nils-herrmann/clean_scirepeval huggingface dataset:
Automated Test Generation to Detect Individual Discrimination in AI Models

Dependability on AI models is of utmost importance to ensure full acceptance of the AI systems. One of the key aspects of the dependable AI system is to ensure that all its decisions are fair and not biased towards any individual. In this paper, we address the problem of detecting whether a model has an individual discrimination. Such a discrimination exists when two individuals who differ only in the values of their protected attributes (such as, gender/race) while the values of their non-protected ones are exactly the same, get different decisions. Measuring individual discrimination requires an exhaustive testing, which is infeasible for a nontrivial system. In this paper, we present an automated technique to generate test inputs, which is geared towards finding individual discrimination. Our technique combines the wellknown technique called symbolic execution along with the local explainability for generation of effective test cases. Our experimental results clearly demonstrate that our technique produces 3.72 times more successful test cases than the existing state-of-the-art across all our chosen benchmarks.
Revisiting Word Embedding for Contrasting Meaning

Contrasting meaning is a basic aspect of semantics. Recent word-embedding models based on distributional semantics hypothesis are known to be weak for modeling lexical contrast. We present in this paper the embedding models that achieve an F-score of 92% on the widely-used, publicly available dataset, the GRE “most contrasting word” questions (Mohammad et al., 2008). This is the highest performance seen so far on this dataset. Surprisingly at the first glance, unlike what was suggested in most previous work, where relatedness statistics learned from corpora is claimed to yield extra gains over lexicon-based models, we obtained our best result relying solely on lexical resources (Roget’s and WordNet)—corpora statistics did not lead to further improvement. However, this should not be simply taken as that distributional statistics is not useful. We examine several basic concerns in modeling contrasting meaning to provide detailed analysis, with the aim to shed some light on the future directions for this basic semantics modeling problem.
The antibiotic resistome: the nexus of chemical and genetic diversity

Over the millennia, microorganisms have evolved evasion strategies to overcome a myriad of chemical and environmental challenges, including antimicrobial drugs. Even before the first clinical use of antibiotics more than 60 years ago, resistant organisms had been isolated. Moreover, the potential problem of the widespread distribution of antibiotic resistant bacteria was recognized by scientists and healthcare specialists from the initial use of these drugs. Why is resistance inevitable and where does it come from? Understanding the molecular diversity that underlies resistance will inform our use of these drugs and guide efforts to develop new efficacious antibiotics.
Does corporate social responsibility impact on employee engagement ?

Despite the claim that internal corporate social responsibility plays an important role, the understanding of this phenomenon has been neglected. This paper intends to contribute to fill this gap by looking into the relation between CSR and employee engagement.  A survey research was conducted and three different groups of respondents were faced with three different CSR scenarios (general, internal, external) and respondents’ employee engagement was measured.  The results show that there are no statistically significant differences in levels of engagement between employees exposed to external and internal CSR practices. Nevertheless, employees exposed to internal CSR are more engaged than those exposed only to external CSR practices.  The use of scenarios, although a grounded approach, involves risks, including the difficulty of participants to put themselves in a fictional situation. Also, the scale used to measure employee engagement puts the emphasis on work rather than on the organisation.  Although this study is not conclusive it raises the need for companies to look at their CSR strategy in a holistic approach, i.e. internal and external.  This paper represents a contribution to understand CSR strategic status and the need to enlighten the impact that social responsible practices can have on employees’ engagement.
The Evolving Field of Organizational Learning and Knowledge Management

Organizational learning (OL) and knowledge management (KM) research has gone through dramatic changes in the last twenty years and, without doubt, the fi eld will continue to change in the next ten years. Our research suggests that Cyert and March were the fi rst authors to reference organizational learning in their publication of 1963. It was just twenty years ago that a conference was held at Carnegie Mellon University to honor March and his contribution to the fi eld of organizational learning. Many of these presentations were published in a special issue of Organization Science in 1991. Since that time we have seen a rapid expansion in the number of journal articles— both academic and practitioner—devoted to organizational learning. Fields such as information technology, marketing and human resources have also jumped on the bandwagon. Doctoral programs are including seminars on organizational learning, and MBA courses on organizational learning are appearing. All of this refl ects acceptance of the concept that organizations have knowledge, do learn over time, and consider their knowledge base and social capital as valuable assets. It also reaffi rms the legitimacy of research on organizational learning and its practical applications to organizations. The fi rst edition of this Handbook was published in 2003 but most chapters were completed in 2001 or 2002. Our fi rst edition was widely used and it was clear—given the advancement of the fi eld—that a second edition was necessary. Some people might claim that it is foolhardy to seek to cover the full range of the literature within one volume. Our intent is to provide a resource that is useful to academics, practitioners, and students who want an overview of the current fi eld with full recognition that—to our delight—the fi eld continues to have major impact on research and management practices. Our response is
Challenging SMT solvers to verify neural networks

In this paper we evaluate state-of-the-art SMT solvers on encodings of verification problems involving Multi-Layer Perceptrons (MLPs), a widely used type of neural network. Verification is a key technology to foster adoption of MLPs in safety-related applications, where stringent requirements about performance and robustness must be ensured and demonstrated. In previous contributions, we have shown that safety problems for MLPs can be attacked by solving Boolean combinations of linear arithmetic constraints. However, the generated encodings are hard for current state-of-the-art SMT solvers, limiting our ability to verify MLPs in practice. The experimental results herewith presented are meant to provide the community with a precise picture of current achievements and standing open challenges in this intriguing application domain.
A Review of Research Evidence on the Antecedents of Transformational Leadership

As the most-studied form of leadership across disciplines in both Western and Chinese contexts, transformational school leadership has the potential to suit diverse national and cultural contexts. Given the growing evidence showing the positive effects of transformational leadership on various school outcomes as it relates to school environment, teacher and student achievement, we wanted to explore the factors that gave rise to transformational leadership. The purpose of this study was to identify and compare the antecedents fostering transformational leadership in the contexts of both the United States and China. This paper reviews and discusses the empirical studies of the last two decades, concentrating on the variables that are antecedent to transformational leadership mainly in the educational context, but also in public management, business and psychology. Results show that transformational leadership is related to three sets of antecedents, which include: (1) the leader’s qualities (e.g., self-efficacy, values, traits, emotional intelligence); (2) organizational features (e.g., organization fairness); and (3) the leader’s colleagues’ characteristics (e.g., follower’s initial developmental level). Some antecedents were common to both contexts, while other antecedents appeared to be national context specific. The implications of the findings for future research and leader preparation in different national contexts are discussed.
A critical evaluation of the trigger point phenomenon.

The theory of myofascial pain syndrome (MPS) caused by trigger points (TrPs) seeks to explain the phenomena of muscle pain and tenderness in the absence of evidence for local nociception. Although it lacks external validity, many practitioners have uncritically accepted the diagnosis of MPS and its system of treatment. Furthermore, rheumatologists have implicated TrPs in the pathogenesis of chronic widespread pain (FM syndrome). We have critically examined the evidence for the existence of myofascial TrPs as putative pathological entities and for the vicious cycles that are said to maintain them. We find that both are inventions that have no scientific basis, whether from experimental approaches that interrogate the suspect tissue or empirical approaches that assess the outcome of treatments predicated on presumed pathology. Therefore, the theory of MPS caused by TrPs has been refuted. This is not to deny the existence of the clinical phenomena themselves, for which scientifically sound and logically plausible explanations based on known neurophysiological phenomena can be advanced.
Analgesic Potential of Essential Oils

Pain is an unpleasant sensation associated with a wide range of injuries and diseases, and affects approximately 20% of adults in the world. The discovery of new and more effective drugs that can relieve pain is an important research goal in both the pharmaceutical industry and academia. This review describes studies involving antinociceptive activity of essential oils from 31 plant species. Botanical aspects of aromatic plants, mechanisms of action in pain models and chemical composition profiles of the essential oils are discussed. The data obtained in these studies demonstrate the analgesic potential of this group of natural products for therapeutic purposes.
Capital cost estimation of RO plants : GCC countries versus southern Europe

Parameters affecting the direct capital costs of BWRO and SWRO plants were assessed. • Plants delivered through EPC contracts were considered. • Assessment was based on cost data from 950 RO plants in the GCC and southern Europe. • Plant capacity, type, award year, and region were found to affect RO CAPEX cost. • A model was also developed and verified for RO CAPEX estimation. a b s t r a c t a r t i c l e i n f o The installation of reverse osmosis (RO) desalination plants has been on the rise throughout the world. Thus, the estimation of their capital cost (CAPEX) is of major importance for governments, potential investors and consulting engineers of the industry. In this paper, parameters potentially affecting the direct capital costs of brackish water RO (BWRO) and seawater RO (SWRO) desalination plants, delivered through Engineering, Procurement & Construction (EPC) contracts, were assessed. The assessment was conducted based on cost data from 950 RO desalination plants contracted in the Gulf Cooperation Council (GCC) countries and in five southern European countries. The parameters assessed include plant capacity, location, award year, feed salinity, and the cumulative installed capacity within a region. Our results showed that plant capacity has the strongest correlation with the EPC cost. Plant type (SWRO or BWRO), plant award year and the region of the RO plant were also found to be statistically important. By utilizing multiple linear regression, a model was also developed to estimate the direct CAPEX (EPC cost) of RO desalination plants to be located either in the GCC countries or southern Europe, which was then verified using the k-fold test. In 2009, over 15,000 desalination plants were in operation worldwide with approximately half of them being reverse osmosis (RO) plants . Although many countries have begun to utilize desalination to produce drinking water, no region of the world has implemented desalination as widely as the Middle East, where 50% of the world's production of desalinated water is installed . Over the past 40 years, use of RO has been gradually gaining momentum in the Gulf Cooperation Council (GCC) countries, due to its lower cost, simplicity, novelties in the membrane fabrication, and the high salt rejection accomplished by RO membranes today [1–4]. It is foreseen that RO will play a key role in increasing fresh water availability globally in the future, but more so in …
Big Data analytics frameworks

Big Data concerns massive, heterogeneous, autonomous sources with distributed and decentralized control. These characteristics make it an extreme challenge for organizations using traditional data management mechanism to store and process these huge datasets. It is required to define a new paradigm and re-evaluate current system to manage and process Big Data. In this paper, the important characteristics, issues and challenges related to Big Data management has been explored. Various open source Big Data analytics frameworks that deal with Big Data analytics workloads have been discussed. Comparative study between the given frameworks and suitability of the same has been proposed.
The Geography of Financial Misconduct ∗

Financial misconduct (FM) varies across cities, and over time. City-level FM is highly correlated with political corruption (enforced non-locally), and with ethically questionable (but not illegal) behavior by local physicians, both inconsistent with regional variation in enforcement. Further, among firms for whom detection is likely very similar (ex-clients of Arthur Andersen forced to switch auditors), a similar ranking of FM rates across cities emerges. FM is uncorrelated with economic and demographic variation, but is consistent with social norms transmitted by peer effects. In particular, FM rates in a city’s “dominant” industry predict FM among local firms outside the dominant sector.
Effective Crop Productivity and Nutrient Level Monitoring in Agriculture Soil Using IOT

In today's world, there is an increase in population growth. Hence there is a need in increase in food production. Fertilizers are being used to increase crop productivity but what is ideal to produce more crop, nutrient level of soil and crop monitoring is more important. Hence we introduce Internet of Things for monitoring the soil parameters including pH level, soil moisture, temperature and humidity. Soil moisture and pH value are the important parameters for crop growth. The soil parameters are detected and monitored using various sensors. Infrared sensors are also used in order to detect disease in plants. The values detected are stored and updated in cloud server for future use. Also an application is developed for farmers in order to know the pH level of nutrients in soil, soil moisture and temperature for efficient productivity of crops.
Intelligent Hybrid Vehicle Power Control—Part I: Machine Learning of Optimal Vehicle Power

In this series of two papers, we present our research on intelligent energy management for hybrid electric vehicles (HEVs). These two papers cover the modeling of power flow in HEVs, the mathematical background of optimization in energy management in HEVs, a machine learning framework that combines dynamic programming (DP) with machine learning to learn about roadway-type- and traffic-congestion-level-specific energy optimization, machine learning algorithms, and real-time quasi-optimal control of energy flow in an HEV. This first paper presents our research on machine learning for optimal energy management in HEVs. We will present a machine learning framework ML_EMO_HEV developed for the optimization of energy management in an HEV, machine learning algorithms for predicting driving environments, and the generation of an optimal power split for a given driving environment. Experiments are conducted based on a simulated Ford Escape Hybrid vehicle model provided by Argonne National Laboratory's Powertrain Systems Analysis Toolkit (PSAT). Based on the experimental results on the test data, we can conclude that the neural networks trained under the ML_EMO_HEV framework are effective in predicting roadway type and traffic congestion levels, predicting driving trends, and learning optimal engine speed and optimal battery power from DP.
Modernity and narcissistic personality disorder.

Narcissistic personality disorder (NPD) is a trait-based disorder that can be understood as a pathological amplification of narcissistic traits. While temperamental vulnerability and psychological adversity are risk factors for NPD, sociocultural factors are also important. This review hypothesizes that increases in narcissistic traits and cultural narcissism could be associated with changes in the prevalence of NPD. These shifts seem to be a relatively recent phenomenon, driven by social changes associated with modernity. While the main treatment for NPD remains psychotherapy, that form of treatment is itself a product of modernity and individualism. The hypothesis is presented that psychological treatment, unless modified to address the specific problems associated with NPD, could run the risk of supporting narcissism.
Team Cornell's Skynet: Robust Perception and Planning in an Urban Environment

Isaac Miller and Mark Campbell Sibley School of Mechanical and Aerospace Engineering Dan Huttenlocher and Frank-Robert Kline Computer Science Department Aaron Nathan, Sergei Lupashin, and Jason Catlin School of Electrical and Computer Engineering Brian Schimpf School of Operations Research and Information Engineering Pete Moran, Noah Zych, Ephrahim Garcia, Mike Kurdziel, and Hikaru Fujishima Sibley School of Mechanical and Aerospace Engineering Cornell University Ithaca, New York 14853
A 60-GHz quadrature PLL in 90nm CMOS

A 1.2 V 60 GHz 120 mW phase-locked loop employing a quadrature differential voltage-controlled oscillator, a programmable charge pump, and a frequency quadrupler is presented. Implemented in a 90 m CMOS process and operating at 60 GHz with a 1.2 V supply, the PLL achieves a phase noise of −91 dBc/Hz at a frequency offset of 1 MHz.
Using self-report assessment methods to explore facets of mindfulness.

The authors examine the facet structure of mindfulness using five recently developed mindfulness questionnaires. Two large samples of undergraduate students completed mindfulness questionnaires and measures of other constructs. Psychometric properties of the mindfulness questionnaires were examined, including internal consistency and convergent and discriminant relationships with other variables. Factor analyses of the combined pool of items from the mindfulness questionnaires suggested that collectively they contain five clear, interpretable facets of mindfulness. Hierarchical confirmatory factor analyses suggested that at least four of the identified factors are components of an overall mindfulness construct and that the factor structure of mindfulness may vary with meditation experience. Mindfulness facets were shown to be differentially correlated in expected ways with several other constructs and to have incremental validity in the prediction of psychological symptoms. Findings suggest that conceptualizing mindfulness as a multifaceted construct is helpful in understanding its components and its relationships with other variables.
Semantic Segmentation of 3D LiDAR Data in Dynamic Scene Using Semi-supervised Learning

This work studies the semantic segmentation of 3D LiDAR data in dynamic scenes for autonomous driving applications. A system of semantic segmentation using 3D LiDAR data, including range image segmentation, sample generation, inter-frame data association, track-level annotation and semisupervised learning, is developed. To reduce the considerable requirement of fine annotations, a CNN-based classifier is trained by considering both supervised samples with manually labeled object classes and pairwise constraints, where a data sample is composed of a segment as the foreground and neighborhood points as the background. A special loss function is designed to account for both annotations and constraints, where the constraint data are encouraged to be assigned to the same semantic class. A dataset containing 1838 frames of LiDAR data, 39934 pairwise constraints and 57927 human annotations is developed. The performance of the method is examined extensively. Qualitative and quantitative experiments show that the combination of a few annotations and large amount of constraint data significantly enhances the effectiveness and scene adaptability, resulting in greater than 10% improvement.
Black-box data-efficient policy search for robotics

The most data-efficient algorithms for reinforcement learning (RL) in robotics are based on uncertain dynamical models: after each episode, they first learn a dynamical model of the robot, then they use an optimization algorithm to find a policy that maximizes the expected return given the model and its uncertainties. It is often believed that this optimization can be tractable only if analytical, gradient-based algorithms are used; however, these algorithms require using specific families of reward functions and policies, which greatly limits the flexibility of the overall approach. In this paper, we introduce a novel model-based RL algorithm, called Black-DROPS (Black-box Data-efficient RObot Policy Search) that: (1) does not impose any constraint on the reward function or the policy (they are treated as black-boxes), (2) is as data-efficient as the state-of-the-art algorithm for data-efficient RL in robotics, and (3) is as fast (or faster) than analytical approaches when several cores are available. The key idea is to replace the gradient-based optimization algorithm with a parallel, black-box algorithm that takes into account the model uncertainties. We demonstrate the performance of our new algorithm on two standard control benchmark problems (in simulation) and a low-cost robotic manipulator (with a real robot).
Face Synthesis from Facial Identity Features

We present a method for synthesizing a frontal, neutralexpression image of a person’s face given an input face photograph. This is achieved by learning to generate facial landmarks and textures from features extracted from a facial-recognition network. Unlike previous generative approaches, our encoding feature vector is largely invariant to lighting, pose, and facial expression. Exploiting this invariance, we train our decoder network using only frontal, neutral-expression photographs. Since these photographs are well aligned, we can decompose them into a sparse set of landmark points and aligned texture maps. The decoder then predicts landmarks and textures independently and combines them using a differentiable image warping operation. The resulting images can be used for a number of applications, such as analyzing facial attributes, exposure and white balance adjustment, or creating a 3-D avatar.