Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal Coordinated Management of a Plug-In Electric Vehicle Charging Station under a Flexible Penalty Contract for Voltage Security

The increasing penetration of plug-in electric vehicles (PEVs) may cause a low-voltage problem in the distribution network. In particular, the introduction of charging stations where multiple PEVs are simultaneously charged at the same bus can aggravate the low-voltage problem. Unlike a distribution network operator (DNO) who has the overall responsibility for stable and reliable network operation, a charging station operator (CSO) may schedule PEV charging without consideration for the resulting severe voltage drop. Therefore, there is a need for the DNO to impose a coordination measure to induce the CSO to adjust its charging schedule to help mitigate the voltage problem. Although the current time-of-use (TOU) tariff is an indirect coordination measure that can motivate the CSO to shift its charging demand to off-peak time by imposing a high rate at the peak time, it is limited by its rigidity in that the network voltage condition cannot be flexibly reflected in the tariff. Therefore, a flexible penalty contract (FPC) for voltage security to be used as a direct coordination measure is proposed. In addition, the optimal coordinated management is formulated. Using the Pacific Gas and Electric Company (PG&E) 69-bus test distribution network, the effectiveness of the coordination was verified by comparison with the current TOU tariff.
Introduction to the Logical investigations : a draft of a preface to the Logical investigations (1913)

The Author's Abstracts 1900/01. - Author's Abstract to Volume One in Vierteljahrsschrift fur wissenschaftliche Philosophie, Vol. 24 (1900), pp. 511-12. - Author's Abstract to Volume Two in Vierteljahrsschrift fur wissenschaftliche Philosophie und Soziologie, Vol. 25 (1901), pp. 260-63. - A Draft of a "Preface" to the Logical Investigations, 1913. - I. Eugen Fink's Editorial Remarks. - II. Husserl's Text. - Section 1. The necessity of a preparatory introduction into the meaning of the work. - The Logical Investigations and the philosophical public. - Section 2. The sense of the "Prolegomena": the separation of the logical and psychological motifs within the unity of a single problem (critical discussion on Natorp). - Section 3. The Demand for an intuitive method of philosophy in the return to intuitive experience (Anschauung). - Section 4. Repudiation of the charge of so-called "Platonism" ideas as objects. - Section 5. The concept of pure logic as mathesis universalis (the unity of the "analytic" doctrine of forms for that which can be an object on the one hand with the categories of meaning on the other). The "positivity" of logic and the philosophical problem of its phenomenological elucidation. - Positive science in general and phenomenology. - Section 6. The Logical Investigations as a breakthrough work. - The antecedent history of its problematic: the beginning with the questions about the psychological origin of basic mathematical concepts the study of Lotze and Bolzano. - The thematic orientation of the mathematician its naive justification and the problem of a phenomenological turn. - Section 7. Critical discussion of Meinong. - Demarcations in the region of the a priori: The distinction between 1) the formal and material a priori, and 2) the "phenomenological" and "ontological" a priori. - Section 8. Critical Differentiation from Lotze. - Section 9. Critical differentiation from Bolzano. - Section 10. Repudiation of the slogan: "Phenomenology as the analysis of the meanings of words. - Section 11. A misunderstanding of the author's in the Logical Investigations. The misleading characterization of phenomenology as descriptive psychology. - Section 12. The charge of "logicism" critical discussion on Wilhelm Wundt.
Modelling and control of a hexa-copter

The paper describes mathematical model and control algorithm for hexa-copter flying machine. Hexa-copter is a member of multi-rotor flying devices which consist of multiple fixed rotors attached to a simple mechanical construction. Hexa-copter itself consists of 6 six rotors with fixed pitch blades. The main property of such devices is their instability. Thus the control algorithm is non-trivial and its tuning is relatively difficult. Since incorrectly tuned control algorithm can cause device damage the use of mathematical model for testing is appropriate. The model of flying hexa-copter is derived using classical Newton laws. As the perfect model would be complicated, some approximations have been done. Using the state variables of model and linearization a control algorithm is designed. Then the results of control algorithm are presented.
‘Pedagogies of resistance’ and critical peace education praxis

This paper explores ‘pedagogies of resistance’ – or critical and democratic educational models utilized by social movements – and how global examples of engaged educational praxis may inform peace education. The central inquiry of this article is ‘How can educational projects that resist larger social, political and economic inequalities offer understandings about how we learn, teach, and act for peace in diverse settings? ’ Drawing upon literature from various fields, ideas and insights are offered about how the field of peace education can better respond to multiple and diverse realities, particularly those facing marginalized communities. The article provides an overview of key tenets of peace education and ideas central to ‘critical peace education;’ offers a framing of ‘pedagogies of resistance;’ and, lastly, details what directions emerge by putting these two educational forms in conversation.
Fast mining of massive tabular data via approximate distance computations

Tabular data abound in many data stores: traditional relational databases store tables, and new applications also generate massive tabular datasets. We present methods for determining similar regions in massive tabular data. Our methods are for computing the "distance" between any two subregions of tabular data: they are approximate, but highly accurate as we prove mathematically, and they are fast, running in time nearly linear in the table size. Our methods are general since these distance computations can be applied to any mining or similarity algorithms that use L/sub p/ norms. A novelty of our distance computation procedures is that they work for any L/sub p/ norms, not only the traditional p = 2 or p = 1, but for all p /spl les/ 2; the choice of p, say fractional p, provides an interesting alternative similarity behavior! We use our algorithms in a detailed experimental study of the clustering patterns in real tabular data obtained from one of AT&T's data stores and show that our methods are substantially faster than straightforward methods while remaining highly accurate, and able to detect interesting patterns by varying the value of p.
Finding Event Videos via Image Search Engine

Searching desirable events in uncontrolled videos isa challenging task. Current researches mainly focus on obtaining concepts from numerous labeled videos. But it is time consumingand labor expensive to collect a large amount of required labeled videos to model events under various circumstances. To alleviate the labeling process, we propose to learn models for videos by leveraging abundant Web images which contains a rich source of information with many events taken under various conditions and roughly annotated. However, knowledge from the Web is noisy and diverse, brute force knowledge transfer may hurt the retrieval performance. To address such negative transfer problem, we propose a novel Joint Group Weighting Learning (JGWL) framework to leverage different but related groups of knowledge (source domain) queried from the Web image searching engine to real-world videos (target domain). Under this framework, weights of different groups are learned in a joint optimization framework, and each weight represents how contributive the corresponding image group is to the knowledge transferred to the videos. Moreover, to deal with the feature distribution mismatching between video feature space and image feature space, we build a common feature subspace to bridge these two heterogeneous feature spaces in an unsupervised manner. Experimental results on two challenging video datasets demonstrate that it is effective to use grouped knowledge gained from Web images for video retrieval.
Building development tools interactively using the EKEKO meta-programming library

EKEKO is a Clojure library for applicative logic meta-programming against an Eclipse workspace. EKEKO has been applied successfully to answering program queries (e.g., “does this bug pattern occur in my code? ”), to analyzing project corpora (e.g., “how often does this API usage pattern occur in this corpus? ”), and to transforming programs (e.g., “change occurrences of this pattern as follows”) in a declarative manner. These applications rely on a seamless embedding of logic queries in applicative expressions. While the former identify source code of interest, the latter associate error markers with, compute statistics about, or rewrite the identified source code snippets. In this paper, we detail the logic and applicative aspects of the EKEKO library. We also highlight key choices in their implementation. In particular, we demonstrate how a causal connection with the Eclipse infrastructure enables building development tools interactively on the Clojure read-eval-print loop.
Receiver design for non-linear satellite channels: Equalizer training and symbol detection on the compressed constellation

Because of the small energy available aboard a satellite, the power amplifier must work with a restricted power supply which limits its maximum output power. To ensure a sufficient signal-to-noise power ratio (SNR) at the receiving side, the amplifier must work close to the saturation point. This is power efficient but, unfortunately, adds non-linear distortions in the communication channel. Several algorithms have been proposed to equalize this non-linear channel. The most widely used in the literature is the baseband Volterra filter. Recently, the Echo State Network (ESN), coming from the artificial neural network field, has been shown to perform equally well. To compensate for this channel, both equalizers adapt their coefficients with the help of a training sequence in order to recover the transmitted constellation points. We will show that, the usual detection, based on Euclidean distances, is no longer optimal. The aim of this paper is to first propose a new detection criterion which meets with the Maximum Likelihood (ML) criterion. Secondly, we will propose a modification of the training reference points to improve the performances of these equalizers and make the detection based on Euclidean distances optimal again. This last solution can offer a significant reduction of the Bit Error Rate (BER) without increasing the equalizers complexity. Only the new training reference points must be evaluated.
CGC

Multi-view graph clustering aims to enhance clustering performance by integrating heterogeneous information collected in different domains. Each domain provides a different view of the data instances. Leveraging cross-domain information has been demonstrated an effective way to achieve better clustering results. Despite the previous success, existing multi-view graph clustering methods usually assume that different views are available for the same set of instances. Thus, instances in different domains can be treated as having strict one-to-one relationship. In many real-life applications, however, data instances in one domain may correspond to multiple instances in another domain. Moreover, relationships between instances in different domains may be associated with weights based on prior (partial) knowledge. In this article, we propose a flexible and robust framework, Co-regularized Graph Clustering (CGC), based on non-negative matrix factorization (NMF), to tackle these challenges. CGC has several advantages over the existing methods. First, it supports many-to-many cross-domain instance relationship. Second, it incorporates weight on cross-domain relationship. Third, it allows partial cross-domain mapping so that graphs in different domains may have different sizes. Finally, it provides users with the extent to which the cross-domain instance relationship violates the in-domain clustering structure, and thus enables users to re-evaluate the consistency of the relationship. We develop an efficient optimization method that guarantees to find the global optimal solution with a given confidence requirement. The proposed method can automatically identify noisy domains and assign smaller weights to them. This helps to obtain optimal graph partition for the focused domain. Extensive experimental results on UCI benchmark datasets, newsgroup datasets, and biological interaction networks demonstrate the effectiveness of our approach.
Wireless media access depending on packet size distribution over error-prone channels

Ad Hoc Network is a decentralized type of network where wireless devices are allowed to discover each other and communicate in peer to peer fashion without involving central access points. In most ad hoc networks, nodes compete for access to shared wireless medium, often resulting in collision (interference). IEEE 802.11, a well-known standard, uses medium access control (MAC) protocol to support delivery of radio data packets for both ad hoc networks and infrastructure based network. But designing a Medium Access Control(MAC) protocol for ad hoc wireless networks is challenging, particularly when the protocol needs to achieve optimal performance both in terms of throughput and efficiency to deliver a packet. Error-prone channel has a significant impact on unsuccessful transmission probability which is often ignored by previous researches. Standard DCF (Distributed Coordination Function) operation of IEEE 802.11 enacted by binary exponential back-off (BEB) algorithm cannot differentiate collision from corruption and therefore sets forth a (time) separation between multiple nodes accessing the channel by (appropriately) adjusting contention window (CW) upon a failure. This leads to increased delay in error-prone network when nodes are not contending at all. Since packet corruption depends on bit error rate (BER) and length of packets, packet size can have significant impact on the throughput in error-prone environment. In this paper, we analyze effect of packet size in determining optimal CW to improve throughput and efficiency for error-prone networks. We propose a dynamic learning based scheme to adaptively select CW sub-range instead of whole selection range for different packet distribution. To validate our scheme extensive simulations have been done and simulation results show significant improvement in E2E delay performance.
An Investigation into the Cross-Cultural Differences of Aesthetic Value Cognition and Visio-Semantic Evaluation of Product Designs : Focus on Japan and Nigeria

In English). 8 ABSTRACT (In Japanese). 13In Japanese). 13 LIST OF FIGURES.22 LIST OF TABLES.25 OPERATIONAL DEFINITIONS.26 1. BACKGROUND OF THE STUDY . 29 1.1. Rationale for the Study . 29 1.1.1. Global Challenges and Design Opportunities . 29 1.2. Design for the User’s Aesthetic Value . 32 1.2.1. Aesthetics within Kansei Study Framework . 32 1.2.2. Aesthetic Value in Product Design . 34 1.2.3. Considering User’s Aesthetic Value over Cultural Diversity . 39 2. OBJECTIVES OF THE STUDY . 45 2.1. Main Objective and Goals of Research . 45 2.2. Scope of the Study . 46 2.3. Structure of the Thesis . 48 3. RESEARCH METHOD . 50 3.1. Assumptions . 50 3.2. Research Design . 52 3.2.1. Preliminary Study . 53 3.2.2. Main Study . 54 4. THEORIES AND CONCEPTS . 57 4.
Neural correlates of semantic and morphological processing of Hebrew nouns and verbs

Neuropsychological evidence regarding grammatical category suggests that deficits affecting verbs tend to localize differently from those affecting nouns, but previous functional imaging studies on healthy subjects fail to show consistent results that correspond to the clinical dissociation. In the current imaging study, we addressed this issue by manipulating not only the grammatical category but also the processing mode, using auditory presentation of Hebrew words. Subjects were presented with verbs and nouns and were instructed to make either a semantic decision (“Does the word belong to a given semantic category? ”) or a morphological decision (“Is the word inflected in plural? ”). The results showed different patterns of activation across distinct regions of interest. With respect to grammatical category effects, we found increased activation for verbs in the posterior portion of the left superior temporal sulcus, left dorsal premotor area, and posterior inferior frontal gyrus. In each of these regions, the effect was sensitive to task. None of the ROIs showed noun advantage. With respect to task effects, we found a semantic advantage in left anterior inferior frontal gyrus, as well as in left posterior middle temporal gyrus. The results suggest that cerebral verb‐noun dissociation is a result of localized and subtle processes that take place in a set of left frontal and temporal regions, and that the cognitive and neural processes involved in analyzing grammatical category depend on the lexical characteristics of the stimuli, as well as on task requirements. The discrepancy between functional imaging and patient data is also discussed. Hum. Brain Mapp, 2007. © 2006 Wiley‐Liss, Inc.
Automated Segmentation and extraction of Literature Citations

Identifying common citations in patents is an important task because it helps to detect related patents, to identify important literature (w.r.t. patents), and to navigate based on patents and literature. Current methods for citation matching often use edit distance, but it does not work in all cases. For example, it cannot match two patents cite to the same literature but in different format and order. A better way to do citation matching is to match individual fields, this requires citation parsing (or segmentation). This research has been done for ’The lens’, a patent archive web site. A high quality citation parser is considered as an essential component in any scholarly web sites as it is needed by many applications such as citation field-based search, author analysis, and citation analysis. Sequential models like HMMs and CRFs often used for tagging/segmentation. CRFs can exploit lots of features unlike HMMs as well as it considers dependencies between input observed variables. Hence in this work, we focus on the design and implementation of a CRF-based system. The final results indicate that CRFs worked well for the type of citations that appear in patents and selected features were considerably useful for citation parsing with CRFs. The major contribution of this work was effective feature engineering to find out useful features that can be extracted automatically and contribute to the system performance. Each feature type applied to the CRF analyzed for observing the marginal improvement. All features together added 15% to the overall accuracy. The Recall over 98% achieved for important fields like Author, Year, and Title, which show the system performed well in tagging related fields correctly out of all true tags. The accuracy, 96.4% obtained over all tags which means 3.6 tag errors per 100 words. It is more than sufficient for citation matching, because we only need a subset of fields (authors and year, or journal and year) to make an exact match. According to the results, a CRF-based system can be reliably used for citation segmentation in patents.
Towards Automated Threat Intelligence Fusion

The volume and frequency of new cyber attacks have exploded in recent years. Such events have very complicated workflows and involve multiple criminal actors and organizations. However, current practices for threat analysis and intelligence discovery are still performed piecemeal in an ad-hoc manner. For example, a modern malware analysis system can dissect a piece of malicious code by itself. But, it cannot automatically identify the criminals who developed it or relate other cyber attack events with it. Consequently, it is imperative to automatically assemble the jigsaw puzzles of cybercrime events by performing threat intelligence fusion on data collected from heterogeneous sources, such as malware, underground social networks, cryptocurrency transaction records, etc. In this paper, we propose an Automated Threat Intelligence fuSion framework (ATIS) that is able to take all sorts of threat sources into account and discover new intelligence by connecting the dots of apparently isolated cyber events. To this end, ATIS consists of 5 planes, namely analysis, collection, controller, data and application planes. We discuss the design choices we made in the function of each plane and the interfaces between two adjacent planes. In addition, we develop two applications on top of ATIS to demonstrate its effectiveness.
On Class-probability Estimates and Cost-sensitive Evaluation of Classiiers 1. Class-probability Estimates

This paper addresses two cost-sensitive learning methodology issues. First, we ask the question of whether Bagging is always an appropriate procedure to compute accurate class-probability estimates for cost-sensitive classiication. Second, we will point the reader to a potential source of erroneous results in the most common procedure of evaluating cost-sensitive classiiers when the real misclassiication costs are unknown. The paper concludes with an experimental comparison of MetaCost and BagCost, a procedure that labels unseen examples based their class probability estimates. Let hx i ; y i i be a set of training instances, and Y = fy 1 ; y 2 ; : : : ; y k g be the set of possible labels. Let us assume that the misclassiication costs are static and are described by a k k cost matrix C , with C (i; j) specifying the cost incurred when an example is predicted to be in class i when in fact it belongs to class j. If we denote with P (yjx) the class probability of an arbitrary example x, then the optimal decision for x is given by (Duda & Hart, 1973): h(x) = argmin y2Y k X j=1 P (y j jx)C(y; y j); (1) This gives us a cost-sensitive classiication procedure whose performance relies on the accuracy of the computed class-probability estimates. MetaCost (Domingos, 1999) is an algorithm that re-labels each training example with the cost-optimal label (computed according to (1)), and outputs the decision of a 0/1-loss classiier trained on the relabeled data. MetaCost uses Bagging (Breiman, 1996) to compute the class probability estimates. Bagging builds an ensemble of classiiers by training the same learning algorithm on a series of bootstrap samples of the training data. The class probability P (y i jx) is estimated by fraction of votes (each classiier in the ensemble outputs itself a vote between 0 and 1). In the meantime, a careful analysis of Bagging shows that these votes represent a measure of the variance of the base classiier, and this is diierent from the probability that we want to estimate (which is a parameter of the data). For example, in the extreme case, if the concept can be 0/1-learned perfectly by an algorithm trained on an arbitrary bootstrap sample, then the cost-sensitive decision boundaries of computed by MetaCost will be the same as the 0/1-loss decision boundaries and can be far from being optimal. Although …