Miracle-dz/UniFAR-FTUs huggingface dataset:
Formal Model and Conceptual Architecture of the Annotation Service for Dynamic Ubiquitous Knowledge Environments

This thesis is a study of the themes and the issues concerning the annotation of digital contents, such as textual documents, images, and multimedia documents in general. These digital contents are automatically managed by different kinds of digital library systems and, more generally speaking, by different kinds of information management systems. Even though this topic has already been partially studied, the previous research work on annotations has left us with many open issues. These issues concerned the lack of clarity about: what an annotation is, what the features and the way of using an annotation are, and what architecture and functionalities a system with annotation capabilities has to provide. These issues are mainly due to the fact that, up to now, models and systems for annotations have been developed for specific purposes. As a result of this, there is a fragmentary picture on the annotation and its management, which are tied to specific usages and lack a general validity. The goal of the thesis is to provide a unified and integrated picture on the annotation. Thus, the thesis ranges from defining what an annotation is by providing a formal model of the annotation to designing the architecture of a system with annotation capabilities. The major contributions of this thesis are the provision of the groundwork needed to properly delineate the complexity of the problem, which is often overlooked, and the definition of both a conceptual model and a unified formal model of the annotation, which is not touched upon in previous research work. Finally, the thesis designs the conceptual architecture of a system capable of providing different information management systems with annotation functionalities.
Supervised Learning for Multi-Domain Text Classification

Digital information available on the Internet is increasing day by day. As a result of this, the demand for tools that help people in finding and analyzing all these resources are also growing in number. Text Classification, in particular, has been very useful in managing the information. Text Classification is the process of assigning natural language text to one or more categories based on the content. It has many important applications in the real world. For example, finding the sentiment of the reviews, posted by people on restaurants, movies and other such things are all applications of Text classification. In this project, focus has been laid on Sentiment Analysis, which identifies the opinions expressed in a piece of text. It involves categorizing opinions in text into categories like 'positive' or 'negative'. Existing works in Sentiment Analysis focused on determining the polarity (Positive or negative) of a sentence. This comes under binary classification, which means classifying the given set of elements into two groups. The purpose of this research is to address a different approach for Sentiment Analysis called Multi Class Sentiment Classification. In this approach the sentences are classified under multiple sentiment classes like positive, negative, neutral and so on. Classifiers are built on the Predictive Model, that consists of multiple phases. Analysis of different sets of features on the data set, like stemmers, n-grams, tf-idf and so on, will be considered for classification of the data. Different classification models like Bayesian Classifier, Random Forest and SGD classifier are taken into consideration for classifying the data and their results are compared. Frameworks like Weka, Apache Mahout and Scikit are used for building the classifiers.
Evaluating Persistent Memory Range Indexes: Part Two

Scalable persistent memory (PM) has opened up new opportunities for building indexes that operate and persist data directly on the memory bus, potentially enabling instant recovery, low latency and high throughput. When real PM hardware (Intel Optane Persistent Memory) first became available, previous work evaluated PM indexes proposed in the pre-Optane era. Since then, newer indexes based on real PM have appeared, but it is unclear how they compare to each other and to previous proposals, and what further challenges remain. This paper addresses these issues by analyzing and experimentally evaluating state-of-the-art PM range indexes built for real PM. We find that newer designs inherited past techniques with new improvements, but do not necessarily outperform pre-Optane era proposals. Moreover, PM indexes are often very competitive with or even outperform indexes tailored for DRAM, highlighting the potential of using a unified design for both PM and DRAM. Functionality-wise, these indexes still lack good support for variable-length keys and handling NUMA effect. Based on our findings, we distill new design principles and highlight future directions.
The influence of genetic variants on serotonin transporter binding under SSRI treatment in Major Depressive Disorder

5 Zusammenfassung . 6 Background & Current State of Research . 7 I. Depression – Public Health, Symptoms, Classification . 7 II. Treatment . 12 III. Etiology . 14 From Twin Studies to Genome Wide Association and further . 19 1. Psychiatric disorders as multicausal diseases . 19 2. SNP (single nucleotide polymorphism) assessment . 20 3. Neuroimaging – the solution? 20 Study Protocol . 27 Genes of the Serotonergic System . 29 I. Serotonin Transport . 29 1. The Serotonin Transporter (5-HTT) . 29 2. The Vesicular Monoamine Transporter (VMAT) . 32 II. The Serotonin Receptor Family . 34 3. 5-HT1A Receptor . 36 4. The 5-HT1B Receptor . 40 5. Other 5-HT1 Receptors . 42 6. The 5-HT2A receptor . 43 III. Serotonin Metabolism . 46 1. Tryptophan Hydroxylase . 46 4 2. Monoamine Oxidase (MAO) . 48 Material & Methods . 49 I. Probands . 49 II. Treatment . 50 III. Blood Sampling & Genotyping . 50 IV. PET Image Acquisition& Quantification . 51 Results . 54 I. Baseline Analysis (before Treatment) . 55 1. Baseline Results (beforeTreatment) . 57 II. Repeated Measures ANOVA . 60 1. RMA Results . 61 Discussion . 74 Literature . 83
IIITG-ADBU at HASOC 2019: Automated Hate Speech and Offensive Content Detection in English and Code-Mixed Hindi Text

This paper presents the results obtained by using Logistic Regression (LR), Support Vector Machine (SVM), bi-directional long short-term memory (BiLSTM) and Neural Network (NN) models for subtask A of the shared task “Hate Speech and Oﬀensive Content Iden-tiﬁcation in Indo-European Languages” (HASOC). This paper presents the results for English and code-mixed Hindi language. Embeddings from Language Models (ELMo), Glove and fastText embeddings, and TF-IDF features of character and word n-grams have been used to train the models. Our best models for Hindi and English language obtained F1 score of 81.05 and 74.62 respectively on the oﬃcial run. The models obtained the 4 th and 8 th position in the oﬃcial ranking.
Emotion based voted classifier for Arabic irony tweet identification

In this paper, we have worked on irony detection in the Arabic language, a task which is organized by FIRE 2019. The tweets have been preprocessed and tokenized to extract the frequency-based, emotion-based features. These features are used to irony identiﬁcation using the voted classiﬁer. The F-score of our proposed approach is 0.807 and the top-ranking developed method having F-score of . 037, so the diﬀerence between F-score makes our approach better.
Sexual Dimorphism of the Maxilla in European Hare Using Geometric Morphometric Techniques

In this study, 40 European hares (20 male and 20 female) that were obtained from the provinces of Elazığ and Malatya in Turkey, were used. The samples were evaluated by placing 23 landmarks on the ventral of the maxilla. After the landmarks were placed onto the pictures, which were developed by means of Thin Plate Spline (TPS) program, the landmarks were standardized by using Morpheus program. Pairwise test, Principle Component Analysis (PCA) and Discriminant Function Analysis (DFA) were applied to the data. The result of the statistical analysis with geometric morphometric techniques showed no sexual dimorphism (SD) in Europan hares (Lepus europaeus).
Exploiting Unstructured Sparsity on Next-Generation Datacenter Hardware

Recurrent neural networks (RNNs) form a significant proportion of data center deep learning inference (29% [1]). This includes workloads like machine translation, speech synthesis and speech transcription. Such models can be substantially compressed, reducing computational cost. In this paper we show, with explicit reference to a benchmark transcription model, how to train such models to very high levels of sparsity (greater than 95%) with minimal loss of accuracy (less than 0.23%). We then present our scalable tile-based accelerator architecture, which can exploit the unstructured sparsity of such models to accelerate inference. We demonstrate the performance of our MAU accelerator on an Intel® Stratix® 10 FPGA ,2 data center board highlighting this platform as an ideal target for data center RNN inference tasks. We demonstrate how algorithm-accelerator co-design can achieve extremely high performance on models with high levels of unstructured sparsity.
High Loading of Hydrophobic and Hydrophilic Agents via Small Immunostimulatory Carrier for Enhanced Tumor Penetration and Combinational Therapy

Development of small-sized nanoformulations for effective tumor penetration, particularly for those tumors with dense stroma is a major challenge in cancer nanomedicine. It is even more challenging to achieve effective co-loading of both hydrophobic and hydrophilic anticancer agents through a small-sized nanocarrier. In this work, we designed a novel redox-responsive gemcitabine (GEM)-conjugated polymer POEG-co-PVDGEM (PGEM) as a small-sized nanocarrier to co-deliver hydrophilic GEM and hydrophobic paclitaxel (PTX). Methods: The in vitro physicochemical and biological properties of PTX/PGEM NPs were characterized. The efficiency of the PGEM carrier in selective codelivery of GEM and PTX in two murine tumor models as well as a patient derived xenograft model (PDX) was also evaluated. In addition, we investigated the changes in tumor immune microenvironment after treatment with PTX/PGEM nanoparticles. Results: We discovered that GEM conjugation could significantly decrease the nanoparticle size from 160 nm to 13 nm. Moreover, different from most reported GEM-conjugated polymers, PGEM polymer could serve as a prodrug carrier to load a wide variety of hydrophobic agents with high drug loading capacity and excellent stability. More importantly, our strategy could be extended to various nucleotides-based drugs such as azacytidine, decitabine and cytarabine, suggesting a new platform for co-delivery of various first line hydrophilic and hydrophobic anticancer agents. Imaging showed that our small-sized carrier was much more effective in tumor accumulation and penetration compared to the relatively large-sized drug carrier. The PGEM prodrug-based carrier not only well retained the pharmacological activity of GEM, but also boosted T-cell immune response. Furthermore, delivery of PTX via PGEM led to significantly improved antitumor activity in several murine cancer models and a PDX model of colon cancer. Conclusion: This work not only provided a small-sized carrier platform that was able to load multiple hydrophilic and hydrophobic drugs with high loading capacity, but also provided an effective regimen for enhanced tumor penetration and improved anti-tumor immunity.
A Feature Selection Layer Integrated Within A Deep Learning Framework

In a standard Industrial Internet-of-things (IoT) setting where large amount of sensors are monitoring a single process multiple problems may arise. Mis-relevancy between the feature variables and the target, large redundancy in different sensors and missing data points increase the importance of applying a feature selection phase. In our work we present a method that allows to train a deep neural network model and simultaneously learn which features are informative in the process. Our contribution differs from previous methods in a sense that the features selected don’t relay on any imposed non-linearity. Moreover, our method proposes to learn the neural net parameters and select the features in a single end to end training methodology.
Analysis of Spatio-temporal Properties of Stochastic Systems Using TSTL

In this article, we present Three-Valued spatio-temporal Logic (TSTL), which enriches the available spatiotemporal analysis of properties expressed in Signal spatio-temporal Logic (SSTL), to give further insight into the dynamic behavior of systems. Our novel analysis starts from the estimation of satisfaction probabilities of given SSTL properties and allows the analysis of their temporal and spatial evolution. Moreover, in our verification procedure, we use a three-valued approach to include the intrinsic and unavoidable uncertainty related to the simulation-based statistical evaluation of the estimates; this can be also used to assess the appropriate number of simulations to use depending on the analysis needs. We present the syntax and three-valued semantics of TSTL and specific extended monitoring algorithms to check the validity of TSTL formulas. We introduce a reliability requirement for TSTL monitoring and an automatic procedure to verify it. Two case studies demonstrate how TSTL broadens the application of spatio-temporal logics in realistic scenarios, enabling analysis of threat monitoring and privacy preservation based on spatial stochastic population models.
Treatment Effect Estimation via Differentiated Confounder Balancing and Regression

Treatment effect plays an important role on decision making in many fields, such as social marketing, healthcare, and public policy. The key challenge on estimating treatment effect in the wild observational studies is to handle confounding bias induced by imbalance of the confounder distributions between treated and control units. Traditional methods remove confounding bias by re-weighting units with supposedly accurate propensity score estimation under the unconfoundedness assumption. Controlling high-dimensional variables may make the unconfoundedness assumption more plausible, but poses new challenge on accurate propensity score estimation. One strand of recent literature seeks to directly optimize weights to balance confounder distributions, bypassing propensity score estimation. But existing balancing methods fail to do selection and differentiation among the pool of a large number of potential confounders, leading to possible underperformance in many high-dimensional settings. In this article, we propose a data-driven Differentiated Confounder Balancing (DCB) algorithm to jointly select confounders, differentiate weights of confounders and balance confounder distributions for treatment effect estimation in the wild high-dimensional settings. Besides, under some settings with heavy confounding bias, in order to further reduce the bias and variance of estimated treatment effect, we propose a Regression Adjusted Differentiated Confounder Balancing (RA-DCB) algorithm based on our DCB algorithm by incorporating outcome regression adjustment. The synergistic learning algorithms we proposed are more capable of reducing the confounding bias in many observational studies. To validate the effectiveness of our DCB and RA-DCB algorithms, we conduct extensive experiments on both synthetic and real-world datasets. The experimental results clearly demonstrate that our algorithms outperform the state-of-the-art methods. By incorporating regression adjustment, our RA-DCB algorithm achieves more precise estimation on treatment effect than DCB algorithm, especially under the settings with heavy confounding bias. Moreover, we show that the top features ranked by our algorithm generate accurate prediction of online advertising effect.
Random Playlists Smoothly Commuting Between Styles

Someone enjoys listening to playlists while commuting. He wants a different playlist of n songs each day, but always starting from Locked Out of Heaven, a Bruno Mars song. The list should progress in smooth transitions between successive and randomly selected songs until it ends up at Stairway to Heaven, a Led Zeppelin song. The challenge of automatically generating random and heterogeneous playlists is to find the appropriate balance among several conflicting goals. We propose two methods for solving this problem. One is called ROPE, and it depends on a representation of the songs in a Euclidean space. It generates a random path through a Brownian Bridge that connects any two songs selected by the user in this music space. The second is STRAW, which constructs a graph representation of the music space where the nodes are songs and edges connect similar songs. STRAW creates a playlist by traversing the graph through a steering random walk that starts on a selected song and is directed toward a target song also selected by the user. When compared with the state-of-the-art algorithms, our algorithms are the only ones that satisfy the following quality constraints: heterogeneity, smooth transitions, novelty, scalability, and usability. We demonstrate the usefulness of our proposed algorithms by applying them to a large collection of songs and make available a prototype.
Towards Automatically Classifying Case Law Citation Treatment Using Neural Networks

In common law legal systems, judges decide issues between parties (legal decision or case law) by reference to previous decisions that consider similar factual situations. Accordingly, these decisions typically feature rich citation networks, i.e., a new decision frequently cites previous relevant decisions (citation). These citations may, in varying degrees, express that a cited decision is applicable, not-applicable, or no longer current law. Such treatment label is important to a lawyer's process of determining whether a case is proper law. These labels serve as a matter of convenience in citation indices enabling lawyers to prioritise decisions to examine to understand the current state of the law. They also prove useful in other areas such as prioritisation for manual summarisation of cases, where not all cases can be summarised, and automatic summarisation, or, potentially, as a ranking feature in case law retrieval. While a lawyer can determine the treatment of a cited case by reading a decision, this is time consuming and can increase legal costs. Currently, not all newly decided cases feature these treatment labels. Further, older cases typically do not. Given the large amount of new legal decisions decided each year, manual annotation of such treatment is not feasible. In this paper, we explore the effectiveness of neural network architectures for identifying case law citation treatment and importance (whether a case is important to a lawyer's reasoning process). We find that these tasks are very difficult and various methods for text classification perform poorly. We address more comprehensively the task of citation importance for this reason while limiting our examination of the task of citation treatment to the modelling of the problem and the highlight of the intrinsic difficulty of the task. We make a test dataset available at github.com/ielab/caselaw-citations to stimulate further research that tackles this challenging problem. We also contribute a range of word embeddings learned over a large amount of processed case law text.
On the Correct Measurement of Application Memory Bandwidth and Memory Access Latency

Diagnosing if an application suffers from DRAM contention can be a challenging task. One method is to compare the hardware memory bandwidth limit with the measured memory bandwidth of an application. Another method is based on memory access latency. The latency of a DRAM access in an uncontended state is a hardware characteristic. If an application shows higher DRAM access latency, the increase comes from queuing delays and the application is limited by DRAM bandwidth. Hardware-based measurement of the application's latency and bandwidth can be done with low-overhead and is agnostic of the application's implementation. But the practical implementation of such a diagnosis system on CPUs is difficult. In modern CPUs, there is an abundance of performance counters and only superficial documentation. Different types of counters for bandwidth or latency, that seemingly measure the same thing, produce different results. There is no in-depth understanding of those performance counters and naive usage may lead to incorrect measurements. Because there is no hardware feature to measure DRAM access latency directly, the implementation of the above-mentioned latency based method may seem impossible. In this paper, we compare various hardware latency and bandwidth measurement methods on CPUs by using micro-benchmarks. We show results of Intel Haswell, Broadwell and Skylake systems. With our experiments, we show how and why performance counters for bandwidth and latency differ. Only the counters inside of the memory controller correctly measure bandwidth. Latency measured by instruction sampling is suitable to find DRAM contention, even though it is not a pure DRAM access latency.
Enhancing a manycore-oriented compressed cache for GPGPU

GPUs can achieve high performance by exploiting massive-thread parallelism. However, some factors limit performance on GPUs, one of which is the negative effects of L1 cache misses. In some applications, GPUs are likely to suffer from L1 cache conflicts because a large number of cores share a small L1 cache capacity. A cache architecture that is based on data compression is a strong candidate for solving this problem as it can reduce the number of cache misses. Unlike previous studies, our data compression scheme attempts to exploit the value locality existing within not only intra cache lines but also inter cache lines. We enhance the structure of a last-level compression cache proposed for general purpose manycore processors to optimize against shared L1 caches on GPUs. The experimental results reveal that our proposal outperforms the other compression cache for GPUs by 11 points on average.
A Novel Approach to Distributed Quantization via Multivariate Information Bottleneck Method

Consider following setup: A number of observations from a data source shall be compressed jointly prior to a forward transmission via several rate- limited links to a central processing unit. To design the respective quantizers, here, Mutual Information is chosen as the fidelity criterion and the broad-ranging structure of Multivariate Information Bottleneck is then aptly tailored to that purpose. This, indeed, not only yields a novel design approach for the considered distributed scenario but also paves the way towards perceiving the chance of leveraging this flexible conceptual frame in a vast variety of applications regarding digital data transmission. Explicitly, it immediately enables addressing various extensions of the presumed arrangement, incorporating the parallel construction of intertwined compression systems for several correlated sources.