nils-herrmann/clean_scirepeval huggingface dataset:
A Learning Market-Maker in the Glosten-Milgrom Model

This paper develops a model of a learning market-maker by extending the GlostenMilgrom model of dealer markets. The market-maker tracks the changing true value of a stock in settings with informed traders (with noisy signals) and liquidity traders, and sets bid and ask prices based on its estimate of the true value. We empirically evaluate the performance of the market-maker in markets with different parameter values to demonstrate the effectiveness of the algorithm, and then use the algorithm to derive properties of price processes in simulated markets. When the true value is governed by a jump process, there is a two regime behavior marked by significant heterogeneity of information and large spreads immediately following a price jump, which is quickly resolved by the market-maker, leading to a rapid return to homogeneity of information and small spreads. We also discuss the similarities and differences between our model and real stock market data in terms of distributional and time series properties of returns.  Finance
Ideological Segregation and the Effects of Social Media on News Consumption

Scholars have argued that online social networks and personalized web search increase ideological segregation. We investigate the impact of these potentially polarizing channels on news consumption by examining web browsing histories for 50,000 U.S.-located users who regularly read online news. We find that individuals indeed exhibit substantially higher segregation when reading articles shared on social networks or returned by search engines, a pattern driven by opinion pieces. However, these polarizing articles from social media and web search constitute only 2% of news consumption. Consequently, while recent technological changes do increase ideological segregation, the magnitude of the effect is limited.
Performance evaluation of cryptographic algorithms: DES and AES

With the fast progression of digital data exchange in electronic way, information security is becoming much more important in data storage and transmission. Cryptography has come up as a solution which plays a vital role in information security system against malicious attacks. This security mechanism uses some algorithms to scramble data into unreadable text which can be only being decoded or decrypted by party those possesses the associated key. These algorithms consume a significant amount of computing resources such as CPU time, memory and computation time. In this paper two most widely used symmetric encryption techniques i.e. data encryption standard (DES) and advanced encryption standard (AES) have been implemented using MATLAB software. After the implementation, these techniques are compared on some points. These points are avalanche effect due to one bit variation in plaintext keeping the key constant, avalanche effect due to one bit variation in key keeping the plaintext constant, memory required for implementation and simulation time required for encryption.
The Cricket indoor location system

Indoor environments present opportunities for a rich set of location-aware applications such as navigation tools for humans and robots, interactive virtual games, resource discovery, asset tracking, location-aware sensor networking etc. Typical indoor applications require better accuracy than what current outdoor location systems provide. Outdoor location technologies such as GPS have poor indoor performance because of the harsh nature of indoor environments. Further, typical indoor applications require different types of location information such as physical space, position and orientation. This dissertation describes the design and implementation of the Cricket indoor location system that provides accurate location in the form of user space, position and orientation to mobile and sensor network applications. Cricket consists of location beacons that are attached to the ceiling of a building, and receivers, called listeners, attached to devices that need location. Each beacon periodically transmits its location information in an RF message. At the same time, the beacon also transmits an ultrasonic pulse. The listeners listen to beacon transmissions and measure distances to nearby beacons, and use these distances to compute their own locations. This active-beacon passive-listener architecture is scalable with respect to the number of users, and enables applications that preserve user privacy. This dissertation describes how Cricket achieves accurate distance measurements between beacons and listeners. Once the beacons are deployed, the MAT and AFL algorithms, described in this dissertation, use measurements taken at a mobile listener to configure the beacons with a coordinate assignment that reflects the beacon layout. This dissertation presents beacon interference avoidance and detection algorithms, as well as outlier rejection algorithms to prevent and filter out outlier distance estimates caused by uncoordinated beacon transmissions. The Cricket listeners can measure distances with an accuracy of 5 cm. The listeners can detect boundaries with an accuracy of 1 cm. Cricket has a position estimation accuracy of 10 cm and an orientation accuracy of 3 degrees.  Hari Balakrishnan Title: Associate Professor of Computer Science and Engineering
Graph neural networks for ranking Web pages

An artificial neural network model, capable of processing general types of graph structured data, has recently been proposed. This paper applies the new model to the computation of customised page ranks problem in the World Wide Web. The class of customised page ranks that can be implemented in this way is very general and easy because the neural network model is learned by examples. Some preliminary experimental findings show that the model generalizes well over unseen web pages, and hence, may be suitable for the task of page rank computation on a large web graph.
A Classifier-Based Parser with Linear Run-Time Complexity

We present a classifier-based parser that produces constituent trees in linear time. The parser uses a basic bottom-up shiftreduce algorithm, but employs a classifier to determine parser actions instead of a grammar. This can be seen as an extension of the deterministic dependency parser of Nivre and Scholz (2004) to full constituent parsing. We show that, with an appropriate feature set used in classification, a very simple one-path greedy parser can perform at the same level of accuracy as more complex parsers. We evaluate our parser on section 23 of the WSJ section of the Penn Treebank, and obtain precision and recall of 87.54% and 87.61%, respectively.
A fully automated greedy square jigsaw puzzle solver

In the square jigsaw puzzle problem one is required to reconstruct the complete image from a set of non-overlapping, unordered, square puzzle parts. Here we propose a fully automatic solver for this problem, where unlike some previous work, it assumes no clues regarding parts' location and requires no prior knowledge about the original image or its simplified (e.g., lower resolution) versions. To do so, we introduce a greedy solver which combines both informed piece placement and rearrangement of puzzle segments to find the final solution. Among our other contributions are new compatibility metrics which better predict the chances of two given parts to be neighbors, and a novel estimation measure which evaluates the quality of puzzle solutions without the need for ground-truth information. Incorporating these contributions, our approach facilitates solutions that surpass state-of-the-art solvers on puzzles of size larger than ever attempted before.
Differential involvement of left prefrontal cortexin inductive and deductive reasoning

While inductive and deductive reasoning are considered distinct logical and psychological processes, little is known about their respective neural basis. To address this issue we scanned 16 subjects with fMRI, using an event-related design, while they engaged in inductive and deductive reasoning tasks. Both types of reasoning were characterized by activation of left lateral prefrontal and bilateral dorsal frontal, parietal, and occipital cortices. Neural responses unique to each type of reasoning determined from the Reasoning Type (deduction and induction) by Task (reasoning and baseline) interaction indicated greater involvement of left inferior frontal gyrus (BA 44) in deduction than induction, while left dorsolateral (BA 8/9) prefrontal gyrus showed greater activity during induction than deduction. This pattern suggests a dissociation within prefrontal cortex for deductive and inductive reasoning.
Live-streaming changes the (video) game

Video games are inherently an active medium, without interaction a video game is benign. Yet there is a growing community of video game spectating that exists on the Internet, at events across the world and, in part, as traditional television broadcasts. In this paper we look at the different communities that have grown around video game spectating, the incentives of all stakeholders and the technologies involved. An interesting part of this phenomenon is its relation to the malleability of activity and passivity; video games are traditionally active but spectatorship brings an element of passivity, whereas television is traditionally passive but interactive television brings an element of activity. We explore this phenomenon based on selected examples and stimulate a discussion around how such understanding from the video game field could be interesting for interactive television.
Organization of the songbird basal ganglia, including area X.

Area X is a songbird basal ganglia nucleus that is required for vocal learning. Both Area X and its immediate surround, the medial striatum (MSt), contain cells displaying either striatal or pallidal characteristics. We used pathway-tracing techniques to compare directly the targets of Area X and MSt with those of the lateral striatum (LSt) and globus pallidus (GP). We found that the zebra finch LSt projects to the GP, substantia nigra pars reticulata (SNr) and pars compacta (SNc), but not the thalamus. The GP is reciprocally connected with the subthalamic nucleus (STN) and projects to the SNr and motor thalamus analog, the ventral intermediate area (VIA). In contrast to the LSt, Area X and surrounding MSt project to the ventral pallidum (VP) and dorsal thalamus via pallidal-like neurons. A dorsal strip of the MSt contains spiny neurons that project to the VP. The MSt, but not Area X, projects to the ventral tegmental area (VTA) and SNc, but neither MSt nor Area X projects to the SNr. Largely distinct populations of SNc and VTA dopaminergic neurons innervate Area X and surrounding the MSt. Finally, we provide evidence consistent with an indirect pathway from the cerebellum to the basal ganglia, including Area X. Area X projections thus differ from those of the GP and LSt, but are similar to those of the MSt. These data clarify the relationships among different portions of the oscine basal ganglia as well as among the basal ganglia of birds and mammals.
Compact GaAs HEMT D flip-flop for the integration of a SAR MMIC core-chip digital control logic

The paper presents a novel, ultra-compact, reduced-area implementation of a D-type flip-flop using the GaAs Enhancement-Depletion (ED) PHEMT process of the OMMIC with the gate metal layout modified, at the device process level. The D cell has been developed as the building block of a serial to parallel 13-bit shifter embedded within an integrated core-chip for satellite X band SAR applications, but can be exploited for a wide set of logical GaAs-based applications. The novel D cell design, based on the Enhancement-Depletion Super-Buffer (EDSB) logical family, allows for an area reduction of about 20%, with respect to the conventional design, and simplified interconnections. Design rules have been developed to optimize the cell performances. Measured and simulated NOR transfer characteristics show good agreement. A dedicated layout for RF probing has been developed to test the D-type flip-flop behaviour and performances.
Developing an artificial intelligence engine

As computer games become more complex and consumers demand more sophisticated computer controlled agents, developers are required to place a greater emphasis on the artificial intelligence aspects of their games. One source of sophisticated AI techniques is the artificial intelligence research community. This paper discusses recent efforts by our group at the University of Michigan Artificial Intelligence Lab to apply state of the art artificial intelligence techniques to computer games. Our experience developing intelligent air combat agents for DARPA training exercises, described in John Laird's lecture at the 1998 Computer Game Developer's Conference, suggested that many principles and techniques from the research community are applicable to games. A more recent project, called the Soar/Games project, has followed up on this by developing agents for computer games, including Quake II and Descent 3. The result of these two research efforts is a partially implemented design of an artificial intelligence engine for games based on well established AI systems and techniques.
Community detection using multilayer edge mixture model

Multilayer networks are networks where edges exist in multiple layers that encode different types or sources of interactions. As one of the most important problems in network science, discovering the underlying community structure in multilayer networks has received an increasing amount of attention in recent years. One of the challenging issues is to develop effective community structure quality functions for characterizing the structural or functional properties of the expected community structure. Although several quality functions have been developed for evaluating the detected community structure, little has been explored about how to explicitly bring our knowledge of the desired community structure into such quality functions, in particular for the multilayer networks. To address this issue, we propose the multilayer edge mixture model (MEMM), which is positioned as a general framework that enables us to design a quality function that reflects our knowledge about the desired community structure. The proposed model is based on a mixture of the edges, and the weights reflect their role in the detection process. By decomposing a community structure quality function into the form of MEMM, it becomes clear which type of community structure will be discovered by such quality function. Similarly, after such decomposition we can also modify the weights of the edges to find the desired community structure. In this paper, we apply the quality functions modified with the knowledge of MEMM to different multilayer benchmark networks as well as real-world multilayer networks and the detection results confirm the feasibility of MEMM.
Learning High-Level Feature by Deep Belief Networks for 3-D Model Retrieval and Recognition

3-D shape analysis has attracted extensive research efforts in recent years, where the major challenge lies in designing an effective high-level 3-D shape feature. In this paper, we propose a multi-level 3-D shape feature extraction framework by using deep learning. The low-level 3-D shape descriptors are first encoded into geometric bag-of-words, from which middle-level patterns are discovered to explore geometric relationships among words. After that, high-level shape features are learned via deep belief networks, which are more discriminative for the tasks of shape classification and retrieval. Experiments on 3-D shape recognition and retrieval demonstrate the superior performance of the proposed method in comparison to the state-of-the-art methods.
Automatic Assessment of Open Ended Questions with a Bleu-Inspired Algorithm and Shallow NLP

This paper compares the accuracy of several variations of the B LEU algorithm when applied to automatically evaluating student essays. The different configurations include closed-class word removal, stemming, two baseline wordsense disambiguation procedures, and translating the texts into a simple semantic representation. We also prove empirically that the accuracy is kept when the student answers are translated automatically. Although none of the representations clearly outperform the others, some conclusions are drawn from the results.
Linear optical quantum computing with photonic qubits

Linear optics with photon counting is a prominent candidate for practical quantum computing. The protocol by Knill, Laflamme, and Milburn 2001,  explicitly demonstrates that efficient scalable quantum computing with single photons, linear optical elements, and projective measurements is possible. Subsequently, several improvements on this protocol have started to bridge the gap between theoretical scalability and practical implementation. The original theory and its improvements are reviewed, and a few examples of experimental two-qubit gates are given. The use of realistic components, the errors they induce in the computation, and how these errors can be corrected is discussed.
Free-energy-based reinforcement learning in a partially observable environment

Free-energy-based reinforcement learning (FERL) can handle Markov decision processes (MDPs) with high-dimensional state spaces by approximating the state-action value function with the negative equilibrium free energy of a restricted Boltzmann machine (RBM). In this study, we extend the FERL framework to handle partially observable MDPs (POMDPs) by incorporating a recurrent neural network that learns a memory representation sufficient for predicting future observations and rewards. We demonstrate that the proposed method successfully solves POMDPs with high-dimensional observations without any prior knowledge of the environmental hidden states and dynamics. After learning, task structures are implicitly represented in the distributed activation patterns of hidden nodes of the RBM.
IP-Geolocation Mapping for Moderately Connected Internet Regions

Most IP-geolocation mapping schemes [14], , , [18] take delay-measurement approach, based on the assumption of a strong correlation between networking delay and geographical distance between the targeted client and the landmarks. In this paper, however, we investigate a large region of moderately connected Internet and find the delay-distance correlation is weak. But we discover a more probable rule - with high probability the shortest delay comes from the closest distance. Based on this closest-shortest rule, we develop a simple and novel IP-geolocation mapping scheme for moderately connected Internet regions, called GeoGet. In GeoGet, we take a large number of webservers as passive landmarks and map a targeted client to the geolocation of the landmark that has the shortest delay. We further use JavaScript at targeted clients to generate HTTP/Get probing for delay measurement. To control the measurement cost, we adopt a multistep probing method to refine the geolocation of a targeted client, finally to city level. The evaluation results show that when probing about 100 landmarks, GeoGet correctly maps 35.4 percent clients to city level, which outperforms current schemes such as GeoLim [16] and GeoPing  by 270 and 239 percent, respectively, and the median error distance in GeoGet is around 120 km, outperforming GeoLim and GeoPing by 37 and 70 percent, respectively.
Towards a smart world and ubiquitous intelligence: A walkthrough from smart things to smart hyperspaces and UbicKids

A cyber world (CW) is a digitized world created on cyberspaces inside computers interconnected by networks including the Internet. Following ubiquitous computers, sensors, e-tags, networks, information, services, etc., is a road towards a smart world (SW) created on both cyberspaces and real spaces. It is mainly characterized by ubiquitous intelligence or computational intelligence pervasion in the physical world filled with smart things. In recent years, many novel and imaginative researcheshave been conducted to try and experiment a variety of smart things including characteristic smart objects and specific smart spaces or environments as well as smart systems. The next research phase to emerge, we believe, is to coordinate these diverse smart objects and integrate these isolated smart spaces together into a higher level of spaces known as smart hyperspace or hyper-environments, and eventually create the smart world. In this paper, we discuss the potential trends and related challenges toward the smart world and ubiquitous intelligence from smart things to smart spaces and then to smart hyperspaces. Likewise, we show our efforts in developing a smart hyperspace of ubiquitous care for kids, called UbicKids.
An analysis of NP-completeness in novelty and diversity ranking

A useful ability for search engines is to be able to rank objects with novelty and diversity: the top k documents retrieved should cover possible intents of a query with some distribution, or should contain a diverse set of subtopics related to the user’s information need, or contain nuggets of information with little redundancy. Evaluation measures have been introduced to measure the effectiveness of systems at this task, but these measures have worst-case NP-hard computation time. The primary consequence of this is that there is no ranking principle akin to the Probability Ranking Principle for document relevance that provides uniform instruction on how to rank documents for novelty and diversity. We use simulation to investigate the practical implications of this for optimization and evaluation of retrieval systems.
Predicting customers' future demand using data mining analysis: A case study of wireless communication customer

Due to high competition in today's business and the need for satisfactory communication with customers, companies understand the inevitable necessity to focus not only on preventing customer churn but also on predicting their needs and providing the best services for them. The purpose of this article is to predict future services needed by wireless users, with data mining techniques. For this purpose, the database of customers of an ISP in Shiraz, which logs the customer usage of wireless internet connections, is utilized. Since internet service has three main factors to define (Time, Speed, Traffics) we predict each separately. First, future service demand is predicted by implementing a simple Recency, Frequency, Monetary (RFM) as a basic model. Other factors such as duration from first use, slope of customer's usage curve, percentage of activation, Bytes In, Bytes Out and the number of retries to establish a connection and also customer lifetime value are considered and added to RFM model. Then each one of R, F, M criteria is alternately omitted and the result is evaluated. Assessment is done through analysis node which determines the accuracy of evaluated data among partitioned data. The result shows that CART and C5.0 are the best algorithms to predict future services in this case. As for the features, depending upon output of each features, duration and transfer Bytes are the most important after RFM. An ISP may use the model discussed in this article to meet customers' demands and ensure their loyalty and satisfaction.
The global assessment scale. A procedure for measuring overall severity of psychiatric disturbance.

The Global Assessment Scale (GAS) is a rating scale for evaluating the overall functioning of a subject during a specified time period on a continuum from psychological or psychiatric sickness to health. In five studies encompassing the range of population to which measures of overall severity of illness are likely to be applied, the GAS was found to have good reliability. GAS ratings were found to have a greater sensitivity to change over time than did other ratings of overall severity or specific symptom dimensions. Former inpatients in the community with a GAS rating below 40 had a higher probability of readmission to the hospital than did patients with higher GAS scores. The relative simplicity, reliability, and validity of the GAS suggests that it would be useful in a wide variety of clinical and research settings.