Miracle-dz/UniFAR-FTUs huggingface dataset:
One Person in the Battlefield is Not a Warrior: Self-Construal, Perceived Ability to Make a Difference, and Socially Responsible Behavior

We suggest that cultivating an individual's connectedness to others promotes socially responsible behavior both directly and indirectly – through increased perceived ability to make a difference. Individuals whose interdependent self is more prominent feel they have more of an impact on larger scale societal outcomes and, therefore, engage more in socially responsible behaviors than do individuals whose independent self is more prominent. We test these hypotheses in two experiments in which participants make financial contributions or exert an effort for a social cause. In a survey, we find that perceived effectiveness mediates the effect of self-construal on socially responsible consumption.
Machine Learning Application to Transmission Quality Assessment in Optical Networks

This paper presents the application of 2 selected machine learning methods for transmission quality assessment in optical networks. The proposed solution uses real data to train classification models that predict whether a given optical channel meets transmission quality requirements. The training data is extracted directly from the running network through the control plane. Several attribute selection strategies have been analysed. The available attributes are divided into several subsets. The performance of the selected machine learning algorithms for each subset is compared using ROC and PR curves. The results are used to evaluate the predictive performance of the selected machine learning algorithms when applied to the optical channel classification task and to determine how this performance is related to the set of attributes used.
Information transfer techniques for mobile devices by "toss" and "swing" actions

In recent years, mobile devices have rapidly penetrated into our daily lives. Several drawbacks of mobile devices have been mentioned so far, such as their limited computational capability, small screen real estate, and, so on, as compared with notebook or desktop computers. However, by fully utilizing the most notable feature of mobile devices, that is, mobility, we can explore possibilities for a new user interface of the devices. In this paper, we use PDAs and propose intuitive information transfer techniques for them, which could not be achieved with notebook or desktop computers. By using the system called Toss-It, a user can send information from the user's PDA to other electronic devices with a "toss" or "swing" action, as the user would toss a ball or deals cards to others. We have developed a circuit board designed to be attached to a PDA and algorithms for recognizing "toss" and "swing" actions. Preliminary user studies of Toss-It indicated that it could correctly identify receivers of information by "toss" or "swing" actions. Our research project is in progress, and this paper describes the current status of the project by focusing on issues related to HCI (human computer interaction). We discuss about several critical issues to be investigated in our future studies.
Visual Exploration of Large-Scale Image Datasets for Machine Learning with Treemaps

— In this paper, we present DendroMap , a novel approach to interactively exploring large-scale image datasets for machine learning. Machine learning practitioners often explore image datasets by generating a grid of images or projecting high-dimensional representations of images into 2-D using dimensionality reduction techniques (e.g., t-SNE). However, neither approach effectively scales to large datasets because images are ineffectively organized and interactions are insufﬁciently supported. To address these challenges, we develop DendroMap by adapting Treemaps, a well-known visualization technique. DendroMap effectively organizes images by extracting hierarchical cluster structures from high-dimensional representations of images. It enables users to make sense of the overall distributions of datasets and interactively zoom into speciﬁc areas of interests at multiple levels of abstraction. Our case studies with widely-used image datasets for deep learning demonstrate that users can discover insights about datasets and trained models by examining the diversity of images, identifying underperforming subgroups, and analyzing classiﬁcation errors. We conducted a user study that evaluates the effectiveness of DendroMap in grouping and searching tasks by comparing it with a gridiﬁed version of t-SNE and found that participants preferred DendroMap over the compared method.
Chatting Makes Perfect - Chat-based Image Retrieval

Chats emerge as an effective user-friendly approach for information retrieval, and are successfully employed in many domains, such as customer service, healthcare, and finance. However, existing image retrieval approaches typically address the case of a single query-to-image round, and the use of chats for image retrieval has been mostly overlooked. In this work, we introduce ChatIR: a chat-based image retrieval system that engages in a conversation with the user to elicit information, in addition to an initial query, in order to clarify the user’s search intent. Motivated by the capabilities of today’s foundation models, we leverage Large Language Models to generate follow-up questions to an initial image description. These questions form a dialog with the user in order to retrieve the desired image from a large corpus. In this study, we explore the capabilities of such a system tested on a large dataset and reveal that engaging in a dialog yields significant gains in image retrieval. We start by building an evaluation pipeline from an existing manually generated dataset and explore different modules and training strategies for ChatIR. Our comparison includes strong baselines derived from related applications trained with Reinforcement Learning. Our system is capable of retrieving the target image from a pool of 50K images with over 78% success rate after 5 dialogue rounds, compared to 75% when questions are asked by humans, and 64% for a single shot text-to-image retrieval. Extensive evaluations reveal the strong capabilities and examine the limitations of CharIR under different settings.
Visual Methods for the Retrieval of Guitarist Fingering

This article presents a method to visually detect and recognize fingering gestures of the left hand of a guitarist. This method has been developed following preliminary manual and automated analysis of video recordings. These first analyses led to some important findings about the design methodology of a vision system for guitarist fingering, namely the focus on the effective gesture, the consideration of the action of each individual finger, and a recognition system not relying on comparison against a knowledge base of previously learned fingering positions. Motivated by these results, studies on three aspects of a complete fingering system were conducted: the first on finger tracking; the second on strings and frets detection; and the last one on movement segmentation. Finally, these concepts were integrated into a prototype and a system for left hand fingering detection was developed.
Narrowing the gap between verification and systematic testing

This dissertation focuses on narrowing the gap between verification and systematic testing, in two directions: (1) by complementing verification with systematic testing, and (2) by pushing systematic testing toward reaching verification. In the first direction, we explore how to effectively combine unsound static analysis with systematic testing, so as to guide test generation toward properties that have not been checked (soundly) by a static analyzer. Our combination significantly reduces the test effort while checking more unverified properties. In the process of testing properties that are typically not soundly verified by static analyzers, we identify important limitations in existing test generation tools, with respect to considering sufficient oracles and all factors that affect the outcome of these oracles. Specifically, testing tools that use object invariants as filters on input data do not thoroughly check whether these invariants are actually maintained by the unit under test. Moreover, existing test generation tools ignore the potential interaction of static state with a unit under test. We address these issues by presenting novel techniques based on static analysis and dynamic symbolic execution. Our techniques detected a significant number of errors that were previously missed by existing tools. We also investigate in which ways it is beneficial to complement sound, interactive verification with systematic testing for the properties that have not been verified yet. In the second direction, we push systematic testing toward verification, in particular, toward proving memory safety of the ANI Windows image parser. This is achieved by concentrating on the main limitations of systematic dynamic test generation, namely, imperfect symbolic execution and path explosion, in the context of this parser. Based on insights from attempting to reach verification of the ANI parser using only systematic testing, we then define IC-Cut, a new compositional search strategy for automatically and dynamically discovering simple function interfaces, where large programs can be effectively decomposed. IC-Cut preserves code coverage and increases bug finding in significantly less exploration time compared to the current search strategy of the dynamic symbolic execution tool SAGE. An additional novelty of IC-Cut is that it can identify which decomposed program units are exhaustively tested and, thus, dynamically verified.
"Am I A Good Therapist?" Automated Evaluation Of Psychotherapy Skills Using Speech And Language Technologies

With the growing prevalence of psychological interventions, it is vital to have measures which rate the e ﬀ ectiveness of psychological care, in order to assist in training, supervision, and quality assurance of services. Traditionally, quality assessment is addressed by human raters who evaluate recorded sessions along speciﬁc dimensions, often codiﬁed through constructs relevant to the approach and domain. This is however a cost-prohibitive and time-consuming method which leads to poor feasibility and limited use in real-world settings. To facilitate this process, we have developed an automated competency rating tool able to process the raw recorded audio of a session, analyzing who spoke when, what they said, and how the health professional used language to provide therapy. Focusing on a use case of a speciﬁc type of psychotherapy called Motivational Interviewing, our system gives comprehensive feedback to the therapist, including information about the dynamics of the session (e.g., therapist’s vs. client’s talking time), low-level psychological language descriptors (e.g., type of questions asked), as well as other high-level behavioral constructs (e.g., the extent to which the therapist understands the clients’ perspective). We describe our platform and its performance, using a dataset of more than 5,000 recordings drawn from its deployment in a real-world clinical setting used to assist training of new therapists. We are conﬁdent that a widespread use of automated psychotherapy rating tools in the near future will augment experts’ capabilities by providing an avenue for more e ﬀ ective training and skill improvement and will eventually lead to more positive clinical outcomes.
Controlling Your PLACE in the File System with Gray-box Techniques

We present the design and implementation of PLACE, a gray-box library for controlling file layout on top of FFSlike file systems. PLACE exploits its knowledge of FFS layout policies to let users place files and directories into specific and localized portions of disk. Applications can use PLACE to collocate files that exhibit temporal locality of access, thus improving performance. Through a series of microbenchmarks, we analyze the overheads of controlling file layout on top of the file system, showing that the overheads are not prohibitive, and also discuss the limitations of our approach. Finally, we demonstrate the utility of PLACE through two case studies: we demonstrate the potential of file layout rearrangement in a web-server environment, and we build a benchmarking tool that exploits control over file placement to quickly extract low-level details from the disk system. In the traditional gray-box manner, the PLACE library achieves these ends entirely at user level, without changing a single line of operating
Investigations on methods of land cover classification of TM image in mountain area

The main purpose of this study was to develop a methodology for classification of Landsat imagery for mountain area cover type mapping. Single-stage classification and multi-stage iterative classification were evaluated to determine which classification of satellite imagery could be employed to obtain accurate land cover information in Mentougou district located in western Beijing with diverse topography. TM data used in the study consists of one quarter-scene acquired on 19 June, 2001. The use of ancillary information in the process of deriving thematic maps from satellite imagery was analyzed. Field surveyed data were not only used for the production of the vegetation map of Mentougou but for the ground information of the land covers in the study area Other ancillary data layers such as topography was used in the analysis. Five classification methods were used for classifying the TM data in the study: 1) single-stage classification, 2) single-stage classification with DEM analysis, 3) single-stage classification with PCA analysis, 4) iterative classification with band selection, and unsupervised classification. The accuracy of each classification is expressed as an error matrix from which the Kappa statistic and its large sample variance are derived. The results indicate that the iterative multi-stage classification approach was significantly better than the single-stage classification approach. And this organizational methodology for classification is feasible and reliable in mountain areas image classification.
Managing a project with open source components

managing large SOftWare prOjectS is daunting. The demands of changing technology, customer requirements, schedules, and business competition can drive you to the breaking point. Depending on your needs, there is immeasurable value in open source code. Although it takes oversight, legal work, training, and additional management to accomplish, use of open source can dramatically improve your time to market and the quality of your product. Imagine the following situation: You are leading a mission-critical project for a large corporation, you’re behind schedule, and you are understaffed and frantic. What to do? You start looking for answers on the Internet. A Web search finds a module that implements the exact function you need. Eureka! You have found just the code that will solve a critical problem in your project. Wait—not so fast. Before you download and use that component, you need to consider all the implications of using open source software in your project. The implications of using open source vary significantly, depending on the nature of your project and the license under which the code was released. It makes a big difference whether you are running an internal IT project that wouldn’t mind sharing its work with others outside the company or whether you have some uncopyable “secret sauce” that gives your product unique value. Depending on how the much needed code was licensed, you may be setting yourself up for future legal, PR, and financial issues. You need to establish a structure that enables you to gain all the value of open source, while avoiding any potential pitfalls. The quality of open source varies. Some mainstream open source code has had so much careful inspection, usage, and tuning that it would take thousands of person hours by the most experienced programmers to recreate. Other, more obscure projects may vary wildly in quality. At ACCESS, we have set up an infrastructure which has helped us manage our open source projects. Depending on the nature of your project and the code you want to use, many of these steps may be helpful to you. Step 1: Create an infrastructure to keep a record of all the code you download and use. It is critical to be able to trace back the source of any open source code you have in your project and to understand the license under which it was provided. To understand what you are allowed to do by Mitch Bayersdorfer | ACCESS | mitch.bayers dorfer@access-compan y.com :} { With M anaging a Proje ct
A Conceptual Model for the Application of Business Analytics in the Horizontal Strategic Alliance

The formation of strategic alliances amongst organisations has grown in recent years as globalisation has opened new markets for firms to pursue. It is of ever-increasing importance that organisations have a thorough understanding of the performance of their alliance to achieve competitive advantage. However, much research on strategic alliance has focused on examining the organisations within the alliance rather than focusing on the alliance itself. Using the new discipline of business analytics, this paper proposes a hierarchical model blending the functional roles of business analytics with data standardisation and context mediation frameworks to allow business analytics to be applied to horizontal strategic alliances with the aim of producing valid, alliance-wide strategic options. A proof of concept whereby the proposed model is theoretically applied to a real-life alliance in the airline industry is given, demonstrating how the model generates insight at the alliance level at each of the model's five levels.
Variability with depth of some physico-chemical and biological parameters of Atlantic Ocean water in part of the coastal area of Nigeria

Results of in-situ measurements of some physico-chemical and biological parameters (temperature, electrical conductivity, density, dissolved oxygen, turbidity, pH, and salinity) of Atlantic Ocean water at various depths are presented. It is shown that temperature and dissolved oxygen vary significantly with depth, while variations of the other parameters with depth are minimal. Temperature, dissolved oxygen, electrical conductivity, density, pH, and salinity vary in the ranges: 3.60C–28.320C, 1.8mg/l–8.4mg/l, 3 . 29 i s/cm – 4 . 71 i s/cm, 1017 . 34 kg//m 3 –1036 . 61 kg//m 3 , 7.1–8.4, and 34.52ppt –35.8ppt respectively. Turbidity is less than 1.0NTU, indicating that the water is clear and transparent.
Non-Symmetric Multi-Antenna Coded Caching for Location-Dependent Content Delivery

Immersive viewing, as the next-generation interface for human-computer interaction, is emerging as a wireless application. A genuinely wireless immersive experience necessitates immense data delivery with ultra-low latency, raising stringent requirements for future wireless networks. In this regard, efficient usage of in-device storage and computation capabilities is a potential candidate for addressing these requirements. In addition, recent advancement in multi-antenna transmission has significantly enhanced wireless communication. Hence, this paper proposes a novel location-based multi-antenna coded cache placement and delivery scheme. We first formulate a linear programming cache allocation problem to provide a uniform quality of experience in different network locations; then, cache-placement is done for each location independently. Subsequently, based on the users’ spatial realizations, a transmission vector is created considering diverse available memory at each user. Moreover, a weighted-max-min optimization is used for the beamformers to support different transmission rates. Finally, numerical results are used to show the performance of the proposed scheme.
Fast Multi-View Rendering for Real-Time Applications

Efficient rendering of multiple views can be a critical performance factor for real-time rendering applications. Generating more than one view multiplies the amount of rendered geometry, which can cause a huge performance impact. Minimizing that impact has been a target of previous research and GPU manufacturers, who have started to equip devices with dedicated acceleration units. However, vendor-specific acceleration is not the only option to increase multi-view rendering (MVR) performance. Available graphics API features, shader stages and optimizations can be exploited for improved MVR performance, while generally offering more versatile pipeline configurations, including the preservation of custom tessellation and geometry shaders. In this paper, we present an exhaustive evaluation of MVR pipelines available on modern GPUs. We provide a detailed analysis of previous techniques, hardware-accelerated MVR and propose a novel method, leading to the creation of an MVR catalogue. Our analyses cover three distinct applications to help gain clarity on overall MVR performance characteristics. Our interpretation of the observed results provides a guideline for selecting the most appropriate one for various use cases on different GPU architectures. CCS Concepts • Computing methodologies → Rasterization; Visibility; Virtual reality;
Simulated and observed decadal variability in ocean heat content

Previous analyses by Levitus et al. [2000] (“Levitus”) of ocean temperature data have shown that ocean heat content has increased over the last fifty years with substantial temporal variability superimposed. The HadCM3 coupled atmosphere–ocean general circulation model (AOGCM) simulates the Levitus trend if both natural and anthropogenic forcings are included. In the relatively well‐observed northern hemisphere upper ocean, HadCM3 has similar temporal variability to Levitus but, like other AOGCMs, it has generally less variability than Levitus for the world ocean. We analyse the causes of this discrepancy, which could result from deficiencies in either the model or the observational dataset. A substantial contribution to the Levitus variability comes from a strong maximum around 500 m depth, absent in HadCM3. We demonstrate a possibly large sensitivity to the method of filling in the observational dataset outside the well‐observed region, and advocate caution in using it to assess AOGCM heat content changes.
Symbolic Modeling of Signal Transduction in Pathway Logic

Pathway logic is a step towards a vision of symbolic systems biology. It is an approach to modeling cellular processes based on formal methods. In particular, formal executable models of processes such as signal transduction, metabolic pathways, and immune system cell-cell signaling are developed using the rewriting logic language Maude and a variety of formal tools are used to query these models. An important objective of Pathway Logic is to reflect the ways that biologists think about problems using informal models, and to provide bench biologists with tools for computing with and analyzing these models that are natural. In this paper we describe the pathway logic approach to the modeling and analysis of signal transduction, and the use of the pathway logic assistant tool to browse and query these models. The Rac1 signaling pathway is used to illustrate the concepts