nils-herrmann/clean_scirepeval huggingface dataset:
Solving Verbal Crypto-Arithmetic Problem by Parallel Genetic Algorithm (PGA)

51   Cryptarithmetic puzzles are quite old and their inventor is not known. An example in The American Agriculturist of 1864 disproves the popular notion that it was invented by Sam Loyd. The name cryptarithmetic was coined by puzzlist Minos (pseudonym of Maurice Vatriquant) in the May 1931 issue of Sphinx, a Belgian magazine of recreational mathematics. In the 1955, J. A. H. Hunter introduced the word "alphabetic" to designate cryptarithms, such as Dudeney's, whose letters from meaningful words or phrases. Solving a cryptarithm by hand usually involves a mix of deductions and exhaustive tests of possibilities. Cryptarithmetic is a puzzle consisting of an arithmetic problem in which the digits have been replaced by letters of the alphabet. The goal is to decipher the letters (i.e. Map them back onto the digits) using the constraints provided by arithmetic and the additional constraint that no two letters can have the same numerical value. Cryptarithmetic is a class of constraint satisfaction problems which includes making mathematical relations between meaningful words using simple arithmetic operators like 'plus' in a way that the result is conceptually true, and assigning digits to the letters of these words and generating numbers in order to make correct arithmetic operations as well
Market Liquidity and Funding Liquidity ∗

We provide a model that links an asset’s market liquidity — i.e., the ease with which it is traded — and traders’ funding liquidity — i.e., the ease with which they can obtain funding. Traders provide market liquidity, and their ability to do so depends on their availability of funding. Conversely, traders’ funding, i.e., their capital and the margins they are charged, depend on the assets’ market liquidity. We show that, under certain conditions, margins are destabilizing and market liquidity and funding liquidity are mutually reinforcing, leading to liquidity spirals. The model explains the empirically documented features that market liquidity (i) can suddenly dry up, (ii) has commonality across securities, (iii) is related to volatility, (iv) is subject to “flight to quality”, and (v) comoves with the market, and it provides new testable predictions.
A Real-Time Detecting Algorithm for Tracking Community Structure of Dynamic Networks

In this paper a simple but efficient real-time detecting algorithm is proposed for tracking community structure of dynamic networks. Community structure is intuitively characterized as divisions of network nodes into subgroups, within which nodes are densely connected while between which they are sparsely connected. To evaluate the quality of community structure of a network, a metric called modularity is proposed and many algorithms are developed on optimizing it. However, most of the modularity based algorithms deal with static networks and cannot be performed frequently, due to their high computing complexity. In order to track the community structure of dynamic networks in a finegrained way, we propose a modularity based algorithm that is incremental and has very low computing complexity. In our algorithm we adopt a two-step approach. Firstly we apply the Blondel et al’s algorithm for detecting static communities to obtain an initial community structure. Then, apply our incremental updating strategies to track the dynamic communities. The performance of our algorithm is measured in terms of the modularity. We test the algorithm on tracking community structure of Enron Email and three other real world datasets. The experimental results show that our algorithm can keep track of community structure in time and outperform the well known CNM algorithm in terms of modularity.
Failure sketching: a technique for automated root cause diagnosis of in-production failures

Developers spend a lot of time searching for the root causes of software failures. For this, they traditionally try to reproduce those failures, but unfortunately many failures are so hard to reproduce in a test environment that developers spend days or weeks as ad-hoc detectives. The shortcomings of many solutions proposed for this problem prevent their use in practice.
 We propose failure sketching, an automated debugging technique that provides developers with an explanation ("failure sketch") of the root cause of a failure that occurred in production. A failure sketch only contains program statements that lead to the failure, and it clearly shows the differences between failing and successful runs; these differences guide developers to the root cause. Our approach combines static program analysis with a cooperative and adaptive form of dynamic program analysis.
 We built Gist, a prototype for failure sketching that relies on hardware watchpoints and a new hardware feature for extracting control flow traces (Intel Processor Trace). We show that Gist can build failure sketches with low overhead for failures in systems like Apache, SQLite, and Memcached.
Categorical Data Clustering using Cosine based similarity for Enhancing the Accuracy of Squeezer Algorithm

DISC Measure, Squeezer, Categorical Data Clustering, Cosine similarity References Rishi Sayal and Vijay Kumar. V. 2011. A novel Similarity Measure for Clustering Categorical Data Sets. ). Aditya Desai, Himanshu Singh and Vikram Pudi. 2011. DISC Data-Intensive Similarity Measure for Categorical Data. Pacific-Asia Conferences on Knowledge Discovery Data Mining. Shyam Boriah, Varun Chandola and Vipin Kumar. 2008.
An X-Band SiGe Single-MMIC Transmit / Receive Module for Radar Applications

The development of a single-MMIC transmit/receive (T/R) module for radar applications is described. The T/R module is fabricated in the IBM 8HP SiGe BiCMOS process technology. The topology of the T/R module is described. The performance of the underlying RF components is shown, including LNA, SP2T switches, SP3T switches, phase shifters, and power amplifiers, all with simulated and measured performance results. The simulated performance of the complete T/R module is presented, as well as the MMIC layout. The applicability to various radar systems and concepts is discussed. It is shown that the RF performance and the low power consumption of the device make it attractive for the described applications.
Sentiment classification on Turkish hotel reviews

Sentiment analysis refers to classify the emotion of a text whether positive or negative. The studies conducted on sentiment analysis are generally based on English and other languages while there are limited studies on Turkish. In this study, after constructing a dataset using a well-known hotel reservation site booking.com, we compare the performances of different machine learning approaches. We also apply dictionary-based method, SentiTFIDF, which differs from the traditional methods due to their logarithmic differential term frequency and term presence distribution usage. The results are evaluated using the area under of a Receiver Operating Characteristic (ROC) curve (AUC). The results show that, using document term matrix as input gives better classification results than TFIDF matrix. We also observe that the best results are obtained using Random Forest classifier with an AUC value of %89 on both positive and negative comments.
A pedagogical framework for mobile learning: Categorizing educational applications of mobile technologies into four types

Instructional designers and educators recognize the potential of mobile technologies as a learning tool for students and have incorporated them into the distance learning environment. However, little research has been done to categorize the numerous examples of mobile learning in the context of distance education, and few instructional design guidelines based on a solid theoretical framework for mobile learning exist. In this paper I compare mobile learning (m-learning) with electronic learning (e-learning) and ubiquitous learning (u-learning) and describe the technological attributes and pedagogical affordances of mobile learning presented in previous studies. I modify transactional distance (TD) theory and adopt it as a relevant theoretical framework for mobile learning in distance education. Furthermore, I attempt to position previous studies into four types of mobile learning: 1) high transactional distance socialized m-learning, 2) high transactional distance individualized m-learning, 3) low transactional distance socialized mlearning and 4) low transactional distance individualized m-learning. As a result, this paper can be used by instructional designers of open and distance learning to learn about the concepts of mobile learning and how mobile technologies can be incorporated into their teaching and learning more effectively.
Sherlock: Scalable Fact Learning in Images

The human visual system is capable of learning an unbounded number of facts from images including not only objects but also their attributes, actions and interactions. Such uniform understanding of visual facts has not received enough attention. Existing visual recognition systems are typically modeled differently for each fact type such as objects, actions, and interactions. We propose a setting where all these facts can be modeled simultaneously with a capacity to understand an unbounded number of facts in a structured way. The training data comes as structured facts in images, including (1) objects (e.g., <boy>), (2) attributes (e.g., <boy, tall>), (3) actions (e.g., <boy, playing>), and (4) interactions (e.g., <boy, riding, a horse >). Each fact has a language view (e.g., < boy, playing>) and a visual view (an image). We show that learning visual facts in a structured way enables not only a uniform but also generalizable visual understanding. We propose and investigate recent and strong approaches from the multiview learning literature and also introduce a structured embedding model. We applied the investigated methods on several datasets that we augmented with structured facts and a large scale dataset of > 202,000 facts and 814,000 images. Our results show the advantage of relating facts by the structure by the proposed model compared to the baselines.
Data muling with mobile phones for sensornets

Sensors are all around us, in buildings, vehicles and public places, from commodity thermostats to custom sensornets. Yet today these sensors are often disconnected from the world, either because they are distant from infrastructure, and wide-area networking (by 3G cellular, satellite, or other approaches) is too expensive to justify. Data muling makes communication cost-effective by leveraging short-range wireless and mobility, perhaps by zebras, buses or farmworkers. In this paper we propose that human-carried mobile phones can serve as data mules for sensornet deployments, exploiting ubiquity of mobile phones and human mobility to bring low-cost communication to sensors. We use two mobile phone datasets to show that Bluetooth can serve as a viable muling network, and humans already see many potential sensors regularly. We have implemented a mobile-phone-based data muling system, and used it in four sensornet deployments totaling ten months operation. We find that muling can be the only cost-effective option for rural deployments, where it is critical to monitoring remote sensor networks. We also show opportunistic mobility can collect data without any extra effort in residential and office environments. Finally, we systematically evaluate our deployments to understand how contact duration and data size interact, and to evaluate the effect of muling on phone batteries.
Be smart, exercise your heart: exercise effects on brain and cognition

An emerging body of multidisciplinary literature has documented the beneficial influence of physical activity engendered through aerobic exercise on selective aspects of brain function. Human and non-human animal studies have shown that aerobic exercise can improve a number of aspects of cognition and performance. Lack of physical activity, particularly among children in the developed world, is one of the major causes of obesity. Exercise might not only help to improve their physical health, but might also improve their academic performance. This article examines the positive effects of aerobic physical activity on cognition and brain function, at the molecular, cellular, systems and behavioural levels. A growing number of studies support the idea that physical exercise is a lifestyle factor that might lead to increased physical and mental health throughout life.
First Impressions: Users' Judgments of Virtual Agents' Personality and Interpersonal Attitude in First Encounters

In first encounters people quickly form impressions of each other’s personality and interpersonal attitude. We conducted a study to investigate how this transfers to first encounters between humans and virtual agents. In the study, subjects’ avatars approached greeting agents in a virtual museum rendered in both first and third person perspective. Each agent exclusively exhibited nonverbal immediacy cues (smile, gaze and proximity) during the approach. Afterwards subjects judged its personality (extraversion) and interpersonal attitude (hostility/friendliness). We found that within only 12.5 seconds of interaction subjects formed impressions of the agents based on observed behavior. In particular, proximity had impact on judgments of extraversion whereas smile and gaze on friendliness. These results held for the different camera perspectives. Insights on how the interpretations might change according to the user’s own personality are also provided.
Identifying Purchase Intent from Social Posts

In present times, social forums such as Quora and Yahoo! Answers constitute powerful media through which people discuss on a variety of topics and express their intentions and thoughts. Here they often reveal their potential intent to purchase ‘Purchase Intent’ (PI). A purchase intent is defined as a text expression showing a desire to purchase a product or a service in future. Extracting posts having PI from a user’s social posts gives huge opportunities towards web personalization, targeted marketing and improving community observing systems. In this paper, we explore the novel problem of detecting PIs from social posts and classifying them. We find that using linguistic features along with statistical features of PI expressions achieves a significant improvement in PI classification over ‘bag-ofwords’ based features used in many present day socialmedia classification tasks. Our approach takes into consideration the specifics of social posts like limited contextual information, incorrect grammar, language ambiguities, etc. by extracting features at two different levels of text granularity word and phrase based features and grammatical dependency based features. Apart from these, the patterns observed in PI posts help us to identify some specific features.
A novel scheme for speaker recognition using a phonetically-aware deep neural network

We propose a novel framework for speaker recognition in which extraction of sufficient statistics for the state-of-the-art i-vector model is driven by a deep neural network (DNN) trained for automatic speech recognition (ASR). Specifically, the DNN replaces the standard Gaussian mixture model (GMM) to produce frame alignments. The use of an ASR-DNN system in the speaker recognition pipeline is attractive as it integrates the information from speech content directly into the statistics, allowing the standard backends to remain unchanged. Improvement from the proposed framework compared to a state-of-the-art system are of 30% relative at the equal error rate when evaluated on the telephone conditions from the 2012 NIST speaker recognition evaluation (SRE). The proposed framework is a successful way to efficiently leverage transcribed data for speaker recognition, thus opening up a wide spectrum of research directions.
Task-Specific Attentive Pooling of Phrase Alignments Contributes to Sentence Matching

This work studies comparatively two typical sentence matching tasks: textual entailment (TE) and answer selection (AS), observing that weaker phrase alignments are more critical in TE, while stronger phrase alignments deserve more attention in AS. The key to reach this observation lies in phrase detection, phrase representation, phrase alignment, and more importantly how to connect those aligned phrases of different matching degrees with the final classifier. Prior work (i) has limitations in phrase generation and representation, or (ii) conducts alignment at word and phrase levels by handcrafted features or (iii) utilizes a single framework of alignment without considering the characteristics of specific tasks, which limits the framework’s effectiveness across tasks. We propose an architecture based on Gated Recurrent Unit that supports (i) representation learning of phrases of arbitrary granularity and (ii) task-specific attentive pooling of phrase alignments between two sentences. Experimental results on TE and AS match our observation and show the effectiveness of our approach.
Artificial intelligence in cyber defense

The speed of processes and the amount of data to be used in defending the cyber space cannot be handled by humans without considerable automation. However, it is difficult to develop software with conventional fixed algorithms (hard-wired logic on decision making level) for effectively defending against the dynamically evolving attacks in networks. This situation can be handled by applying methods of artificial intelligence that provide flexibility and learning capability to software. This paper presents a brief survey of artificial intelligence applications in cyber defense (CD), and analyzes the prospects of enhancing the cyber defense capabilities by means of increasing the intelligence of the defense systems. After surveying the papers available about artificial intelligence applications in CD, we can conclude that useful applications already exist. They belong, first of all, to applications of artificial neural nets in perimeter defense and some other CD areas. From the other side - it has become obvious that many CD problems can be solved successfully only when methods of artificial intelligence are being used. For example, wide knowledge usage is necessary in decision making, and intelligent decision support is one of yet unsolved problems in CD.
Weighted Sampling for Large-Scale Boosting

This paper addresses the problem of learning from very large databases where batch learning is impractical or even infeasible. Bootstrap is a popular technique applicable in such situations. We show that sampling strategy used for bootstrapping has a significant impact on the resulting classifier performance. We design a new general sampling strategy ”quasi-random weighted sampling + trimming” (QWS+) that includes well established strategies as special cases. The QWS+ approach minimizes the variance of hypothesis error estimate and leads to significant improvement in performance compared to standard sampling techniques. The superior performance is demonstrated on several problems including profile and frontal face detection.
Putting the users center stage: role playing and low-fi prototyping enable end users to design mobile systems

This paper sums up lessons learned from a sequence of cooperative design workshops where end users were enabled to design mobile systems through scenario building, role playing, and low-fidelity prototyping. We present a resulting fixed workshop structure with well-chosen constraints that allows for end users to explore and design new technology and work practices. In these workshops, the systems developers get input to design from observing how users stage and act out current and future use scenarios and improvise new technology to fit their needs. A theoretical framework is presented to explain the creative processes involved and the workshop as a user-centered design method. Our findings encourage us to recommend the presented workshop structure for design projects involving mobility and computer-mediated communication, in particular project where the future use of the resulting products and services also needs to be designed.
The state of CRM adoption by the financial services in the UK: an empirical investigation

In recent years, organisations have begun to realise the importance of knowing their customers better. Customer relationship management (CRM) is an approach to managing customer related knowledge of increasing strategic significance. The successful adoption of IT-enabled CRM redefines the traditional models of interaction between businesses and their customers, both nationally and globally. It is regarded as a source for competitive advantage because it enables organisations to explore and use knowledge of their customers and to foster profitable and long-lasting one-to-one relationships. This paper discusses the results of an exploratory survey conducted in the UK financial services sector; it discusses CRM practice and expectations, the motives for implementing it, and evaluates post-implementation experiences. It also investigates the CRM tools functionality in the strategic, process, communication, and business-to-customer (B2C) organisational context and reports the extent of their use. The results show that despite the anticipated potential, the benefits from such tools are rather small.
AttS2S-VC: Sequence-to-Sequence Voice Conversion with Attention and Context Preservation Mechanisms

This paper describes a method based on a sequenceto-sequence learning (Seq2Seq) with attention and context preservation mechanism for voice conversion (VC) tasks. Seq2Seq has been outstanding at numerous tasks involving sequence modeling such as speech synthesis and recognition, machine translation, and image captioning. In contrast to current VC techniques, our method 1) stabilizes and accelerates the training procedure by considering guided attention and proposed context preservation losses, 2) allows not only spectral envelopes but also fundamental frequency contours and durations of speech to be converted, 3) requires no context information such as phoneme labels, and 4) requires no time-aligned source and target speech data in advance. In our experiment, the proposed VC framework can be trained in only one day, using only one GPU of an NVIDIA Tesla K80, while the quality of the synthesized speech is higher than that of speech converted by Gaussian mixture model-based VC and is comparable to that of speech generated by recurrent neural network-based text-to-speech synthesis, which can be regarded as an upper limit on VC performance.
Future e-Government Research: 13 Research Themes Identified in the eGovRTD2020 Project

E-government research has become a recognized research domain and many policies and strategies are formulated for e-government implementations. Most of these target the next few years and limited attention has been giving to the long term. The eGovRTD2020, a European Commission co-funded project, investigated the future research on e-government driven by changing circumstances and the evolution of technology. This project consists of an analysis of the state of play, a scenario-building, a gap analysis and a roadmapping activity. In this paper the roadmapping methodology fitting the unique characteristics of the e-government field is presented and the results are briefly discussed. The use of this methodology has resulted in the identification of a large number of e-government research themes. It was found that a roadmapping methodology should match the unique characteristics of e-government. The research shows the need of multidisciplinary research.