nils-herrmann/clean_scirepeval huggingface dataset:
News impact on stock price return via sentiment analysis

Financial news articles are believed to have impacts on stock price return. Previous works model news pieces in bag-of-words space, which analyzes the latent relationship between word statistical patterns and stock price movements. However, news sentiment, which is an important ring on the chain of mapping from the word patterns to the price movements, is rarely touched. In this paper, we first implement a generic stock price prediction framework, and plug in six different models with different analyzing approaches. To take one step further, we use Harvard psychological dictionary and Loughran–McDonald financial sentiment dictionary to construct a sentiment space. Textual news articles are then quantitatively measured and projected onto the sentiment space. Instance labeling method is rigorously discussed and tested. We evaluate the models’ prediction accuracy and empirically compare their performance at different market classification levels. Experiments are conducted on five years historical Hong Kong Stock Exchange prices and news articles. Results show that (1) at individual stock, sector and index levels, the models with sentiment analysis outperform the bag-of-words model in both validation set and independent testing set; (2) the models which use sentiment polarity cannot provide useful predictions; (3) there is a minor difference between the models using two different sentiment dictionaries.
Combination of Evidence in Dempster-Shafer Theory

Dempster-Shafer theory offers an alternative to traditional probabilistic theory for the mathematical representation of uncertainty. The significant innovation of this framework is that it allows for the allocation of a probability mass to sets or intervals. DempsterShafer theory does not require an assumption regarding the probability of the individual constituents of the set or interval. This is a potentially valuable tool for the evaluation of risk and reliability in engineering applications when it is not possible to obtain a precise measurement from experiments, or when knowledge is obtained from expert elicitation. An important aspect of this theory is the combination of evidence obtained from multiple sources and the modeling of conflict between them. This report surveys a number of possible combination rules for Dempster-Shafer structures and provides examples of the implementation of these rules for discrete and interval-valued data.
The Wheels on the Bot Go Round and Round: Robotics Curriculum in Pre-Kindergarten.

This paper qualitatively examines the implementation of an intensive weeklong robotics curriculum in three Pre-Kindergarten classrooms (N=37) at an early childhood STEM (science, technology, engineering, and math) focused magnet school in the Harlem area of New York City. Children at the school spent one week participating in computer programming activities using a developmentally appropriate tangible programming language called CHERP, which is specifically designed to program a robot’s behaviors. The children used CHERP to program “Robot Recyclers” that they constructed using parts from LEGO Education WeDoTM Robotics Construction Sets. The Robot Recyclers were designed to help carry, push, and/or sort recyclable materials found in the classroom. Researchers were participant-observers in the robotics lessons over the course of curriculum implementation. Each lesson was taught by the researchers, with classroom teachers present in order to facilitate classroom management and assist with small group work. A combination of interviews, video, photographs, and classroom observations were used to document the students’ experiences. Classroom teachers were also interviewed and asked to complete anonymous pre and post surveys. Results from this study provide preliminary evidence that PreKindergarten children can design, build, and program a robot after just one week of concentrated robotics work. Additionally, results indicate that teachers were able to successfully integrate robotics work into their classrooms that included foundational math and literacy concepts while also engaging children in the arts. However, this study also highlights the difficulties and challenges that must be considered before implementing a robotics curriculum into a PreKindergarten classroom, including opportunities for one-to-one adult assistance during building and programming activities.
Hierarchical Bayesian Networks: A Probabilistic Reasoning Model for Structured Domains

Bayesian Networks are being used extensively for reasoning under uncertainty. Inference mechanisms for Bayesian Networks are compromised by the fact that they can only deal with propositional domains. In this work, we introduce an extension of that formalism, Hierarchical Bayesian Networks, that can represent additional information about the structure of the domains of variables. Hierarchical Bayesian Networks are similar to Bayesian Networks, in that they represent probabilistic dependencies between variables as a directed acyclic graph, where each node of the graph corresponds to a random variable and is quanti ed by the conditional probability of that variable given the values of its parents in the graph. What extends the expressive power of Hierarchical Bayesian Networks is that a node may correspond to an aggregation of simpler types. A component of one node may itself represent a composite structure; this allows the representation of complex hierarchical domains. Furthermore, probabilistic dependencies can be expressed at any level, between nodes that are contained in the same structure.
A hierarchical clustering-based routing protocol for wireless sensor networks supporting multiple data aggregation qualities

Wireless Sensor Networks (WSNs) consist of a large number of energy-limited sensor nodes that are densely deployed in a large geographical region. For WSNs, energy efficiency is always a key design issue to improve the life span of the network. In this paper, we propose a routing protocol, called the Clustering-Based Expanding–Ring Routing Protocol (CBERRP), which mainly focuses on the network layer while integrating factors from other layers to gain the preferred performance. CBERRP is a centralised scheme which is controlled by the Base Station (BS) and utilises the two-level hierarchical structure of clusters and chains to route the sensed data to the BS. In addition, CBERRP can be fine-tuned to support multiple Data Aggregation Qualities. The performance of CBERRP is compared to Low-Energy Adaptive Clustering Hierarchy (LEACH) and LEACH-Centralised (LEACH-C). Simulation results show that CBERRP presents significant improvement on power consumption, throughput and the network lifetime.
Data MULEs: modeling and analysis of a three-tier architecture for sparse sensor networks

This paper presents and analyzes a three-tier architecture for collecting sensor data in sparse sensor networks. Our approach exploits the presence of mobile entities (called MULEs) present in the environment. When in close range, MULEs pick up data from the sensors, buffer it, and deliver it to wired access points. This can lead to substantial power savings at the sensors as they only have to transmit over a short-range. This paper focuses on a simple analytical model for understanding performance as system parameters are scaled. Our model assumes a two-dimensional random walk for mobility and incorporates key system variables such as number of MULEs, sensors and access points. The performance metrics observed are the data success rate (the fraction of generated data that reaches the access points), latency and the required buffer capacities on the sensors and the MULEs. The modeling and simulation results can be used for further analysis and provide certain guidelines for deployment of such systems.
TCP Offload Is a Dumb Idea Whose Time Has Come

Network interface implementors have repeatedl y attempted to offload TCP processingfrom the host CPU. Theseefforts met with li ttle success,becausetheywere basedon faulty premises.TCPoffloadper se is neither of much overall benefitnor free from significant costs and risks. But TCPoffloadin theserviceof very specific goalsmight actually beuseful. In thecontext of the replacement of storage-spec ific interconnect via commoditized network hardware, TCP offload (and more generally, offloading the transport protocol) appropriately solvesanimportant problem.
Defining Mobile Learning in the Higher Education Landscape

The article seeks to clarify the meaning of mobile learning by applying its key concepts to learning experiences in post-school education. In other words, it seeks not to discuss one fixed meaning of mobile learning but to disassemble the basic components and provide an interpretation of the model in the context of higher education. The article argues that in order to comprehensively understand and define mobile learning, we should from the outset separate its key components and arrange them under three different concepts. The first concept relates to the mobility of the technology. The second concept hinges on increased learner mobility. The third concept examines the mobility and dynamism of the learning processes and the flow of information. The article concludes that knowledge in the modern world is transformed by the development of revolutionary technologies in society.
A Two-Way Time of Flight Ranging Scheme for Wireless Sensor Networks

Relative ranging between Wireless Sensor Network (WSN) nod es is considered to be an important requirement for a number of dis tributed applications. This paper focuses on a two-way, time of flight (ToF) te chnique which achieves good accuracy in estimating the point-to-point di s ance between two wireless nodes. The underlying idea is to utilize a two-way t ime transfer approach in order to avoid the need for clock synchronization b etween the participating wireless nodes. Moreover, by employing multipl e ToF measurements, sub-clock resolution is achieved. A calibration stage is us ed to estimate the various delays that occur during a message exchange and require subtraction from the initial timed value. The calculation of the range betwee n the nodes takes place on-node making the proposed scheme suitable for distribute d systems. Care has been taken to exclude the erroneous readings from the set of m easurements that are used in the estimation of the desired range. The two-way T oF technique has been implemented on commercial off-the-self (COTS) device s without the need for additional hardware. The system has been deployed in var ous experimental locations both indoors and outdoors and the obtained result s reveal that accuracy between 1m RMS and 2.5m RMS in line-of-sight conditions over a 42m range can be achieved.
An Experimental Investigation of the Tradeoff between Switching Losses and EMI Generation With Hard-Switched All-Si, Si-SiC, and All-SiC Device Combinations

Silicon carbide (SiC) switching power devices (MOSFETs, JFETs) of 1200 V rating are now commercially available, and in conjunction with SiC diodes, they offer substantially reduced switching losses relative to silicon (Si) insulated gate bipolar transistors (IGBTs) paired with fast-recovery diodes. Low-voltage industrial variable-speed drives are a key application for 1200 V devices, and there is great interest in the replacement of the Si IGBTs and diodes that presently dominate in this application with SiC-based devices. However, much of the performance benefit of SiC-based devices is due to their increased switching speeds ( di/dt, dv/ dt), which raises the issues of increased electromagnetic interference (EMI) generation and detrimental effects on the reliability of inverter-fed electrical machines. In this paper, the tradeoff between switching losses and the high-frequency spectral amplitude of the device switching waveforms is quantified experimentally for all-Si, Si-SiC, and all-SiC device combinations. While exploiting the full switching-speed capability of SiC-based devices results in significantly increased EMI generation, the all-SiC combination provides a 70% reduction in switching losses relative to all-Si when operated at comparable dv/dt. It is also shown that the loss-EMI tradeoff obtained with the Si-SiC device combination can be significantly improved by driving the IGBT with a modified gate voltage profile.
A secure environment for untrusted helper applications confining the Wily Hacker

Many popular programs, such as Netscape, use untrusted helper applications to process data from the network. Unfortunately, the unauthenticated network data they interpret could well have been created by an adversary, and the helper applications are usually too complex to be bug-free. This raises signi cant security concerns. Therefore, it is desirable to create a secure environment to contain untrusted helper applications. We propose to reduce the risk of a security breach by restricting the program's access to the operating system. In particular, we intercept and lter dangerous system calls via the Solaris process tracing facility. This enabled us to build a simple, clean, user-mode implementation of a secure environment for untrusted helper applications. Our implementation has negligible performance impact, and can protect pre-existing applications.
Time Based Measurement of the Impedance of the Skin-Electrode Interface for Dry Electrode ECG Recording

This paper reports the measurement of the properties of dry or pasteless conductive electrodes to be used for long-term recording of the human electrocardiogram (ECG). Knowledge of these properties is essential for the correct design of the input stage of associated recording amplifiers. Measurements were made on three commercially available conductive carbon based electrodes at pressures of 5mmHg and 20mmHg, located on the lower abdomen of the body on three subjects having different skin types. Parameter values were fitted to a two-time-constant based model of the electrode using data measured over a period of 10s. Values of resistance, ranging from 40kOmega to 1590kOmega and of capacitance ranging from 0.05muF to 38muF were obtained for the components, while the values of the time-constants varied from 0.07s to 3.9s.
A Taxonomy of Cyber Events Affecting Communities

Communities, whose reliance on critical cyber infrastructures is growing, are threatened by a wide range of cyber events that can adversely affect these systems and networks. The development of computer security taxonomies to classify computer and network vulnerabilities and attacks has led to a greater insight into the causes, effects, mitigation, and remediation of cyber attacks. In developing these taxonomies researchers are better able to understand and address the many different attacks that can occur. No current taxonomy, however, has been developed that takes into account the community aspects of cyber attacks or other cyber events affecting communities. We present a new taxonomy that considers the motivation, methodology, and effects of cyber events that can affect communities. We include a discussion on how our taxonomy is useful to e-government, industry, and security researchers.
Procedural Landscapes with Overhangs

Overhangs have been a major stumbling block in the context of terrain synthesis models. These models resort invariably to a heightfield paradigm, which immediately precludes the existence of any type of overhang. This article presents a new technique for the generation of surfaces, with the ability to model overhangs in a procedural way. This technique can be used generally to model landscape elements, not only terrains but also the surface of the sea. The technique applies non-linear deformations to an initial heightfield surface. The deformations occur after the surface has been displaced along some specified vector field. The method is conceptually simple and enhances greatly the class of landscapes synthesized with procedural models.
Cross-domain Text Classification with Multiple Domains and Disparate Label Sets

Advances in transfer learning have let go the limitations of traditional supervised machine learning algorithms for being dependent on annotated training data for training new models for every new domain. However, several applications encounter scenarios where models need to transfer/adapt across domains when the label sets vary both in terms of count of labels as well as their connotations. This paper presents first-of-its-kind transfer learning algorithm for cross-domain classification with multiple source domains and disparate label sets. It starts with identifying transferable knowledge from across multiple domains that can be useful for learning the target domain task. This knowledge in the form of selective labeled instances from different domains is congregated to form an auxiliary training set which is used for learning the target domain task. Experimental results validate the efficacy of the proposed algorithm against strong baselines on a real world social media and the 20 Newsgroups datasets.
An Overview of Research Activities in Facial Age Estimation Using the FG-NET Aging Database

The FG-NET aging database was released in 2004 in an attempt to support research activities related to facial aging. Since then a number of researchers used the database for carrying out research in various disciplines related to facial aging. Based on the analysis of published work where the FG-NET aging database was used, conclusions related to the type of research carried out in relation to the impact of the dataset in shaping up the research topic of facial aging, are presented. In particular we focus our attention on the topic of age estimation that proved to be the most popular among users of the FG-NET aging database. Through the review of key papers in age estimation and the presentation of benchmark results the main approaches/directions in facial aging are outlined and future trends, requirements and research directions are drafted.
Speech Enhancement with LSTM Recurrent Neural Networks and its Application to Noise-Robust ASR

We evaluate some recent developments in recurrent neural network (RNN) based speech enhancement in the light of noise-robust automatic speech recognition (ASR). The proposed framework is based on Long Short-Term Memory (LSTM) RNNs which are discriminatively trained according to an optimal speech reconstruction objective. We demonstrate that LSTM speech enhancement, even when used ‘näıvely’ as front-end processing, delivers competitive results on the CHiME-2 speech recognition task. Furthermore, simple, feature-level fusion based extensions to the framework are proposed to improve the integration with the ASR back-end. These yield a best result of 13.76 % average word error rate, which is, to our knowledge, the best score to date.
A Multi-dimensional Comparison of Toolkits for Machine Learning with Big Data

Big data is a big business, and effective modeling of this data is key. This paper provides a comprehensive multidimensional analysis of various open source tools for machine learning with big data. An evaluation standard is proposed along with detailed comparisons of the frameworks discussed, with regard to algorithm availability, scalability, speed, and more. The major tools profiled are Mahout, MLlib, H2O, and SAMOA, along with the big data processing engines they utilize, including Hadoop MapReduce, Apache Spark, and Apache Storm. There is not yet one framework that "does it all", but this paper provides insight into each tool's strengths and weaknesses along with guidance on tool choice for specific needs.
Continuum regression for cross-modal multimedia retrieval

Understanding the relationship among different modalities is a challenging task. The frequently used canonical correlation analysis (CCA) and its variants have proved effective for building a common space in which the correlation between different modalities is maximized. In this paper, we show that CCA and its variants may cause information dissipation when switching the modals, and thus propose to use the continuum regression (CR) model to handle this problem. In particular, the CR model with a fixed variance coefficient of 1/2 is adopted here. We also apply the multinomial logistic regression model for further classification task. To evaluate the CR model, we perform a series of cross-modal retrieval experiments in terms of two kinds of modals, namely image and text. Compared with previous methods, experimental results show that the CR model has achieved the best retrieval precision, which demonstrates the potential of our method for real internet search applications.
Something in the way we move: Motion dynamics, not perceived sex, influence head movements in conversation.

During conversation, women tend to nod their heads more frequently and more vigorously than men. An individual speaking with a woman tends to nod his or her head more than when speaking with a man. Is this due to social expectation or due to coupled motion dynamics between the speakers? We present a novel methodology that allows us to randomly assign apparent identity during free conversation in a video-conference, thereby dissociating apparent sex from motion dynamics. The method uses motion-tracked synthesized avatars that are accepted by naive participants as being live video. We find that 1) motion dynamics affect head movements but that apparent sex does not; 2) judgments of sex are driven almost entirely by appearance; and 3) ratings of masculinity and femininity rely on a combination of both appearance and dynamics. Together, these findings are consistent with the hypothesis of separate perceptual streams for appearance and biological motion. In addition, our results are consistent with a view that head movements in conversation form a low level perception and action system that can operate independently from top-down social expectations.
Dynamic pricing for smart grid with reinforcement learning

In the smart grid system, dynamic pricing can be an efficient tool for the service provider which enables efficient and automated management of the grid. However, in practice, the lack of information about the customers' time-varying load demand and energy consumption patterns and the volatility of electricity price in the wholesale market make the implementation of dynamic pricing highly challenging. In this paper, we study a dynamic pricing problem in the smart grid system where the service provider decides the electricity price in the retail market. In order to overcome the challenges in implementing dynamic pricing, we develop a reinforcement learning algorithm. To resolve the drawbacks of the conventional reinforcement learning algorithm such as high computational complexity and low convergence speed, we propose an approximate state definition and adopt virtual experience. Numerical results show that the proposed reinforcement learning algorithm can effectively work without a priori information of the system dynamics.
Enterprise Integration Patterns: Designing, Building, and Deploying Messaging Solutions

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