nils-herrmann/clean_scirepeval huggingface dataset:
Bisecting K-Means for Clustering Web Log data

Web usage mining is the area of web mining which deals with extraction of useful knowledge from web log information produced by web servers. One of the most important tasks of Web Usage Mining (WUM) is web user clustering which forms groups of users exhibiting similar interests or similar browsing patterns. This paper presents results of clustering techniques for Web log data using K-means and Bisecting K-means algorithm. Clusters are formed with respect to similar IP address and packet combinations. The clustering framework is further used as an approach for intrusion detection from the log files. The system is trained first by labeling the classes and then tested to check for any intrusions. Recommendation output is generated which help in classifying the whether the input IP&apos;s are &quot;safe&quot; or &quot;infected&quot;. Comparison of both algorithms is done and performance is evaluated with respect to time and accuracy. From the experimental results, it is found that Bisecting K-means overcomes the major drawbacks of basic K-means algorithm.
Reliable and intuitive teleoperation of LineScout: a mobile robot for live transmission line maintenance

Power line inspection and maintenance is a slowly but surely emerging field for robotics. This paper describes the control scheme implemented in LineScout technology, one of the first teleoperated obstacle crossing systems that has progressed to the stage of actually performing very-high-voltage power line jobs. Following a brief overview of the hardware and software architecture, key challenges associated with the objectives of achieving reliability, robustness and ease of operation are presented. The coordinated control through visual feedback of all motors needed for obstacle crossing calls for a coherent strategy, an effective graphical user interface and rules to ensure safe, predictable operation. Other features such as automatic weight balancing are introduced to lighten the workload and let the operator concentrate on inspecting power line components. Open architecture was considered for progressive improvements. The features required to succeed in making power line robots fully autonomous are also discussed.
Four ethical priorities for neurotechnologies and AI

A challenging goal in neuroscience is to be able to read out, or decode, mental content from brain activity. Recent functional magnetic resonance imaging (fMRI) studies have decoded orientation, position and object category from activity in visual cortex. However, these studies typically used relatively simple stimuli (for example, gratings) or images drawn from fixed categories (for example, faces, houses), and decoding was based on previous measurements of brain activity evoked by those same stimuli or categories. To overcome these limitations, here we develop a decoding method based on quantitative receptive-field models that characterize the relationship between visual stimuli and fMRI activity in early visual areas. These models describe the tuning of individual voxels for space, orientation and spatial frequency, and are estimated directly from responses evoked by natural images. We show that these receptive-field models make it possible to identify, from a large set of completely novel natural images, which specific image was seen by an observer. Identification is not a mere consequence of the retinotopic organization of visual areas; simpler receptive-field models that describe only spatial tuning yield much poorer identification performance. Our results suggest that it may soon be possible to reconstruct a picture of a person’s visual experience from measurements of brain activity alone.
Convicting exploitable software vulnerabilities: An efficient input provenance based approach

Software vulnerabilities are the root cause of a wide range of attacks. Existing vulnerability scanning tools are able to produce a set of suspects. However, they often suffer from a high false positive rate. Convicting a suspect and vindicating false positives are mostly a highly demanding manual process, requiring a certain level of understanding of the software. This limitation significantly thwarts the application of these tools by system administrators or regular users who are concerned about security but lack of understanding of, or even access to, the source code. It is often the case that even developers are reluctant to inspect/fix these numerous suspects unless they are convicted by evidence. In this paper, we propose a lightweight dynamic approach which generates evidence for various security vulnerabilities in software, with the goal of relieving the manual procedure. It is based on data lineage tracing, a technique that associates each execution point precisely with a set of relevant input values. These input values can be mutated by an offline analysis to generate exploits. We overcome the efficiency challenge by using Binary Decision Diagrams (BDD). Our tool successfully generates exploits for all the known vulnerabilities we studied. We also use it to uncover a number of new vulnerabilities, proved by evidence.
Leafsnap: A Computer Vision System for Automatic Plant Species Identification

We describe the first mobile app for identifying plant species using automatic visual recognition. The system – called Leafsnap – identifies tree species from photographs of their leaves. Key to this system are computer vision components for discarding non-leaf images, segmenting the leaf from an untextured background, extracting features representing the curvature of the leaf’s contour over multiple scales, and identifying the species from a dataset of the 184 trees in the Northeastern United States. Our system obtains state-of-the-art performance on the real-world images from the new Leafsnap Dataset – the largest of its kind. Throughout the paper, we document many of the practical steps needed to produce a computer vision system such as ours, which currently has nearly a million users.
A Silicon-Based 0.3 THz Frequency Synthesizer With Wide Locking Range

A 300 GHz frequency synthesizer incorporating a triple-push VCO with Colpitts-based active varactor (CAV) and a divider with three-phase injection is introduced. The CAV provides frequency tunability, enhances harmonic power, and buffers/injects the VCO fundamental signal from/to the divider. The locking range of the divider is vastly improved due to the fact that the three-phase injection introduces larger allowable phase change and injection power into the divider loop. Implemented in 90 nm SiGe BiCMOS, the synthesizer achieves a phase-noise of -77.8 dBc/Hz (-82.5 dBc/Hz) at 100 kHz (1 MHz) offset with a crystal reference, and an overall locking range of 280.32-303.36 GHz (7.9%).
JStill: mostly static detection of obfuscated malicious JavaScript code

The dynamic features of the JavaScript language not only promote various means for users to interact with websites through Web browsers, but also pose serious security threats to both users and websites. On top of this, obfuscation has become a popular technique among malicious JavaScript code that tries to hide its malicious purpose and to evade the detection of anti-virus software. To defend against obfuscated malicious JavaScript code, in this paper we propose a mostly static approach called JStill. JStill captures some essential characteristics of obfuscated malicious code by function invocation based analysis. It also leverages the combination of static analysis and lightweight runtime inspection so that it can not only detect, but also prevent the execution of the obfuscated malicious JavaScript code in browsers. Our evaluation based on real-world malicious JavaScript samples as well as Alexa top 50,000 websites demonstrates high detection accuracy (all in our experiment) and low false positives of JStill. Meanwhile, JStill only incurs negligible performance overhead, making it a practical solution to preventing obfuscated malicious JavaScript code.
The Simplified Disease Activity Index (SDAI) and the Clinical Disease Activity Index (CDAI): a review of their usefulness and validity in rheumatoid arthritis.

Composite indices or pooled indices are useful tools for the evaluation of disease activity in patients with rheumatoid arthritis (RA). They allow the integration of various aspects of the disease into a single numerical value, and may therefore facilitate consistent patient care and improve patient compliance, which both can lead to improved outcomes. The Simplified Disease Activity Index (SDAI) and the Clinical Disease Activity Index (CDAI) are two new tools for the evaluation of disease activity in RA. They have been developed to provide physicians and patients with simple and more comprehensible instruments. Moreover, the CDAI is the only composite index that does not incorporate an acute phase response and can therefore be used to conduct a disease activity evaluation essentially anytime and anywhere. These two new tools have not been developed to replace currently available instruments such as the DAS28, but rather to provide options for different environments. The comparative construct, content, and discriminant validity of all three indices--the DAS28, the SDAI, and the CDAI--allow physicians to base their choice of instrument on their infrastructure and their needs, and all of them can also be used in clinical trials.
Weakly supervised learning of actions from transcripts

We present an approach for weakly supervised learning of human actions from video transcriptions. Our system is based on the idea that, given a sequence of input data and a transcript, i.e. a list of the order the actions occur in the video, it is possible to infer the actions within the video stream and to learn the related action models without the need for any frame-based annotation. Starting from the transcript information at hand, we split the given data sequences uniformly based on the number of expected actions. We then learn action models for each class by maximizing the probability that the training video sequences are generated by the action models given the sequence order as defined by the transcripts. The learned model can be used to temporally segment an unseen video with or without transcript. Additionally, the inferred segments can be used as a starting point to train high-level fully supervised models. We evaluate our approach on four distinct activity datasets, namely Hollywood Extended, MPII Cooking, Breakfast and CRIM13. It shows that the proposed system is able to align the scripted actions with the video data, that the learned models localize and classify actions in the datasets, and that they outperform any current state-of-the-art approach for aligning transcripts with video data.
RC-Based Temperature Prediction Scheme for Proactive Dynamic Thermal Management in Throttle-Based 3D NoCs

The three-dimensional Network-on-Chip (3D NoC) has been proposed to solve the complex on-chip communication issues in multicore systems using die stacking in recent days. Because of the larger power density and the heterogeneous thermal conductance in different silicon layers of 3D NoC, the thermal problems of 3D NoC become more exacerbated than that of 2D NoC and become a major design constraint for a high-performance system. To control the system temperature under a certain thermal limit, many Dynamic Thermal Managements (DTMs) have been proposed. Recently, for emergent cooling, the full throttling scheme is usually employed as the system temperature reaches the alarming level. Hence, the conventional reactive DTM suffers from significant performance impact because of the pessimistic reaction. In this paper, we propose a throttle-based proactive DTM(T-PDTM) scheme to predict the future temperature through a new Thermal RC-based temperature prediction (RCTP) model. The RCTP model can precisely predict the temperature with heterogeneous workload assignment with low constant computational complexity. Based on the predictive temperature, the proposed T-PDTM scheme will assign the suitable clock frequency for each node of the NoC system to perform early temperature control through power budget distribution. Based on the experimental results, compared with the conventional reactive throttled-based DTMs, the T-PDTM scheme can help to reduce 11.4~80.3 percent fully throttled nodes and improves the network throughput by around 1.5~211.8 percent.
Characterization of Caco-2 and HT29-MTX cocultures in an in vitro digestion/cell culture model used to predict iron bioavailability.

Cocultures of two human cell lines, Caco-2 and HT29-MTX mucus-producing cells, have been incorporated into an in vitro digestion/cell culture model used to predict iron bioavailability. A range of different foods were subjected to in vitro digestion, and iron bioavailability from digests was assessed with Caco-2, Caco-2 overlaid with porcine mucin, HT29-MTX or cocultures of Caco-2 and HT29-MTX at varying ratios. It was found that increasing the ratio of HT29-MTX cells decreased the amount of ferritin formed and resulted in an overall decline in the ability of the model to detect differences in iron bioavailability. At the physiologically relevant ratios of 90% Caco-2/10% HT29-MTX and 75% Caco-2/25% HT29-MTX, however, a mucus layer completely covered the cell monolayer and the in vitro digestion model was nearly as responsive to changes in sample iron bioavailability as pure Caco-2 cultures. The in vitro digestion/Caco-2 cell culture model correlates well with human iron bioavailability studies, but, as mucus appears to play a role in iron absorption, the addition of a physiologically realistic mucus layer and goblet-type cells to this model may give more accurate iron bioavailability predictions.
First record of the red gum lerp psyllid, Glycaspis brimblecombei Moore (Hemiptera Psyllidae), in Tunisia

During a survey carried out in August 2013 along all coastal areas of north-eastern Tunisia (governorships of Bizerte, Ariana, Tunis, Ben Arous, Nabeul, Sousse), eucalyptus trees were found to be highly infested by the invasive pest Glycaspis brimblecombei Moore, 1964, also known as red gum lerp psyllid. This insect, native to the Australian region and secondarily dispersed also in the Americas, Mauritius, Madagascar and South Africa, very recently started to invade the Mediterranean region and in almost 5 years has spread to the Iberian Peninsula, Italy, Greece and Morocco. Its presence in Tunisia (which is recorded here for the first time) most probably dates back to summer 2012, since typical necrotic spots caused by the lerp of the psyllid had already been noted on leaves during spring 2013. No presence of its main parasitoid – Psyllaephagus bliteus Riek – nor of any other natural enemy, was noted up to now during our survey in Tunisia.
AMOLED Pixel Circuit With Electronic Compensation of Luminance Degradation

A new voltage-programmed pixel circuit using hydrogenated amorphous silicon (a-Si:H) thin-film transistors (TFTs) for active-matrix organic light-emitting diodes (AMOLEDs) is presented. In addition to compensating for the shift in threshold voltage of TFTs, the circuit is capable of compensating for OLED luminance degradation by employing the shift in OLED voltage as a feedback of OLED degradation
Effects of rapamycin on cell apoptosis in MCF-7 human breast cancer cells.

Rapamycin is an effective anti-angiogenic drug. However, the mode of its action remains unclear. Therefore, in this study, we aimed to elucidate the antitumor mechanism of rapamycin, hypothetically via apoptotic promotion, using MCF-7 breast cancer cells.



MCF-7 cells were plated at a density of 15105 cells/well in 6-well plates. After 24h, cells were treated with a series of concentrations of rapamycin while only adding DMEM medium with PEG for the control regiment and grown at 37oC, 5% CO2 and 95% air for 72h. Trypan blue was used to determine the cell viability and proliferation. Untreated and rapamycin-treated MCF-7 cells were also examined for morphological changes with an inverted-phase contrast microscope. Alteration in cell morphology was ascertained, along with a stage in the cell cycle and proliferation. In addition, cytotoxicity testing was performed using normal mouse breast mammary pads.



Our results clearly showed that rapamycin exhibited inhibitory activity on MCF-7 cell lines. The IC50 value of rapamycin on the MCF-7 cells was determined as 0.4μg/ml (p<0.05). Direct observation by inverted microscopy demonstrated that the MCF-7 cells treated with rapamycin showed characteristic features of apoptosis including cell shrinkage, vascularization and autophagy. Cells underwent early apoptosis up to 24% after 72h. Analysis of the cell cycle showed an increase in the G0G1 phase cell population and a corresponding decrease in the S and G2M phase populations, from 81.5% to 91.3% and 17.3% to 7.9%, respectively.



This study demonstrated that rapamycin may potentially act as an anti-cancer agent via the inhibition of growth with some morphological changes of the MCF-7 cancer cells, arrest cell cycle progression at G0/G1 phase and induction of apoptosis in late stage of apoptosis. Further studies are needed to further characterize the mode of action of rapamycin as an anti-cancer agent.
Understanding and Designing New Server Architectures for Emerging Warehouse-Computing Environments

This paper seeks to understand and design next-generation servers for emerging "warehouse-computing" environments. We make two key contributions. First, we put together a detailed evaluation infrastructure including a new benchmark suite for warehouse-computing workloads, and detailed performance, cost, and power models, to quantitatively characterize bottlenecks. Second, we study a new solution that incorporates volume non-server-class components in novel packaging solutions, with memory sharing and flash-based disk caching. Our results show that this approach has promise, with a 2X improvement on average in performance-per-dollar for our benchmark suite.
Friendships among Competitors in the Sydney Hotel Industry 1

Friendships with competitors can improve the performance of organizations through the mechanisms of enhanced collaboration, mitigated competition, and better information exchange. Moreover, these benefits are best achieved when competing managers are embedded in a cohesive network of friendships (i.e., one with many friendships among competitors), since cohesion facilitates the verification of information culled from the network, eliminates the structural holes faced by customers, and facilitates the normative control of competitors. The first part of this analysis examines the performance implications of the friendship-network structure within the Sydney hotel industry, with performance being the yield (i.e., revenue per available room) of a given hotel. This shows that friendships with competitors lead to dramatic improvements in hotel yields. Performance is further improved if a manager’s competitors are themselves friends, evidencing the benefit of cohesive friendship networks. The second part of the analysis examines the structure of friendship ties among hotel managers and shows that friendships are more likely between managers who are competitors.
Deep Learning for Distant Speech Recognition

Deep learning is an emerging technology that is considered one of the most promising directions for reaching higher levels of artificial intelligence. Among the other achievements, building computers that understand speech represents a crucial leap towards intelligent machines. Despite the great efforts of the past decades, however, a natural and robust human-machine speech interaction still appears to be out of reach, especially when users interact with a distant microphone in noisy and reverberant environments. The latter disturbances severely hamper the intelligibility of a speech signal, making Distant Speech Recognition (DSR) one of the major open challenges in the field. This thesis addresses the latter scenario and proposes some novel techniques, architectures, and algorithms to improve the robustness of distanttalking acoustic models. We first elaborate on methodologies for realistic data contamination, with a particular emphasis on DNN training with simulated data. We then investigate on approaches for better exploiting speech contexts, proposing some original methodologies for both feed-forward and recurrent neural networks. Lastly, inspired by the idea that cooperation across different DNNs could be the key for counteracting the harmful effects of noise and reverberation, we propose a novel deep learning paradigm called “network of deep neural networks”. The analysis of the original concepts were based on extensive experimental validations conducted on both real and simulated data, considering different corpora, microphone configurations, environments, noisy conditions, and ASR tasks.
Self-supervised learning of grasp dependent tool affordances on the iCub Humanoid robot

The ability to learn about and efficiently use tools constitutes a desirable property for general purpose humanoid robots, as it allows them to extend their capabilities beyond the limitations of their own body. Yet, it is a topic that has only recently been tackled from the robotics community. Most of the studies published so far make use of tool representations that allow their models to generalize the knowledge among similar tools in a very limited way. Moreover, most studies assume that the tool is always grasped in its common or canonical grasp position, thus not considering the influence of the grasp configuration in the outcome of the actions performed with them. In the current paper we present a method that tackles both issues simultaneously by using an extended set of functional features and a novel representation of the effect of the tool use. Together, they implicitly account for the grasping configuration and allow the iCub to generalize among tools based on their geometry. Moreover, learning happens in a self-supervised manner: First, the robot autonomously discovers the affordance categories of the tools by clustering the effect of their usage. These categories are subsequently used as a teaching signal to associate visually obtained functional features to the expected tool's affordance. In the experiments, we show how this technique can be effectively used to select, given a tool, the best action to achieve a desired effect.
A formal approach to finding explanations for database queries

As a consequence of the popularity of big data, many users with a variety of backgrounds seek to extract high level information from datasets collected from various sources and combined using data integration techniques. A major challenge for research in data management is to develop tools to assist users in explaining observed query outputs. In this paper we introduce a principled approach to provide explanations for answers to SQL queries based on intervention: removal of tuples from the database that significantly affect the query answers. We provide a formal definition of intervention in the presence of multiple relations which can interact with each other through foreign keys. First we give a set of recursive rules to compute the intervention for any given explanation in polynomial time (data complexity). Then we give simple and efficient algorithms based on SQL queries that can compute the top-K explanations by using standard database management systems under certain conditions. We evaluate the quality and performance of our approach by experiments on real datasets.