences of early stress include reduced size of the mid-portions of the corpus callosum and attenuated development of the left neocortex, hippocampus, and amygdala. Major functional consequences include increased electrical irritability in limbic structures and reduced functional activity of the cerebellar vermis. There are also gender differences in vulnerability and functional consequences. The neurobiological sequelae of early stress and maltreatment may play a significant role in the emergence of psychiatric disorders during development.
The multiple facets of drug resistance: one history, different approaches

Some cancers like melanoma and pancreatic and ovarian cancers, for example, commonly display resistance to chemotherapy, and this is the major obstacle to a better prognosis of patients. Frequently, literature presents studies in monolayer cell cultures, 3D cell cultures or in vivo studies, but rarely the same work compares results of drug resistance in different models. Several of these works are presented in this review and show that usually cells in 3D culture are more resistant to drugs than monolayer cultured cells due to different mechanisms. Searching for new strategies to sensitize different tumors to chemotherapy, many methods have been studied to understand the mechanisms whereby cancer cells acquire drug resistance. These methods have been strongly advanced along the years and therapies using different drugs have been increasingly proposed to induce cell death in resistant cells of different cancers. Recently, cancer stem cells (CSCs) have been extensively studied because they would be the only cells capable of sustaining tumorigenesis. It is believed that the resistance of CSCs to currently used chemotherapeutics is a major contributing factor in cancer recurrence and later metastasis development. This review aims to appraise the experimental progress in the study of acquired drug resistance of cancer cells in different models as well as to understand the role of CSCs as the major contributing factor in cancer recurrence and metastasis development, describing how CSCs can be identified and isolated.
Pipeline Generative Adversarial Networks for Facial Images Generation with Multiple Attributes

Generative Adversarial Networks are proved to be efficient on various kinds of image generation tasks. However, it is still a challenge if we want to generate images precisely. Many researchers focus on how to generate images with one attribute. But image generation under multiple attributes is still a tough work. In this paper, we try to generate a variety of face images under multiple constraints using a pipeline process. The Pip-GAN (Pipeline Generative Adversarial Network) we present employs a pipeline network structure which can generate a complex facial image step by step using a neutral face image. We applied our method on two face image databases and demonstrate its ability to generate convincing novel images of unseen identities under multiple conditions previously.
American Sign Language Posture Understanding with Deep Neural Networks

Sign language is a visually oriented, natural, nonverbal communication medium. Having shared similar linguistic properties with its respective spoken language, it consists of a set of gestures, postures and facial expressions. Though, sign language is a mode of communication between deaf people, most other people do not know sign language interpretations. Therefore, it would be constructive if we can translate the sign postures artificially. In this paper, a capsule-based deep neural network sign posture translator for an American Sign Language (ASL) fingerspelling (posture), has been presented. The performance validation shows that the approach can successfully identify sign language, with accuracy like 99%. Unlike previous neural network approaches, which mainly used fine-tuning and transfer learning from pre-trained models, the developed capsule network architecture does not require a pre-trained model. The framework uses a capsule network with adaptive pooling which is the key to its high accuracy. The framework is not limited to sign language understanding, but it has scope for non-verbal communication in Human-Robot Interaction (HRI) also.
The Rules of Facebook Friendship : A Two-Stage Examination of Interaction Rules in Close , Casual , and Acquaintance Friendships

The present study examined friendship rules on the online social networking site Facebook. Study one used focus group data to inductively create a list of 36 Facebook friendship rules. Study two utilized survey data to examine college students’ endorsement of the various rules in close, casual, and acquaintance friendships. Results indicated five categories of Facebook friendship rules, which included rules regarding: communication channels, deception and control, relational maintenance, negative consequences for the self, and negatives consequences for a friend. Additionally, close friends, casual friends, and acquaintances significantly differed in their endorsement of four of the five rules categories. Results suggested that interaction rules provide a useful framework for the study of online social networking sites.
Retrieve and Refine: Improved Sequence Generation Models For Dialogue

Sequence generation models for dialogue are known to have several problems: they tend to produce short, generic sentences that are uninformative and unengaging. Retrieval models on the other hand can surface interesting responses, but are restricted to the given retrieval set leading to erroneous replies that cannot be tuned to the specific context. In this work we develop a model that combines the two approaches to avoid both their deficiencies: first retrieve a response and then refine it – the final sequence generator treating the retrieval as additional context. We show on the recent CONVAI2 challenge task our approach produces responses superior to both standard retrieval and generation models in human evaluations.