nils-herrmann/clean_scirepeval huggingface dataset:
Spacetime expression cloning for blendshapes

The goal of a practical facial animation retargeting system is to reproduce the character of a source animation on a target face while providing room for additional creative control by the animator. This article presents a novel spacetime facial animation retargeting method for blendshape face models. Our approach starts from the basic principle that the source and target movements should be similar. By interpreting movement as the derivative of position with time, and adding suitable boundary conditions, we formulate the retargeting problem as a Poisson equation. Specified (e.g., neutral) expressions at the beginning and end of the animation as well as any user-specified constraints in the middle of the animation serve as boundary conditions. In addition, a model-specific prior is constructed to represent the plausible expression space of the target face during retargeting. A Bayesian formulation is then employed to produce target animation that is consistent with the source movements while satisfying the prior constraints. Since the preservation of temporal derivatives is the primary goal of the optimization, the retargeted motion preserves the rhythm and character of the source movement and is free of temporal jitter. More importantly, our approach provides spacetime editing for the popular blendshape representation of facial models, exhibiting smooth and controlled propagation of user edits across surrounding frames.
Visual Analytics for Explainable Deep Learning

Recently, deep learning has been advancing the state of the art in artificial intelligence to a new level, and humans rely on artificial intelligence techniques more than ever. However, even with such unprecedented advancements, the lack of explanation regarding the decisions made by deep learning models and absence of control over their internal processes act as major drawbacks in critical decision-making processes, such as precision medicine and law enforcement. In response, efforts are being made to make deep learning interpretable and controllable by humans. In this paper, we review visual analytics, information visualization, and machine learning perspectives relevant to this aim, and discuss potential challenges and future research directions.
A Dose of Reality: Overcoming Usability Challenges in VR Head-Mounted Displays

This video presents insights into the usability challenges present in consumer VR Head-Mounted Displays regarding a users' capability to interact with and be aware of reality. We demonstrate how these issues can be overcome through selectively incorporating necessary elements of reality into VR, as a user engages with reality. We term this approach Engagement-Dependent Augmented Virtuality.
CAMP: Content-Agnostic Malware Protection

--In spite of recent advances, the world wide web remains an important vector for malware installation. Approaches to evaluating potentially malicious code before execution in a browser, such as blacklisting or content-based detection are hindered by an attacker’s ability to easily change hosting domains or mutate malware binaries. On the other hand, whitelistbased approaches are challenged by the large, dynamic, and heterogenous space of benign binaries that they must track. In practice these approaches continue to provide value for popular binaries at either extreme of maliciousness (e.g., the current large outbreak of malware, the benign binaries shipped with an OS), but bridging the gap between whitelist and blacklist detection for web malware remains a significant challenge. This paper presents CAMP, a content-agnostic malware protection system based on binary reputation that is designed to address these shortcomings. CAMP is built into the browser and determines the reputation of most downloads locally, relying on server-side reputation data only when a local decision cannot be made. This paper gives a detailed overview of CAMP and its architecture and provides an evaluation of the system through a six-month deployment in which 200 million users of Google Chrome requested between eight to ten million reputation requests a day. Our evaluation shows that CAMP exhibits accuracy close to 99% relative to proprietary VM-based dynamic analysis, is able to process requests in less than 130 ms on average, and was able to detect approximately five million intentional malware downloads per month that were not detected by existing solutions.
Classification of Moral Foundations in Microblog Political Discourse

Previous works in computer science, as well as political and social science, have shown correlation in text between political ideologies and the moral foundations expressed within that text. Additional work has shown that policy frames, which are used by politicians to bias the public towards their stance on an issue, are also correlated with political ideology. Based on these associations, this work takes a first step towards modeling both the language and how politicians frame issues on Twitter, in order to predict the moral foundations that are used by politicians to express their stances on issues. The contributions of this work includes a dataset annotated for the moral foundations, annotation guidelines, and probabilistic graphical models which show the usefulness of jointly modeling abstract political slogans, as opposed to the unigrams of previous works, with policy frames for the prediction of the morality underlying political tweets.
Patterns of Enterprise Application Architecture

protected void doInsert(DomainObject subject, PreparedStatement insertStatement) throws SQLException; class PersonMapper... protected String insertStatement() { return "INSERT INTO people VALUES (?, ?, ?, ?)"; } protected void doInsert( DomainObject abstractSubject, PreparedStatement stmt) throws SQLException { Person subject = (Person) abstractSubject; stmt.setString(2, subject.getLastName()); stmt.setString(3, ()); stmt.setInt(4, subject.getNumberOfDependents()); } Example: Separating the Finders (Java) To allow domain objects to invoke finder behavior I can use Separated Interface (476) to separate the finder interfaces from the mappers . I can put these finder interfaces in a separate package that's visible to the domain layer, or, as in this case, I can put them in the domain layer itself.  Defining a finder interface in the domain package.
Brief report: female-to-male transsexual people and autistic traits.

The 'extreme male brain' theory suggests females with Autism Spectrum Conditions are hyper-masculinized in certain aspects of behavior. We predicted that females with Gender Identity Disorder (who are masculinized) would have elevated Autism Spectrum Quotient (AQ) scores. AQ scores from five groups were compared: (1) n = 61 transmen (female-to-male transsexual people); (2) n = 198 transwomen (male-to-female transsexual people); (3) n = 76 typical males; (4) n = 98 typical females; and (5) n = 125 individuals with Asperger Syndrome (AS). Transmen had a higher mean AQ than typical females, typical males and transwomen, but lower than individuals with AS. Transmen have more autistic traits and may have had difficulty socializing with female peers and thus found it easier to identify with male peer groups.
Developing a Feasible and Maintainable Ontology for Automatic Landscape Design

In general, landscape architecture includes analysis, planning, design, administration and management of natural and artificial. An important aspect is the formation of socalled sustainable landscapes that allow maximum use of the environment, natural resources and promote sustainable restoration of ecosystems. For such purposes, a designer needs a complete database with existing and suitable plants, but no designing tool has one. Therefore it is presented the structure and the development of on ontology suitable for storing and managing all information and knowledge about plants. The advantage is that the format of the ontology allows the storage of any plant species (e.g. live or fossil) and automated reasoning. Ontology is a formal conceptualization of a particular knowledge about the world, through the explicit representation of basic concepts, relations, and inference rules about themselves. Therefore the ontology may be used by a design tool for helping the designer and choosing the best options for a sustainable landscape. environment; landscapes; ontology; ontology-based simulation; sustainable landscapes
A genetic algorithm optimized MPPT controller for a PV system with DC-DC boost converter

Because of the non-linearity of the P-V characteristic of the Photovoltaic (PV) panel, a power electronic interface is mandatory to reach the desired fixed voltage level. This paper deals with the design of a PID controller in order to control a DC-DC boost converter in the PV system. For the design of the whole system, the used approach is based on the Energetic Macroscopic Representation (EMR). This technique goes from a complex command to a simpler one and is characterized by the use of cascading of one or more controllers. The three parameters of the controller are tuned by using Genetic Algorithm (GA). Both of the system responses using PID MPPT controller tuned by genetic algorithm and PID controller tuned by Pole Placement (PP) are tested when fast changing the solar irradiation is experienced. Finally, in order to obtain the best system performance, Perturb and Observe (P&O) MPPT method is applied to generate the output voltage reference Vref. The simulation results are compared in terms of mean squared error (MSE) criterion, percentage overshoot and rising time. The results show that parameters tuned by GA PID controller gives better performance.
Network-Based Marketing : Identifying Likely Adopters via Consumer Networks

Network-based marketing refers to a collection of marketing techniques that take advantage of links between consumers to increase sales. We concentrate on the consumer networks formed using direct interactions (e.g., communications) between consumers. We survey the diverse literature on such marketing with an emphasis on the statistical methods used and the data to which these methods have been applied. We also provide a discussion of challenges and opportunities for this burgeoning research topic. Our survey highlights a gap in the literature. Because of inadequate data, prior studies have not been able to provide direct, statistical support for the hypothesis that network linkage can directly affect product/service adoption. Using a new data set that represents the adoption of a new telecommunications service, we show very strong support for the hypothesis. Specifically, we show three main results: (1) “Network neighbors”—those consumers linked to a prior customer—adopt the service at a rate 3–5 times greater than baseline groups selected by the best practices of the firm’s marketing team. In addition, analyzing the network allows the firm to acquire new customers who otherwise would have fallen through the cracks, because they would not have been identified based on traditional attributes. (2) Statistical models, built with a very large amount of geographic, demographic and prior purchase data, are significantly and substantially improved by including network information. (3) More detailed network information allows the ranking of the network neighbors so as to permit the selection of small sets of individuals with very high probabilities of adoption.
Labeled Faces in the Wild: A Survey

In 2007, Labeled Faces in the Wild was released in an effort to spur research in face recognition, specifically for the problem of face verification with unconstrained images. Since that time, more than 50 papers have been published that improve upon this benchmark in some respect. A remarkably wide variety of innovative methods have been developed to overcome the challenges presented in this database. As performance on some aspects of the benchmark approaches 100% accuracy, it seems appropriate to review this progress, derive what general principles we can from these works, and identify key future challenges in face recognition. In this survey, we review the contributions to LFW for which the authors have provided results to the curators (results found on the LFW results web page). We also review the cross cutting topic of alignment and how it is used in various methods. We end with a brief discussion of recent databases designed to challenge the next generation of face recognition algorithms. Erik Learned-Miller University of Massachusetts, Amherst, Massachusetts,  elm@cs.umass.edu Gary B. Huang Howard Hughes Medical Institute, Janelia Research Campus,  University of Massachusetts, Amherst, Massachusetts,  aruni@cs.umass.edu Haoxiang Li Stevens Institute of Technology, Hoboken, New Jersey,  Hoboken, New Jersey, e-mail: ganghua@gmail.com
Patient factors influencing dermal filler complications: prevention, assessment, and treatment

While rare, complications do occur with the esthetic use of dermal fillers. Careful attention to patient factors and technique can do much to avoid these complications, and a well-informed practitioner can mitigate problems when they do occur. Since cosmetic surgery is usually an elective process, requested by the patient, clinical trials are complex to organize and run. For this reason, an international group of practicing physicians in the field of esthetics came together to share knowledge and to try and produce some informed guidance for their colleagues, considering the literature and also pooling their own extensive clinical experience. This manuscript aims to summarize the crucial aspects of patient selection, including absolute contraindications as well as situations that warrant caution, and also covers important considerations for the pre- and posttreatment periods as well as during the procedure itself. Guidance is given on both immediate and long-term management of adverse reactions. The majority of complications are related to accepting patients inappropriate for treatment or issues of sterility, placement, volume, and injection technique. It is clear that esthetic practitioners need an in-depth knowledge of all aspects of treatment with dermal fillers to achieve optimal outcomes for their patients.
A cognitive architecture for a humanoid robot: a first approach

Future life pictures humans having intelligent humanoid robotic systems taking part in their everyday life. Thus researchers strive to supply robots with an adequate artificial intelligence in order to achieve a natural and intuitive interaction between human being and robotic system. Within the German Humanoid Project we focus on learning and cooperating multimodal robotic systems. In this paper we present a first cognitive architecture for our humanoid robot: The architecture is a mixture of a hierarchical three-layered form on the one hand and a composition of behaviour-specific modules on the other hand. Perception, learning, planning of actions, motor control, and human-like communication play an important role in the robotic system and are embedded step by step in our architecture
On Two Desiderata for Creativity Support Tools

This paper discusses two important desiderata for developing creativity support tools, namely ideation and empowerment. We then use them to guide us in designing a new individual creativity support tool codenamed Creative-Pad. Pad is designed to assist individual advertising creative to develop creative ideas for advertisements. For ideation, Creative-Pad searches and filters information automatically from the internet to present to the user with related words and exemplar sentences. For empowerment, CreativePad is designed in such a way that the user is neither distracted nor burdened to do any other tasks unrelated to conjuring up a creative idea for a new advertisement. Pad is fully implemented and some preliminary results of its use by advertising creatives are reported.
The Technology Behind Personal Digital Assistants: An overview of the system architecture and key components

We have long envisioned that one day computers will understand natural language and anticipate what we need, when and where we need it, and proactively complete tasks on our behalf. As computers get smaller and more pervasive, how humans interact with them is becoming a crucial issue. Despite numerous attempts over the past 30 years to make language understanding (LU) an effective and robust natural user interface for computer interaction, success has been limited and scoped to applications that were not particularly central to everyday use. However, speech recognition and machine learning have continued to be refined, and structured data served by applications and content providers has emerged. These advances, along with increased computational power, have broadened the application of natural LU to a wide spectrum of everyday tasks that are central to a user's productivity. We believe that as computers become smaller and more ubiquitous [e.g., wearables and Internet of Things (IoT)], and the number of applications increases, both system-initiated and user-initiated task completion across various applications and web services will become indispensable for personal life management and work productivity. In this article, we give an overview of personal digital assistants (PDAs); describe the system architecture, key components, and technology behind them; and discuss their future potential to fully redefine human?computer interaction.
EdgeIoT: Mobile Edge Computing for the Internet of Things

In order to overcome the scalability problem of the traditional Internet of Things architecture (i.e., data streams generated from distributed IoT devices are transmitted to the remote cloud via the Internet for further analysis), this article proposes a novel approach to mobile edge computing for the IoT architecture, edgeIoT, to handle the data streams at the mobile edge. Specifically, each BS is connected to a fog node, which provides computing resources locally. On the top of the fog nodes, the SDN-based cellular core is designed to facilitate packet forwarding among fog nodes. Meanwhile, we propose a hierarchical fog computing architecture in each fog node to provide flexible IoT services while maintaining user privacy: each user's IoT devices are associated with a proxy VM (located in a fog node), which collects, classifies, and analyzes the devices' raw data streams, converts them into metadata, and transmits the metadata to the corresponding application VMs (which are owned by IoT service providers). Each application VM receives the corresponding metadata from different proxy VMs and provides its service to users. In addition, a novel proxy VM migration scheme is proposed to minimize the traffic in the SDNbased core.
Comparing Electric Motors: An Analysis Using Four Standard Driving Schedules

Permanent magnets with rare-earth materials are widely used in interior permanent magnet synchronous motors (IPMSMs) in hybrid electric vehicles (HEVs). The recent price increase in rare-earth materials has become a serious concern. A switched reluctance motor (SRM) is one of the candidates for HE V rare-earth-free motors. An SRM has been developed with dimensions, maximum torque, operating area, and maximum efficiency that all compete with the IPMSM. The efficiency map of the designed SRM is different from that of the IPMSM; thus, direct comparisons have been rather difficult. In this article, a comparison of the energy consumption between the SRM and the IPMSM using four standard driving schedules is carried out. In the Highway Fuel Economy Driving Schedule (HWFET) and the New European Driving Cycle (NEDC), the SRM is found to have better efficiency because its efficiency is high at the high rotational speed region.
Cyber Security Backdrop: A SCADA testbed

Supervisory Control and Data Acquisition (SCADA) systems are traditionally proprietary and well protected. Due to increasing use of commercial/open source technology and communication protocols, there are growing concerns about the associated security threats. SCADA networks are usually employed in critical infrastructure, therefore, not much technical data of the actual systems is accessible to the research community. Most of the researchers simulate the SCADA functioning through development of testbed. Such projects are usually expensive and requiring financial sponsorship. In this paper we present a simple, inexpensive and flexible approach to develop a SCADA testbed utilizing TrueTime, a MATLAB/ Simulink based tool. The paper describes TrueTime simulation blocks, our control system, simulation of Denial of Service (DoS) attack and its effects. The main aim was to assess the effectiveness and suitability of TrueTime for the intended use in the development of a larger scale SCADA testbed. The results shown reflect that TrueTime can be effectively used for the purpose of SCADA network simulations and collection of necessary data for security analysis.
Modeling Avoidance in Mood and Anxiety Disorders Using Reinforcement Learning

Serious and debilitating symptoms of anxiety are the most common mental health problem worldwide, accounting for around 5% of all adult years lived with disability in the developed world. Avoidance behavior-avoiding social situations for fear of embarrassment, for instance-is a core feature of such anxiety. However, as for many other psychiatric symptoms the biological mechanisms underlying avoidance remain unclear.



Reinforcement learning models provide formal and testable characterizations of the mechanisms of decision making; here, we examine avoidance in these terms. A total of 101 healthy participants and individuals with mood and anxiety disorders completed an approach-avoidance go/no-go task under stress induced by threat of unpredictable shock.



We show an increased reliance in the mood and anxiety group on a parameter of our reinforcement learning model that characterizes a prepotent (pavlovian) bias to withhold responding in the face of negative outcomes. This was particularly the case when the mood and anxiety group was under stress.



This formal description of avoidance within the reinforcement learning framework provides a new means of linking clinical symptoms with biophysically plausible models of neural circuitry and, as such, takes us closer to a mechanistic understanding of mood and anxiety disorders.
On Laser Ranging Based on High-Speed/Energy Laser Diode Pulses and Single-Photon Detection Techniques

This paper discusses the construction principles and performance of a pulsed time-of-flight (TOF) laser radar based on high-speed (FWHM ~100 ps) and high-energy (~1 nJ) optical transmitter pulses produced with a specific laser diode working in an “enhanced gain-switching” regime and based on single-photon detection in the receiver. It is shown by analysis and experiments that single-shot precision at the level of 2W3 cm is achievable. The effective measurement rate can exceed 10 kHz to a noncooperative target (20% reflectivity) at a distance of > 50 m, with an effective receiver aperture size of 2.5 cm2. The effect of background illumination is analyzed. It is shown that the gating of the SPAD detector is an effective means to avoid the blocking of the receiver in a high-level background illumination case. A brief comparison with pulsed TOF laser radars employing linear detection techniques is also made.