Miracle-dz/UniFAR-FTUs huggingface dataset:
Mixed-initiative clustering

Mixed-initiative clustering is a task where a user and a machine work collaboratively to analyze a large set of documents. We hypothesize that a user and a machine can both learn better clustering models through enriched communication and interactive learning from each other. The first contribution or this thesis is providing a framework of mixed-initiative clustering. The framework consists of machine learning and teaching phases, and user learning and teaching phases connected in an interactive loop which allows bi-directional communication. The bi-directional communication languages define types of information exchanged in an interface. Coordination between the two communication languages and the adaptation capability of the machine's clustering model is the key to building a mixed-initiative clustering system. The second contribution comes from successfully building several systems using our proposed framework. Two systems are built with incrementally enriched communication languages — one enables user feedback on features for non-hierarchical clustering and the other accepts user feedback on hierarchical clustering results. This achievement validates our framework and also demonstrates the possibilities to develop machine learning algorithms to work with conceptual properties. The third contribution comes from the study of enabling real-time interactive capability in our full-fledged mixed-initiative clustering system. We provide several guidelines on practical issues that developers of mixed-initiative learning systems may encounter. The fourth contribution is the design of user studies for examining effectiveness of a mixed-initiative clustering system. We design the studies according to two scenarios, a learning scenario where a user develops a topic ontology from an unfamiliar data set, and a teaching scenario where a user knows the ontology and wants to transfer this knowledge to a machine. Results of the user studies demonstrate the mixed-initiative clustering has advantages over non-mixed-initiative approaches in terms of helping users learn an ontology as well as helping users teach a known ontology to a machine.
Cloud services and marketing

Nowadays, most of the companies use cloud tools. Well-known services, such as Gmail, Google Docs and Dropbox are used every day. Salesforce is commonly used in companies' business routine. Through social networks (Facebook, LinkedIn, Twitter), significant part of personal and business data is collected and saved in the cloud. So, all these services and their data are easily accessible and available for different purposes. On the other hand, users have to give up their privacy and control. Anyone with moderate analytical skills can research, profile and use all published data for their own gain and interest (advertising, sales, different analysis, etc.). Nothing excites managers and marketers more than the massive application of cloud services and infinite flow of fresh information about consumers, which is then used to improve their products and services. In this paper we present process improvements in marketing automation, lead tracking, and inbound marketing as a result of cloud services. No matter which cloud supported services are used, they are less time and money consuming and easier to access and manage. They are accepted by more users than traditional desktop tools. In the future, processes could be even more automated which could put businesses on autopilot. This might be the Holy Grail everyone is seeking.
Observations of [C II] 158 Micron Line and Far-Infrared Continuum Emission toward the High-Latitude Molecular Clouds in Ursa Major

We report the results of a rocket-borne observation of the [C II] 158 μm line and far-infrared continuum emission at 152.5 μm toward the high-latitude molecular clouds in Ursa Major. We also present the results of a follow-up observation of the millimeter 12CO J = 1 → 0 line over a selected region observed by the rocket-borne experiment. We have discovered three small CO cloudlets from the follow-up 12CO observations. We show that these molecular cloudlets, as well as the MBM clouds (MBM 27, 28, 29, and 30), are not gravitationally bound. Magnetic pressure and turbulent pressure dominate the dynamic balance of the clouds. After removing the H I-correlated and background contributions, we find that the [C II] emission peak is displaced from the 152.5 μm and CO peaks, while the 152.5 μm continuum emission is spatially correlated with the CO emission. We interpret this behavior by attributing the origin of the [C II] emission to the photodissociation regions around the molecular clouds illuminated by the local UV radiation field. We also find that the ratio of the molecular hydrogen column density to the velocity-integrated CO intensity is 1.19 ± 0.29 × 1020 cm-2 (K km s-1)-1, from the FIR continuum and the CO data. The average [C II]/FIR intensity ratio over the MBM clouds is 0.0071, which is close to the all-sky average of 0.0082 reported by FIRAS on the COBE satellite. The average [C II]/CO ratio over the same regions is 420, significantly lower than in molecular clouds in the Galactic plane.
Trust , Reciprocity and the Strength of Ties in Online Social Networks

We conduct a field experiment using a customized Facebook application that allows us to study the linkage between online social tie measures and an economic measure of trust. We endogenously identify latent heterogeneity in Facebook users’ notion of friendship and partition users as selective or non-selective based on their type. For non-selective pairs of users, each of whom are likely to befriend anybody and everybody, we find that their intersection of common friends is insignificant in its ability to predict trust. This provides the first evidence that traditional measures of dyadic-trust, such as embeddedness, used widely in physical world social networks do not directly carry over to online social networks. In contrast to existing network theory, our field study conducted within an online social network is able to capture revealed preference style measures that capture dyadic interactions between users. We find these measures have strong explanatory power in varying levels of trust, and thus we contribute to existing network theory in this new and expanding domain. Methodologically, our study is part of a nascent body of literature that showcases how large-scale online social networks can serve as platform to take social science and economics experimental research traditionally done in the physical laboratory to real-world subjects in a highly generalizable setting.
Profiling and classification of scientific documents with SAS Text Miner TM

The automatic classification of documents into categories is an increasingly important task. As in life sciences scientific document collections continue to grow at exponential growth rates, the task of retrieving and classifying the appropriate documents by hand can become unmanageable. In fact it is impossible to follow a scientific field by manual methods. SAS Institute (www.sas.com) and the European Molecular Biology Laboratory (EMBL)/ the ELM Consortium (http://elm.eu.org) are cooperating on the development of a text mining-application for the automated identification and ranking of scientific articles. The so-called topic scoring engine is based on the SAS Text Miner. The topic scoring engine identifies documents with similar contents and creates search-profiles which will comply with the congruencies of the documents. This paper will provide an overview over the key features of the topic scoring engine. Text categorization techniques have traditionally determined a subset of terms that are most diagnostic of particular categories and then tried to predict the categories using the weighted frequencies of each of those terms in each document. We will refer to this technique as the truncation approach (since only a subset of terms are used). This approach is subject to several deficiencies: 1. It does not take into account terms that are highly correlated with each other, such as synonyms. As a result, it is very important to employ a useful stemming algorithm, as well. 2. Documents are rated close to each other only according to co-occurrence of terms. Documents may be semantically similar to each other while having very few of the truncated terms in common. Most of these terms only occur in a small percentage of the documents. 3. The words used need to be recomputed for each category of interest. These problems present themselves also for text retrieval ; as a result it has become de rigueur to use a reduced-dimensionality vector-space model when retrieving documents using search terms. In the vector-space model, vectors in a multi-dimensional space can represent both documents and terms. To determine which documents match retrieval terms, Latent Semantic Analysis [3] is used to find the nearest documents in that space to the search terms. The use of a reduced-dimensionality normalized vector space model to represent documents in multi-dimensional space can be useful for both classification and categorization of text documents, particularly in the context of categorization approaches that are based on Euclidean distances between documents, such as Discriminant and Regression …
Human Action Recognition Using Compact Global Descriptors Derived from 2DPCA-2DLDA

Dense feature representations are recently popular for human action recognition based on video. They achieve better performance than sparse local representations. However, dense representations usually lead to high-dimensional data which contain plenty of redundancy information. In order to solve this issue, a novel compact representation based on global Gist feature and 2DPCA-2DLDA is proposed. Firstly Global grid based Gist features are extracted from image sequences and described as Gist matrices. The Gist matrices reflect global structure distribution of local grids, and at the same time depict frequency characteristics of the local grids. Secondly, bilateral dimensionality reduction is developed to our proposed Gist matrices. The most discriminative spatial structure and frequency characteristic of local grids are separately selected with 2DPCA and 2DLDA. Finally, the derived structure and frequency properties are simultaneously embedded into a low dimensional compact global descriptor. Experimental results on UCF Sports dataset validate that the proposed method makes a good compromise between feature discrimination and dimensions. And the action recognition accuracy is promising.
Exploiting Adaptive Packet-Sampling Measurements for Multimedia Traffic Classification

Abstract—With the huge amount of ubiquitous multimedia data transmitted in nowadays Internet, the use of packet sampling for traffic measurements has become widely employed for network operators. In this paper, we present an adaptive packet sampling technique from the classification perspective, the main sampling principle of which is to select as many packets with low occurrence rate as possible based on two useful features for multimedia traffic: Packet Size (PS) and Packet Inter Arrival Time (IAT). We build a model of the ideal packet sampling technique for classifying multimedia traffic, which adjusts adaptively the sampling probability of selecting packets according to PS and IAT predicted simultaneously by multi-output support vector regression, and define general indexes for evaluating the sampling performance of the proposed approach. We compare our approach with other sampling methods and evaluate their impact on the performance of traffic classification using two machine learning methods with real multimedia traffic data. The experimental results show that this approach has good sampling performance and is able to enhance the performance of the traffic classification methods.
MASAQ : A Multi-Agent System for Answering Questions Based on an Encyclopedic Knowledge Base

In this paper, we present a multi-agent system, called MASAQ, for answering users’ queries based on an encyclopedic knowledge base. MASAQ has three major components: (1) a natural language interface; (2) an executable specification language (EASL) for developing multi-agent systems for answering or reasoning about users’ queries; (3) an encyclopedic knowledge base covering twenty-one domains. In addition to those features, another novel feature of MASAQ is that the agents can run on the Internet as a distributed system, on a supercomputer as a parallel system, or on a desktop PC as a centralized system.
Optimal ankle compliance regulation for humanoid balancing control

Keeping balance is the main concern for humanoids in standing and walking tasks. This paper endeavors to acquire optimal ankle stabilization methods for humanoids with passive and active compliance and explain ankle balancing strategy from the compliance regulation perspective. Unlike classical stiff humanoids, the compliant ones can control both impedance and position during task operation. Optimal compliance regulation is resolved to maximize the stability of the humanoids. The linearized model is proposed to obtain the optimal ankle impedance for stabilizing against impacts. The nonlinear model is proposed as well and compared with the linear one. The proposed methods are validated by experiments on an intrinsically compliant humanoid using passivity based admittance and impedance controllers both in joint and Cartesian space.
Comparing Climate Change and Species Invasions as Drivers of Coldwater Fish Population Extirpations

Species are influenced by multiple environmental stressors acting simultaneously. Our objective was to compare the expected effects of climate change and invasion of non-indigenous rainbow smelt (Osmerus mordax) on cisco (Coregonus artedii) population extirpations at a regional level. We assembled a database of over 13,000 lakes in Wisconsin, USA, summarising fish occurrence, lake morphology, water chemistry, and climate. We used A1, A2, and B1 scenarios from the Intergovernmental Panel on Climate Change (IPCC) of future temperature conditions for 15 general circulation models in 2046–2065 and 2081–2100 totalling 78 projections. Logistic regression indicated that cisco tended to occur in cooler, larger, and deeper lakes. Depending upon the amount of warming, 25–70% of cisco populations are predicted to be extirpated by 2100. In addition, cisco are influenced by the invasion of rainbow smelt, which prey on young cisco. Projecting current estimates of rainbow smelt spread and impact into the future will result in the extirpation of about 1% of cisco populations by 2100 in Wisconsin. Overall, the effect of climate change is expected to overshadow that of species invasion as a driver of coldwater fish population extirpations. Our results highlight the potentially dominant role of climate change as a driver of biotic change.
Experiencing absurdity of organizational life: a dive in a kafkaesque universe

Dysfunctional bureaucracy is a major challenge for developing countries like Brazil today. There are innumerable problems, among which the lack of effectiveness, inefficiency, and absurd contexts that lead to stress, suffering and burn-out are highlighted. Although such organizations have received attention from the area of Administration and Organizational Studies, the studies related to coping with absurd contexts are very incipient. More rare are the empirical studies that analyse the daily life of people who face such situations. In order to address such gaps, this research is based on an empirical study carried out in a 15-month period (October 2015 to December 2016) of data collection through 50 interviews, conversations and daily observation of managers of research, development and innovation (R&D&I) projects in the largest company in the electric sector in Brazil. Secondary data and documents were also collected. The goal of this research is to develop theory about the absurd in bureaucratic organizations. This research contributes to the development of academic knowledge in two aspects: first, from the theoretical point of view, when dealing with absurdity in a bureaucratic organizational context, a subject not much discussed; second, from a practical point of view, to deepen the knowledge about the daily life of managers, and to bring elements that allow to rethink several of the daily challenges of these managers. Data was analyzed through an iterative thematic analysis. Through this I identified that the organizational context is complex and involves: (1) Bureaucracy; (2) Multiples cultures; (3) Leadership changes and the lack of sensegiver; (4) R&D&I colliding with organizational routine; (5) Personality in the processes; (6) Informality and non-registration of activities; (7) Politics. The data theory presents that the absurd is understood from the notions of: (a) Environment of contradictions; (b) Fear and guilt; and (c) Loneliness. The consequences of the absurd experience in the context studied involve: (i) Emotional work; (ii). Turnover; (iii) Non-implementation of projects; (iv) Metamorphosis of the sense of innovation; (v) Loss of sense of innovation; and (vi) Inaction. In answering the proposed research question, the contributions of this thesis are: (i) the elaboration of a working definition for absurdity; (ii) contribution to the practice of innovation management in the Brazilian electric sector, based on the reflection of its innovation dynamics. The theory generated is under construction and reflects the interpretation of a particular researcher. The effort of this research is expected to stimulate researchers in the continuity of research into the absurd in bureaucratic organizations.
Skeletal myocytes are a source of interleukin‐6 mRNA expression and protein release during contraction: evidence of fiber type specificity

In this study, we aimed to determine whether skeletal muscle cells per se are a source of interleukin (IL)‐6 during contraction and whether IL‐6 production is fiber type specific. Muscle biopsy samples were collected from seven males before (PRE) and after (POST) completing 120 min of continuous bicycle ergometry. Biopsies were sectioned and analyzed for the following: IL‐6 protein detected by immunohistochemistry (IHC), IL‐6 mRNA content detected by in situ hybridization, fiber type measured by either IHC or myofibrillar ATPase activity stain, and glycogen content measured by periodic acid schiff (PAS) assay. Fibers were qualitatively categorized according to glycogen content to one of five groups (1–5), with 1 being very low (LOW) and 5 being very high (HIGH) glycogen. Total fluorescence (PRE vs. POST) and glycogen‐dependent fluorescence (LOW vs. HIGH) of IL‐6 protein were quantitated using Metamorph software. Total IL‐6 protein was elevated from PRE to POST exercise (P<0.05). At PRE, IL‐6 protein was evenly distributed across all fibers at low levels, consistent with glycogen distribution. At POST, IL‐6 protein was greater (P<0.05) in HIGH compared with LOW glycogen fibers, which coincided with type 2 fibers. IL‐6 mRNA was distributed peripherally in all fibers at PRE. At POST, however, IL‐6 mRNA appeared predominantly in type 2 fibers, which also had higher glycogen content (P<0.05). These data demonstrate that myocytes per se are a source of IL‐6 produced during contraction. Our data also suggest that type 2 fibers predominantly produce IL‐6 during muscle contractile activity.
Regionalism in Standards: Good or Bad for Trade?

Regional agreements on standards have been largely ignored by economists and unconditionally blessed by multilateral trade rules. The authors find, theoretically and empirically, that such agreements increase trade between participating countries but not necessarily with the rest of the world. Adopting a common standard in a region-that is, harmonization-boosts exports of excluded industrial countries to the region. But it reduces exports of excluded developing countries, possibly because developing country firms are hurt more by an increase in the stringency of standards and benefit less from economies of scale in integrated markets. Mutual recognition agreements are more uniformly trade promoting unless they contain restrictive rules of origin, in which case intra-regional trade increases at the expense of trade with other, especially developing, countries. The authors propose a modification of international trade rules to strike a better balance between the interests of integrating and excluded countries.
Weighted bag hybrid multiple classifier machine for boosting prediction accuracy

Ensemblelearning of classifier has been a hot topic in pattern recognition problems for the last twenty years. This is because standalone classifier does not improve the performance when the dataset suffers from class imbalance.Ensemble learning is generally based on boosting and bagging techniques. Boostingcombines multiple classifiers of the same type, trained with weighted sample sets. Our aim is to improve the general boosting algorithm by usingdiversekinds of classifiers to build the ensemble of classifiers. Two different kinds of classifier - BP-MLP and RBFNN are considered for constructing the initial ensemble in our algorithm. Thestrategy is to assign an adaptive weight to the different types of classifiers based on their individual performancein order toboost a particular kind of classifier amongst the above two. Benchmark datasets from UCI repository are used for analysis which confirm that our method outperforms single type of learner based boosting.
Performance Analysis of a Dual-Hop Relay With STBC-CIOD over Rayleigh Fading Channels

In this paper, a symbol error rate (SER) study is presented for the dual-hop relay networks employing space-time block code (STBC) from coordinate interleaved orthogonal design (STBC-CIOD), where a relay with either a decode-and-forward (DF) or an amplify-and-forward (AF) protocol is equipped with multiple antennas. More specifically, we provide the union upper and lower bounds on the average SER by deriving an accurate closed-form formula for the corresponding symbol pairwise error rate (SPER). In addition, the asymptotic diversity order is analyzed. The theoretically derived bounds are in good agreement with the simulation results.
Unstructured video-based rendering: interactive exploration of casually captured videos

We present an algorithm designed for navigating around a performance that was filmed as a "casual" multi-view video collection: real-world footage captured on hand held cameras by a few audience members. The objective is to easily navigate in 3D, generating a video-based rendering (VBR) of a performance filmed with widely separated cameras. Casually filmed events are especially challenging because they yield footage with complicated backgrounds and camera motion. Such challenging conditions preclude the use of most algorithms that depend on correlation-based stereo or 3D shape-from-silhouettes. Our algorithm builds on the concepts developed for the exploration of photo-collections of empty scenes. Interactive performer-specific view-interpolation is now possible through innovations in interactive rendering and offline-matting relating to i) modeling the foreground subject as video-sprites on billboards, ii) modeling the background geometry with adaptive view-dependent textures, and iii) view interpolation that follows a performer. The billboards are embedded in a simple but realistic reconstruction of the environment. The reconstructed environment provides very effective visual cues for spatial navigation as the user transitions between viewpoints. The prototype is tested on footage from several challenging events, and demonstrates the editorial utility of the whole system and the particular value of our new inter-billboard optimization.