nils-herrmann/clean_scirepeval huggingface dataset:
Twitter spam detection based on deep learning

Twitter spam has long been a critical but difficult problem to be addressed. So far, researchers have developed a series of machine learning-based methods and blacklisting techniques to detect spamming activities on Twitter. According to our investigation, current methods and techniques have achieved the accuracy of around 80%. However, due to the problems of spam drift and information fabrication, these machine-learning based methods cannot efficiently detect spam activities in real-life scenarios. Moreover, the blacklisting method cannot catch up with the variations of spamming activities as manually inspecting suspicious URLs is extremely time-consuming. In this paper, we proposed a novel technique based on deep learning techniques to address the above challenges. The syntax of each tweet will be learned through WordVector Training Mode. We then constructed a binary classifier based on the preceding representation dataset. In experiments, we collected and implemented a 10-day real Tweet datasets in order to evaluate our proposed method. We first studied the performance of different classifiers, and then compared our method to other existing text-based methods. We found that our method largely outperformed existing methods. We further compared our method to non-text-based detection techniques. According to the experiment results, our proposed method was more accurate.
Archimetrix: Improved Software Architecture Recovery in the Presence of Design Deficiencies

Maintaining software systems requires up-to-date models of these systems to systematically plan, analyse and execute the necessary reengineering steps. Often, no or only outdated models of such systems exist. Thus, a reverse engineering step is needed that recovers the system's components, subsystems and connectors. However, reverse engineering methods are severely impacted by design deficiencies in the system's code base, e.g., they lead to wrong component structures. Several approaches exist today for the reverse engineering of component-based systems, however, none of them explicitly integrates a systematic design deficiency removal into the process to improve the quality of the reverse engineered architecture. Therefore, in our Archimetrix approach, we propose to regard the most relevant deficiencies with respect to the reverse engineered component-based architecture and support reengineers by presenting the architectural consequences of removing a given deficiency. We validate our approach on the Common Component Modeling Example and show that we are able to identify relevant deficiencies and that their removal leads to an improved reengineered architecture.
ELDEN: Improved Entity Linking Using Densified Knowledge Graphs

Entity Linking (EL) systems aim to automatically map mentions of an entity in text to the corresponding entity in a Knowledge Graph (KG). Degree of connectivity of an entity in the KG directly affects an EL system’s ability to correctly link mentions in text to the entity in KG. This causes many EL systems to perform well for entities well connected to other entities in KG, bringing into focus the role of KG density in EL. In this paper, we propose Entity Linking using Densified Knowledge Graphs (ELDEN). ELDEN is an EL system which first densifies the KG with co-occurrence statistics from a large text corpus, and then uses the densified KG to train entity embeddings. Entity similarity measured using these trained entity embeddings result in improved EL. ELDEN outperforms stateof-the-art EL system on benchmark datasets. Due to such densification, ELDEN performs well for sparsely connected entities in the KG too. ELDEN’s approach is simple, yet effective. We have made ELDEN’s code and data publicly available.
Fast and Accurate Detection and Classification of Plant Diseases

We propose and experimentally evaluate a software solution for automatic detection and classification of plant leaf diseases. The proposed solution is an improvement to the solution proposed in  as it provides faster and more accurate solution. The developed processing scheme consists of four main phases as in . The following two steps are added successively after the segmentation phase. In the first step we identify the mostlygreen colored pixels. Next, these pixels are masked based on specific threshold values that are computed using Otsu's method, then those mostly green pixels are masked. The other additional step is that the pixels with zeros red, green and blue values and the pixels on the boundaries of the infected cluster (object) were completely removed. The experimental results demonstrate that the proposed technique is a robust technique for the detection of plant leaves diseases. The developed algorithm‟s efficiency can successfully detect and classify the examined diseases with a precision between 83% and 94%, and can achieve 20% speedup over the approach proposed in .
Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network

Automated tissue characterization is one of the most crucial components of a computer aided diagnosis (CAD) system for interstitial lung diseases (ILDs). Although much research has been conducted in this field, the problem remains challenging. Deep learning techniques have recently achieved impressive results in a variety of computer vision problems, raising expectations that they might be applied in other domains, such as medical image analysis. In this paper, we propose and evaluate a convolutional neural network (CNN), designed for the classification of ILD patterns. The proposed network consists of 5 convolutional layers with 2 × 2 kernels and LeakyReLU activations, followed by average pooling with size equal to the size of the final feature maps and three dense layers. The last dense layer has 7 outputs, equivalent to the classes considered: healthy, ground glass opacity (GGO), micronodules, consolidation, reticulation, honeycombing and a combination of GGO/reticulation. To train and evaluate the CNN, we used a dataset of 14696 image patches, derived by 120 CT scans from different scanners and hospitals. To the best of our knowledge, this is the first deep CNN designed for the specific problem. A comparative analysis proved the effectiveness of the proposed CNN against previous methods in a challenging dataset. The classification performance ( ~ 85.5%) demonstrated the potential of CNNs in analyzing lung patterns. Future work includes, extending the CNN to three-dimensional data provided by CT volume scans and integrating the proposed method into a CAD system that aims to provide differential diagnosis for ILDs as a supportive tool for radiologists.
A Bayesian Model of Diachronic Meaning Change

Word meanings change over time and an automated procedure for extracting this information from text would be useful for historical exploratory studies, information retrieval or question answering. We present a dynamic Bayesian model of diachronic meaning change, which infers temporal word representations as a set of senses and their prevalence. Unlike previous work, we explicitly model language change as a smooth, gradual process. We experimentally show that this modeling decision is beneficial: our model performs competitively on meaning change detection tasks whilst inducing discernible word senses and their development over time. Application of our model to the SemEval-2015 temporal classification benchmark datasets further reveals that it performs on par with highly optimized task-specific systems.
Learning a Classification Model for Segmentation

We propose a two-class classification model for grouping. Human segmented natural images are used as positive examples. Negative examples of grouping are constructed by randomly matching human segmentations and images. In a preprocessing stage an image is oversegmented into superpixels. We define a variety of features derived from the classical Gestalt cues, including contour, texture, brightness and good continuation. Information-theoretic analysis is applied to evaluate the power of these grouping cues. We train a linear classifier to combine these features. To demonstrate the power of the classification model, a simple algorithm is used to randomly search for good segmentations. Results are shown on a wide range of images.
Real-Time Tracking via On-line Boosting

Very recently tracking was approached using classification techniques such as support vector machines. The object to be tracked is discriminated by a classifier from the background. In a similar spirit we propose a novel on-line AdaBoost feature selection algorithm for tracking. The distinct advantage of our method is its capability of on-line training. This allows to adapt the classifier while tracking the object. Therefore appearance changes of the object (e.g. out of plane rotations, illumination changes) are handled quite naturally. Moreover, depending on the background the algorithm selects the most discriminating features for tracking resulting in stable tracking results. By using fast computable features (e.g. Haar-like wavelets, orientation histograms, local binary patterns) the algorithm runs in real-time. We demonstrate the performance of the algorithm on several (publically available) video sequences.
Conceptual modeling for ETL processes

Extraction-Transformation-Loading (ETL) tools are pieces of software responsible for the extraction of data from several sources, their cleansing, customization and insertion into a data warehouse. In this paper, we focus on the problem of the definition of ETL activities and provide formal foundations for their conceptual representation. The proposed conceptual model is (a) customized for the tracing of inter-attribute relationships and the respective ETL activities in the early stages of a data warehouse project; (b) enriched with a 'palette' of a set of frequently used ETL activities, like the assignment of surrogate keys, the check for null values, etc; and (c) constructed in a customizable and extensible manner, so that the designer can enrich it with his own re-occurring patterns for ETL activities.
Limiting privacy breaches in privacy preserving data mining

There has been increasing interest in the problem of building accurate data mining models over aggregate data, while protecting privacy at the level of individual records. One approach for this problem is to randomize the values in individual records, and only disclose the randomized values. The model is then built over the randomized data, after first compensating for the randomization (at the aggregate level). This approach is potentially vulnerable to privacy breaches: based on the distribution of the data, one may be able to learn with high confidence that some of the randomized records satisfy a specified property, even though privacy is preserved on average.In this paper, we present a new formulation of privacy breaches, together with a methodology, "amplification", for limiting them. Unlike earlier approaches, amplification makes it is possible to guarantee limits on privacy breaches without any knowledge of the distribution of the original data. We instantiate this methodology for the problem of mining association rules, and modify the algorithm from [9] to limit privacy breaches without knowledge of the data distribution. Next, we address the problem that the amount of randomization required to avoid privacy breaches (when mining association rules) results in very long transactions. By using pseudorandom generators and carefully choosing seeds such that the desired items from the original transaction are present in the randomized transaction, we can send just the seed instead of the transaction, resulting in a dramatic drop in communication and storage cost. Finally, we define new information measures that take privacy breaches into account when quantifying the amount of privacy preserved by randomization.
On Using Very Large Target Vocabulary for Neural Machine Translation

Neural machine translation, a recently proposed approach to machine translation based purely on neural networks, has shown promising results compared to the existing approaches such as phrasebased statistical machine translation. Despite its recent success, neural machine translation has its limitation in handling a larger vocabulary, as training complexity as well as decoding complexity increase proportionally to the number of target words. In this paper, we propose a method based on importance sampling that allows us to use a very large target vocabulary without increasing training complexity. We show that decoding can be efficiently done even with the model having a very large target vocabulary by selecting only a small subset of the whole target vocabulary. The models trained by the proposed approach are empirically found to match, and in some cases outperform, the baseline models with a small vocabulary as well as the LSTM-based neural machine translation models. Furthermore, when we use an ensemble of a few models with very large target vocabularies, we achieve performance comparable to the state of the art (measured by BLEU) on both the English→German and English→French translation tasks of WMT’14.
Conservation cores: reducing the energy of mature computations

Growing transistor counts, limited power budgets, and the breakdown of voltage scaling are currently conspiring to create a utilization wall that limits the fraction of a chip that can run at full speed at one time. In this regime, specialized, energy-efficient processors can increase parallelism by reducing the per-computation power requirements and allowing more computations to execute under the same power budget. To pursue this goal, this paper introduces conservation cores. Conservation cores, or c-cores, are specialized processors that focus on reducing energy and energy-delay instead of increasing performance. This focus on energy makes c-cores an excellent match for many applications that would be poor candidates for hardware acceleration (e.g., irregular integer codes). We present a toolchain for automatically synthesizing c-cores from application source code and demonstrate that they can significantly reduce energy and energy-delay for a wide range of applications. The c-cores support patching, a form of targeted reconfigurability, that allows them to adapt to new versions of the software they target. Our results show that conservation cores can reduce energy consumption by up to 16.0x for functions and by up to 2.1x for whole applications, while patching can extend the useful lifetime of individual c-cores to match that of conventional processors.
A durable and energy efficient main memory using phase change memory technology

Using nonvolatile memories in memory hierarchy has been investigated to reduce its energy consumption because nonvolatile memories consume zero leakage power in memory cells. One of the difficulties is, however, that the endurance of most nonvolatile memory technologies is much shorter than the conventional SRAM and DRAM technology. This has limited its usage to only the low levels of a memory hierarchy, e.g., disks, that is far from the CPU.
 In this paper, we study the use of a new type of nonvolatile memories -- the Phase Change Memory (PCM) as the main memory for a 3D stacked chip. The main challenges we face are the limited PCM endurance, longer access latencies, and higher dynamic power compared to the conventional DRAM technology. We propose techniques to extend the endurance of the PCM to an average of 13 (for MLC PCM cell) to 22 (for SLC PCM) years. We also study the design choices of implementing PCM to achieve the best tradeoff between energy and performance. Our design reduced the total energy of an already low-power DRAM main memory of the same capacity by 65%, and energy-delay2 product by 60%. These results indicate that it is feasible to use PCM technology in place of DRAM in the main memory for better energy efficiency.
A quasi-passive compliant stance control Knee-Ankle-Foot Orthosis

In this paper, we present the design of a novel quasi-passive stance-control orthosis that implements a natural amount of knee compliance during the weight acceptance phase and potentially the entire stance phase of the gait, and allows for free motion during the rest of the gait. We explain that the unaffected knee behaves close to a linear torsional spring in stance and hypothesize that an assistive device that places a linear spring of appropriate stiffness in parallel with the knee can help restore the natural behavior of the joint in stance. We present the design of a friction-based latching mechanism and a control algorithm that engages the spring in parallel with the knee in stance and disengages it during the swing phase of gait, and explain how this module is implemented into a brace in order to create a novel class of compliant stance control orthosis. The device is quasi-passive in that a small actuator serves to lock and unlock the spring module, but the device otherwise requires no actuation and very little power, computation, and control to operate.
Automatic Book Spine Extraction and Recognition for Library Inventory Management

Manual inventory management in a library is by far arduous. Automation of book inspection can be achieved by using a simple camera based system that can recognize book spines in a book shelf. The book spines contain printed information such as title, author and publisher name, which can be extracted and verified with the library's database. Book spines can be segmented by detecting their rectangular boundaries which appear as straight lines. Line detection using hough transform and line segment detector may result in spurious boundaries due to the presence of long titles or graphics on the book spine. In this paper, we propose a technique to improve book spine border detection by devising set of constraints based on structural properties that can be used to filter the detected line segments so as to obtain book spine boundaries. The segmented book spines are binarized to extract the printed information such as title, author and publisher name. The text is recognized using Tesseract Optical Character Recognition Engine. The proposed algorithm was tested successfully on book shelf images with vertically oriented, uniformly inclined and multi-oriented book spines.
4W1H in mobile crowd sensing

With the rapid proliferation of sensor-rich smartphones, mobile crowd sensing has become a popular research field. In this article, we propose a four-stage life cycle (i.e., task creation, task assignment, individual task execution, and crowd data integration) to characterize the mobile crowd sensing process, and use 4W1H (i.e., what, when, where, who, and how) to sort out the research problems in the mobile crowd sensing domain. Furthermore, we attempt to foresee some new research directions in future mobile crowd sensing research.
Three Approaches to the Quantitative Definition of Information in an Individual Pure Quantum State

In analogy of classical Kolmogorov complexity we develop a theory of the algorithmic information in bits contained in any one of continuously many pure quantum states: quantum Kolmogorov complexity. Classical Kolmogorov complexity coincides with the new quantum Kolmogorov complexity restricted to the classical domain. Quantum Kolmogorov complexity is upper bounded and can be effectively approximated from above. With high probability a quantum object is incompressible. There are two alternative approaches possible: to define the complexity as the length of the shortest qubit program that effectively describes the object, and to use classical descriptions with computable real parameters.
The molecular neurobiology of depression

Unravelling the pathophysiology of depression is a unique challenge. Not only are depressive syndromes heterogeneous and their aetiologies diverse, but symptoms such as guilt and suicidality are impossible to reproduce in animal models. Nevertheless, other symptoms have been accurately modelled, and these, together with clinical data, are providing insight into the neurobiology of depression. Recent studies combining behavioural, molecular and electrophysiological techniques reveal that certain aspects of depression result from maladaptive stress-induced neuroplastic changes in specific neural circuits. They also show that understanding the mechanisms of resilience to stress offers a crucial new dimension for the development of fundamentally novel antidepressant treatments.
Neural Relation Extraction via Inner-Sentence Noise Reduction and Transfer Learning

Extracting relations is critical for knowledge base completion and construction in which distant supervised methods are widely used to extract relational facts automatically with the existing knowledge bases. However, the automatically constructed datasets comprise amounts of low-quality sentences containing noisy words, which is neglected by current distant supervised methods resulting in unacceptable precisions. To mitigate this problem, we propose a novel word-level distant supervised approach for relation extraction. We first build Sub-Tree Parse (STP) to remove noisy words that are irrelevant to relations. Then we construct a neural network inputting the subtree while applying the entity-wise attention to identify the important semantic features of relational words in each instance. To make our model more robust against noisy words, we initialize our network with a priori knowledge learned from the relevant task of entity classification by transfer learning. We conduct extensive experiments using the corpora of New York Times (NYT) and Freebase. Experiments show that our approach is effective and improves the area of Precision/Recall (PR) from 0.35 to 0.39 over the state-of-the-art work.
A miniaturized electrolytic pump sensorized with a strain gauge based on thermoplastic nanocomposite for drug delivery systems

In this paper we present a miniature electrolytic pump sensorized with a novel strain sensor to be used as active component of a drug delivery system. It consists of an electrolytic solution reservoir where inert electrodes are immersed. By polarizing the electrodes, the electrolytic reaction is activated and the produced gases (i.e. oxygen and hydrogen) displace an elastic membrane delimiting the electrolytic solution reservoir. In order to measure and monitor the membrane displacement, and therefore the volume of drug ejected, a strain gauge sensor has been prepared using a conductive thermoplastic nanocomposite elastomer (CTPE). The sensor has been fixed on the deformable membrane. The conductive thermoplastic elastomer is a good candidate for this application because of its high sensitivity. Furthermore, the CTPE allows to customize the resistance of the device in order to obtain low power consumption.
Square Root SAM: Simultaneous Localization and Mapping via Square Root Information Smoothing

Solving the SLAM (simultaneous localization and mapping) problem is one way to enable a robot to explore, map, and navigate in a previously unknown environment. Smoothing approaches have been investigated as a viable alternative to extended Kalman filter (EKF)based solutions to the problem. In particular, approaches have been looked at that factorize either the associated information matrix or the measurement Jacobian into square root form. Such techniques have several significant advantages over the EKF: they are faster yet exact; they can be used in either batch or incremental mode; are better equipped to deal with non-linear process and measurement models; and yield the entire robot trajectory, at lower cost for a large class of SLAM problems. In addition, in an indirect but dramatic way, column ordering heuristics automaticallyexploit the locality inherent in the geographic nature of the SLAM problem. This paper presents the theory underlying these methods, along with an interpretation of factorization in terms of the graphical model associated with the SLAM problem. Both simulation results and actual SLAM experiments in large-scale environments are presented that underscore the potential of these methods as an alternative to EKF-based approaches. mobile robots, SLAM, graphical models