nils-herrmann/clean_scirepeval huggingface dataset:
Self-Transfer Learning for Weakly Supervised Lesion Localization

Recent advances of deep learning have achieved remarkable performances in various computer vision tasks including weakly supervised object localization. Weakly supervised object localization is practically useful since it does not require fine-grained annotations. Current approaches overcome the difficulties of weak supervision via transfer learning from pre-trained models on large-scale general images such as ImageNet. However, they cannot be utilized for medical image domain in which do not exist such priors. In this work, we present a novel weakly supervised learning framework for lesion localization named as self-transfer learning (STL). STL jointly optimizes both classification and localization networks to help the localization network focus on correct lesions without any types of priors. We evaluate STL framework over chest X-rays and mammograms, and achieve significantly better localization performance compared to previous weakly supervised localization
Combining Classifiers for Chinese Word Segmentation

In this paper we report results of a supervised machine-learning approach to Chinese word segmentation. First, a maximum entropy tagger is trained on manually annotated data to automatically labels the characters with tags that indicate the position of character within a word. An error-driven transformation-based tagger is then trained to clean up the tagging inconsistencies of the first tagger. The tagged output is then converted into segmented text. The preliminary results show that this approach is competitive compared with other supervised machine-learning segmenters reported in previous studies.
Feature Extraction and Image Processing

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True-Randomness and Pseudo-Randomness in Ring Oscillator-Based True Random Number Generators

The paper deals with true random number generators employing oscillator rings, namely, with the one proposed by Sunar et al. in 2007 and enhanced by Wold and Tan in 2009. Our mathematical analysis shows that both architectures behave identically when composed of the same number of rings and ideal logic components. However, the reduction of the number of rings, as proposed by Wold and Tan, would inevitably cause the loss of entropy. Unfortunately, this entropy insufficiency is masked by the pseudo-randomness caused by XOR-ing clock signals having different frequencies. Our simulation model shows that the generator, using more than 18 ideal jitter-free rings having slightly different frequencies and producing only pseudo-randomness, will let the statistical tests pass. We conclude that a smaller number of rings reduce the security if the entropy reduction is not taken into account in post-processing. Moreover, the designer cannot avoid that some of rings will have the same frequency, which will cause another loss of entropy. In order to confirm this, we show how the attacker can reach a state where over 25% of the rings are locked and thus completely dependent. This effect can have disastrous consequences on the system security.
The state of the art in semantic relatedness: a framework for comparison

Semantic relatedness (SR) is a form of measurement that quantitatively identifies the relationship between two words or concepts based on the similarity or closeness of their meaning. In the recent years, there have been noteworthy efforts to compute SR between pairs of words or concepts by exploiting various knowledge resources such as linguistically structured (e.g. WordNet) and collaboratively developed knowledge bases (e.g. Wikipedia), among others. The existing approaches rely on different methods for utilizing these knowledge resources, for instance, methods that depend on the path between two words, or a vector representation of the word descriptions. The purpose of this paper is to review and present the state of the art in SR research through a hierarchical framework. The dimensions of the proposed framework cover three main aspects of SR approaches including the resources they rely on, the computational methods applied on the resources for developing a relatedness metric, and the evaluation models that are used for measuring their effectiveness. We have selected 14 representative SR approaches to be analyzed using our framework. We compare and critically review each of them through the dimensions of our framework, thus, identifying strengths and weaknesses of each approach. In addition, we provide guidelines for researchers and practitioners on how to select the most relevant SR method for their purpose. Finally, based on the comparative analysis of the reviewed relatedness measures, we identify existing challenges and potentially valuable future research directions in this domain.
A ground level causal learning algorithm

Open domain causal learning involves learning and establishing causal connections between events directly from sensory experiences. It has been established in psychology that this often requires background knowledge. However, background knowledge has to be built from first experiences, which we term ground level causal learning, which basically involves observing temporal correlations. Subsequent knowledge level causal learning can then be based on this ground level causal knowledge. The causal connections between events, such as between lightning and thunder, are often hard to discern based on simple temporal correlations because there might be noise - e.g., wind, headlights, sounds of vehicles, etc. - that intervene between lightning and thunder. In this paper, we adopt the position that causal learning is inductive and pragmatic, and causal connections exist on a scale of graded strength. We describe a method that is able to filter away noise in the environment to obtain likely causal connections between events.
Towards a better understanding of Vector Quantized Autoencoders

Deep neural networks with discrete latent variables offer the promise of better symbolic reasoning, and learning abstractions that are more useful to new tasks. There has been a surge in interest in discrete latent variable models, however, despite several recent improvements, the training of discrete latent variable models has remained challenging and their performance has mostly failed to match their continuous counterparts. Recent work on vector quantized autoencoders (VQ-VAE) has made substantial progress in this direction, with its perplexity almost matching that of a VAE on datasets such as CIFAR-10. In this work, we investigate an alternate training technique for VQ-VAE, inspired by its connection to the Expectation Maximization (EM) algorithm. Training the discrete bottleneck with EM helps us achieve better image reconstruction results on SVHN together with knowledge distillation, allows us to develop a non-autoregressive machine translation model whose accuracy almost matches a strong greedy autoregressive baseline Transformer, while being 3.3 times faster at inference.
RGB-D Mapping: Using Depth Cameras for Dense 3D Modeling of Indoor Environments

RGB-D cameras are novel sensing systems that capture RGB images along with per-pixel depth information. RGB-D cameras rely on either structured light patterns combined with stereo sensing [6,10] or time-of-flight laser sensing  to generate depth estimates that can be associated with RGB pixels. Very soon, small, high-quality RGB-D cameras developed for computer gaming and home entertainment applications will become available at cost below $100. In this paper we investigate how such cameras can be used in the context of robotics, specifically for building dense 3D maps of indoor environments. Such maps have applications in robot navigation, manipulation, semantic mapping, and telepresence. The robotics and computer vision communities have developed a variety of techniques for 3D mapping based on laser range scans [8, 11], stereo cameras [7], monocular cameras , and unsorted collections of photos . While RGB-D cameras provide the opportunity to build 3D maps of unprecedented richness, they have drawbacks that make their application to 3D mapping difficult: They provide depth only up to a limited distance (typically less than 5m), depth values are much noisier than those provided by laser scanners, and their field of view (∼ 60◦) is far more constrained than that of specialized cameras or laser scanners typically used for 3D mapping (∼ 180◦). In our work, we use a camera developed by PrimeSense . The key insights of this investigation are: first, that existing frame matching techniques are not sufficient to provide robust visual odometry with these cameras; second, that a tight integration of depth and color information can yield robust frame matching and loop closure detection; third, that building on best practice techniques in SLAM and computer graphics makes it possible to build and visualize accurate and extremely rich 3D maps with such cameras; and, fourth, that it will be feasible to build complete robot navigation and interaction systems solely based on cheap depth cameras.
Notification and awareness: synchronizing task-oriented collaborative activity

People working collaboratively must establish and maintain awareness of one another’s intentions, actions and results. Notification systems typically support awareness of the presence, tasks and actions of collaborators, but they do not adequately support awareness of persistent and complex activities. We analysed awareness breakdowns in use of our Virtual School system—stemming from problems related to the collaborative situation, group, task and tool support—to motivate the concept of activity awareness. Activity awareness builds on prior conceptions of social and action awareness, but emphasizes the importance of activity context factors like planning and coordination. This work suggests design strategies for notification systems to better support collaborative activity. r 2003
Gamification - A Novel Phenomenon or a New Wrapping for Existing Concepts?

Adding game elements to products and services has become a popular approach for enhancing users’ experiences. Accordingly, gamification is widely considered an important method for intrinsically motivating users toward a preferred behavior. But what exactly about gamification is actually novel? In a broad literature review, we compare and contrast gamification to similar concepts such as hedonic, persuasive and intrinsically motivating information systems. By decomposing and classifying game elements found in the literature, we distinguish between already existing elements and ones that can be considered new. In order to drive this area of research forward, we develop an extended framework for gamification, identify gaps in the literature, and propose future avenues for research.
Critical success factor categories for big data: A preliminary analysis of the current academic landscape

Big Data (BD) is a popular talking point currently. Companies invest huge amounts of resources to manage and maintain Big Data with mixed results. Understanding the Critical Success Factors (CSFs) associated with the successful management and maintenance of BD can aid organisations to obtain higher returns on their investment. This study focused on the identification of Critical Success Factors (CSFs) categories published in academic journals and conference proceedings on the topic of Big Data. It adopted a qualitative approach to classify the factors into categories and proposes a high level, preliminary model of CSF categorization. The model postulates that CSFs can be classified in three main categories namely Organisational, People and Technology. These categories can be used in future studies to test for the applicability and utilisation of BD by organisations. Furthermore, the authors postulate that the CSFs for all three categories should prevail in order to achieve the maximum value and success of BD implementations. The study also indicated that further research is necessary to refine the classification by considering alternative frameworks such as BD capability frameworks.
RECOGNIZING EMOTION IN SPEECH USING NEURAL NETWORKS

Emotion recognition is an important factor of affective computing and has potential use in assistive technologies. In this paper we used landmark and other acoustic features to recognize different emotional states in speech. We analyzed 2442 utterances from the Emotional Prosody Speech and Transcripts corpus and extracted 62 features from each utterance. A neural network classifier was built to recognize different emotional states of these utterances. We obtained over 90% accuracy in distinguishing hot anger and neutral states, over 80% accuracy in distinguishing happy and sadness as well as in distinguishing hot anger and cold anger. We also achieved 62% and 49% accuracy for classifying 4 and 6 emotions respectively. We had 20% accuracy in classifying all 15 emotions in the corpus which is a large improvement over other studies. We plan to apply our work to developing a tool to help people who have difficulty in identifying emotion.
System Integration and Power-Flow Management for a Series Hybrid Electric Vehicle Using Supercapacitors and Batteries

In this paper, system integration and power-flow management algorithms for a four-wheel-driven series hybrid electric vehicle (HEV) having multiple power sources composed of a diesel-engine-based generator, lead acid battery bank, and supercapacitor bank are presented. The super-capacitor is utilized as a short-term energy storage device to meet the dynamic performance of the vehicle, while the battery is utilized as a mid-term energy storage for the electric vehicle (EV) mode operation due to its higher energy density. The generator based on an interior permanent magnet machine (IPMM), run by a diesel engine, provides the average power for the normal operation of the vehicle. Thanks to the proposed power-flow management algorithm, each of the energy sources is controlled appropriately and also the dynamic performance of the vehicle has been improved. The proposed power-flow management algorithm has been experimentally verified with a full-scale prototype vehicle.
A Low-Noise Amplifier With Tunable Interference Rejection for 3.1- to 10.6-GHz UWB Systems

An ultrawideband common-gate low noise amplifier with tunable interference rejection is presented. The proposed LNA embeds a tunable active notch filter to eliminate interferer at 5-GHz WLAN and employs a common-gate input stage and dual-resonant loads for wideband implementation. This LNA has been fabricated in a 0.18-¿m CMOS process. The measured maximum power gain is 13.2 dB and noise figure is 4.5-6.2 dB with bandwidth of 3.1-10.6 GHz. The interferer rejection is 8.2 dB compared to the maximum gain and 7.6 dB noise figure at 5.2 GHz , respectively. The measured input P1dB is -11 dBm at 10.3 GHz. It consumes 12.8 mA from 1.8-V supply voltage.
Focus on Formative Feedback

This paper reviews the corpus of research on feedback, with a particular focus on formative feedback—defined as information communicated to the learner that is intended to modify the learner’s thinking or behavior for the purpose of improving learning. According to researchers in the area, formative feedback should be multidimensional, nonevaluative, supportive, timely, specific, credible, infrequent, and genuine (e.g., Brophy, 1981; Schwartz & White, 2000). Formative feedback is usually presented as information to a learner in response to some action on the learner’s part. It comes in a variety of types (e.g., verification of response accuracy, explanation of the correct answer, hints, worked examples) and can be administered at various times during the learning process (e.g., immediately following an answer, after some period of time has elapsed). Finally, there are a number of variables that have been shown to interact with formative feedback’s success at promoting learning (e.g., individual characteristics of the learner and aspects of the task). All of these issues will be discussed in this paper. This review concludes with a set of guidelines for generating formative feedback.
One Million Sense-Tagged Instances for Word Sense Disambiguation and Induction

Supervised word sense disambiguation (WSD) systems are usually the best performing systems when evaluated on standard benchmarks. However, these systems need annotated training data to function properly. While there are some publicly available open source WSD systems, very few large annotated datasets are available to the research community. The two main goals of this paper are to extract and annotate a large number of samples and release them for public use, and also to evaluate this dataset against some word sense disambiguation and induction tasks. We show that the open source IMS WSD system trained on our dataset achieves stateof-the-art results in standard disambiguation tasks and a recent word sense induction task, outperforming several task submissions and strong baselines.
DeepSeek: Content Based Image Search & Retrieval

Most of the internet today is composed of digital media that includes videos and images. With pixels becoming the currency in which most transactions happen on the internet, it is becoming increasingly important to have a way of browsing through this ocean of information with relative ease. YouTube has 400 hours of video uploaded every minute and many million images are browsed on Instagram, Facebook, etc. Inspired by recent advances in the field of deep learning and success that it has gained on various problems like image captioning (Karpathy and Fei-Fei, 2015) and (Xu et al., 2015), machine translation (Bahdanau et al., 2014), word2vec , skip thoughts (Kiros et al., 2015), etc, we present DeepSeek a natural language processing based deep learning model that allows users to enter a description of the kind of images that they want to search, and in response the system retrieves all the images that semantically and contextually relate to the query. Two approaches are described in the following sections.
An electrostatic gripper for flexible objects

We demonstrate a flexible, electrostatic adhesive gripper designed to controllably grasp and manipulate soft goods in space. The 8-fingered gripper has 50 cm2 of active electrodes operating at 3 kV. It generates electrostatic adhesion forces up to 3.5 N (0.70 kPa) on Ge-coated polyimide film and 1.2 N on MLI blanket, a film composite used for satellite thermal insulation. Extremely low-force gripper engagement (0.08 N) and release (0.04 N) of films is ideal for micro-gravity. Individual fingers generate shear adhesion forces up to 4.76 N (5.04 kPa) using electrostatic adhesive and 45.0 N (47.6 kPa) with a hybrid electrostatic / gecko adhesive. To simulate a satellite servicing task, the gripper was mounted on a 7-DoF robot arm and performed a supervised grasp, manipulate, and release sequence on a hanging, Al-coated PET film.
Replicating Milgram: Would people still obey today?

The author conducted a partial replication of Stanley Milgram's (1963, 1965, 1974) obedience studies that allowed for useful comparisons with the original investigations while protecting the well-being of participants. Seventy adults participated in a replication of Milgram's Experiment 5 up to the point at which they first heard the learner's verbal protest (150 volts). Because 79% of Milgram's participants who went past this point continued to the end of the shock generator's range, reasonable estimates could be made about what the present participants would have done if allowed to continue. Obedience rates in the 2006 replication were only slightly lower than those Milgram found 45 years earlier. Contrary to expectation, participants who saw a confederate refuse the experimenter's instructions obeyed as often as those who saw no model. Men and women did not differ in their rates of obedience, but there was some evidence that individual differences in empathic concern and desire for control affected participants' responses.
The maturing of gamification research

Throughout history, many have championed the use of play, games, and game-inspired design to improve the human condition. In the mid-2000s, the confluence of web technologies, digital business models, and online and location-based gaming gave rise to the most recent manifestation of this basic idea. Mobile applications like foursquare and websites like StackOverflow borrowed design elements like point scores, badges, or leaderboards from social network games and meta-gaming systems like Xbox Live to motivate user activity. This industry practice quickly became known as gamification, which can be defined as the use of game design elements in non-game contexts (Deterding, Dixon, Khaled, & Nacke, 2011). Many startups and design agencies emerged to offer gamification design or software-as-a-service (SaaS) packages, and large organisations (e.g., Oracle and SAP) across the globe began exploring gamification as a way to motivate people and improve the user experience. Applications reach from education and training to health, self-management, innovation, employee engagement, heritage, crowdsourcing, civic engagement, and marketing (Seaborn & Fels, 2015). Today, gamification is an established practice and industry segment, by some estimates poised to grow to over US$ 11 billion by 2020 (Markets and Markets, 2016). A key enabler of this groundswell has been now-ubiquitous sensor and computing technology: smart cities, smartphones, and wearables are increasingly tracking and processing our every step, effectively turning our life-world into a digital game in waiting. In parallel, we see a shift to post-material values of selfexpression and experience, catered to by a dematerialized ‘experience economy’ and a new profession and practice of experience designers, as well as the growth of digital games into a dominant cultural form, complete with a whole ‘gamer generation’ socialised into them. Economically, we can observe the transformation of business models and market differentiators towards innovation, user experience, customer relations, and the tight integration of customers into value chains with user-led innovation,
Hidden anomaly detection in telecommunication networks

Nowadays one of the challenges of telecommunication systems management is to detect in real-time unexpected or hidden malfunctions in extremely complex environments. In this article, we present an on-line algorithm that performs a flow of messages analysis. More precisely, it is able to highlight hidden abnormal behaviors that existing network management methods would not detect. Our algorithm uses the notion of constraint curves, introduced in the Network Calculus theory, defining successive time windows that bound the flow.