nils-herrmann/clean_scirepeval huggingface dataset:
An information integration theory of consciousness

Consciousness poses two main problems. The first is understanding the conditions that determine to what extent a system has conscious experience. For instance, why is our consciousness generated by certain parts of our brain, such as the thalamocortical system, and not by other parts, such as the cerebellum? And why are we conscious during wakefulness and much less so during dreamless sleep? The second problem is understanding the conditions that determine what kind of consciousness a system has. For example, why do specific parts of the brain contribute specific qualities to our conscious experience, such as vision and audition? This paper presents a theory about what consciousness is and how it can be measured. According to the theory, consciousness corresponds to the capacity of a system to integrate information. This claim is motivated by two key phenomenological properties of consciousness: differentiation – the availability of a very large number of conscious experiences; and integration – the unity of each such experience. The theory states that the quantity of consciousness available to a system can be measured as the Φ value of a complex of elements. Φ is the amount of causally effective information that can be integrated across the informational weakest link of a subset of elements. A complex is a subset of elements with Φ>0 that is not part of a subset of higher Φ. The theory also claims that the quality of consciousness is determined by the informational relationships among the elements of a complex, which are specified by the values of effective information among them. Finally, each particular conscious experience is specified by the value, at any given time, of the variables mediating informational interactions among the elements of a complex. The information integration theory accounts, in a principled manner, for several neurobiological observations concerning consciousness. As shown here, these include the association of consciousness with certain neural systems rather than with others; the fact that neural processes underlying consciousness can influence or be influenced by neural processes that remain unconscious; the reduction of consciousness during dreamless sleep and generalized seizures; and the time requirements on neural interactions that support consciousness. The theory entails that consciousness is a fundamental quantity, that it is graded, that it is present in infants and animals, and that it should be possible to build conscious artifacts.
FILA: Fine-grained indoor localization

Indoor positioning systems have received increasing attention for supporting location-based services in indoor environments. WiFi-based indoor localization has been attractive due to its open access and low cost properties. However, the distance estimation based on received signal strength indicator (RSSI) is easily affected by the temporal and spatial variance due to the multipath effect, which contributes to most of the estimation errors in current systems. How to eliminate such effect so as to enhance the indoor localization performance is a big challenge. In this work, we analyze this effect across the physical layer and account for the undesirable RSSI readings being reported. We explore the frequency diversity of the subcarriers in OFDM systems and propose a novel approach called FILA, which leverages the channel state information (CSI) to alleviate multipath effect at the receiver. We implement the FILA system on commercial 802.11 NICs, and then evaluate its performance in different typical indoor scenarios. The experimental results show that the accuracy and latency of distance calculation can be significantly enhanced by using CSI. Moreover, FILA can significantly improve the localization accuracy compared with the corresponding RSSI approach.
A Comparison of Vector-based Representations for Semantic Composition

In this paper we address the problem of modeling compositional meaning for phrases and sentences using distributional methods. We experiment with several possible combinations of representation and composition, exhibiting varying degrees of sophistication. Some are shallow while others operate over syntactic structure, rely on parameter learning, or require access to very large corpora. We find that shallow approaches are as good as more computationally intensive alternatives with regards to two particular tests: (1) phrase similarity and (2) paraphrase detection. The sizes of the involved training corpora and the generated vectors are not as important as the fit between the meaning representation and compositional method.
Deep Joint Rain Detection and Removal from a Single Image

In this paper, we address a rain removal problem from a single image, even in the presence of heavy rain and rain streak accumulation. Our core ideas lie in our new rain image model and new deep learning architecture. We add a binary map that provides rain streak locations to an existing model, which comprises a rain streak layer and a background layer. We create a model consisting of a component representing rain streak accumulation (where individual streaks cannot be seen, and thus visually similar to mist or fog), and another component representing various shapes and directions of overlapping rain streaks, which usually happen in heavy rain. Based on the model, we develop a multi-task deep learning architecture that learns the binary rain streak map, the appearance of rain streaks, and the clean background, which is our ultimate output. The additional binary map is critically beneficial, since its loss function can provide additional strong information to the network. To handle rain streak accumulation (again, a phenomenon visually similar to mist or fog) and various shapes and directions of overlapping rain streaks, we propose a recurrent rain detection and removal network that removes rain streaks and clears up the rain accumulation iteratively and progressively. In each recurrence of our method, a new contextualized dilated network is developed to exploit regional contextual information and to produce better representations for rain detection. The evaluation on real images, particularly on heavy rain, shows the effectiveness of our models and architecture.
Distancing from experienced self: how global-versus-local perception affects estimation of psychological distance.

In 4 studies, the authors examined the prediction derived from construal level theory (CLT) that higher level of perceptual construal would enhance estimated egocentric psychological distance. The authors primed participants with global perception, local perception, or both (the control condition). Relative to the control condition, global processing made participants estimate larger psychological distances in time (Study 1), space (Study 2), social distance (Study 3), and hypotheticality (Study 4). Local processing had the opposite effect. Consistent with CLT, all studies show that the effect of global-versus-local processing did emerge when participants estimated egocentric distances, which are distances from the experienced self in the here and now, but did not emerge with temporal distances not from now (Study 1), spatial distances not from here (Study 2), social distances not from the self (Study 3), or hypothetical events that did not involve altering an experienced reality (Study 4).
Dependency Parsing and Domain Adaptation with LR Models and Parser Ensembles

We present a data-driven variant of the LR algorithm for dependency parsing, and extend it with a best-first search for probabilistic generalized LR dependency parsing. Parser actions are determined by a classifier, based on features that represent the current state of the parser. We apply this parsing framework to both tracks of the CoNLL 2007 shared task, in each case taking advantage of multiple models trained with different learners. In the multilingual track, we train three LR models for each of the ten languages, and combine the analyses obtained with each individual model with a maximum spanning tree voting scheme. In the domain adaptation track, we use two models to parse unlabeled data in the target domain to supplement the labeled out-ofdomain training set, in a scheme similar to one iteration of co-training.
Machine Learning under Attack: Vulnerability Exploitation and Security Measures

Learning to discriminate between secure and hostile patterns is a crucial problem for species to survive in nature. Mimetism and camouflage are well-known examples of evolving weapons and defenses in the arms race between predators and preys. It is thus clear that all of the information acquired by our senses should not be considered necessarily secure or reliable. In machine learning and pattern recognition systems, however, we have started investigating these issues only recently. This phenomenon has been especially observed in the context of adversarial settings like malware detection and spam filtering, in which data can be purposely manipulated by humans to undermine the outcome of an automatic analysis. As current pattern recognition methods are not natively designed to deal with the intrinsic, adversarial nature of these problems, they exhibit specific vulnerabilities that an attacker may exploit either to mislead learning or to evade detection. Identifying these vulnerabilities and analyzing the impact of the corresponding attacks on learning algorithms has thus been one of the main open issues in the novel research field of adversarial machine learning, along with the design of more secure learning algorithms.
 In the first part of this talk, I introduce a general framework that encompasses and unifies previous work in the field, allowing one to systematically evaluate classifier security against different, potential attacks. As an example of application of this framework, in the second part of the talk, I discuss evasion attacks, where malicious samples are manipulated at test time to evade detection. I then show how carefully-designed poisoning attacks can mislead some learning algorithms by manipulating only a small fraction of their training data. In addition, I discuss some defense mechanisms against both attacks in the context of real-world applications, including biometric identity recognition and computer security. Finally, I briefly discuss our ongoing work on attacks against clustering algorithms, and sketch some promising future research directions.
A Comparison of String Metrics for Matching Names and Records

We describe an open-source Java toolkit of methods for matching names and records. We summarize results obtained from using various string distance metrics on the task of matching entity names: these metrics include distance functions proposed by several different communities, including edit-distance metrics, fast heuristic string comparators, token-based distance metrics, and hybrid methods. We then describe an extension to the toolkit which allows records to be compared. We discuss some issues involved in performing a similar comparison for record-matching techniques, and finally present results for some baseline record-matching algorithms which are based on string comparisons between fields.
A framework for the quantitative evaluation of disentangled representations

Recent AI research has emphasised the importance of learning disentangled representations of the explanatory factors behind data. Despite the growing interest in models which can learn such representations, visual inspection remains the standard evaluation metric. While various desiderata have been implied in recent definitions, it is currently unclear what exactly makes one disentangled representation better than another. In this work we propose a framework for the quantitative evaluation of disentangled representations when the ground-truth latent structure is available. Three criteria are explicitly defined and quantified to elucidate the quality of learnt representations and thus compare models on an equal basis. To illustrate the appropriateness of the framework, we employ it to compare quantitatively the representations learned by recent state-of-the-art models.
Obstacle Avoidance System for a Quadrotor UAV

This is the summary of the research being done at Cal Poly Pomona at an undergraduate level on the development of obstacle avoidance capability for unmanned aerial vehicles (UAVs). A quadrotor UAV is used as the research platform. Push and Rapidly Exploring Random Tree (RRT) algorithms are used for the obstacle avoidance capability. The paper shows the use of cheaper commercial off-the-shelf sensors and processor board in the design and implementation of obstacle avoidance algorithm.
A Common Backend for Hardware Acceleration on FPGA

Field Programmable Gate Arrays (FPGAs) are configurable integrated circuits able to provide a good trade-off in terms of performance, power consumption, and flexibility with respect to other architectures, like CPUs, GPUs and ASICs. The main drawback in using FPGAs, however, is their steep learning curve. An emerging solution to this problem is to write algorithms in a Domain Specific Language (DSL) and to let the DSL compiler generate efficient code targeting FPGAs. This work proposes FROST, a unified backend that enables different DSL compilers to target FPGA architectures. Differently from other code generation frameworks targeting FPGA, FROST exploits a scheduling co-language that enables users to have full control over which optimizations to apply in order to generate efficient code (e.g. loop pipelining, array partitioning, vectorization). At first, FROST analyzes and manipulates the input Abstract Syntax Tree (AST) in order to apply FPGA-oriented transformations and optimizations, then generates a C/C++ implementation suitable for High-Level Synthesis (HLS) tools. Finally, the output of HLS phase is synthesized and implemented on the target FPGA using Xilinx SDAccel toolchain. The experimental results show a speedup up of 15&#xd7; with respect to O3-optimized implementations of the same algorithms on CPU.
Show Me the Money! Finding Flawed Implementations of Third-party In-app Payment in Android Apps

The massive growth of transaction via third-party cashier has attracted numerous mobile apps to embed in-app payment functionality. Although this feature makes the payment easy within apps, transactions via current third-party in-app payment involve more sophisticated interactions between multiple participants compared to those using traditional payments. The implementations in mobile apps also lack security considerations. Therefore, such transaction exposes new attack vectors and could be exploited more easily, leading to serious deceptions such as payment forging. To investigate current third-party mobile payment ecosystem and find potential security threats, we conduct an in-depth analysis on world’s largest mobile payment market–China’s mobile payment market. We study four mainstream third-party mobile payment cashiers, and conclude unified security rules that must be regulated by both cashier and merchant. We also illustrate the serious consequences of violating these security rules, which may cause up to four types of attacks against online and offline transactions. Besides, we detect the seven security rule violations to the payment in Android apps. Our detection result shows not only the prevalence of third-party in-app payment, but also the awful status quo of its security. Over 37% Android apps with at least 100,000 users embed third-party payment functionality. Hundreds of them violate security rule(s) and face with various potential security risks, allowing an attacker to consume almost every aspect of commodities or services in life without actually purchasing them or deceiving others to pay for them. Our further investigation reveals that the cashiers not only have improperly designed SDK, which may expand the attack effects, but also release ambiguous documents and even vulnerable sample codes, directly leading to the mistakes committed by merchants. Besides the cashiers’ ignorance for security, our successful exploits to several apps show that these flawed implementations can cause financial loss in real world. We have reported these findings to all the related parties and received positive feedbacks.
An Integrated Framework for Service Quality, Customer Value, Satisfaction: Evidence from China's Telecommunication Industry

Service quality, customer satisfaction and customer value have become the priority of both manufacturers and service provider in the increasingly intensified competition for customers in today’s customer-centered era. However, findings regarding service quality, customer satisfaction and customer value are rather divergent and related studies are fragmented, especially for the complicated interrelationships among them. Thus, less is known about the relative impacts of quality-related factors on customer value and customer satisfaction up to now and the moderating role of customer value in the relationship between service quality and customer satisfaction has been neglected. Further, it is very difficult to find related studies, supported by evidence, that focus on service quality, customer satisfaction and customer value, and their influences on customer behavior intentions in the telecommunication industry. In this paper, much attention is paid to the measurement model of service quality in China’s mobile communication market based on the well-known SERVQUAL model, but with reasonable modification on the basis of focus group discussions and expert opinions to reflect the specific industry attributes and the special culture of China. By taking a disaggregated approach, the key drivers of service quality, customer value and customer satisfaction are first identified and the impact of customer perceived sacrifice on customer value is emphasized. Then attention is given to the systematic study of the dynamic relationships among them, especially the moderating effect of customer value on the relationship between service quality and customer satisfaction, which is followed by the examination of their influences on behavior intentions of customers. Results are based on the development of structural equation models using Partial Least Square technique.
Secure communications with D2D cooperation

Device to Device (D2D) communications provides a promising technique for 5G wireless networks, supporting higher data rates. Security of data transmission over wireless clouds could put constraints on devices; whether to cooperate or not. Therefore, our aim is to provide analytical framework for the security at the physical layer and to define the constraints embodied with cooperation in wireless clouds1. In this paper, two legitimate transmitters Alice and John cooperate to increase the reliable transmission rate received by their common legitimate receiver Bob, where one eavesdropper, Eve exists. We propose a distributed algorithm that allows the devices to select whether to cooperate or not and to adapt their optimal power allocation based on the cooperation framework selected. Moreover, we define distance constraints to enforce the benefits of cooperation between devices in a wireless cloud.
Dynamic Airline Pricing and Seat Availability

Airfares are determined by both intertemporal price discrimination and dynamic adjustment to stochastic demand given limited capacity. In this paper I estimate a model of dynamic airline pricing taking both forces into account. I use an original data set of daily fares and seat availabilities at the flight level. With model estimates, I disentangle key interactions between the arrival pattern of consumer types and remaining capacity under stochastic demand. I find dynamic adjustment to stochastic demand is particularly important as a means to secure seats for high-valuing consumers who arrive close to the departure date. It leads to substantial revenue gains compared to pricing policies which depend on date of purchase but not remaining capacity. In aggregate consumers benefit, despite facing higher fares on average, as a result of more efficient capacity allocation. Finally, I show that failing to account for stochastic demand leads to a systematic bias in estimating demand elasticities.
The influence of social hierarchy on primate health.

Dominance hierarchies occur in numerous social species, and rank within them can greatly influence the quality of life of an animal. In this review, I consider how rank can also influence physiology and health. I first consider whether it is high- or low-ranking animals that are most stressed in a dominance hierarchy; this turns out to vary as a function of the social organization in different species and populations. I then review how the stressful characteristics of social rank have adverse adrenocortical, cardiovascular, reproductive, immunological, and neurobiological consequences. Finally, I consider how these findings apply to the human realm of health, disease, and socioeconomic status.
Towards a Business Process Management Maturity Model

Business Process Management (BPM) has been identified as the number one business priority by a recent Gartner study (Gartner, 2005). However, BPM has a plethora of facets as its origins are in Business Process Reengineering, Process Innovation, Process Modelling, and Workflow Management to name a few. Organisations increasingly recognize the requirement for an increased process orientation and require appropriate comprehensive frameworks, which help to scope and evaluate their BPM initiative. This research project aims toward the development of a holistic and widely accepted BPM maturity model, which facilitates the assessment of BPM capabilities. This paper provides an overview about the current model with a focus on the actual model development utilizing a series of Delphi studies. The development process includes separate studies that focus on further defining and expanding the six core factors within the model, i.e. strategic alignment, governance, method, Information Technology, people and culture.
The Smart Home Concept : our immediate future

This general paper aims at presenting the Smart Home concept. In this paper, we will detail a) the Smart Home concept b) the various networks infrastructures specific to the habitat c) our concepts to model the habitat and to provide the most adapted services to the inhabitants. Contrary to the other projects, we direct our work towards a sensors approach and an ontology modelling of the Smart Home. Our work has the originality to take into account the real heterogeneity of information present in a habitat and use a service oriented approach (SOA). We can say that our paper is a good overview to present what is a Smart Home and which are the necessary hardware and software components to make a Smart Home
CompRec-Trip: A composite recommendation system for travel planning

Classical recommender systems provide users with a list of recommendations where each recommendation consists of a single item, e.g., a book or a DVD. However, applications such as travel planning can benefit from a system capable of recommending packages of items, under a user-specified budget and in the form of sets or sequences. In this context, there is a need for a system that can recommend top-k packages for the user to choose from. In this paper, we propose a novel system, Trip, which can automatically generate composite recommendations for travel planning. The system leverages rating information from underlying recommender systems, allows flexible package configuration and incorporates users' cost budgets on both time and money. Furthermore, the proposed CompRec-Trip system has a rich graphical user interface which allows users to customize the returned composite recommendations and take into account external local information.