nils-herrmann/clean_scirepeval huggingface dataset:
Exploring Emotion Embedding and the Dimensional Model of Emotion ’ s Predictive Power in Sentiment by

Artificial intelligence (AI) has been integrated to people’s daily lives. However, the AI system’s lack of understanding of human emotions impede an e↵ective communication between the system and the human interactants. As an example, if a search engine understands the intent of the searcher, it should be able to return favorable results regardless of the correct queries. Emotions take a big part in communication as they allow humans to empathize and understand one another. An AI would also need to understand emotions for an e↵ective interaction with humans. A↵ective computing research has surged recently in order to tackle this problem. Sentiment analysis, which can be thought as a subset of a↵ective computing, allows an AI system to understand a limited portion of human emotions, and has been used widely in systems that involve reviews to recommend related products, such as movies, electronics, or books. The problem with the traditional sentiment analysis is that it only uses the polarity label as a proxy to the full emotion, which makes the system di cult to make fine-grained judgments. There exists a model of emotion that represents an emotion as a point in the emotion space [26], a more detailed model than the one-dimension polarity model. The model consists of three independent axes: evaluation, potency, and activity in the EPA format [29], or equivalently, valence, dominance, and arousal in the VAD format . We believe that by learning the three-dimension emotion model and extracting sentiment from it, we can predict the sentiment more precisely than the use of the typical polarity based model. In this paper, we explore validity of using the three-dimension emotion model as opposed to the naive polarity model in predicting the sentiment of given text data. We set the work of Tang et al.  as the baseline where they construct word embeddings with integrated sentiment information, called sentiment embeddings. As opposed to their approach of using polarity label to guide the word embedding and the corresponding classifier, we use the three-dimension emotion model, namely the VAD vectors [36], and train emotion embeddings. In the experiment, a recently established corpus with emotion labels, EmoBank [5], is used along with a common corpus for sentiment, Stanford Sentiment Treebank (SST) [32], and a large text dataset, text8 [22]. We compare and contrast the prediction power between the sentiment embeddings and the emotion embeddings on EmoBank corpus, while checking their generality on the SST corpus. We also analyze emotion embedding itself by visualizing the embeddings using t-distributed Stochastic Neighbor Embedding (t-SNE) . The visualization showed the lack of context generalization in emotion embedding,
The Bhabhatron : an Affordable Solution for Radiation Therapy

Radiation therapy as a mode of cancer treatment is well-established. Telecobalt and telecaesium units were used extensively during the early days. Now, medical linacs offer more options for treatment delivery. However, such systems are prohibitively expensive and beyond the reach of majority of the worlds population living in developing and under-developed countries. In India, there is shortage of cancer treatment facilities, mainly due to the high cost of imported machines. Realizing the need of technology for affordable radiation therapy machines, Bhabha Atomic Research Centre (BARC), the premier nuclear research institute of Government of India, started working towards a sophisticated telecobalt machine. The Bhabhatron is the outcome of the concerted efforts of BARC and Panacea Medical Technologies Pvt. Ltd., India. It is not only less expensive, but also has a number of advanced features. It incorporates many safety and automation features hitherto unavailable in the most advanced telecobalt machine presently available. This paper describes various features available in Bhabhatron-II. The authors hope that this machine has the potential to make safe and affordable radiation therapy accessible to the common people in India as well as many other countries.
A Novel Approach to Automatic Road-Accident Detection using Machine Vision Techniques

In this paper, a novel approach for automatic road accident detection is proposed. The approach is based on detecting damaged vehicles from footage received from surveillance cameras installed in roads and highways which would indicate the occurrence of a road accident. Detection of damaged cars falls under the category of object detection in the field of machine vision and has not been achieved so far. In this paper, a new supervised learning method comprising of three different stages which are combined into a single framework in a serial manner which successfully detects damaged cars from static images is proposed. The three stages use five support vector machines trained with Histogram of gradients (HOG) and Gray level co-occurrence matrix (GLCM) features. Since damaged car detection has not been attempted, two datasets of damaged cars Damaged Cars Dataset-1 (DCD-1) and Damaged Cars Dataset-2 (DCD-2) – was compiled for public release. Experiments were conducted on DCD-1 and DCD-2 which differ based on the distance at which the image is captured and the quality of the images. The accuracy of the system is 81.83% for DCD-1 captured at approximately 2 meters with good quality and 64.37% for DCD-2 captured at approximately 20 meters with poor quality. Feature extraction; Image denoising; Machine vision; object detection; Supervised learning; Support vector machines
Real-Time Visual SLAM for Autonomous Underwater Hull Inspection Using Visual Saliency

This paper reports a real-time monocular visual simultaneous localization and mapping (SLAM) algorithm and results for its application in the area of autonomous underwater ship hull inspection. The proposed algorithm overcomes some of the specific challenges associated with underwater visual SLAM, namely, limited field of view imagery and feature-poor regions. It does so by exploiting our SLAM navigation prior within the image registration pipeline and by being selective about which imagery is considered informative in terms of our visual SLAM map. A novel online bag-of-words measure for intra and interimage saliency are introduced and are shown to be useful for image key-frame selection, information-gain-based link hypothesis, and novelty detection. Results from three real-world hull inspection experiments evaluate the overall approach, including one survey comprising a 3.4-h/2.7-km-long trajectory.
Classification and Phylogenetic Analysis of African Ternary Rhythm Timelines

A combinatorial classification and a phylogenetic analysis of the ten 12/8 time, seven-stroke bell rhythm timelines in African and Afro-American music are presented. New methods for rhythm classification are proposed based on measures of rhythmic oddity and off-beatness. These combinatorial classifications reveal several new uniqueness properties of the Bembé bell pattern that may explain its widespread popularity and preference among the other patterns in this class. A new distance measure called the swap-distance is introduced to measure the non-similarity of two rhythms that have the same number of strokes. A swap in a sequence of notes and rests of equal duration is the location interchange of a note and a rest that are adjacent in the sequence. The swap distance between two rhythms is defined as the minimum number of swaps required to transform one rhythm to the other. A phylogenetic analysis using Splits Graphs with the swap distance shows that each of the ten bell patterns can be derived from one of two “canonical” patterns with at most four swap operations, or from one with at most five swap operations. Furthermore, the phylogenetic analysis suggests that for these ten bell patterns there are no “ancestral” rhythms not contained in this set.
Body composition and susceptibility to type 2 diabetes: an evolutionary perspective

Type 2 diabetes is rapidly increasing in prevalence worldwide, in concert with epidemics of obesity and sedentary behavior that are themselves tracking economic development. Within this broad pattern, susceptibility to diabetes varies substantially in association with ethnicity and nutritional exposures through the life-course. An evolutionary perspective may help understand why humans are so prone to this condition in modern environments, and why this risk is unequally distributed. A simple conceptual model treats diabetes risk as the function of two interacting traits, namely ‘metabolic capacity’ which promotes glucose homeostasis, and ‘metabolic load’ which challenges glucose homoeostasis. This conceptual model helps understand how long-term and more recent trends in body composition can be considered to have shaped variability in diabetes risk. Hominin evolution appears to have continued a broader trend evident in primates, towards lower levels of muscularity. In addition, hominins developed higher levels of body fatness, especially in females in relative terms. These traits most likely evolved as part of a broader reorganization of human life history traits in response to growing levels of ecological instability, enabling both survival during tough periods and reproduction during bountiful periods. Since the emergence of Homo sapiens, populations have diverged in body composition in association with geographical setting and local ecological stresses. These long-term trends in both metabolic capacity and adiposity help explain the overall susceptibility of humans to diabetes in ways that are similar to, and exacerbated by, the effects of nutritional exposures during the life-course.
Personalized Privacy-Preserving Social Recommendation

Privacy leakage is an important issue for social recommendation. Existing privacy preserving social recommendation approaches usually allow the recommender to fully control users’ information. This may be problematic since the recommender itself may be untrusted, leading to serious privacy leakage. Besides, building social relationships requires sharing interests as well as other private information, which may lead to more privacy leakage. Although sometimes users are allowed to hide their sensitive private data using privacy settings, the data being shared can still be abused by the adversaries to infer sensitive private information. Supporting social recommendation with least privacy leakage to untrusted recommender and other users (i.e., friends) is an important yet challenging problem. In this paper, we aim to address the problem of achieving privacy-preserving social recommendation under personalized privacy settings. We propose PrivSR, a novel framework for privacy-preserving social recommendation, in which users can model ratings and social relationships privately. Meanwhile, by allocating different noise magnitudes to personalized sensitive and non-sensitive ratings, we can protect users’ privacy against the untrusted recommender and friends. Theoretical analysis and experimental evaluation on real-world datasets demonstrate that our framework can protect users’ privacy while being able to retain effectiveness of the underlying recommender system.
Complex regional pain syndrome: An optimistic perspective.

Complex regional pain syndrome (CRPS) presents with clinical symptoms that can no longer be explained by the initial trauma, including pain, sensory, motor, and trophic symptoms, and impairment of autonomic control of the limb. These symptoms spread distally and go beyond single nerve innervation territories. Typically, the symptoms change through the course of CRPS as a result of the varying pathophysiology. Diagnosis is made clinically after the rigorous elimination of other possible causes, and 3-phase bone scintigraphy can be a useful tool for confirming CRPS. In acute stages, inflammatory symptoms prevail and should be treated with anti-inflammatory agents (steroids), bisphosphonates, or topical application of dimethyl sulfoxide. In chronic stages, many symptoms are related to so-called central neuroplasticity; these include hyperalgesia, sensory loss, motor symptoms, body perception disturbance, autonomic symptoms, and learned incorrect behavior such as nonuse. At this stage, the only medical treatment that is effective against pain without improving the function is ketamine infusions, but this has side effects. Physical therapy, graded motor imagery, and pain exposure/graded exposure in vivo therapy can help to overcome central reorganization. If a relevant mental comorbidity is present, the patient should be referred for psychotherapeutic treatment. Invasive treatment should be restricted to special cases and only offered after psychosomatic assessment. If these recommendations are followed, CRPS prognosis is not as poor as commonly assumed. Whether the patients can return to their previous life depends on particular individual factors.
The use of activity theory to guide information systems research

Activity Theory (AT) is increasingly employed as a lens to guide data analysis in information systems (IS) studies. The theory is also applied to assess and evaluate information systems and technologies (IS/IT) in organisations. Even though its popularity continues to increase in both business and academic domains, there is no formal or assessment guide through which the theory can be applied, which makes it sometimes difficult or complicated. The challenge is significant and critical in that when applied, it influences and shapes the results of the phenomena being studied. This is a problem as results of studies cannot or should not be misconstrued or misrepresented. This study was undertaken to examine how the use of AT in IS studies can be made easy. Based on the findings, a three phase approach was developed and proposed, to guide: (i) the selection of AT in an IS/IT study; (ii) use of elements for data analysis; and (iii) how the elements can be linked with AT components in the analysis of qualitative data. The approach therefore provides a formal guide that can be followed, to ease the selection and application of AT in IS/IT studies as well as assessment of IS/IT artefacts in an organisation. The approach can be useful to researchers for data analysis and interpretation of results. Also, the guidelines can be employed by educators for teaching and learning in the areas of research.
The missing link: evolution of the primate cerebellum.

The cerebellum has too often been seen as the "little brain," subservient to the "big brain," the cerebrum. That is changing, as neuroimaging uncovers the cerebellum as the "missing link" in the neurological underpinnings of many cognitive domains. Connections between the neocortex and the cerebellum are now more precisely defined, with functionally localized areas of cerebellar cortex understood for cognitive tasks in humans. Comparative volumetric studies of the primate cerebellum have isolated some elements of circuitry, and our field is moving toward a better integration with the neurosciences in a systematic comparative framework. The next decade may show great advances, as relatively noninvasive techniques of neuroimaging have the potential to build a comparative model of the evolution of primate neurocircuitry.
Diode-triggered SCR (DTSCR) for RF-ESD protection of BiCMOS SiGe HBTs and CMOS ultra-thin gate oxides

A novel diode-triggered SCR (DTSCR) ESD protection element is introduced for low-voltage application (signal, supply voltage /spl les/1.8 V) and extremely narrow ESD design margins. Trigger voltage engineering in conjunction with fast and efficient SCR voltage clamping is applied for the protection of ultra-sensitive circuit nodes, such as SiGe HBT bases (e.g. f/sub Tmax/=45 GHz in BiCMOS-0.35 /spl mu/m LNA input) and thin gate-oxides (e.g. tox=1.7 nm in CMOS-0.09 /spl mu/m input). SCR integration is possible based on CMOS devices or can alternatively be formed by high-speed SiGe HBTs.
MVCWalker: Random Walk-Based Most Valuable Collaborators Recommendation Exploiting Academic Factors

In academia, scientific research achievements would be inconceivable without academic collaboration and cooperation among researchers. Previous studies have discovered that productive scholars tend to be more collaborative. However, it is often difficult and time-consuming for researchers to find the most valuable collaborators (MVCs) from a large volume of big scholarly data. In this paper, we present MVCWalker, an innovative method that stands on the shoulders of random walk with restart (RWR) for recommending collaborators to scholars. Three academic factors, i.e., coauthor order, latest collaboration time, and times of collaboration, are exploited to define link importance in academic social networks for the sake of recommendation quality. We conducted extensive experiments on DBLP data set in order to compare MVCWalker to the basic model of RWR and the common neighbor-based model friend of friends in various aspects, including, e.g., the impact of critical parameters and academic factors. Our experimental results show that incorporating the above factors into random walk model can improve the precision, recall rate, and coverage rate of academic collaboration recommendations.
Emergence of disparity tuning during the development of vergence eye movements

The role of behavior for the acquisition of sensory representations has been underestimated in the past. We study this question for the task of learning vergence eye movements allowing proper fixation of objects. We model the development of this skill with an artificial neural network based on reinforcement learning. A biologically plausible reward mechanism that is responsible for driving behavior and learning of the representation of disparity is proposed. The network learns to perform vergence eye movements between natural images of objects by receiving a reward whenever an object is fixated with both eyes. Disparity tuned neurons emerge robustly in the hidden layer during development. The characteristics of the cells' tuning curves depend strongly on the task: if mostly small vergence movements are to be performed, tuning curves become narrower at small disparities, as has been measured experimentally in barn owls. Extensive training to discriminate between small disparities leads to an effective enhancement of sensitivity of the tuning curves.
One-shot learning for RGB-D hand-held object recognition

With the advance of computer technology and smart device, many applications, such as face recognition and object recognition, have been developed to facilitate human-computer interaction (HCI) efficiently. In this respect, the hand-held object recognition plays an important role in HCI. It can be used not only to help computer understand useros intentions but also to meet useros requirements. In recent years the appearance of convolutional neural networks (CNNs) greatly enhances the performance of object recognition and this technology has been applied to hand-held object recognition in some works. However, these supervised learning models need large number of labelled data and many iterations to train their large number of parameters. This is a huge challenge for HCI, because HCI need to deal with in-time and itos difficult to collect enough labeled data. Especially when a new category need to be learnt, it will spend a lot of time to update the model. In this work, we adopt the one-shot learning method to solve this problem. This method does not need to update the model when a new category need to be learnt. Moreover, depth image is robust to light and color variation. We fuse depth image information to harness the complementary relationship between the two modalities to improve the performance of hand-held object recognition. Experimental results on our handheld object dataset demonstrate that our method for hand-held object recognition achieves an improvement of performance.
A Multibeam End-Fire Magnetoelectric Dipole Antenna Array for Millimeter-Wave Applications

A novel substrate integrated waveguide (SIW)-fed end-fire magnetoelectric (ME) dipole antenna is proposed. The antenna consisting of an open-ended SIW and a pair of electric dipoles has a simple structure that can be integrated into substrates conveniently. Both the open-ended SIW and the electric dipoles are effectively radiated together. Excellent performance, including a bandwidth of 44%, symmetrical radiation patterns that are almost identical in two orthogonal planes, low backward radiation, low cross polarizations, stable gain of around 5 dBi, and wide beamwidth of around 110°, are also obtained. An 8×8 SIW Butler matrix is then designed. Modifications to the geometry of the matrix provide more spacing to locate SIW phase shifters and phase compensation structures with wide bandwidth. By employing the proposed end-fire ME-dipole antenna array and the 8×8 Butler matrix, an eight-beam antenna array is realized. The fabricated prototype demonstrates that wide bandwidth, stable radiation patterns with cross polarizations of less than -28 dB and gain varying from 9 to 12 dBi can be obtained. The proposed multibeam end-fire ME-dipole antenna array would be an attractive candidate for millimeter-wave wireless applications due to its good performance, ease of integration, and low fabrication cost.
How emotion is made and measured

How we design and evaluate for emotions depends crucially on what we take emotions to be. In affective computing, affect is often taken to be another kind of information discrete units or states internal to an individual that can be transmitted in a loss-free manner from people to computational systems and back. While affective computing explicitly challenges the primacy of rationality in cognitivist accounts of human activity, at a deeper level it often relies on and reproduces the same information-processing model of cognition. Drawing on cultural, social, and interactional critiques of cognition which have arisen in HCI, as well as anthropological and historical accounts of emotion, we explore an alternative perspective on emotion as interaction: dynamic, culturally mediated, and socially constructed and experienced. We demonstrate how this model leads to new goals for affective systems instead of sensing and transmitting emotion, systems should support human users in understanding, interpreting, and experiencing emotion in its full complexity and ambiguity. In developing from emotion as objective, externally measurable unit to emotion as experience, evaluation, too, alters focus from externally tracking the circulation of emotional information to co-interpreting emotions as they are made in interaction.
Specification-Based Monitoring of Cyber-Physical Systems: A Survey on Theory, Tools and Applications

The term Cyber-Physical Systems (CPS) typically refers to engineered, physical and biological systems monitored and/or controlled by an embedded computational core. The behaviour of a CPS over time is generally characterised by the evolution of physical quantities, and discrete software and hardware states. In general, these can be mathematically modelled by the evolution of continuous state variables for the physical components interleaved with discrete events. Despite large effort and progress in the exhaustive verification of such hybrid systems, the complexity of CPS models limits formal verification of safety of their behaviour only to small instances. An alternative approach, closer to the practice of simulation and testing, is to monitor and to predict CPS behaviours at simulation-time or at runtime. In this chapter, we summarise the state-of-the-art techniques for qualitative and quantitative monitoring of CPS behaviours. We present an overview of some of the important applications and, finally, we describe the tools supporting CPS monitoring and compare their main features.