nils-herrmann/clean_scirepeval huggingface dataset:
Vision System for AGI: Problems and Directions

What frameworks and architectures are necessary to create a vision system for AGI? In this paper, we propose a formal model that states the task of perception within AGI. We show the role of discriminative and generative models in achieving efficient and general solution of this task, thus specifying the task in more detail. We discuss some existing generative and discriminative models and demonstrate their insufficiency for our purposes. Finally, we discuss some architectural dilemmas and open questions.
Choice of Agile Methodologies in Software Development : A Vendor Perspective

The purpose of this research was to develop understanding about how vendor firms make choice about agile methodologies in software projects and their fit. Two analytical frameworks were developed from extant literature and the findings were compared with real world decisions. Framework 1 showed that the choice of XP for one project was not supported by the guidelines given by the framework. The choices of SCRUM for other two projects, were partially supported. Analysis using the framework 2 showed that except one XP project, all others had sufficient project management support, limited scope for adaptability and had prominence for rules.
Global change and species interactions in terrestrial ecosystems.

The main drivers of global environmental change (CO2 enrichment, nitrogen deposition, climate, biotic invasions and land use) cause extinctions and alter species distributions, and recent evidence shows that they exert pervasive impacts on various antagonistic and mutualistic interactions among species. In this review, we synthesize data from 688 published studies to show that these drivers often alter competitive interactions among plants and animals, exert multitrophic effects on the decomposer food web, increase intensity of pathogen infection, weaken mutualisms involving plants, and enhance herbivory while having variable effects on predation. A recurrent finding is that there is substantial variability among studies in both the magnitude and direction of effects of any given GEC driver on any given type of biotic interaction. Further, we show that higher order effects among multiple drivers acting simultaneously create challenges in predicting future responses to global environmental change, and that extrapolating these complex impacts across entire networks of species interactions yields unanticipated effects on ecosystems. Finally, we conclude that in order to reliably predict the effects of GEC on community and ecosystem processes, the greatest single challenge will be to determine how biotic and abiotic context alters the direction and magnitude of GEC effects on biotic interactions.
Artificial Intelligence and Cognitive Psychology Applications , Models Gabriella

How can we connect artificial intelligence with cognitive psychology? What kind of models and approaches were developed in these scientific fields? The main aim of this paper is to provide a broad summary and analyses about the relationships between psychology and artificial intelligence. I present the state of the art applications, human like thinking and acting systems (Human Computer Interface, Modelling Mental Processes, Data Mining Application) Application can be divided into several groups and aspects. Main goal of the artificial intelligence was/is to develop human level intelligence, but the technology transfer turned out to be much comprehensive, and these systems are used widely, and the research is blooming. The first part of the paper introduces the development, and the basic knowledge, general models of the cognitive psychology (gives also its relevant connecting points to artificial intelligence), it describes also the information processing model of the human brain. The second part provides analyses of the human computing interaction, its tasks, application fields, the psychological models used for HCI, and the barriers of the field. In order to extend or defeat these barriers, the science has to face several scientific, pragmatic, and technical challenges (such as the problem of complexity, disturbing coefficients... etc). Other important area demonstrated in this paper is the mental modelling used to prevent, prognoses, manipulate, or to support the human mental processes, like learning. By a prognoses (for example prognoses of the children affected by mental illnesses according to their environments. etc), data mining, knowledge discovery, or expert systems are applied. The paper gives an outline about in the system used coefficients, and analyses the missing attributes. The last part deals with the expert systems used to help people and relatives with autism and with the life simulation (applied mental model) in the virtual reality/virtual environment.
Adaptive Input Representations for Neural Language Modeling

We introduce adaptive input representations for neural language modeling which extend the adaptive softmax of Grave et al. (2017) to input representations of variable capacity. There are several choices on how to factorize the input and output layers, and whether to model words, characters or sub-word units. We perform a systematic comparison of popular choices for a self-attentional architecture. Our experiments show that models equipped with adaptive embeddings are more than twice as fast to train than the popular character input CNN while having a lower number of parameters. We achieve a new state of the art on the WIKITEXT-103 benchmark of 20.51 perplexity, improving the next best known result by 8.7 perplexity. On the BILLION WORD benchmark, we achieve a state of the art of 24.14
A Control Methodology and Characterization of Dynamics for a Photovoltaic (PV) System Interfaced With a Distribution Network

This paper proposes a control strategy for a single-stage, three-phase, photovoltaic (PV) system that is connected to a distribution network. The control is based on an inner current-control loop and an outer DC-link voltage regulator. The current-control mechanism decouples the PV system dynamics from those of the network and the loads. The DC-link voltage-control scheme enables control and maximization of the real power output. Proper feedforward actions are proposed for the current-control loop to make its dynamics independent of those of the rest of the system. Further, a feedforward compensation mechanism is proposed for the DC-link voltage-control loop, to make the PV system dynamics immune to the PV array nonlinear characteristic. This, in turn, permits the design and optimization of the PV system controllers for a wide range of operating conditions. A modal/sensitivity analysis is also conducted on a linearized model of the overall system, to characterize dynamic properties of the system, to evaluate robustness of the controllers, and to identify the nature of interactions between the PV system and the network/loads. The results of the modal analysis confirm that under the proposed control strategy, dynamics of the PV system are decoupled from those of the distribution network and, therefore, the PV system does not destabilize the distribution network. It is also shown that the PV system dynamics are not influenced by those of the network (i.e., the PV system maintains its stability and dynamic properties despite major variations in the line length, line X/R ratio, load type, and load distance from the PV system).
Thermal energy storage technologies and systems for concentrating solar power plants

This paper presents a review of thermal energy storage system design methodologies and the factors to be considered at different hierarchical levels for concentrating solar power (CSP) plants. Thermal energy storage forms a key component of a power plant for improvement of its dispatchability. Though there have been many reviews of storage media, there are not many that focus on storage system design along with its integration into the power plant. This paper discusses the thermal energy storage system designs presented in the literature along with thermal and exergy efficiency analyses of various thermal energy storage systems integrated into the power plant. Economic aspects of these systems and the relevant publications in literature are also summarized in this effort.
On-the-fly Table Generation

Many information needs revolve around entities, which would be better answered by summarizing results in a tabular format, rather than presenting them as a ranked list. Unlike previous work, which is limited to retrieving existing tables, we aim to answer queries by automatically compiling a table in response to a query. We introduce and address the task of on-the-fly table generation: given a query, generate a relational table that contains relevant entities (as rows) along with their key properties (as columns). This problem is decomposed into three specific subtasks: (i) core column entity ranking, (ii) schema determination, and (iii) value lookup. We employ a feature-based approach for entity ranking and schema determination, combining deep semantic features with task-specific signals. We further show that these two subtasks are not independent of each other and can assist each other in an iterative manner. For value lookup, we combine information from existing tables and a knowledge base. Using two sets of entity-oriented queries, we evaluate our approach both on the component level and on the end-to-end table generation task.
The Role of Community in Disaster Response : Conceptual Models

We focus on the role that community plays in the continuum of disaster preparedness, response and recovery, and we explore where community fits in conceptual frameworks concerning disaster decision-making. We offer an overview of models developed in the literature as well as insights drawn from research related to Hurricane Katrina. Each model illustrates some aspect of the spectrum of disaster preparedness and recovery, beginning with risk perception and vulnerability assessments, and proceeding to notions of resiliency and capacity building. Concepts like social resilience are related to theories of ‘‘social capital,’’ which stress the importance of social networks, reciprocity, and interpersonal trust. These allow individuals and groups to accomplish greater things than they could by their isolated efforts. We trace two contrasting notions of community to Tocqueville. On the one hand, community is simply an aggregation of individual persons, that is, a population. As individuals, they have only limited capacity to act effectively or make decisions for themselves, and they are strongly subject to administrative decisions that authorities impose on them. On the other hand, community is an autonomous actor, with its own interests, preferences, resources, and capabilities. This definition of community has also been embraced by community-based participatory researchers and has been thought to offer an approach that is more active and advocacy oriented. We conclude with a discussion of the strengths and weaknesses of community in disaster response and in disaster research.
The Hofstede model Applications to global branding and advertising strategy and research

versus concrete thinking Whereas in individualistic cultures brands are made by adding values or abstract personality traits to products, members of collectivistic cultures are more interested in concrete product features than in abstract brands because they are less used to conceptual thinking. for members of collectivistic cultures where context and situation are important, the brand concept is too abstract to be discussed the way members of individualistic cultures do. The Reader’s Digest Trusted Brands survey in 2002 asked people in 18 different countries in europe about the probability of buying unknown brands. The responses ‘extremely/quite likely to consider buying a brand which I’ve heard of but haven’t tried before’ correlated significantly with individualism (r = 0.82***).2 Instead of adding abstract personal characteristics to the product, in collectivistic cultures the brand is linked to concrete persons, in Japan called talents (Praet 2001).  American companies have developed product brands with unique characteristics, Japanese companies have generally emphasised the corporate brand. In essence, this means inspiring trust among consumers in a company and so persuading them to buy its products. As a result, Japanese and Korean companies, in their television advertisements, display corporate identity logos more frequently than do uS and German companies (Souiden et al. 2006). The unfamiliarity with abstract brand associations leads to variation when measuring brand equity of global brands across cultures. An important element of brand equity is consumer equity, which is measured in 2 for correlation analysis, the Pearson product-moment correlation coefficient is used. Correlation analysis is one-tailed. Significance levels are indicated by *p < 0.05, **p < 0.01 and ***p < 0.005. Regression analysis is stepwise. The coefficient of determination or R2 is the indicator of the percentage of variance explained.
Hey, You, Get Off of My Market: Detecting Malicious Apps in Official and Alternative Android Markets

In this paper, we present a systematic study for the detection of malicious applications (or apps) on popular Android Markets. To this end, we first propose a permissionbased behavioral footprinting scheme to detect new samples of known Android malware families. Then we apply a heuristics-based filtering scheme to identify certain inherent behaviors of unknown malicious families. We implemented both schemes in a system called DroidRanger. The experiments with 204, 040 apps collected from five different Android Markets in May-June 2011 reveal 211 malicious ones: 32 from the official Android Market (0.02% infection rate) and 179 from alternative marketplaces (infection rates ranging from 0.20% to 0.47%). Among those malicious apps, our system also uncovered two zero-day malware (in 40 apps): one from the official Android Market and the other from alternative marketplaces. The results show that current marketplaces are functional and relatively healthy. However, there is also a clear need for a rigorous policing process, especially for non-regulated alternative marketplaces.
Emergence of Locomotion Behaviours in Rich Environments

The reinforcement learning paradigm allows, in principle, for complex behaviours to be learned directly from simple reward signals. In practice, however, it is common to carefully hand-design the reward function to encourage a particular solution, or to derive it from demonstration data. In this paper explore how a rich environment can help to promote the learning of complex behavior. Specifically, we train agents in diverse environmental contexts, and find that this encourages the emergence of robust behaviours that perform well across a suite of tasks. We demonstrate this principle for locomotion – behaviours that are known for their sensitivity to the choice of reward. We train several simulated bodies on a diverse set of challenging terrains and obstacles, using a simple reward function based on forward progress. Using a novel scalable variant of policy gradient reinforcement learning, our agents learn to run, jump, crouch and turn as required by the environment without explicit reward-based guidance. A visual depiction of highlights of the learned behavior can be viewed in this video.
Open access, readership, citations: a randomized controlled trial of scientific journal publishing.

Does free access to journal articles result in greater diffusion of scientific knowledge? Using a randomized controlled trial of open access publishing, involving 36 participating journals in the sciences, social sciences, and humanities, we report on the effects of free access on article downloads and citations. Articles placed in the open access condition (n=712) received significantly more downloads and reached a broader audience within the first year, yet were cited no more frequently, nor earlier, than subscription-access control articles (n=2533) within 3 yr. These results may be explained by social stratification, a process that concentrates scientific authors at a small number of elite research universities with excellent access to the scientific literature. The real beneficiaries of open access publishing may not be the research community but communities of practice that consume, but rarely contribute to, the corpus of literature.
Context Mover's Distance & Barycenters: Optimal transport of contexts for building representations

We propose a unified framework for building unsupervised representations of entities and their compositions, by viewing each entity as a histogram (or distribution) over its contexts. This enables us to take advantage of optimal transport and construct representations that effectively harness the geometry of the underlying space containing the contexts. Our method captures uncertainty via modelling the entities as distributions and simultaneously provides interpretability with the optimal transport map, hence giving a novel perspective for building rich and powerful feature representations. As a guiding example, we formulate unsupervised representations for text, and demonstrate it on tasks such as sentence similarity and word entailment detection. Empirical results show strong advantages gained through the proposed framework. This approach can potentially be used for any unsupervised or supervised problem (on text or other modalities) with a co-occurrence structure, such as any sequence data. The key tools at the core of this framework are Wasserstein distances and Wasserstein barycenters, hence raising the question from our title.
A 1.8-V 1.8 GHz LC VCO for WSN application

A 1.8 GHz LC VCO in 1.8-V supply is presented. The VCO achieves low power consumption by optimum selection of inductance in the L-C tank. To increase the tuning range, a three-bit switching capacitor array is used for digital switched tuning. Designed in 0.18μm RF CMOS technology, the proposed VCO achieves a phase noise of -126.2dBc/Hz at 1MHz offset and consumes 1.38mA core current at 1.8-V voltage supply.
3D Human Pose Estimation with Siamese Equivariant Embedding

In monocular 3D human pose estimation a common setup is to first detect 2D positions and then lift the detection into 3D coordinates. Many algorithms suffer from overfitting to camera positions in the training set. We propose a siamese architecture that learns a rotation equivariant hidden representation to reduce the need for data augmentation. Our method is evaluated on multiple databases with different base networks and shows a consistent improvement of error metrics. It achieves state-of-the-art cross-camera error rate among algorithms that use estimated 2D joint coordinates only.
Context Refinement for Object Detection

Current two-stage object detectors, which consists of a region proposal stage and a refinement stage, may produce unreliable results due to ill-localized proposed regions. To address this problem, we propose a context refinement algorithm that explores rich contextual information to better refine each proposed region. In particular, we first identify neighboring regions that may contain useful contexts and then perform refinement based on the extracted and unified contextual information. In practice, our method effectively improves the quality of the final detection results as well as region proposals. Empirical studies show that context refinement yields substantial and consistent improvements over different baseline detectors. Moreover, the proposed algorithm brings around 3% performance gain on PASCAL VOC benchmark and around 6% gain on MS COCO benchmark respectively.
Detecting Adversarial Examples - A Lesson from Multimedia Forensics

Adversarial classification is the task of performing robust classification in the presence of a strategic attacker. Originating from information hiding and multimedia forensics, adversarial classification recently received a lot of attention in a broader security context. In the domain of machine learningbased image classification, adversarial classification can be interpreted as detecting so-called adversarial examples, which are slightly altered versions of benign images. They are specifically crafted to be misclassified with a very high probability by the classifier under attack. Neural networks, which dominate among modern image classifiers, have been shown to be especially vulnerable to these adversarial examples. However, detecting subtle changes in digital images has always been the goal of multimedia forensics and steganalysis. In this paper, we highlight the parallels between these two fields and secure machine learning. Furthermore, we adapt a linear filter, similar to early steganalysis methods, to detect adversarial examples that are generated with the projected gradient descent (PGD) method, the state-ofthe-art algorithm for this task. We test our method on the MNIST database and show for several parameter combinations of PGD that our method can reliably detect adversarial examples. Additionally, the combination of adversarial re-training and our detection method effectively reduces the attack surface of attacks against neural networks. Thus, we conclude that adversarial examples for image classification possibly do not withstand detection methods from steganalysis, and future work should explore the effectiveness of known techniques from multimedia forensics in other adversarial settings.
PyNetLogo: Linking NetLogo with Python

Methods for testing and analyzing agent-basedmodels have drawn increasing attention in the literature, in thecontextof e orts toestablish standard frameworks for thedevelopmentanddocumentationofmodels. This process can benefit from the use of established so ware environments for data analysis and visualization. For instance, the popular NetLogo agent-based modelling so ware can be interfaced with Mathematica and R, letting modellers use the advanced analysis capabilities available in these programming languages. To extend these capabilities to anadditional user base, this paper presents thepyNetLogo connector, which allows NetLogo to be controlled from the Python general-purpose programming language. Given Python’s increasing popularity for scientific computing, this provides additional flexibility for modellers and analysts. PyNetLogo’s features are demonstrated by controlling one of NetLogo’s example models from an interactive Python environment, then performing a global sensitivity analysis with parallel processing.
Driver Drowsiness Classification Using Fuzzy Wavelet-Packet-Based Feature-Extraction Algorithm

Driver drowsiness and loss of vigilance are a major cause of road accidents. Monitoring physiological signals while driving provides the possibility of detecting and warning of drowsiness and fatigue. The aim of this paper is to maximize the amount of drowsiness-related information extracted from a set of electroencephalogram (EEG), electrooculogram (EOG), and electrocardiogram (ECG) signals during a simulation driving test. Specifically, we develop an efficient fuzzy mutual-information (MI)- based wavelet packet transform (FMIWPT) feature-extraction method for classifying the driver drowsiness state into one of predefined drowsiness levels. The proposed method estimates the required MI using a novel approach based on fuzzy memberships providing an accurate-information content-estimation measure. The quality of the extracted features was assessed on datasets collected from 31 drivers on a simulation test. The experimental results proved the significance of FMIWPT in extracting features that highly correlate with the different drowsiness levels achieving a classification accuracy of 95%-97% on an average across all subjects.