nils-herrmann/clean_scirepeval huggingface dataset:
Automated directed fairness testing

Fairness is a critical trait in decision making. As machine-learning models are increasingly being used in sensitive application domains (e.g. education and employment) for decision making, it is crucial that the decisions computed by such models are free of unintended bias. But how can we automatically validate the fairness of arbitrary machine-learning models? For a given machine-learning model and a set of sensitive input parameters, our Aeqitas approach automatically discovers discriminatory inputs that highlight fairness violation. At the core of Aeqitas are three novel strategies to employ probabilistic search over the input space with the objective of uncovering fairness violation. Our Aeqitas approach leverages inherent robustness property in common machine-learning models to design and implement scalable test generation methodologies. An appealing feature of our generated test inputs is that they can be systematically added to the training set of the underlying model and improve its fairness. To this end, we design a fully automated module that guarantees to improve the fairness of the model. We implemented Aeqitas and we have evaluated it on six stateof- the-art classifiers. Our subjects also include a classifier that was designed with fairness in mind. We show that Aeqitas effectively generates inputs to uncover fairness violation in all the subject classifiers and systematically improves the fairness of respective models using the generated test inputs. In our evaluation, Aeqitas generates up to 70% discriminatory inputs (w.r.t. the total number of inputs generated) and leverages these inputs to improve the fairness up to 94%.
Internet of Vehicles: Architecture, Protocols, and Security

Today, vehicles are increasingly being connected to the Internet of Things which enable them to provide ubiquitous access to information to drivers and passengers while on the move. However, as the number of connected vehicles keeps increasing, new requirements (such as seamless, secure, robust, scalable information exchange among vehicles, humans, and roadside infrastructures) of vehicular networks are emerging. In this context, the original concept of vehicular ad-hoc networks is being transformed into a new concept called the Internet of Vehicles (IoV). We discuss the benefits of IoV along with recent industry standards developed to promote its implementation. We further present recently proposed communication protocols to enable the seamless integration and operation of the IoV. Finally, we present future research directions of IoV that require further consideration from the vehicular research community.
A Generative Approach for Dynamically Varying Photorealistic Facial Expressions in Human-Agent Interactions

This paper presents an approach for generating photorealistic video sequences of dynamically varying facial expressions in human-agent interactions. To this end, we study human-human interactions to model the relationship and influence of one individual's facial expressions in the reaction of the other. We introduce a two level optimization of generative adversarial models, wherein the first stage generates a dynamically varying sequence of the agent's face sketch conditioned on facial expression features derived from the interacting human partner. This serves as an intermediate representation, which is used to condition a second stage generative model to synthesize high-quality video of the agent face. Our approach uses a novel L1 regularization term computed from layer features of the discriminator, which are integrated with the generator objective in the GAN model. Session constraints are also imposed on video frame generation to ensure appearance consistency between consecutive frames. We demonstrated that our model is effective at generating visually compelling facial expressions. Moreover, we quantitatively showed that agent facial expressions in the generated video clips reflect valid emotional reactions to behavior of the human partner.
Face Recognition via Centralized Coordinate Learning

Owe to the rapid development of deep neural network (DNN) techniques and the emergence of large scale face databases, face recognition has achieved a great success in recent years. During the training process of DNN, the face features and classification vectors to be learned will interact with each other, while the distribution of face features will largely affect the convergence status of network and the face similarity computing in test stage. In this work, we formulate jointly the learning of face features and classification vectors, and propose a simple yet effective centralized coordinate learning (CCL) method, which enforces the features to be dispersedly spanned in the coordinate space while ensuring the classification vectors to lie on a hypersphere. An adaptive angular margin is further proposed to enhance the discrimination capability of face features. Extensive experiments are conducted on six face benchmarks, including those have large age gap and hard negative samples. Trained only on the small-scale CASIA Webface dataset with 460K face images from about 10K subjects, our CCL model demonstrates high effectiveness and generality, showing consistently competitive performance across all the six benchmark databases.
The Potential of Blockchain in Education and Health Care.

n our nursing program, we require a transcript for every course taken at any university or college, and it is always frustrating when we have to wait for copies to arrive before making our decisions. To be honest, if a candidate took Religion 101 at a community college and later transferred to the BSN program, I would be willing to pass on the community college transcript, but the admissions office is less flexible. And, although we used to be able to ask the student to have another copy sent if we did not have a transcript in the file, we now must wait for the student to have the college upload the transcript into an admissions system andwait for verification. I can assure you, most nurses, like other students today, take a lot of courses across many colleges without getting a degree. I sometimes have as many as 10 transcripts to review. When I saw an article titled “Blockchain: Letting Students Own Their Credentials” (Schaffnauser, 2017), I was therefore intrigued. I had already heard of blockchain as a tool to take the middleman out of the loop when doing financial transactions with Bitcoin. Now the thought of students owning their own credentials got me thinking about the movement toward new forms of credentialing from professional organizations (e.g., badges, certification documents). Hence, my decision to explore blockchain and its potential. Let’s start with some definitions. Simply put, blockchain is a distributed digital ledger. Technically speaking, it is “a peer-to-peer (P2P) distributed ledger technology for a new generation of transactional applications that establishes transparency and trust” (Linn & Koo, n.d.). Watter (2016) noted that “the blockchain is a distributed database that provides an unalterable, (semi-) public record of digital transactions. Each block aggregates a timestamped batch of transactions to be included in the ledger — or rather, in the blockchain. Each block is identified by a cryptographic signature. The blockchain contains an un-editable record of all the transactions made.” If we take this apart, here is what we have: a database that is distributed to computers associated with members of the network. Thus, rather than trying to access one central database, all members have copies of the database. Each time a transaction occurs, it is placed in a block that is given a time stamp and is “digitally signed using public key cryptography — which uses both a public and private key” (Watter, 2016). Locks are then connected so there is a historical record and they cannot be altered. According to Lin and Koo
Waterloo Exploration Database: New Challenges for Image Quality Assessment Models

The great content diversity of real-world digital images poses a grand challenge to image quality assessment (IQA) models, which are traditionally designed and validated on a handful of commonly used IQA databases with very limited content variation. To test the generalization capability and to facilitate the wide usage of IQA techniques in real-world applications, we establish a large-scale database named the Waterloo Exploration Database, which in its current state contains 4744 pristine natural images and 94 880 distorted images created from them. Instead of collecting the mean opinion score for each image via subjective testing, which is extremely difficult if not impossible, we present three alternative test criteria to evaluate the performance of IQA models, namely, the pristine/distorted image discriminability test, the listwise ranking consistency test, and the pairwise preference consistency test (P-test). We compare 20 well-known IQA models using the proposed criteria, which not only provide a stronger test in a more challenging testing environment for existing models, but also demonstrate the additional benefits of using the proposed database. For example, in the P-test, even for the best performing no-reference IQA model, more than 6 million failure cases against the model are “discovered” automatically out of over 1 billion test pairs. Furthermore, we discuss how the new database may be exploited using innovative approaches in the future, to reveal the weaknesses of existing IQA models, to provide insights on how to improve the models, and to shed light on how the next-generation IQA models may be developed. The database and codes are made publicly available at: https://ece.uwaterloo.ca/~k29ma/exploration/.
Math 180B Homework 1 Solutions

be the first time that the Markov chain reaches a state in A. Then for each i ∈ S \ A, we have E[T | X0 = i] = 1 + ∑ j∈S\A P (X1 = j | X0 = i)E[T | X0 = j].  Suppose i ∈ S \ A and j ∈ S. By the Markov property and time homogeneity, we have E[T | X0 = i,X1 = j] = ∞ ∑ n=0 P (T > n | X0 = i,X1 = j) = ∞ ∑ n=0 P (X0 / ∈ A, . . . , Xn / ∈ A | X0 = i,X1 = j) = 1 + ∞ ∑ n=1 P (X1 / ∈ A, . . . , Xn / ∈ A | X0 = i,X1 = j) = 1 + ∞ ∑ n=1 P (X1 / ∈ A, . . . , Xn / ∈ A | X1 = j) = 1 + ∞ ∑ n=1 P (X0 / ∈ A, . . . , Xn−1 / ∈ A | X0 = j) = 1 + ∞ ∑ n=1 P (T ≥ n | X1 = j) = 1 + E[T | X0 = j]. Also, note that if j ∈ A, then E[T | X0 = j] = 0. Therefore, by first-step analysis, if i / ∈ A, then E[T | X0 = i] = ∞ ∑ n=0 nP (T = n | X0 = i)
A double-ridged horn antenna design in canola oil for medical imaging

The design of a double-ridged horn antenna for medical imaging is presented in this paper. The antenna is immersed in canola oil to reduce its size and to achieve efficient coupling of the electromagnetic energy to the breast tissue. The antenna has wideband performance from 1.5 GHz to 5 GHz (3.5 GHz or 108% of impedance bandwidth), which is suitable for radar-based medical imaging. The double-ridged horn antenna has exponential tapered section for impedance matching. The detail design equations and antenna parameters are presented. The fabricated antenna was tested and compared with simulated results, and excellent agreement has been observed.
Antibiotic Use in Agriculture and Its Consequential Resistance in Environmental Sources: Potential Public Health Implications

Due to the increased demand of animal protein in developing countries, intensive farming is instigated, which results in antibiotic residues in animal-derived products, and eventually, antibiotic resistance. Antibiotic resistance is of great public health concern because the antibiotic-resistant bacteria associated with the animals may be pathogenic to humans, easily transmitted to humans via food chains, and widely disseminated in the environment via animal wastes. These may cause complicated, untreatable, and prolonged infections in humans, leading to higher healthcare cost and sometimes death. In the said countries, antibiotic resistance is so complex and difficult, due to irrational use of antibiotics both in the clinical and agriculture settings, low socioeconomic status, poor sanitation and hygienic status, as well as that zoonotic bacterial pathogens are not regularly cultured, and their resistance to commonly used antibiotics are scarcely investigated (poor surveillance systems). The challenges that follow are of local, national, regional, and international dimensions, as there are no geographic boundaries to impede the spread of antibiotic resistance. In addition, the information assembled in this study through a thorough review of published findings, emphasized the presence of antibiotics in animal-derived products and the phenomenon of multidrug resistance in environmental samples. This therefore calls for strengthening of regulations that direct antibiotic manufacture, distribution, dispensing, and prescription, hence fostering antibiotic stewardship. Joint collaboration across the world with international bodies is needed to assist the developing countries to implement good surveillance of antibiotic use and antibiotic resistance.
A solar concentrating photovoltaic / thermal collector

This thesis discusses aspects of a novel solar concentrating photovoltaic / thermal (PV/T) collector that has been designed to produce both electricity and hot water. The motivation for the development of the Combined Heat and Power Solar (CHAPS) collector is twofold: in the short term, to produce photovoltaic power and solar hot water at a cost which is competitive with other renewable energy technologies, and in the longer term, at a cost which is lower than possible with current technologies. To the author’ s knowledge, the CHAPS collector is the first PV/T system using a reflective linear concentrator with a concentration ratio in the range 20-40x. The work contained in this thesis is a thorough study of all facets of the CHAPS collector, through a combination of theoretical and experimental investigation. A theoretical discussion of the concept of ‘energy value’ is presented, with the aim of developing methodologies that could be used in optimisation studies to compare the value of electrical and thermal energy. Three approaches are discussed; thermodynamic methods, using second law concepts of energy usefulness; economic valuation of the hot water and electricity through levelised energy costs; and environmental valuation, based on the greenhouse gas emissions associated with the generation of hot water and electricity. It is proposed that the value of electrical energy and thermal energy is best compared using a simple ratio. Experimental measurement of the thermal and electrical efficiency of a CHAPS receiver was carried out for a range of operating temperatures and fluid flow rates. The effectiveness of internal fins incorporated to augment heat transfer was examined. The glass surface temperature was measured using an infrared camera, to assist in the calculation of thermal losses, and to help determine the extent of radiation absorbed in the cover materials. FEA analysis, using the software package Strand7, examines the conductive heat transfer within the receiver body to obtain a temperature profile under operating conditions. Electrical efficiency is not only affected by temperature, but by non-uniformities in the radiation flux profile. Highly non-uniform illumination across the cells was found to reduce the efficiency by about 10% relative. The radiation flux profile longitudinal to the receivers was measured by a custom-built flux scanning device. The results show significant fluctuations in the flux profile and, at worst, the minimum flux intensity is as much as 27% lower than the median. A single cell with low flux intensity limits the current and performance of all cells in series, causing a significant drop in overall output. Therefore, a detailed understanding of the causes of flux non-uniformities is essential for the design of a single-axis tracking PV trough concentrator. Simulation of the flux profile was carried out
Review of the applications of neural networks in chemical process control - simulation and online implementation

As a result of good modeling capabilities, neural networks have been used extensively for a number of chemical engineering applications such as sensor data analysis, fault detection and nonlinear process identi®cation. However, only in recent years, with the upsurge in the research on nonlinear control, has its use in process control been widespread. This paper intend to provide an extensive review of the various applications utilizing neural networks for chemical process control, both in simulation and online implementation. We have categorized the review under three major control schemes; predictive control, inverse-model-based control, and adaptive control methods, respectively. In each of these categories, we summarize the major applications as well as the objectives and results of the work. The review reveals the tremendous prospect of using neural networks in process control. It also shows the multilayered neural network as the most popular network for such process control applications and also shows the lack of actual successful online applications at the present time. q 1998
INTEGRATION OF PLM SOLUTIONS AND BIM SYSTEMS FOR THE AEC

Product Lifecycle Management (PLM) solutions have been serving as the basis for collaborative product definition, manufacturing, and service management in many industries. They capture and provide access to product and process information and preserve integrity of information throughout the lifecycle of a product. Efficient growth in the role of Building Information Modeling (BIM) can benefit vastly from unifying solutions to acquire, manage and make use of information and processes from various project and enterprise level systems, selectively adapting functionality from PLM systems. However, there are important differences between PLM’s target industries and the Architecture, Engineering, and Construction (AEC) industry characteristics that require modification and tailoring of some aspects of current PLM technology. In this study we examine the fundamental PLM functionalities that create synergy with the BIM-enabled AEC industry. We propose a conceptual model for the information flow and integration between BIM and PLM systems. Finally, we explore the differences between the AEC industry and traditional scope of service for PLM solutions.
Counterfactual Analysis in Macroeconometrics : An Empirical Investigation into the Effects of Quantitative Easing *

Counterfactual Analysis in Macroeconometrics: An Empirical Investigation into the Effects of Quantitative Easing This paper is concerned with ex ante and ex post counterfactual analyses in the case of macroeconometric applications where a single unit is observed before and after a given policy intervention. It distinguishes between cases where the policy change affects the model’s parameters and where it does not. It is argued that for ex post policy evaluation it is important that outcomes are conditioned on ex post realized variables that are invariant to the policy change but nevertheless influence the outcomes. The effects of the control variables that are determined endogenously with the policy outcomes can be solved out for the policy evaluation exercise. An ex post policy ineffectiveness test statistic is proposed. The analysis is applied to the evaluation of the effects of the quantitative easing (QE) in the UK after March 2009. It is estimated that a 100 basis points reduction in the spread due to QE has an impact effect on output growth of about one percentage point, but the policy impact is very quickly reversed with no statistically significant effects remaining within 9-12 months of the policy intervention.
An Electrically Small Planar Quasi-Isotropic Antenna

A singly fed, electrically small, planar antenna that generates a quasi-isotropic radiation pattern is investigated. The antenna consists of a folded dipole, a pair of capacitively loaded loops (CLLs), and a coplanar stripline (CPS), which are printed on the top and bottom surfaces of a single-layer printed circuit board. Through near-field coupling with the driven CPS, the folded dipole and CLLs are both effectively excited and behave like an electric dipole and a magnetic dipole, respectively. A quasi-isotropic radiation pattern can therefore be obtained by combining the two orthogonal dipoles with the same radiation intensities and quadrature phases. To verify the idea, a prototype operating at 2.4 GHz is designed, fabricated, and measured. It has been shown that this electrically small antenna (0.165 × 0.164 × 0.006 λ3, ka = 0.73) has a −10 dB impedance bandwidth of 0.99%, a total efficiency of ∼90%, and a nearly isotropic pattern with the difference between the maximum and minimum radiated power densities given by ∼3 dB over the entire spherical radiating surface.
PSD based primary user detection in Cognitive Radio systems operating in impulsive noise environment

Anjali Mishra1, Amit Mishra2 1 Master’s Degree Student, Electronics and Communication Engineering 2 Assistant Professor, Electronics and Communication Engineering 1,2 Vindhya Institute of Technology & Science, Jabalpur, Madhya Pradesh, India PIN – 482021 Email: 10309anjali@gmail.com , 2 amit12488@gmail.com ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract Cognitive Radio presents a new opportunity area to explore for better utilization of a scarce natural resource like spectrum which is under focus due to increased presence of new communication devices, density of users and development of new data intensive applications. Cognitive Radio utilizes dynamic utilization of spectrum and is positioned as a promising solution to spectrum underutilization problem. However, reliability of a CR system in a noisy environment remains a challenge area. Especially manmade impulsive noise makes spectrum sensing difficult. In this paper we have presented a simulation model to analyze the effect of impulsive noise in Cognitive Radio system. Primary user detection in presence of impulsive noise is investigated for different noise thresholds and other signal parameters of interest using the unconventional power spectral density based detection approach. Also, possible alternatives for accurate primary user detection which are of interest for future research in this area are discussed for practical implementation.
State of the art and challenges facing consensus protocols on blockchain

Nowadays, the blockchain is a favorite platform, for instance, for use with cryptocurrency, smart contracts, IoT, and so forth. Blockchains are distributed ledgers that enable parties who do not trust each other to maintain states. The parties agree on the existence, values, and histories of the states. The blockchain applies the consensus protocol to verify the block which is distributing the network node. Consensus has many practices such as, for example, Byzantine general problem, Proof of work, and Proof of stake. However, there have not been numerous papers that have undertaken analysis with regard to various aspects, and which have incorporated the adoption summary as appropriate to the application. In this paper, we have provided a technical verification review with regard to the consensus algorithm taken from previous research. The audience will receive a consensus protocol, and the algorithm typically analyzes the application to match which platform is appropriate by viewing the node identity, energy saving and the tolerated power of the adversary, the data model, language, execution, application, and examples. Finally, we finish by presenting several research directions with regard to the consensus protocol.